Reviewerly/exHarmony huggingface dataset:
Histograms lie about distribution shapes and Pearson's coefficient of variation lies about variability.

Background and Objective: Histograms and Pearson's coefficient of variation are among the most popular summary statistics. Researchers use them to judge the shape of quantitative data distribution by visual inspection of histograms. The coefficient of variation is taken as an estimator of relative variability of these data. We explore properties of histograms and coefficient of variation by examples in R, thus offering better alternatives: density plots and Eisenhauer's relative dispersion coefficient. Methods: Hypothetical examples developed in R are applied to create histograms and density and to compute coefficient of variation and relative dispersion coefficient. Results: These hypothetical examples clearly show that these two traditional approaches are flawed. Histograms are incapable of reflecting the distribution of probabilities and the coefficient of variation has issues with negative and positive values in the same dataset, it is sensible to outliers, and it is severely affected by mean value of a distribution. Potential replacements are explained and applied for contrast. Conclusions: With the use of modern computers and R language it is easy to replace histograms by density plots, which are able to approximate the theoretical probability distribution. In addition, Eisenhauer's relative dispersion coefficient is suggested as a suitable estimator of relative variability, including corrections for lower and upper bounds.
Symmetric states and dynamics of three quantum bits

The unitary group acting on the Hilbert space ${\cal H}:=(C^2)^{\otimes 3}$ of three quantum bits admits a Lie subgroup, $U^{S_3}(8)$, of elements which permute with the symmetric group of permutations of three objects. Under the action of such a Lie subgroup, the Hilbert space ${\cal H}$ splits into three invariant subspaces of dimensions $4$, $2$ and $2$ respectively, each corresponding to an irreducible representation of $su(2)$. The subspace of dimension $4$ is uniquely determined and corresponds to states that are themselves invariant under the action of the symmetric group. This is the so called {\it symmetric sector.} The subspaces of dimension two are not uniquely determined and we parametrize them all. We provide an analysis of pure states that are in the subspaces invariant under $U^{S_3}(8)$. This concerns their entanglement properties, separability criteria and dynamics under the Lie subgroup $U^{S_3}(8)$. As a physical motivation for the states and dynamics we study, we propose a physical set-up which consists of a symmetric network of three spin $\frac{1}{2}$ particles under a common driving electro-magnetic field. {For such system, we solve the control theoretic problem of driving a separable state to a state with maximal distributed entanglement.
Optimality and Stability in Federated Learning: A Game-theoretic Approach

Federated learning is a distributed learning paradigm where multiple agents, each only with access to local data, jointly learn a global model. There has recently been an explosion of research aiming not only to improve the accuracy rates of federated learning, but also provide certain guarantees around social good properties such as total error. One branch of this research has taken a game-theoretic approach, and in particular, prior work has viewed federated learning as a hedonic game, where error-minimizing players arrange themselves into federating coalitions. This past work proves the existence of stable coalition partitions, but leaves open a wide range of questions, including how far from optimal these stable solutions are. In this work, we motivate and define a notion of optimality given by the average error rates among federating agents (players). First, we provide and prove the correctness of an efficient algorithm to calculate an optimal (error minimizing) arrangement of players. Next, we analyze the relationship between the stability and optimality of an arrangement. First, we show that for some regions of parameter space, all stable arrangements are optimal (Price of Anarchy equal to 1). However, we show this is not true for all settings: there exist examples of stable arrangements with higher cost than optimal (Price of Anarchy greater than 1). Finally, we give the first constant-factor bound on the performance gap between stability and optimality, proving that the total error of the worst stable solution can be no higher than 9 times the total error of an optimal solution (Price of Anarchy bound of 9).
Generating Diverse Realistic Laughter for Interactive Art.

We propose an interactive art project to make those rendered invisible by the COVID-19 crisis and its concomitant solitude reappear through the welcome melody of laughter, and connections created and explored through advanced laughter synthesis approaches. However, the unconditional generation of the diversity of human emotional responses in high-quality auditory synthesis remains an open problem, with important implications for the application of these approaches in artistic settings. We developed LaughGANter, an approach to reproduce the diversity of human laughter using generative adversarial networks (GANs). When trained on a dataset of diverse laughter samples, LaughGANter generates diverse, high quality laughter samples, and learns a latent space suitable for emotional analysis and novel artistic applications such as latent mixing/interpolation and emotional transfer.
ALATI ZA ANALIZU SENTIMENTA

Sentiment analysis (or opinion mining) is anatural language processing technique used todetermine whether data is positive, negative, orneutral. Given the exceptional importance ofthis technique in making business decisions thatlargely depend on the evaluation of the opinionsof end users and customers, this paper provides anoverview of several commonly used tools that canbe used for these purposes. They use keywords tosearch certainInternet content such as social networks, forums,blogs and the like. Then, using built-in algorithmsand keywords, they analyze sentiment and assignpositive, negative, or neutral connotations.The paper deals with three topics of differentcategories. Their purpose is only to show thesimilarities and differences of the tools and thetypes of concepts that are analyzed. The purposeis not to provide a general analysis and judgmentof the tools but to show by concrete exampleshow they work and what can be achieved by themin market opinion research.
Numerical reasoning in machine reading comprehension tasks: are we there yet?

Numerical reasoning based machine reading comprehension is a task that involves reading comprehension along with using arithmetic operations such as addition, subtraction, sorting and counting. The DROP benchmark (Dua et al., 2019) is a recent dataset that has inspired the design of NLP models aimed at solving this task. The current standings of these models in the DROP leaderboard, over standard metrics, suggests that the models have achieved near-human performance. However, does this mean that these models have learned to reason? In this paper, we present a controlled study on some of the top-performing model architectures for the task of numerical reasoning. Our observations suggest that the standard metrics are incapable of measuring progress towards such tasks.
FacialFilmroll: High-resolution multi-shot video editing.

We present FacialFilmroll, a solution for spatially and temporally consistent editing of faces in one or multiple shots. We build upon unwrap mosaic [Rav-Acha et al. 2008] by specializing it to faces. We leverage recent techniques to fit a 3D face model on monocular videos to (i) improve the quality of the mosaic for edition and (ii) permit the automatic transfer of edits from one shot to other shots of the same actor. We explain how FacialFilmroll is integrated in post-production facility. Finally, we present video editing results using FacialFilmroll on high resolution videos.
Understanding High-Level Behavior with a Light-Traces Visualization Metaphor

We propose to exploit a visualization metaphor and a set of filters to assist developers grasping high-level behaviors of programs. Our interactive visualization is based on a metaphor of light-traces as part of an animation to explore execution scenarios. The animation is augmented with a set of structural and temporal filters to reduce the volume of information displayed. We showcase our visualization environment on two case studies featuring programs of a chess game and a UML diagram editor.
The ‘New Normal’ Concept Critique

There is increased use of the phrase “new normal” in the current times to refer to the state of the economies in Africa. The word normal in its formal meaning is linked to terms such as “usual”, “regular”, “standard”, “customary”, “expected” and “accustomed” among others. Generally, normal outcome is preferred than any other outcome. If something is not normal it is a defect, it has deviated and ways to have it corrected are usually sort as a remedy. A positive deviation may be acceptable, however, in a careful manner, checking possible future effects. The “new normal” concept now exist to praise what is not normal by making it acceptable as the new version of normality. In the name of ‘new normal’, it is increasingly being noticed of ‘inefficiency’ being labelled as new normal, hence no effective remedies sort to address the deviation. Many development indicators for African nations indicates low levels of performance, a lot being caused by instability and bad policies. Without adequate effort to address the problems, policy makers and authorities have been always busy preaching the gospel of ‘new normal’, to force the general populace to accept and settle for economic and social conditions brought about by inefficiency. African states are characterized by high levels of unemployment and poverty, civil wars and instability, unstable currencies, unfair income distribution, nepotism and corruption, infrastructure deficits, among other negative indicators. The study argues that the prevailing conditions in African states should never be linked to the term ‘new normal’ rather what is required is to make appropriate development policies to turn the economies in the right path that is sustainable. Noted drivers of inefficiency in African states include poor governance, weak institutions, poor policy formulation and implementation, low private sector engagement, low regional and international cooperation, political will among many others. The study recommends various measures that may be taken to correct the economic and social conditions of African states hence enabling attainment of positive development results.
Performance evaluation and influential factor analysis for stacking-based seismic location

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]Performance evaluation and influential factor analysis for stacking-based seismic locationAuthorsLeiLiiDFrantišekStaněkiDTingtingZhanSee all authors Lei LiiDCorresponding Author• Submitting AuthorCentral South UniversityiDhttps://orcid.org/0000-0002-3391-5608view email addressThe email was not providedcopy email addressFrantišek StaněkiDColorado School of MinesiDhttps://orcid.org/0000-0002-0037-9239view email addressThe email was not providedcopy email addressTingting ZhanInstitute of Acoustics, Chinese Academy of Sciences; University of Chinese Academy of Sciencesview email addressThe email was not providedcopy email address
Neuron-based Pruning of Deep Neural Networks with Better Generalization using Kronecker Factored Curvature Approximation

Existing methods of pruning deep neural networks focus on removing unnecessary parameters of the trained net-work and fine tuning the model afterwards to find a good solution that recovers the initial performance of the trained model. Unlike other works, our method pays special attention to the quality of the solution in the compressed model and inference computation time by pruning neurons. The proposed algorithm directs the parameters of the compressed model toward a flatter solution by exploring the spectral radius of Hessian which results in better generalization on unseen data. Moreover, the method does not work with a pre-trained network and performs training and pruning simultaneously. Our result shows that it improves the state-of-the-art results on neuron compression. The method is able to achieve very small networks with small accuracy degradation across different neural network models.
Stochastic Bias-Reduced Gradient Methods

We develop a new primitive for stochastic optimization: a low-bias, low-cost estimator of the minimizer $x_\star$ of any Lipschitz strongly-convex function. In particular, we use a multilevel Monte-Carlo approach due to Blanchet and Glynn to turn any optimal stochastic gradient method into an estimator of $x_\star$ with bias $\delta$, variance $O(\log(1/\delta))$, and an expected sampling cost of $O(\log(1/\delta))$ stochastic gradient evaluations. As an immediate consequence, we obtain cheap and nearly unbiased gradient estimators for the Moreau-Yoshida envelope of any Lipschitz convex function, allowing us to perform dimension-free randomized smoothing. We demonstrate the potential of our estimator through four applications. First, we develop a method for minimizing the maximum of $N$ functions, improving on recent results and matching a lower bound up logarithmic factors. Second and third, we recover state-of-the-art rates for projection-efficient and gradient-efficient optimization using simple algorithms with a transparent analysis. Finally, we show that an improved version of our estimator would yield a nearly linear-time, optimal-utility, differentially-private non-smooth stochastic optimization method.
Autogestión del conocimiento en la Licenciatura en Educación, especialidad Español-Literatura: estudio histórico

The objective of the article is to carry out an analysis of the planning of the didactic process of self-management of knowledge in the study plans through which the Bachelor of Education career, a Spanish-Literature specialty, has passed since its emergence in Cuba as part of the training pedagogical professional superior.For this, theoretical methods such as historical-logical and induction deduction and empirical methods such as bibliographic analysis were used, which allowed determining that the didactic process of self-management of knowledge has not been sufficiently promoted in the years to come. the different study plans and programs conceived for this career, which has made it impossible for students to use different study techniques to assimilate the information contained in the bibliography, carry out varied research work with a professional approach and apply linguistic knowledge independently and creatively and lit erarios in their pre-professional work. It is concluded that this lack has prevented the graduates from demonstrating in their modes of action the professional skills and distinctive qualities of cognitive independence typical of a good teacher of the Spanish-Literature subject.
On the Sample Complexity of Privately Learning Axis-Aligned Rectangles

We revisit the fundamental problem of learning Axis-Aligned-Rectangles over a finite grid $X^d\subseteq{\mathbb{R}}^d$ with differential privacy. Existing results show that the sample complexity of this problem is at most $\min\left\{ d{\cdot}\log|X| \;,\; d^{1.5}{\cdot}\left(\log^*|X| \right)^{1.5}\right\}$. That is, existing constructions either require sample complexity that grows linearly with $\log|X|$, or else it grows super linearly with the dimension $d$. We present a novel algorithm that reduces the sample complexity to only $\tilde{O}\left\{d{\cdot}\left(\log^*|X|\right)^{1.5}\right\}$, attaining a dimensionality optimal dependency without requiring the sample complexity to grow with $\log|X|$.The technique used in order to attain this improvement involves the deletion of exposed data-points on the go, in a fashion designed to avoid the cost of the adaptive composition theorems. The core of this technique may be of individual interest, introducing a new method for constructing statistically-efficient private algorithms.
CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) aims to align the labeled source distribution with the unlabeled target distribution to obtain domain invariant predictive models. However, the application of well-known UDA approaches does not generalize well in Semi-Supervised Domain Adaptation (SSDA) scenarios where few labeled samples from the target domain are available. In this paper, we propose a simple Contrastive Learning framework for semi-supervised Domain Adaptation (CLDA) that attempts to bridge the intra-domain gap between the labeled and unlabeled target distributions and inter-domain gap between source and unlabeled target distribution in SSDA. We suggest employing class-wise contrastive learning to reduce the inter-domain gap and instance-level contrastive alignment between the original (input image) and strongly augmented unlabeled target images to minimize the intra-domain discrepancy. We have shown empirically that both of these modules complement each other to achieve superior performance. Experiments on three well-known domain adaptation benchmark datasets namely DomainNet, Office-Home, and Office31 demonstrate the effectiveness of our approach. CLDA achieves state-of-the-art results on all the above datasets.
Self-Supervised Multi-Object Tracking with Cross-Input Consistency

In this paper, we propose a self-supervised learning procedure for training a robust multi-object tracking (MOT) model given only unlabeled video. While several self-supervisory learning signals have been proposed in prior work on single-object tracking, such as color propagation and cycle-consistency, these signals cannot be directly applied for training RNN models, which are needed to achieve accurate MOT: they yield degenerate models that, for instance, always match new detections to tracks with the closest initial detections. We propose a novel self-supervisory signal that we call cross-input consistency: we construct two distinct inputs for the same sequence of video, by hiding different information about the sequence in each input. We then compute tracks in that sequence by applying an RNN model independently on each input, and train the model to produce consistent tracks across the two inputs. We evaluate our unsupervised method on MOT17 and KITTI -- remarkably, we find that, despite training only on unlabeled video, our unsupervised approach outperforms four supervised methods published in the last 1--2 years, including Tracktor++, FAMNet, GSM, and mmMOT.
Provably Strict Generalisation Benefit for Invariance in Kernel Methods

It is a commonly held belief that enforcing invariance improves generalisation. Although this approach enjoys widespread popularity, it is only very recently that a rigorous theoretical demonstration of this benefit has been established. In this work we build on the function space perspective of Elesedy and Zaidi arXiv:2102.10333 to derive a strictly non-zero generalisation benefit of incorporating invariance in kernel ridge regression when the target is invariant to the action of a compact group. We study invariance enforced by feature averaging and find that generalisation is governed by a notion of effective dimension that arises from the interplay between the kernel and the group. In building towards this result, we find that the action of the group induces an orthogonal decomposition of both the reproducing kernel Hilbert space and its kernel, which may be of interest in its own right.
Digital Competence of Teachers and Students in Yakutia: Problems and Prospects of E-Learning During he Pandemic

Under self-isolation and quarantine measures during the COVID-19 pandemic in 2020, distance learning is urgent at every educational level. These events affected the digital competence of teachers and students of pedagogical specialties who teach students and schoolchildren. The article analyzes the digital competencies required for a modern teacher to successfully implement students' educational needs when using distance educational technologies and e-learning. The research objective is to increase the level of digital competence by assessing this level and identifying problems in the e-learning usage. The study was carried out through a questionnaire developed by the NAFR analytical center Russian Teachers' Digital Literacy, allowing identification of the main competencies required for a modern teacher, such as work in distance learning environments, knowledge of the basics of cloud technologies, and the ability to organize online and offline interaction, using diverse services. According to the calculation of Kendall's concordance coefficient, a ranking of the plus and minus sides identified in the organization of remote interaction among teachers and students of the Republic of Sakha (Yakutia) during the pandemic was made. The study outcomes allowed increasing the efficiency of work with students and teachers and organizing refresher courses and support in the e-learning implementation.
Twice regularized MDPs and the equivalence between robustness and regularization

Robust Markov decision processes (MDPs) aim to handle changing or partially known system dynamics. To solve them, one typically resorts to robust optimization methods. However, this significantly increases computational complexity and limits scalability in both learning and planning. On the other hand, regularized MDPs show more stability in policy learning without impairing time complexity. Yet, they generally do not encompass uncertainty in the model dynamics. In this work, we aim to learn robust MDPs using regularization. We first show that regularized MDPs are a particular instance of robust MDPs with uncertain reward. We thus establish that policy iteration on reward-robust MDPs can have the same time complexity as on regularized MDPs. We further extend this relationship to MDPs with uncertain transitions: this leads to a regularization term with an additional dependence on the value function. We finally generalize regularized MDPs to twice regularized MDPs (R${}^2$ MDPs), i.e., MDPs with $\textit{both}$ value and policy regularization. The corresponding Bellman operators enable developing policy iteration schemes with convergence and robustness guarantees. It also reduces planning and learning in robust MDPs to regularized MDPs.
Apuntes para una universidad común

These notes comprise an organization of notions that we have considered important to distinguish from the questions that the meeting called «Public University and Virtuality. Disputed narratives» updated us.These are fundamentally the notions that we believe are relevant to continue reflecting about: The frameworks of university teaching and studies -assuming that school and university share some demands as a school form- and the university autonomy.The meeting called «Public University and Virtuality. Narratives in Dispute» implies a memory as a starting point. A feedback between questions about the way that work adopted on the situation of the health emergency by COVID-19, how we know and make questions to ourselves, the university autonomy, and the particularities of our territory, the National University of Entre Rios.