nils-herrmann/clean_scirepeval huggingface dataset:
Learning agile and dynamic motor skills for legged robots

A method for learning agile control policies uses simulated data to enable precise, efficient movements in a complex physical robot. Legged robots pose one of the greatest challenges in robotics. Dynamic and agile maneuvers of animals cannot be imitated by existing methods that are crafted by humans. A compelling alternative is reinforcement learning, which requires minimal craftsmanship and promotes the natural evolution of a control policy. However, so far, reinforcement learning research for legged robots is mainly limited to simulation, and only few and comparably simple examples have been deployed on real systems. The primary reason is that training with real robots, particularly with dynamically balancing systems, is complicated and expensive. In the present work, we introduce a method for training a neural network policy in simulation and transferring it to a state-of-the-art legged system, thereby leveraging fast, automated, and cost-effective data generation schemes. The approach is applied to the ANYmal robot, a sophisticated medium-dog–sized quadrupedal system. Using policies trained in simulation, the quadrupedal machine achieves locomotion skills that go beyond what had been achieved with prior methods: ANYmal is capable of precisely and energy-efficiently following high-level body velocity commands, running faster than before, and recovering from falling even in complex configurations.
The Assistive Multi-Armed Bandit

Learning preferences implicit in the choices humans make is a well studied problem in both economics and computer science. However, most work makes the assumption that humans are acting (noisily) optimally with respect to their preferences. Such approaches can fail when people are themselves learning about what they want. In this work, we introduce the assistive multi-armed bandit, where a robot assists a human playing a bandit task to maximize cumulative reward. In this problem, the human does not know the reward function but can learn it through the rewards received from arm pulls; the robot only observes which arms the human pulls but not the reward associated with each pull. We offer sufficient and necessary conditions for successfully assisting the human in this framework. Surprisingly, better human performance in isolation does not necessarily lead to better performance when assisted by the robot: a human policy can do better by effectively communicating its observed rewards to the robot. We conduct proof-of-concept experiments that support these results. We see this work as contributing towards a theory behind algorithms for human-robot interaction.
Is Pretraining Necessary for hyperspectral image classification?

We address two questions for training a convolutional neural network (CNN) for hyperspectral image classification: i) is it possible to build a pre-trained network? and ii) is the pretraining effective in furthering the performance? To answer the first question, we have devised an approach that pre-trains a network on multiple source datasets that differ in their hyperspectral characteristics and fine-tunes on a target dataset. This approach effectively resolves the architectural issue that arises when transferring meaningful information between the source and the target networks. To answer the second question, we carried out several ablation experiments. Based on the experimental results, a network trained from scratch performs as good as a network fine-tuned from a pre-trained network. However, we observed that pre-training the network has its own advantage in achieving better performances when deeper networks are required.
New nonasymptotic convergence rates of stochastic proximal pointalgorithm for convex optimization problems with many constraints

\noindent Significant parts of the recent stochastic optimization literature focused on analyzing the theoretical and practical behaviour of stochastic first order schemes under various convexity properties. Due to its simplicity, the traditional method of choice for most supervised machine learning problems is the stochastic gradient descent (SGD) method, which is known to have a relatively slow convergence. Many iteration improvements and accelerations have been added to the pure SGD in order to boost its convergence under different (strong) convexity conditions when constraints are present. However, full projections on complicated feasible set, smoothness or strong convexity assumptions are an essential requirement for these improved stochastic first-order schemes. In this paper novel convergence results are presented for the stochastic proximal point (SPP) algorithm for (non-)strongly convex optimization with many constraints. We show that a prox-quadratic growth assumption is sufficient to guarantee for SPP $\mathcal{O}\left(\frac{1}{k}\right)$ convergence rate, in terms of the distance to the optimal set, using only projections onto a simple component set. Furthermore, linear convergence is obtained for interpolation setting, when the optimal set of the expected cost is included into the optimal sets of each functional component.
Fairness risk measures

Ensuring that classifiers are non-discriminatory or fair with respect to a sensitive feature (e.g., race or gender) is a topical problem. Progress in this task requires fixing a definition of fairness, and there have been several proposals in this regard over the past few years. Several of these, however, assume either binary sensitive features (thus precluding categorical or real-valued sensitive groups), or result in non-convex objectives (thus adversely affecting the optimisation landscape). In this paper, we propose a new definition of fairness that generalises some existing proposals, while allowing for generic sensitive features and resulting in a convex objective. The key idea is to enforce that the expected losses (or risks) across each subgroup induced by the sensitive feature are commensurate. We show how this relates to the rich literature on risk measures from mathematical finance. As a special case, this leads to a new convex fairness-aware objective based on minimising the conditional value at risk (CVaR).
Python script used as a simulator for the teaching of the electric field in electromagnetism course

We present in this article a Python script, based on a methodology to obtain the electric field produced by n electric charges. This tool was implemented in courses of electromagnetism and its laboratory in three institutions of higher education. The aim of this work was to incorporate information and communication technologies (ICTs) at the physics subjects, in accordance with the programs promoted by the Colombian Ministry of Education. We wanted to connect the students with sensory experiences of the physical phenomena that allow them to improve their experience of learning of subjects traditionally studied through the board. Finally, in this work, an interactive computational code was obtained, in which the electric field of the discrete and continuous charge distributions can be calculated, for the classical problems that are shown in an electrical physics course.
The Local Perspective on the Hubble Tension: Local Structure Does Not Impact Measurement of the Hubble Constant

We use the largest sample to date of spectroscopic supernova (SN) Ia distances and redshifts to look for evidence in the Hubble diagram of large-scale outflows caused by local voids suggested to exist at z < 0.15. Our sample combines data from the Pantheon sample with the Foundation survey, and the most recent release of light curves from the Carnegie Supernova Project, to create a sample of 1295 SNe over a redshift range of 0.01 < z < 2.26. We make use of an inhomogeneous and isotropic Lemaitre–Tolman–Bondi metric to model a void in the SN Ia distance–redshift relation. We conclude that the SN luminosity distance–redshift relation is inconsistent at the 4–5σ confidence level with large local underdensities ( , where the density contrast δ = Δρ/ρ) proposed in some galaxy count studies, and find no evidence of a change in the Hubble constant corresponding to a void with a sharp edge in the redshift range 0.023 < z < 0.15. With an empirical precision of , we conclude that the distance ladder measurement is not affected by local density contrasts, in agreement with a cosmic variance of , predicted from simulations of large-scale structure. Given that uncertainty in the distance ladder value is , this does not affect the Hubble tension. We derive a 5σ constraint on local density contrasts on scales larger than of . The presence of local structure does not appear to impede the possibility of measuring the Hubble constant to 1% precision.
Ambitious Data Science Can Be Painless

Modern data science research can involve massive computational experimentation; an ambitious PhD in computational fields may do experiments consuming several million CPU hours. Traditional computing practices, in which researchers use laptops or shared campus-resident resources, are inadequate for experiments at the massive scale and varied scope that we now see in data science. On the other hand, modern cloud computing promises seemingly unlimited computational resources that can be custom configured, and seems to offer a powerful new venue for ambitious data-driven science. Exploiting the cloud fully, the amount of work that could be completed in a fixed amount of time can expand by several orders of magnitude. 
As potentially powerful as cloud-based experimentation may be in the abstract, it has not yet become a standard option for researchers in many academic disciplines. The prospect of actually conducting massive computational experiments in today's cloud systems confronts the potential user with daunting challenges. Leading considerations include: (i) the seeming complexity of today's cloud computing interface, (ii) the difficulty of executing an overwhelmingly large number of jobs, and (iii) the difficulty of monitoring and combining a massive collection of separate results. Starting a massive experiment `bare-handed' seems therefore highly problematic and prone to rapid `researcher burn out'. 
New software stacks are emerging that render massive cloud experiments relatively painless. Such stacks simplify experimentation by systematizing experiment definition, automating distribution and management of tasks, and allowing easy harvesting of results and documentation. In this article, we discuss several painless computing stacks that abstract away the difficulties of massive experimentation, thereby allowing a proliferation of ambitious experiments for scientific discovery.
Emergent Linguistic Phenomena in Multi-Agent Communication Games

We describe a multi-agent communication framework for examining high-level linguistic phenomena at the community-level. We demonstrate that complex linguistic behavior observed in natural language can be reproduced in this simple setting: i) the outcome of contact between communities is a function of inter- and intra-group connectivity; ii) linguistic contact either converges to the majority protocol, or in balanced cases leads to novel creole languages of lower complexity; and iii) a linguistic continuum emerges where neighboring languages are more mutually intelligible than farther removed languages. We conclude that at least some of the intricate properties of language evolution need not depend on complex evolved linguistic capabilities, but can emerge from simple social exchanges between perceptually-enabled agents playing communication games.
Surrogate Supervision for Medical Image Analysis: Effective Deep Learning From Limited Quantities of Labeled Data

We investigate the effectiveness of a simple solution to the common problem of deep learning in medical image analysis with limited quantities of labeled training data. The underlying idea is to assign artificial labels to abundantly available unlabeled medical images and, through a process known as surrogate supervision, pre-train a deep neural network model for the target medical image analysis task lacking sufficient labeled training data. In particular, we employ 3 surrogate supervision schemes, namely rotation, reconstruction, and colorization, in 4 different medical imaging applications representing classification and segmentation for both 2D and 3D medical images. 3 key findings emerge from our research: 1) pre-training with surrogate supervision is effective for small training sets; 2) deep models trained from initial weights pre-trained through surrogate supervision outperform the same models when trained from scratch, suggesting that pre-training with surrogate supervision should be considered prior to training any deep 3D models; 3) pre-training models in the medical domain with surrogate supervision is more effective than transfer learning from an unrelated domain (e.g., natural images), indicating the practical value of abundant unlabeled medical image data.
Almost Boltzmann Exploration

Boltzmann exploration is widely used in reinforcement learning to provide a trade-off between exploration and exploitation. Recently, in (Cesa-Bianchi et al., 2017) it has been shown that pure Boltzmann exploration does not perform well from a regret perspective, even in the simplest setting of stochastic multi-armed bandit (MAB) problems. In this paper, we show that a simple modification to Boltzmann exploration, motivated by a variation of the standard doubling trick, achieves $O(K\log^{1+\alpha} T)$ regret for a stochastic MAB problem with $K$ arms, where $\alpha>0$ is a parameter of the algorithm. This improves on the result in (Cesa-Bianchi et al., 2017), where an algorithm inspired by the Gumbel-softmax trick achieves $O(K\log^2 T)$ regret. We also show that our algorithm achieves $O(\beta(G) \log^{1+\alpha} T)$ regret in stochastic MAB problems with graph-structured feedback, without knowledge of the graph structure, where $\beta(G)$ is the independence number of the feedback graph. Additionally, we present extensive experimental results on real datasets and applications for multi-armed bandits with both traditional bandit feedback and graph-structured feedback. In all cases, our algorithm performs as well or better than the state-of-the-art.
When Can Neural Networks Learn Connected Decision Regions?

Previous work has questioned the conditions under which the decision regions of a neural network are connected and further showed the implications of the corresponding theory to the problem of adversarial manipulation of classifiers. It has been proven that for a class of activation functions including leaky ReLU, neural networks having a pyramidal structure, that is no layer has more hidden units than the input dimension, produce necessarily connected decision regions. In this paper, we advance this important result by further developing the sufficient and necessary conditions under which the decision regions of a neural network are connected. We then apply our framework to overcome the limits of existing work and further study the capacity to learn connected regions of neural networks for a much wider class of activation functions including those widely used, namely ReLU, sigmoid, tanh, softlus, and exponential linear function.
Local Distance Restricted Bribery in Voting

Studying complexity of various bribery problems has been one of the main research focus in computational social choice. In all the models of bribery studied so far, the briber has to pay every voter some amount of money depending on what the briber wants the voter to report and the briber has some budget at her disposal. Although these models successfully capture many real world applications, in many other scenarios, the voters may be unwilling to deviate too much from their true preferences. In this paper, we study the computational complexity of the problem of finding a preference profile which is as close to the true preference profile as possible and still achieves the briber's goal subject to budget constraints. We call this problem Local Distance Restricted $Bribery. We consider three important measures of distances, namely, swap distance, footrule distance, and maximum displacement distance, and resolve the complexity of the local distance restricted problem for many common voting rules.
Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization

Dynamic portfolio optimization is the process of sequentially allocating wealth to a collection of assets in some consecutive trading periods, based on investors' return-risk profile. Automating this process with machine learning remains a challenging problem. Here, we design a deep reinforcement learning (RL) architecture with an autonomous trading agent such that, investment decisions and actions are made periodically, based on a global objective, with autonomy. In particular, without relying on a purely model-free RL agent, we train our trading agent using a novel RL architecture consisting of an infused prediction module (IPM), a generative adversarial data augmentation module (DAM) and a behavior cloning module (BCM). Our model-based approach works with both on-policy or off-policy RL algorithms. We further design the back-testing and execution engine which interact with the RL agent in real time. Using historical {\em real} financial market data, we simulate trading with practical constraints, and demonstrate that our proposed model is robust, profitable and risk-sensitive, as compared to baseline trading strategies and model-free RL agents from prior work.
Towards a Deeper Understanding of Adversarial Losses

Recent work has proposed various adversarial losses for training generative adversarial networks. Yet, it remains unclear what certain types of functions are valid adversarial loss functions, and how these loss functions perform against one another. In this paper, we aim to gain a deeper understanding of adversarial losses by decoupling the effects of their component functions and regularization terms. We first derive some necessary and sufficient conditions of the component functions such that the adversarial loss is a divergence-like measure between the data and the model distributions. In order to systematically compare different adversarial losses, we then propose DANTest, a new, simple framework based on discriminative adversarial networks. With this framework, we evaluate an extensive set of adversarial losses by combining different component functions and regularization approaches. This study leads to some new insights into the adversarial losses. For reproducibility, all source code is available at this https URL .
Lattice investment projects support process model with corruption

Lattice investment projects support process model with corruption is formulated and analyzed. The model is based on the Ising lattice model of ferromagnetic but takes deal with the social phenomenon. Set of corruption agents is considered. It is supposed that agents are placed in sites of the lattice. Agents take decision about participation in corruption activity at discrete moments of time. The decision may lead to profit or to loss. It depends on prehistory of the system. Profit and its dynamics are defined by stochastic Markov process. Stochastic nature of the process models influence of external and individual factors on agents profits. The model is formulated algorithmically and is studied by means of computer simulation. Numerical results are given which demonstrate different asymptotic state of a corruption network for various conditions of simulation.
Robust estimation of tree structured Gaussian Graphical Model

Consider jointly Gaussian random variables whose conditional independence structure is specified by a graphical model. If we observe realizations of the variables, we can compute the covariance matrix, and it is well known that the support of the inverse covariance matrix corresponds to the edges of the graphical model. Instead, suppose we only have noisy observations. If the noise at each node is independent, we can compute the sum of the covariance matrix and an unknown diagonal. The inverse of this sum is (in general) dense. We ask: can the original independence structure be recovered? We address this question for tree structured graphical models. We prove that this problem is unidentifiable, but show that this unidentifiability is limited to a small class of candidate trees. We further present additional constraints under which the problem is identifiable. Finally, we provide an O(n^3) algorithm to find this equivalence class of trees.
Psychological model of the project investor and manager behavior in risk

All people have to make risky decisions in everyday life. And we do not know how true they are. But is it possible to mathematically assess the correctness of our choice? This article discusses the model of decision making under risk on the example of project management. This is a game with two players, one of which is Investor, and the other is the Project Manager. Each player makes a risky decision for himself, based on his past experience. With the help of a mathematical model, the players form a level of confidence, depending on who the player accepts the strategy or does not accept. The project manager assesses the costs and compares them with the level of confidence. An investor evaluates past results. Also visit the case where the strategy of the player accepts the part. 1 ∗ o.malafeev@spbu.ru 2 anastasya.malova97@yandex.ru 3 aleksandra_tsybaeva@mail.ru 2
Results from a hidden photon dark matter search using a multi-cathode counter

Here we present measurements of the rates of emission of single electrons from the cathode of a proportional counter filled with a mixture of Ar + CH4 (10%) at 1 bar. We interpret the results as a possible photoelectric effect associated with hidden photons (HPs). Our results set upper limits for HPs from cold dark matter (CDM). We also discuss future options for searches for HPs from CDM using a multi-cathode counter technique.
Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously

We develop the first general semi-bandit algorithm that simultaneously achieves $\mathcal{O}(\log T)$ regret for stochastic environments and $\mathcal{O}(\sqrt{T})$ regret for adversarial environments without knowledge of the regime or the number of rounds $T$. The leading problem-dependent constants of our bounds are not only optimal in some worst-case sense studied previously, but also optimal for two concrete instances of semi-bandit problems. Our algorithm and analysis extend the recent work of (Zimmert & Seldin, 2019) for the special case of multi-armed bandit, but importantly requires a novel hybrid regularizer designed specifically for semi-bandit. Experimental results on synthetic data show that our algorithm indeed performs well uniformly over different environments. We finally provide a preliminary extension of our results to the full bandit feedback.