A major challenge in Bayesian Optimization is the boundary issue (Swersky, 2017) where an algorithm spends too many evaluations near the boundary of its search space. In this paper, we propose BOCK, Bayesian Optimization with Cylindrical Kernels, whose basic idea is to transform the ball geometry of the search space using a cylindrical transformation. Because of the transformed geometry, the Gaussian Process-based surrogate model spends less budget searching near the boundary, while concentrating its efforts relatively more near the center of the search region, where we expect the solution to be located. We evaluate BOCK extensively, showing that it is not only more accurate and efficient, but it also scales successfully to problems with a dimensionality as high as 500. We show that the better accuracy and scalability of BOCK even allows optimizing modestly sized neural network layers, as well as neural network hyperparameters.
Generalization performance of classifiers in deep learning has recently become a subject of intense study. Deep models, typically over-parametrized, tend to fit the training data exactly. Despite this "overfitting", they perform well on test data, a phenomenon not yet fully understood. The first point of our paper is that strong performance of overfitted classifiers is not a unique feature of deep learning. Using six real-world and two synthetic datasets, we establish experimentally that kernel machines trained to have zero classification or near zero regression error perform very well on test data, even when the labels are corrupted with a high level of noise. We proceed to give a lower bound on the norm of zero loss solutions for smooth kernels, showing that they increase nearly exponentially with data size. We point out that this is difficult to reconcile with the existing generalization bounds. Moreover, none of the bounds produce non-trivial results for interpolating solutions. Second, we show experimentally that (non-smooth) Laplacian kernels easily fit random labels, a finding that parallels results for ReLU neural networks. In contrast, fitting noisy data requires many more epochs for smooth Gaussian kernels. Similar performance of overfitted Laplacian and Gaussian classifiers on test, suggests that generalization is tied to the properties of the kernel function rather than the optimization process. Certain key phenomena of deep learning are manifested similarly in kernel methods in the modern "overfitted" regime. The combination of the experimental and theoretical results presented in this paper indicates a need for new theoretical ideas for understanding properties of classical kernel methods. We argue that progress on understanding deep learning will be difficult until more tractable "shallow" kernel methods are better understood.
We present a VAE architecture for encoding and generating high dimensional sequential data, such as video or audio. Our deep generative model learns a latent representation of the data which is split into a static and dynamic part, allowing us to approximately disentangle latent time-dependent features (dynamics) from features which are preserved over time (content). This architecture gives us partial control over generating content and dynamics by conditioning on either one of these sets of features. In our experiments on artificially generated cartoon video clips and voice recordings, we show that we can convert the content of a given sequence into another one by such content swapping. For audio, this allows us to convert a male speaker into a female speaker and vice versa, while for video we can separately manipulate shapes and dynamics. Furthermore, we give empirical evidence for the hypothesis that stochastic RNNs as latent state models are more efficient at compressing and generating long sequences than deterministic ones, which may be relevant for applications in video compression.
Stein variational gradient descent (SVGD) is a recently proposed particle-based Bayesian inference method, which has attracted a lot of interest due to its remarkable approximation ability and particle efficiency compared to traditional variational inference and Markov Chain Monte Carlo methods. However, we observed that particles of SVGD tend to collapse to modes of the target distribution, and this particle degeneracy phenomenon becomes more severe with higher dimensions. Our theoretical analysis finds out that there exists a negative correlation between the dimensionality and the repulsive force of SVGD which should be blamed for this phenomenon. We propose Message Passing SVGD (MP-SVGD) to solve this problem. By leveraging the conditional independence structure of probabilistic graphical models (PGMs), MP-SVGD converts the original high-dimensional global inference problem into a set of local ones over the Markov blanket with lower dimensions. Experimental results show its advantages of preventing vanishing repulsive force in high-dimensional space over SVGD, and its particle efficiency and approximation flexibility over other inference methods on graphical models.
We develop nested automatic differentiation (AD) algorithms for exact inference and learning in integer latent variable models. Recently, Winner, Sujono, and Sheldon showed how to reduce marginalization in a class of integer latent variable models to evaluating a probability generating function which contains many levels of nested high-order derivatives. We contribute faster and more stable AD algorithms for this challenging problem and a novel algorithm to compute exact gradients for learning. These contributions lead to significantly faster and more accurate learning algorithms, and are the first AD algorithms whose running time is polynomial in the number of levels of nesting.
We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: under certain common circumstances, it is possible to learn to restore signals without ever observing clean ones, at performance close or equal to training using clean exemplars. We show applications in photographic noise removal, denoising of synthetic Monte Carlo images, and reconstruction of MRI scans from undersampled inputs, all based on only observing corrupted data.
Predicting structured outputs can be computationally onerous due to the combinatorially large output spaces. In this paper, we focus on reducing the prediction time of a trained black-box structured classifier without losing accuracy. To do so, we train a speedup classifier that learns to mimic a black-box classifier under the learning-to-search approach. As the structured classifier predicts more examples, the speedup classifier will operate as a learned heuristic to guide search to favorable regions of the output space. We present a mistake bound for the speedup classifier and identify inference situations where it can independently make correct judgments without input features. We evaluate our method on the task of entity and relation extraction and show that the speedup classifier outperforms even greedy search in terms of speed without loss of accuracy.
The generalization properties of Gaussian processes depend heavily on the choice of kernel, and this choice remains a dark art. We present the Neural Kernel Network (NKN), a flexible family of kernels represented by a neural network. The NKN architecture is based on the composition rules for kernels, so that each unit of the network corresponds to a valid kernel. It can compactly approximate compositional kernel structures such as those used by the Automatic Statistician (Lloyd et al., 2014), but because the architecture is differentiable, it is end-to-end trainable with gradient-based optimization. We show that the NKN is universal for the class of stationary kernels. Empirically we demonstrate pattern discovery and extrapolation abilities of NKN on several tasks that depend crucially on identifying the underlying structure, including time series and texture extrapolation, as well as Bayesian optimization.
The optimization of expensive-to-evaluate black-box functions over combinatorial structures is an ubiquitous task in machine learning, engineering and the natural sciences. The combinatorial explosion of the search space and costly evaluations pose challenges for current techniques in discrete optimization and machine learning, and critically require new algorithmic ideas (NIPS BayesOpt 2017). This article proposes, to the best of our knowledge, the first algorithm to overcome these challenges, based on an adaptive, scalable model that identifies useful combinatorial structure even when data is scarce. Our acquisition function pioneers the use of semidefinite programming to achieve efficiency and scalability. Experimental evaluations demonstrate that this algorithm consistently outperforms other methods from combinatorial and Bayesian optimization.
Recent work in unsupervised representation learning has focused on learning deep directed latent-variable models. Fitting these models by maximizing the marginal likelihood or evidence is typically intractable, thus a common approximation is to maximize the evidence lower bound (ELBO) instead. However, maximum likelihood training (whether exact or approximate) does not necessarily result in a good latent representation, as we demonstrate both theoretically and empirically. In particular, we derive variational lower and upper bounds on the mutual information between the input and the latent variable, and use these bounds to derive a rate-distortion curve that characterizes the tradeoff between compression and reconstruction accuracy. Using this framework, we demonstrate that there is a family of models with identical ELBO, but different quantitative and qualitative characteristics. Our framework also suggests a simple new method to ensure that latent variable models with powerful stochastic decoders do not ignore their latent code.
We provide theoretical and empirical evidence that using tighter evidence lower bounds (ELBOs) can be detrimental to the process of learning an inference network by reducing the signal-to-noise ratio of the gradient estimator. Our results call into question common implicit assumptions that tighter ELBOs are better variational objectives for simultaneous model learning and inference amortization schemes. Based on our insights, we introduce three new algorithms: the partially importance weighted auto-encoder (PIWAE), the multiply importance weighted auto-encoder (MIWAE), and the combination importance weighted auto-encoder (CIWAE), each of which includes the standard importance weighted auto-encoder (IWAE) as a special case. We show that each can deliver improvements over IWAE, even when performance is measured by the IWAE target itself. Furthermore, our results suggest that PIWAE may be able to deliver simultaneous improvements in the training of both the inference and generative networks.
We propose a method of moments (MoM) algorithm for training large-scale implicit generative models. Moment estimation in this setting encounters two problems: it is often difficult to define the millions of moments needed to learn the model parameters, and it is hard to determine which properties are useful when specifying moments. To address the first issue, we introduce a moment network, and define the moments as the network's hidden units and the gradient of the network's output with the respect to its parameters. To tackle the second problem, we use asymptotic theory to highlight desiderata for moments -- namely they should minimize the asymptotic variance of estimated model parameters -- and introduce an objective to learn better moments. The sequence of objectives created by this Method of Learned Moments (MoLM) can train high-quality neural image samplers. On CIFAR-10, we demonstrate that MoLM-trained generators achieve significantly higher Inception Scores and lower Frechet Inception Distances than those trained with gradient penalty-regularized and spectrally-normalized adversarial objectives. These generators also achieve nearly perfect Multi-Scale Structural Similarity Scores on CelebA, and can create high-quality samples of 128x128 images.
Semi-implicit variational inference (SIVI) is introduced to expand the commonly used analytic variational distribution family, by mixing the variational parameter with a flexible distribution. This mixing distribution can assume any density function, explicit or not, as long as independent random samples can be generated via reparameterization. Not only does SIVI expand the variational family to incorporate highly flexible variational distributions, including implicit ones that have no analytic density functions, but also sandwiches the evidence lower bound (ELBO) between a lower bound and an upper bound, and further derives an asymptotically exact surrogate ELBO that is amenable to optimization via stochastic gradient ascent. With a substantially expanded variational family and a novel optimization algorithm, SIVI is shown to closely match the accuracy of MCMC in inferring the posterior in a variety of Bayesian inference tasks.
The ADAM optimizer is exceedingly popular in the deep learning community. Often it works very well, sometimes it doesn't. Why? We interpret ADAM as a combination of two aspects: for each weight, the update direction is determined by the sign of stochastic gradients, whereas the update magnitude is determined by an estimate of their relative variance. We disentangle these two aspects and analyze them in isolation, gaining insight into the mechanisms underlying ADAM. This analysis also extends recent results on adverse effects of ADAM on generalization, isolating the sign aspect as the problematic one. Transferring the variance adaptation to SGD gives rise to a novel method, completing the practitioner's toolbox for problems where ADAM fails.
Normalizing flows and autoregressive models have been successfully combined to produce state-of-the-art results in density estimation, via Masked Autoregressive Flows (MAF), and to accelerate state-of-the-art WaveNet-based speech synthesis to 20x faster than real-time, via Inverse Autoregressive Flows (IAF). We unify and generalize these approaches, replacing the (conditionally) affine univariate transformations of MAF/IAF with a more general class of invertible univariate transformations expressed as monotonic neural networks. We demonstrate that the proposed neural autoregressive flows (NAF) are universal approximators for continuous probability distributions, and their greater expressivity allows them to better capture multimodal target distributions. Experimentally, NAF yields state-of-the-art performance on a suite of density estimation tasks and outperforms IAF in variational autoencoders trained on binarized MNIST.
Training of discrete latent variable models remains challenging because passing gradient information through discrete units is difficult. We propose a new class of smoothing transformations based on a mixture of two overlapping distributions, and show that the proposed transformation can be used for training binary latent models with either directed or undirected priors. We derive a new variational bound to efficiently train with Boltzmann machine priors. Using this bound, we develop DVAE++, a generative model with a global discrete prior and a hierarchy of convolutional continuous variables. Experiments on several benchmarks show that overlapping transformations outperform other recent continuous relaxations of discrete latent variables including Gumbel-Softmax (Maddison et al., 2016; Jang et al., 2016), and discrete variational autoencoders (Rolfe 2016).
MAP perturbation models have emerged as a powerful framework for inference in structured prediction. Such models provide a way to efficiently sample from the Gibbs distribution and facilitate predictions that are robust to random noise. In this paper, we propose a provably polynomial time randomized algorithm for learning the parameters of perturbed MAP predictors. Our approach is based on minimizing a novel Rademacher-based generalization bound on the expected loss of a perturbed MAP predictor, which can be computed in polynomial time. We obtain conditions under which our randomized learning algorithm can guarantee generalization to unseen examples.
Uncertainty computation in deep learning is essential to design robust and reliable systems. Variational inference (VI) is a promising approach for such computation, but requires more effort to implement and execute compared to maximum-likelihood methods. In this paper, we propose new natural-gradient algorithms to reduce such efforts for Gaussian mean-field VI. Our algorithms can be implemented within the Adam optimizer by perturbing the network weights during gradient evaluations, and uncertainty estimates can be cheaply obtained by using the vector that adapts the learning rate. This requires lower memory, computation, and implementation effort than existing VI methods, while obtaining uncertainty estimates of comparable quality. Our empirical results confirm this and further suggest that the weight-perturbation in our algorithm could be useful for exploration in reinforcement learning and stochastic optimization.
We seek to automate the design of molecules based on specific chemical properties. In computational terms, this task involves continuous embedding and generation of molecular graphs. Our primary contribution is the direct realization of molecular graphs, a task previously approached by generating linear SMILES strings instead of graphs. Our junction tree variational autoencoder generates molecular graphs in two phases, by first generating a tree-structured scaffold over chemical substructures, and then combining them into a molecule with a graph message passing network. This approach allows us to incrementally expand molecules while maintaining chemical validity at every step. We evaluate our model on multiple tasks ranging from molecular generation to optimization. Across these tasks, our model outperforms previous state-of-the-art baselines by a significant margin.
Gaussian multiplicative noise is commonly used as a stochastic regularisation technique in training of deterministic neural networks. A recent paper reinterpreted the technique as a specific algorithm for approximate inference in Bayesian neural networks; several extensions ensued. We show that the log-uniform prior used in all the above publications does not generally induce a proper posterior, and thus Bayesian inference in such models is ill-posed. Independent of the log-uniform prior, the correlated weight noise approximation has further issues leading to either infinite objective or high risk of overfitting. The above implies that the reported sparsity of obtained solutions cannot be explained by Bayesian or the related minimum description length arguments. We thus study the objective from a non-Bayesian perspective, provide its previously unknown analytical form which allows exact gradient evaluation, and show that the later proposed additive reparametrisation introduces minima not present in the original multiplicative parametrisation. Implications and future research directions are discussed.
Amortized inference allows latent-variable models trained via variational learning to scale to large datasets. The quality of approximate inference is determined by two factors: a) the capacity of the variational distribution to match the true posterior and b) the ability of the recognition network to produce good variational parameters for each datapoint. We examine approximate inference in variational autoencoders in terms of these factors. We find that divergence from the true posterior is often due to imperfect recognition networks, rather than the limited complexity of the approximating distribution. We show that this is due partly to the generator learning to accommodate the choice of approximation. Furthermore, we show that the parameters used to increase the expressiveness of the approximation play a role in generalizing inference rather than simply improving the complexity of the approximation.
Generative Adversarial Networks (GANs) have been shown to be able to sample impressively realistic images. GAN training consists of a saddle point optimization problem that can be thought of as an adversarial game between a generator which produces the images, and a discriminator, which judges if the images are real. Both the generator and the discriminator are commonly parametrized as deep convolutional neural networks. The goal of this paper is to disentangle the contribution of the optimization procedure and the network parametrization to the success of GANs. To this end we introduce and study Generative Latent Optimization (GLO), a framework to train a generator without the need to learn a discriminator, thus avoiding challenging adversarial optimization problems. We show experimentally that GLO enjoys many of the desirable properties of GANs: learning from large data, synthesizing visually-appealing samples, interpolating meaningfully between samples, and performing linear arithmetic with noise vectors.
Recently there have been increasing interests in learning and inference with implicit distributions (i.e., distributions without tractable densities). To this end, we develop a gradient estimator for implicit distributions based on Stein's identity and a spectral decomposition of kernel operators, where the eigenfunctions are approximated by the Nystr\"om method. Unlike the previous works that only provide estimates at the sample points, our approach directly estimates the gradient function, thus allows for a simple and principled out-of-sample extension. We provide theoretical results on the error bound of the estimator and discuss the bias-variance tradeoff in practice. The effectiveness of our method is demonstrated by applications to gradient-free Hamiltonian Monte Carlo and variational inference with implicit distributions. Finally, we discuss the intuition behind the estimator by drawing connections between the Nystr\"om method and kernel PCA, which indicates that the estimator can automatically adapt to the geometry of the underlying distribution.
We introduce instancewise feature selection as a methodology for model interpretation. Our method is based on learning a function to extract a subset of features that are most informative for each given example. This feature selector is trained to maximize the mutual information between selected features and the response variable, where the conditional distribution of the response variable given the input is the model to be explained. We develop an efficient variational approximation to the mutual information, and show the effectiveness of our method on a variety of synthetic and real data sets using both quantitative metrics and human evaluation.
Analyzing large-scale, multi-experiment studies requires scientists to test each experimental outcome for statistical significance and then assess the results as a whole. We present Black Box FDR (BB-FDR), an empirical-Bayes method for analyzing multi-experiment studies when many covariates are gathered per experiment. BB-FDR learns a series of black box predictive models to boost power and control the false discovery rate (FDR) at two stages of study analysis. In Stage 1, it uses a deep neural network prior to report which experiments yielded significant outcomes. In Stage 2, a separate black box model of each covariate is used to select features that have significant predictive power across all experiments. In benchmarks, BB-FDR outperforms competing state-of-the-art methods in both stages of analysis. We apply BB-FDR to two real studies on cancer drug efficacy. For both studies, BB-FDR increases the proportion of significant outcomes discovered and selects variables that reveal key genomic drivers of drug sensitivity and resistance in cancer.
Datasets are growing not just in size but in complexity, creating a demand for rich models and quantification of uncertainty. Bayesian methods are an excellent fit for this demand, but scaling Bayesian inference is a challenge. In response to this challenge, there has been considerable recent work based on varying assumptions about model structure, underlying computational resources, and the importance of asymptotic correctness. As a result, there is a zoo of ideas with few clear overarching principles. In this paper, we seek to identify unifying principles, patterns, and intuitions for scaling Bayesian inference. We review existing work on utilizing modern computing resources with both MCMC and variational approximation techniques. From this taxonomy of ideas, we characterize the general principles that have proven successful for designing scalable inference procedures and comment on the path forward.
Importance sampling is widely used in machine learning and statistics, but its power is limited by the restriction of using simple proposals for which the importance weights can be tractably calculated. We address this problem by studying black-box importance sampling methods that calculate importance weights for samples generated from any unknown proposal or black-box mechanism. Our method allows us to use better and richer proposals to solve difficult problems, and (somewhat counter-intuitively) also has the additional benefit of improving the estimation accuracy beyond typical importance sampling. Both theoretical and empirical analyses are provided.
In this paper, we propose the "adversarial autoencoder" (AAE), which is a probabilistic autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform variational inference by matching the aggregated posterior of the hidden code vector of the autoencoder with an arbitrary prior distribution. Matching the aggregated posterior to the prior ensures that generating from any part of prior space results in meaningful samples. As a result, the decoder of the adversarial autoencoder learns a deep generative model that maps the imposed prior to the data distribution. We show how the adversarial autoencoder can be used in applications such as semi-supervised classification, disentangling style and content of images, unsupervised clustering, dimensionality reduction and data visualization. We performed experiments on MNIST, Street View House Numbers and Toronto Face datasets and show that adversarial autoencoders achieve competitive results in generative modeling and semi-supervised classification tasks.
Variational Autoencoders (VAEs) are expressive latent variable models that can be used to learn complex probability distributions from training data. However, the quality of the resulting model crucially relies on the expressiveness of the inference model. We introduce Adversarial Variational Bayes (AVB), a technique for training Variational Autoencoders with arbitrarily expressive inference models. We achieve this by introducing an auxiliary discriminative network that allows to rephrase the maximum-likelihood-problem as a two-player game, hence establishing a principled connection between VAEs and Generative Adversarial Networks (GANs). We show that in the nonparametric limit our method yields an exact maximum-likelihood assignment for the parameters of the generative model, as well as the exact posterior distribution over the latent variables given an observation. Contrary to competing approaches which combine VAEs with GANs, our approach has a clear theoretical justification, retains most advantages of standard Variational Autoencoders and is easy to implement.
Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks. However, due to the model capacity required to capture such representations, they are often susceptible to overfitting and therefore require proper regularization in order to generalize well. In this paper, we show that the simple regularization technique of randomly masking out square regions of input during training, which we call cutout, can be used to improve the robustness and overall performance of convolutional neural networks. Not only is this method extremely easy to implement, but we also demonstrate that it can be used in conjunction with existing forms of data augmentation and other regularizers to further improve model performance. We evaluate this method by applying it to current state-of-the-art architectures on the CIFAR-10, CIFAR-100, and SVHN datasets, yielding new state-of-the-art results of 2.56%, 15.20%, and 1.30% test error respectively. Code is available at https://github.com/uoguelph-mlrg/Cutout
Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks. However, due to the model capacity required to capture such representations, they are often susceptible to overfitting and therefore require proper regularization in order to generalize well. In this paper, we show that the simple regularization technique of randomly masking out square regions of input during training, which we call cutout, can be used to improve the robustness and overall performance of convolutional neural networks. Not only is this method extremely easy to implement, but we also demonstrate that it can be used in conjunction with existing forms of data augmentation and other regularizers to further improve model performance. We evaluate this method by applying it to current state-of-the-art architectures on the CIFAR-10, CIFAR-100, and SVHN datasets, yielding new state-of-the-art results of 2.56%, 15.20%, and 1.30% test error respectively. Code is available at https://github.com/uoguelph-mlrg/Cutout


public interface MoveController extends MoveProvider
| Modifier and Type | Field and Description |
|---|---|
static double |
WHEEL_SIZE_EV3
EV3 kit wheel diameter, in centimeters
|
static double |
WHEEL_SIZE_NXT1
NXT 1.0 kit wheel diameter, in centimeters
|
static double |
WHEEL_SIZE_NXT2
NXT 2.0 kit wheel diameter, in centimeters
|
static double |
WHEEL_SIZE_RCX
White RCX "motorcycle" wheel diameter, in centimeters
|
| Modifier and Type | Method and Description |
|---|---|
void |
backward()
Starts the NXT robot moving backwards.
|
void |
forward()
Starts the NXT robot moving forward.
|
double |
getLinearAcceleration()
Returns the acceleration at which the robot accelerates at the start of a move and decelerates at the end of a move.
|
double |
getLinearSpeed()
Returns the speed at which the robot will travel forward and backward (and to some extent arcs, although actual arc speed
is slightly less).
|
double |
getMaxLinearSpeed()
Returns the maximum speed at which this robot is capable of traveling forward and backward.
|
boolean |
isMoving()
true if the robot is moving
|
void |
setLinearAcceleration(double acceleration)
Sets the acceleration at which the robot will accelerate at the start of a move and decelerate at the end of a move.
|
void |
setLinearSpeed(double speed)
Sets the speed at which the robot will travel forward and backward (and to some extent arcs, although actual arc speed
is slightly less).
|
void |
stop()
Halts the NXT robot
|
void |
travel(double distance)
Moves the NXT robot a specific distance.
|
void |
travel(double distance,
boolean immediateReturn)
Moves the NXT robot a specific distance.
|
addMoveListener, getMovement
static final double WHEEL_SIZE_NXT1
static final double WHEEL_SIZE_NXT2
static final double WHEEL_SIZE_EV3
static final double WHEEL_SIZE_RCX
void forward()
void backward()
void stop()
boolean isMoving()
void travel(double distance)
distance
- The positive or negative distance to move the robot.void travel(double distance, boolean immediateReturn)
distance
- The positive or negative distance to move the robot, in wheel diameter units.immediateReturn
- If immediateReturn is true then the method returns immediately.void setLinearSpeed(double speed)
speed
- In chosen units per second (e.g. cm/sec)double getLinearSpeed()
double getMaxLinearSpeed()
void setLinearAcceleration(double acceleration)
If acceleration is set during a move it will not be in used for the current move, it will be in effect with the next move.
acceleration
- in chosen units/second^2double getLinearAcceleration()


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So This is What Reggae Feels Like
On Spotify’s puzzle page, they’ve got three tech puzzles for potential hires to play around with. This is the first of them, whose difficulty is rated Reggae.
Here’s what we need to do: write a program to take some integer in decimal form between 1 and 1,000,000,000 (inclusive), convert it to binary form, reverse that binary string, and then convert the result back into decimal form. For example, on input 11, we convert to binary number 1011, reverse to get 1101, then convert back to decimal form to get 13 (11 > 1011 > 1101 > 13).
Let’s continue with 11. 11 is 1011, or (1*23 + 0*22 + 1*21 + 1*20), in binary. Why is this?
If we divide 11 mod 2, we’ll get a 1, telling us the remainder of 11/2 and consequently whether the 1s place of 11 in binary form is 1 or 0.
Now, let’s divide 11/2, rounding down to the nearest integer, which makes 5. This division allows us to raise our modulous divisor by a power of 2, or shift left one place in binary. When we compute 5 mod 2 what we’re really seeing is the remainder of 11/4 expressed in terms of 2. Notice that if we didn’t divide by 2 here and simply doubled the modulous divisor, we would get our remainder in 1s, which in this case would be (11%4 = 3), a very unhelpful number in binary.
The central idea is to find the remainder of the number you’re converting (in this case 11)/2n+1 in terms of 2n, which will always either be a 1 or a 0, in order to find the value of the nth binary slot from the right.
11%2 = 1 --> append to binary string: 1
11/2 = 5
5%2 = 1 --> append to binary string: 11
5/2 = 2
2%2 = 0 --> append to binary string: 011
2/2 = 1
1%2 = 1 --> append to binary string: 1011
1/2 = 0 --> stop because we reach 0
Notice that the example above appends new 1s and 0s to the left. This means that if we were to append to the right in a method, as I do below, it would yield an already-reversed binary string, thus killing two birds with one piece of code!
String convertDecimalToReversedBinary(int value) {
String result = "";
while (value > 0) {
result += ""+(value%2);
value = value/2;
}
return result;
}
Now, all we need is a method to convert a binary string back into an integer. This isn’t hard, as we already know how binary strings are put together. My method works backwards from the end of the String, adding increasing powers of 2 to a final result.
int convertBinaryToDecimal(String binary) {
int value = 0;
int count = 0;
for (int i = (binary.length()-1); i >= 0; i--) {
if (binary.charAt(i) == '1')
value += Math.pow(2, count);
count++;
}
return value;
}
And that’s it! We solved the first Spotify programming challenge.
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This tool seeks to simplify and make accessible the emerging branch of astrophysics called multi-messenger astronomy. Those pioneering this field are working to combine observations of ‘messenger particles’ - neutrinos, cosmic rays, electromagnetic radiation and gravitational waves - in order to gain an enhanced understanding of their sources. The recent confirmation of both the existence and detectability of gravitational waves has helped to propel the field to the forefront of the physics community, and there is much anticipation as to what it might hold for the future.
With this in mind, it will become increasingly important to make sure that multi-messenger astronomy does not remain confined only to those at the cutting edge of research and academia. The Multi-Messenger Skymap is designed to be an easy-to-use tool for visualizing the core concepts of multi-messenger astronomy, as well as conducting low level data analysis by eye. Implemented by default are data releases from the IceCube neutrino detector in Antarctica, NASA’s gamma-ray LAT (Large Area Telescope) aboard the Fermi satellite, and the High-Altitude Water Cherenkov Gamma-Ray Observatory (HAWC) in Mexico. Data from these detectors, as well as from any of the user’s choosing, can also be uploaded and displayed on the skymap.
This map was created by five high-school students during a six week internship at WIPAC in the summer of 2017. Special thanks goes to Joshua Wood, David Schultz, Sílvia Bravo and Amanda Nothem for their advice, support, and guidance during the project.
Learn more about these detectors at the following links:
http://www.hawc-observatory.org/Although not necessary, you may choose to upload your own files rather than use the ones we provide. In this case, find the button in the upper left corner labeled "choose files", and when clicked, will let you choose a file from your computer. However, this site will only be able to display data from files in a certain format, and this format will be further explained in the section below called "File Conversion". Once uploaded, you will have the option of specifying the file as "neutrino", "source", "gamma ray", or "other". Doing so will place the file in one of the following four sections:
This selection allows the user to display neutrino data from IceCube.
First, select which file you want to pull data from. If you would like to select more than one file at a time, press and hold command or control and select all the files you wish to display.
Next, since neutrinos are labeled as either tracks or showers (based on angle error), select which you want to show.
Lastly, the slider will allow you to choose a range of energy to display. The maximum and minimum values of this energy slider are automatically set to the maximum and minimum of the files selected. The boxes with colors, when clicked, will allow you to choose the gradient of color on which the energy is displayed, specifying which color is to be associated with the maximum and which with the minimum. Also, beneath this slider, you will find a checkbox option which will allow you to switch the range of energy between a logarithmic and a linear scale.
Nothing new here. This section has a selection of files to choose from and the energy slider below operates in the same way.
Similarly to the last two, this also has a selection of files to choose from. However, unlike the last two, the data to be manipulated by the user is different, and thus the options are as well.
First, and fairly self-explanatory, is the array of checkboxes allowing you to choose which source types are displayed, out of the following: unidentified, composite SNR, shell, pulsar wind nebula, HBL, massive star cluster, and SNR molecule cloud.
Next, the user may choose between displaying the color of the data according to spectral index, flux, or neither. The energy sliders and the color work the same as they did previously, only they are specifically for spectral index or flux, and the linear/logarithmic option exists here as well.
Unlike the previous three options, this one has to do with data you upload yourself and do not specify which section it should fall under (otherwise, if you do specify, the name of your file should appear in the file selections of the other sections).
Having selected a file, the name of this file should have been added to the area which in previous sections had selectable files. Similarly to previous sections, you can choose any or several of the files you upload.
Having selected which files from which telescopes you want to display, find the options at the top of the page (IceCube, Fermi Events, Gamma Ray Sources, Other), select which you would like displayed and press submit to run the process. Only press submit if all of the options you want to be displayed are selected, including any options described below.
In the upper right-hand corner of the page will be an option between two checkboxes labeled "Galactic Coordinates" and "Equatorial Coordinates". The purpose of this option should be fairly self explanatory. If Galactic Coordinates is selected, the data will appear on the SkyMap in Galactic Coordinates, the equator being the galactic plane. If Equatorial Coordinates is selected, the data will be displayed on the SkyMap in equatorial coordinates, drawing the galactic plane on the map.
The last two options which you will see are labeled as "Toggle filtering by time" and "Toggle zoom and pan".
When this option is selected, you will be presented with several more options in order to specify which data is shown: year, month, day, hour, minute, second, and margin. These options will allow you to choose which time period will be displayed. The "margin" option will allow you to choose the range of time above and below what you selected. The default, 31536000 seconds, is one year.
This option, when selected, will allow you to zoom and pan on the SkyMap. Zooming can be accomplished by scrolling while hovering over the map. Panning can be accomplished by clicking and dragging the map.
In order to understand the file conversion, one must understand the format of the files which this site reads. First, the text file (it must be a text file) must have, as its first line, a header, which must have at least these two headers: dec and ra (for declination and right ascension). Other headers, if they are values included in the options in our site, will be read as well, however are not necessary. If any headers which are more than one word long (anything separated by a space), the file must be edited (ex: Neutrino En. will become NeutrinoEn.). Anything inside parentheses or brackets will not be read by the program in both the header and the following data.
If the file you want to convert is a .FITS file, the button in the upper left of the screen will give you information on how to convert that file. Otherwise, if the file is in a .txt format, the site itself will convert it for you, as long as the file follows the parameters already described.
An acceptable file header would be:


{# Copyright 2011 Google Inc. All Rights Reserved. #} {% comment %} Form for noting an imported migration. Args: project_name: The name of the project. If not specified, we will ask the user to input a project name. Author<EMAIL_ADDRESS>(Nick Santos) {% endcomment %}
Did you have to make a small, temporary change in the public codebase that shouldn't be brought back into the internal repository? (e.g., fixing a character that MOE messed up). Tell MOE that it doesn't need to be brought back in here. (Though, this will also mean that any change before it will not be imported, either).


Create New Group
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THERE'S SOMETHING ABOUT MARY 1998 A film review by Timothy Voon Copyright 1998 Timothy Voon 2 :-) :-) for that girl!
Director: Bobby Farrelly & Peter Farrelly Writers: Peter Farrelly, Bobby Farrelly & Ed Decter & John J. Strauss Cast: Cameron Diaz, Matt Dillon, Ben Stiller, Lee Evans, Chris Elliott
There certainly is something about Mary. In one of the funniest and darkest comedies this year we have a mixture of the vile and the hilarious, that will keep you simultaneously shocked and amused at the same time. This seems to be the winning combination for the Farrelly brother team.
Cameron Diaz is undeniably charming as Mary, who has up to five guys obsessed with her throughout the movie, if not the entire male audience. She is the gal of every straight guy's fantasy – pretty, wholesome, intelligent and outright nice. Of course a woman like Mary doesn't exist in the real world, that's why we have Hollywood to create the illusion. There's a lot to disbelieve about Mary, but because we are always looking for the perfect someone, she exists in all of us.
This movie has interesting ways of dealing with issues like masturbation, kissing dogs, wrinkled breasts, hair gel, missing sperm, intellectually disabled people, hives, testes and zippers. Every time you are on the brink of throwing up, the humour some how pulls you back in time before you barf in shock. It is a dangerous tight rope of balancing what is acceptable to watch on screen and pushing the limits of the viewer's tolerance. This could easily have been a disaster if it was just out to shock, but the witty script and intelligent plotting saves the day and somehow makes Mary a success, when it could easily have been a Turkey.
Other notable notes is Matt Dillon, who plays against his usual stereotypic role of body hunk and plays a sleaze with big teeth, graciously. Ben Stiller is wonderfully naïve and everybody else seems to team up extremely well, making Mary one of the zanniest experiences you'll come across this Spring.
Timothy Voon e-mail<EMAIL_ADDRESS>Movie Archives http://us.imdb.com/M/reviews_by?Tim+Voon Movies In Melbourne http://www.labyrinth.net.au/~pfowler/week/movies.html
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


China software designers ready UOS for end-of-2019 release
by Staff reporter, Shanghai; Joseph Tsai, DIGITIMESUnity Operating System (UOS), an in-house developed operating system by China-based software designers including NewStart, Wuhan Deepin Technology and stated-owned CEC, is expected to make the first version available at the end of 2019.
The designers signed agreement to form a team for developing an operating system in May 2019 with the team officially coming online in July. The team is composed of a couple hundreds of engineers stationed in China's Wuhan, Nanjing and Beijing.
UOS already began testing in October 2019 and will support processors made by China-based makers including Loongson, ShenWei, FeiTeng, Hisilicon, Zhaoxin and Hygon.
UOS will be an open-source platform encouraging players in the supply chain and developers to participate in its R&D with unified app store and architecture.


--- layout: default date: 2019-12-06 title: EEmacs ---
Home/ Computing/ Editors/ Emacs/
Emacs(1976) is written in C and Lisp. Being so old, it was developed using terminology that is not used today. What most people would call a Window, Emacs calls a Frame.


Its-Licious is a 48 total hour challenge to design and build a project from scratch, with as little outside help or work as possible.
The goal of Its-Licious is to help people find a good place to eat for Summerlicious 2019 in Toronto.
Like what you see? Feel free to give me a shout!
This project greatly benefitted from the work from the following places and people:


In this experiment, we wanted to test whether or not participants could exploit which direction a demon will go.
If you were in the random condition, there was no systematic pattern for the directionality of a demon.
If you were in the predictable condition, certain attributes of the demon determined the directionality of that demon.
Your SONA credit will be available to you within the next 2 business days.


LoggerPro (1.0)A simple, pluggable and modern logging framework for Delphi
Unit LoggerPro
Description
Uses
- System.Generics.Collections
- System.SysUtils
- System.Classes
Overview
Classes, Interfaces, Objects and Records
Functions and Procedures
Types
Variables
Description
Functions and Procedures
function BuildLogWriter(aAppenders: array of ILogAppender; aEventsHandlers: TLoggerProEventsHandler = nil; aLogLevel: TLogType = TLogType.Debug): ILogWriter; |
|
Builds a new ILogWriter instance. Call this global function to start logging like a pro.
Here's a sample unit that you can use in your code
unit LoggerProConfig;
interface
uses
LoggerPro;
function Log: ILogWriter;
implementation
uses
LoggerPro.FileAppender;
var
_Log: ILogWriter;
function Log: ILogWriter;
begin
Result := _Log;
end;
initialization
_Log := BuildLogWriter([TLoggerProFileAppender.Create(10, 5)]);
end.
Add this unit to your project, then when you need to use the logger, include the unit and call one of the followings:
Log.Debug('This is a debug message', 'tag1')
Log.Info('This is an information message', 'tag1')
Log.Warn('This is a warning message', 'tag1')
Log.Error('This is an error message', 'tag1')
|
Types
TLogType = (...); |
Values
-
Debug = 0:
-
Info:
-
Warning:
-
Error:
|
TLogErrorReason = (...); |
Values
|
TLogErrorAction = (...); |
Values
|
Variables
DefaultLoggerProMainQueueSize: Cardinal = 100000; |
|
|
DefaultLoggerProAppenderQueueSize: Cardinal = 10000; |
|
|
Copyright 2016 Daniele Teti
Generated by PasDoc 0.14.0.
|


read-only
A stream of dragover
events fired when a dragged object is currently
being dragged over this element.
Other resources
-
Drag and drop sample based on the tutorial from HTML5Rocks.
-
Drag and drop specification from WHATWG.
A stream of dragover
events fired when a dragged object is currently
being dragged over this element.
Drag and drop sample based on the tutorial from HTML5Rocks.
Drag and drop specification from WHATWG.


Have a bunch of buttons that all trigger the same modal with slightly different contents? Use event.relatedTarget and HTML data-* attributes (possibly via jQuery) to vary the contents of the modal depending on which button was clicked.
Below is a live demo followed by example HTML and JavaScript. For more information, read the modal events docs for details on relatedTarget.
Anim pariatur cliche reprehenderit, enim eiusmod high life accusamus terry richardson ad squid. Nihil anim keffiyeh helvetica, craft beer labore wes anderson cred nesciunt sapiente ea proident.
Holy guacamole! You should check in on some of those fields below.


Gateway
BOT
02-Jul-19 10:54 AM
[dscrd-hush] <halo kitty rules> @ComputerGenie - MineHush.com Hello. That LooTz guy you spoke with on KMD channel 2 days ago... Pretending ˝what is that b*tch coin˝... He pranked you. He sent coins few minutes prior to Coinexchange. He is just pretending not to know a thing.
[dscrd-hush] <halo kitty rules> Coins are from DEX. At airdrop time DEX was hundreds of USD and now he sold his airdrop at few cents. Count the selloff at 500 and 501 sats few hours ago. Amount is the same. https://www.coinexchange.io/market/BTCH/BTC BTCH might not be a world beater right now, but anyway... Weird people.
Market: BTCH / BTC | Last Price: 0.00000500 BTC | Currency: Bitcoin Hush | Base Currency: Bitcoin |
Join CoinExchange.io and start trading today!
[dscrd-hush] <ComputerGenie - MineHush.com> dafuq you on about?
[dscrd-hush] <halo kitty rules> Lootz, some oldtimer you spoke with 2 days ago on KMD general...
[dscrd-hush] <ComputerGenie - MineHush.com> oldtimer
he's younger than I am
(edited)
[dscrd-hush] <halo kitty rules> He just made fun of all of you. he knew excictly what is BTCH, if he sent to Coinexchange prior talk.
[dscrd-hush] <ComputerGenie - MineHush.com> so he sold an airdrop that he didn't know how/why he had, what's your point?
(edited)
[dscrd-hush] <halo kitty rules> Yes.
[dscrd-hush] <halo kitty rules> I don't know what are his intentions, but he is just pretending not to know a thing.
[dscrd-hush] <ComputerGenie - MineHush.com> there's a lot of people that don't know why they have a lot of asset coins
(edited)
[dscrd-hush] <halo kitty rules> But he knew BTCH is on Coinexchange before he knew what BTCH is...
[dscrd-hush] <ComputerGenie - MineHush.com> 99% of people that hold NINJA ask the same question "Wha't this NINJA coin and how did I get it"
[dscrd-hush] <halo kitty rules> But sending to exchange prior knowing a thing does take some research.
[dscrd-hush] <ComputerGenie - MineHush.com> errr no
[dscrd-hush] <halo kitty rules> Well, if you say so. However, check the timestamps and you will see.
[dscrd-hush] <ComputerGenie - MineHush.com> if you send me BTC, I'm not going to research it, I'm going to dump it
[dscrd-hush] <halo kitty rules> You can sell BTC anywhere. BTCH has only one CEX. Well, in fact there is another BTCH on Livecoin I think...
[dscrd-hush] <ComputerGenie - MineHush.com> is there a moral to this or is this just "that guy that you know from one server and talked to on another server sold some coins that you don't care about"?
[dscrd-hush] <halo kitty rules> Sorry. Don't you know him? It looked so. Maybe you should him.
[dscrd-hush] <ComputerGenie - MineHush.com> I know lootz from the pirate server
I commented as much on the KMD server
You're on about a coin that I never even liked and a conversation that I didn't actually pay any real attention to
(edited)
[dscrd-hush] <halo kitty rules> Ok, fine.
[dscrd-hush] <halo kitty rules> I will not mention you again.
[dscrd-hush] <ComputerGenie - MineHush.com> lol


Reference jQuery and SpriteSpin scripts
<script src='jquery-1.4.2.js' type='text/javascript' />
<script src='spritespin.js' type='text/javascript' />
Create a container for SpriteSpin
<div id='mySpriteSpin'/>
Initialize the plugin
<script type='text/javascript'>
$("#mySpriteSpin").spritespin({
// path to the source images.
source: [
"path/to/frame_001.jpg",
"path/to/frame_002.jpg",
"path/to/frame_003.jpg",
"path/to/frame_004.jpg",
"path/to/frame_005.jpg",
"path/to/frame_006.jpg",
"path/to/frame_007.jpg",
"path/to/frame_008.jpg",
"path/to/frame_009.jpg",
"path/to/frame_010.jpg",
],
width : 480, // width in pixels of the window/frame
height : 327, // height in pixels of the window/frame
});
</script>
There are many options that can be used to initialize the plugin. The following is a set with default values and their meaning. However, it is recommended to take a look at the examples where the options are described with more detail.
Spin.defaults = {
source : undefined, // Stitched source image or array of files
width : undefined, // actual display width
height : undefined, // actual display height
frames : undefined, // Total number of frames
framesX : undefined, // Number of frames in one row of sprite sheet (if source is a spritesheet)
lanes : 1, // Number of 360 sequences. Used for 3D like effect.
module : '360', // The presentation module to use
behavior : 'drag', // The interaction module to use
renderer : 'canvas', // The rendering mode to use
lane : 0, // The initial sequence number to play
frame : 0, // Initial (and current) frame number
frameTime : 40, // Time in ms between updates. 40 is exactly 25 FPS
animate : true, // If true starts the animation on load
reverse : false, // If true animation is played backward
loop : true, // If true loops the animation
stopFrame : 0, // Stops the animation at this frame if loop is disabled
wrap : true, // If true wraps around the frame index on user interaction
wrapLane : false, // If true wraps around the lane index on user interaction
sense : 1, // Interaction sensitivity used by behavior implementations
senseLane : undefined, // Interaction sensitivity used by behavior implementations
orientation : 'horizontal', // Preferred axis for user interaction
detectSubsampling : true, // Tries to detect whether the images are downsampled by the browser.
scrollThreshold : 50, // Number of pixels the user must drag within a frame to enable page scroll (for touch devices)
preloadCount : undefined, // Number of frames to preload. If nothing is set, all frames are preloaded.
onInit : undefined, // Occurs when plugin has been initialized, but before loading the source files
onProgress : undefined, // Occurs when any source file has been loaded
onLoad : undefined, // Occurs when all source files have been loaded
onFrame : undefined, // Occurs when the frame number has been updated (e.g. during animation)
onDraw : undefined // Occurs when all update is complete and frame can be drawn
};
SpriteSpin offers an API object with functions that simplify certain tasks.
var api = $("#mySpriteSpin").spritespin({
// initialization options
}).spritespin("api");
For the available API methods take a look at the annotated source code.
You can add your own methods to the api.
SpriteSpin.extendApi({
myCustomFunction: function(){
this.data.customValue = 'i have added something to the sprite spin data object';
}
});
You can access the data object that represents the state of the SpriteSpin instance and manipulate it directly.
var data = $("#mySpriteSpin").spritespin({
// initialization options
}).data("spritespin");
Most of the initialization parameters are available in the data object. However, changing some of them may break the instance. For example you can not change the source images or the size properties directly. Those options need a special processing by the plugin before they can be actively used.


Colorado Avalanche angry over refs' decision to continue possession as player was bloodied from shot
by Ryan GaydosThe Colorado Avalanche expressed their frustration with NHL officiating Saturday after referees declined to stop the game against the Vancouver Canucks while Matt Calvert was down on the ice in pain from taking a puck to the head.
Calvert was bleeding from the ear after being hit on the side of the head by an Elias Patterson wrist shot. The Canucks had possession of the puck and the officials let the play continue. Alex Elder eventually scored on the possession to cut the Avalanche lead down to one goal.
CALGARY FLAMES’ T.J. BRODIE OUT INDEFINITELY AFTER FAINTING, CONVULSING DURING PRACTICE
Colorado coach Jared Bednar was not happy with the decision.
“For me, we’re talking about head injuries and what not, and that’s the second time now in two weeks for us that a guy takes a puck in the face and is bleeding all over the ice and [officials are] letting it go,” Bednar said. “He’s not moving so I just think we should blow it dead. Sometimes it’s a tough call to make because you’re trying to let the play go and seeing if he’s going to get up. But I think eventually you should just blow it dead.”
Avalanche defenseman Erik Johnson was irate.
“It’s a f---ing joke, man,” he told The Athletic. “Like, you want to protect a guy? The guy's got a family at home.  It's a complete joke.
PENGUINS STAR SIDNEY CROSBY OUT 6 WEEKS AFTER CORE SURGERY
Avalanche center Nathan McKinnon scored the eventual game-winning goal in overtime but echoed his teammate’s sentiments.
“I can only imagine if that was LeBron James, his head was bleeding and they let the other team take a three-pointer to tie the game,” McKinnon said, according to Sportsnet. “I know it’s not the ref’s fault, it’s the league rule, but I think you need to look and who’s laying on the ice.”
The league rule states: “When a player is injured so that he cannot continue play or go to his bench, the play shall not be stopped until the injured player’s team has secured control of the puck. If the player’s team is in control of the puck at the time of injury, play shall be stopped immediately unless his team is in a scoring position.
CAROLINA HURRICANES SHADE THE WASHINGTON CAPITALS ON WORLD KINDNESS DAY
“In the case where it is obvious that a player has sustained a serious injury, the Referee and/or Linesman may stop the play immediately.”
Canucks coach Travis Green said his team had a similar incident recently when defenseman Chris Tanev was injured blocking a shot against the Winnipeg Jets.
 “Right or wrong, that’s the way the rule has been for a while. We got fortunate tonight.
The Associated Press contributed to this report.


{% extends 'base.html' %} {% load static %} {% load tz %} {% block main %}
Share ongoing data? Symptom reports and related data can be published via your random ID – and you'll have a link you can share with others.
Shared data helps us do more! We'll also share data used for retrospective analyses if available (e.g. heartrate, temperature up to six weeks before). Your data can help others develop visualizations and analyses, and hopefully soon we'll have similar visualizations here. 🤞
Sharing data! Please visit your account page to modify this setting. Thank you for sharing! 💖
Use this URL to share with others: {{ request.get_host }}{% url 'reports:list_member' request.user.openhumansmember.oh_id %}
Timezone: {{ timezone }}
{% timezone timezone %}| Report date | Symptoms | Fever |
|---|---|---|
| {{ report.created }} | {% if report.report_none %} None {% else %} {% for symptomitem in report.reported_symptoms %} {{ symptomitem.symptom.get_label_display }} ({{ symptomitem.get_intensity_display }}){% if not forloop.last %}, {% endif %} {% endfor %} {% endif %} | {% if report.fever_guess %} {{ report.get_fever_guess_display }} {% else %} No fever {% endif %} |


Jaime Velasquez has 32 years of accounting experience and has served as the second-highest-ranking official in the Webb County Treasury Department.
It's simple. In the March 6 Democratic primary, vote for an accountant — not a politican — for treasurer.
Find Out MoreYou'll receive updates from the campaign trail via an email newsletter.
5 years as Webb County's chief deputy treasurer
Certified county investment officer, professional tax return preparer
Family man, active parishioner, committed to community service
Born and bred here in Webb County


Andrews, Lovett lift FIU over Cleveland St. 107-61
by EndPlayCLEVELAND (AP) - Devon Andrews had 16 points to lead five Florida International players in double figures, Osasumwen Osaghae had a double-double, and the Panthers rolled past Cleveland State 107-61 on Tuesday night.
Osaghae finished with 13 points and 10 rebounds. Eric Lovett added 14 points, Isaiah Banks scored 13 and Cameron Corcoran had 12 for the Panthers.
Florida International (2-2) is averaging 104 points per game in its two wins this season.
Hugo Ferreira had 12 points for the Vikings (1-4) and Craig Beaudion scored 11.
Florida International plays Eastern Kentucky on Friday. Cleveland State plays UNC Wilmington on Friday.
___
For more AP college basketball coverage: https://apnews.com/Collegebasketball and http://twitter.com/AP_Top25
___
Elements of this story were generated by Automated Insights, http://www.automatedinsights.com/ap, using data from STATS LLC, https://www.stats.com


Instructions for installing the SDK are language specific.
The Go SDK supports Go modules and requires Go 1.12 or later. To use the SDK, import this package into your application:
import "github.com/cobaltspeech/sdk-cubic/grpc/go-cubic"
The Python SDK depends on Python >= 3.5. You may use pip to perform a system-wide install, or use virtualenv for a local install.
pip install --upgrade pip
pip install "git+https://github.com/cobaltspeech/sdk-cubic#egg=cobalt-cubic&subdirectory=grpc/py-cubic"
The C# SDK utilizes the NuGet package manager. The package is called Cubic-SDK
, under the owners name of CobaltSpeech
.
NuGet allows 4 different ways to install. Further instructions can be found on the nuget webpage. Installing via the dotnet
cli through the command:
dotnet add package Cubic-SDK
Building for Android requires more steps, so it is described on the Android Integrations page.
The Swift Package Manager is a tool for automating the distribution of Swift code and is integrated into the swift
compiler.
Once you have your Swift package set up, adding swift-cubic as a dependency is as easy as adding it to the dependencies
value of your Package.swift
.
dependencies: [
.package(url: "https://github.com/cobaltspeech/sdk-cubic.git", .upToNextMajor(from: "1.4.0"))
]


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Except where otherwise noted, copyright content on this site is licensed under a Creative Commons Attribution 4.0 International Licence.


| Path: | README.rdoc |
| Last Update: | Tue Oct 18 17:45:44 -0400 2011 |
Webrat lets you quickly write expressive and robust acceptance tests for a Ruby web application.
class SignupTest < ActionController::IntegrationTest def test_trial_account_sign_up visit home_path click_link "Sign up" fill_in "Email", :with =><EMAIL_ADDRESS>select "Free account" click_button "Register" end end
Behind the scenes, Webrat will ensure:
Users of Debian Linux (e.g. Ubuntu) need to run:
sudo apt-get install libxslt1-dev libxml2-dev.
Otherwise the Nokogiri gem, which Webrat depends on, won‘t install properly.
To install the latest release as a gem:
sudo gem install webrat
To install the latest code as a plugin: (_Note:_ This may be less stable than using a released version)
script/plugin install git://github.com/brynary/webrat.git
In your test_helper.rb or env.rb (for Cucumber) add:
require "webrat" Webrat.configure do |config| config.mode = :rails end
Merb 1.0 has built-in, seamless Webrat support. Just start using methods from Webrat::Session in your specs.
Copyright (c) 2007-2008 Bryan Helmkamp, Seth Fitzsimmons. See MIT-LICENSE.txt in this directory.


public class ListenerManager
extends java.lang.Object
GenericEvents
are fired to all managed
EventListeners
that are subscribed to the tournament the event is from.
Events exist for all theoretically possible properties, including events that
will never be fired like
TournamentIdChangedEvent
. If the id of a tournament really changed, it would
not be seen as the same tournament anymore, therefore a
TournamentDeletedEvent
would be fired rather than a
TournamentIdChangedEvent
.
| Modifier and Type | Class and Description |
|---|---|
static class |
ListenerManager.ManagerState
Represents a possible state of a ListenerManager.
|
| Constructor and Description |
|---|
ListenerManager(ChallongeExtension challonge)
Creates a running instance that tries to update every 5 seconds.
|
ListenerManager(ChallongeExtension challonge,
long interval)
Creates a running instance that tries to update at the specified
interval.
|
| Modifier and Type | Method and Description |
|---|---|
void |
addListener(EventListener... listener)
Adds all of the listeners to the list of managed listeners.
|
void |
awaitRunning()
Blocks the current thread until the main cycle is started.
|
void |
awaitState(ListenerManager.ManagerState state)
Blocks the current thread until the given state is reached
|
ChallongeCache |
getCache()
Gets the cache of all of the managed tournaments.
|
long |
getInterval()
The interval in milliseconds the listener tries to update in.
|
java.util.List<EventListener> |
getListeners()
List of all the listeners the manager currently manages.
|
ListenerManager.ManagerState |
getState()
The current state of the manager.
|
boolean |
isRunning()
Deprecated.
Use
getState() instead |
boolean |
removeListener(EventListener... listener)
Removes all the specified listeners from the list of managed listeners if
they exist in that list.
|
void |
setExceptionHandler(java.util.function.Consumer<? super at.stefangeyer.challonge.exception.DataAccessException> exceptionHandler)
Sets the consumer invoked whenever an DataAccessException is caught
|
void |
shutdown()
Shuts down the update cycle and removes all listeners from its managed
listeners list.
|
public ListenerManager(ChallongeExtension challonge) throws at.stefangeyer.challonge.exception.DataAccessException
shutdown()
method is called.challonge
- The ChallongeExtension instance used to get all the tournament
dataat.stefangeyer.challonge.exception.DataAccessException
- Exchange with the rest api or validation failedpublic ListenerManager(ChallongeExtension challonge, long interval) throws at.stefangeyer.challonge.exception.DataAccessException
shutdown()
method is called.challonge
- The ChallongeExtension instance used to get all the tournament
datainterval
- The interval to try to update at in milliseconds. If the
interval is 0, the manager tries to update as quickly as
possibleat.stefangeyer.challonge.exception.DataAccessException
- Exchange with the rest api or validation failedpublic void awaitRunning() throws java.lang.InterruptedException, java.lang.IllegalStateException
java.lang.InterruptedException
- if the thread is interruptedjava.lang.IllegalStateException
- if the manager is shutting down or shut downpublic void awaitState(ListenerManager.ManagerState state) throws java.lang.InterruptedException, java.lang.IllegalStateException
state
- The state to be reachedjava.lang.InterruptedException
- if the thread is interruptedjava.lang.IllegalStateException
- if the given state is unreachablepublic void shutdown()
public long getInterval()
public void addListener(EventListener... listener)
listener
- The listeners to be addedpublic boolean removeListener(EventListener... listener)
listener
- The listeners to remove from the list of managed listenerspublic java.util.List<EventListener> getListeners()
public ListenerManager.ManagerState getState()
public void setExceptionHandler(java.util.function.Consumer<? super at.stefangeyer.challonge.exception.DataAccessException> exceptionHandler)
exceptionHandler
- The exception handler@Deprecated public boolean isRunning()
getState()
insteadpublic ChallongeCache getCache()


keywords Mediation, conditional mediation, moderated mediation, mediated moderation
Draft version, mistakes may be around
This example shows how to estimate a conditional (moderated) mediation model with four variables in jAMM. Data come from Muller, Judd, Yzerbyt, 2005 and contain variables related to a social dilemma experiment. The dataset can be downloaded here
Participants were primed with either “might” or “morality” primes and then engaged in a one-trial prisoner’s dilemma with a fictitious partner. Additionally, participants’ social value orientation (from pro-self and pro-social) was measured. Social value orientation is a stable trait, which differentiates people in their tendency to cooperate. Because cooperative behavior is known to be linked to expectations about the other cooperation, participants were asked to report their expectations about other’s cooperation. The aim of the study is to show whether prime has an effect on cooperation, if social value orientation moderates the effect of prime, and if expectations have a mediating role in the experimental effects.
There are four variables:
prime
: a two-group experimental conditionEXP
: expectations about the other cooperationSVO
: continuous measure of social value orientation (higher levels mean more cooperative attitude)BEH
: behavior, the amount of experimental tokens given to the public good by the participant.The mediation model implied by the researcher’s hypotheses is: PRIME -> EXP -> BEH. Additionally, the researcher expects SVO to moderate the mediation model, such that the mediation effect through EXP should be different at different levels of SVO. Thus, SVO is the moderator of the mediation model. At this stage, we do not know whether SVO moderates the path PRIME -> EXP or the path EXP - BEH, so we simply estimate SVO moderation effect on both paths. This implies that SVO should be allowed to interact with PRIME in predicting EXP, and to interact with EXP is predicting BEH. Basically, SVO*PRIME interaction should be present in both the mediator model (the one with EXP as dependent variable) and in the full model (the one with BEH as dependent variable)
We first define the variables role in order to obtain the simple mediation model PRIME -> EXP -> BEH. In jAMM, we put each variable in the corresponding field.
Notice that we put PRIME
in the Factors
field because the variable is categorical. However, being the variable a dichotomous variable, putting it in the Covariates
would yield similar results. By putting it in the Factors
field makes the variable to be coded as a categorical variable, as signaled by the Model diagram notes
table.
The resulting path diagram is the classical simple mediation diagram.
The module automatically estimates the simple model parameters, but we can ignore the results.
To add SVO as moderator, we first need to define it as an independent variable. Being SVO a continuous variable, we put it in Covariates
.
Notice that jAMM automatically updates the diagram, but we still need to declare that SVO is not only an additional independent variable, it should function as a moderator. To do that, we go to Moderators
tab, and put SVO in the moderator field.
Now the model is set-up, and the diagram shows the intended model.
For simplicity, the diagram does not show the moderator main effects (cf. the Model diagram notes
in your output), but they are correctly inserted in the model. You can check out the model in the Mediators models
and Full model
tabs.
The Mediators models
tab shows that in predicting EXP (i.e Mediator=EXP), we defined PRIME, SVO, and their interaction as predictors.
The Full model
tab shows that in predicting BEH (i.e the dependent variable), we defined PRIME, EXP and SVO, as predictors, and the two-way interaction between the moderator and the other variables in the model. This guarantees the model to be completed.
In conditional mediation models, the first thing we want to check is whether the moderator actually has a moderating effect on the components of the mediated effect. We can check that in the results table Moderation effects (interactions)
.
In the table we see that SVO
does not moderate the path from PRIME
to EXP
(at least not in a substantial way), because the interaction between PRIME
and SVO
in predicting EXP
is not significant. However, SVO
moderates the path from EXP
to BEH
, because the interaction between EXP
and SVO
in predicting BEH
is clearly different from zero. This means that the conditional mediation is due to the fact that when PRIME
affects EXP
, its effect depends on SVO
. This model is sometimes called mediated moderation, because the interaction comes before the mediator in the mediation pathway.
After finding a significant interaction, we know that the mediated effect depends on the levels of the moderator. Thus, we should see how the mediated effects look when estimated at different levels of the moderators. We can call these simple mediated effects.
Simple mediated effects are in the Conditional Mediation
results section. The results show the mediatated (indirect), direct, and total effects at different levels of the moderators. By default, the levels of the moderators are SVO=mean-SD
, SVO=mean
, and SVO=mean+SD
. The levels of the moderator used to computed the conditional mediation parameters can be changed in the Covariates Scaling
tab.
Let’s start by inspecting the results for SVO=Mean
, that show the average mediated effect.
The Indirect
row reports the mediated effect. Thus, this is the classical \(a \cdot b\) mediation effect, where \(a\) is the coefficient from PRIME
to EXP
and \(b\) is the coefficient from EXP
to BEH
, computed for the average level of SVO. In other words, this is the average mediated effect across levels of the moderator. The Coefficients \(a\) and \(b\) are the components of the mediated effect, and you can see them in the table in the Component
rows. As expected, \(a \cdot b= 2.692 \cdot .840 = 2.261\).
The z
test associated with the mediated effect is the large sample z test of the mediated effect, which is a slightly more accurate version of the Sobel test. The confidence intervals are also computed using large sample approximation, but the bootstrap version of confidence intervals can be asked in the Mediation options
tab (see below).
The Direct
row shows the effect of the independent variable not mediated by EXP
computed for the average level of SVO
. The coefficient is usually denoted by \(c^\prime\) in the mediation jargon. The Total
effect is the overall effect of prime
on BEH
, that is \(c=a \cdot b + c^\prime\), again computed for the average level of SVO
. All the z
tests associated with these coefficients test the null-hypothesis that the corresponding coefficient is zero. Confidence intervals are computed with the same method used for the indirect effect.
The \(\beta\) column shows the standardized solution coefficients.
As regards the confidence intervals, by default jAMM computes the confidence intervals using the large sample delta method, that is using the z test as the starting point of computation. However, often users prefer to use a bootstrap method. That can be done in the Mediation options
tab. Details of the methods can be found in the lavaan R package.
Thus, we can say that for the average level of SVO (mildly cooperative people), expectations mediate the effect of prime on behavior, IE=2.261, CI=[.862,3.66],beta=.147, z=3.17, p.=.002.
We can now explore what happens for low levels of cooperative attitude (SVO=mean-SD).
The mediated effect is much smaller than before, IE=-.589, CI=[-1.400,.2218], beta=-.0385, z=-1.424, p.=.155, and does not seem to be different from zero. Indeed, also the total effect of PRIME is small (.982). This means that for low levels of SVO, prime has a weak effect on behavior, and this small effect is not mediated by expectations.
Finally, we can explore what happens for high levels of cooperative attitude (SVO=mean+SD).
The mediated effect is larger than for the average SVO, IE=5.38, CI=[1.373, 9.38], beta=.242, z=2.63, p.=.008, and seems to be different from zero. The total effect of PRIME is also larger (8.18). Thus, for high levels of SVO, prime has a strong effect on behavior, and this effect is partially due to the mediated effect of expectations.
If you prefer to have a shorter table of conditional mediation effects, you can ask to hide the components of the mediated effects in the Mediation options
tab. That produces a simpler table.
If you are interested in checking the convergence of jAMM results with other software results, please visit the rosetta store Rosetta store: conditional mediation
Got comments, issues or spotted a bug? Please open an issue on GAMLj at github“ or send me an email


Table view is the default option for all pages. To get it responsive, you should usually define a visibility for each column. Table view can be customized according to the developer needs.


Ingredients
12 oz whole tomatoes 1 clove crushed garlic 1 onion sliced small and sauteed Tsp sugar Tsp crushed red pepper 1/2 Tsp fennel seeds Salt and Pepper to taste
Directions
Puree whole tomatoes, add all ingredients and simmer until sauce thickens.


Scouter demo web
Load simulator start
Load simulator stop
Call API - get user ID
Call API - Login
Call API - Simulate millis delay
Call API - Simulate seconds delay
Call API - Simulate millis delay include async calls
Call API - Simulate seconds delay include async calls
Call API - Sql Error case
Call API - Sql Generation Error case
Call API - Simulate delay by dbms lock(millis)
Call API - Simulate delay by dbms lock(seconds)
Call API - Simulate cross service traceablity - simple
Call API - Simulate cross service traceablity - complex


{% extends 'client_base.html' %} {% load widget_tweaks %} {% load static %} {% block left_col %}
Character
Inventory
Comms
Comms are not currently connected.
logout


var notes = [['C4',1],['D4',1],['E4',1],['F4',1],['G4',1],['A4',1],['B4',1],['C5',1]];
//create player
var player = new simple_player();
//play sheet music
player.play(notes);
You can set 'tempo' and 'volume' properties, i'm now adding more properties like tone-colours.
var notes = [['E4',1],['E4',1],['E4',2],['E4',1],['E4',1],['E4',2],['E4',1],['G4',1],['C4',1.5],['D4',0.5],['E4',1]];
var player = new simple_player();
//Tempo(beats per min), 120 by default
player.tempo = 360;
//vilume(0.0 ~ 1.0), 0.7 by default
player.volume = 0.2;
player.play(notes);
Use Array to present notes，in format like: [['Name',beats], ...],use '0' for pause,sharp and flat should be :C#4 or Cb4. Below is an example:
[['C4',2],['A4',1],['0',1],...]
Here's a sample for main theme of《Castle in the sky》
[
['A4',1],['B4',1],['C5',3],['B4',1],['C5',2],['E5',2],['B4',6],
['E4',1],['E4',1],['A4',3],['G4',1],['A4',2],['C5',2],['G4',4],
['0',2],['E4',1],['E4',1],['F4',3],['E4',1],['F4',1],['C5',3],['E4',4],
['0',1],['C5',1],['C5',1],['C5',1],['B4',3],['F#4',1],['F#4',2],['B4',2],['B4',2]
]
You can also try it by input:
I'm now adding more properties like tune-color of piano,chords,multi-tracks。The player works not so proper now, and welcom to offer any advice and help.Any contribute will be aprreciated ,just fork send me pull request(github)。
E-mail: xiaoquan.vip@gmail.com


Package com.jme.bullet.collision
Class CollisionFlag
- java.lang.Object
-
- com.jme.bullet.collision.CollisionFlag
-
public class CollisionFlag extends java.lang.Object
Named collision flags for aPhysicsCollisionObject
. Values must agree with those in BulletCollision/CollisionDispatch/btCollisionObject.h- Author:
- Stephen Gold<EMAIL_ADDRESS>- See Also:
PhysicsCollisionObject.getCollisionFlags(long)
-
-
Field Summary
Fields Modifier and Type Field Description static int
CHARACTER_OBJECT
flag for a character object, such as a PhysicsCharacterstatic int
CUSTOM_MATERIAL_CALLBACK
flag to enable a custom material callback for per-triangle friction/restitution (not used by JME)static int
DISABLE_SPU_COLLISION_PROCESSING
flag to disable parallel/SPU processing (not used by JME)static int
DISABLE_VISUALIZE_OBJECT
flag to disable debug visualization (not used by JME)static int
HAS_COLLISION_SOUND_TRIGGER
flag not used by JMEstatic int
HAS_CONTACT_STIFFNESS_DAMPING
flag not used by JMEstatic int
HAS_CUSTOM_DEBUG_RENDERING_COLOR
flag not used by JMEstatic int
HAS_FRICTION_ANCHOR
flag not used by JMEstatic int
KINEMATIC_OBJECT
flag for a kinematic objectstatic int
NO_CONTACT_RESPONSE
flag for an object with no contact response, such as a PhysicsGhostObjectstatic int
STATIC_OBJECT
flag for a static object
-
Constructor Summary
Constructors Constructor Description CollisionFlag()
-
-
-
Field Detail
-
STATIC_OBJECT
public static final int STATIC_OBJECT
flag for a static object- See Also:
- Constant Field Values
-
KINEMATIC_OBJECT
public static final int KINEMATIC_OBJECT
flag for a kinematic object- See Also:
- Constant Field Values
-
NO_CONTACT_RESPONSE
public static final int NO_CONTACT_RESPONSE
flag for an object with no contact response, such as a PhysicsGhostObject- See Also:
- Constant Field Values
-
CUSTOM_MATERIAL_CALLBACK
public static final int CUSTOM_MATERIAL_CALLBACK
flag to enable a custom material callback for per-triangle friction/restitution (not used by JME)- See Also:
- Constant Field Values
-
CHARACTER_OBJECT
public static final int CHARACTER_OBJECT
flag for a character object, such as a PhysicsCharacter- See Also:
- Constant Field Values
-
DISABLE_VISUALIZE_OBJECT
public static final int DISABLE_VISUALIZE_OBJECT
flag to disable debug visualization (not used by JME)- See Also:
- Constant Field Values
-
DISABLE_SPU_COLLISION_PROCESSING
public static final int DISABLE_SPU_COLLISION_PROCESSING
flag to disable parallel/SPU processing (not used by JME)- See Also:
- Constant Field Values
-
HAS_CONTACT_STIFFNESS_DAMPING
public static final int HAS_CONTACT_STIFFNESS_DAMPING
flag not used by JME- See Also:
- Constant Field Values
-
HAS_CUSTOM_DEBUG_RENDERING_COLOR
public static final int HAS_CUSTOM_DEBUG_RENDERING_COLOR
flag not used by JME- See Also:
- Constant Field Values
-
HAS_FRICTION_ANCHOR
public static final int HAS_FRICTION_ANCHOR
flag not used by JME- See Also:
- Constant Field Values
-
HAS_COLLISION_SOUND_TRIGGER
public static final int HAS_COLLISION_SOUND_TRIGGER
flag not used by JME- See Also:
- Constant Field Values
-
-


--- layout: base title: 'Cluckles Options Reference' description: 'Cluckles is a Live Theme Editor for CSS Framework based on Less such as Twitter Bootstrap, and currently allows Bootstrap Themes to be completely customised.' permalink: /options/ ---
{{ site.title }} has several options which can configure things like loading a theme (on initialisation), exporting, prefixing generated CSS/Less with a selector (in the editor) and more!


ECJ: Poland's judicial independence in doubt
by EUobserverThe EU's top court ruled Tuesday that there are reasons to question the independence of a new judicial chamber in Poland, created under the right-wing Law and Justice (PiS) government, which monitors and potentially punishes judges. But the European Court of Justice (ECJ) left it to the Polish Supreme Court to examine the new chamber's independence, where some members have been recommended by PiS.


This game is not complete yet.
Make sure the gamearea has focus, and use the arrow keys to move the red square around.
DPI 1


This function is used when a several buttons use the same action procedure to determine which button was pressed.
import GUI in "%oot/lib/GUI"
View.Set ("graphics:150;210")
var buttonNames : array 1 .. 5 of string := init ("Red", "Green",
"Blue", "Yellow", "Purple")
var buttons : array 1 .. 5 of int
procedure ButtonPush
for i : 1 .. 5
if GUI.GetEventWidgetID = buttons (i) then
Text.Locate (1, 1)
put buttonNames (i), " selected"
end if
end for
end ButtonPush
for i : 1 .. 5
buttons (i) := GUI.CreateButton (10, 210 40 * i, 110,
buttonNames (i), ButtonPush)
end for
loop
exit when GUI.ProcessEvent
end loop
This means that you can only call the function by calling GUI.GetEventWidgetID, not by calling GetEventWidgetID.


✎ ⚙
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|
<project name>
|
mpu6050 accelerometer library More...
#include <mpulibrary.hpp>
Public Member Functions | |
| mpulibrary (int mpu_addr, hwlib::i2c_bus_bit_banged_scl_sda &bus) | |
| constructor of class More... | |
| void | wake_up () |
| wakeup function More... | |
| int16_t | numbers_x () |
| X axis function. More... | |
| int16_t | numbers_y () |
| Y axis function. More... | |
| int16_t | numbers_z () |
| Z axis function. More... | |
mpu6050 accelerometer library
This is a library for the accelerometer within the mpu6050. In this library you can wake the chip up and ask for the x,y and z axis of the accelerometer.
|
inline |
constructor of class
This constructor initializes the mpu6050 address and the i2c bus so that they can be used in the function later on.
|
inline |
X axis function.
This function gives back the x axis integer that the mpu6050 gives back. It does this by reading out the correct addressess and changing those bytes back to an integer by pushing the first byte to the left so that there is an 16 bit number.
|
inline |
Y axis function.
This function gives back the y axis integer that the mpu6050 gives back. It does this by reading out the correct addressess and changing those bytes back to an integer by pushing the first byte to the left so that there is an 16 bit number.
|
inline |
Z axis function.
This function gives back the z axis integer that the mpu6050 gives back. It does this by reading out the correct addressess and changing those bytes back to an integer by pushing the first byte to the left so that there is an 16 bit number.
|
inline |
wakeup function
This function wakes up the mpu6050 by writing 0 to the 0x6b address within the mpu6050.


{% extends "responsive-layout.html" %} {% block page_title %} NHS Service finder {% endblock %} {% block content %} Find a type of service Example: Urgent Care Centre, Pharmacy Acute Assessment unit Ambulance Service Care Home Clinics Community Based Services Community Hospital Dental Emergency Dental Services Distance Selling Pharmacy (DSP) District/Community Nurse Domiciliary Dentist Domiciliary Optician Emergency Care Practitioner (ECP) Emergency Department Eye Casualty GP Choice GP Extended Hours GP in hours GP-Led UCC with ED GP Out of Hours Provider (OOH) Health Information Health Visitor Hyper-Acute Stroke Unit (HASU) Integrated Urgent Care (IUC) Clinical Assessment Service (CAS) Intermediate Care Mental Health Midwifery Minor Eye Condition service (MECS) Minor Injuries Unit (MIU) Multi-Disciplinary Services NHS111 Provider Optician Palliative Care Pharmacist Pharmacist Enhanced Service Pharmacist - Extended Hours Pharmacist Urgent Prescription Primary Care Practitioner (PCP) Primary Percutaneous Coronary Intervention (PPCI) Safeguarding Sexual Health Single Point of Access (SPoA) Social Care Specialist Service Speciality Emergency Department (Spec ED) Therapist Urgent Care Centre (UCC) Voluntary agency Walk In Centre (WIC) + add another type of service Continue {% endblock %}
+ add another type of service


BANDITS
Reviewed by Harvey Karten MGM Director: Barry Levinson Writer: Harley Peyton Cast: Bruce Willis, Cate Blanchett, Billy Bob Thornton, Troy Garity, January Jones, Rocky LaRochelle, Jaye K. Danford Screened at:Kips Bay Thea., NYC 10/6/01
Here's a question for our women readers who are dating. What sort of man would you prefer: one who is strong and knows what he wants, or one who is quiet, somewhat intellectual, and has various symptoms of illness which are merely forms of hypochondria? The answer would seem obvious, and I wouldn't be surprised if 90% or more of you would list the first choice. Imagine what a relatively meek hypochondriac would feel if he pals around with the strong, confident type, what with all the women looking at the other guy and treating him as though he were invisible! Yet think of the kind of ecstasy the Woody-Allen type would experience if a woman actually chose him instead of his hardy pal!
Though "Bandits" is about bank robbery, the real theme of Harley Peyton's story directed by Barry Levinson--whose previous projects afford him experience with the genre like gangster pic "Donnie Brasco" and one about a mentally challenged guy, "Rain Man"--is the conflict between two pals who fall in love with the same woman and compete actively with each other for her favor. They're equally famous (or infamous) with the general public who know the pair as The Sleepover Bandits, but the forbearing Terry (Billy Bob Thornton) becomes increasingly competitive with his partner Joe (Bruce Willis) because he believes he will inevitably lose Kate (Cate Blanchett), the woman of his dreams, to him.
The story opens as a flashback. We watch Joe and Terry in one of their numerous disguises at work pulling a job on a west coast bank (the movie is filmed in gorgeous Oregon and in parts of California). They have apparently been sold out by an accomplice. The cops have the place surrounded. A narrator, who runs a TV show on crime and famous criminals, reports that the men have actually shot each other seemingly to avoid surrendering and going back to the jail from which they escaped, and then tells the TV audience the account he had heard directly from the well-known duo when Joe and Terry slipped into his house and insisted that he record their tale.
As a romantic comedy, "Bandits" thankfully avoids the saccharine nothingness of such current movies as "Serendipity," choosing an off-beat style to depict the unusual methods of these two robbers--who never fail to say "good morning" to their victims as they are initiating a job and wouldn't think of hurting any of them despite their possession of guns. Levinson shows Kate to be a woman who seems to have it all--a spacious house, the time and inclination to pursue her hobby of gourmet cooking--but who is unhappy because of her passive-aggressive husband and for whom living dangerously with these celebrated bandits would be just the prescription.
 There are cute meetings that the robbers, aided by their very willing female hostage, have with the people they are going to rob. What they do is enter the houses of bank managers the night before a planned job, have dinner with the executive and his family, and then force the guy to open the bank early and withdraw the money. But these victims, who are taken in by the charming personalities of their urban pirates, act in predictable manners, as does one family whose kids think the whole affair is a pleasant enough diversion and one older woman who escorts the duo in her bank and then refuses to open the vault knowing that "you wouldn't hurt a fly."
There is a surprising twist at the conclusion, one that I would challenge any in the audience to have guessed, and far fetched though it may be is the cleverest part of this generally average fare.
Rated R. Running time: 125 minutes. (C) 2001 by Harvey Karten<EMAIL_ADDRESS>========== X-RAMR-ID: 29727 X-Language: en X-RT-ReviewID: 253041 X-RT-TitleID: 1110275 X-RT-SourceID: 570 X-RT-AuthorID: 1123
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


Money Play. How should Red play 42?
pips Red=142 White=135
(CorrectAnswer= 0/0, ErrorScore= 0)
This is another positional type where bot rollouts have clearly established the correct strategy.
Giving up the mid-point for a hit when you are outboarded is the wrong concept.
Red needs to look at this as a potential prime-versus-prime game. The correct move is 8/4, 6/4, creating a broken five-point prime, with the idea of trying to subsequently make the 5-pt though of course the dice may argue with that idea.
Giving up the mid-point for a hit when you are outboarded is the wrong concept.
Red needs to look at this as a potential prime-versus-prime game. The correct move is 8/4, 6/4, creating a broken five-point prime, with the idea of trying to subsequently make the 5-pt though of course the dice may argue with that idea.
1. Cubeful 2-ply 8/4 6/4 Eq.: -0.094 0.477 0.156 0.005 - 0.523 0.167 0.010 2. Cubeful 2-ply 13/11 13/9* Eq.: -0.136 (-0.042) 0.467 0.169 0.007 - 0.533 0.178 0.013 3. Cubeful 2-ply 13/9*/7 Eq.: -0.304 (-0.210) 0.426 0.143 0.006 - 0.574 0.186 0.013 4. Cubeful 0-ply 13/9* 8/6 Eq.: -0.323 (-0.229) 0.414 0.136 0.006 - 0.586 0.205 0.018 5. Cubeful 0-ply 13/9* 6/4 Eq.: -0.534 (-0.440) 0.387 0.118 0.006 - 0.613 0.264 0.033


[Teleport to Hunting Grounds]
You may teleport to any of the following hunting locations. Each teleport requires a sum of ancient adena.
Pilgrim's Necropolis (30-40) - 3900 AA
Necropolis of Worship (40-50) - 11000 AA
Catacomb of the Heretic (30-40) - 2600 AA
Catacomb of the Branded (40-50) - 5900 AA


MasterChef finalist Dean Banks launches Arbroath gin
by Scott ReidMasterChef: The Professionals finalist and restaurant owner Dean Banks has launched a new premium Scottish gin.
Lunun is said to benefit from the “single-fold” distillation process used to create it while its taste has been developed with the chef’s palette in mind.
The new gin has been influenced by Banks’ travels to more than 40 different countries and his expertise in fusion cooking.
Distilled in the chef’s home town of Arbroath, the gin’s name is an adaptation of Lunan Bay. The product will be distributed using eco-friendly packaging.
Banks said: “I’ve always wanted to create my own gin and it seems like the next natural step for me after establishing my restaurant in St Andrews.
“I wanted to create something that people judge solely on the taste and not the ‘hype’ that often comes with contemporary artisan gins – in fact, Lunun is the opposite to an artisan gin. There’s no smoke and mirrors with Lunun.”


Gluten Freedom
Here I would like to have general information that I update regularly in regard to a recipe's that I am creating. But in the mean time since I completely changed the idea for my website. Heres some gluten free flower for all my chef's. You dont have to worry about parts combined! Its even to the measurements of normal flour in a recipe.
- Top suggestion from popular page
- My own personal current favorite
- The Dessert


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