class: center, middle # Lecture 10: ## From notebooks to projects
.bold[Andrei Bursuc ]
.bold[.gray[valeo]_.ai_]
--- # Outline - Under the hood - Tools for visualization and logging - Example project --- class: center, middle # GPUs .center[
] --- # CPU vs GPU
.left[ .center[CPU]
.center[
] ] .right[ .center[GPU]
.center[
] ] --- # CPU vs GPU .left-column[ - CPU: + fewer cores; each core is faster and more powerful + useful for sequential tasks ] .right-column[ - GPU: + more cores; each core is slower and weaker + great for parallel tasks ] .reset-column[] .center.width-70[![](images/part10/cpu_vs_gpu_2.png)] --- # CPU vs GPU .left-column[ - CPU: + fewer cores; each core is faster and more powerful + useful for sequential tasks ] .right-column[ - GPU: + more cores; each core is slower and weaker + great for parallel tasks ] .reset-column[]
| | Cores | Clock Speed | Memory | Price | Speed | |---|---|---|---|---| | .bold[CPU]
Intel Core i7-7700k| $4$
(8 threads with hyperthreading) | $4.2$ Ghz | System RAM | $339$ € | $\sim540$ GFLOPs FP32 | | .bold[GPU]
Nvidia GTX 1080 Ti| $3584$ | $1.6$ Ghz | $11$ GB GDDR5X | $699$ € | $\sim11.4$ TFLOPs FP32 | --- # CPU vs GPU .center.width-70[![](images/part10/cpu_vs_gpu_3.png)] .left-column[ - SP = single precision, 32 bits / 4 bytes ] .right-column[ - DP = double precision, 64 bits / 8 bytes ] --- # CPU vs GPU .center[
] .citation[Benchmarking State-of-the-Art Deep Learning Software Tools, Shi et al., 2016] --- # CPU vs GPU .center.width-80[![](images/part10/cpu_vs_gpu_4.png)] - more benchmarks available at [https://github.com/jcjohnson/cnn-benchmarks](https://github.com/jcjohnson/cnn-benchmarks) .credit[Figure credit: J. Johnson] --- # CPU vs GPU .center.width-80[![](images/part10/cpu_vs_gpu_5.png)] - more benchmarks available at [https://github.com/jcjohnson/cnn-benchmarks](https://github.com/jcjohnson/cnn-benchmarks) .credit[Figure credit: J. Johnson] --- # CPU vs GPU .center.width-80[![](images/part10/flops_per_dollar_1.png)] .credit[Figure credit: J. Johnson] --- # CPU vs GPU
| | Cores | Clock Speed | Memory | Price | Speed | |---|---|---|---|---| | .bold[CPU]
Intel Core i7-7700k| $4$
(8 threads with hyperthreading) | $4.2$ Ghz | System RAM | $339$ € | $\sim540$ GFLOPs FP32 | | .bold[GPU]
Nvidia GTX 1080 Ti| $3584$ | $1.6$ Ghz | $11$ GB GDDR5X | $699$ € | $\sim11.4$ TFLOPs FP32 | | .bold[GPU]
Nvidia Titan V| $5120$ | $1.5$ Ghz | $12$ GB HBM2 | $2999$ € | $\sim14$ TFLOPs FP32
$\sim 112$ TFLOPs FP16 | --- # CPU vs GPU .center.width-80[![](images/part10/flops_per_dollar_2.png)] .credit[Figure credit: J. Johnson] --- # CPU vs GPU
| | Cores | Clock Speed | Memory | Price | Speed | |---|---|---|---|---| | .bold[CPU]
Intel Core i7-7700k| $4$
(8 threads with hyperthreading) | $4.2$ Ghz | System RAM | $339$ € | $\sim540$ GFLOPs FP32 | | .bold[GPU]
Nvidia GTX 1080 Ti| $3584$ | $1.6$ Ghz | $11$ GB GDDR5X | $699$ € | $\sim11.4$ TFLOPs FP32 | | .bold[GPU]
Nvidia Titan V| $5120$ | $1.5$ Ghz | $12$ GB HBM2 | $2999$ € | $\sim14$ TFLOPs FP32
$\sim 112$ TFLOPs FP16 | | .bold[TPU]
Google Cloud TPUS| - | - | $64$ GB HBM | $6.5$ $/hour | $\sim180$ TFLOPs
with 4 stacked TPUs| -- count: false
.tiny[Some advice for trying to setup your GPU at home: http://timdettmers.com/2018/11/05/which-gpu-for-deep-learning/] --- # The ImageNet sprint .left-column[ .italic[aka] Who's the _Usain Bolt_ of Deep Learning? ] .right-column[.center.width-50[![](images/part10/dawnbench_logo.png)] ] .reset-column[ ] .center.width-70[![](images/part10/dawnbench_1.png)] .citation[DAWNBench: https://dawn.cs.stanford.edu/benchmark/] --- # The ImageNet sprint ### Current SoTA - Train ResNet-50 on ImageNet to $76.3\%$ accuracy in $2.2$ minutes on a 1024-chip TPU v3 Pod - Training throughput of over $1.05\text{M}$ images/second and no accuracy drop. .center.width-50[![](images/part10/tweet_szegedy.png)] .citation[Image Classification at Supercomputer Scale, C. Ying et al., arXiv 2018] --- # The ImageNet sprint .left-column[ .italic[aka] Who's the _Usain Bolt_ of Deep Learning? ] .right-column[.center.width-50[![](images/part10/dawnbench_logo.png)] ] .reset-column[ ] .center.width-70[![](images/part10/dawnbench_2.png)] .citation[DAWNBench: https://dawn.cs.stanford.edu/benchmark/] --- # The ImageNet sprint .left-column[ .italic[aka] Who's the _Usain Bolt_ of Deep Learning? ] .right-column[.center.width-50[![](images/part10/dawnbench_logo.png)] ] .reset-column[ ] .center.width-70[![](images/part10/dawnbench_3.png)] .citation[DAWNBench: https://dawn.cs.stanford.edu/benchmark/] --- # System .center[
] .credit[Figure credit: F. Fleuret] --- # System .center[
] .credit[Figure credit: F. Fleuret] --- # System .center[
] .credit[Figure credit: F. Fleuret] --- # System .center[
] .credit[Figure credit: F. Fleuret] --- # System .center[
] .credit[Figure credit: F. Fleuret] --- # System .center[
] .credit[Figure credit: F. Fleuret] --- # System .center[
] .credit[Figure credit: F. Fleuret] --- # Programming GPUs - NVIDIA GPUs are programmed through .bold[CUDA] (_Compute Unified Device Architecture_) -- count:false - The alternative is .bold[OpenCL], supported by several manufacturers but with significant less investments than Nvidia -- count: false - Recently .bold[HIP](_Heterogeneous-compute Interface for Portability_) converts CUDA code to C++ code running on AMD GPus -- count:false - Google developed their own processor for deep learning .bold[TPU] (_Tensor Processing Unit_) with great flops/watt performance. It's for in-house use and it will be soon PyTorch-compatible. -- count:false - Nvidia and CUDA are dominating the field by far, though some alternatives start emerging: Intel Nervana, GraphCore, Groq, etc. --- # Libraries The interface between frameworks (PyTorch, TensorFlow) with the computational backend (CPU or GPU) is ensured by a couple of libraries -- count: false - BLAS (_Basic Linear Algebra Subprograms_): vector/matrix products, and the cuBLAS implementation for NVIDIA GPUs - LAPACK (_Linear Algebra Package_): linear system solving, Eigen-decomposition, etc. - cuDNN (_NVIDIA CUDA Deep Neural Network library_) computations specific to deep-learning on NVIDIA GPUs. --- # GPUs in pytorch Tensors of torch.cuda types are in the GPU memory. Operations on them are done by the GPU and resulting tensors are stored in its memory. -- count: false Checking if GPU exists on the machine and can be used ```py >>> torch.cuda.is_available() ``` -- count: false The device can be specified when at `Tensor` creation. ```py >>> x = torch.zeros(10, 10) >>> x.device device(type='cpu') >>> x = torch.zeros(10, 10, device = torch.device('cuda')) >>> x.device device(type='cuda', index=0) >>> x = torch.zeros(10, 10, device = torch.device('cuda:1')) >>> x.device device(type='cuda', index=1) ``` --- # GPUs in pytorch Moving Tensors between CPU and GPU ```py >>> x = torch.zeros(10, 10) >>> x.to('cuda') ... >>> x.to('cpu') ... >>> x.cuda() ... >>> x.cpu() ... ``` -- count:false Moving data between the CPU and the GPU memories is far slower than moving it inside the GPU memory. --- # GPUs in pytorch Operations maintain the types and devices of the tensors. You do not need to worry about making your code generic regarding these aspects. Most of the functions available on the CPU are over-rided on the GPU --- # GPUs in pytorch Operations cannot mix different tensor types (CPU vs. GPU, or different numerical types); except `copy_()` ```py >>> x = torch.zeros(2, 3) >>> y = torch.zeros(2, 3).fill_(4).cuda() >>> x.copy_(y) tensor([[ 4., 4., 4.], [ 4., 4., 4.]]) >>> x + y Traceback (most recent call last): File "
", line 1, in
RuntimeError: Expected object of type torch.FloatTensor but found type torch.cuda.FloatTensor for argument #3 'other' ``` --- # GPUs in pytorch A simple manner for using multiple GPUs is to wrap the model in `nn.DataParallel`. -- count:false `nn.DataParallel`: + generates instances of the current model and sends them to all GPUs + splits the input mini-batch in as many new mini-batches as indicated GPUs + regroups by concatenation results from all GPUs --- # GPUs in pytorch .left-column[ `nn.DataParallel`: ```py class EmptyNet(nn.Module): def __init__(self, m): super(EmptyNet, self).__init__() self.m = m def forward(self, x): print('EmptyNet.forward', x.size(), x.device) return self.m(x) ``` ] .right-column[ ] --- # GPUs in pytorch .left-column[ `nn.DataParallel`: ```py class EmptyNet(nn.Module): def __init__(self, m): super(EmptyNet, self).__init__() self.m = m def forward(self, x): print('EmptyNet.forward', x.size(), x.device) return self.m(x) ``` ] .right-column[ ```py x = torch.empty(50, 10).normal_() model = EmptyNet(nn.Linear(10, 5)) print('On CPU') y = model(x) x = x.cuda() model.cuda() print('On GPU w/o nn.DataParallel') y = model(x) print('On GPU w/ nn.DataParallel') parallel_model = nn.DataParallel(model) y = parallel_model(x) ``` Running on a machine with two GPUs: ```py # On CPU EmpytNet.forward torch.Size([50, 10]) cpu # On GPU w/o nn.DataParallel EmpytNet.forward torch.Size([50, 10]) cuda:0 # On GPU w/ nn.DataParallel EmpytNet.forward torch.Size([25, 10]) cuda:0 EmpytNet.forward torch.Size([25, 10]) cuda:1 ``` ] --- class: middle # Keeping track of experiments and data visualization --- # Visdom .center.width-70[![](images/part10/visdom.gif)] .citation[Visdom: https://github.com/facebookresearch/visdom] --- # Visdom .left-column[ ```py from visdom import Visdom viz = Visdom(port=8030, server='http://localhost') # matplotlib demo: try: import matplotlib.pyplot as plt plt.plot([1, 23, 2, 4]) plt.ylabel('some numbers') viz.matplot(plt) except BaseException as err: print('Skipped matplotlib example') print('Error message: ', err) # histogram viz.histogram(X=np.random.rand(10000), opts=dict(numbins=20)) ``` ] .right-column[ ```py viz.bar( X=np.random.rand(20, 3), opts=dict( stacked=False, legend=['The Netherlands', 'France', 'United States'] ) ) # image demo viz.image( np.random.rand(3, 512, 256), opts=dict(title='Random!', caption='How random.'), ) # grid of images viz.images( np.random.randn(20, 3, 64, 64), opts=dict(title='Random images', caption='How random.') ) ``` ] .citation[Visdom: https://github.com/facebookresearch/visdom] --- # Visdom Pros: - lightweight and easy to deploy - a lot of functionalities available by default - sessions can be saved Cons: - you must manually define/compute/plot the things you have to have displayed .citation[Visdom: https://github.com/facebookresearch/visdom] --- # Tensorboard .center.width-80[![](images/part10/tensorboard.gif)] .citation[tensorboardX: https://github.com/lanpa/tensorboardX] --- # Tensorboard .left-column[ .center.width-80[![](images/part10/tensorboard_1.png)] ] .right-column[ .center.width-80[![](images/part10/tensorboard_2.png)] ] .citation[tensorboardX: https://github.com/lanpa/tensorboardX] --- ```py from tensorboardX import SummaryWriter resnet18 = models.resnet18(False) writer = SummaryWriter() ... dummy_s1 = torch.rand(1) dummy_s2 = torch.rand(1) writer.add_scalar('data/scalar1', dummy_s1[0], n_iter) writer.add_scalar('data/scalar2', dummy_s2[0], n_iter) writer.add_scalars('data/scalar_group', {'xsinx': n_iter * np.sin(n_iter), 'xcosx': n_iter * np.cos(n_iter), 'arctanx': np.arctan(n_iter)}, n_iter) if n_iter % 10 == 0: writer.add_image('Image', x, n_iter) writer.add_text('Text', 'text logged at step:' + str(n_iter), n_iter) for name, param in resnet18.named_parameters(): writer.add_histogram(name, param.clone().cpu().data.numpy(), n_iter) ... writer.add_embedding(features, metadata=label, label_img=images.unsqueeze(1)) # export scalar data to JSON for external processing writer.export_scalars_to_json("./all_scalars.json") writer.close() ``` .citation[tensorboardX: https://github.com/lanpa/tensorboardX] --- # Tensorboard .left-column[ Pros: - a lot of functionalities - less manual intervention required Cons: - can become quickly heavyweight with computations - not compatible with many situations ] .right-column[ .center.width-80[![](images/part10/tensorboard_3.png)] ] .citation[tensorboardX: https://github.com/lanpa/tensorboardX] --- # pytorchviz .center.width-70[![](images/part10/pytorchviz_1.png)] .citation[pytorchviz: https://github.com/szagoruyko/pytorchviz] --- class: end-slide, center count: false The end.


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This is my playground for COP4813
I will use this site to try out new thing and present my assignments to the class and the instructor.
Created by Chris Kruger in 2019.


CUSTOMER MANAGEMENT
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You've just seen how the Simple Search Service can power a search engine, now you'll see how it can power content discovery. Here, the interface uses context from the page to query the service.
In your application, you may not want to present the results as a table. No problem, you can present the results however you see fit. See the Resources tab in the Bluemix catalog for an example.


We hosted HACS Temporal Action Localization Challenge at ICCV'19 Workshop on Multi-modal Video Analysis. The goal of this challenge is to detect actions in untrimmed videos. The action localization challenge uses HACS Segments dataset, which contains:
In this year's challenge, we have recieved 21 submissions from 5 teams. Winner teams' performance and reports can be found below.
Please follow instructions in THIS PAGE to download HACS Segments dataset.
We use mAP as our evaluation metric, which is the same as ActivityNet localization metric.
Interpolated Average Precision (AP) is used as the metric for evaluating the results on each activity category. Then, the AP is averaged over all the activity categories (mAP). To determine if a detection is a true positive, we inspect the temporal intersection over union (tIoU) with a ground truth segment, and check whether or not it is greater or equal to a given threshold (e.g. tIoU > 0.5). The official metric used in this task is the average mAP, which is defined as the mean of all mAP values computed with tIoU thresholds between 0.5 and 0.95 (inclusive) with a step size of 0.05.
Submission portal is now closed, stay tuned for our next year's challenge.
You should submit a JSON file in the following format, where each video ID has a list of predicted action segments. Submission portal will be available on August 1st.
{ "results": { "--0edUL8zmA": [ { "label": "Dodgeball", "score": 0.84, "segment": [5.40, 11.60] }, { "label": "Dodgeball", "score": 0.71, "segment": [12.60, 88.16] } ] } }
Please contact HangZhao AT csail.mit.edu for further questions.


I am a young professional, lover of verbal languages, dogs, tech, and the great outdoors. Growing up in a bilingual
household, I've always found excitement in learning more verbal languages to communicate with people and help them
with anything they may need. When I was introduced to programming and their languages, I was just as amazed and desired
to learn as much as possible. A critical thinker, I enjoy being presented with issues that require a solution. In
my young developer career, I am ready to provide people with solutions to their problems in business by developing
useful websites, apps, and more!


This inspection checks that arguments to
property()
and functions annotated with
@property
and friends look reasonably.


MDS 1.0 is an XML Schema implementation derived from ISO ISO 19115-1, Geographic Information - Metadata - Part 1: Fundamentals, Clause 6.5.2. It includes this namespace contains all classes needed for a complete metadata record for any resource. It is used for metadata records that do not include geospatial common extensions (gex), extenbsions to the schema (mex), data series (DS_*) elements (mda) or metadata for data transfer (mdt). The XML schema was encoded using the rules described in ISO/TS 19139:2007, Clause 8 and implementation approach from ISO/TS19115-3, Clause 8.
The namespace URI for mds 1.0 is http://standards.iso.org/iso/19115/-3/mds/1.0.
mds.xsd is the XML Schema document to be referenced by XML documents containing XML elements in the mds 1.0 namespace or by XML Schema documents importing the mds 1.0 namespace. This XML schema includes (indirectly) all the implemented concepts of the mds namespace, but it does not contain the declaration of any types.
NOTE: The XML Schema for mds 1.0 are available here. A zip archive including all the XML Schema Implementations defined in ISO/TS 19115-3 and related standards is also available.
metadataDataServices.xsd implements the UML conceptual schema defined in ISO 19115-1, Geographic Information - Metadata - Part 1: Fundamentals, Clause 6.5.2. It was created using the encoding rules defined in ISO 19118, ISO 19139, and the implementation approach described in ISO 19115-3 and contains the following classes (codeLists are bold):
| Name | Standard Prefix | Namespace Location | Schema Location |
|---|---|---|---|
| Feature Catalog Common | fcc | http://standards.iso.org/iso/19110/fcc/1.0 | ../../../../19110/fcc/1.0/fcc.xsd |
| Geospatial EXtent | gex | http://standards.iso.org/iso/19115/-3/gex/1.0 | ../../../../19115/-3/gex/1.0/gex.xsd |
| Metadata for Acquisition Metadata for Acquisition | mac | http://standards.iso.org/iso/19115/-3/mac/1.0 | ../../../../19115/-3/mac/1.0/mac.xsd |
| Metadata for Application Schema | mas | http://standards.iso.org/iso/19115/-3/mas/1.0 | ../../../../19115/-3/mas/1.0/mas.xsd |
| Metadata for Constraints | mco | http://standards.iso.org/iso/19115/-3/mco/1.0 | ../../../../19115/-3/mco/1.0/mco.xsd |
| Metadata Base Metadata Base | mdb | http://standards.iso.org/iso/19115/-3/mdb/1.0 | ../../../../19115/-3/mdb/1.0/mdb.xsd |
| Metadata for Data Quality | mdq | http://standards.iso.org/iso/19157/-2/mdq/1.0 | ../../../../19157/-2/mdq/1.0/mdq.xsd |
| Metadata for Maintenance Information | mmi | http://standards.iso.org/iso/19115/-3/mmi/1.0 | ../../../../19115/-3/mmi/1.0/mmi.xsd |
| Metadata for Portrayal Catalog | mpc | http://standards.iso.org/iso/19115/-3/mpc/1.0 | ../../../../19115/-3/mpc/1.0/mpc.xsd |
| Metadata for Resource Content Metadata for Resource Content | mrc | http://standards.iso.org/iso/19115/-3/mrc/1.0 | ../../../../19115/-3/mrc/1.0/mrc.xsd |
| Metadata for Resource Distribution | mrd | http://standards.iso.org/iso/19115/-3/mrd/1.0 | ../../../../19115/-3/mrd/1.0/mrd.xsd |
| Metadata for Resource Lineage Metadata for Resource Lineage | mrl | http://standards.iso.org/iso/19115/-3/mrl/1.0 | ../../../../19115/-3/mrl/1.0/mrl.xsd |
| Metadata for Reference System | mrs | http://standards.iso.org/iso/19115/-3/mrs/1.0 | ../../../../19115/-3/mrs/1.0/mrs.xsd |
| Metadata for Spatial Representation Metadata for Spatial Representation | msr | http://standards.iso.org/iso/19115/-3/msr/1.0 | ../../../../19115/-3/msr/1.0/msr.xsd |
| Metadata for Services | srv | http://standards.iso.org/iso/19115/-3/srv/2.0 | ../../../../19115/-3/srv/2.0/srv.xsd |
Written by writeHTMLFiles Version: 2015-06-06 at 2018-03-10T10:56:57.622-07:00


--- type: blog order: 101 ---
We send a periodic newsletter announcing new features as well as some ML-related papers, label techniques that are innovative and funny stories.


Resolution
Multiple measurements per day
Single daily summary
Period
One day
Seven days
Thirty days
Ninety days
360 days
All data
Ending date


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Neil understands that; "Giving someone you love something hand crafted that has been thought out from the smallest detail and designed with love and care is a gift from the heart."
Working in many locations and
being a well sought after jeweller, Neil has now made a place for himself at his workshop in Gosford. Being located only an hour drive from Sydney, Neil has gathered a strong Sydney base, as well as being a favourite amongst
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Israel's Attorney General charges PM Netanyahu with bribery and breach of trust in connection with 3 separate cases
by Naveel KrishantIsrael's Attorney General has charged Prime Minister Benjamin Netanyahu with bribery, fraud, and breach of trust in connection with three separate cases.
Netanyahu is alleged to have accepted gifts from wealthy businessmen and dispensed favours to try to get more positive press coverage.
Netanyahu has denied any wrongdoing and said he is the victim of a "witch-hunt" by his left-wing opponents and the media.
He has also insisted he will not resign and is not legally obliged to do so.
[Source:BBC]


Money Play. How should Red play 54?
pips Red=124 White=109
(CorrectAnswer= 0/0, ErrorScore= 0)
Hopefully nobody found 9/4, 6/2 which is the equivalent of waving a white flag.
When I set the problem I assumed that my over the board play of 8/3, 7/3, making a broken prime would be the easy winner. Imagine my surprise when it dead-heated with the wide-open 9/5*, 6/1*!
This latter play seeks to build a counter-prime by force. It can backfire spectacularly but when it works it reaps a rich reward. The play wins 24% of the time which many will find a surprisingly high percentage.
The position is worthy of a longer analysis and commentary but for now just enjoy the result.
When I set the problem I assumed that my over the board play of 8/3, 7/3, making a broken prime would be the easy winner. Imagine my surprise when it dead-heated with the wide-open 9/5*, 6/1*!
This latter play seeks to build a counter-prime by force. It can backfire spectacularly but when it works it reaps a rich reward. The play wins 24% of the time which many will find a surprisingly high percentage.
The position is worthy of a longer analysis and commentary but for now just enjoy the result.
1. Cubeful 2-ply 8/3 7/3 Eq.: -0.711 0.234 0.039 0.001 - 0.766 0.275 0.024 2. Cubeful 2-ply 9/5* 6/1* Eq.: -0.727 (-0.016) 0.240 0.059 0.002 - 0.760 0.317 0.033 3. Cubeful 2-ply 9/5* 7/2 Eq.: -0.733 (-0.023) 0.221 0.051 0.002 - 0.779 0.283 0.022 4. Cubeful 2-ply 9/4 7/3 Eq.: -0.762 (-0.051) 0.192 0.031 0.001 - 0.808 0.228 0.015 5. Cubeful 2-ply 9/5* 8/3 Eq.: -0.768 (-0.057) 0.256 0.051 0.002 - 0.744 0.367 0.052


Results of a run – how to access all the information¶
-
class
hpbandster.core.result.
Result
(HB_iteration_data, HB_config)[source]¶ Object returned by the HB_master.run function
This class offers a simple API to access the information from a Hyperband run.
-
get_all_runs
(only_largest_budget=False)[source]¶ returns all runs performed
Parameters: only_largest_budget (boolean) – if True, only the largest budget for each configuration is returned. This makes sense if the runs are continued across budgets and the info field contains the information you care about. If False, all runs of a configuration are returned
-
get_fANOVA_data
(config_space, budgets=None, loss_fn=<function Result.<lambda>>, failed_loss=None)[source]¶
-
get_id2config_mapping
()[source]¶ returns a dict where the keys are the config_ids and the values are the actual configurations
-
get_incumbent_id
()[source]¶ Find the config_id of the incumbent.
The incumbent here is the configuration with the smallest loss among all runs on the maximum budget! If no run finishes on the maximum budget, None is returned!
-
get_incumbent_trajectory
(all_budgets=True, bigger_is_better=True, non_decreasing_budget=True)[source]¶ Returns the best configurations over time
Parameters: - all_budgets (bool) – If set to true all runs (even those not with the largest budget) can be the incumbent. Otherwise, only full budget runs are considered
- bigger_is_better (bool) – flag whether an evaluation on a larger budget is always considered better. If True, the incumbent might increase for the first evaluations on a bigger budget
- non_decreasing_budget (bool) – flag whether the budget of a new incumbent should be at least as big as the one for the current incumbent.
Returns: dictionary with all the config IDs, the times the runs finished, their respective budgets, and corresponding losses
Return type: dict
-
get_learning_curves
(lc_extractor=<function extract_HBS_learning_curves>, config_ids=None)[source]¶ extracts all learning curves from all run configurations
Parameters: - lc_extractor (callable) – a function to return a list of learning_curves. defaults to hpbanster.HB_result.extract_HP_learning_curves
- config_ids (list of valid config ids) – if only a subset of the config ids is wanted
Returns: a dictionary with the config_ids as keys and the learning curves as values
Return type: dict
-
-
class
hpbandster.core.result.
Run
(config_id, budget, loss, info, time_stamps, error_logs)[source]¶ Not a proper class, more a ‘struct’ to bundle important information about a particular run
-
hpbandster.core.result.
extract_HBS_learning_curves
(runs)[source]¶ function to get the hyperband learning curves
This is an example function showing the interface to use the HB_result.get_learning_curves method.
Parameters: runs (list of HB_result.run objects) – the performed runs for an unspecified config Returns: list of learning curves – An individual learning curve is a list of (t, x_t) tuples. This function must return a list of these. One could think of cases where one could extract multiple learning curves from these runs, e.g. if each run is an independent training run of a neural network on the data. Return type: list of lists of tuples
-
class
hpbandster.core.result.
json_result_logger
(directory, overwrite=False)[source]¶ convenience logger for ‘semi-live-results’
Logger that writes job results into two files (configs.json and results.json). Both files contain propper json objects in each line.
This version opens and closes the files for each result. This might be very slow if individual runs are fast and the filesystem is rather slow (e.g. a NFS).
Parameters: - directory (string) – the directory where the two files ‘configs.json’ and ‘results.json’ are stored
- overwrite (bool) –
In case the files already exist, this flag controls the behavior:
- True: The existing files will be overwritten. Potential risk of deleting previous results
- False: A FileExistsError is raised and the files are not modified.
-
hpbandster.core.result.
logged_results_to_HBS_result
(directory)[source]¶ function to import logged ‘live-results’ and return a HB_result object
You can load live run results with this function and the returned HB_result object gives you access to the results the same way a finished run would.
Parameters: directory (str) – the directory containing the results.json and config.json files Returns: hpbandster.core.result.Result – TODO Return type: object:


Great product as always plus good team and wonderful support. Worth 5 stars and more. Please keep up the great work.
Enabled | ThemeForestGreat product as always plus good team and wonderful support. Worth 5 stars and more. Please keep up the great work.
Enabled | ThemeForestWorth 5 stars for code quality, design, and documentation. Keep your hard work and produce more quality themes! I'm so happy with my purchase!
Enabled | ThemeForest


#include <boost/logging/format/named_write_fwd.hpp>
#include <boost/logging/format_fwd.hpp>
#include <boost/logging/logging.hpp>
// when you log messages on a dedicated thread (see writer::on_dedicated_thread class) #include <boost/logging/writer/on_dedicated_thread.hpp>
#include <boost/logging/format/named_write.hpp>
#include <boost/logging/format.hpp>
#include <boost/logging/formatter/tags.hpp>
#include <boost/logging/logging.hpp>


The source code for this project is available on Github.
If you have issues or bugreports, you can report them there as well.
I recommend 'Anonymous Pro' as a coding font, because its readable and fixed width.
This program was made because I didn't like the program (Reads51) we used in school.
The syntax highlighting was made possible because of a include file made by Roggemans M. (MGM), a teacher of mine.
The school in question is Thomas More, Campus De Nayer
Copyright (c) 2014, Dries007
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the name of the project nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.


{% extends "base/template.html" %}
{% block content %}
Parties
Available Stock
Products
Human Resources
Orders
About
{% endblock content %}


CodeMirror 2: NTriples mode
.
"literal 1" .
_:bnode3 . _:bnode4
"literal 2"@lang . _:bnode5
"literal 3"^^
.
Press
ctrl-space
to activate autocompletion.
MIME types defined:
text/n-triples
.
Get Cursor State


To allow PNGER from accessing your
camera please click on the link bellow.
Why should i do this ?
chrome://flags/#unsafely-treat-insecure-origin-as-secure.
Press the (+) bellow icon to take your shot, then choose your background removal
method.
(Make sure you provided an API in the extension settings)
Once your background is succefully removed, a modal will show asking for saving destination and image name.
PNGER is an extension to make images integration and background removal easier than ever!
It started as a Weekend Project to write my first VScode extension and understand the concepts for building
extensions,
It's idea is to make it easier for designers to integrate images directly from their cameras to their projects
with a background removal feature.
Find more about this extension in my Github.
Or you can contact me using my email.


April 15, 1998
Number Two of Ten Movies That Shook Wanniski
Memo To: Website browsers, fans & clients Author: Jude Wanniski <http://www.polyconomics.com> Re: Ten movies that helped shape my life: #2
"Rashomon." (1950) There are several foreign films I saw as a boy that made me think of how different people were in other parts of the world. Rashomon, from the Japanese director Akira Kurosawa, was about the universality of human nature -- the elusiveness of truth everywhere in the world. There are four people involved in an event that occurs in the middle of a forest in 16th century Japan. There is a nobleman, his beautiful consort, a bandit, and a bystander. There is conflict, a sexual episode between the woman and the bandit, and a murder. The heart of the film consists of the same event played out four different ways, according to the recollections of the four participants. This troubling film taught me that there is no such thing as objective fact or pure objectivity and built upon the lesson of The Ox-Bow Incident. It made it easy for me to see how Clarence Thomas and Anita Hill told the truth, although their stories were 180 degrees apart. It also enabled me to appreciate the conflicting stories of President Clinton and Kathleen Willey. People sometimes remember what was in their mind at the time of a stressful situation because it really was in their mind.
-------------------------------------------------------------------- Posted using Reference.COM http://WWW.Reference.COM FREE Usenet and Mailing list archive, directory and clipping service --------------------------------------------------------------------
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


Remember to state Start time(s) for each round, Rotation time and general location. For choruses it is critical to provide location for each chorus. "It's on your CSA or eval schedule" is only appropriate for quartets.
Administration
CSA: sent to group admins
Recording distribution?
_________________________
{% if pos %}
Total Participants on Stage: {{pos}}
{% endif %}
{% if contests %}
Awards
{% for contest in contests %}
{{contest.award.name}}: {{contest.group.name}}
{% endfor %}
{% for competitor in competitors %}
With a score of {{competitor.tot_points}}, a {{competitor.tot_score|floatformat:1}}
=> {{competitor.group.name}}


Sugar Stars
Here's a collection of firework star recipes using common sugar (sucrose) as the fuel source. This approach is impractical for commercial pyrotechnics due to being so sensitive to moisture and heat. But for homemade fireworks it has several advantages. It doubles as a binder and is water soluble so there's no need for noxious solvents. It's also widely available and cheap.
None of these use any additional chlorine donors other than potassium perchlorate, which is also the main oxidizer. This means that the colors are all on the pale end of the spectrum but there are fewer ingredients required.
These compositions can be moistened and formed into stars by either pressing or cutting. If you get the consistency right you can even roll them by hand. Be careful not to add too much water or it turns soupy fast. You want to add just enough for a damp sand consistency. It usually takes 2-4 days to fully dry depending on the humidity and size.
Colors can be tuned using different sources and ratios of barium, copper, potassium, and strontium. These were just the supplies I had enough of on hand to experiment with. The addition of a strong chlorine donor should significantly increase the saturation for most of these colors.
One quirk of these colors is that strontium nitrate + copper oxychloride don't seem to mix in the flame the way I expected. They produced a reddish flame at the base with blue tips. The entire flame appears like a cold white from far away. This made it difficult to get a better violet/purple than potassium nitrate, which is quite pale itself and burns more aggressively.
Pink
- 2 Potassium perchlorate
- 1 Strontium nitrate
- 1 Sugar
Orange
- 2 Potassium perchlorate
- 0.75 Strontium nitrate
- 0.25 Barium nitrate
- 1 Sugar
Yellow
- 2 Potassium perchlorate
- 0.25 Strontium nitrate
- 0.75 Barium nitrate
- 1 Sugar
Green
- 2 Potassium perchlorate
- 0.75 Barium nitrate
- 0.25 Copper oxychloride
- 1 Sugar
Blue
- 2 Potassium perchlorate
- 1 Copper oxychloride
- 1 Sugar
Lavender
- 2 Potassium perchlorate
- 1 Potassium nitrate
- 1 Sugar


INSTINCT A film review by David N. Butterworth Copyright 1999 David N. Butterworth
** (out of ****)
Subtlety is not one of Jon Turteltaub's strong suits.
In his previous outing, "Phenomenon," the director proved guilty of beating us about the face and neck with heavy-handed metaphors. For his latest film, a psychological thriller with the curiously unmemorable title "Instinct" (perhaps "The Silence of the Gorillas in the Mist" would have been more appropriate), Turteltaub has toned the meaningful allusions down some, but he still doesn't miss the opportunity to close in on his actors the minute their eyes well with tears. This happens not once but twice in "Instinct," and by the second time it's clear that Turteltaub still favors sledgehammer manipulation over nuance.
That's not the only problem with "Instinct." Perhaps its biggest failing is that it's just not very interesting. Whatever potential there was in Daniel Quinn's book remains unrealized by the director, the writer, and the actors--hard as they may try.
Dr. Ethan Powell (the reliable Anthony Hopkins) is an anthropologist who disappears for two years after studying gorillas (courtesy Stan Winston) in the African bush. He surfaces in a Rwandan jail, found guilty of killing two game wardens and injuring three others. Following Powell's transfer to a Florida penitentiary, an ambitious University of Miama psychiatry resident called Theo Caulder (Cuba Gooding, Jr.) takes a crack at evaluating the intriguing primatologist, who hasn't spoken a word in over a year.
Caulder's goal is to determine what turned Powell into a killer, but the deeper he digs into his patient's psyche, the stronger becomes his determination to win the man's freedom.
One of the catches behind Caulder's assignment is that he's required to serve as the staff psychiatrist at the ironically-named Harmony Bay Correctional Facility for the duration of his appraisal.  The filmmakers, however, choose to milk this situation of its "One Flew Over the Cuckoo's Nest" parallels, with the stubborn Caulder fighting to change the maximum security prison's rules by giving the mild-mannered felons a fair shot at privilege against the big gorillas holding all the aces.
All while dealing with Powell, whom he slowly reaches and befriends.
It's hard referring to the film as a thriller since what few thrills there are culminate in a real bonehead of a revelation. In addition, the prison officials are depicted as one-dimensional caricatures--the brutal cell guard, the insensitive warden, etc.--and the inmates are even more clichéd. This subtracts from the drama, and provides an embarrassing backdrop to all the anthropological philosophizing.
Hopkins, however, does a fine job as the troubled "ape man." With his flowing white hair and disheveled appearance, our first sighting of him is a throwback to Sean Connery's character in "The Rock," shuffling, manacled, and badly in need of a barber. Or, come to think of it, Anthony Hopkins' character in "The Mask of Zorro"!  This isn't a performance on a par with, say, Hannibal Lecter, but it's got its fascinating moments. Gooding, Jr. proves a satisfying complement to Hopkins, but Maura Tierney ("Forces of Nature"), who plays Powell's estranged daughter, isn't given much to do.
Except to tear up in close-up, that is.
Rather than dealing with serious psychological or celestial issues like those in "Instinct" and "Phenomenon," Turteltaub might do better by returning to lighter, "While You Were Sleeping"-type fare.
-- David N. Butterworth<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


Home
Passengers
Commuter Passes
Train Lines
Stations
Prefectures
Trainlines and Stations
Passengers
Add a Passenger
First Name:
Last Name:
Birthdate:
Occupation:
Email:
Filter Passengers
By Occupation:
Show All
{% for r in rows[1] %}
{{r[0]}}
{% endfor %}
Results
Passenger ID
First Name
Last Name
Birthdate
Occupation
Email
{% for r in rows[0] %}
{{ r[0] }}
{{ r[1] }}
{{ r[2] }}
{{ r[3] }}
{{ r[4] }}
{{ r[5] }}
{% endfor %}


 The date of this entry above (August 8, 2015) is a heading element (h4, to be precise). This paragraph you are reading right now is a paragraph element. The icons at the bottom of this page for my Twitter etc. accounts are separate image elements. Elements of a webpage are, by default, displayed in a certain manner on a website. Two comment default display values for elements are "inline" and "block". Block elements form a block that takes up the entire width of the browser window (i.e., the block stretches from the left all the way to the right of the window and no other elements sit on the same line this block occupies). The paragraph element and the heading elements are, by default, block elements.
Digression: bullies and sharers
This whole block thing is a little confusing, because the date heading above is really short. What do you mean it takes up the entire width of the window? The content of a block element does not effect the width of the element. Think of the date heading as having an invisible bar running horizantally from from "2015" to the right edge of the window. That invisible bar doesn't care that it is invisible, and is keeping all other elements out of its turf. Here is the invisible bar, highlighted:
display:inline
"Inline" elements only take up the width they need (unless you add to their width with different CSS properties), and thus do not need to start on a new line all their own (they also don't force the elements that come after them to start on a new line). Block elements do force any element that comes after them to start on a new line. Why? That's right: block elements take up the whole line! They are line bullies. The social media icon images at the bottom of this page are by default inline elements. Inline elements are not surrounded by invisible no-go zones to their left and right, and you can place other elements on the same line as an inline element (assuming that they too are inline elements). Inline elements are line sharers. Awwww, inline elements!You Can Change Display Values Using CSS
While HTML elements have a default display value, you can use CSS to change this value. Want to line up a paragraph and an image next to eachother, maybe so the text of the paragraph can wrap around the image? This wouldn't be possible if the default values were in place, because the paragraph element, no matter how wide the actual text of the paragraph, must take up the whole line and an image couldn't sit next to it. With CSS, we can fix this problem, and simply change the display for the paragraph (by default a block element) to display:inline. The image is by default already an inline element, so nothing to do there. Your text and image should (fingers crossed!) now be able to occupy the same lines on your webpage, so the text can fill in where the image does not.
Want more CSS?
The Mozilla Developer Network is a great resource, as is W3Schools.


DashBoard
Last 10 Seconds
Timesamp
00:00:00
Pulsometer
Efficiency
Color Status
from
to
Collect The Data!
Collecting The Data...
Reset!


Education and Skills
Bachelor of IS, CSULB (Expected Graduation 2019)
Programming Languages: Python, HTML, CSS


Node version 6.10.1
React version 15.6.1
Redux version 3.7.2
Last updated 2017-08-17
We have a brilliant idea! We NEED to create an app which fetches the public repositories from a users GitHub...
And we've also heard about this newfangled tech called React, and that it meshes well with Redux. Cool~!
I know NOTHING about either of those frameworks, but how hard could it be?...
Oh... and I don't like code being scaffolded out for me...
AHHHhahhahahaaahahaahaaaaaaa...... 😈🙈
Let's create our index.html and main.js files in the src directory
But that's ok. ONWARDS! ==>


--- layout: post title: Sharks with Lasers date: '2011-08-08T22:52:32-07:00' tags: tumblr_url: http://alexhwilliams.tumblr.com/post/8679800088/sharks-with-lasers --- Sharks with Lasers
Are they ill-tempered sea bass?


[!-- View of Hello RSP --] [:page partOf="hello_rsp"]
Now is [=new DateTime.now()].
This page is served by Rikulo Stream [=connect.server.version].
Please refer to Github for how it is implemented.


ExpenseTracker
welcome {{username}}
All Transaction
Expense
Income
Profile
{% block content %} {% endblock %}


There are souls in this world who have the gift of finding joy everywhere, and leaving it behind them when they go. It's a shame though we can't actually wrap and offer joy. So when you find yourself in need of something a bit more physical, GIFTER will come up with the best ideas!
Maybe you got a last-minute invitation to a party? Or forgot one of your friends have their birthday this week? You get constrained by time to find a gift fast, and some flowers or money won't do the trick - you need something more thoughtful than that.
Gifter is a platform for gift recommendations, based on the personality traits, hobbies and interests of the receiver. You can provide us details about their friend/coworker/family member, then you choose from the resulted suggestions.


Family members of formers PMs will not be guarded by SPG: Sources
Minister of State for Parliamentary Affairs Arjun Ram Meghwal told the Lok Sabha on Friday that the SPG Act (Amendment) Bill had been listed in the government's business for next week.
by PTIThe family members of former prime ministers will not be guarded by the elite Special Protection Group commandos, as per the amendments approved by the Union cabinet to the SPG Act, official sources said.
Minister of State for Parliamentary Affairs Arjun Ram Meghwal told the Lok Sabha on Friday that the SPG Act (Amendment) Bill had been listed in the government's business for next week.
This comes days after the government withdrew the SPG protection given to Congress president Sonia Gandhi and her children, Rahul Gandhi and Priyanka Gandhi Vadra, after almost three decades.


---
layout: plainarticle
permalink: '/homecoming/2016/jacobs-gives/'
title: JacobsGives - Homecoming 2016
---
This homecoming, even if you can’t make it back to campus you can still be a part of making a better future for Jacobs. Donate to either the Generation Fund or the JUFA Alumni Scholarship Fund by September 25, 2016.
Generation Fund
- Self sustainable model for student loans to finance studies at Jacobs
- Yearly fundraising target of 9’000 EUR in the first years, until self sustainable
- Based on merit and need, awarded to students of all nationalities
- Repayment starts once a minimum salary is reached, set at a fixed percentage of their income, for 5 years after graduation
- Self-sustainable after several years through student repayment into the fund
- Managed by the Jacobs Alumni Association
- Tax Deductible for German Citizens
- For more information on the Generation Fund, click here.
JUFA Alumni Scholarship Fund
- Endowed scholarship fund with a goal to raise $100,000 by 2020
- All scholarships are merit based and available to students from all nationalities
- Sustainable through endowment model where scholarship is only paid out of interest on endowment
- Each dollar donated will be matched, dollar for dollar up to $10,000
- Managed by the Jacobs University Foundation of America
- Tax deductible for US Citizens and Residents
- For more information on the JUFA Alumni Scholarship Fund, click here.


More about Oka bRionZ blog
Some categories will seen on Oka Brionz Blog is Web Development (Main Category), Programming, Technology, Personal Post, Inventions, Virals, Businesses, Entrepreneur, News.
Web Development
As I said before, Each site has a unique design, keyword, programming language (build), and uses. I want to make Blog using Angular with the newest Angular Materials. It was fast and consistent finely tuned performance, because every millisecond counts. And, it fully tested across modern browsers.


|
DashingPlatforms
1.0
|
Public Member Functions | |
| FaultMessage () | |
| FaultMessage (const FaultMessage &rhs) | |
| ~FaultMessage (void) | |
| void | reset (void) |
| FaultMessage & | operator= (const FaultMessage &rhs) |
| bool | operator== (const FaultMessage &rhs) const |
| bool | operator< (const FaultMessage &rhs) const |
Public Attributes | |
| char | neid [NEID_LENGTH] |
| ACE_Based_Pointer_Basic< char > | neidPI |
| ManagedObjectType | managedObject |
| unsigned int | managedObjectInstance |
| AlarmCodeType | alarmCode |
| EventCodeType | eventCode |
| AlarmSeverityType | alarmSeverity |
| unsigned int | pid |
| unsigned int | timeStamp |
| FaultMessage::FaultMessage | ( | const FaultMessage & | rhs | ) |
Copy Constructor
References alarmCode, alarmSeverity, eventCode, managedObject, managedObjectInstance, neid, neidPI, pid, and timeStamp.
| FaultMessage::~FaultMessage | ( | void | ) |
Destructor
| bool FaultMessage::operator< | ( | const FaultMessage & | rhs | ) | const |
Overloaded comparison operator
References alarmCode, alarmSeverity, eventCode, managedObject, managedObjectInstance, neidPI, pid, and timeStamp.
| FaultMessage & FaultMessage::operator= | ( | const FaultMessage & | rhs | ) |
Overloaded assignment operator
References alarmCode, alarmSeverity, eventCode, managedObject, managedObjectInstance, neid, neidPI, pid, and timeStamp.
| bool FaultMessage::operator== | ( | const FaultMessage & | rhs | ) | const |
Overloaded equality operator
References alarmCode, alarmSeverity, eventCode, managedObject, managedObjectInstance, neidPI, pid, and timeStamp.
| void FaultMessage::reset | ( | void | ) |
Reset data members
References alarmCode, alarmSeverity, eventCode, managedObject, managedObjectInstance, neid, neidPI, pid, and timeStamp.
| AlarmCodeType FaultMessage::alarmCode |
Alarm code that applies to the fault condition
Referenced by Faults::clearAlarm(), FaultMessage(), operator<(), operator=(), operator==(), FaultManager::processMailbox(), Faults::raiseAlarm(), and reset().
| AlarmSeverityType FaultMessage::alarmSeverity |
Severity. This field is used for event reports and clear alarms, but for raising alarms, this severity will be re-assigned/overriden by the severity in the database since we need to support severity reassignment by the user
Referenced by Faults::clearAlarm(), FaultMessage(), operator<(), operator=(), operator==(), FaultManager::processMailbox(), Faults::raiseAlarm(), Faults::reportEvent(), and reset().
| EventCodeType FaultMessage::eventCode |
Event Report code if the fault message is an event report instead
Referenced by FaultMessage(), operator<(), operator=(), operator==(), FaultManager::processMailbox(), Faults::reportEvent(), and reset().
| ManagedObjectType FaultMessage::managedObject |
Managed Object type (MEMORY, COMMUNICATIONS LINK, HARD DISK, etc.)
Referenced by Faults::clearAlarm(), FaultMessage(), operator<(), operator=(), operator==(), FaultManager::processMailbox(), Faults::raiseAlarm(), Faults::reportEvent(), and reset().
| unsigned int FaultMessage::managedObjectInstance |
Managed Object instance (LINK 12, LINK 13, etc.)
Referenced by Faults::clearAlarm(), FaultMessage(), operator<(), operator=(), operator==(), FaultManager::processMailbox(), Faults::raiseAlarm(), Faults::reportEvent(), and reset().
| char FaultMessage::neid[NEID_LENGTH] |
NEID (network element Id) that the Fault applies to. 9 character string
Referenced by Faults::clearAlarm(), FaultMessage(), operator=(), Faults::raiseAlarm(), and reset().
| ACE_Based_Pointer_Basic<char> FaultMessage::neidPI |
Position Independent pointer to the NEID
Referenced by Faults::clearAlarm(), FaultMessage(), operator<(), operator=(), operator==(), FaultManager::processMailbox(), Faults::raiseAlarm(), and reset().
| unsigned int FaultMessage::pid |
OS Process ID originating the alarm
Referenced by Faults::clearAlarm(), FaultMessage(), operator<(), operator=(), operator==(), FaultManager::processMailbox(), Faults::raiseAlarm(), Faults::reportEvent(), and reset().
| unsigned int FaultMessage::timeStamp |
Time stamp for alarm origination
Referenced by Faults::clearAlarm(), FaultMessage(), operator<(), operator=(), operator==(), FaultManager::processMailbox(), Faults::raiseAlarm(), Faults::reportEvent(), and reset().


Halo 1 was made in 2001. The game featured many things such as local play and would eventually go into online play. This original game sparked a new fire in the company, as the game was very successful. Many people started getting into the FPS (First person shooter) genre and started to love the idea of it. After a few more years Halo 2 was published in 2004. People would be scattering and running to buy this new game Bungie released due to the popularity of Halo 1. As usual the game was successful and introduced online play, meaning you could play with complete strangers from across the country. I could go on about the other games, but the devlopment of this series grew out extremely well. It would create communities and tournaments of different players just because of one game.


"x"
"1" "JEONG,HK, SHLYANKEVICH,M AND SURH,YJ, INTRINSIC MUTAGENICITY ANDELECTROPHILICITY OF 1-SULFOOXY-3-METHYLCHOLANTHRENE: IMPLICATIONS FOR METABOLICACTIVATION OF THE CARCINOGEN 3-METHYLCHOLANTHRENE, BIOCHE __ M. MOL. BIOL. INT.37(5):885-893, 1995"


{% extends 'frame.html' %}
{% block content %}
Friends
My Friends
Friends can see whatever you set friends to be able to see in your profile.
{% if friended %}
{% else %}
Add friends below!
{% endif %}
Following
Users you follow will show up here and in the creep page, for your convenience. They can't see that you follow them.
{% if followed %}
{% else %}
Follow other users below!
{% endif %}
Add and Follow
{% if not new %}
{% if count %}
Found {{count}} users.
{% for user in results %}
| {{user.username}} |
{% if user not in friended %}
friend |
{% else %}
(already friends) |
{% endif %}
{% if user not in followed %}
follow |
{% else %}
(already following) |
{% endif %}
{% if user not in friended and user not in followed %}
both |
{% endif %}
{% endfor %}
{% else %}
No users found.
{% endif %}
{% endif %}
{% endblock %}


You are best fit for JAVASCRIPT/JQUERY
You like to have some freedom and you are also interested in UI Logic. With javascript you can mix both of them and get the best of both worlds.
You are best fit for C#/.NET
You like structure and backend logic, you can learn pretty fast too.
You are best fit for SQL/Project Manager
You want to learn about Data Types and might want to have your own startup one day


|
SuanShu, a Java numerical and statistical library | |||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object com.numericalmethod.suanshu.matrix.doubles.linearsystem.OLSSolver
public class OLSSolver
This class solves a system of over-determined linear equations in the ordinary least square sense.
An over-determined system, which is represented by
Ax = y
has more rows than columns.
That is, there are more equations than unknowns.
One application of this is linear regression, where A
is the independent factors of a data set.
y
is the dependent observation of the data set.
The solution x_hat
is such that
|| Ax - y ||2
is minimized.
That is, it is the best approximation that
minimizes the sum of squared differences between the data values and their corresponding modeled values.
The approach is called "linear" least squares since the solution,
x_hat = (AtA)-1Aty,
depends linearly on the data.
Our implementations do not use the above formula to solve for x_hat
because of the numerical stability problem in computing AtA
.
Instead, we use the orthogonal decomposition methods.
| Nested Class Summary | |
|---|---|
static class |
OLSSolver.Method
the methods available to solve an Ordinary Least Square problem |
| Field Summary | |
|---|---|
double |
epsilon
a precision parameter: when a number |x| ≤ ε, it is considered 0 |
| Constructor Summary | |
|---|---|
OLSSolver(Matrix A)
Construct an OLS solver for a matrix A , where
Ax = y . |
|
OLSSolver(Matrix A,
OLSSolver.Method method,
double epsilon)
Construct an OLS solver for a matrix A , where
Ax = y |
| Method Summary | |
|---|---|
Vector |
solve(Vector y)
In the ordinary least square sense, solve Ax = y . |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
|---|
public final double epsilon
| Constructor Detail |
|---|
public OLSSolver(Matrix A, OLSSolver.Method method, double epsilon)
A
, where
Ax = y
A
- a matrixmethod
- one of the OLSSolver.Method
to useepsilon
- a precision parameter: when a number |x| ≤ ε, it is considered 0public OLSSolver(Matrix A)
A
, where
Ax = y
.
A
- a matrix| Method Detail |
|---|
public Vector solve(Vector y)
Ax = y
.
y
- a vector
x_hat
such that
|| Ax - y ||2
is minimized.
|
SuanShu, a Java numerical and statistical library | |||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


Loading the Scrawl-canvas library using a script tag in the HTML code
Scrawl-canvas is added to the page through a <script> tag at the bottom of the html code. The tag takes the following attributes:
<script type="module">[Scrawl-canvas code goes here]</script>
... or:
<script src="dom-001.js" type="module"></script>
- src= - path to the user code which imports the scrawl.js file
- type="module" - user code needs to be modular (ES6 style) - it has an import instruction at the top of the file
By importing the scrawl.js file, the browser will also import all the related files required by Scrawl-canvas. The import code goes at the top of the user code (in this demo, the dom-001.js file):
import * as scrawl from '../../relative/path/to/scrawl.js';
... or if the library has been included via NPM or Yarn:
import * as scrawl from 'scrawl-canvas';
... or if using the remote CDN version:
import * as scrawl from<EMAIL_ADDRESS>The import doesn't need to be called 'scrawl' - it can be whatever the user prefers.
element in a nested stack
Test purpose
- Import the scrawl.js file
- Import all other required Scrawl-canvas files
- Discover and prepare stack elements in the DOM
- Discover and prepare child elements in each stack element
- Discover and prepare the <canvas> element
- Track the mouse over the root stack element
Annotated code


Constructor
new TallyLabAccessController()
aasdsadsa d
- Source:
Returns:
TallyLabAccessController
Members
(static) type
Static type getter
- Source:
type
Non-static type getter, passthrough to static
- Source:
Methods
(async, static) create()
Factory method to statically create a new instance of the TallyLabAccessController.
This method is used internally to OrbitDB and is generally never called directly.
Returns:
TallyLabAccessController
(async) canAppend(entry, identityProvider)
The main function of the TallyLabAccessController: Write Access.
This method is used internally to OrbitDB and is generally never called directly. However, it's helpful to know that the function checks four things:
- Does TallyLabIdentityProvider#verifyIdentity pass?
- Does the ID of the running OrbitDB node match the identity of the attempted entry?
- Does the
write
array inside the ACL contain the public key trying to write? - Does the local keystore cache contain the public key trying to write?
If any of the above fail, the write will be denied.
Parameters:
| Name | Type | Description |
|---|---|---|
entry |
object | |
identityProvider |
TallyLabIdentityProvider |
- Source:
Returns:
Boolean can append / can not append
(async) load() → {Object}
Reads ACL from IPFS via ipfs.dag.get
Returns:
JSON of the manifest details
- Type
- Object
(async) save() → {Object}
Writes the ACL options to IPFS via ipfs.dag.put
Returns:
JSON object { address: $HASH }
- Type
- Object


Cars
Create
All Cars
reg
make
model
price
Updates
Delete
191 MO 123
Ford
Modeo
25,000
update
delete
123 MO 456
Kia
Ceed
56,000
update
delete
123 MO 456
Opel
Astra
44,000
update
delete
Create a
Update this
Car
Reg
Make
Ford
Kia
Opel
Model
Price
Create
Update


Welcome To New York is an Indian 3D Comedy film, directed by Chakri Toleti, produced by Vashu Bhagnani and Jackky Bhagnani, and starring Diljit Dosanjh, Sonakshi Sinha, Karan Johar, Riteish Deshmukh, Lara Dutta and Boman Irani. It is set to be released worldwide on 23 February 2018.
Plot Teji (Diljit Dosanjh), a sloppy recovery agent, and Jeenat Patel (Sonakshi Sinha), a fashion designer, become part of a big Bollywood event in New York - and chaos ensues.


--- template: example.html title: Stamen example shortdesc: Example of a Stamen tile source. docs: > Two layers are composed: the watercolor base layer with the terrain labels. tags: "stamen, watercolor, terrain-labels, two-layers" ---


|
|||||||||
| PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |
See:
Description
| Interface Summary | |
|---|---|
| ClassPath | ClassPath is an interface implemented by objects
representing a class search path. |
| CodeConverter.ArrayAccessReplacementMethodNames | Interface containing the method names to be used as array access replacements. |
| Translator | An observer of Loader . |
| Class Summary | |
|---|---|
| ByteArrayClassPath | A ByteArrayClassPath contains bytes that is served as
a class file to a ClassPool . |
| ClassClassPath | A search-path for obtaining a class file
by getResourceAsStream() in java.lang.Class . |
| ClassMap | A hash table associating class names with different names. |
| ClassPool | A container of CtClass objects. |
| CodeConverter | Simple translator of method bodies
(also see the javassist.expr package). |
| CodeConverter.DefaultArrayAccessReplacementMethodNames | Default implementation of the ArrayAccessReplacementMethodNames
interface giving default values for method names to be used for replacing
accesses to array elements. |
| CtBehavior | CtBehavior represents a method, a constructor,
or a static constructor (class initializer). |
| CtClass | An instance of CtClass represents a class. |
| CtConstructor | An instance of CtConstructor represents a constructor. |
| CtField | An instance of CtField represents a field. |
| CtField.Initializer | Instances of this class specify how to initialize a field. |
| CtMember | An instance of CtMember represents a field, a constructor,
or a method. |
| CtMethod | An instance of CtMethod represents a method. |
| CtMethod.ConstParameter | Instances of this class represent a constant parameter. |
| CtNewConstructor | A collection of static methods for creating a CtConstructor . |
| CtNewMethod | A collection of static methods for creating a CtMethod . |
| CtPrimitiveType | An instance of CtPrimitiveType represents a primitive type. |
| Loader | The class loader for Javassist. |
| LoaderClassPath | A class search-path representing a class loader. |
| Modifier | The Modifier class provides static methods and constants to decode class and member access modifiers. |
| SerialVersionUID | Utility for calculating serialVersionUIDs for Serializable classes. |
| URLClassPath | A class search-path specified with URL (http). |
| Exception Summary | |
|---|---|
| CannotCompileException | Thrown when bytecode transformation has failed. |
| NotFoundException | Signals that something could not be found. |
The Javassist Core API.
Javassist (Java programming assistant) makes bytecode engineering simple. It is a class library for editing bytecode in Java; it enables Java programs to define a new class at runtime and to modify a given class file when the JVM loads it.
The most significant class of this package is CtClass
.
See the description of this class first.
To know the version number of this package, type the following command:
java -jar javassist.jar
It prints the version number on the console.
|
|||||||||
| PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |


Machine learning tools applied to datasets for prediction and classification. I added mathematical explanations wherever I could.
scikit-learn
, pytorch
, tensorflow
, numpy
, pandas
, matplotlib
happiness.py
: Predicting happiness quotient for different countriesHousing.ipynb
: Predicting housing price in CaliforniaMNIST.ipynb
: Exploring MNIST data and classifying handwritten digitslightdata.ipynb
: Learning the optimum brightness level of laptop screen based on ambient light and content of the screenlogistic.py
: Classification of iris flowers using petal information using logistic regressionlogistic-money.py
: Logistic regression on expendituresignal.ipynb
: Signal processing on time series data using scipy
ml-notes.tex
: My notes of machine learningModels.ipynb
: Various modelsSupport Vector Machine.ipynb
: Support vector machine


{% extends 'base_app.html' %} {% block title %}{% trans 'Product group' %} {{ productgroup }}{% endblock title %} {% block content %}
{% trans 'Are you sure you want to delete this product group? The following products will also be deleted:' %}


To view this video please enable JavaScript, and consider upgrading to a web browser that supports HTML5 video


Lost tribe discovered in the burbs
Krippendorf's Tribe A Film Review By Michael Redman Copyright 1998 By Michael Redman
**1/2 (out of ****)
Just when we think that we've evolved culturally from our tribal cousins, we're reminded that the trappings of our customs are just overlays on primitive rituals. The universality of humanity hasn't changed much in thousands of years. Lucky for James Krippendorf.
Anthropology professor Krippendorf (Richard Dreyfuss) has been in New Guinea researching a lost tribe for two years. At least he was supposed to have been. After his wife died, he spent that time - and the $100,000 grant money - lying around his house. Now he has to give a presentation documenting his findings and he has nothing.
At the lecture to reveal all the strange mysteries he has supposedly discovered, Krippendorf looks at his speech notes which consist of blank pages and comes up with what he thinks is a brilliant scam. Inventing the Shelmikedmu (named after the first few letters of each of his children) people on the spot, he talks about the uniqueness of the tribe. The family unit of this culture consists of single fathers. He bases his extemporaneous talk on his own family's interactions.
The ruse works well until the Shelmikedmu capture the imagination of academia and the pop anthropology crowd. Krippendorf becomes an over-night celebrity, sought after by high society and the press. He is forced to invent more and more about the tribe and to produce his filmed documentaries.
Enlisting the help of his kids, Shelly (Natasha Lyonne), Mikey (Gregory Smith) and Edmund (Carl Michael Lindner), he dresses them up as tribesmen and throws together a New Guinea scene in his back yard complete with pigs and huts. At first the children are reluctant to co-operate. Their relationship with their father has gone down the tubes since their mother died. Young Edmund hasn't spoken a word to dad in two years. Then they get into it and the family begins to pull together for the first time since the tragedy.
Krippendorf is pushed towards the limelight by fellow anthropologist Veronica Micelli (Jenna Elfman from television's "Dharma And Greg"). Dragged kicking and screaming before the television cameras, he embellishes his lie with "facts" and numerous films.
Not everyone is fooled though. Suspicious professor Ruth Allen (Lily Tomlin) believes something is amiss and sets out to prove that the Shelmikedmu don't exist.
There are some truly hilarious bits in the film. When Mikey constructs a Shelmikedmu set for his school pageant, the movie works well. Pulling a miniature grass hut onto a stage, he tells of a ritual where the young virgin is blessed with pig urine during the time of her first menses. As the parents storm the stage, his classmate screams that she's not leaving the hut until she is purified.
There are other scenes that are just as funny. During a lecture, Krippendorf describes the early morning rituals of the teenage Shelmikedmu girl. As he talks about how she goes to a small room near the sleeping area to an altar and douses herself with potions and tonics, the film shows Shelly standing before the bathroom mirror putting on her make-up.
Where the movie runs into problems is its inability to focus on a target audience. It shuffles back and forth from being a smart comedy to bland Disneyesque pap. We get scenes with decidedly adult situations.  He and Veronica get drunk, dress in Shelmikedmu garb and jump into bed while he secretly films the "mating ritual". Then there are scenes where the kids come to the rescue as Shelly drives a car for the first time narrowly avoiding slapstick accidents.
Richard Dreyfuss is always entertaining in his roles. Whether he's the fixated alien-searcher from "Close Encounters Of The Third Kind" or the inventive Krippendorf, there's something about him that makes you want to invite him over for dinner. His friendliness and ability to think on his feet are refreshing.
Elfman is an odd one. Sometimes she is so goofy that you want to cringe. At other times she has a lanky somewhat gawky energetic appeal. Whatever it is, it's difficult to take your eyes off her. She holds her own on the screen and just needs to figure out exactly what her own actually is.
Lily Tomlin is one of the most gifted comedians around, but here she is mostly wasted. Not given much to do, she only shines in a few scenes.
The kids are...well, they're kids. Cute and occasionally engaging, the actors do a credible job in their parts, but could have been replaced by any number of other child actors and the film would have been unchanged.
Director Todd Holland ("The Larry Sanders Show") would have done better to have jettisoned the child audience and gone for a comedy more on the edge. The Shelmikedmu with their invented culture pulled together from authentic New Guinea artifacts and rituals and stapled onto items from Pier One held more promise than we got.
(Michael Redman has written this column for more years than he can remember and is currently attempting to recuperate from the Wild Raccoons Party Mardi. If you email him at<EMAIL_ADDRESS>please do so quietly.)
[This appeared in the 2/26/98 "Bloomington Voice", Bloomington, Indiana. Michael Redman can be contacted at<EMAIL_ADDRESS>--<EMAIL_ADDRESS>This week's film review at http://www.bvoice.com/ Film reviews archive at http://us.imdb.com/M/reviews_by?Michael%20Redman
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


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+++ title = "El Palomar Hostel, Playa" slug = "el-palomar-hostel-playa" date = 2007-03-03T15:00:00+10:00 tags = ["accommodation"] locations = ["mexico/playa-del-carmen"] draft = false summary = """
This place, the no. 1 youth hostel in Playa Del Carmen, has its advantages and its disadvantages. Its plusses are: its location (middle of town, across from the beach); and the people you can meet there. Its minuses are: the lack of space (really cramped dorms, and a twisting spiral staircase); and the cost (cheap for Playa, but overpriced for what you get). If you can handle the minuses, it's a great place to stay.
""" +++This place, the no. 1 youth hostel in Playa Del Carmen, has its advantages and its disadvantages. Its plusses are: its location (middle of town, across from the beach); and the people you can meet there. Its minuses are: the lack of space (really cramped dorms, and a twisting spiral staircase); and the cost (cheap for Playa, but overpriced for what you get). If you can handle the minuses, it's a great place to stay.
Rooms: crammed like sardines.
People: friendly, party-rearing bunch.
Staff: reasonably friendly, and speak some English.
Location: middle of town, next to the beach — perfect.
Food: basic breakfast.
Internet: free for 15 mins (1 PC).


Successful logins are worthy to log since they can be used for forensic analysis. Since here we with to log potential malicious data, input validation does not help.
Wrong / A2: Validate the input data and perform output encoding
Since here we with to log potential malicious data, input validation does not help. It might be hard to know which tools will process the logs - therefore we cannot know in which context to perform output encoding
Wrong / A4: Perform output encoding and rate-limiting
It might be hard to know which tools will process the logs - therefore we cannot know in which context to perform output encoding
Right / A3: Sanitize the username and perform rate-limiting
The best solution is to sanitize the username such that no injection can occur in the log files
One example of sanitization is to allow only letters and numbers up to 20 characters and convert all other caracters to underscores
For further information read here (SEI CERT Secure Coding Java Standard - IDS03-J)
Here is your flag: {{cfg.flag}}
{% else %}
Nay :-(
{% endif %}


Program
Technical Program
Please check our list of accepted papers and schedule.
Outstanding Keynotes
SSBSE’17 is proud to have Prof. Myra Cohen and Dr. Joachim Wegener as our keynote speakers. Make sure to check keynotes page for details.
Tutorials
SSBSE’17 is going to host four tutorials with a wide range of search-based software engineering material, including an introduction to jMetal, multiobjective algorithms benchmarking, GUI and cyber-physical systems optimization, and unit test generation with EvoSuite
SSBSE Challenge
The highly sucessful SSBSE challenge continues at SSBSE’17. Are you up for the task? Check our challenge call to see how you can apply and win up to €1000.
Journal-First Presentations at SSBSE’17
SSBSE’17 is excited to present, for the first time in its history, journal first publications at this conference.
The goal of the journal first program is to provide a mechanism whereby research results that may be a better fit for publication via the journal review process can be broadly disseminated to the community through presentation in the technical research tracks of other technical conferences. As in other communities that provide a journal-first publication route (such as the VLDB conference in the database community), authors choosing this route benefit from the absence of page limits and annual submission deadlines, and the possibility of having their submissions undergo multiple rounds of review and revision.
In software engineering, the journal first program has gained considerable momentum in recent years. For example, now TSE has agreed to allow many such papers to be presented at ICSE.
Inspired by their lead, we are doing the same with SBSE-related papers. To qualify:
- The paper has to discuss SBSE material;
- The paper must not have appeared previous;
- The paper has to have appeared recently in a major journal;
- The editors of the journal must agree to such a journal first presentation.
Applying this criteria, the SSBSE’17 PC co-chairs selected the following papers for journal first presentation at SSBSE’17:
-
Roberto E. Lopez-Herrejon, Lukas Linsbauer, and Alexander Egyed. 2015. A systematic mapping study of search-based software engineering for software product lines. Inf. Softw. Technol. 61, C (May 2015), 33-51.
-
Michael Mohan, Des Greer, and Paul McMullan. Technical debt reduction using search based automated refactoring. Journal of Systems and Software 120 (2016): 183-194.
Student Paper Track
This track gives students the opportunity to showcase their SBSE research and receive feedback from senior members of the SBSE community. To be eligible, papers should be primarily the work of students that have not yet completed their studies. The papers may have co-authors who are not students but the student author is expected to present the paper at the symposium.


More better documentation is in the works and coming soon. For now please review the ocaml typespec below.


You are currently not subscribed to any journal. This is either your first login or you removed your last journal subscription. You will now be redirected to the subscriptions page where you can choose which academic journals to follow.
Once you have chosen the journals you can return to the home page to see the papers interesting to you.


Machine Perception Primitive:
An implementation of the "Fast Saliency Using Natural-statistics" algorithm from Butko, et al., 2008. FastSUN is an efficient implementation of Zhang et al.'s SUN algorithm, which is documented in Zhang, et al., 2008 (see Related Publications).
More...
#include <FastSaliency.h>
List of all members.
Public Member Functions |
| | FastSaliency (int salwidth, int salheight, int numtemporal=2, int numspatial=7, float firsttau=1.0, int firstrad=0) |
| | Detailed Constructor.
|
| | ~FastSaliency () |
| | Simple Destructor.
|
| void | updateSaliency (IplImage *colorframe) |
| | Compute the saliency map for a new input frame.
|
| void | setGGDistributionPower (double value) |
| | Set the Generalized-Gaussian feature distribution to be something other than Laplacian.
|
| void | setUseDoEFeatures (int flag) |
| | Toggle use of Difference of Exponential (motion) features. On by default.
|
| void | setUseDoBFeatures (int flag) |
| | Toggle use of Difference of Box (spatial) features. On by default.
|
| void | setUseColorInformation (int flag) |
| | Toggle use of Color Contrast (red-green and blue-yellow) information. On by default.
|
| void | setUseGGDistributionParams (int flag) |
| | Toggle use of custom Generalized Gaussian Distribution parameters. On by default.
|
| void | setEstimateGGDistributionParams (int flag) |
| | Toggle estimation of custom Generalized Gaussian Distribution parameters. On by default.
|
| std::vector< CvPoint > | getKeyPoints (int radius=1) |
| | Find key-point interest detectors using non-maximal suppression on the saliency map.
|
Public Attributes |
|
IplImage * | salImageDouble |
| | The current saliency image, in Double format, i.e. IPL_DEPTH_64F with 1 channel. These are the raw saliency values (negative log likelihood) computed by the FastSUN algorithm.
|
|
IplImage * | salImageFloat |
| | The current saliency iamge, in Float format, i.e. IPL_DEPTH_32F with 1 channel. This is an image representation meant for display, and has values normalized to the range 0-1.
|
Detailed Description
Machine Perception Primitive:
An implementation of the "Fast Saliency Using Natural-statistics" algorithm from Butko, et al., 2008. FastSUN is an efficient implementation of Zhang et al.'s SUN algorithm, which is documented in Zhang, et al., 2008 (see Related Publications).
- Author:
- Nicholas Butko
- Date:
- 2010 version 0.4
Constructor & Destructor Documentation
| FastSaliency::FastSaliency |
( |
int |
salwidth, |
|
|
int |
salheight, |
|
|
int |
numtemporal = 2 , |
|
|
int |
numspatial = 7 , |
|
|
float |
firsttau = 1.0 , |
|
|
int |
firstrad = 0 |
|
) |
| |
Detailed Constructor.
- Parameters:
-
| salwidth | The width of the input images supplied to the algorithm. Note: No image scaling is done by the FastSaliency algorithm, so if you want a saliency map of a downscaled image, you must scale the image down to salwidth before calling updateSaliency. |
| salheight | The height of the input images supplied to the algorithm. Note: No image scaling is done by the FastSaliency algorithm, so if you want a saliency map of a downscaled image, you must scale the image down to salheight before calling updateSaliency. |
| numtemporal | Number of timescales of Difference of Expontential filters to track. |
| numspatial | Number of sizes of Difference of Box filters to use. |
| firsttau | Exponential Falloff parameter for the first Difference of Exponentials scale. Lower numbers give slower falloff. Must be greater than 0. |
| firstrad | Radius of smallest Difference of Boxes filter center. The diameter of the box is 2*rad+1, so the smallest allowed first radius is 0. |
| FastSaliency::~FastSaliency |
( |
) |
|
Simple Destructor.
Deallocates all memory associated with the FastSaliency object.
Member Function Documentation
| vector< CvPoint > FastSaliency::getKeyPoints |
( |
int |
radius = 1 ) |
|
Find key-point interest detectors using non-maximal suppression on the saliency map.
- Parameters:
-
| radius | Non-maximal suppression radius. |
| void FastSaliency::setEstimateGGDistributionParams |
( |
int |
flag ) |
|
Toggle estimation of custom Generalized Gaussian Distribution parameters. On by default.
Initially we assume that image features are drawn from a Laplace distribution of zero mean and unit variance. However, these parameters can be estimated and adjusted online to better reflect the statistics of the current environment. If off, the current estimate of the mean and variance are used. If on, the estimate updates with each new frame (at small computational cost). One approach would be to estimate the parameters for a few frames, and then when the estimates no longer change much, to turn off estimation and just use the "learned" parameters.
- Parameters:
-
| flag | If 0, turn off estimation of histogram information. Otherwise, calculate it. |
| void FastSaliency::setGGDistributionPower |
( |
double |
value ) |
|
Set the Generalized-Gaussian feature distribution to be something other than Laplacian.
Image features often exhibit marginal histograms that are of a generalized Gaussian form. The power of these distributions is often between .5 and .7, but performing this power computation is numerically slow. By using a Laplacian distribution (power of one) by defuault, the salience estimate is worse, but much faster to compute. Setting "power" to some lower value may give better but slower resultes.
- Parameters:
-
| value | The power of a generalized-gaussian feature distribution. |
| void FastSaliency::setUseColorInformation |
( |
int |
flag ) |
|
Toggle use of Color Contrast (red-green and blue-yellow) information. On by default.
- Parameters:
-
| flag | If 0, turn off Color Contrast information. Otherwise, calculate it. |
| void FastSaliency::setUseDoBFeatures |
( |
int |
flag ) |
|
Toggle use of Difference of Box (spatial) features. On by default.
- Parameters:
-
| flag | If 0, turn off DoB features. Otherwise, calculate them. |
| void FastSaliency::setUseDoEFeatures |
( |
int |
flag ) |
|
Toggle use of Difference of Exponential (motion) features. On by default.
- Parameters:
-
| flag | If 0, turn off DoE features. Otherwise, calculate them. |
| void FastSaliency::setUseGGDistributionParams |
( |
int |
flag ) |
|
Toggle use of custom Generalized Gaussian Distribution parameters. On by default.
If this is off, we assume that image features are drawn from a Laplace distribution of zero mean and unit variance. However, these parameters can be estimated and adjusted online to better reflect the statistics of the current environment. If on, these online estimates will be used (at small computational cost).
- Parameters:
-
| flag | If 0, turn off the use of histogram estimate information. Otherwise, use it. |
| void FastSaliency::updateSaliency |
( |
IplImage * |
colorframe ) |
|
Compute the saliency map for a new input frame.
- Parameters:
-
| colorframe | A BGR Color image with width=salwidth and height=salheight. The input image type should be IPL_DEPTH_32F with 3 channels. The saliency map that is computed is accessible via the variables salImageDouble and salImageFloat |
The documentation for this class was generated from the following files:
- /Users/nick/projects/NickThesis/Code/OpenCV/src/FastSaliency.h
- /Users/nick/projects/NickThesis/Code/OpenCV/src/FastSaliency.cpp


ImageReview checks if the set of images in a pod are allowed.
| Field | Description | ||||||
|---|---|---|---|---|---|---|---|
apiVersion string |
imagepolicy.k8s.io/v1alpha1 |
||||||
kind string |
ImageReview |
||||||
metadata
meta/v1.ObjectMeta
|
Standard object's metadata. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#metadata Refer to the Kubernetes API documentation for the fields of themetadata field.
|
||||||
spec
*
ImageReviewSpec
|
Spec holds information about the pod being evaluated
|
||||||
status
ImageReviewStatus
|
Status is filled in by the backend and indicates whether the pod should be allowed. |
(Appears in: ImageReviewSpec)
ImageReviewContainerSpec is a description of a container within the pod creation request.
| Field | Description |
|---|---|
image
string
|
This can be in the form image:tag or image@SHA:012345679abcdef. |
(Appears in: ImageReview)
ImageReviewSpec is a description of the pod creation request.
| Field | Description |
|---|---|
containers
[]ImageReviewContainerSpec
|
Containers is a list of a subset of the information in each container of the Pod being created. |
annotations
map[string]string
|
Annotations is a list of key-value pairs extracted from the Pod's annotations.
It only includes keys which match the pattern |
namespace
string
|
Namespace is the namespace the pod is being created in. |
(Appears in: ImageReview)
ImageReviewStatus is the result of the review for the pod creation request.
| Field | Description |
|---|---|
allowed
*
bool
|
Allowed indicates that all images were allowed to be run. |
reason
string
|
Reason should be empty unless Allowed is false in which case it may contain a short description of what is wrong. Kubernetes may truncate excessively long errors when displaying to the user. |
auditAnnotations
map[string]string
|
AuditAnnotations will be added to the attributes object of the admission controller request using 'AddAnnotation'. The keys should be prefix-less (i.e., the admission controller will add an appropriate prefix). |
Generated with genref
on git commit 4247aac7


Apache Flink ML 2.2.0 Release Announcement
April 19, 2023 - Dong LinThe Apache Flink community is excited to announce the release of Flink ML 2.2.0! This release focuses on enriching Flink ML’s feature engineering algorithms. The library now includes 33 feature engineering algorithms, making it a more comprehensive library for feature engineering tasks.
With the addition of these algorithms, we believe Flink ML library is ready for use in production jobs that require feature engineering capabilities, whose input can then be consumed by both offline and online machine learning tasks.
We encourage you to download the release and share your feedback with the community through the Flink mailing lists or JIRA! We hope you like the new release and we’d be eager to learn about your experience with it.
Notable Features #
Introduced API and infrastructure for online serving #
In machine learning, one of the main goals of model training is to deploy the trained model to perform online inference, where the model server must respond to incoming requests with millisecond-level latency. However, prior releases of Flink ML only supported nearline inference using the Flink runtime, which may not meet the requirements of online inference use-cases.
With FLIP-289, Flink ML now provides an API and infrastructure for users to load a ModelServable from model data generated by an Estimator. This ModelServable can be replicated across multiple model servers to process online inference requests in parallel. As the ModelServable is effectively a UDF that does not rely on Flink runtime, it can also be integrated as a UDF into other serving or processing frameworks to serve the model trained by Flink ML.
As a first step, the LogisticRegressionModelServable has been added to serve the logistic regression model online, and more servables will be added in the future. This new feature enables Flink ML to be used for both offline and online machine learning tasks, making it more versatile for a wider range of use cases.
Added 27 feature engineering algorithms #
Flink ML 2.2.0 significantly expanded the coverage of feature engineering algorithms, increasing the number from 6 to 33. Flink ML now covers 28 out of the 33 feature engineering algorithms provided in Spark ML, making it a more comprehensive library for feature engineering tasks.
Feature engineering is a critical step in modern AI infrastructures as it can preprocess data not only for traditional machine learning algorithms like GBT but also for deep learning algorithms and large language models like Transformer, which are increasingly popular. With the addition of these algorithms, we hope Flink ML can be more useful in machine-learning tasks for Flink users.
All feature engineering algorithms can be easily accessed through the drop-down list on the left side of this Flink ML page. For each algorithm, we have provided Python and Java examples to demonstrate how to use them.
Added two production-validated online learning algorithms #
Flink ML offers a significant advantage over other machine learning libraries in terms of its ability to perform online learning using Flink’s streaming runtime. To leverage this strength, we implemented two online algorithms in Flink ML and successfully used them in a production machine learning job at Alibaba.
This job involves dynamically clustering similar logs and detecting errors in the logs to help site reliability engineers. By using OnlineStandardScaler and AgglomerativeClustering to standardize and cluster logs in real-time, the job is able to update models more frequently with a much simpler infrastructure setup. We presented this work at Flink Forward Asia last year, and it will soon be integrated into the open-source project SREWorks.
With these online algorithms, Flink ML provides users with the ability to continuously update models using new data in real-time, resulting in more accurate and up-to-date predictions. This can be particularly useful in use cases where data is constantly streaming in, and it’s important to make quick decisions based on the latest available information.
Upgrade Notes #
This release is fully backward compatible with Flink ML 2.1. Users should be able to upgrade to Flink ML 2.2.0 without worrying about any incompatibilities or breaking changes.
Release Notes and Resources #
Please take a look at the release notes for a detailed list of changes and new features.
The binary distribution and source artifacts are now available on the updated Downloads page of the Flink website, and the most recent distribution of Flink ML Python package is available on PyPI.
List of Contributors #
The Apache Flink community would like to thank each one of the contributors that have made this release possible:
Zhipeng Zhang, Dong Lin, Fan Hong, JiangXin, Zsombor Chikan, huangxingbo, taosiyuan163, vacaly, weibozhao, yunfengzhou-hub