zoo.pipeline.api.net package¶
Submodules¶
zoo.pipeline.api.net.graph_net module¶
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class
zoo.pipeline.api.net.graph_net.
GraphNet
(input, output, jvalue=None, bigdl_type='float', **kwargs)[source]¶ Bases:
bigdl.nn.layer.Model
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freeze_up_to
(names)[source]¶ Freeze the model from the bottom up to the layers specified by names (inclusive). This is useful for finetuning a model
Parameters: names – A list of module names to be Freezed Returns: current graph model
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static
from_jvalue
(jvalue, bigdl_type='float')[source]¶ Create a Python Model base on the given java value
Parameters: jvalue – Java object create by Py4j Returns: A Python Model
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layers
¶
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new_graph
(outputs)[source]¶ Specify a list of nodes as output and return a new graph using the existing nodes
Parameters: outputs – A list of nodes specified Returns: A graph model
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predict
(x, batch_per_thread=4, distributed=True)[source]¶ Use a model to do prediction.
# Arguments x: Prediction data. A Numpy array or RDD of Sample or ImageSet. batch_per_thread: The default value is 4. When distributed is True,the total batch size is batch_per_thread * rdd.getNumPartitions. When distributed is False the total batch size is batch_per_thread * numOfCores. distributed: Boolean. Whether to do prediction in distributed mode or local mode. Default is True. In local mode, x must be a Numpy array.
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unfreeze
(names=None)[source]¶ “unfreeze” module, i.e. make the module parameters(weight/bias, if exists) to be trained(updated) in training process. If ‘names’ is a non-empty list, unfreeze layers that match given names
Parameters: names – list of module names to be unFreezed. Default is None. Returns: current graph model
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zoo.pipeline.api.net.net_load module¶
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class
zoo.pipeline.api.net.net_load.
Net
[source]¶ Bases:
object
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static
load
(model_path, weight_path=None, bigdl_type='float')[source]¶ Load an existing Analytics Zoo model defined in Keras-style(with weights).
Parameters: model_path – The path to load the saved model. Local file system, HDFS and Amazon S3 are supported. HDFS path should be like ‘hdfs://[host]:[port]/xxx’. Amazon S3 path should be like ‘s3a://bucket/xxx’. :param weight_path: The path for pre-trained weights if any. Default is None. :return: An Analytics Zoo model.
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static
load_bigdl
(model_path, weight_path=None, bigdl_type='float')[source]¶ Load a pre-trained BigDL model.
Parameters: - model_path – The path to the pre-trained model.
- weight_path – The path to the weights of the pre-trained model. Default is None.
Returns: A pre-trained model.
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static
load_caffe
(def_path, model_path, bigdl_type='float')[source]¶ Load a pre-trained Caffe model.
Parameters: - def_path – The path containing the caffe model definition.
- model_path – The path containing the pre-trained caffe model.
Returns: A pre-trained model.
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static
load_keras
(json_path=None, hdf5_path=None, by_name=False)[source]¶ Load a pre-trained Keras model.
Parameters: - json_path – The json path containing the keras model definition. Default is None.
- hdf5_path – The HDF5 path containing the pre-trained keras model weights
with or without the model architecture. Default is None. :param by_name: by default the architecture should be unchanged. If set as True, only layers with the same name will be loaded. :return: A BigDL model.
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static
zoo.pipeline.api.net.tf_dataset module¶
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class
zoo.pipeline.api.net.tf_dataset.
MapDataset
(pre_dataset, map_fn)[source]¶ Bases:
zoo.pipeline.api.net.tf_dataset.TFDataset
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get_evaluation_data
()[source]¶ Returns: an object that can be used for TFNet.evaluate, e.g. an RDD of Sample or a ImageSet
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get_prediction_data
()[source]¶ Returns: an object that can be used for TFNet.predict e.g. an RDD of Sample or a ImageSet
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class
zoo.pipeline.api.net.tf_dataset.
MergeFeatureLabelFeatureTransformer
(bigdl_type='float')[source]¶ Bases:
bigdl.transform.vision.image.FeatureTransformer
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class
zoo.pipeline.api.net.tf_dataset.
MergeFeatureLabelImagePreprocessing
(bigdl_type='float')[source]¶ Bases:
zoo.feature.image.imagePreprocessing.ImagePreprocessing
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class
zoo.pipeline.api.net.tf_dataset.
TFBytesDataset
(string_rdd, batch_size, batch_per_thread, hard_code_batch_size=False, validation_string_rdd=None, sequential_order=False, shuffle=True)[source]¶ Bases:
zoo.pipeline.api.net.tf_dataset.TFDataset
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get_evaluation_data
()[source]¶ Returns: an object that can be used for TFNet.evaluate, e.g. an RDD of Sample or a ImageSet
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get_prediction_data
()[source]¶ Returns: an object that can be used for TFNet.predict e.g. an RDD of Sample or a ImageSet
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class
zoo.pipeline.api.net.tf_dataset.
TFDataset
(tensor_structure, batch_size, batch_per_thread, hard_code_batch_size=False)[source]¶ Bases:
object
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feature_tensors
¶
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static
from_bytes_rdd
(bytes_rdd, batch_size=-1, batch_per_thread=-1, hard_code_batch_size=False, validation_bytes_rdd=None)[source]¶ Create a TFDataset from a RDD of bytes. Each element is the RDD should be a bytes object. The returning TFDataset’s feature_tensors has only one Tensor. the type of the Tensor is tf.string, and the shape is (None,). The returning don’t have label_tensors. If the dataset is used for training, the label should be encoded in the bytes. :param bytes_rdd: the RDD of bytes :param batch_size: the batch size, used for training, should be a multiple of total core num :param batch_per_thread: the batch size for each thread, used for inference or evaluation :param hard_code_batch_size: whether to hard code the batch_size into tensorflow graph, if True, the static size of the first dimension of the resulting tensors is batch_size/total_core_num (training) or batch_per_thread for inference; if False, it is None. :param validation_bytes_rdd: the RDD of bytes to be used in validation :return: a TFDataset
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static
from_feature_set
(dataset, features, labels=None, batch_size=-1, batch_per_thread=-1, hard_code_batch_size=False, validation_dataset=None)[source]¶ Create a TFDataset from a FeatureSet. Currently, the element in this Feature set must be a Sample, i.e. the result of ImageFeatureToSample transformer :param dataset: the feature set used to create this TFDataset :param features: a tuple of two, the first element is the type of this input feature, the second element is the shape of this element, i.e. (tf.float32, [224, 224, 3])). text can also be nested structure of this tuple of two. :param labels: a tuple of two, the first element is the type of label, the second element is the shape of this element, i.e. (tf.int32, [1])). label can also be nested structure of this tuple of two. :param batch_size: the batch size, used for training, should be a multiple of total core num :param batch_per_thread: the batch size for each thread, used for inference or evaluation :param hard_code_batch_size: whether to hard code the batch_size into tensorflow graph, if True, the static size of the first dimension of the resulting tensors is batch_size/total_core_num (training) or batch_per_thread for inference; if False, it is None. :param validation_dataset: The FeatureSet used for validation during training :return: a TFDataset
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static
from_image_set
(image_set, image, label=None, batch_size=-1, batch_per_thread=-1, hard_code_batch_size=False, validation_image_set=None, sequential_order=False, shuffle=True)[source]¶ Create a TFDataset from a ImagetSet. Each ImageFeature in the ImageSet should already has the “sample” field, i.e. the result of ImageSetToSample transformer
Parameters: - image_set – the ImageSet used to create this TFDataset
- image – a tuple of two, the first element is the type of image, the second element
is the shape of this element, i.e. (tf.float32, [224, 224, 3])) :param label: a tuple of two, the first element is the type of label, the second element is the shape of this element, i.e. (tf.int32, [1])) :param batch_size: the batch size, used for training, should be a multiple of total core num :param batch_per_thread: the batch size for each thread, used for inference or evaluation :param hard_code_batch_size: whether to hard code the batch_size into tensorflow graph, if True, the static size of the first dimension of the resulting tensors is batch_size/total_core_num (training) or batch_per_thread for inference; if False, it is None. :param validation_image_set: the ImageSet used for validation during training :param sequential_order: whether to iterate the elements in the Dataset in sequential order when training. :param shuffle: whether to shuffle the elements in each partition before each epoch when training :return: a TFDataset
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static
from_ndarrays
(*args, **kwargs)[source]¶ Create a TFDataset from a nested structure of numpy ndarrays. Each element in the resulting TFDataset has the same structure of the argument tensors and is created by indexing on the first dimension of each ndarray in the tensors argument.
This method is equivalent to sc.parallize the tensors and call TFDataset.from_rdd
Parameters: - tensors – the numpy ndarrays
- batch_size – the batch size, used for training, should be a multiple of
total core num :param batch_per_thread: the batch size for each thread, used for inference or evaluation :param hard_code_batch_size: whether to hard code the batch_size into tensorflow graph, if True, the static size of the first dimension of the resulting tensors is batch_size/total_core_num (training) or batch_per_thread for inference; if False, it is None. :param val_tensors: the numpy ndarrays used for validation during training :param sequential_order: whether to iterate the elements in the Dataset in sequential order when training. :param shuffle: whether to shuffle the elements in each partition before each epoch when training :return: a TFDataset
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static
from_rdd
(*args, **kwargs)[source]¶ Create a TFDataset from a rdd.
For training and evaluation, both features and labels arguments should be specified. The element of the rdd should be a tuple of two, (features, labels), each has the same structure of numpy.ndarrays of the argument features, labels.
E.g. if features is [(tf.float32, [10]), (tf.float32, [20])], and labels is {“label1”:(tf.float32, [10]), “label2”: (tf.float32, [20])} then a valid element of the rdd could be
( [np.zeros(dtype=float, shape=(10,), np.zeros(dtype=float, shape=(10,)))], {“label1”: np.zeros(dtype=float, shape=(10,)), “label2”:np.zeros(dtype=float, shape=(10,))))} )
If labels is not specified, then the above element should be changed to [np.zeros(dtype=float, shape=(10,), np.zeros(dtype=float, shape=(10,)))]
For inference, labels can be not specified. The element of the rdd should be some ndarrays of the same structure of the features argument.
A note on the legacy api: if you are using names, shapes, types arguments, each element of the rdd should be a list of numpy.ndarray.
Parameters: rdd – a rdd containing the numpy.ndarrays to be used for training/evaluation/inference :param features: the structure of input features, should one the following: - a tuple (dtype, shape), e.g. (tf.float32, [28, 28, 1]) - a list of such tuple [(dtype1, shape1), (dtype2, shape2)], e.g. [(tf.float32, [10]), (tf.float32, [20])], - a dict of such tuple, mapping string names to tuple {“name”: (dtype, shape}, e.g. {“input1”:(tf.float32, [10]), “input2”: (tf.float32, [20])}
Parameters: - labels – the structure of input labels, format is the same as features
- batch_size – the batch size, used for training, should be a multiple of
total core num :param batch_per_thread: the batch size for each thread, used for inference or evaluation :param hard_code_batch_size: whether to hard code the batch_size into tensorflow graph, if True, the static size of the first dimension of the resulting tensors is batch_size/total_core_num (training) or batch_per_thread for inference; if False, it is None. :param val_rdd: validation data with the same structure of rdd :param sequential_order: whether to iterate the elements in the Dataset in sequential order when training. :param shuffle: whether to shuffle the elements in each partition before each epoch when training :return: a TFDataset
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static
from_string_rdd
(string_rdd, batch_size=-1, batch_per_thread=-1, hard_code_batch_size=False, validation_string_rdd=None)[source]¶ Create a TFDataset from a RDD of strings. Each element is the RDD should be a single string. The returning TFDataset’s feature_tensors has only one Tensor. the type of the Tensor is tf.string, and the shape is (None,). The returning don’t have label_tensors. If the dataset is used for training, the label should be encoded in the string. :param string_rdd: the RDD of strings :param batch_size: the batch size, used for training, should be a multiple of total core num :param batch_per_thread: the batch size for each thread, used for inference or evaluation :param hard_code_batch_size: whether to hard code the batch_size into tensorflow graph, if True, the static size of the first dimension of the resulting tensors is batch_size/total_core_num (training) or batch_per_thread for inference; if False, it is None. :param validation_string_rdd: the RDD of strings to be used in validation :return: a TFDataset
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static
from_text_set
(text_set, text, label=None, batch_size=-1, batch_per_thread=-1, hard_code_batch_size=False, validation_image_set=None, sequential_order=False, shuffle=True)[source]¶ Create a TFDataset from a TextSet. The TextSet must be transformed to Sample, i.e. the result of TextFeatureToSample transformer. :param text_set: the TextSet used to create this TFDataset :param text: a tuple of two, the first element is the type of this input feature, the second element is the shape of this element, i.e. (tf.float32, [10, 100, 4])). text can also be nested structure of this tuple of two. :param label: a tuple of two, the first element is the type of label, the second element is the shape of this element, i.e. (tf.int32, [1])). label can also be nested structure of this tuple of two. :param batch_size: the batch size, used for training, should be a multiple of total core num :param batch_per_thread: the batch size for each thread, used for inference or evaluation :param hard_code_batch_size: whether to hard code the batch_size into tensorflow graph, if True, the static size of the first dimension of the resulting tensors is batch_size/total_core_num (training) or batch_per_thread for inference; if False, it is None. :param validation_image_set: The TextSet used for validation during training :param sequential_order: whether to iterate the elements in the Dataset in sequential order when training. :param shuffle: whether to shuffle the elements in each partition before each epoch when training :return: a TFDataset
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static
from_tfrecord_file
(sc, file_path, batch_size=-1, batch_per_thread=-1, hard_code_batch_size=False, validation_file_path=None, sequential_order=False, shuffle=True)[source]¶ Create a TFDataset from tfrecord files. :param sc: The SparkContext :param file_path: comma seperated tfrecord file(s) path structure of tensors. Follows the signature: * Args: * example: a string TensorFlow tensor representing a single record * Returns: a tuple or dictionary of output tensors and the output tensors must be of numeric type :param batch_size: the batch size, used for training, should be a multiple of total core num :param batch_per_thread: the batch size for each thread, used for inference or evaluation :param hard_code_batch_size: whether to hard code the batch_size into tensorflow graph, if True, the static size of the first dimension of the resulting tensors is batch_size/total_core_num (training) or batch_per_thread for inference; if False, it is None. :param validation_file_path: The tfrecord files used for validation :param sequential_order: whether to iterate the elements in the Dataset in sequential order when training. :param shuffle: whether to shuffle the elements in each partition before each epoch when training :return: a TFDataset
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get_evaluation_data
()[source]¶ Returns: an object that can be used for TFNet.evaluate, e.g. an RDD of Sample or a ImageSet
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get_prediction_data
()[source]¶ Returns: an object that can be used for TFNet.predict e.g. an RDD of Sample or a ImageSet
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get_training_data
()[source]¶ Returns: an object that can be used to create a BigDL optimizer, e.g. an RDD of Sample or a DataSet
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get_validation_data
()[source]¶ Returns: an object that can be used to set validation in a BigDL optimizer, e.g. an RDD of Sample or a DataSet
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label_tensors
¶
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tensors
¶ a nested structure of TensorFlow tensor object in TensorFlow graph. The elements of this dataset will be fed into these tensors on each iteration. :return: the nested structure of TensorFlow tensor object
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class
zoo.pipeline.api.net.tf_dataset.
TFFeatureDataset
(dataset, tensor_structure, batch_size, batch_per_thread, hard_code_batch_size=False, validation_dataset=None)[source]¶ Bases:
zoo.pipeline.api.net.tf_dataset.TFDataset
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get_evaluation_data
()[source]¶ Returns: an object that can be used for TFNet.evaluate, e.g. an RDD of Sample or a ImageSet
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get_prediction_data
()[source]¶ Returns: an object that can be used for TFNet.predict e.g. an RDD of Sample or a ImageSet
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class
zoo.pipeline.api.net.tf_dataset.
TFImageDataset
(image_set, tensor_structure, batch_size, batch_per_thread, hard_code_batch_size=False, validation_image_set=None, sequential_order=False, shuffle=True)[source]¶ Bases:
zoo.pipeline.api.net.tf_dataset.TFDataset
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get_evaluation_data
()[source]¶ Returns: an object that can be used for TFNet.evaluate, e.g. an RDD of Sample or a ImageSet
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get_prediction_data
()[source]¶ Returns: an object that can be used for TFNet.predict e.g. an RDD of Sample or a ImageSet
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-
class
zoo.pipeline.api.net.tf_dataset.
TFNdarrayDataset
(rdd, tensor_structure, batch_size, batch_per_thread, hard_code_batch_size=False, val_rdd=None, sequential_order=True, shuffle=False)[source]¶ Bases:
zoo.pipeline.api.net.tf_dataset.TFDataset
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static
from_ndarrays
(tensors, batch_size=-1, batch_per_thread=-1, hard_code_batch_size=False, val_tensors=None, sequential_order=False, shuffle=True)[source]¶ Create a TFDataset from a nested structure of numpy ndarrays. Each element in the resulting TFDataset has the same structure of the argument tensors and is created by indexing on the first dimension of each ndarray in the tensors argument.
This method is equivalent to sc.parallize the tensors and call TFDataset.from_rdd
Parameters: - tensors – the numpy ndarrays
- batch_size – the batch size, used for training, should be a multiple of
total core num :param batch_per_thread: the batch size for each thread, used for inference or evaluation :param hard_code_batch_size: whether to hard code the batch_size into tensorflow graph, if True, the static size of the first dimension of the resulting tensors is batch_size/total_core_num (training) or batch_per_thread for inference; if False, it is None. :param val_tensors: the numpy ndarrays used for validation during training :param sequential_order: whether to iterate the elements in the Dataset in sequential order when training. :param shuffle: whether to shuffle the elements in each partition before each epoch when training :return: a TFDataset
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static
from_rdd
(rdd, names=None, shapes=None, types=None, batch_size=-1, batch_per_thread=-1, hard_code_batch_size=False, val_rdd=None, features=None, labels=None, sequential_order=False, shuffle=True)[source]¶ Create a TFDataset from a rdd.
For training and evaluation, both features and labels arguments should be specified. The element of the rdd should be a tuple of two, (features, labels), each has the same structure of numpy.ndarrays of the argument features, labels.
E.g. if features is [(tf.float32, [10]), (tf.float32, [20])], and labels is {“label1”:(tf.float32, [10]), “label2”: (tf.float32, [20])} then a valid element of the rdd could be
( [np.zeros(dtype=float, shape=(10,), np.zeros(dtype=float, shape=(10,)))], {“label1”: np.zeros(dtype=float, shape=(10,)), “label2”:np.zeros(dtype=float, shape=(10,))))} )
If labels is not specified, then the above element should be changed to [np.zeros(dtype=float, shape=(10,), np.zeros(dtype=float, shape=(10,)))]
For inference, labels can be not specified. The element of the rdd should be some ndarrays of the same structure of the features argument.
A note on the legacy api: if you are using names, shapes, types arguments, each element of the rdd should be a list of numpy.ndarray.
Parameters: rdd – a rdd containing the numpy.ndarrays to be used for training/evaluation/inference :param features: the structure of input features, should one the following: - a tuple (dtype, shape), e.g. (tf.float32, [28, 28, 1]) - a list of such tuple [(dtype1, shape1), (dtype2, shape2)], e.g. [(tf.float32, [10]), (tf.float32, [20])], - a dict of such tuple, mapping string names to tuple {“name”: (dtype, shape}, e.g. {“input1”:(tf.float32, [10]), “input2”: (tf.float32, [20])}
Parameters: - labels – the structure of input labels, format is the same as features
- batch_size – the batch size, used for training, should be a multiple of
total core num :param batch_per_thread: the batch size for each thread, used for inference or evaluation :param hard_code_batch_size: whether to hard code the batch_size into tensorflow graph, if True, the static size of the first dimension of the resulting tensors is batch_size/total_core_num (training) or batch_per_thread for inference; if False, it is None. :param val_rdd: validation data with the same structure of rdd :param sequential_order: whether to iterate the elements in the Dataset in sequential order when training. :param shuffle: whether to shuffle the elements in each partition before each epoch when training :return: a TFDataset
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get_evaluation_data
()[source]¶ Returns: an object that can be used for TFNet.evaluate, e.g. an RDD of Sample or a ImageSet
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get_prediction_data
()[source]¶ Returns: an object that can be used for TFNet.predict e.g. an RDD of Sample or a ImageSet
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static
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class
zoo.pipeline.api.net.tf_dataset.
TFTextDataset
(text_set, tensor_structure, batch_size, batch_per_thread, hard_code_batch_size=False, validation_text_set=None, sequential_order=False, shuffle=True)[source]¶ Bases:
zoo.pipeline.api.net.tf_dataset.TFDataset
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get_evaluation_data
()[source]¶ Returns: an object that can be used for TFNet.evaluate, e.g. an RDD of Sample or a ImageSet
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get_prediction_data
()[source]¶ Returns: an object that can be used for TFNet.predict e.g. an RDD of Sample or a ImageSet
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zoo.pipeline.api.net.tf_optimizer module¶
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class
zoo.pipeline.api.net.tf_optimizer.
BigDLMetric
(val_method, outputs, labels)[source]¶ Bases:
object
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class
zoo.pipeline.api.net.tf_optimizer.
IdentityCriterion
[source]¶ Bases:
bigdl.nn.criterion.Criterion
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class
zoo.pipeline.api.net.tf_optimizer.
StatelessMetric
(metric_name, idx)[source]¶ Bases:
bigdl.util.common.JavaValue
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class
zoo.pipeline.api.net.tf_optimizer.
TFModel
(training_helper_layer, criterion, val_methods)[source]¶ Bases:
object
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class
zoo.pipeline.api.net.tf_optimizer.
TFOptimizer
(tf_model, optim_method, sess=None, dataset=None, val_split=0.0, clip_norm=None, clip_value=None, model_dir=None)[source]¶ Bases:
object
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static
export_training_model
(export_dir, loss, sess, inputs, metrics=None, tensor_with_value=None, updates=None)[source]¶
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classmethod
from_keras
(keras_model, dataset, optim_method=None, val_split=0.0, session_config=None, model_dir=None)[source]¶ Create a TFOptimizer from a tensorflow.keras model. The model must be compiled. :param keras_model: the tensorflow.keras model, which must be compiled. :param dataset: a TFDataset :param optim_method: the optimization method to be used, such as bigdl.optim.optimizer.Adam :param val_split: Float between 0 and 1. Fraction of the training data to be used as validation data. :return:
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classmethod
from_loss
(loss, optim_method, session=None, val_outputs=None, val_labels=None, val_method=None, val_split=0.0, clip_norm=None, clip_value=None, metrics=None, tensor_with_value=None, session_config=None, model_dir=None, updates=None)[source]¶ Create a TFOptimizer from a TensorFlow loss tensor. The loss tensor must come from a TensorFlow graph that only takes TFDataset.tensors and the tensors in tensor_with_value as inputs. :param loss: The loss tensor of the TensorFlow model, should be a scalar :param optim_method: the optimization method to be used, such as bigdl.optim.optimizer.Adam :param session: the current TensorFlow Session, if you want to used a pre-trained model, you should use the Session to load the pre-trained variables and pass it to TFOptimizer. :param val_outputs: the validation output TensorFlow tensor to be used by val_methods :param val_labels: the validation label TensorFlow tensor to be used by val_methods :param val_method: the BigDL val_method(s) to be used. :param val_split: Float between 0 and 1. Fraction of the training data to be used as validation data. :param clip_norm: float >= 0. Gradients will be clipped when their L2 norm exceeds this value. :param clip_value: float >= 0. Gradients will be clipped when their absolute value exceeds this value. :param metrics: a dictionary. The key should be a string representing the metric’s name and the value should be the corresponding TensorFlow tensor, which should be a scalar. :param tensor_with_value: a dictionary. The key is TensorFlow tensor, usually a placeholder, the value of the dictionary is a tuple of two elements. The first one of the tuple is the value to feed to the tensor in training phase and the second one is the value to feed to the tensor in validation phase. :return: a TFOptimizer
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optimize
(end_trigger=None, checkpoint_trigger=None)[source]¶ Run the training loop of the this optimizer :param end_trigger: BigDL’s Trigger to indicate when to stop the training. :param checkpoint_trigger: When to save a checkpoint and evaluate model.
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static
zoo.pipeline.api.net.tf_predictor module¶
zoo.pipeline.api.net.tfnet module¶
-
class
zoo.pipeline.api.net.tfnet.
TFNet
(path, input_names=None, output_names=None, tf_session_config=None, jvalue=None, bigdl_type='float')[source]¶ Bases:
bigdl.nn.layer.Layer
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static
check_input
(input)[source]¶ Parameters: input – ndarray or list of ndarray or JTensor or list of JTensor. Returns: (list of JTensor, isTable)
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static
from_export_folder
(folder, tf_session_config=None)[source]¶ Create a TFNet from an exported folder produced by export_tf :param folder: the folder the TensorFlow model exported to :param tf_session_config: an optional tf.ConfigProto object to set the session config in java side. This config does not necessarily be the same with your current session. E.g. sess_config = tf.ConfigProto(inter_op_parallelism_threads=1, intra_op_parallelism_threads=1) net = TFNet.from_session(sess, inputs, outputs, sess_config) :return: a TFNet
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static
from_saved_model
(model_path, tag=None, signature=None, inputs=None, outputs=None, tf_session_config=None)[source]¶ Create a TFNet from an TensorFlow saved model :param model_path: the path to the SavedModel path :param tag: the tag to load in the saved model, default to “serve” :param signature: The signature of the SignatureDef that defines inputs and outputs of the graph. TFNet assumes inputs is sorted by their corresponding key in SignatureDef. :param inputs: a list input tensor names of this model, you may want to use TensorFlow’s command line tool to inspect the saved model to find the input tensor names e.g. saved_model_cli show –dir {saved_model_path} –all :param outputs: a list output tensor names of this model, you may want to use TensorFlow’s command line tool to inspect the saved model to find the output tensor names e.g. saved_model_cli show –dir {saved_model_path} –all :param tf_session_config: an optional tf.ConfigProto object to set the session config in java side. This config does not necessarily be the same with your current session. E.g. sess_config = tf.ConfigProto(inter_op_parallelism_threads=1, intra_op_parallelism_threads=1) net = TFNet.from_session(sess, inputs, outputs, sess_config) :return: a TFNet
-
static
from_session
(sess, inputs, outputs, generate_backward=False, allow_non_differentiable_input=True, tf_session_config=None)[source]¶ Create a TFNet from an a session and the inputs and outpus endpoints of the TensorFlow graph. :param sess: the TensorFlow session contain all the variables :param inputs: a list of TensorFlow Tensor represents the input endpoints of the TensorFlow graph :param outputs: a list of TensorFlow Tensor represents the output endpoints of the TensorFlow graph :param generate_backward: whether to generated a the backward graph, set true if you want to train this TFNet :param allow_non_differentiable_input: if set to yes, when input are not differentiable, the gradient will be set to zero. if set to false, an error will be thrown. :param tf_session_config: an optional tf.ConfigProto object to set the session config in java side. This config does not necessarily be the same with your current session. E.g. sess_config = tf.ConfigProto(inter_op_parallelism_threads=1, intra_op_parallelism_threads=1) net = TFNet.from_session(sess, inputs, outputs, sess_config) :return a TFNet
-
static
zoo.pipeline.api.net.torch_criterion module¶
-
class
zoo.pipeline.api.net.torch_criterion.
LossWrapperModule
(lossFunc)[source]¶ Bases:
torch.nn.modules.module.Module
-
forward
(x, y)[source]¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-
-
class
zoo.pipeline.api.net.torch_criterion.
TorchCriterion
(path, bigdl_type='float')[source]¶ Bases:
bigdl.nn.criterion.Criterion
TorchCriterion wraps a loss function for distributed inference or training. Use TorchCriterion.from_pytorch to initialize.
-
static
from_pytorch
(loss, input, label=None)[source]¶ Create a TorchCriterion directly from PyTorch function. We need users to provide example input and label (or just their sizes) to trace the loss function.
Parameters: loss – this can be a torch loss (e.g. nn.MSELoss()) or a function that takes two Tensor parameters: input and label. E.g. def lossFunc(input, target): return nn.CrossEntropyLoss().forward(input, target.flatten().long()) :param input: example input. It can be: 1. a torch tensor, or tuple of torch tensors for multi-input models 2. list of integers, or tuple of int list for multi-input models. E.g. For ResNet, this can be [1, 3, 224, 224]. A random tensor with the specified size will be used as the example input. :param label: example label. It can be: 1. a torch tensor, or tuple of torch tensors for multi-input models 2. list of integers, or tuple of int list for multi-input models. E.g. For ResNet, this can be [1, 3, 224, 224]. A random tensor with the specified size will be used as the example input. When label is None, input will also be used as label.
-
static
zoo.pipeline.api.net.torch_net module¶
-
class
zoo.pipeline.api.net.torch_net.
TorchNet
(path, bigdl_type='float')[source]¶ Bases:
bigdl.nn.layer.Layer
TorchNet wraps a TorchScript model as a single layer, thus the Pytorch model can be used for distributed inference or training. :param path: path to the TorchScript model.
-
static
from_pytorch
(module, input, check_trace=True)[source]¶ Create a TorchNet directly from PyTorch model, e.g. model in torchvision.models. Users need to provide an example input or the input tensor shape. :param module: a PyTorch model :param input: To trace the tensor operations, torch jit trace requires users to provide an example input. Here the input parameter can be: 1. a torch tensor, or tuple of torch tensors for multi-input models 2. list of integers, or tuple of int list for multi-input models. E.g. For ResNet, this can be [1, 3, 224, 224]. A random tensor with the specified size will be used as the example input. :param check_trace: boolean value, optional. check if the same inputs run through traced module produce the same outputs. Default:
True
. You might want to disable this if, for example, your network contains non-deterministic ops or if you are sure that the network is correct despite a checker failure.
-
static


Plugin test and demonstration page
Plugin homepage
jQuery plugins directory page
QUnit example
Show all tweets, unstyled
Show one tweet at a time with animation, unstyled
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on twitter
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---
layout: post
title: rough layout for a web illustration
date: '2015-06-23T18:30:41+02:00'
categories:
- Illustration
picture_url: https://film-Storyboards.tumblr.com/post/122290344751/rough-layout-for-a-web-Illustration-client
---
Rough layout for a web Illustration.
Client: 24Vision
Agency: LUON


HRMS Web App
Web application which is complete solution for HRMS requirement.
Contains 4 Module Payroll, Leave, Attendance, Income tax,
Employee Self Service Portal .
Technology Used
Asp.Net MVC, C#, Bootstrap 4, jquery, Jquery UI, JQGrid, EF6,
Ado.net, RDLC, Web Api 2


The focus of Chapter 3, Getting Data from Visitors, is on the HTML form, not on the Perl CGI script used to parse the form. Therefore, when you click on each section, you'll see the HTML page shown on the corresponding page and you'll be able to input data and submit the data to test the examples as I've shown in the figures. The actual Perl CGI script that's doing all the work from pages 45-54 is called namevalue.cgi. It basically calls the script described in Appendix A, Parsing Form Input, and then prints out the results. Page 55 uses a script called zonemap.cgi which is virtually identical to namevalue.cgi. On page 56, I use a script called gettime.cgi. On page 57, I use a script that gets data from the link that activates it. Finally, on page 58, there's a script that gets default data from the form.
Although I'll let you see the script (click on that link in the previous paragraph), I'd rather not explain it just yet. We'll get there. For now, focus on reviewing your HTML form creating skills and looking at how what you use for the NAME and VALUE attributes affects the results.


Register
FirstName
LastName
Male
Female
Other
Email
BirthDay
Date
13
14
15
16
Month
Feb
mar
Apr
May
Year
1999
2000
2001
2002
I agree to the terms and conditions


The EntityLogger object records the outputs, attributes, and state data for each entity that it receives.
The TraceEntityStates input provides an alternative mode of operation. When this input is set to TRUE, the entities received by the EntityLogger are instructed to record a log entry each time they change state.
The output log file is created automatically when the simulation run begins. The output file is named <configuration file name>-<EntityLogger name>.log to ensure that it is unique for the simulation run. For example, if the configuration file is named run1.cfg and the EntityLogger's name is EntityLogger1, then the name of the log file will be run1-EntityLogger1.log. A pre-existing file with this name will be overwritten once the simulation run is started.
EntityLogger Inputs
|
Keyword |
Description |
|
Key Inputs |
|
|
Description |
A free-form string describing the object. |
|
DataSource |
One or more sources of data to be logged. Each source is specified by an Expression. It is best to include only dimensionless quantities and non-numeric outputs in the DataSource input. An output with dimensions can be made non-dimensional by dividing it by 1 in the desired unit, e.g. '[Queue1].AverageQueueTime / 1[h]' is the average queue time in hours. A dimensional number will be displayed along with its unit. The 'format' function can be used if a fixed number of decimal places is required. |
|
IncludeInitialization |
If TRUE, entries are logged during the initialization period. |
|
StartTime |
The time at which the log starts recording entries. |
|
EndTime |
The time at which the log stops recording entries. |
|
NextComponent |
The next object to which the processed entity is passed. |
|
TraceEntityStates |
If TRUE, an entry will made in the log file every time one of the received entities changes state. |
|
Options |
|
|
Active |
If TRUE, the object is used in the simulation run. |
|
AttributeDefinitionList, CustomOutputList |
See Section 6.2. |
|
Graphics |
|
|
Position, Alignment, Size, Orientation, Region, RelativeEntity, DisplayModel, Show, Movable, VisibleViews, DrawRange |
See Section 11.1. |
EntityLogger Outputs
|
Output Name |
Description |
|
Entity and DisplayEntity |
|
|
Name, ObjectType, SimTime, Parent, Position, Size, Orientation, Alignment, GraphicalLength, ObserverList, NextList, PreviousList, EntityReferenceList |
See Section 11.1. |
|
Logger |
|
|
LogTime |
The simulation time at which the last log entry was made. |
|
EntityLogger |
|
|
obj |
The entity that was received most recently. |
|
User-defined attributes and custom outputs |
As defined by the AttributeDefinitionList and CustomOutputList inputs. |


---
layout: post
title: 'PhD Talk interview '
date: '2015-06-16T23:00:00.000+01:00'
author: Amidst Science
tags:
- tools & workflow
modified_time: '2016-02-27T21:39:04.176Z'
blogger_id: tag:blogger.com,1999:blog-7745412915677497145.post-6804477460615938261
blogger_orig_url: http://amidstscience.blogspot.com/2015/06/phd-talk-interview.html
---
I have done an interview for the great PhD Talk blog. If you don't know it, you should definitely check it out: Eva does a great job, there is sooooo much good advice over there. I really like all the guest posts too, it's nice to hear from a variety of people from different backgrounds and at different stages of their careers.
I am a sucker for workflows and optimisation, so the new series of posts describing how different academics work was going to be a must-read for me. Eva was looking for more academics to take part in the series and I thought why not?
If you would like to read the interview you can find it here: This is how I work!


{% extends "layout.html" %} {% block page_title %} GOV.UK prototyping kit {% endblock %} {% block content %} ALPHA This is a new service – your feedback will help us to improve it. What type of waste did you transfer? Wastes from agriculture, horticulture, aquaculture, forestry, hunting and fishing Oil and Liquid Fuel Waste Waste packaging; absorbents, wiping cloths, filter materials and protective clothing not otherwise specified Construction and Demolition Waste Healthcare Waste Materials from Waste and Water Treatment Municipal Waste and Similar Materials From Commerce and Industry Wastes not otherwise specified in the list {% endblock %}
ALPHA This is a new service – your feedback will help us to improve it.


{% extends 'base_http_fail.html' %} {% load static %} {% block custom_body %}
wow~这个页面被外星人抢走了~
Wow~ this page was the alien took ~
{% endblock %}


14 February 2003
Disease Outbreak Reported
The Ministry of Health, China has reported that the outbreak in Guangdong Province is clinically consistent with atypical pneumonia. Cases had been detected in the province as far back as November 16, 2002. The majority of the patients have experienced non-specific symptoms including fever, headache, joint ache and overall fatigue and weariness.
As of 11 February 2003, a total of 305 cases and 5 deaths have been reported. National authorities report that outbreak is coming under control. Investigations carried out by the Chinese authorities have ruled out anthrax, pulmonary plague, leptospirosis and haemorrhagic fever.


It is not suprising that Christmas turns out to be the most popular holiday/occasion to send gifts for. The gift search term for Christmas has the highest search interest across all years around the globe. After Christmas gift search are birthday gifts and anniversary gifts. Birthday as a common occasion for celebration, gift
When looking at gift search terms by relationships, parents and girlfriend/boyfriend seem to gain more attention and got more gifts compare to others.
Buying gifts for males seems like a struggle on a global level. A lot more people including me ask for Google's help for the issue. However, it is possible that the phenomenon is caused due to people in general are having problem with buying gifts for the opposite sex and females just happen to give more gifts.


This website uses technical and third parties profiling cookies to send advertising messages in line with the user's online navigation preferences. By proceeding you agree to the use of these cookies. For the complete information notice or to deny you consent please click here »


SIE CONFIDENTIAL
Copyright (C) 2017 Sony Interactive Entertainment Inc. All Rights Reserved.
libgnm - simplet-simple-fs Sample
Overview
This sample demonstrates simple fetch shading.
Screenshot
Result
Explanation
This sample creates a fetch shader to match a vertex shader, and uses it to shade vertices in a simple rendering.
Changes
0.80
First release
Keywords
render
texture
graphics


I'm a shy person. I always have been. As a child I had some selective mutism going on. That was always fun in school, being called on randomly by a teacher when my hand was clearly not up, and just freezing. I knew the answer. Heck, I could talk circles around the answer, I just couldn't bring myself to say it. It's hard to describe.
Around second or third grade, I started rationalizing the anxiety away, telling myself I had no reason to be afraid. Since there was no physical harm being threatened, I shouldn't feel so scared, and I should just ignore it. After some time, the rationalizations became more natural, and I got used to forcing myself out of my comfort zone. Being anxious when interacting with others became the new norm.
When I read that we would have to constantly work with strangers at Dev Bootcamp, I almost didn't apply. I pushed myself with my rationalization again, though, and I am very, very glad I did.
When it came time for my first pair, I was super anxious that he would think I was just some dumb girl who didn't belong there because honestly, I'm not very good at communicating my ideas. That, and I'm not sure if I do belong here. However, my first pairing buddy was super amazing and was able to put me at ease quite easily. That seems to be one of the many things he's good at since he seems to make friends naturally. I'm really, really jealous of people like that. Not "omg I hate you" kind of jealous, but "man, I wish I coud do that," kind of jealous. It's cool, though. I've used him as inspiration to step up and talk to complete strangers and so far it's worked out awesomely!
That's been one of the best things about the pairing experience- learning from others. I don't know about any of you other geeks out there, but I get this almost-euphoric feeling when I learn a new approach to coding or something novel that I can assimilate into my knowledge system. Again, it's hard to describe, but pairing with my cohorts has been freaking fantastic because of it. It's amazing to see how other people can obtain the same information that I do, and then see how they apply it to solve problems in ways I would never have thought of. Not one session goes by where I don't learn something mind-blowing. And more often than not, their way ends up being better than mine.
I do have to point out that I am super-blessed with an amazing cohort. Everyone in it is crazy-smart in their own way and I think we all complement each other very well. Well, they all do, anyway. Maybe they could do without me, since I'm not sure what niche I fill, yet. But I am learning from each and every one of them, and they're all so very open to my questions, even the dumb ones I should probably know. I only hope to one day be good enough to maybe be as helpful to them as they have been to me.
The problem with pairing with such awesome people, though, is the part where I have to give feedback. As I understand it, we're supposed to give our pairing buddy something they can improve on for next time. You may have noticed, but I am pretty critical of myself. When I try to be critical of others, it always turns into, "Who am I to judge them? They're actually not too bad, because I'm guiltier of that same thing and probably even worse things by miles!" Because of that, feedback is hard enough on its own. But trying to give critical feedback to folks who blow your mind and leave you in the dust? That's practically an impossible task for me. I'm still working on getting better at leaving feedback for amazing people, but I'm not quite sure where to start.
Receiving feedback has been pretty awesome, so far. Everyone has been extremely kind to me. I do see a common theme of my apologizing too much. Yeah, I recognize I do that. It's just a reflex whenever I think I've given anyone reason to be annoyed with me. But now I see it's probably pretty annoying on it's own, turning me into a spiral of never-ending annoyances. Perhaps it would be best if I never entered the spiral in the first place? I don't want to never apologize, though. That'd be exchanging one problem for another! I will make an attempt to cut it down, but I'm not too confident that I'll be able to. It's second nature to me at this point.
 I know that doesn't sound too helpful; it's like telling people who are struggling to breathe to just breathe. Trust me, I've been there. But please know that the programming world is full of amazing non-judgmental people who have so much to teach you. You have a lot more to gain than to lose. And to anyone who's been on the receiving end of my spiral of annoyances, I apologize... And thus the sprial continues. *sigh* Clearly, I have my work cut out for me.


Register and Book Appointment!!!
Write online Prescription and see your appointments!
Maintain Docter's and Patient's!!


-
Breaking News, Sports, Weather & More | Miami Herald & MiamiHerald.com
www.miamiherald.com
Miami Herald newspaper in Miami, FL is proud to offer you local news coverage online. Serving South Florida, MiamiHerald.com has local, breaking, ...
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The Telegraph - Telegraph online, Daily Telegraph, Sunday Telegraph - ...
www.telegraph.co.uk
Latest news, business, sport, comment, lifestyle and culture from the Daily Telegraph and Sunday Telegraph newspapers and video from Telegraph TV.
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Scoop - New Zealand News
www.scoop.co.nz
Scoop Provides up to the minute New Zealand News. Press Releases, Analysis, Opinion Pieces, all published the instant they are available
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Phoenix and Arizona Breaking News And News Headlines - Home
ktar.com
Phoenix, Arizona News - KTAR.com. The page brings you up to date news from state of Arizona. KTAR.com is the home of News/Talk 92.3 KTAR.
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Sustainable Business, Green Business, Renewable Energy, Organic & Green ...
www.sustainablebusiness.com
Online community for green business: Green Dream Jobs, Progressive Investor, Renewable Energy stocks, Healthy Living stocks, Green Capital, daily ...
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NewsOK: Oklahoma City News, Sports, Weather, Business & Entertainment OKC
newsok.com
NewsOK is Oklahoma's most trusted source for breaking news, sports, weather, obituaries, politics, business, art, entertainment, blogs, video, photos ...
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Southernminn.com
www.southernminn.com
A 6-year-old watches his parents abuse illegally obtained prescription drugs.
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Your guide to dining, nightlife, music, arts and people of Columbus, Ohio
www.columbusalive.com
Your guide to dining, nightlife, music, arts and people of Columbus, Ohio
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Timesonline.com
www.timesonline.com
The Beaver County Times covers local news and events from Beaver County, Northern Allegheny County and Southern Lawrence County.
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Home
www.krcrtv.com
Redding, Chico & Red Bluff severe weather information, breaking news, community events and more brought to you by KRCR News Channel 7.
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EnergyTrends | An online resource for comparing state energy patterns.
www.energytrends.org
More New York energy news, this time from the Big Apple: This week, New York City Mayor Bill de Blasio made public a plan to reduce greenhouse gas ...
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Breaking News, Sports, Weather & More | The Wichita Eagle & Kansas.com
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The Wichita Eagle newspaper in Wichita, KS is proud to offer you local news coverage online. Serving the Wichita Metro, Kansas.com has local, ...
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Lehigh Valley news, Easton news, Bethlehem news, Allentown news, ...
www.lehighvalleylive.com
Get the latest Lehigh Valley PA Local News, Easton, Bethlehem, Allentown, Phillipsburg, Warren County, Slate Belt, Upper Bucks news, sports & US ...
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Northcentralpa.com Home
www.northcentralpa.com
Pennsylvania State Police Montoursville reported the following incidents
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ENGINEERING.com | The Engineer's Ultimate Resource Tool
www.engineering.com
Engineering information and connections for the global community of engineers. Find engineering games, videos, jobs, disciplines, calculators and ...
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The Alternative Press
thealternativepress.com
Hyperlocal online news and information for communities in Pennsylvania and New Jersey including Berkeley Heights NJ, Chatham NJ, East Brunswick NJ, ...
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Care2 - largest online community for healthy and green living, human ...
www.care2.com
Largest online community empowering people to lead a healthy and green lifestyle while taking action on important causes such as human rights, animal ...
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Zacks Investment Research: Stock Research, Analysis, & Recommendations
www.zacks.com
Zacks is the leading investment research firm focusing on stock research, analysis and recommendations. Gain free stock research access to stock ...
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Moyers & Company | BillMoyers.com
billmoyers.com
Veteran journalist Bill Moyers returned to television in January with "Moyers & Company." Visit the website to watch video from the new show and his ...
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Marketplace.org
www.marketplace.org
American Public Media's Marketplace presents news on business, economics, and money for the rest of us. Daily broadcasts can be heard on public radio ...
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Best of Patch | Local News, Community, Sports, Shopping, Restaurants, ...
patch.com
Stay in the know, connect with your neighborhood and make more of your day in USA. Patch brings you great local news, events and more.


Models: by Rome.
Size: Full 1/2 1/16
Try WebXR's AR mode on Android with Chrome (Beta) v81+ or Mozilla's WebXR Viewer on iOS.


@* * Copyright 2021 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import models.viewmodels.notificationspage.NotificationsPageSingleElement @(index: Int, element: NotificationsPageSingleElement)
@{element.timestampInfo}


Music Theory
Table of Contents
The following lists may come from many authors all over the entire Internet world, I list their original addresses here:
- Reddit Music Thery self-study book list
- 43 Music Theory Concepts That EVERY Modern Composer Should Master {The Music Theory Map}
1 A learning map you can refer to
- Musical Alphabet
- Musical Staff
- Knowing the Fretboard
- Scale Shapes and the Major Scale
- Rhythm Basics
- Triplets, Swing, and Advanced Tuplets
- Meter and Time Signature
- Audiation and Melody
- Key Signatures and Enharmonics
- Scale Degrees
- Triads
- Movable Chords
- Diatonic Major Chords + Roman Numerals
- Writing Chord Progressions in Major
- Solo-ing with the Pentatonic Major Scale
- Suspended Chords
- The Minor Scale + Diatonic Chords
- Writing progressions in Minor
- Soloing with the Pentatonic Minor Scale
- Ear Training Intervals and Chords
- Power Chords
- Relative Scales and Tonul Center
- Combining Pentatonic Shapes
- Arpeggios and Chord Tone Soloing
- Inversions and Slash Chords
- Popular 7th Chords
- Diatonic 7th Chords
- Blues
- Secondary Dominant Chords
- Diminished Chords
- Augmented Chords.
- Harmonic Minor
- Writing Melodies
- Passing Tones and Chromaticism
- Introducing Modes
- Transpositions and Modulations
- Modal Interchange
- Song Sections
- Song Structures
- Writing Drum Parts
- Writing Bass Lines
- Creating a Full Song
- Advanced Theory Concepts: Timbre, Harmony, Tunings, and World Music
2 General
- The Complete Idiot's Guide To Music Theory
- The Complete Musician: An Integrated Approach to Tonal Theory, Analysis, and Listening
3 Harmony
3.1 Classical Period Harmony
- Harmony and Voice Leading - Carl Schachter
- Tonal Harmony - Stefan Kostka This book is quite concise and friendly:)
- Theory of Harmony - Arnold Schoenberg
- Structural Functions of Harmony - Arnold Schoenberg
- Practical Manual of Harmony - Nikolay Rimsky-Korsakov
- Analyzing Classical Form: An Approach for the Classroom - William Caplin
- Harmony and Melody by Elise Siegmeister
3.2 Modern 20th Century Harmony
- Twentieth-Century Harmony: Creative Aspects and Practice
4 Jazz
- The Jazz Harmony Book
- Bert Ligon - JAZZ THEORY RESOURCES, VOL. 1 & TWO (Really complete)
- Mark Levine - The Jazz Theory Book (Really good but not as complete as my first choice) (University Standard)
- Bert Ligon - Connecting Chords with Linear Harmony (new way to think…)
- Bert Ligon - COMPREHENSIVE TECHNIQUE for JAZZ MUSICIANS (Really good for practice)
5 Composition
- Alan Belkin - Musical composition
- Analyzing Classical form (duplication, but it is recommended for classical composition)


mkofm(1) teTeX mkofm(1) NAME mkofm - front end to mktextfm(1) SYNOPSIS mkofm ARGS DESCRIPTION mkofm takes all ARGS and passes them to mktextfm(1). For more informa‐ tion about appropriate arguments, see mktextfm(1). SEE ALSO mktextfm(1) BUGS None known. AUTHOR Author unknown. This manual page was written by C.M. Connelly<EMAIL_ADDRESS>for the Debian GNU/Linux system. It may be used by other distributions without contacting the author. Any mistakes or omissions in the manual page are my fault; inquiries about or corrections to this manual page should be directed to me (and not to the primary author). teTeX September 2000 mkofm(1)


BBQ FUNDRAISER
Two tmes per year, the FVBB hosts a huge BBQ. This is the second most important fundraiser for the Band Program.
I created the artwork we used for all of the promo collateral {banners, road signs, invitations, emails, social media posts etc}.


The eerily lifelike robot dog ‘Spot’ is now working with the police
by Ben Gilbert, Business Insider US- The array of robots from Boston Dynamics often appear in sometimes silly, sometimes terrifying videos that the company publishes on YouTube.
- Like most of Boston Dynamics’ robots, the company’s dog-like robots – Spot and Spot Mini – are in ongoing development, but commercial sales via lease agreements have begun.
- The Spot robot is already being used by the Massachusetts State Police.
- The Massachusetts State Police borrowed a Spot robot from Boston Dynamics for several months in 2019, and it was used by police in two unnamed “incidents.”
- Visit Business Insider’s homepage for more stories.
It seemed like only a matter of time before the sometimes silly, sometimes terrifying robots from Boston Dynamics made their way into police work.
That time has come, apparently: The Massachusetts State Police employed the dog-like Spot from Boston Dynamics from August until early November, Boston public radio station WBUR reported on Monday.
So, what was the Massachusetts State Police doing with a robot dog?
The loan agreement between Boston Dynamics and Massachusetts State Police explains it’s being used, “For the purpose of evaluating the robot’s capabilities in law enforcement applications, particularly remote inspection of potentially dangerous environments which may contain suspects and ordinances.”
Videos of Spot in action depict the dog-like robot opening doors and performing surveillance – it was used by the Bomb Squad and only the Bomb Squad, according to the lease agreement.
Though Spot was loaned to the Massachusetts State Police for testing, a representative told WBUR that Spot was deployed in two “incidents” without specifying details.
Both Boston Dynamics and the Massachusetts State Police say that the agreement didn’t allow robots to physically harm or threaten anyone.
“Part of our early evaluation process with customers is making sure that we’re on the same page for the usage of the robot,” Boston Dynamics VP of business development Michael Perry told WBUR. “So upfront, we’re very clear with our customers that we don’t want the robot being used in a way that can physically harm somebody.”
State police spokesman David Procopio echoed that sentiment. “Robot technology is a valuable tool for law enforcement because of its ability to provide situational awareness of potentially dangerous environments.”
Moreover, that’s how Boston Dynamics is handling the first commercial sales of Spot.
“As a part of our lease agreement, for people who enter our early adopter program, we have a clause that says you cannot use a robot in a way that physically harms or intimidates people,” Perry told Business Insider in a phone call on Monday afternoon.
Boston Dynamics announced earlier this year that Spot would be its first robot to go on sale to the public.
Those sales have already begun through the company’s “Early Adopter Program,” which offers leases to customers with certain requirements. If a customer violates that agreement, Boston Dynamics can terminate the relationship and reclaim its robot – it also allows the company to repair and replace the Spot robots it sells.
Perry said the Massachusetts State Police is the only law enforcement or military organization that Boston Dynamics is working with currently.


Back to home page
Privacy Policy
- Your data is safe.
- Your data is not going to be store anywhere.
- This is just for demostration purpose only.
- We are just taking data and showing it on screen.
- We are not sharing your data anywhere not even our server or to any other server.
- It is used just for educational purpose or demostration purpose only.
Terms Of Service
- This is part of intership tasks.
- We are not using your data for any purpose other than mentioned in privacy policy.


Information
A solution was found.
The objective value (weighted
slack) of this solution is {{solution.objective}}.
It
took {{solution.solveTime}} seconds to find this solution. The relative gap of the
solution is {{solution.relativeGap}}
Solution
| Event |
Time |
| {{event}} |
{{solution.eventTimes[event]}} |


{%extends "base.html"%} {% block script %} {% include "site_view_script.html" %} {%endblock%} {% block breadcrumb %} | View a Site {% endblock %} {%block about_this_page %}
{{ entries.0.program }}
Edit Site Info | View all Site Expenses | Reporting Summary {% if user.captain %}
If you just made an order, please confirm that it's correct and check your budget.
Click here to go back to your Captain's Page.


NBMRoomClient Class Reference
| Inherits from | NSObject |
|---|---|
| Declared in | NBMRoomClient.h |
Overview
It’s actually only a wrapper over the JSON-RPC protocol used to communicate with Room Server. The developer of room applications can use this API when implementing an iOS client.
delegate
The delegate object for the client.
@property (nonatomic, weak) id<NBMRoomClientDelegate> delegate
Declared In
NBMRoomClient.h
peers
A list of connected NBMPeer objects.
@property (nonatomic, readonly) NSArray *peers
Declared In
NBMRoomClient.h
room
The NBMRoom instance representing the joined room.
@property (nonatomic, readonly) NBMRoom *room
Declared In
NBMRoomClient.h
timeout
The timeout interval for API requests, in seconds. Default value is 5.
@property (nonatomic, assign, readonly) NSTimeInterval timeout
Declared In
NBMRoomClient.h
connectionState
@property (nonatomic, assign, readonly) NBMRoomClientConnectionState connectionState
connected
A Boolean that indicates the WebSocket connection status.
@property (nonatomic, assign, readonly, getter=isConnected) BOOL connected
Declared In
NBMRoomClient.h
joined
A Boolean that indicates if the room was joined by the local peer.
@property (nonatomic, assign, readonly, getter=isJoined) BOOL joined
Declared In
NBMRoomClient.h
– initWithRoom:delegate:
Creates and initializes a NBMRoomClient with default API timeout interval.
- (instancetype)initWithRoom:(NBMRoom *)room delegate:(id<NBMRoomClientDelegate>)delegate
Parameters
room |
The room to join with. |
|---|---|
delegate |
The delegate object for the client |
Return Value
An initialized NBMRoomClient object.
Declared In
NBMRoomClient.h
– initWithRoom:timeout:delegate:
Creates and initializes a NBMRoomClient with a custom API timeout interval.
- (instancetype)initWithRoom:(NBMRoom *)room timeout:(NSTimeInterval)timeout delegate:(id<NBMRoomClientDelegate>)delegate
Parameters
room |
The room to join with. |
|---|---|
timeout |
The timeout interval for API requests, in seconds. |
delegate |
The delegate object for the client. |
Return Value
An initialized NBMRoomClient object.
Declared In
NBMRoomClient.h
– connect
Connects client to the Room Server using WebSocket, giving access to its API when the connection is established successfully.
- (void)connect
Discussion
Note: See [NBMRoomClientDelegate client:isConnected:] method.
Declared In
NBMRoomClient.h
– joinRoom
Represents a client’s request to join a room. If the room does not exists, it is created. When the request is processed, the [NBMRoomClientDelegate client:didJoinRoom:] message is sent to the client’s delegate.
- (void)joinRoom
Declared In
NBMRoomClient.h
– joinRoom:
Represents a client’s request to join a room. If the room does not exists, it is created.
- (void)joinRoom:(void ( ^ ) ( NSSet *peers , NSError *error ))block
Parameters
block |
A block object to be executed when the request is processed. This block has no return value and takes an NSSet of remote participants and an error if the request failed. |
|---|
Discussion
Note: No message is sent to client’s delegate.
Declared In
NBMRoomClient.h
– leaveRoom
Represent a client’s notification that it’s leaving the room. When the request is processed, the [NBMRoomClientDelegate client:didLeaveRoom:] message is sent to the client’s delegate.
- (void)leaveRoom
Declared In
NBMRoomClient.h
– leaveRoom:
Represent a client’s notification that it’s leaving the room.
- (void)leaveRoom:(void ( ^ ) ( NSError *error ))block
Parameters
block |
A block object to be executed when the request is processed. This block has no return value and takes an error if the request failed. |
|---|
Discussion
Note: No message is sent to client’s delegate.
Declared In
NBMRoomClient.h
– publishVideo:loopback:
Represents a client’s request to start streaming her local media to anyone inside the room. When the request is processed, the [NBMRoomClientDelegate client:didPublishVideo:loopback:error:] message is sent to the client’s delegate.
- (void)publishVideo:(NSString *)sdpOffer loopback:(BOOL)doLoopback
Parameters
sdpOffer |
A NSString representing an SDP offer. |
|---|---|
doLoopback |
A Boolean enabling media loopback. |
Declared In
NBMRoomClient.h
– publishVideo:loopback:completion:
Represents a client’s request to start streaming her local media to anyone inside the room.
- (void)publishVideo:(NSString *)sdpOffer loopback:(BOOL)doLoopback completion:(void ( ^ ) ( NSString *sdpAnswer , NSError *error ))block
Parameters
sdpOffer |
A NSString representing an SDP offer. |
|---|---|
doLoopback |
A Boolean enabling media loopback. |
block |
A block object to be executed when the request is processed. This block has no return value and takes the SDP answer sent by KMS and an error if the request failed. |
Discussion
Note: No message is sent to client’s delegate.
Declared In
NBMRoomClient.h
– unpublishVideo
Represents a client’s request to stop streaming its local media to the room peers. When the request is processed, the [NBMRoomClientDelegate client:didUnPublishVideo:] message is sent to the client’s delegate.
- (void)unpublishVideo
Declared In
NBMRoomClient.h
– unpublishVideo:
Represents a client’s request to stop streaming its local media to the room peers.
- (void)unpublishVideo:(void ( ^ ) ( NSError *error ))block
Parameters
block |
A block object to be executed when the request is processed. This block has no return value and takes an error if the request failed. |
|---|
Discussion
Note: No message is sent to client’s delegate.
Declared In
NBMRoomClient.h
– receiveVideoFromPeer:offer:
Represents a client’s request to receive media from participants in the room that published their media. This method can also be used for loopback connections. When the request is processed, the [NBMRoomClientDelegate client:didReceiveVideoFrom:sdpAnswer:error:] message is sent to the client’s delegate.
- (void)receiveVideoFromPeer:(NBMPeer *)peer offer:(NSString *)sdpOffer
Parameters
peer |
A NBMPeer that is publishing media. |
|---|---|
sdpOffer |
A NSString representing an SDP offer. |
Declared In
NBMRoomClient.h
– receiveVideoFromPeer:offer:completion:
Represents a client’s request to receive media from participants in the room that published their media.
- (void)receiveVideoFromPeer:(NBMPeer *)peer offer:(NSString *)sdpOffer completion:(void ( ^ ) ( NSString *sdpAnswer , NSError *error ))block
Parameters
peer |
A NBMPeer that is publishing media. |
|---|---|
sdpOffer |
A NSString representing an SDP offer. |
block |
A block object to be executed when the request is processed. This block has no return value and takes the SDP answer sent by KMS and an error if the request failed. |
Discussion
Note: No message is sent to client’s delegate.
Declared In
NBMRoomClient.h
– unsubscribeVideoFromPeer:
Represents a client’s request to stop receiving media from a given publisher. When the request is processed, the [NBMRoomClientDelegate client:didUnsubscribeVideoFrom:sdpAnswer:error:] message is sent to the client’s delegate.
- (void)unsubscribeVideoFromPeer:(NBMPeer *)peer
Parameters
peer |
A NBMPeer that is publishing media. |
|---|
Declared In
NBMRoomClient.h
– unsubscribeVideoFromPeer:completion:
Represents a client’s request to stop receiving media from a given publisher.
- (void)unsubscribeVideoFromPeer:(NBMPeer *)peer completion:(void ( ^ ) ( NSString *sdpAnswer , NSError *error ))block
Parameters
peer |
The NBMPeer that is publishing media. |
|---|---|
block |
A block object to be executed when the request is processed. This block has no return value and takes the SDP answer sent by KMS and an error if the request failed. |
Discussion
Note: No message is sent to client’s delegate.
Declared In
NBMRoomClient.h
– sendICECandidate:forPeer:
Request that carries info about an ICE candidate gathered on the client side. This information is required to implement the trickle ICE mechanism. When the request is processed, the [NBMRoomClientDelegate client:didUnsubscribeVideoFrom:sdpAnswer:error:] message is sent to the client’s delegate.
- (void)sendICECandidate:(RTCICECandidate *)candidate forPeer:(NBMPeer *)peer
Parameters
candidate |
The RTCICECandidate object to send. |
|---|---|
peer |
The NBMPeer whose ICE candidate was found. |
Declared In
NBMRoomClient.h
– sendICECandidate:forPeer:completion:
Request that carries info about an ICE candidate gathered on the client side. This information is required to implement the trickle ICE mechanism.
- (void)sendICECandidate:(RTCICECandidate *)candidate forPeer:(NBMPeer *)peer completion:(void ( ^ ) ( NSError *error ))block
Parameters
candidate |
The RTCICECandidate object to send. |
|---|---|
peer |
The NBMPeer whose ICE candidate was found. |
block |
A block object to be executed when the request is processed. This block has no return value and takes an error if the request failed. |
Discussion
Note: No message is sent to client’s delegate.
Declared In
NBMRoomClient.h
– sendMessage:
Represents a client’s request to send written message to all other participants in the room. When the request is processed, the [NBMRoomClientDelegate client:didSentMessage:] message is sent to the client’s delegate.
- (void)sendMessage:(NSString *)message
Parameters
message |
The text message. |
|---|
Declared In
NBMRoomClient.h
– sendMessage:completion:
Represents a client’s request to send written message to all other participants in the room.
- (void)sendMessage:(NSString *)message completion:(void ( ^ ) ( NSError *error ))block
Parameters
message |
The text message. |
|---|---|
block |
A block object to be executed when the request is processed. This block has no return value and takes an error if the request failed. |
Discussion
Note: No message is sent to client’s delegate.
Declared In
NBMRoomClient.h
– sendCustomRequest:
Provides a custom envelope for requests not directly implemented by the Room server. When the request is processed, the [NBMRoomClientDelegate client:didSentCustomRequest:] message is sent to the client’s delegate.
- (void)sendCustomRequest:(NSDictionary<NSString*,NSString*> *)params
Parameters
params |
A NSDictionary of NSString key-value parameters, their specification is left to the actual implementation. |
|---|
Declared In
NBMRoomClient.h
– sendCustomRequest:completion:
Provides a custom envelope for requests not directly implemented by the Room server.
- (void)sendCustomRequest:(NSDictionary<NSString*,NSString*> *)params completion:(void ( ^ ) ( NSError *error ))block
Parameters
params |
A NSDictionary of NSString key-value parameters, their specification is left to the actual implementation. |
|---|---|
block |
A block object to be executed when the request is processed. This block has no return value and takes an error if the request failed. |
Discussion
Note: No message is sent to client’s delegate.
Declared In
NBMRoomClient.h
– peerWithIdentifier:
Returns a remote NBMPeer with specified identifier, if exists.
- (NBMPeer *)peerWithIdentifier:(NSString *)identifier
Parameters
identifier |
A unique NSString identifier (peer’s username). |
|---|
Return Value
a NBMPeer object with specified identifier.
Declared In
NBMRoomClient.h


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%%IGNORE%%Please add<EMAIL_ADDRESS>to your contacts to ensure our e-mail reaches your inbox.
To view as a webpage, click here.%%IGNORE%%
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COVERING
TRAVEL NIGHTMARES! |
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Dear Customer
Often when travelling, scary things can happen - freakily lost
luggage, ghoulish illnesses and nightmarish missed
flights.
With AIG Travel Insurance, we offer a range of options to cater to your travel needs, including an
a spookily good 10%
discount this Halloween.
To avail simply click on the button below, fill out your details and we’ll have our team contact you…unless you’re
too scared that is!
Happy Halloween!


Move static content to static
Jekyll has a rule that any directory not starting with _
will be copied as-is to the _site
output. Hugo keeps all static content under static
. You should therefore move it all there.
With Jekyll, something that looked like
▾ <root>/
▾ images/
logo.png
should become
▾ <root>/
▾ static/
▾ images/
logo.png
Additionally, you’ll want any files that should reside at the root (such as CNAME
) to be moved to static
.
Create your Hugo configuration file
Hugo can read your configuration as JSON, YAML or TOML. Hugo supports parameters custom configuration too. Refer to the Hugo configuration documentation for details.
Set your configuration publish folder to _site
The default is for Jekyll to publish to _site
and for Hugo to publish to public
. If, like me, you have _site
mapped to a git submodule on the gh-pages
branch, you’ll want to do one of two alternatives:
Change your submodule to point to map
gh-pages
to public instead of_site
(recommended).git submodule deinit _site git rm _site git submodule add -b gh-pages git@github.com:your-username/your-repo.git public
Or, change the Hugo configuration to use
_site
instead ofpublic
.{ .. "publishdir": "_site", .. }
Convert Jekyll templates to Hugo templates
That’s the bulk of the work right here. The documentation is your friend. You should refer to Jekyll’s template documentation if you need to refresh your memory on how you built your blog and Hugo’s template to learn Hugo’s way.
As a single reference data point, converting my templates for heyitsalex.net took me no more than a few hours.
Convert Jekyll plugins to Hugo shortcodes
Jekyll has plugins; Hugo has shortcodes. It’s fairly trivial to do a port.
Implementation
As an example, I was using a custom image_tag
plugin to generate figures with caption when running Jekyll. As I read about shortcodes, I found Hugo had a nice built-in shortcode that does exactly the same thing.
Jekyll’s plugin:
module Jekyll
class ImageTag < Liquid::Tag
@url = nil
@caption = nil
@class = nil
@link = nil
// Patterns
IMAGE_URL_WITH_CLASS_AND_CAPTION =
IMAGE_URL_WITH_CLASS_AND_CAPTION_AND_LINK = /(\w+)(\s+)((https?:\/\/|\/)(\S+))(\s+)"(.*?)"(\s+)->((https?:\/\/|\/)(\S+))(\s*)/i
IMAGE_URL_WITH_CAPTION = /((https?:\/\/|\/)(\S+))(\s+)"(.*?)"/i
IMAGE_URL_WITH_CLASS = /(\w+)(\s+)((https?:\/\/|\/)(\S+))/i
IMAGE_URL = /((https?:\/\/|\/)(\S+))/i
def initialize(tag_name, markup, tokens)
super
if markup =~ IMAGE_URL_WITH_CLASS_AND_CAPTION_AND_LINK
@class = $1
@url = $3
@caption = $7
@link = $9
elsif markup =~ IMAGE_URL_WITH_CLASS_AND_CAPTION
@class = $1
@url = $3
@caption = $7
elsif markup =~ IMAGE_URL_WITH_CAPTION
@url = $1
@caption = $5
elsif markup =~ IMAGE_URL_WITH_CLASS
@class = $1
@url = $3
elsif markup =~ IMAGE_URL
@url = $1
end
end
def render(context)
if @class
source = "
"
else
source = "
"
end
if @link
source += ""
end
source += ""
if @link
source += ""
end
source += "
#{@caption}
" if @caption
source += "
"
source
end
end
end
Liquid::Template.register_tag('image', Jekyll::ImageTag)
is written as this Hugo shortcode:

{{ with .Get "link"}}{{ end }}

{{ if .Get "link"}}{{ end }}
{{ if or (or (.Get "title") (.Get "caption")) (.Get "attr")}}

{{ if isset .Params "title" }}
{{ .Get "title" }}{{ end }}
{{ if or (.Get "caption") (.Get "attr")}}

{{ .Get "caption" }}
{{ with .Get "attrlink"}} {{ end }}
{{ .Get "attr" }}
{{ if .Get "attrlink"}} {{ end }}

 {{ end }}

{{ end }}

Usage
I simply changed:
{% image full http://farm5.staticflickr.com/4136/4829260124_57712e570a_o_d.jpg "One of my favorite touristy-type photos. I secretly waited for the good light while we were "having fun" and took this.  ->http://www.flickr.com/photos/alexnormand/4829260124/in/set-72157624547713078/ %}
to this (this example uses a slightly extended version named fig
, different than the built-in figure
):
{{% fig class="full" src="http://farm5.staticflickr.com/4136/4829260124_57712e570a_o_d.jpg" title="One of my favorite touristy-type photos. I secretly waited for the good light while we were having fun and took this.  link="http://www.flickr.com/photos/alexnormand/4829260124/in/set-72157624547713078/" %}}
As a bonus, the shortcode named parameters are, arguably, more readable.
Finishing touches
Fix content
Depending on the amount of customization that was done with each post with Jekyll, this step will require more or less effort. There are no hard and fast rules here except that hugo server --watch
is your friend. Test your changes and fix errors as needed.
Clean up
You’ll want to remove the Jekyll configuration at this point. If you have anything else that isn’t used, delete it.
A practical example in a diff
Hey, it’s Alex was migrated in less than a father-with-kids day from Jekyll to Hugo. You can see all the changes (and screw-ups) by looking at this diff.


| callbacks.c | ENet callback functions |
| callbacks.h [code] | ENet callbacks |
| enet.h [code] | ENet public header file |
| host.c | ENet host management functions |
| list.c | ENet linked list functions |
| list.h [code] | ENet list management |
| packet.c | ENet packet management functions |
| peer.c | ENet peer management functions |
| protocol.c | ENet protocol functions |
| protocol.h [code] | ENet protocol |
| time.h [code] | ENet time constants and macros |
| types.h [code] | Type definitions for ENet |
| unix.c | ENet Unix system specific functions |
| unix.h [code] | ENet Unix header |
| utility.h [code] | ENet utility header |
| win32.c | ENet Win32 system specific functions |
| win32.h [code] | ENet Win32 header |


This calendar demonstrates the differents new options that allow user management and freebusy display / computation.


c
h
o
r
e
o
g
r
a
p
h
e
r
-
j
s
A simple library to take care of complex CSS animations.
$ npm install --save choreographer-js
documentation
scroll down


★★★★★
FEATURES This is a QR code tool you need! * Scanning * Identify from the album * Convert the text you want into a QR code * Scan records are not lost * Automatically identify links * Mark important QR code * Save to album and share with friends
© Andrew Shen


BiKeyHashMap |
A Collection class that supports two keys (K1, K2) mapped to a single value V. class BiKeyHashMap<K1, K2, V> : Iterable<Entry<K1, V>> |
DualHashMap |
A bi-directional HashMap that can be reversed using reverse. class DualHashMap<K, V> : MutableMap<K, V> |
MultiMap |
A class used in mapping multiple values V to a single key K. abstract class MultiMap<K, out V> |
MutableMultiMap |
A class used in mapping multiple values V to a single key K. The values of this particular MultiMap can be altered. class MutableMultiMap<K, V> : MultiMap<K, V> |
SizedList |
A very minimal class that limits the number of elements that can be added to it. class SizedList<T> : LinkedList<T> |
java.util.HashMap |
|
kotlin.collections.List |
|
kotlin.collections.MutableCollection |
cartesianProduct |
fun <T> cartesianProduct(lists: List<List<T>>): List<List<T>> |


LAKE PLACID USA, 1999 Directed by Steve Miner Written and produced by David Kelley Reviewed by Kent Johnson
Of all the films in which TV shows have graduated to the big screen, this left-handed spinoff of ALLY McBEAL, which might be better entitled ALLY GOES TO THE LAKE, has to be the oddest. The film is written by ALLY McBEAL writer/producer David Kelley and the premise is, What would happen if Ally, that neurotic, abrasive city girl (here a paleontologist rather than a lawyer and named Kelly [sounds like "Ally" and is also the writer's last name...]) had to camp out in the woods and interact with the local sherrif, game warden and an eccentric millionaire mythology professor? Kelly is played by Bridget Fonda, who acts like and is costumed to look like Ally McBeal. While Bridget was very good and very sexy, one wonders why the part wasn't played by Callista Flockhart. (The possible answer is that Fonda was Kelley's first choice for the Ally McBeal TV series, but she turned it down in order to stick with feature films, so this was his chance to have her play the role.) Oliver Platt (who was Kelley's first choice for the lead in THE PRACTICE, but also turned it down to stick with feature films) is excellent as the eccentric millionaire mythology professor and Brendan Gleeson (I WENT DOWN, THE GENERAL) and Bill Pullman are very good as, respectively, the sheriff and the game warden.
LAKE PLACID reminds me of the Dashiell Hammet novel THE THIN MAN, in that while ostensibly a mystery novel, the mystery is actually the least interesting part of the book. The interaction between the characters Nick and Nora is the interesting part and the mystery actually interrupts it -- yet, the mystery is the hook that gets the reader to pick up the novel in the first place.
In LAKE PLACID, ostensibly a monster movie, the interesting part is the witty sniping between the four disparate main characters. The dialogue is artificial and not very believable, but it's clever, original and hilarious. The crocodile hunt, the excuse for these characters to be together in the first place and the hook that gets the viewer into the theatre, is actually the least interesting part of the film. In fact, once the characters know what they're up against, it's obvious that the croc is outmatched unless the characters behave stupidly. The beginning and particularly the middle of the film, the parts with the most character conflict, are the most entertaining. The film cools down rapidly in the last third when it has to concentrate on the croc hunt and character reconciliation. Since it's only OK as a monster movie, some viewers may be disappointed, but those who can appreciate witty, well-written comedy will enjoy it tremendously.

Recommended
Note: The characters don't know what they're up against until the middle of the film and the theatrical trailers were similarly designed to give no hint of what the monster might be. The film might have been more effective if viewers had gone in without that knowledge, but of course the two-month barrage of giant croc TV ads (which the characters in the film somehow incredibly managed to miss) spoiled any chance of that.
David Kelley's next projects are the movie MYSTERY, ALASKA and the private eye TV series SNOOPS, starring Gina Gershon.
_______________
Kent Johnson San Francisco<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Network Roundtrips
(if no data -> refresh page)
network roundtrip average over time
the slower the bubbles, the slower the connection between the two peers
(if no data -> refresh page)
network roundtrip average over time
(if no data -> refresh page)


Logout
Here are the items we recommend you pack:
Clothes:
Underwear
5
Socks
5
Shorts
5
Pants
1
Short Sleeve Shirt
5
Long Sleeve Shirt
1
Tank Top
1
Shoes
1
Toiletries:
Toothbrush
Toothpaste
Shampoo
Conditioner
Soap/Body Wash
Razor
Shaving Cream
Lotion
Sunscreen
Food:
Snacks
Drinks
Libations(Alcohol)
Medicine:
Prescriptions
Vitamins
Pain Reliever
First Aid Kit
Face Mask
Hand Sanitizer
Tech:
Phone
Phone Charger
Headphones
Laptop
iPad
Camera
Documents:
Wallet
ID
Passport
Tickets
Print this page


--- layout: post title: A Farmville 2 Profit/Experience Maximization Spreadsheet date: 2012-11-24 19:36:09.000000000 -08:00 type: post parent_id: '0' published: true password: '' status: publish categories: [] tags: - farmville 2 - Optimization - spreadsheet - Video Games meta: _edit_last: '1' _jetpack_related_posts_cache: a:1:{s:32:"8f6677c9d6b0f903e98ad32ec61f8deb";a:2:{s:7:"expires";i:1543549673;s:7:"payload";a:3:{i:0;a:1:{s:2:"id";i:1232;}i:1;a:1:{s:2:"id";i:1039;}i:2;a:1:{s:2:"id";i:735;}}}} author: login: Philihp email<EMAIL_ADDRESS>display_name: Philihp first_name: '' last_name: '' ---
I've been playing Farmville 2 since launch. At first it started out as a mix of nostalgia and curiosity, but after reaching level 5 and opening up the cookhouse, I discovered there was a fun supply-chain management problem in it, and I made this Google Docs spreadsheet. It glosses over a few things like the average number of fruit a tree produces, and the costs involved in farming livestock.
If you have any data or corrections you'd like to contribute, please email me.


Additional Notes on how to use the calendar system are provided in the example lab 1!
Who is your instructor, the role of the website, course policies, and a brief intro into the topic.
All suggested readings have already been posted to the calendar for the semester, primarily from the 13th edition of xxxxxxxxx, Prentice Hall 2014, as well as zzzzzzzzzzzzzzzzzzzzzzz, Fourth Edition, by J Doe and L Thomson 2009. While these books are marked as recommended you are strongly encouraged to follow the readings throughout this course. The online notes serve as a distilled source of information, but the texts will dive much deeper into a subject and may help if you are to get lost or find yourself behind.
This course is an introduction to xxxxxxxxxxxxx
And the end of the notes for almost every lecture will be a couple of example problems with solutions (like what you have seen in previous courses).
Instructor Comment:
Note to instructor - Answers to problems should be inside these student tags, the contents can
be revealed to the student when they click the icon on the top navigation bar.
The calendar is the primary source of information and guidance that we will use
throughout the semester. You are expected to check here often to ensure that no additional readings, notes,
or assignments are posted.
Homeworks: There will be weekly homework assignments, usually assigned on Mondays and due one week later.
Homeworks are to be turned in as paper copies, no electronic submissions will be accepted.
Quizzes: Be prepared for weekly quizzes, usually at the beginning of classes on Fridays, to ensure that you
are following along with the class.
Answers: Answers are set to be automatically revealed by the calendar 2 days after the having been due,
because of this your are strongly encouraged to review the course policy w.r.t. late assignments.
Please fill out the survey and return at the beginning of the next class.


Page: Page URL
URL: RequestURL
Occurred: Timestamp
Response Status: status-code error ...
Note: You would never generate a page that displays a Java exception to an end-user in a real application


“strive 7/31 ~ 8/6”
Use
-
[Gem] vcr
- For test, it could record real third party response to yml file
- Use it with webmock’s body
-
[Gem] webmock
-
[Gem] rubocop
- rubocop –auto-correct
-
[Gem] webmock
- If you use multiple sidekiq processes then you can specify limits per process
-
[Gem] figaro
- Figaro was written to make it easy to securely configure Rails applications.
-
[Gem] docker-api
-
[Gem] active_type
- ActiveType is our take on “presenter models” (or “form models”) in Rails. We want to have controllers (and forms) talk to models that are either not backed by a database table
-
[Gem] png_quantizator
- PngQuantizator is a little wrapper around pngquant
Read
-
[Ruby] Partial Downloads with Enumerators and Fibers
- before download, need to verify MIME type
- use Enumerators and Fiber to execute streaming downlaod
-
[Ruby] Ruby on Rails 基本主義en
-
[Ruby] A Review of Code Reloaders for Ruby
- Different Code Reloaders
-
[Ruby] Rbenv — How it works
-
[Ruby] Hiding secrets in a Vault
- Using Vault, Github authentication and ‘secrets’ gem to make secret files more secure.
-
[Ruby] New Features in Ruby 2.4
-
[Ruby] respond_to |format| is useful even without multiple formats
- benefit of respond_to
-
[Ruby] CONSIDER TURBOLINKS 5 FOR YOUR NEXT RAILS PROJECT
- Turbolinks 5 is a library for web applications that makes pages load faster
-
[Rspec] rspec described_class
- If the first argument to the outermost example group is a class, the class is exposed to each example via the described_class() method
-
[Ruby] run_callbacks
-
[Ruby] http://www.justinweiss.com/articles/the-lesser-known-features-in-rails-4-dot-2/
- Easily load config files => Rails.application.config_for(‘yml file name’)
-
[Ruby] The Recipe for the World's Largest Rails Monolith
- Many handmade tools
-
[Ruby] RSpec 中let 和subject 的區別是什麼？
- 主要語意的差別, subject主角, let配角
Book
-
on page 286
-
on page 172
Work design
-
API Architecture
-
Design
- client auth: follow aws, use current timestamp + private key as checksum, server need to verify checksum, also need to make sure timestamp in some duration (like 10 mins)
- user auth: logined user will have one or more token. use token as key, value is user id, and set expired time on every token, save them on redis, so when client send token, server could know the current user.
- error handler:
- one config, record key, status code of error, error message use key for i18n.
- every api action has its service object, when render api error will raise specific exception class and error key,this exception will rescue from base controller, and according error key to error render. All the data format will be the same, the different are message, key, status code
– learned from MarsZ
-
Implement
- controller only handle flow, other through service.
- use value object to handle user session, due to operation very like model CRUD, so use value, and base is redis hash
- exception only on controller raise, service use active model save error message, when return false, we could get error message from service and raise with exception class
-
** Learn **
- feature driven coding
- every commit with small main part of the feature
- service with callback
- exceptio class
- every concerns only focus one thing
- RSpec from base stack
- how to use service to architecture base of whole application
- feature driven coding


Do you know your icons?Press "Play" to begin!
Play
Example app for Swimming in Elixir Presentation by Benjamin P Lee.


ARROW KEYS = move around the perlin noise algorithm :: MOUSE = Rotate :: SCROLL WHEEL = Zoom
Fungi -
Source at Github


What About the Bylaws?
Back to Top
The 2016 fall GLM was an eventful one that no one in attendance will forget. After the long series of debates
and votes on bylaw amendments many people were left wondering "What actually changed?"
For those wondering, two big things happened:
1) New lodge flaps are now approved by the LEC (previously approved by GLM). However, the standard flap cannot be changed.
2) Any flaps issued during your time as a member of the lodge are permitted for uniform wear.
To clear up the second big change here are some examples:
-All members are permitted to wear the standard issue white and red flap
-Anniversary flaps are issued until the calendar year of the flap. A member who joined in March 2013
cannot wear the 2012 anniversary flap.
-A member who joined in 2013 and paid dues in 2015 may wear the 2015 NOAC sets as they were issued during
while that member was dues paid.
-An Arrowman inducted in 2015 may wear the city skyline flap as it was under a "rolling issue" when they
were inducted. That Arrowman mat not wear the sunset flap as that flap's issue ended before that Arrowman's induction.
The term "Rolling Issue" is used for any flap that had no event or anniversary affiliation and has yet to
be removed from the trading post. Once the stock of a patch is removed from the trading post and can only
be purchased from the lodge as part of an auction or is sold out for the final time its issue is ceased.
Though members may be eligible to wear certain flaps, they should use their best judgement as to what would
be the most appropriate display of their scouting participation before changing flaps.
For further clarification on these amendments please contact your chapter leadership.


WITNESS LIST Finance Committee April 18, 2005 - 9:00A HB 1489 ON: Lippincott, Chris Public Affairs Director (Texas Association Against Sexual Assault), Austin, TX Registering, but not testifying: On: Geeslin, Julie Budget Director (Office of the Attorney General), Austin, TX Millholland, Herman Chief Crime Victim Service Division (Office of the Attorney General), Austin, TX SB 18 Registering, but not testifying: Against: Hankins, Barbara Retired (League of Women Voters of Texas), Austin, TX SB 567 Registering, but not testifying: For: Allison, Jim General Counsel (County Judges and Commissioners Association of Texas), Austin, TX Gonzales, Daniel Director of Legislative Affairs (Self), Austin, TX Molter, Michele (Texas Apartment Association), Austin, TX SB 1501 FOR: Garcia, Sylvia County Commissioner (Harris County), Houston, TX Lee, Donald Executive Director (Texas Conference of Urban Counties), Austin, TX Mosbacher, Rob President, Mosbacher Energy Company (Self), Houston, TX Svadlenak, Steve Director (Texas Association of Public and Nonprofit Hospitals), Austin, TX Yanas, Chris Director/Ambulatory Services (University Health System), Houston, TX AGAINST: Canto, William Compliance Officer (Texas Money Exchange), Hidalso, TX Figueroa, Luis Legislative Staff Attorney (MALAEF (Mexican American Legal Active and Educational Fund)), San Antonio, TX Heasley, John (Texas Bankers Association), Austin, TX Scurlock, Steve Association Executive (IBAT - Independent Bankers Association of Texas), Austin, TX Van, Allan Lawyer (Texas Appleseed), Houston, TX ON: Dunkelberg, Anne Assistant Director (Center for Public Policy Priorities), Austin, TX Fleming, Bob Catholic Charities (Catholic Charities/The Metropolitan Organization), Houston, TX Flores, Rebecca (United Farm Workers), San Antonio, TX Jobe, Everette General Counsel (Texas Department of Banking), Austin, TX Marchman, Denice Attorney, Hughes and Luce, LLP (Harris County), Austin, TX Newberg, Stephanie Deputy Commissioner (Texas Department of Banking), Austin, TX Yanez-Correa, Ana Ph.D Student (Texas LULAC), Ausitn, TX Registering, but not testifying: For: Berger, Ed Vice President, Government Relations (Seton Healthcare Network), Austin, TX Cutrer, Jennifer Executive Director Government Relations (Parkland Health and Hospital System), Dallas, TX Dennis, Hillary Associate Director Legislative Affairs (Texas Medical Association), Austin, TX Hillier, King Director of Government Relations (Harris County Hospital District), Houston, TX Hinojosa, Walter Lobbyist (Texas AFL-CIO), Austin, TX Jones, Amanda Legislative Cordinator (Harris County/Harris County Commissioners Committee), Houston, TX Mendez, Mark Assistant County Administrator (Tarrant County), Ft. Worth, TX Pardue, Craig Governmental Affairs (Dallas County), Dallas, TX Against: Baddour, Ann Senior Policy Analyst (Texas Appleseed), Austin, TX Lovoi, Annette Non Profit Management (Texas Appleseed), Austin, TX Munoz, Jessica Policy/Advocacy (National Council of La Raza), Houston, TX Sandberg, Eric Trade Association (Texas Savings and Community Bankers Association), Austin, TX On: Hagert, Celia Senior Policy Analyst (Center for Public Policy Priorities), Austin, TX Providing written testimony: For: Ramirez, Richard Public Finance Investment Banking (Gooldman Sache Company), Houston, TX SB 1538 Registering, but not testifying: For: Arnold, Ellen Lobbyist (Texas Association of Goodwills/Goodwill Industries), Austin, TX SB 1569 Registering, but not testifying: On: Childs, Hayden LBB Staff (LBB), Austin, TX SB 1646 FOR: Patterson, Jerry Land Commissioner (Texas General Land Office), Austin, TX Registering, but not testifying: On: Tanner, Bo Deputy Commissioner - Asset Management (Texas General Land Office), Austin, TX SB 1655 FOR: Hutcheson, Mark Attorney (Texas Association of Property Tax Professionals), Austin, TX Kliewer, Edward Attorney (Self), Houston, TX AGAINST: Amezquita, Michael Chiel Appraiser Bexar Appraisal District (Bexar Appraisal District), Houston, TX Cory, Art Chief Appraiser - Travis Appraisal District (Texas Association of Appraisal Districts), Austin, TX Registering, but not testifying: For: Christian, George Attorney (Texas Taxpayer and Research Association), Austin, TX Gonzales, Daniel Director of Legislative Affairs (Texas Association of Realtors), Austin, TX Ikard, William Attorney (Self), Austin, TX Against: Powell, Leilah Government Relations Manager (Bexar County Commissioners Court), San Antonio, TX SB 1693 FOR: Scurlock, Steve Association Executive (IBAT - Independent Bankers Association of Texas), Austin, TX Registering, but not testifying: On: Dennis, Rachel Deputy General Counsel (Texas Bankers Association), Austin, TX


Navy veteran was dead for 3 years before he was found in apartment
by Jackie Salo(NEW YORK POST) — A Navy veteran who lived in Texas was dead for around three years before his body was found inside his apartment, officials said.
The body of Ronald Wayne White was discovered on the kitchen floor of his Dallas home by apartment complex staffers checking last week on units not using water, news station WFAA reported.
“What I can tell you is it is very clear when officers entered that he had been there for a while,” DeSoto Police Det. Pete Schulte told the outlet.
Police said 51-year-old White had paid his rent through an automatic withdrawal system linked to his bank account.
The third-floor apartment was new, and all of the windows had been sealed when he died, authorities said. Inside his home, there were diabetes medications, which were dated 2016.
“The way he was found, the way the apartment was arranged and so forth, there was zero indication of foul play,” Schulte told WFAA.

“My biggest question is, how in the world could my son have been dead in that apartment and nobody knows anything?” said his mom, Doris Stevens.
Stevens said she became concerned when she was unable to reach him on his birthday in April 2017.
She was unaware that he was living in the complex where he was found dead and a search of past homes didn’t lead to any clues about his whereabouts, WFAA reported.
Authorities declined to launch a missing persons investigations, citing that he was an adult known to travel, she said.
“All them days, holidays, I just suffered. Because nobody wanted to help find him,” Stevens said.
She confirmed he suffered from diabetes, though it’s unclear how he died, the outlet reported. Toxicology tests have been ordered to determine a cause of death.
More International News Stories
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‘Jealous’ Ohio mom gets 37 years in prison for killing her 3 sons November 22, 2019
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Video shows moment Brazilian man shoots, kills homeless woman who asked for 23 cents November 22, 2019
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First male birth control injection almost ready for penises November 22, 2019
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Daughter saves mum from domestic violence by pretending to order pizza November 22, 2019


pdsend(1) pdsend(1) NAME pdsend - send messages to pd on this or a remote machine SYNOPSIS pdsend port-number [hostname] [udp|tcp] DESCRIPTION Pdsend sends messages to pd(1), via a socket conection, from pdsend's standard input. This input can be any stream of Pd messages separated by semicolons. This is probably the easiest way to control pd from another application. The protocol used is easy to implement and is called FUDI. The port number should agree with the port number of a "netreceive" object within pd. The hostname is "localhost" by default and can be a domain name or an IP address. The protocol is "tcp" by default; this does a handshake to guarantee that all messages arrive complete and in their correct order; if you are sending messages locally or point-to- point you can often get away with the faster udp protocol instead. You can also use this to talk to a Max "pdnetreceive" object or even just a "pdreceive" in another shell. If you're writing another program you're welcome to just grab the sources for pdsend/pdreceive and adapt them to your own ends; they're part of the Pd distribution. SEE ALSO pd(1), pdreceive(1) GNU 1996 Mar 20 pdsend(1)