Reviewerly/exHarmony huggingface dataset:
Sampling Sketches for Concave Sublinear Functions of Frequencies

We consider massive distributed datasets that consist of elements modeled as key-value pairs and the task of computing statistics or aggregates where the contribution of each key is weighted by a function of its frequency (sum of values of its elements). This fundamental problem has a wealth of applications in data analytics and machine learning, in particular, with concave sublinear functions of the frequencies that mitigate the disproportionate effect of keys with high frequency. The family of concave sublinear functions includes low frequency moments ($p \leq 1$), capping, logarithms, and their compositions. A common approach is to sample keys, ideally, proportionally to their contributions and estimate statistics from the sample. A simple but costly way to do this is by aggregating the data to produce a table of keys and their frequencies, apply our function to the frequency values, and then apply a weighted sampling scheme. Our main contribution is the design of composable sampling sketches that can be tailored to any concave sublinear function of the frequencies. Our sketch structure size is very close to the desired sample size and our samples provide statistical guarantees on the estimation quality that are very close to that of an ideal sample of the same size computed over aggregated data. Finally, we demonstrate experimentally the simplicity and effectiveness of our methods.
Structured Variational Inference in Continuous Cox Process Models

We propose a scalable framework for inference in a continuous sigmoidal Cox process that assumes the corresponding intensity function is given by a Gaussian process (GP) prior transformed with a scaled logistic sigmoid function. We present a tractable representation of the likelihood through augmentation with a superposition of Poisson processes. This view enables a structured variational approximation capturing dependencies across variables in the model. Our framework avoids discretization of the domain, does not require accurate numerical integration over the input space and is not limited to GPs with squared exponential kernels. We evaluate our approach on synthetic and real-world data showing that its benefits are particularly pronounced on multivariate input settings where it overcomes the limitations of mean-field methods and sampling schemes. We provide the state of-the-art in terms of speed, accuracy and uncertainty quantification trade-offs.
Generalization Bounds for Neural Networks via Approximate Description Length

We investigate the sample complexity of networks with bounds on the magnitude of its weights. In particular, we consider the class \[ \cn = \left\{W_t\circ\rho\circ W_{t-1}\circ\rho\ldots\circ \rho\circ W_{1} : W_1,\ldots,W_{t-1}\in M_{d\times d}, W_t\in M_{1,d} \right\} \] where the spectral norm of each $W_i$ is bounded by $O(1)$, the Frobenius norm is bounded by $R$, and $\rho$ is the sigmoid function $\frac{e^x}{1 + e^x}$ or the smoothened ReLU function $ \ln\left(1 + e^x\right)$. We show that for any depth $t$, if the inputs are in $[-1,1]^d$, the sample complexity of $\cn$ is $\tilde O\left(\frac{dR^2}{\epsilon^2}\right)$. This bound is optimal up to log-factors, and substantially improves over the previous state of the art of $\tilde O\left(\frac{d^2R^2}{\epsilon^2}\right)$, that was established in a recent line of work. We furthermore show that this bound remains valid if instead of considering the magnitude of the $W_i$'s, we consider the magnitude of $W_i - W_i^0$, where $W_i^0$ are some reference matrices, with spectral norm of $O(1)$. By taking the $W_i^0$ to be the matrices in the onset of the training process, we get sample complexity bounds that are sub-linear in the number of parameters, in many {\em typical} regimes of parameters. To establish our results we develop a new technique to analyze the sample complexity of families $\ch$ of predictors. We start by defining a new notion of a randomized approximate description of functions $f:\cx\to\reals^d$. We then show that if there is a way to approximately describe functions in a class $\ch$ using $d$ bits, then $\frac{d}{\epsilon^2}$ examples suffices to guarantee uniform convergence. Namely, that the empirical loss of all the functions in the class is $\epsilon$-close to the true loss. Finally, we develop a set of tools for calculating the approximate description length of classes of functions that can be presented as a composition of linear function classes and non-linear functions.
Learning Sample-Specific Models with Low-Rank Personalized Regression

Modern applications of machine learning (ML) deal with increasingly heterogeneous datasets comprised of data collected from overlapping latent subpopulations. As a result, traditional models trained over large datasets may fail to recognize highly predictive localized effects in favour of weakly predictive global patterns. This is a problem because localized effects are critical to developing individualized policies and treatment plans in applications ranging from precision medicine to advertising. To address this challenge, we propose to estimate sample-specific models that tailor inference and prediction at the individual level. In contrast to classical ML models that estimate a single, complex model (or only a few complex models), our approach produces a model personalized to each sample. These sample-specific models can be studied to understand subgroup dynamics that go beyond coarse-grained class labels. Crucially, our approach does not assume that relationships between samples (e.g. a similarity network) are known a priori. Instead, we use unmodeled covariates to learn a latent distance metric over the samples. We apply this approach to financial, biomedical, and electoral data as well as simulated data and show that sample-specific models provide fine-grained interpretations of complicated phenomena without sacrificing predictive accuracy compared to state-of-the-art models such as deep neural networks.
A finite analogue of the ring of algebraic numbers

We construct an analogue of the ring of algebraic numbers, living in a quotient of the product of all finite fields of prime order. We use this ring to deduce some results about linear recurrent sequences.
Covariate-Powered Empirical Bayes Estimation

We study methods for simultaneous analysis of many noisy experiments in the presence of rich covariate information. The goal of the analyst is to optimally estimate the true effect underlying each experiment. Both the noisy experimental results and the auxiliary covariates are useful for this purpose, but neither data source on its own captures all the information available to the analyst. In this paper, we propose a flexible plug-in empirical Bayes estimator that synthesizes both sources of information and may leverage any black-box predictive model. We show that our approach is within a constant factor of minimax for a simple data-generating model. Furthermore, we establish robust convergence guarantees for our method that hold under considerable generality, and exhibit promising empirical performance on both real and simulated data.
Joint Lightweight Object Tracking and Detection for Unmanned Vehicles

In this paper, we address the problem of lightweight and effective visual object tracking and we present a real-time tracking system suitable for integration in embedded autonomous platforms. We propose a novel tracking framework for classification-based re-detection and tracking, with learnable management of tracking and detection results. The proposed framework includes a novel, very efficient object reidentification method, which filters the detection candidates and systematically corrects the tracking results. In our experiments, we demonstrate the effectiveness of the proposed system by comparing its performance against several other state-of-the art trackers and report the results on the UAV123 and UAV20L datasets. The results indicate that the proposed method is significantly more robust and accurate against recent state-of-the-art trackers, surpassing problems caused by real-world scenarios, while maintaining fast tracking speeds, making it suitable for use in real-time vision applications for autonomous robots, such as Unmanned Aerial Vehicles (UAVs).
A Novel Density-Based Clustering Approach for Outlier Detection in High-Dimensional Data

Outlier detection is a primary aspect in data-mining and machine learning applications, also known as outlier mining. The importance of outlier detection in medical data came from the fact that outliers may carry some precious information however outlier detection can show very bad performance in the presence of high dimensional data. In this paper, a new outlier detection technique is proposed based on a feature selection strategy to avoid the curse of dimensionality, named Infinite Feature Selection DBSCAN. The main purpose of our proposed method is to reduce the dimensions of a high dimensional data set in order to efficiently identify outliers using clustering techniques. Simulations on real databases proved the effectiveness of our method taking into account the accuracy, the error-rate, F-score and the retrieval time of the algorithm.
Setting tasks for performing creative assignments by students of first-second years of a technical university

In this article we decided to focus on one of the significant stages of the experiment being conducted: on setting assignments for students' creative work asking them to describe in details the problems encountered and suggest ways of solving. We are conducting an experiment with biomimetic approach in teaching students of technical universities to graphic disciplines. At the same time creative work includes not only the material of the main discipline, in frames of which we set the task, but also affects related disciplines. Let us see how the attitude of students to the set creative tasks changed depending on the accents we placed. How the students' understanding of the purpose of the work was changing. Formulation of the task that we set for the students, change greatly the motivational factor of fulfilling work and the understanding of the problem. If the task is oriented correctly, the student not only just draws a model, but deliberately sets a goal, for which he does it, develops criteria for choosing a specific object for research, suggests ways of solving problem. Based on the data obtained, we justify and demonstrate why this format of work allows the student to gradually get used to doing work at the point of intersection of several disciplines. The article provides an overview analysis of the theoretical literature, which we use as a scientific basis, and also presents statistical calculations of the results of student surveys and analysis of their creative works, depending on the change of the tasks and goals.
Atalaya at TASS 2019: Data Augmentation and Robust Embeddings for Sentiment Analysis.

In this article we describe our participation in TASS 2019, a shared task aimed at the detection of sentiment polarity of Spanish tweets. We combined different representations such as bag-of-words, bag-of-characters, and tweet embeddings. In particular, we trained robust subword-aware word embeddings and computed tweet representations using a weighted-averaging strategy. We also used two data augmentation techniques to deal with data scarcity: two-way translation augmentation, and instance crossover augmentation, a novel technique that generates new instances by combining halves of tweets. In experiments, we trained linear classifiers and ensemble models, obtaining highly competitive results despite the simplicity of our approaches.
New Loss Functions for Fast Maximum Inner Product Search

Quantization based methods are popular for solving large scale maximum innerproduct search problems. However, in most traditional quantization works, theobjective is to minimize the reconstruction error for datapoints to besearched. In this work, we focus directly on minimizing error in inner productapproximation and derive a new class of quantization loss functions. One keyaspect of the new loss functions is that we weight the error term based on thevalue of the inner product, giving more importance to pairs of queries anddatapoints whose inner products are high. We provide theoretical grounding tothe new quantization loss function, which is simple, intuitive and able to workwith a variety of quantization techniques, including binary quantization andproduct quantization. We conduct experiments on standard benchmarking datasetsto demonstrate that our method using the new objective outperforms otherstate-of-the-art methods.
Computational Separations between Sampling and Optimization

Two commonly arising computational tasks in Bayesian learning are Optimization (Maximum A Posteriori estimation) and Sampling (from the posterior distribution). In the convex case these two problems are efficiently reducible to each other. Recent work (Ma et al. 2019) shows that in the non-convex case, sampling can sometimes be provably faster. We present a simpler and stronger separation. We then compare sampling and optimization in more detail and show that they are provably incomparable: there are families of continuous functions for which optimization is easy but sampling is NP-hard, and vice versa. Further, we show function families that exhibit a sharp phase transition in the computational complexity of sampling, as one varies the natural temperature parameter. Our results draw on a connection to analogous separations in the discrete setting which are well-studied.
Balancing Memory Accesses for Energy-Efficient Graph Analytics Accelerators

Domain-specific accelerators for graph analytics leverage a large on-chip memory in order to tackle the intensive random memory accesses, offering higher performance and energy efficiency than conventional architectures. However, limited by the inefficient usage of on-chip memory, current accelerators suffer from energy and performance bottlenecks due to the large amount of off-chip memory accesses. In this work, we introduce an online preprocessing step for the vertex-centric programming model based on our observation of imbalanced memory bandwidth utilization between two execution phases. Our scheme improves energy efficiency and performance by significantly reducing off-chip accesses in two ways. First, we sequence random off-chip memory accesses to balance memory bandwidth demands and improve the utilization of on-chip memory. Second, we prune active leaf vertices to avoid redundant memory accesses. We evaluate our method on a state-of-the-art graph analytics accelerator and achieve 1.6× speedup while reducing energy consumption by 42% on average.
Mixture Content Selection for Diverse Sequence Generation

Generating diverse sequences is important in many NLP applications such as question generation or summarization that exhibit semantically one-to-many relationships between source and the target sequences. We present a method to explicitly separate diversification from generation using a general plug-and-play module (called SELECTOR) that wraps around and guides an existing encoder-decoder model. The diversification stage uses a mixture of experts to sample different binary masks on the source sequence for diverse content selection. The generation stage uses a standard encoder-decoder model given each selected content from the source sequence. Due to the non-differentiable nature of discrete sampling and the lack of ground truth labels for binary mask, we leverage a proxy for ground truth mask and adopt stochastic hard-EM for training. In question generation (SQuAD) and abstractive summarization (CNN-DM), our method demonstrates significant improvements in accuracy, diversity and training efficiency, including state-of-the-art top-1 accuracy in both datasets, 6% gain in top-5 accuracy, and 3.7 times faster training over a state of the art model. Our code is publicly available at https://github.com/clovaai/FocusSeq2Seq.
Classification of H&amp;E Stained Breast Cancer Histopathology Images Based on Convolutional Neural Network

Abstract Automatic classification of H&amp;E breast cancer histopathology images is a challenging task. Computer-aided diagnostic systems help reduce costs and increase the efficiency of the process. Although the existing research on breast cancer histopathology image classification is higher than 90% accurate in the binary classifications (non-carcinoma/carcinoma), the classification accuracy of four classifications (normal, benign, in situ, invasive) is less than 80%. This paper proposes a framework for the classification of H&amp;E stained breast cancer histopathological images, which includes two methods based on convolutional neural network. The first method is based on the convolutional neural network structure of the SE-ResNet module, and the second method is based on the transfer learning hybrid model structure, which achieves the accuracy of 80.33% and 86.11% respectively. Compared with the state-of-the-art method, the accuracy is improved by 2.56% and 8.33% respectively. The proposed framework achieves 91.67% accuracy in binary classification and is competitive with state-of-the-art methods.
A theoretical study of process dependence for critical statistics in standard serial models and standard parallel models

Long before the mathematical developments that led inexorably to the development of systems factorial technology, the somewhat arduous, but arguably requisite labors which precisely defined parallel and serial architectures had begun (e.g., Townsend, 1969, 1972). Both then and now, what are now referred to as standard serial models and standard parallel models not only play an important role in psychological science, they are often what non-mathematical psychologists are (sometimes in an unschooled fashion) referring to when they bring up architectural concepts. Interestingly, two strategic and critical properties, and therefore implicit predictions of the canonical serial and parallel models have witnessed little analysis. In this article, we address three issues: (1) Standard parallel models predict stochastically independent processing times and therefore total completion times. There is a partially valid intuition that standard serial models will predict positive dependence among totally completion times and standard serial models based on exponential processing time do predict this (Townsend and Ashby, 1983, p. 73). This also holds if only one order of processing is possible. However, if there is a mixture of processing orders, certain distributions can predict negative dependencies on this statistic. (2) Analogously, standard serial models predict independent and identically distributed processing times and therefore intercompletion times. Certain examples of standard parallel models (again, based on exponential processing times) suggest that the intercompletion times tend to increase over time and stages although there has been no information on their interdependence. Interestingly, we again find that there is an inclination for standard parallel models to emulate this behavior but that this tendency can be over-ridden by dramatically increasing parallel-channel hazard functions. (3) Are there psychological questions that pertain to these distinctions and are there any experiments that have emerged to assess the latter? With regard to experimental data on these issues, it turns out that there exists some data from categorical free recall that bear on some of the behavior of intercompletion times, which we discuss. To date, we know of no investigations that explore correlations of total completion times, although such analyses appear relatively straightforward.
Towards a Pragmatic Model of an Artificial Conversational Partner: Opening the Blackbox

In this paper, we suggest a new model of chatbot aiming to enhance the pragmatic aspects of language processing. We believe that dissociating the sentence processing aspect and the information processing aspect to be of the utmost importance in any system aiming to generate relevant utterances in response to the user’s input. Therefore, we suggest a cognitively plausible model of a conversation processing system, containing: (1) a sentence decoder, responsible for translating a sentence into a purely semantic information; (2) an information processor, responsible for choosing the appropriate information to send back to the user; (3) a sentence encoder, responsible for generating a sentence able to convey the information chosen by the information processor in a relevant way; (4) a world model, containing all the information the system has learnt about the world; and (5) a user’s mental state model, containing all the information the system assumes the user is or is not aware of. We review the literature giving rise to this model and attempt to detail it.
Choice of law for property issues regarding Bitcoin under English law

The dominant recent legal discourse concerning Bitcoin and later virtual currencies has been focused on regulatory issues such as anti-money laundering and financial and securities regulation. By contrast, scant attention has been given to the intersection between the uses of Bitcoin and commercial law even as its popularity has grown as both an asset class and as a means of payment for goods and services. In determining the commercial law consequences of transactions involving Bitcoin, the likely cross-border nature of Bitcoin-related transactions will give rise to choice of law issues which may have a determinative effect on a given dispute. Commercial disputes involving virtual currencies have already made their way into common law courts. This paper examines one of the most significant choice of law problems that arises – the law applicable to determine disputes concerning property in Bitcoins (and determining the situs of Bitcoins) through the lens of English conflict of laws.
Sistem Informasi Sekolah Berbasis Web

Abstract: A system of data input and the grade of students who still use manual way namely by filling form of data on student and the grades of students in the form of paper sheet done by administration and teacher. Besides needed a place the immense storage, to find files need a require a long time, because that is the way are considered less effective. Websites is information in word wide web stored in different file as a page web. Academic information system web-based can manage academic information with more effective. The software used for develop information system web is PHP and MySQL for databases.&#x0D; &#x0D; Keywords : Php, MySQL, System, Information, Education
Simplified Computation and Interpretation of Fisher Matrices in Incremental Learning with Deep Neural Networks

Import recent advances in the domain of incremental or continual learning with DNNs, such as Elastic Weight Consolidation (EWC) or Incremental Moment Matching (IMM) rely on a quantity termed the Fisher information matrix (FIM). While the results obtained in this way are very promising, the use of the FIM relies on the assumptions that (a) the FIM can be approximated by its diagonal, and (b) that FIM diagonal entries are related to the variance of a DNN parameter in the context of Bayesian neural networks. In addition, the FIM is notoriously difficult to compute in automatic differentiation (AD) systems frameworks like TensorFlow, and existing implementations require an excessive use of memory due to this problem. We present the Matrix of SQuares (MaSQ), computed similarly as the FIM, but whose use in EWC-like algorithms follows directly from the calculus of derivatives and requires no additional assumptions. Additionally, MaSQ computation in AD frameworks is much simpler and more memory-efficient FIM computation. When using MaSQ together with EWC we show superior or equal performance to FIM/EWC on a variety of benchmark tasks.