Reviewerly/exHarmony huggingface dataset:
Random Shuffling Beats SGD Only After Many Epochs on Ill-Conditioned Problems

Recently, there has been much interest in studying the convergence rates of without-replacement SGD, and proving that it is faster than with-replacement SGD in the worst case. However, these works ignore or do not provide tight bounds in terms of the problem's geometry, including its condition number. Perhaps surprisingly, we prove that when the condition number is taken into account, without-replacement SGD \emph{does not} significantly improve on with-replacement SGD in terms of worst-case bounds, unless the number of epochs (passes over the data) is larger than the condition number. Since many problems in machine learning and other areas are both ill-conditioned and involve large datasets, this indicates that without-replacement does not necessarily improve over with-replacement sampling for realistic iteration budgets. We show this by providing new lower and upper bounds which are tight (up to log factors), for quadratic problems with commuting quadratic terms, precisely quantifying the dependence on the problem parameters.
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features

Effectively modeling phenomena present in highly nonlinear dynamical systems whilst also accurately quantifying uncertainty is a challenging task, which often requires problem-specific techniques. We present a novel, domain-agnostic approach to tackling this problem, using compositions of physics-informed random features, derived from ordinary differential equations. The architecture of our model leverages recent advances in approximate inference for deep Gaussian processes, such as layer-wise weight-space approximations which allow us to incorporate random Fourier features, and stochastic variational inference for approximate Bayesian inference. We provide evidence that our model is capable of capturing highly nonlinear behaviour in real-world multivariate time series data. In addition, we find that our approach achieves comparable performance to a number of other probabilistic models on benchmark regression tasks.
NLP based grievance redressal system for Indian Railways

The current grievance redressal system has a dedicated 24X7 Twitter Cell, wherein the human experts take actions and respond to the tweets of customers addressed to Ministry of Railways. It is done quite promptly by the human experts. It is understood that the software plugin to process the tweets addressed towards Ministry of Railways can not match the human expertise. Still, efforts can be done to build a software plugin which can ease the human effort. This project aims at building a software plug-in to minimize the human effort involved in analysis of tweets addressed to Indian Railways and aid in existing complaints redressal system by identifying the complaints from the tweets. It is understood that it is not possible to match human promptness in terms of handling the tweets, still we can try to reduce the human efforts by working on the following objectives: 1.
Dynamic population-based meta-learning for multi-agent communication with natural language

In this work, our goal is to train agents that can coordinate with seen, unseen as well as human partners in a multi-agent communication environment involving natural language. Previous work using a single set of agents has shown great progress in generalizing to known partners, however it struggles when coordinating with unfamiliar agents. To mitigate that, recent work explored the use of population-based approaches, where multiple agents interact with each other with the goal of learning more generic protocols. These methods, while able to result in good coordination between unseen partners, still only achieve so in cases of simple languages, thus failing to adapt to human partners using natural language. We attribute this to the use of static populations and instead propose a dynamic population-based meta-learning approach that builds such a population in an iterative manner. We perform a holistic evaluation of our method on two different referential games, and show that our agents outperform all prior work when communicating with seen partners and humans. Furthermore, we analyze the natural language generation skills of our agents, where we find that our agents also outperform strong baselines. Finally, we test the robustness of our agents when communicating with out-of-population agents and carefully test the importance of each component of our method through ablation studies.
Brazil: The impact of the epicenter of the COVID-19 pandemic among medical doctors

Abstract Health-care workers (HCWs) on the frontlines of the 2019 Coronavirus Disease 2019 (COVID-19) have a high risk of acquiring and dying from the disease. Brazil is a country with high morbimortality and ranks highest in general mortality from COVID-19. A large proportion of medical doctors (MDs) is dying, impacting in the workforce. As part of the research by Fiocruz on working conditions among HCWs, we have conducted this study of death among MDs. We analyzed data from Federal Council of Medicine, the only national platform with details about deaths in MDs. A broad search of other sources, including the Ministry of Health, was done, but unfortunately, the data does not exist. A total of 622 MDs died from March 2020 to March 2021: 87.6% male and 75% over age 60. The Southeast had the highest percentage (34.7%), followed by the Northeast (27.2%), and rankings by state showed Rio de Janeiro (15.8%), followed by São Paulo, Pará, Paraná and Paraíba, totalizing 50.1%. The specialties with highest mortality were gynecology (12.2%), pediatrics (10.0%), internal medicine (9.3%), general surgery (7.8%) and cardiology (7.6%). May 2020 was the month of worst mortality (16.9%) followed by July and June. A slow decline was observed until October, when Brazil begun to experience the “second wave”. The vaccination of MDs began at the end of January 2021 and has not been sufficient time to evaluate the impact on morbimortality. The results of a study of 50 professional categories are under analysis and will be published soon as well as a study of “invisible” HCWs, who do not have specific training but are essential to supporting health care system, from primary care units to high-complexity hospitals. We hope that the results of these studies will improve links among managers, those who define public policies, and union leaders, achieving better workplace and living conditions, minimizing health and socioeconomic disparities. Key messages This study is about death by COVID-19 among medical doctors in Brazil, as part of a large one on professional categories, essentials to supporting health care system, conducted by FIOCRUZ. This stdy is part of a large one on professional categories, essentials to supporting health care system, conducted by FIOCRUZ.
On Derandomizing Yao’s Weak-to-Strong OWF Construction

The celebrated result of Yao (Yao, FOCS’82) shows that concatenating \(n\cdot p(n)\) copies of a weak one-way function (OWF) f, which can be inverted with probability \(1-\tfrac{1}{p(n)}\), suffices to construct a strong OWF g, showing that weak and strong OWFs are black-box equivalent. This direct product theorem for hardness amplification of OWFs has been very influential. However, the construction of Yao is not security-preserving, i.e., the input to g needs to be much larger than the input to f. Understanding whether a larger input is inherent is a long-standing open question.In this work, we explore necessary features of constructions which achieve short input length by proving the following: for any direct product construction of a strong OWF g from a weak OWF f, which can be inverted with probability \(1-\tfrac{1}{p(n)}\), the input size of g must grow as \(\varOmega (p(n))\). By direct product construction, we refer to any construction with the following structure: the construction g executes some arbitrary pre-processing function (independent of f) on its input, obtaining a vector \((y_1, \cdots , y_l)\), and outputs \(f(y_1), \cdots , f(y_l)\). Note that Yao’s construction is obtained by setting the pre-processing to be the identity. Our result generalizes to functions g with post-processing, as long as the post-processing function is not too lossy. Thus, in essence, any weak-to-strong OWF hardness amplification must either (1) be very far from security-preserving, (2) use adaptivity, or (3) must be very far from a direct-product structure (in the sense of having a very lossy post-processing of the outputs of f).On a technical level, we use ideas from lower bounds for secret-sharing to prove the impossibility of derandomizing Yao in a black-box way. Our results are in line with Goldreich, Impagliazzo, Levin, Venkatesan, and Zuckerman (FOCS 1990) who derandomize Yao’s construction for regular weak OWFs by evaluating the OWF along a random walk on an expander graph—the construction is adaptive, since it alternates steps on the expander graph with evaluations of the weak OWF.
Bayesian Framework for Gradient Leakage

Federated learning is an established method for training machine learning models without sharing training data. However, recent work has shown that it cannot guarantee data privacy as shared gradients can still leak sensitive information. To formalize the problem of gradient leakage, we propose a theoretical framework that enables, for the first time, analysis of the Bayes optimal adversary phrased as an optimization problem. We demonstrate that existing leakage attacks can be seen as approximations of this optimal adversary with different assumptions on the probability distributions of the input data and gradients. Our experiments confirm the effectiveness of the Bayes optimal adversary when it has knowledge of the underlying distribution. Further, our experimental evaluation shows that several existing heuristic defenses are not effective against stronger attacks, especially early in the training process. Thus, our findings indicate that the construction of more effective defenses and their evaluation remains an open problem.
Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning.

Reinforcement learning can train policies that effectively perform complex tasks. However for long-horizon tasks, the performance of these methods degrades with horizon, often necessitating reasoning over and composing lower-level skills. Hierarchical reinforcement learning aims to enable this by providing a bank of low-level skills as action abstractions. Hierarchies can further improve on this by abstracting the space states as well. We posit that a suitable state abstraction should depend on the capabilities of the available lower-level policies. We propose Value Function Spaces: a simple approach that produces such a representation by using the value functions corresponding to each lower-level skill. These value functions capture the affordances of the scene, thus forming a representation that compactly abstracts task relevant information and robustly ignores distractors. Empirical evaluations for maze-solving and robotic manipulation tasks demonstrate that our approach improves long-horizon performance and enables better zero-shot generalization than alternative model-free and model-based methods.
Probabilistic Attention for Interactive Segmentation

We provide a probabilistic interpretation of attention and show that the standard dot-product attention in transformers is a special case of Maximum A Posteriori (MAP) inference. The proposed approach suggests the use of Expectation Maximization algorithms for online adaptation of key and value model parameters. This approach is useful for cases in which external agents, e.g., annotators, provide inference-time information about the correct values of some tokens, e.g, the semantic category of some pixels, and we need for this new information to propagate to other tokens in a principled manner. We illustrate the approach on an interactive semantic segmentation task in which annotators and models collaborate online to improve annotation efficiency. Using standard benchmarks, we observe that key adaptation boosts model performance ($\sim10\%$ mIoU) in the low feedback regime and value propagation improves model responsiveness in the high feedback regime. A PyTorch layer implementation of our probabilistic attention model will be made publicly available here: this https URL.
The Macroeconomic Impact of Recent Political Conflicts in Africa: Generalized Synthetic Counterfactual Evidence

This paper measures the macroeconomic impact of recent political crisis, protest and uprisings in Africa with the generalized synthetic control method and evaluates the role played by natural resource dependence in the modulation of the impact. We find that political crisis, protests and uprisings have a significant and negative impact on economic growth while the impact is positive on investment and price level. For economic growth, the deviation of the actual series from the counterfactual is negative, instantaneous, persistent and highly significant; indicating non-negligible costs of the shock. Indeed, dependence on natural resources amplifies the negative effect of political crisis, protests and uprisings on GDP. Finally, the more the treated country depends on natural resources, the more it becomes resilient from the investment losses caused by political crisis.
Powerpropagation: A sparsity inducing weight reparameterisation

The training of sparse neural networks is becoming an increasingly important tool for reducing the computational footprint of models at training and evaluation, as well enabling the effective scaling up of models. Whereas much work over the years has been dedicated to specialised pruning techniques, little attention has been paid to the inherent effect of gradient based training on model sparsity. In this work, we introduce Powerpropagation, a new weight-parameterisation for neural networks that leads to inherently sparse models. Exploiting the behaviour of gradient descent, our method gives rise to weight updates exhibiting a rich get richer dynamic, leaving low-magnitude parameters largely unaffected by learning. Models trained in this manner exhibit similar performance, but have a distribution with markedly higher density at zero, allowing more parameters to be pruned safely. Powerpropagation is general, intuitive, cheap and straight-forward to implement and can readily be combined with various other techniques. To highlight its versatility, we explore it in two very different settings: Firstly, following a recent line of work, we investigate its effect on sparse training for resource-constrained settings. Here, we combine Powerpropagation with a traditional weight-pruning technique as well as recent state-of-the-art sparse-to-sparse algorithms, showing superior performance on the ImageNet benchmark. Secondly, we advocate the use of sparsity in overcoming catastrophic forgetting, where compressed representations allow accommodating a large number of tasks at fixed model capacity. In all cases our reparameterisation considerably increases the efficacy of the off-the-shelf methods.
A Probabilistic State Space Model for Joint Inference from Differential Equations and Data

Mechanistic models with differential equations are a key component of scientific applications of machine learning. Inference in such models is usually computationally demanding, because it involves repeatedly solving the differential equation. The main problem here is that the numerical solver is hard to combine with standard inference techniques. Recent work in probabilistic numerics has developed a new class of solvers for ordinary differential equations (ODEs) that phrase the solution process directly in terms of Bayesian filtering. We here show that this allows such methods to be combined very directly, with conceptual and numerical ease, with latent force models in the ODE itself. It then becomes possible to perform approximate Bayesian inference on the latent force as well as the ODE solution in a single, linear complexity pass of an extended Kalman filter / smoother - that is, at the cost of computing a single ODE solution. We demonstrate the expressiveness and performance of the algorithm by training, among others, a non-parametric SIRD model on data from the COVID-19 outbreak.
A Aceleração da Tecnologia e Implicações Jurídicas

portuguesO direito e fruto da vida social. Sendo esta norteada pelo uso de meios tecnologicos em quase todas as areas do dia-a-dia, necessita entao o direito andar de maos dadas com a tecnologia. A metodologia seguida resulta da analise dos impactos da tecnologia sobre o direito, com base no levantamento bibliografico de livros, artigos cientificos e sitios de noticias, alcancando tambem o campo da ciencia politica. O texto esta dividido em quatro partes sendo a primeira, uma abordagem do direito com a tecnologia; na segunda as sentencas e os emojis; a terceira parte, sobre o Twitter e #BlackLivesMatter e por ultimo sobre a Covid-19, todos com ligacao e implicacoes com o direito. No entanto, tem-se como objetivo fazer uma analise dos meios tecnologicos na doutrina do direito, o que tem valor matricial para facilitar a compreensao das principais questoes sobre o tema proposto. Pretende-se conhecer a atual evolucao do direito relacionado com a evolucao da tecnologia, associados a formacao legislativa atual, particularmente os objetivos que a lei se propoe tutelar e a agilidade necessaria. Procurar-se-a tambem analisar os meios que foram utilizados para o combate a Pandemia Covid-19 relativo ao teletrabalho e as implicacoes juridicas. Segundo a amplitude e dignidade do assunto, abordar-se-ao as vantagens e as vulnerabilidades do ciberespaco para o teletrabalho face a Covid.19, representando tal um desafio juridico EnglishThe law is the fruit of social life. Since this is guided by the use of technological means in almost all areas of daily life, then the law needs to go hand in hand with technology. The method followed results from the analysis of the impacts of technology on law, based on the bibliographic survey of books, scientific articles and news sites, also reaching the field of political science. The text is divided into four parts, the first being an approach to law with technology; in the second, sentences and emojis; the third part, on Twitter and #BlackLivesMatter and finally on Covid-19, all with links and implications with the law. However, the goal is to make an analysis of technological means in the doctrine of law, which has a matrix value to ease the understanding of the main issues on the proposed theme. It is intended to know the current evolution of law related to the evolution of technology, associated with the current legislative formation, particularly the aims that the law proposes to protect and the necessary agility. It will also seek to analyze the means that were used to combat Pandemic Covid-19 related to telework and the legal implications. Depending on the breadth and dignity of the subject, the advantages and vulnerabilities of cyberspace for teleworking against Covid.19 will be addressed, representing such a legal challenge.
Promoting English vocabulary of young learners through songs, games, and stories

Teaching English to young learners has become part of the language education curriculum in Indonesia, and several other developing countries, such as the Philippines, Vietnam, Thailand, and Cambodia. Teaching English to young learners is intended to provide a foundation for mastery of the basic language for students so that they can be even more skilled when they study in high school. For that, we need a learning strategy that can help them to be motivated and happy to learn English. Therefore, the English teacher must be creative in using tools that can create a pleasant learning atmosphere. The use of games, songs, and stories is expected to help elementary school students to improve their mastery of basic vocabulary as a provision for communicating in English according to their education level. This paper describes several ways to introduce English language teaching through songs, games, and stories through some fun learning strategies. This paper is one kind of best practice. Thus, teachers are expected to be able to take advantage of these learning media, especially for students who live in remote areas that cannot be reached by digital technology.
Learning a Single Neuron with Bias Using Gradient Descent

We theoretically study the fundamental problem of learning a single neuron with a bias term ($\mathbf{x} \mapsto \sigma( + b)$) in the realizable setting with the ReLU activation, using gradient descent. Perhaps surprisingly, we show that this is a significantly different and more challenging problem than the bias-less case (which was the focus of previous works on single neurons), both in terms of the optimization geometry as well as the ability of gradient methods to succeed in some scenarios. We provide a detailed study of this problem, characterizing the critical points of the objective, demonstrating failure cases, and providing positive convergence guarantees under different sets of assumptions. To prove our results, we develop some tools which may be of independent interest, and improve previous results on learning single neurons.
Профессиональная поддержка педагогов в системе непрерывного образования как продукт гуманизации и здоровьеформирующего мышления

The article identifies the possibilities of scientific comprehension of ideas and prospects to use technologies for the teacher professional support in the education system. The orientation of ideas and theorized models, technologies and methods of the teacher professional support in the lifelong education system allow approaching the task to create a new resource and concept of ensuring the quality of forming values and meanings of humanization and health-forming thinking. The authors highlight the priorities of professional support for teachers in the lifelong education system. They consider the ideas of choosing models and technologies to support teachers professionally in the lifelong education system; define and specify the functions of professional support for teachers in the of lifelong education system; present the principles of teacher professional support in this system as a linear model. The paper reveals pedagogical conditions to improve the quality of applying technologies for the teacher professional support in the lifelong education system; determines the prospects tor model and  theorize, manage and optimize decisions and ideas of the teacher professional support in the lifelong education system.
A Minimalist Approach to Offline Reinforcement Learning

Offline reinforcement learning (RL) defines the task of learning from a fixed batch of data. Due to errors in value estimation from out-of-distribution actions, most offline RL algorithms take the approach of constraining or regularizing the policy with the actions contained in the dataset. Built on pre-existing RL algorithms, modifications to make an RL algorithm work offline comes at the cost of additional complexity. Offline RL algorithms introduce new hyperparameters and often leverage secondary components such as generative models, while adjusting the underlying RL algorithm. In this paper we aim to make a deep RL algorithm work while making minimal changes. We find that we can match the performance of state-of-the-art offline RL algorithms by simply adding a behavior cloning term to the policy update of an online RL algorithm and normalizing the data. The resulting algorithm is a simple to implement and tune baseline, while more than halving the overall run time by removing the additional computational overheads of previous methods.