nils-herrmann/clean_scirepeval huggingface dataset:
An Agent-Based Model to Explain the Emergence of Stylised Facts in Log Returns

This paper outlines an agent-based model of a simple financial market in which a single asset is available for trade by three different types of traders. The model was first introduced in the PhD thesis of one of the authors, see reference . The simulated log returns are examined for the presence of the stylised facts of financial data. The features of leptokurtosis, volatility clustering and aggregational Gaussianity are especially highlighted and studied in detail. The following ingredients are found to be essential for the production of these stylised facts: the memory of noise traders who make random trade decisions; the inclusion of technical traders that trade in line with trends in the price and the inclusion of fundamental traders who know the "fundamental value" of the stock and trade accordingly. When these three basic types of traders are included log returns are produced with a leptokurtic distribution and volatility clustering as well as some further statistical features of empirical data. This enhances and broadens our understanding of the fundamental processes involved in the production of empirical data by the market.
Going back to the roots: how to create stroboscopic photos from digital videos

We show here how to create from digital films using the well-known software Tracker stroboscopic photos in order to analyze different types of movements. The advantage of this procedure is that it is possible to analyze the printed photo or on a computer screen in an intuitive way for the students. After presenting a historical perspective of the use of stroboscopic photos in secondary education we discuss several examples: the movement of a remote control car, an elastic planar collision, the movement of a projectile and also an experiment in electromagnetism, more specifically, the discharge of a capacitor measured using an analog multimeter.
Near actions.

A near permutation of a set is a bijection between two cofinite subsets, modulo coincidence on smaller cofinite subsets. Near permutations of a set form its near symmetric group. In this monograph, we define near actions as homomorphisms into this group, and perform a systematic study of this notion. Notably, we deal with the problem of realizability: how far is a near action from being induced by an action? Also, what invariants of group actions are actually invariants of the underlying near action? the most fundamental one is the space of ends. We notably provide a classification of near actions of finitely generated abelian groups. There are several examples of exotic near actions of the free abelian group of rank two, which we classify according to a positive integer, called winding number, and an integral vector, called additive holonomy. In contrast, we prove that there are no such exotic near actions in higher rank abelian groups. We also obtain results for other groups; for instance, every near action of SL(2,Z) is realizable; this is not true for arbitrary amalgams of two finite cyclic groups. We introduce and study almost and near automorphism groups, which have been considered for some particular graphs or labeled graphs, in the general setting of relational structures.
Physics experiments using simultaneously more than one smartphone sensors

In the last years, numerous Physics experiments using smartphone sensors have been reported in the literature. In this presentation we focus on a less-explored feature of the smartphones: the possibility of using (measure and register data) simultaneously with more than one sensor. To illustrate, in the field of mechanics simultaneous use of the accelerometer and gyroscope (angular velocity sensor) or in optics experiments synchronous use of the ambient light and orientation sensors have been proposed. Indeed, this is a characteristic that simplifies experimental setups allowing to see through the physics concepts and, last but not least, reducing the costs.
Sensors based on micromechanical devices: mobile labs at the service of experimental science teaching

In this paper we discuss the use of sensors incorporated in mobile devices as possible mobile laboratories at the service of teaching experimental sciences. Mobile devices, smartphones, tablets, laptops, microbit cards, are a resource for making measurements of the physical world, since they have a set of built-in sensors that allow to measure position, linear speed, angular speed, acceleration, pressure, sound, color, magnetic field, proximity or luminosity, among others. Also, these devices have greatly improved the performance of their video cameras, allowing to easily shoot at high speed and high resolution. Given its portability it is possible to work in the laboratory itself or in other areas such as a gym, a park or your own home, transcending the traditional scope of the laboratory. In general, the measurements obtained can be analyzed in the device itself or uploaded to the cloud to be analyzed la
ReNeg and Backseat Driver: Learning from Demonstration with Continuous Human Feedback

In autonomous vehicle (AV) control, allowing mistakes can be quite dangerous and costly in the real world. For this reason we investigate methods of training an AV without allowing the agent to explore and instead having a human explorer collect the data. Supervised learning has been explored for AV control, but it encounters the issue of the covariate shift. That is, training data collected from an optimal demonstration consists only of the states induced by the optimal control policy, but at runtime, the trained agent may encounter a vastly different state distribution with little relevant training data. To mitigate this issue, we have our human explorer make sub-optimal decisions. In order to have our agent not replicate these sub-optimal decisions, supervised learning requires that we either erase these actions, or replace these action with the correct action. Erasing is wasteful and replacing is difficult, since it is not easy to know the correct action without driving. We propose an alternate framework that includes continuous scalar feedback for each action, marking which actions we should replicate, which we should avoid, and how sure we are. Our framework learns continuous control from sub-optimal demonstration and evaluative feedback collected before training. We find that a human demonstrator can explore sub-optimal states in a safe manner, while still getting enough gradation to benefit learning. The collection method for data and feedback we call "Backseat Driver." We call the more general learning framework ReNeg, since it learns a regression from states to actions given negative as well as positive examples. We empirically validate several models in the ReNeg framework, testing on lane-following with limited data. We find that the best solution is a generalization of mean-squared error and outperforms supervised learning on the positive examples alone.
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation

Computer graphics, 3D computer vision and robotics communities have produced multiple approaches to representing 3D geometry for rendering and reconstruction. These provide trade-offs across fidelity, efficiency and compression capabilities. In this work, we introduce DeepSDF, a learned continuous Signed Distance Function (SDF) representation of a class of shapes that enables high quality shape representation, interpolation and completion from partial and noisy 3D input data. DeepSDF, like its classical counterpart, represents a shape's surface by a continuous volumetric field: the magnitude of a point in the field represents the distance to the surface boundary and the sign indicates whether the region is inside (-) or outside (+) of the shape, hence our representation implicitly encodes a shape's boundary as the zero-level-set of the learned function while explicitly representing the classification of space as being part of the shapes interior or not. While classical SDF's both in analytical or discretized voxel form typically represent the surface of a single shape, DeepSDF can represent an entire class of shapes. Furthermore, we show state-of-the-art performance for learned 3D shape representation and completion while reducing the model size by an order of magnitude compared with previous work.
Uncertainty-Aware Driver Trajectory Prediction at Urban Intersections

Predicting the motion of a driver’s vehicle is crucial for advanced driving systems, enabling detection of potential risks towards shared control between the driver and automation systems. In this paper, we propose a variational neural network approach that predicts future driver trajectory distributions for the vehicle based on multiple sensors.Our predictor generates both a conditional variational distribution of future trajectories, as well as a confidence estimate for different time horizons. Our approach allows us to handle inherently uncertain situations, and reason about information gain from each input, as well as combine our model with additional predictors, creating a mixture of experts.We show how to augment the variational predictor with a physics-based predictor, and based on their confidence estimations, improve overall system performance. The resulting combined model is aware of the uncertainty associated with its predictions, which can help the vehicle autonomy to make decisions with more confidence. The model is validated on real-world urban driving data collected in multiple locations. This validation demonstrates that our approach improves the prediction error of a physics-based model by 25% while successfully identifying the uncertain cases with 82% accuracy.
Instantaneous Arbitrage and the CAPM

This paper studies the concept of instantaneous arbitrage in continuous time and its relation to the instantaneous CAPM. Absence of instantaneous arbitrage is equivalent to the existence of a trading strategy which satisfies the CAPM beta pricing relation in place of the market. Thus the difference between the arbitrage argument and the CAPM argument in Black and Scholes (1973) is this: the arbitrage argument assumes that there exists some portfolio satisfying the capm equation, whereas the CAPM argument assumes, in addition, that this portfolio is the market portfolio.
Memory Augmented Deep Generative Models for Forecasting the Next Shot Location in Tennis

This paper presents a novel framework for predicting shot location and type in tennis. Inspired by recent neuroscience discoveries, we incorporate neural memory modules to model the episodic and semantic memory components of a tennis player. We propose a Semi-Supervised Generative Adversarial Network architecture that couples these memory models with the automatic feature learning power of deep neural networks, and demonstrate methodologies for learning player level behavioral patterns with the proposed framework. We evaluate the effectiveness of the proposed model on tennis tracking data from the 2012 Australian Tennis Open and exhibit applications of the proposed method in discovering how players adapt their style depending on the match context.
Attention-Aware Multi-Stroke Style Transfer

Neural style transfer has drawn considerable attention from both academic and industrial field. Although visual effect and efficiency have been significantly improved, existing methods are unable to coordinate spatial distribution of visual attention between the content image and stylized image, or render diverse level of detail via different brush strokes. In this paper, we tackle these limitations by developing an attention-aware multi-stroke style transfer model. We first propose to assemble self-attention mechanism into a style-agnostic reconstruction autoencoder framework, from which the attention map of a content image can be derived. By performing multi-scale style swap on content features and style features, we produce multiple feature maps reflecting different stroke patterns. A flexible fusion strategy is further presented to incorporate the salient characteristics from the attention map, which allows integrating multiple stroke patterns into different spatial regions of the output image harmoniously. We demonstrate the effectiveness of our method, as well as generate comparable stylized images with multiple stroke patterns against the state-of-the-art methods.
DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization

For optimization of a sum of functions in a distributed computing environment, we present a novel communication efficient Newton-type algorithm that enjoys a variety of advantages over similar existing methods. Similar to Newton-MR, our algorithm, DINGO, is derived by optimization of the gradient's norm as a surrogate function. DINGO does not impose any specific form on the underlying functions, and its application range extends far beyond convexity. In addition, the distribution of the data across the computing environment can be arbitrary. Further, the underlying sub-problems of DINGO are simple linear least-squares, for which a plethora of efficient algorithms exist. Lastly, DINGO involves a few hyper-parameters that are easy to tune. Moreover, we theoretically show that DINGO is not sensitive to the choice of its hyper-parameters in that a strict reduction in the gradient norm is guaranteed, regardless of the selected hyper-parameters. We demonstrate empirical evidence of the effectiveness, stability and versatility of our method compared to other relevant algorithms.
Predicting Variable Types in Dynamically Typed Programming Languages

Dynamic Programming Languages are quite popular because they increase the programmer's productivity. However, the absence of types in the source code makes the program written in these languages difficult to understand and virtual machines that execute these programs cannot produced optimized code. To overcome this challenge, we develop a technique to predict types of all identifiers including variables, and function return types. 
We propose the first implementation of $2^{nd}$ order Inside Outside Recursive Neural Networks with two variants (i) Child-Sum Tree-LSTMs and (ii) N-ary RNNs that can handle large number of tree branching. We predict the types of all the identifiers given the Abstract Syntax Tree by performing just two passes over the tree, bottom-up and top-down, keeping both the content and context representation for all the nodes of the tree. This allows these representations to interact by combining different paths from the parent, siblings and children which is crucial for predicting types. Our best model achieves 44.33\% across 21 classes and top-3 accuracy of 71.5\% on our gathered Python data set from popular Python benchmarks.
The Winning Solution to the IEEE CIG 2017 Game Data Mining Competition

Machine learning competitions such as those organized by Kaggle or KDD represent a useful benchmark for data science research. In this work, we present our winning solution to the Game Data Mining competition hosted at the 2017 IEEE Conference on Computational Intelligence and Games (CIG 2017). The contest consisted of two tracks, and participants (more than 250, belonging to both industry and academia) were to predict which players would stop playing the game, as well as their remaining lifetime. The data were provided by a major worldwide video game company, NCSoft, and came from their successful massively multiplayer online game Blade and Soul. Here, we describe the long short-term memory approach and conditional inference survival ensemble model that made us win both tracks of the contest, as well as the validation procedure that we followed in order to prevent overfitting. In particular, choosing a survival method able to deal with censored data was crucial to accurately predict the moment in which each player would leave the game, as censoring is inherent in churn. The selected models proved to be robust against evolving conditions—since there was a change in the business model of the game (from subscription-based to free-to-play) between the two sample datasets provided—and efficient in terms of time cost. Thanks to these features and also to their ability to scale to large datasets, our models could be readily implemented in real business settings.
SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning

SkinnerDB is designed from the ground up for reliable join ordering. It maintains no data statistics and uses no cost or cardinality models. Instead, it uses reinforcement learning to learn optimal join orders on the fly, during the execution of the current query. To that purpose, we divide the execution of a query into many small time slices. Different join orders are tried in different time slices. We merge result tuples generated according to different join orders until a complete result is obtained. By measuring execution progress per time slice, we identify promising join orders as execution proceeds. Along with SkinnerDB, we introduce a new quality criterion for query execution strategies. We compare expected execution cost against execution cost for an optimal join order. SkinnerDB features multiple execution strategies that are optimized for that criterion. Some of them can be executed on top of existing database systems. For maximal performance, we introduce a customized execution engine, facilitating fast join order switching via specialized multi-way join algorithms and tuple representations. We experimentally compare SkinnerDB's performance against various baselines, including MonetDB, Postgres, and adaptive processing methods. We consider various benchmarks, including the join order benchmark and TPC-H variants with user-defined functions. Overall, the overheads of reliable join ordering are negligible compared to the performance impact of the occasional, catastrophic join order choice.
Tracy-Widom limit for the largest eigenvalue of high-dimensional covariance matrices in elliptical distributions

Let $X$ be an $M\times N$ random matrix consisting of independent $M$-variate elliptically distributed column vectors $\mathbf{x}_{1},\dots,\mathbf{x}_{N}$ with general population covariance matrix $\Sigma$. In the literature, the quantity $XX^{*}$ is referred to as the sample covariance matrix after scaling, where $X^{*}$ is the transpose of $X$. In this article, we prove that the limiting behavior of the scaled largest eigenvalue of $XX^{*}$ is universal for a wide class of elliptical distributions, namely, the scaled largest eigenvalue converges weakly to the same limit regardless of the distributions that $\mathbf{x}_{1},\dots,\mathbf{x}_{N}$ follow as $M,N\to\infty$ with $M/N\to\phi_0>0$ if the weak fourth moment of the radius of $\mathbf{x}_{1}$ exists . In particular, via comparing the Green function with that of the sample covariance matrix of multivariate normally distributed data, we conclude that the limiting distribution of the scaled largest eigenvalue is the celebrated Tracy-Widom law.
VeriSign: A Secure Contract Consensus Platform on the Blockchain with Amendment Functionality

While electronic signatures are widespread, there currently exists no viable signing solutions that can track amendments. We proposed VeriSign, a secure contract consensus platform where amendments to contracts can be tracked in a decentralized medium. We demonstrate a user-facing app where signatories can vote on original contracts and amendments, and incorporate a Blockchain where we store the transaction history of original contracts and amendments. This platform has possible applications in tracking the history of legislation, and amendments to legislation.
Deep Grid Net (DGN): A Deep Learning System for Real-Time Driving Context Understanding

Grid maps obtained from fused sensory information are nowadays among the most popular approaches for motion planning for autonomous driving cars. In this paper, we introduce Deep Grid Net (DGN), a deep learning (DL) system designed for understanding the context in which an autonomous car is driving. DGN incorporates a learned driving environment representation based on Occupancy Grids (OG) obtained from raw Lidar data and constructed on top of the Dempster-Shafer (DS) theory. The predicted driving context is further used for switching between different driving strategies implemented within EB robinos, Elektrobit's Autonomous Driving (AD) software platform. Based on genetic algorithms (GAs), we also propose a neuroevolutionary approach for learning the tuning hyperparameters of DGN. The performance of the proposed deep network has been evaluated against similar competing driving context estimation classifiers.
Teaching Electronics and Programming in Norwegian Schools Using the air: bit Sensor Kit

We describe lessons learned from using the air:bit project to introduce more than 150 students in the Norwegian upper secondary school to computer programming, engineering and environmental sciences. In the air:bit project, students build and code a portable air quality sensor kit, and use their air:bit to collect data to investigate patterns in air quality in their local environment. When the project ended students had collected more than 400,000 measurements with their air:bit kits, and could describe local patterns in air quality. Students participate in all parts of the project, from soldering components and programming the sensors, to analyzing the air quality measurements. Following the project we conducted a survey to investigate the satisfaction of participating in the project, as well as learning outcomes in the different parts of the project to improve future versions. The results show that both teachers and students were very satisfied with participating in the project. While the students showed positive learning outcomes in electronics and simple programming parts of the project, there is room for improvement regarding more advanced programming tasks. We believe that our work provides insight for other similar maker-inspired projects, and guidelines for educators to incorporate computer programming in traditional science experiments.
From polarized gravitational waves to analytically solvable electromagnetic beams

Using the correspondence between solutions of gravitational and gauge theories (the so-called classical double copy conjecture) some analytically solvable electromagnetic fields with vortices are constructed. The starting point is a certain class of plane gravitational waves exhibiting the conformal symmetry. The notion of the Niederer transformation, crucial for the solvability, is analysed in the case of the Lorentz force equation on the curved spacetimes. Furthermore, the models discussed recently in the context of the intense laser beams are constructed from their gravitational counterparts, with the special emphasis put on the focusing property, and new solvable examples are presented.
LHC analysis-specific datasets with Generative Adversarial Networks

Using generative adversarial networks (GANs), we investigate the possibility of creating large amounts of analysis-specific simulated LHC events at limited computing cost. This kind of generative model is analysis specific in the sense that it directly generates the high-level features used in the last stage of a given physics analyses, learning the N-dimensional distribution of relevant features in the context of a specific analysis selection. We apply this idea to the generation of muon four-momenta in $Z \to \mu\mu$ events at the LHC. We highlight how use-case specific issues emerge when the distributions of the considered quantities exhibit particular features. We show how substantial performance improvements and convergence speed-up can be obtained by including regression terms in the loss function of the generator. We develop an objective criterion to assess the geenrator performance in a quantitative way. With further development, a generalization of this approach could substantially reduce the needed amount of centrally produced fully simulated events in large particle physics experiments.