text:
Mean Spectral Normalization of Deep Neural Networks for Embedded Automation
by Anand Krishnamoorthy Subramanian, et al.
JAIST
Deep Neural Networks (DNNs) have begun to thrive in the field of automation systems, owing to the recent advancements in standardising various aspects such as architecture, optimization techniques, and regularization. In this paper, we take a step towards a better understanding of Spectral Normalization (SN) and its potential for standardizing regularization of a wider range of Deep Learning models, following an empirical approach. We conduct several experiments to study their training dynamics, in comparison with the ubiquitous Batch Normalization (BN) and show that SN increases the gradient sparsity and controls the gradient variance. Furthermore, we show that SN suffers from a phenomenon, we call the mean-drift effect, which mitigates its performance. We, then, propose a weight reparameterization called as the Mean Spectral Normalization (MSN) to resolve the mean drift, thereby significantly improving the network's performance. Our model performs 16 practice, and has fewer trainable parameters. We also show the performance of our MSN for small, medium, and large CNNs - 3-layer CNN, VGG7 and DenseNet-BC, respectively - and unsupervised image generation tasks using Generative Adversarial Networks (GANs) to evaluate its applicability for a broad range of embedded automation tasks.
Anand Krishnamoorthy Subramanian
Nak Young Chong
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mean-spectral-norm
Code for the paper "Mean Spectral Normalization"
view repo
I Introduction
The rise of application of Deep Neural Networks (DNNs) to robot automation motivates various research questions that typically differ from that of the traditional computer vision. While the research direction in DNNs mainly focus towards building architectures, developing loss functions and studying their internal mechanism, standardizing DNN models has been the main motivation and an essential criterion for their successful application to robot automation. The reason being that traditional robot automation relies on well-understood white-box models, while DNNs are black-box models where progress is still being made to reach a consensus about their internal behaviour and dynamics. By
standardizing
, we mean the basic well-behaved framework of architectures, loss functions, regularization techniques and activation functions for the DNN models.
To reinforce our motivation, we shall provide some recent examples of applications of DNNs in automation tasks. Concerning industrial automation, DNNs have found applications that include fault diagnosis [1] , combustion optimization [2] , welding faults detection [3] , traffic control [4] , power line inspection [5] and spectrum sensing [6] . Even Generative Adversarial Networks (GANs) have attracted some attention and have been applied in practice (apart from the standard unsupervised image generation or translation tasks), such as fault detection [7] . The main surge in the interests and applications of DNNs can be reasoned as follows - unlike traditional/classical automation tasks where the features
or the control elements of the task are usually preset and their dynamics/state changes are completely characterised analytically, DNNs try to capture the relevant features and their dynamics automatically given the nominally pre-processed data. The characteristics of DNNs where this feature selection and extraction happens due to their hierarchical structure and their ability to train over modern hardware over thousands of data points are the foremost attractive reasons for their current popularity and success.
The above discussed automation systems often use small-medium sized neural networks in their tasks owing to speed and hardware/cost constraints. DNNs are over-parameterised models, in the sense that they have large number of trainable parameters (typically hundreds of thousands) compared to the size of the dataset (∼ tens of thousands). Though this over-parameterization helps greatly in optimizing the network weights [8] , regularization methods are required to improve the generalization and stability of the network during training. As such, a lot of regularization methods have been developed to address this problem; of which the class of methods called Weight reparameterization have proven to be quite successful.
Weight reparameterization techniques like Batch Normalization (BN) [9] , Weight Normalization (WN) [10] , Layer Normalization (LN) [11] are implicit regularization methods that restrict the capacity of the over-parameterized network by normalizing/reparameterizing the network weights. Among them, BN has established itself as a very effective component of almost all modern DNNs. This is backed by its stability over a wide range of learning rates, ability to train over large minibatch sizes and faster convergence.
Although BN works well for almost all architectures of neural networks, it is an overkill for small-medium sized networks as it introduces additional training parameters. It is in this background that we investigate the application of spectral normalization (SN) for such networks − owing to it requiring no additional parameters; unlike batch normalization. SN [12] is a recently introduced technique for normalizing the Lipschitz constant of intermediate layers of deep neural networks, originally proposed for Wasserstein GANs. We, however, have found that SN performs poorly compared to BN for small to medium-sized networks, and identified the cause of which to be the effect called mean-drift. We rectify this effect using our proposed Mean Spectral Normalization (MSN), thereby improving the performance to be comparable to that of BN.
We demonstrate empirically that our MSN method works across a wide range of model depths with fewer parameters and performs at par with BN. Most of the recent applications of neural networks to automation tasks utilise smaller to medium neural networks with 3-20 layers - usually convolution or fully connected layers [13] . Almost every neural network model employs BN (or its slight variants) for regularization and training stabilization. We, therefore, extend our ideas to a wide range of models, even to very deep networks, with greatly improved performance. Through our method, we propose to standardize the regularization aspect of DNNs, and applicable for robot automation tasks. Our contributions can be summarized as follows:
Figure 1: Sparsity of gradients during training DenseNet-BC with LeakyReLU activation function. SN immediately induces a high percent of sparsity to the gradients of the model and steadily increases the sparsity of the gradients during training.
We provide empirical results for the sparsity of gradients in spectral normalized networks (Refer to Fig. 1
). By bounding the gradient magnitude of the activations (Lipschitz normalization), spectral normalized networks yield a much sparser network compared to the sparsity induced by the rectified linear units (ReLU).
We show that by controlling the mean layer singular values, spectral norm offers better utilization of feature dimensions, unlike other methods such as weight normalization.
We identify the mean-drift effect to be a major cause for the diminishing performance of SN as a regularization technique for small and medium sized networks.
We propose a modified SN technique called Mean Spectral Normalization(MSN) to correct for the mean-drift and accelerate the performance of the spectral normalized network for small, medium and large neural networks.
The structure of this paper is as follows - Section II introduces BN and SN methods formally; In Section III, we introduce our mean spectral normalization. In Section IV, we provide our experimental results (focussing on image-related tasks) and various empirical observations. Note that in this paper, we follow the current trend of empirical insights into deep learning to provide solid experimental footing to understand the dynamics of the SN.
Ii Background
In this section we discuss batch normalization and spectral normalization techniques for convolutional neural networks along with some background about other weight reparameterization methods. The normalization methods discussed here, come under a subclass of regularisation methods where the network parameters are normalised based on some norm of their parameters which limit some of their capabilities. For example, the
L2 norm tends to limit the parameters values to lie on a unit ball centered about the origin i.e. be closer to zero. The key insight here is that a normalization method makes the network invariant to the scaling of the weights. This makes the network more robust to the new data points and parameter initialization strategies. This is true for all currently used normalization techniques as well as our proposed one.
Ii-a Batch Normalization
Batch Normalization (BN) or simply batch norm, was initially introduced to reduce the internal covariate-shift (ICS) in DNNs. The internal covariate shift is the phenomenon when the distribution of activations gk of a layer k
shift due to the weight updates in the previous layers during training. Batch norm rectifies this problem by simply standardizing(z-score normalization) the activations of the intermediate layers to zero mean and unit variance and rescaling them using the affine transformations
γk and βk
along each channel with respect to all the pixels/points in the input tensor.
zk=γkgk−Ec[gk]√Var(gk)+ϵ+βk (1)
where ϵ is a small value adding for numerical stability. By standardizing the layer weights, we essentially remove the dependencies on the previous layer updates. Rescaling the weights based on some learnable parameters (γk, βk) -called the scaling factor and bias respectively- enable the flexibility in choosing appropriate weights during the training. Note that the batch normalized activations have a norm independent of the data, and depends only on the effective layer dimension D and the affine scaling γk. The effective layer dimension D for a input tensor of dimensions m×H×W×C is simply D:=√mHWC where m is the minibatch size, H,W,C are the height, width and depth (number of channels) of the input data.
The concrete reasoning behind the exceptional performance of BN is still being investigated. The recent findings include preventing gradient explosion, improving optimization by smoothing the loss landscape [14] and, most importantly, improving the Lipschitzness of the layer [15] , i.e., the gradients become more concentrated around the mean. This reduction of gradient variance has been accepted [16] as one of the core reasons for the success of BN. The idea of controlling the Lipschitzness of the network, motivated us to probe a related weight reparameterization technique - SN. Furthermore, in practice, BN is difficult to accelerate as it is bounded by memory-bandwidth. Precisely, BN requires two passes through the input data to compute the statistics of the minibatch and then to normalize the output; and this may consume up to a quarter of the total training time for large networks [17] . Other similar normalization methods like Layer normalization and Instance normalization [18] are slight variations to BN with normalization across different dimensions of the output like channels, layers or spatial dimensions. As such, all these suffer from the same drawbacks as that of BN.
Ii-B Spectral Normalization
Spectral Normalization (SN) [12] essentially restricts Lipschitz constant of the network to unity by restricting the spectral norm of each layer. Recall that the a function f is K−Lipschitz if |f(x1)−f(x2)|≤K∥x1−x2∥, for all x1,x2; where K is the Lipschitz norm of f. In other words, small changes in the input of the function causes corresponding small changes in the magnitude of its gradients. From its definition, the Lipschitz constant of a given intermediate layer k of a neural network, whose activations are given by gk=f(Wgk−1), is equal to the spectral norm σ(W) of the weight matrix W. Here, f is the activation function. The SN method is then, defined as follows.
^W=W/σ(W), (2)
where σ(W) is the spectral norm (L2 matrix norm) of the weight matrix W given by
σ(W)=supgk−1σ(∇gk)=maxx≠0∥Wx∥2∥x∥2 (3)
Again, from its definition, the spectral norm is essentially the largest singular value of the matrix W. Furthermore, the spectral normalization of each layer applies to the weights of the layer and not the activation; similar to the Weight Normalization techniques. This is a crucial distinction from BN which applies to the activations. Since the weights are much fewer than the activation of intermediate layers, SN is often computationally faster than BN. In practice, this means that SN is not bounded by memory-bandwidth, unlike BN. An important caveat is that the Lipschitz norm of the activation function used must be equal to 1. Therefore, we are limited to activations such as ReLU and leaky ReLU 111For proof, refer to Lemma A.1 in the Appendix of [19] .
In contrast to spectral norm regularization [20] , which penalizes the spectral norm by adding an explicit regularization term to the loss function, the layer weights are simply divided by their corresponding spectral norm in SN. Furthermore, convolutional neural networks usually have fewer weights compared to pre-activations. Therefore, SN is computationally much cheaper, and does not introduce any additional parameters to be trained as evident from Eq. (2).
Figure 2: Test Accuracy of various normalization methods during training DenseNet-BC, 3-layer CNN and VGG-7 models (with learning rate 0.001). We observe that SN perform poorly for shallow CNNs, though the performance improves with the depth of the neural network (Compare VGG-7 and DenseNet-BC). MSN clearly improves upon SN for both shallow and deep neural networks and performs comparable to BN.
Figure 3: Summary statistics of layer weights showing the internal covariate shift for various normalization methods while training DenseNet-BC. During training, BN controls the mean and variance of the weight distribution while SN only controls the variance. Our proposed MSN corrects for the drift in the mean and variance, thereby improving the performance of the network.
Iii Proposed Method
Iii-a Motivation
In this section, we provide some stronger theoretical motivation for the use of spectral norm for regularizing DNNs. Recent theoretical insights [19] , [21] in analyzing the learning capacity and the generalizability of the neural networks have shown that the those characteristics can be bounded by the network's spectral complexity R. The spectral complexity of a given neural network is given by
which is essentially the product of the spectral norm of the weight matrices Wk of all the L layers in the network times a correction factor ϕ dependent on those weight matrices.
For a given neural network function F, computed as where fk is the activation function at layer k and a dataset {xi,yi}Ni=1, drawn i.i.d from some data distribution, we have
P[argmaxiF(x)i≠y]≤O(R)+E(F) (5)
where E(F) is the risk associated with the network, defined as the expectation of the loss.
The above theorem states that the generalization error can be reduced by reducing the upper bound, given by the spectral complexity of the network222For a complete proof of Theorem 1, refer to [19] . Additionally, recent studies [20] , [22] have been conducted in enforcing such Lipschitz continuity in neural networks, albeit through explicit regularization methods, in contrast to our proposed implicit technique.
Iii-B Mean Drift
The empirical motivation for our proposed Mean Spectral Normalization is the reduced performance of SN for small and medium sized networks. Through our experimentation, we observed that the reason to be the gradual uncontrolled drift of the layer mean during training (Refer to Fig. 4). We hypothesize that the mean drift is directly related to the internal covariate shift, where the distribution of layer activations change during training. This can be clearly observed from Fig. 3, where the shift in some selected layers are shown. It is evident that the spectral norm sufficiently restricts the variance of the distribution of activations, however causes their mean to drift during training. Moreover, the mean-drift is also observable in batch normalized networks, but the drift is controlled by the bias βk during training. Therefore, the rapid and uncontrolled drift of the activation-distribution mean is the foremost cause for diminished performance of spectral normalized networks. We resolve this mean-drift by proposing a modification to the original SN, called as mean spectral normalization.
Iii-C Mean Spectral Normalization
We explore the idea of combining SN with a part of BN, which we call Mean Spectral Normalization (MSN). In this method, we perform the spectral normalization on the weights and then subtract the minibatch means from the activations like with BN, as
hk =Wσ(W)gk−1 (6)
~hk =hk−E[hk]+m (7)
where hk is the preactivation for the given layer and m is the external bias learned during training. The activation gk is then given by passing through the activation function f as gk=f(~hk). By subtracting the mean, we create a normalization method that restricts the variance as well as the mean of the activation distribution, thereby resolving the problem of mean-drift. Moreover, the mean correction introduces only a small computational overhead compared to the full BN.
Figure 4: Mean drift correction by MSN during training DenseNet-BC. Our proposed MSN method reduces the gradual drift of the mean during training, which we hypothesize as the major cause for the reduced performance of SN networks compared to BN networks.
During training, the running average of the minibatch mean E[⋅] is stored to be used for validation data. The spectral norm of the weight matrix can be efficiently computed, with negligible overhead, using the power iteration in practice (as pointed out in [12]
). During stochastic gradient descent, because the weights change slowly during each update, a single power iteration on the latest version of the initial vectors is sufficient for each training iteration; making MSN computationally more efficient than BN. By recentering the pre-activations, the dependency on the inputs of the neurons
x on the pre-activations hl is completely detached. This method of decoupling the norm of the pre-activations from the input vectors have shown to improve the rate of convergence [10] .
We distinguish our MSN from the weight normalization with mean-only batch norm [10] from the fact that unlike weight normalization, spectral norm does not reduce the rank of the weight matrix and therefore can leverage upon a wider range of features to improve the performance. Weight normalization, on the other hand, regularizes the network by forcing the network to produce weight matrices that lie (approximately) in low dimensional vector spaces, compromising the feature dimensions. Besides, by dividing by the Frobenius norm of the weights, weight normalization enforces a stronger restriction on the layer weights, often causing over-fitting. This was empirically shown in [17] where even other methods like dropout and weight decay failed to improve the generalization of the weight normalized network. Our proposed MSN, however, has a stronger regularizing effect than weight normalization as it restricts the layer weights in their gradient space, effectively regulating their learnability.
Iii-D Gradients of MSN
The gradient of MSN can be computed as follows. Consider the gradient of the layer weight ^W after SN, w.r.t. Wij
∂^W∂Wij =1σ(W)[Iij−1σ(W)∂σ(W)∂WijW] (8)
=1σ(W)[Iij−[u1vT1]ij^W] (9)
where Iij is the matrix that has 1 in its (i,j)th entry and zero elsewhere; u1 and v1 are the left and right singular vectors of W respectively. Note that the first column of the left and right singular matrices of W correspond to the largest singular value of W. Therefore, the gradient with respect to the largest singular value at a given element Wij is the (i,j)th entry in the left and right singular vectors of the largest singular value.
Now, the gradient of the loss L with respect to MSN pre-activation hk after the mean subtraction, can be found in a straightforward manner.
∇hkL=∇~hkL−E[∇~hkL] (10)
The recentering of the pre-activations has a much lower computational overhead compared to the classical BN where the second order batch statistics are required.
Figure 5: Gradient Histograms of layers 44 and 75 (chosen randomly) of DenseNet-BC with BN, SN and MSN respectively. For the spectral normalized network, the gradients are more concentrated around the mean compared to the batch normalized network. MSN clearly improves in restricting the gradients around the zero mean compared to BN or MSN.
Figure 6: Mean layer-singular values during training for DenseNet-BC.
Iv Experiments
Iv-a Experimental Setup
To investigate the training dynamics of various normalization techniques discussed thus far, we use a set three different convolutions neural networks - 3-layer CNN (without pooling and dropout), VGG-7, and 100-layer DenseNet BC [23] architectures. These networks were trained on the standard MNIST333http://yann.lecun.com/exdb/mnist/ , SVHN444http://ufldl.stanford.edu/housenumbers/ and CIFAR10555https://www.cs.toronto.edu/ kriz/cifar.html datasets, respectively. In this work, our core focus is on improving the networks for image-based tasks, as the application of DNNs in automation are predominantly image-based. We train these networks with Adam optimizer and set initial learning rates from {0.1,0.001,0.0001}
. We train these models with sufficiently long epochs such that learning plateaus. We always report the best results among those learning rates. The code for all the experiments, plots and trained models are given in the following GitHub repository
− https://github.com/AntixK/mean-spectral-norm. Moreover, the choice of the networks was motivated from their widespread applications in real-world object recognition and segmentation.
Iv-B Training Dynamics and SN techniques
In this section, we discuss our empirical observations of SN during training and the performance of our proposed normalization technique. Firstly, we present the performance comparison of BN, SN and MSN networks for all the three models in Fig. 2, to illustrate the effectiveness of our proposed MSN weight reparametrization. We observe that MSN greatly improves upon SN for small and medium sized networks (3-layer CNN and VGG-7, respectively) and provides a comparable performance to that of BN. Table I provides a comparison of test accuracy of all the models.
Inducing sparsity - Sparsity in DNNs has usually been connected to its robustness, with the reasoning that the network automatically determines the right subset of parameters required to capture the high-level information from the data. From Fig. 1, it is evident that the SN and MSN methods constantly improve the gradient sparsity of the network during training, while the gradient sparsity in the batch normalized network saturates around 20%. One of the advantages of such sparse gradients is that they are well suited for distributed training of large neural networks [24] , as little gradient information has to be shared between the sub-networks. Such distributed training of networks provide exciting opportunities for distributed training of autonomous systems.
Mean Drift Correction - As discussed before, the mean-drift is a consequence of the internal covariate shift, observed in all neural networks in general. From Fig. 3 and Fig. 4, it is clear that the BN and MSN methods control the drift of the mean compared to SN. We also observe that the mean-drift is always in the negative region. Large negative mean for layer weights causes the gradients to be extremely small after the LeakyReLU activation, used in all our models. This, in addition to already sparsified gradient, effectively reduced the learning capacity of the networks with many dead neurons. BN avoids this problem with recentering its layer weights using a learnable bias βk (Refer to Eq. 1). In MSN, we follow a similar approach, where this recentering (Refer to Eq. 6) avoids the mean-drift, causing a balance between creating a robust sparse network but preventing too many dead neurons. Fig. 4 confirms our hypothesis and MSN correctly rectifies the mean-drift to match the performance with that of batch normalised networks.
Lipschitzness of the network - From its definition, SN controls the Lipschitz constant of the hidden layers of the neural network to be 1. Empirically, we observe this in our spectral normalized models as shown in Fig. 5. Neural networks with SN and MSN methods, concentrate the gradients around the mean with much smaller variance compared to batch normalized neural networks with the same learning rate. As noted in [16] , neural nets with larger variance of gradients or the models with heavy-tailed gradient histograms (e.g., unnormalized networks) lead to divergence rather than convergence, as the training progresses.
Another interesting consequence of such concentrated gradients is that the loss landscape of the network becomes smooth as it does not allow erratic or sharp gradient changes. This smoothing of the loss landscape is one of the prime reasons for BN and now our proposed MSN to work over a wider range of learning rates.
Singular value regularization - Fig. 6 shows the variation of mean layer singular values of different layers during training. Specifically, during training, BN causes the singular values to increase monotonically. Furthermore, the average layer singular values of all layers are closely spaced, implying that all the weight matrices lie on the same vector subspace. SN, on the other hand, causes the mean layer singular values to taper more quickly, especially for higher layers, where the the activations are affected by previous weight matrices. We reason that in spectral normalized networks, the weights of each layer lie in different vector subspaces and therefore has lesser freedom in choosing the number of singular components. In MSN, the bias correction term m, improves the average singular value by appropriate factor, learned during training. As a result, the divergence of the mean singular values during training is reduced, forcing the weights to lie in the same vector subspace.
3-layer CNN
SVHN
VGG-7
DenseNet-BC
Table I: Test accuracy comparison
Inception Score (IS)
FID Score
Real Data (CIFAR10)
11.24±0.12 7.8
WGAN-GP(With BN)
6.42±0.10 41.3
SNGAN
MSNGAN (ours)
Table II: Qualitative comparison of MSNGAN, SNGAN and WGAN-GP
Number of Parameters
WGAN (Discriminator) BN 2,938,689
SN 2,935,873(−2816)
MSN 2,937,282(−1407)
3-layer CNN BN 103,562
SN 103,406(−156)
MSN 103,486(−76)
VGG-7 BN 438,314
SN 437,168(−1146)
MSN 437,744(−570)
DenseNet-BC BN 1,009,730
Table III: Comparison of number of trainable parameters.
Fewer trainable parameters - Table III shows the comparison of the number of trainable parameters of various models with BN, SN and MSN normalization methods. The amount of reduction in the number of parameters is given within parentheses. Note that the SN does not introduce any additional parameters to the network. Therefore, spectral normalized models have fewer trainable parameters compared to batch normalized models, and thus are usually faster during training and more memory efficient. Albeit introducing bias correction parameters, MSN still has lesser number of parameters compared to BN. Additionally, this reduction in number of parameters results in faster training. During our experiments, SN models trained 27% faster compared to BN models, and MSN models trained 16% faster than BN model. This reduction in the number of parameters, coupled with highly sparsity, makes the MSN a highly desirable choice for embedded application of DNNs.
Iv-C Comparison with SNGAN
We evaluate our proposed MSN method against the original SN method on the Wasserstein Generative Adversarial Network(WGAN) model (called as SNGAN [12] ), for the task of unsupervised image generation on the CIFAR-10 dataset. Furthermore, we also compare these spectral norm-based Lipschitz regularizers against the gradient penalty (WGAN-GP) regularization [25] method. Also, we use BN for WGAN-GP following the original paper. In this section, we shall very briefly discuss the GAN objective function and Lipschitz regularization.
The SN scheme was initially proposed for improving the training of Wasserstein GANs. Generative adversarial networks [26] are a class of generative models with two dueling neural networks - namely the generator G and the discriminator D. The discriminator D is trained to differentiate between real x∼qdata(x) and fake data, while G is trained to generate fake data z that D identifies as real. However, in the original GAN, the gradient of the optimal discriminator D with respect to its input can be unbounded, and therefore can lead to instability in training or modal collapse. Addressing this problem, various methods [27] , [25] have been proposed for penalizing the Lipschitz constant -essentially regularising the gradients- of the discriminator in the form of Wasserstein distance-based GAN losses. Note that the Wasserstein distance, in its dual form, asserts that the discriminator function must have a Lipschitz constant of K. Thus, employing the Wasserstein distance rather than the original Jensen-Shannon distance for the GAN loss, implicitly requires that the discriminator gradients must be bounded. The WGAN objective function V(G,D) used in our experiments (except for WGAN-GP) is given as follows
For WGAN-GP, we have an additional gradient penalty term, following the original paper. Furthermore, we observe that the recentering of the pre-activations h in MSN, does not alter the Lipschitz norm of the activations g. Therefore MSN still regularizes the Lipschitz norm of the activations effected by SN.
We employ the same DCGAN [28] architecture for both generator and discriminator as described in [12] . To evaluate the quality of the generated image samples, we use the standard inception score (IS) [29] and the Fréchet inception distance (FID) [30] . In Table II, we show the inception scores (higher, the better) and FID666Code obtained from https://github.com/mseitzer/pytorch-fid (lower, the better) for the unsupervised image generation on various models, with optimal setting, on the CIFAR10 dataset. The report the average scores over 5 runs, each with 2000 sampled images. The scores for the real CIFAR10 data is given for a baseline comparison. We observe that MSN clearly improves upon the WGAN-GP and performs at par with the original SNGAN.
V Discussion & Conclusion
Albeit originally proposed to control the Lipschitz constant of WGANs, we believe SN is a generic method to reparameterize the weights, with a goal to build a standardized framework to employ DNNs for robot automation. In this paper, we investigated a consequence of the internal covariate shift, called mean drift, in spectral normalized networks, which affects their performance compared to BN. Furthermore, we presented many experimental results to demonstrate the gradient sparsity and Lipschitzness induced by SN in small, medium and large DNNs. We then proposed a solution to resolve the mean drift, called mean spectral normalization(MSN), deriving ideas from both BN and SN. Through our experiments, we confirm that MSN clearly out-performs SN for supervised classification models for all depths of neural networks. Parallelly, Farnia et al., [31] observe a similar result as ours with spectral normalized DNNs. In contrast to our analysis, they conclude that the naive algorithm used to compute the spectral norm (the power iteration as used in [12] ) was inefficient in regularizing the actual spectral norm of the convolution layers. To correct this, they slightly loosen the spectral norm constrain to be σ(W)≤β, where β is some fixed value. Besides having no such tunable parameter, in our work, we observe a deeper mean-drift effect restricting network's performance and rectify the effect with our MSN method. Furthermore, we also compare the qualitative results of our MSNGAN with that of the SNGAN for unsupervised image generation. In future, we wish to focus on evaluating the performance of our MSN on sequence modelling tasks and on real-time data from robots.
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[15] S. Santurkar, D. Tsipras, A. Ilyas, and A. Madry. How does batch normalization help optimization?(no, it is not about internal covariate shift). arXiv preprint arXiv:1805.11604, 2018.
[16] J. Bjorck, C. Gomes, and B. Selman. Understanding batch normalization. arXiv preprint arXiv:1806.02375, 2018.
[17] I. Gitman and B. Ginsburg. Comparison of batch normalization and weight normalization algorithms for the large-scale image classification. arXiv preprint arXiv:1709.08145, 2017.
[18] D. Ulyanov, A. Vedaldi, and V. Lempitsky. Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022, 2016.
[19] P. L. Bartlett, D. J. Foster, and M. J. Telgarsky. Spectrally-normalized margin bounds for neural networks. In Advances in Neural Information Processing Systems, pages 6240–6249, 2017.
[20] Y. Yoshida and T. Miyato. Spectral norm regularization for improving the generalizability of deep learning. arXiv preprint arXiv:1705.10941, 2017.
[21] B. Neyshabur, S. Bhojanapalli, D. McAllester, and N. Srebro. Exploring generalization in deep learning. In Advances in Neural Information Processing Systems, pages 5947–5956, 2017.
[22] H. Gouk, E. Frank, B. Pfahringer, and M. Cree. Regularisation of neural networks by enforcing lipschitz continuity. arXiv preprint arXiv:1804.04368, 2018.
[23] G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger. Densely connected convolutional networks. In CVPR, volume 1, page 3, 2017.
[24] P. Jiang and G. Agrawal. A linear speedup analysis of distributed deep learning with sparse and quantized communication. In Advances in Neural Information Processing Systems, pages 2526–2537, 2018.
[25] I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville. Improved training of wasserstein gans. In Advances in Neural Information Processing Systems, pages 5767–5777, 2017.
[26] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In Advances in neural information processing systems, pages 2672–2680, 2014.
[27] M. Arjovsky, S. Chintala, and L. Bottou. Wasserstein gan. arXiv preprint arXiv:1701.07875, 2017.
[28] A. Radford, L. Metz, and S. Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434, 2015.
[29] M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. In Advances in Neural Information Processing Systems, pages 6626–6637, 2017.
[30] T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen. Improved techniques for training gans. In Advances in Neural Information Processing Systems, pages 2234–2242, 2016.
[31] F. Farnia, J. M Zhang, and D. Tse. Generalizable adversarial training via spectral normalization. arXiv preprint arXiv:1811.07457, 2018.


text:
" wireless bandwagon to such an extent that the term has no real meaning anymore." === "None"?null:"The major carriers' marketing teams have jumped on the "4G" wireless bandwagon to such an extent that the term has no real meaning anymore.", id: "379902" === "None"?null:"379902", isInsiderContent: false, modifiedAt: "2011-03-16T09:18:29Z" === "None"?null:"2011-03-16T09:18:29Z", publishedAt: "2011-03-16T08:59:20Z" === "None"?null:"2011-03-16T08:59:20Z", source: "Computerworld New Zealand", title: "Breaking down carriers' '4G' wireless spin", type: "Article" === "None"?null:"Article", purchaseIntent: "None" === "None"?null:"None" }, ads: { adblocker: adblockerOn, enabled: true }, gTax: { primaryIds: [], secondaryIds: [] }, tax: { primaryCategories: [], secondaryCategories: [] } } }); window.permutive.track('User', { companySize: "None" === "None"?null:"None", country: "None" === "None"?null:"None", id: "None" === "None"?null:"None", jobIndustry: "None" === "None"?null:"None", jobPosition: "None" === "None"?null:"None", purchaseIntent: "None" === "None"?null:"None", loggedIn: false, })
Breaking down carriers' '4G' wireless spin
Brad Reed (Network World) 16 March, 2011 08:59
The major carriers' marketing teams have jumped on the "4G" wireless bandwagon to such an extent that the term has no real meaning anymore.
The International Telecommunications Union had initially defined "4G" technologies as International Mobile Communications (IMT)-Advanced standards that hit peak theoretical data rates of 100Mbps or higher. Needless to say, none of the current wireless data technologies commercially available anywhere in the world come close to those data rates, and actual IMT-Advanced standards aren't expected to be completed until 2012 at the earliest.
LTE IN 2011: Curb your enthusiasm
NETWORKING'S GREATEST ARGUMENTS: LTE vs. WiMAX
But this doesn't matter much anymore from a marketing point of view, since the ITU threw up its hands late last year and said carriers could use the term 4G to describe any IMT-Advanced forerunner technologies such as LTE, WiMAX and HSPA+. The ITU's reasoning was that such "evolved 3G technologies" provide "a substantial level of improvement in performance and capabilities with respect to the initial third generation systems now deployed." In other words, unless you operate an old-school EDGE network, you can probably get away with calling your network 4G.
So since the term "4G" is used by all major U.S. wireless carriers, and given the fact that it will be a hot topic of conversation at next week's big CTIA Wireless industry event in Orlando, it's a good time to revisit just what kinds of "4G" services carriers are offering and what you can expect from them in terms of data speeds.
-- Verizon became the first major U.S. carrier to adopt LTE (Long Term Evolution) last year and this week is rolling out its first LTE smartphone, the HTC ThunderBolt. LTE is essentially a bridge from 3G technologies such as HSPA and EV-DO Rev. A to the 4G IMT-Advanced technologies that the ITU has in mind. The company made its initial LTE launch covering roughly one-third of the U.S. population. The carrier plans to have its entire current 3G footprint upgraded to LTE by the end of 2013.
As far as speeds go, initial tests of the LTE network showed data downloads frequently topping 10Mbps in most major markets, although these tests were run when the network just started and didn't have much congestion to deal with. A test released this week by PC World showed that Verizon's LTE laptop air cards provided average download speeds of 6.5Mbps and average upload speeds of 5Mbps.
-- AT&T and T-Mobile both use HSPA+, an advanced version of the GSM-based 3G HSPA standard that delivers significantly higher speeds than its predecessor. Whereas older HSPA networks would typically deliver mobile download speeds of under 1Mbps, tests have generally delivered download speeds in the 2Mbps to 4Mbps range. So although HSPA+ may not represent as big a leap forward for mobile data as LTE, it is still a vast improvement over older 3G networks such as EV-DO and HSPA.
The two carriers decided to upgrade their 3G footprints with HSPA+ technology before they take the plunge into LTE over the next two years. AT&T, which said in January that it would be launching more than 20 "4G" devices this year, is planning a limited rollout of commercial LTE services this summer. T-Mobile is expected to start offering LTE services sometime in 2012.
-- Sprint and its partners at Clearwire are the only major players in the wireless industry to have adopted WiMAX as their technology of choice. Their current coverage extends to all major markets and covers more than 120 million points of presence. On average, WiMAX services deliver download speeds of between 3Mbps and 6Mbps, although PC World found in their tests that the network was not available on a consistent basis and also that the speeds of Sprint's 3G EV-DO Rev. A network had actually decreased over the past year.
Sprint has toyed with the idea of adopting LTE to supplement its WiMAX services, as both it and Clearwire would together have enough spectrum to support both technologies if they so desired. The companies will also likely roll out the 802.16m ("WiMAX 2") standard at some point, although that might not occur until 2013 since device certification for the standard is slated to start this year and it will take some time for carriers to commercially deploy the new technology over their existing networks.
Conclusion: Buying "4G services" from a carrier really doesn't tell you anything about its coverage or overall quality. Verizon's LTE network currently offers the fastest data rates of all the networks, although it isn't yet available throughout the entire country and its 3G backup network is significantly slower. T-Mobile and AT&T are currently getting good mileage with their HSPA+ networks and they should remain competitive with Verizon as they start rolling out LTE over the next two years. Sprint's WiMAX network also gets good speeds, although there remains a good deal of uncertainty about the future of its partners at Clearwire.
Read more about anti-malware in Network World's Anti-malware section.
Tags wirelessNetworkingWiMax3G & 4G4G wireless
More about CTIAHTCING AustraliaITUSpeedSprintT-MobileT-MobileVerizonVerizon

redpajama_set_name: RedPajamaCommonCrawl


text:
package org.redis.manager.shell;

import java.io.BufferedReader;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileWriter;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.ArrayList;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;

import org.redis.manager.notify.Notify;
import org.redis.manager.shell.client.SftpInterface;
import org.redis.manager.shell.client.ShellClient;
import org.redis.manager.util.GzipUtil;

/**
 * Redis自动化部署
 * Redis结构如下
 * File : redis.3.0.6.x64.gz
  		redis3.0.6
		└── bin
		    ├── redis-benchmark
		    ├── redis-check-aof
		    ├── redis-check-dump
		    ├── redis-cli
		    ├── redis-sentinel -> redis-server
		    └── redis-server
 */
public class RedisInstallUtil extends LinuxUtil{

	static String redis_home = "redis";
	static String redis_data = "cluster";
	static String sourceRule = "redis.{version}.{system}.gz";
	static String config_template = "redis.conf.template";

	String workpath;
	String source;

	public RedisInstallUtil(SftpInterface ftp, ShellClient client, String workpath, String source, Notify notify) {
		super(ftp, client, notify);
		this.workpath = workpath;
		this.source = source;
	}

	/**
	 * 安装Redis服务
	 * @param name 需要安装的redis文件
	 * @param ports
	 * @throws Exception
	 */
	public void install(String name, List<Integer> ports, Integer memory, String config) throws Exception {
		mkdirs(workpath);
		cd(workpath);
		if(!checkDir(workpath + "/" + redis_home)){//Redis没有部署
			if(!checkFile(workpath + "/redis.gz")){//没有源文件
				File sourceFile = new File(source + "/" + name);
				message(">> upload file:" + sourceFile.getPath());
				ftp.upload(workpath, sourceFile, "redis.gz");
			}
			String currentVersion = GzipUtil.getTarFiles(source + "/" + name).get(0);
			message(">> unzip redis");
			untar(workpath + "/redis.gz", workpath);
			rename(workpath + "/" + currentVersion, workpath + "/redis");
		}
		cd(workpath);
		for (Integer port : ports) {
			mkdirs(workpath + "/" + redis_data + "/" + port);
			File configFile = replaceFile(port, memory, config);
			message(">> upload config file:" + configFile.getPath());
			ftp.upload(workpath + "/" + redis_data + "/" + port, configFile, "redis.conf");
			configFile.delete();
			message(">> start redis port:" + port);
			if(checkFile(workpath + "/" + redis_home + "/bin/redis-server")){
				client.exec("./"+redis_home+"/bin/redis-server ./"+redis_data+"/"+port+"/redis.conf");
			}else{
				client.exec("./"+redis_home+"/src/redis-server ./"+redis_data+"/"+port+"/redis.conf");
			}
			Thread.sleep(3000);
			if(check(port) == null){
				throw new Exception("start redis fail");
			}
		}
		message(">> all node is running");
	}

	public void restart(List<Integer> ports) throws Exception {
		for (Integer port : ports) {
			restart(port);
		}
		message(">> all node is running");
	}

	public void restart(int port) throws Exception{
		String pid = check(port);
		if(pid != null){
			message(">> kill redis port:" + port);
			kill(pid);
		}
		message(">> restart redis port:" + port);
		if(checkFile(workpath + "/" + redis_home + "/bin/redis-server")){
			client.exec("./"+redis_home+"/bin/redis-server ./"+redis_data+"/"+port+"/redis.conf");
		}else{
			client.exec("./"+redis_home+"/src/redis-server ./"+redis_data+"/"+port+"/redis.conf");
		}
		Thread.sleep(3000);
		if(check(port) == null){
			throw new Exception("restart redis fail");
		}
	}

	public void stop(int port) throws Exception{
		String pid = check(port);
		if(pid != null){
			message(">> kill redis port:" + port);
			kill(pid);
		}
	}

	public File replaceFile(int port, Integer memory,String config) throws IOException{
		message(">> Make config file with port: "+ port);
		File file = new File(source + "/redis-" + port + ".conf");
		File template = new File(source + "/" + config_template);
		if(file.exists()){
			file.delete();
		}
		HashMap<String,String> configMap = new HashMap<String,String>();
		InputStreamReader reader = new InputStreamReader(new FileInputStream(template));
		BufferedReader bufferedReader = new BufferedReader(reader);
		{
			String line = null;
	        while((line = bufferedReader.readLine()) != null){
	        	if(line.length() > 2 && line.indexOf(" ") > -1 && (line.length() > line.indexOf(" ")+1)){
	        		String key = line.substring(0,line.indexOf(" "));
	        		String value = line.substring(line.indexOf(" ")+1);
	        		configMap.put(key, value);
	        	}
	        }
	        bufferedReader.close();
	        reader.close();
		}
        configMap.put("port", port + "");
        configMap.put("dir",  workpath + "/" + redis_data + "/" + port + "/");
        configMap.put("cluster-config-file", "nodes.conf");
        if(memory != null){
        	configMap.put("maxmemory", memory+"mb");
        }
        if(config != null && !"".equals(config)){
        	String[] lines = config.split(";");
        	for (String line : lines) {
        		if(line!= null && line.indexOf("=")>0){
        			String key = line.substring(0,line.indexOf("="));
            		String value = line.substring(line.indexOf("=")+1);
            		configMap.put(key, value);
        		}
			}
        }
        file.createNewFile();
        FileWriter writer = new FileWriter(file);
        List<String> keys = new ArrayList<String>(configMap.keySet());
        Collections.sort(keys);
        for (String key : keys) {
        	String value = configMap.get(key);
        	if(value != null){
        		writer.write(key + " " + value + "\n\n");
        	}
		}
        writer.flush();
        writer.close();
		return file;
	}

	public String check(int port) throws Exception{
		String processNum = client.exec("ps -ef | grep *:"+ port +" | grep redis | grep -v grep | awk '{print $2}'");
		if(processNum != null && !"".equals(processNum)){
			return processNum;
		}
		return null;
	}
}

redpajama_set_name: RedPajamaGithub


text:
Q: What is the name of this old GBA game?
I've been trying to find the name of this game for more than 5 years, found out last year and then forgot again, but now with this topic I'll have somewhere to go in case I make the same mistake of forgeting it one more time haha.

A: The game is Galidor: Defenders of the Outer Dimension.

Galidor: Defenders of the Outer Dimension is a third-person action adventure game featuring dynamic environments, extraordinary enemies, and involving gameplay ranging from puzzle-solving to all out, wall-to-wall action. As protagonist Nicholas Bluetooth, you must use your ability to "glinch" or morph his limbs into other objects, gain new capabilities from the creatures you encounter, and navigate environmental obstacles to destroy a dimensional wave emitter on each world. The farther you get into the game, the more abilities and skills you will earn. And that's important, because the fate of the Outer Dimension is in your hands.

Here's a gameplay video, here is the GameFAQs link, here's the IGN link, here's the relevant Wikipedia article.

redpajama_set_name: RedPajamaStackExchange


text:
Q: Is there a method to include CSS background images in print? Is there a method to include CSS background images in print?
If i use image replace techniques for (which is considered as a best practice) Logo  then logo doesn't come in print.
and many places in site CSS background is saving bandwidth and my time both. but client is asking to include many things in print also.
And many places where i used background images but now client need background too at some places but on some places not.
What should i do?
Can we do anything with print css?

A: None of the browsers I'm aware of print backgrounds by default (to save ink/toner). I would also suggest that this is a time when using an image replacement technique is a little overkill -- for a logo the image could appear in the document (and with the relevant alternate text, still accessible)


The sacred scent of freshly picked Araliya and Jasmine flowers waft through the temples and kovils as the familiar call of the Koha heralds the dawn of spring with vibrant hues of chili red Erabadu flowers in bloom over the dusty pavements. Yes, it's beginning to look and feel a lot like Avurudu, one of the most anticipated events in the Sri Lankan calendar. What's so precious about the Sinhalese and Tamil New Year is that no matter what ethnicity you represent, what language you speak or what faith you believe in, everyone celebrates Avurudu at some scale be it by adhering to ancient traditional rituals or just enjoying a piece of delicious Kiribath and spicy Lunu Miris with friends and family for breakfast.


text:
Let Your Green Thumb Blossom at a Floral Arrangement Workshop
by Alana Willerton March 24, 2016 May 30, 2019
Learn how to care for and design beautiful flower arrangements at Amborella Floral Studio's weekly workshops.
Photograph by Sofia Katherine Photography
The class in a nutshell: Learn about flower care and how to choose the best blooms and colours for an arrangement. Go through the steps of putting a flower arrangement together, then take it home at the end of the night.
When: Weekly until June 29, generally on Wednesdays at 7 p.m. Classes stop for the summer, then resume in the fall until Christmas. Classes last between one and two hours.
Where: Amborella Floral Studio, 2515 90 Ave. S.W., 403-386-0330
Cost: Starting at $55.
Click here for more information on classes.
Spring is finally here, but if we're being honest, fresh, beautifully arranged flowers are a must year-round. For those who want to recreate the beauty of a flower shop in their own home, there are shops like Amborella Floral Studio. The studio offers weekly flower arranging workshops several months of the year.
Most of the workshops, which can usually have 12 to 15 participants, are taught by floral designer and workshop lead Lacey Janette. They're usually centered around basic floral design concepts and the themes are often chosen based on trends and seasonality – think terrarium workshops, arrangements focusing on seasonal flowers and DIY wedding workshops. Janette says some of the most popular workshops include their wine and cheese classes, learning how to make flower crowns and seasonal ones like how to make Christmas wreaths and outdoor planters around the holidays.
During the classes, participants learn about flower care, basic design principles like choosing flowers, focal and accent blooms, fillers and greens, and how to make an arrangement last longer once it's complete. Janette takes them through the process of arranging the flowers step by step, and they get to take their arrangement home at the end of the night.
"People are finding a lot of inspiration from Pinterest and social media and they want to learn to do it themselves," Janette says. "The whole DIY culture is sort of blowing up right now."
While they've noticed a trend of the workshops doubling as a unique girls' night experience, Janette says there's usually one or two people every class who have aspirations of becoming a florist themselves. As someone who's only been in the floral industry for about a year and a half, it's a desire Janette can relate to. She enjoys giving students a peek at what it's like to be a floral designer through the classes.
"It's really neat because I kind of see myself in the people coming in and being like, 'Oh, this is totally cool and I would love it if this could be my job.' And I can stand in front of them and say, 'This actually could be your job,'" Janette says. "It is a growing industry and it's fun and it's beautiful. And I think it's a really cool skill to have even if it's not something you want to pursue for a lifetime – even if it's something you want to do on Saturdays with your friends. Who doesn't love touching flowers?"
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> To develop unity and collectivism among the Muslims on the basis of Kalma-e-Taiyyeba and strive to make the Muslims discharge their duties in this country in the capacity of the best Ummah.
> To adopt reasonable measures for the safety of life, property and dignity of Muslims.
> To strive for safeguarding the religious and cultural identity of the Muslims and to coordinate and cooperate with the various efforts being undertaken in this regard.
> To mobilize the public opinion against fast spreading fascism, communal hatred and violence in the country.
> To strengthen the good-will and brotherhood among all the religious communities and units in India and to develop a deep sense of mutual trust among them.
> To strive for the safety of life, property, honour and culture of all the citizens of India, especially, the minorities and the oppressed class.
> To reinstate courage and confidence in the Muslims so also that they do not consider themselves as second class citizens in this country and rather believe that they can equally enjoy and share the seat of power as per the constitution and that they have to play their due role in the development of the country and in establishing a system which guarantees social justice.
> To strive for the establishment of 'Makatib' and 'Madaris' for religious education, schools and colleges for modern education, institutions, colleges, medical centers for professional education; institutions in the field of various vocational trades with an aim to provide the Muslim a level playing field for their intellectual growth and due reward of their efforts.
> To plan for the educational and economic progress and social welfare of Muslims.To develop proactive leadership in the Muslims right from local to national level so that the above mentioned aims and objectives can be accomplished.
> To co-ordinate and consolidate the institutional efforts in this regard.
> To develop political and collective awareness among Muslims and make them aware of the complexities of the situation and provide them adequate guidance.


Handmade tie clip with Infinity. This Aluminum tie clip boasts a polished finish with a hand stamped infinity symbol on the front and initials on the back, or you can have other symbols (see above 5th photo) or words. Finished it with hammered textured on the tie clip or without it with smooth surface of your choice. It makes a thoughtful, personalized gift for father or men in your life.
Measures 3/8 inches long and 2.50" wide or choice of other sizes. It is slightly bendable. These tie bars are 1100 Pure Aluminum which is safe on your skin. There is a much lower rate of skin reaction with PURE ALUMINUM than with sterling silver.
* Tie in the photo is not included.
** initials, name, date or word for your Tie clip up to 20 characters.
* choice of Font - see above photo.
* choice of length & size: 1/4" x 2.5" or 1/4" x 2" or 3/8" x 2.5" or 3/8" x 2". If you choose 2 inches tie clip. Please limited to shorter words/characters.


No, Pluralsight does not offer student discounts. However, some students may qualify for limited Pluralsight library access through our partners.
High school computer science teachers may qualify for Pluralsight access through CSTA -- click here for more information.
For university students, Pluralsight is proud to partner with Microsoft Azure for Students to provide a free 12-month, 35-course limited library benefit.
Click to register for Azure for Students on the official website.
After you've registered, visit the Education Hub and click Software.
Click to the Pluralsight offer, then click and Activate. This takes you to a Pluralsight where you can redeem your access.
Alternatively, you can click View Key and redeem the code here.
For questions about Azure for Students, see the Azure for Students FAQ. Premium Pluralsight features, including Kaplan certification practice exams, interactive courses, and projects, are not part of the Azure for Students benefit.
Do you offer a pilot for business plans?


Notes of honeysuckle, salted cherry blossoms, and umami complement layers of earth and limestone. Hints of freshly spun sugar finish flawlessly with passion fruit and luscious green mineral tones.
This tea is very sensitive to hot water. Please brew at 170F, far below boiling. To best enjoy the floral, honeysuckle, passion fruit and honey notes of this tea, a warm water temperature is best.
We are honored to share with you the worlds most luxurious tea made easy.
Our single estate, premium Sencha is grown in a volcano tea garden in Kagoshima, on the island of Kyushu, Japan. For more than 800 years, Zen Buddhist monks have used Sencha as a meditational drink. Generations of families have stayed true to their roots to grow and increase healthy local work opportunities through tea.
Acidic volcanic soil is perfect for tea plants. Our garden is one of the few that is able to produce authentic Sencha to the standards of international regulation including safe and organic cultivation, no pesticides, and no heavy metals. They work on a microbiological level to ensure the highest standards in quality, food safety and hygiene for their team and the beloved product.
As one of the highest quality teas in the Japanese internationally certified organic Sencha category, this high grade tea is rare even in the Japanese market.


First of all let me excuse me if this is not the right place to post but didn't find the information I was looking for in any place.
Since I'm moving to android studio i've followed the instructions here and exported my project to android studio. I've commited the changes to bitbucket at this point.
After instaling the bitbucket plugin and providing my account details I've tried to import my project with no success.
I enter at bitbucket, select the desired repository and tryed to put in the already existing directory on my computer G:\.....\myappname (This is where my project was located when using eclipse). No can do. Geting a message with that folder already exists.
Step two: Doing the exact same thing but know choosing a diferent folder at my computer.
Now I can press the OK button and at this message selected "Yes" and next selected "Create project from existing sources" and after selecting the project name and project location selected "Next" and got the message "Source files for your projecdt not found"
Step three and final step.
Now I've tried to directely import my project from the harddrive directory.
At the welcome screen, selected "Import project" and as the instructions here selected the build.gradle file on the directory that was the original eclipse project.
Now I can run my project but still can´t connect to bitbucket.
And the IDE error was this "11:05:20 IllegalStateException: Can't deserialize target data of key 'IdeaAndroidProject'. Given class loaders: [com.intellij.util.lang.UrlClassLoader@660b1b14, com.intellij.util.lang.UrlClassLoader@660b1b14]: Can't deserialize target data of key 'IdeaAndroidProject'. Given class loaders: [com.intellij.util.lang.UrlClassLoader@660b1b14, com.intellij.util.lang.UrlClassLoader@660b1b14]"
With eclipse I just ad to install the plugin.
Do I have to install anything more? Can someone help me conecting my android studio project with my bitbucket repository?
Sorry but this question relates to some AndroidStudio-specific stuff we're not familiar with (Can't deserialize target data of key 'IdeaAndroidProject'). Please submit corresponding ticket to AndroidStudio tracker.


Seattle and the state of Washington have determinedly resisted the expanding privatization of our nation's schools. Citizens protested standardized testing and voted three times to oppose charter schools in the state. The issue went to the state's Supreme Court when a fourth charter school initiative passed with a push from big donors. The Washington State Supreme Court ruled that charter schools were unconstitutional. We watched as cities like Chicago and Detroit folded to privatization interests. We saw charter school operators take over entire districts as in New Orleans after Hurricane Katrina, and the mostly minority communities of Michigan.
We wanted no part of it.
Seattle has taken back control of its local school board from individuals who supported a former Broad-trained Superintendent's plans to close schools and convert public schools into charters, and who pushed discredited education reforms including an increase in high-stakes testing, use of under-qualified, short-term Teach for America, Inc. trainees, and tying teacher evaluations to student test scores.
Some other examples of the Seattle School Board's progressive actions include: initiating later school start times to better match students' biological needs; passing a resolution to replace the Common Core SBAC test with more fair and valid assessments; establishing a $2 million "student stability" fund to mitigate upheaval at the start of the school year; demanding that special education students be served in the city's preschool program; moving public testimony to a time when more working parents can participate; placing a moratorium on suspensions of elementary students for non-violent offenses; cutting ties with the Gates Foundation funded Alliance for Education; reaffirming board support of public schools and its opposition to charter schools and taking a stand with the superintendent opposing law-skirting efforts by the Office of the State Superintendent to channel public funding to illegal charter schools via the tiny Mary Walker School District In Eastern Washington.
At the same time, Seattle's Mayor Ed Murray has been working behind the scenes to take mayoral control of Seattle's School Board.
Back when he was a Washington state senator, Murray sponsored a failed bill proposing that any town or city in the state could hand over its school district to mayoral control. In the last few years Murray has assembled his own Department of Education and worked on privatizing preschool, using tax levy dollars to threaten established city- and county-subsidized preschools with a percentage of money taken away based on the number of children who do not perform well on tests.
Seattleites are aware of what has happened around the country with mayoral control and want no part of it. The NAACP opposed a recent bill on mayoral control along with the League of Women Voters of Washington State and various legislative districts.
The Mayor is now looking at other ways to gain control.
In a letter sent to Mayor Murray from Regina Jones, who is now working for the Mayor's office as an "executive on loan," Jones spells out how to take over the school board by "cultivating candidates to serve on the board." The plan is based on the success of two mayors in San Francisco who essentially embedded one of their own staffers on the school board because, "As in Seattle, [the San Francisco Unified School District] SFUSD was concerned about a takeover of the district by the mayor." So the mayor needed to find a more subtle way to achieve that end—or something approximating it.
Ms. Jones recommends that the San Francisco superintendent become an "intermediary" engaging Seattle Public Schools at meetings such as the Council for Great City Schools. The San Francisco Superintendent recently quashed the San Francisco school board's efforts to cut ties with the controversial five-week teacher training enterprise Teach for America.
An example of this is a partnership with the gaming tech company Zynega.
Mayor Lee, in his strategic plan for SFUSD titled Vision 2025, includes "blended learning" also referred to as "personalized learning." What that means is each student has her own computer and uses it for lessons and tests, instead of shared class time with other students and interaction with a teacher. Summit charter school and Rocketship Charters are based on this approach. It's less expensive in terms of operating costs and staff hours.
The agreement with Zynega provides students with an opportunity to work with companies while in high school but this also appears to be a business opportunity for Zynega to develop "talent" for their own use.
Regina Jones' letter is a game plan for gaining control of Seattle's school board and the district. As Chris Hedges would say, it is "a coup d'etat in slow motion".
In cities that have mayoral control of schools, and in Michigan where the entire state public school system is under the governor's control, there is little to no opportunity for a democratic process.
In Chicago, under mayoral control, schools have been closed and many converted into charter schools, disrupting neighborhoods and scattering students to schools throughout the city.
The statement was made at an event where community members were calling for an elected school board in 2015.
Former Secretary of Education Arne Duncan made an important piece of his agenda mayoral control of urban school districts and once said he would consider his time as education secretary a "failure" if more mayors didn't take over city school systems by the end of his tenure.
The most egregious example of a politician's undemocratic control of public schools can be seen in the state of Michigan with the decision by former Michigan Governor Jennifer Granholm to hire Emergency Financial Managers. The emergency managers have the power to take control of a city's government, reduce pay, outsource work, reorganize departments and modify employee contracts. Emergency managers can also deem school districts "failing," close public schools and convert them into charter schools.
The first appointed emergency manager, Robert Bobb, took over the Detroit Public School system in 2009. The County Circuit Court in 2011 found this takeover illegal but soon after, emergency managers were appointed in mostly minority communities around the state, including the city of Flint. In several of these towns, such as Highland Park, Michigan the public schools were closed and taken over by charter operators.
Darnell Earley, the unelected manager of Flint, presided over the devastating decision to switch the city's water supply to the Detroit River resulting in lead poisoning of residents throughout the city. After the water disaster, Mr. Earley was appointed by Governor Rick Snyder to become the CEO of Detroit Public Schools.
Now the Emergency Managers are being named CEOs, as in Chicago, and given more power.
Convert schools into charter schools without the consent of the locally elected board.
The people have no voice or control over how their children are educated or by whom. The same holds true for mayoral control. That's why, in Seattle, people are fighting back.
Not only Magic Mayors, now, but MAYOR APPOINTED SCHOOL BOARDS. Can't we immediately see through this game?


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UOB > Football Bet Tips And Predictions From Experts > England Championship Predictions From Experts > Watford vs Cardiff Prediction & Betting Tips | 05/12/2020 | Football
Our prediction for this Championship match:
Vicarage Road will host Saturday's football game between Watford and Cardiff City. The Hornets played out a goalless draw with Nottingham Forest on Wednesday evening, with the team extending their unbeaten run in the Championship to six games. Vladimir Ivic's men have been impressive at home ground this season, with the team eyeing their seventh win at their Vicarage Road.
Even though the Serbian tactician has some selection worries ahead of Matchday 16, home win should be considered. The Bluebirds, on the other hand, aim to build on back-to-back wins over Luton Town and Huddersfield Town. The Welsh outfit have been struggling on the travels of late, and we do not believe that they are capable of spoiling Watford's party at Vicarage Road. You should bear in mind that in their last meeting in the English second tier Watford rolled over Cardiff 5-1.


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In this week's show we're talking about something that's very important to your business. Very important to every business out there. But something that only a handful of businesses actually have!
Do you find that you don't know what products to create? What niche to do business in? Which customers to serve, what content to produce, what tools to use etc.? The list goes on! If so, then you need to check this out. We'll take you through some simple, actionable tips that you can start using in your business today.
In the Content Report this week, Mike goes back to the epic Robert Kiyosaki, this time with Rich Dad's Guide To Investing. Which you can actually get free at that link! And we'll also be talking about creating a solid mission statement for your business…(hint!).


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Der Ady, auch Adih  oder Adee, war ein Längenmaß in der Präsidentschaft Madras in Ostindien und die Bezeichnung für das Maß Fuß. Er wurde auch mit malabarischer Fuß bezeichnet.
 1 Ady = 117,77 Pariser Linien = 0,26568 Meter
 1 Ady = 10,46 Zoll (engl.)
 gesetzlich 24 Adies = 1 Kjuli/Culi/Culy = 20,92 Fuß (engl.= 0,3048 Meter); in der Praxis 26 Adies = 22,663 Fuß (engl.)

Literatur
 Johann Michael Leuchs: Der Kontorwissenschaft: Teil: Die vollständige Geld-, Münz-, Maß- und Gewichtskunde. Band 3, Verlag Leuchs und Komp., Nürnberg 1834, S. 135.

Einzelnachweise

Altes Maß oder Gewicht (Indien)
Fuß (Einheit)

redpajama_set_name: RedPajamaWikipedia


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WEEK IN REVIEW: Laurels and Darts | Oct. 21, 2016
10/21/16 12:52am The Daily Targum
DON'T WASTE THE TASTE
Rutgers University has opened up about their leftover food from dining halls. Rutgers Dining Services first inspects left-over food to see which food is appropriate for reutilization and then these food items are collected and then transported to a facility. These donations eventually end up in local food pantries and soup kitchens. We laurel the dining services for taking the initiative to not waste food and to help those in need.
GETTING THE BIG PICTURE OF RACISM
Shocking photos taken on university campuses around the nation are circulating the internet. Students from the University of North Dakota, Quinnipiac University and Kansas State University shared racist photos and captions including the use of blackface and racial slurs. A representative of Rutgers blames the popularity of these photos on social media and its prevalence on college campuses. We dart these students for sharing discriminatory and insulting pictures.
RESILIENCE FOR RECOVERY
Rutgers University Behavioral Health Care's Child Division is receiving $2 million to create a Children's Center for Resilience and Trauma Recovery. The University is hoping to use these centers to train existing and new childcare providers to help better assist the students who have experienced traumatic life experiences. We laurel RUBHC for taking a step toward the improvement of mental health consideration in classrooms.
BROWN SHOWERS
Rutgers' students have voiced their complaints about the brown discoloration of water in university facilities. The brown water is the result of New Brunswick flushing out the fire hydrants. Although the water was deemed to be safe, students were unaware that the city would be flushing the water. We dart the University for not alerting its students about the brown water.
STRIVING FOR THE AMERICAN DREAM
UndocuRutgers, a new student organization at the university is working to extend support and advice to undocumented students in Rutgers—New Brunswick. Undocumented students and documented students alike have joined the club to share their support for students who are undocumented. We laurel UndocuRutgers for helping their fellow students cope with their current residency status.
STOP THE SEXUAL VIOLENCE
A report from Safety Matters reveals that there was a general increase in rape cases from 2013 to 2015. Schools are also being investigated for allegedly mishandling harassment cases and not including them in listed reports that are given by the Education Department. Although we are glad that more students are coming forth with their stories, we dart the fact that sexual assault is still so prevalent on college campuses.
The Daily Targum's editorials represent the views of the majority of the 148th editorial board. Columns, cartoons and letters do not necessarily reflect the views of the Targum Publishing Company or its staff


One of the things that attracts people to the internet is possibly a hangover of the dotcom boom. People still believe that they can make their fortunes online without having to work hard.
While it may still be possible to make money online the days of not working hard, alas, are no more.
However that should not dissuade you!
But what if you can't design?
There are plenty of other options open to you.
A couple of the more popular ones are Adsense and Text Link Ads.
Adsense is quite simple to use. All you need to do is sign up for an account and add some code to your website pages. When people click on the ads you earn money! Not that complicated, is it?
If you start using Adsense you'll soon find that there are thousands of websites and blogs dedicated to providing advice on how to make more money from it. There are even a couple of books on the subject!
Text Link Ads is slightly more complicated, but the rewards can be generous.
The basic concept is that text links ie. not graphic links, are valuable both in terms of traffic and seo, so you, as a website publisher, can make money from them.
The only downsides to TLA is that your site needs to be established and the system will not work with static HTML ie. you need to be using PHP, ASP or some other form of server-side scripting language to use the system.
If you're looking for tips and tricks on making money online you may want to check out the Irish Webmaster Forum, which is popular with Irish web professionals.


There were plenty of apples on the trees in the small orchard when we first moved into St Olaves in August. However, the previous owners had warned us that we would be in competition with the deer, who had a knack of waiting until the fruit was just ripe before bothering with it.
So began the waiting game - apple roulette. Nine of the trees are on dwarf rootstock, making the apples easy to pick but also very accessible to even the smallest fallow deer.
We saw our first deer in the large field to the west of the garden in early September. Three animals - probably roe deer- in the early morning light, alert and bounding away across the field once they sensed our movement. A couple of weeks later in the late afternoon we glimpsed a couple of young deer bounding through the undergrowth on the hillside, just above the river.
As the September days shortened into October we gradually picked more and more of the apples. Some fell as windfalls and started to be eaten by slugs. Sunset was the variety that had the heaviest crop this year - well over 100 small eating apples on a tree with five main radiant branches. The Egremont Russet only produced about a dozen fruit, but they were delicious.
With storm Ophelia predicted for Monday 16th October we harvested nearly all the apples rather than losing them to the strong winds. Three large bags in total - around 14 Kg. But a few apples were left on the trees, the slightly more inaccessible ones and some small misshapen fruits. By now there were also plenty of windfalls littering the grass.
Earlier this week, (probably 1st November), we were walking back through the orchard when we realised that there was not a single apple left on the trees and that all the windfalls had also vanished - spirited away by the deer the night before.


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China&India
H5N1 outbreak: China bans imports of Malaysia's bird's nest products
KUALA LUMPUR — China has imposed a temporary restriction on imports of bird's nest products from Malaysia, following an outbreak of the highly-contagious H5N1 avian flu in the east coast state of Kelantan.
Two styles of cleaned bird's nest, Yan Zhan (left) and Su Zhan (right) await repacking at a processing plant in Kuala Lumpur, Feb 17, 2015. Photo: Reuters
The ban prompted Malaysia's Veterinary Services Department (VSD) to send letters to the Chinese authorities explaining the situation and measures currently taken to curb the spread of the pathogenic virus.
"We explained that the number of chickens and ducks in Kelantan only comprised 0.5 per cent of the total number of poultry nationwide ... The production of commercial eggs is fully run outside of Kelantan," the VSD said in a statement on Sunday.
"The sources of raw clean edible bird's nest, which are meant for export, are from swiftlet nests located outside of Kelantan. The supply can be traced through a system adopted by the department."
Additionally, processing plants also used heat treatment which met the requirements set by China during times of disease — which was to heat the bird's nest products at temperatures of up to 70°C for 3.5 seconds.
Last week, Kelantan declared the H5N1 bird flu outbreak a "state disaster" after it spread to two more districts despite attempts by the authorities to contain the virus.
It was first detected on Feb 26 among a few free-range chickens. The virus resurfaced in the country after an absence of a decade.
The VSD had previously said it believed the epidemic detected in Kelantan could have started from cockfighting activities involving roosters from neighbouring countries, citing similarities between the current cases and those in 2004.
The H5N1 strain was first detected in Kampung Pulau Tebu in the state capital of Kota Bharu, before it spread to the five villages nearby.
A second cluster of the bird flu strain had also been detected in three villages at Pasir Mas, a district 20km from Kota Bharu.
Despite taking immediate measures to check the spread of the virus, including setting up road blocks to prevent chickens from being smuggled out of the state's borders and culling all types of fowl, the bird flu spread to two other districts: Pasir Puteh and Bachok.
Meanwhile, Malaysia's Agriculture and Agro-based Ministry secretary-general and the VSD deputy director-general will make a visit to China to explain the current measures taken by the authorities to combat the spread of the virus.
"Through this working visit, we hope China will lift the temporary restriction and allow the importation of the bird's nest in China," the VSD said.
Malaysia is one of the major countries that China import bird's nest from. Last year, Kuala Lumpur exported 20.15 metric tonnes of bird's nest, worth RM134.13 million (S$42.3 million), to Beijing. AGENCIES
World Explore now


Toy retailer Toymaster Kingdom has called in administrators after falling victim to rising costs and tough trading.
KPMG's Allan Graham and Richard Philpott have been appointed as administrators of the Ipswich-based retailer, which operates 14 stores and employs 150 staff.
Ian Corfield, from KPMG's restructuring team said: 'Despite cost-saving measures earlier in the year, the difficult trading conditions coupled with increasing costs have left the directors with no alternative but to place TM Kingdom into administration.
Administrators have told staff that the business is not being closed and that they had begun to look for purchasers for the business to save the jobs.
The business, which is officially known as TM Kingdom is continuing to trade as normal and pay suppliers.


Just some random highlights from the last few months.
"Paint me like one of your French bandos."
"Nothing's finer than being in your diner"
"What's the first rule of slide club?"
Really love the Hobbit Church (4) though! It tells a story.
Wow, great shots, and perfect captioning! The dolls in the stroller are indeed creepy, but I remember that schoolyard chant for sure. That old car, the Grande Entrance, and slide club are probably my favorites, but they're all keepers and instant classics. Thanks for sharing!
That drug store is amazing. it seems as though it hasn't been changed in 60 years.
Really nice shots of some awesome spots!
Loving the hobbit church. How was it inside?
Thank you! I loved the diner as well. Unfortunately I only had time to get about three shots in there before we got kicked out by the white trash neighbors.
They are go karts, so bumping is not their main function lol.
I really like that little stone church. It is near a large abandoned convent.
Thank you for your kind words as always! I love that church for the way the light comes through the stained glass. It is a extremely photogenic spot. Mabel's house has been vacant since 2014, but was completely untouched. Both of the dolls were pretty high on the creepy scale!
Thank you so much! Glad you enjoyed looking at them and appreciate the captions!
The car is a 40s era Cadillac.
Thank you! I enjoy coming up with the captions for my shots. I have a lot of fun with it and I think it helps add a bit extra to them.
#2 was fun but also incredibly nerve wracking. As the place still has power and active security rolling around. Also definitely not a place you want to get caught in!
Quite small, but really neat. It is part of a much larger convent property, which is also abandoned. There isn't much to it inside, although I think there is another room we were unable to access. There is a garden with statues behind it. I imagine it was used by the nuns as a secluded place to sit with nature and pray, or whatever it is nuns do on their free time.
I do have some shots from there. Just need to get around to converting them to JPEG so I can upload.
"Its O'Neill, with two L's"
Very cool shots! Love the wall of vinyl. Nice job!
I like the captions you put!
Thanks! That house was a real surprise. We found it completely by accident and from the outside it looked like it would not contain much of interest.
Just goes to show you can never judge a house by its walls!
A few more panels have been broken, likely by kids throwing bricks onto it from the roof, since the first time I went here back in 2012. The water damage in this room, and the entire complex, has also gotten progressively worse. I honestly fear the dome may not last another decade. I hope it can be saved somehow, but I am not optimistic about that actually happening.
Thanks! I always caption my shots. I feel as though it adds that little extra something.
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Given that the samples on show were pre-production models without final firmware Nikon was understandably reluctant to let anyone take any images with them, although they did at least load some of the test samples with memory cards so that we could get a better feel for them.
either in our original news article or on Nikon's own microsite.
when we get some review samples in for a full test.
factor of each to shine through.


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JAPAN TRAVEL: Satsuma Kiriko Glassworks Features The Art of Glass Making in Kagoshima
Part of the efforts of the Shimadzu clan to modernize Japan is Satsuma Kiriko, the art of glass making.
Satsuma Kiriko, a cut crystal glass, was first manufactured in 1851. It was pioneered by Nairakira, the 28th head of the influential Shimadzu clan.
Nariakira started to modernize Japan in fear of Western colonization; thus he created the Shuseikan Project. This project spawned many factories and workshops, which then included Satsuma Kiriko.
However, when Nariakira died, the production of Satsuma Kiriko had stopped. It was then revived again in 1985 when Shimadzu Ltd. worked and researched on its reproduction once again.
At present, Satsuma Kiriko is very much alive and the art of glassmaking still thrives, especially in Kagoshima Prefecture where it is considered a Traditional Cultural Craft Product.
If you wish to see this fine art of glassmaking, you can visit the site where you can learn how Satsuma Kiriko is produced.
A trip to the Shimadzu Satsuma Kiriko Glassworks will let you observe the step-by-step process of making Satsuma Kiriko.
In this site, highly-skilled artisans carefully work on creating delicate crystal glass which has become a symbolic pride of Kagoshima.
It is significant to note that Satsuma Kiriko is not your ordinary kind of glass product that you can buy elsewhere. With a hundred years of tradition incorporated into creating this craft product, this unique item is considered of great worth.
Satsuma Kiriko has distinct characteristics that separate it from other forms of glassmaking. In making Satsuma Kiriko, intrinsic cuts and colors are made into the different layers of glass. This then produces color gradation that creates a fading effect.
Satsuma Kiriko is elegant-looking, and it can be relatively expensive. It is not to be confused with Edo Kiriko, which originated in Tokyo.
Finished products are displayed on the gallery shop next door, set in an Important Cultural Property in 1909. This European-style gallery houses different Satsuma Kiriko products, ranging from jewelry, vessels, and figurines.
Visit Shimadzu Satsuma Kiriko Glassworks and learn more about this traditional Kagoshima product.
By car: Take the National Highway No. 10 towards Kokubu and Miyazaki. Go through the Torigoe Tunnel and take a left turn.
By bus: Get off at Sengan-en bus stop. From there, it is a 5-minute walk.
Address: 9688-24 Yoshino-cho, Kagoshima City, Kagoshima Prefecture 892-0871
Operating Hours: 9 am to 5 pm (Break time: 10 am to 10:15; 3:00 to 3:15, and 12 to 1 pm)
Closed on: Monday and Every 3rd Sunday of the month
Website: http://www.satsumakiriko.co.jp/index.html
Contact: +81 99-247-2111
This first appeared in Philippine Primer magazine Vol. 36 – March 2019 issue.