nils-herrmann/clean_scirepeval huggingface dataset:
Invariance reduces Variance: Understanding Data Augmentation in Deep Learning and Beyond

Many complex deep learning models have found success by exploiting symmetries in data. Convolutional neural networks (CNNs), for example, are ubiquitous in image classification due to their use of translation symmetry, as image identity is roughly invariant to translations. In addition, many other forms of symmetry such as rotation, scale, and color shift are commonly used via data augmentation: the transformed images are added to the training set. However, a clear framework for understanding data augmentation is not available. One may even say that it is somewhat mysterious: how can we increase performance by simply adding transforms of our data to the model? Can that be information theoretically possible? In this paper, we develop a theoretical framework to start to shed light on some of these problems. We explain data augmentation as averaging over the orbits of the group that keeps the data distribution invariant, and show that it leads to variance reduction. We study finite-sample and asymptotic empirical risk minimization (using results from stochastic convex optimization, Rademacher complexity, and asymptotic statistical theory). We work out as examples the variance reduction in exponential families, linear regression, and certain two-layer neural networks under shift invariance (using discrete Fourier analysis). We also discuss how data augmentation could be used in problems with symmetry where other approaches are prevalent, such as in cryo-electron microscopy (cryo-EM).
Applying Constraint Logic Programming to SQL Semantic Analysis

This paper proposes the use of Constraint Logic Programming (CLP) to model SQL queries in a data-independent abstract layer by focusing on some semantic properties for signalling possible errors in such queries. First, we define a translation from SQL to Datalog, and from Datalog to CLP, so that solving this CLP program will give information about inconsistency, tautology, and possible simplifications. We use different constraint domains which are mapped to SQL types, and propose them to cooperate for improving accuracy. Our approach leverages a deductive system that includes SQL and Datalog, and we present an implementation in this system which is currently being tested in classroom, showing its advantages and differences with respect to other approaches, as well as some performance data.
Self-Supervised Domain Adaptation for Computer Vision Tasks

Recent progress of self-supervised visual representation learning has achieved remarkable success on many challenging computer vision benchmarks. However, whether these techniques can be used for domain adaptation has not been explored. In this work, we propose a generic method for self-supervised domain adaptation, using object recognition and semantic segmentation of urban scenes as use cases. Focusing on simple pretext/auxiliary tasks (e.g. image rotation prediction), we assess different learning strategies to improve domain adaptation effectiveness by self-supervision. Additionally, we propose two complementary strategies to further boost the domain adaptation accuracy on semantic segmentation within our method, consisting of prediction layer alignment and batch normalization calibration. The experimental results show adaptation levels comparable to most studied domain adaptation methods, thus, bringing self-supervision as a new alternative for reaching domain adaptation. The code is available at this link.11https://github.com/Jiaolong/self-supervised-da
An EMG-based Eating Behaviour Monitoring System with Haptic Feedback to Promote Mindful Eating

BACKGROUND
 Mindless eating, or the lack of awareness of the food we are consuming, has been linked to health problems attributed to unhealthy eating behaviour, including obesity. Traditional approaches used to moderate eating behaviour often rely on inaccurate self-logging, manual observations or bulky equipment. Overall, there is a need for an intelligent and lightweight system which can automatically monitor eating behaviour and provide feedback.
 
 
 OBJECTIVE
 In this paper, we investigate: i) the development of an automated system for detecting eating behaviour using wearable Electromyography (EMG) sensors, and ii) the application of such a system in combination with real time wristband haptic feedback to facilitate mindful eating.
 
 
 METHODS
 Data collected from 16 participants were used to develop an algorithm for detecting chewing and swallowing. We extracted 18 features from EMG and presented those features to different machine learning classifiers. Based on this algorithm, we developed a system to enable participants to self-moderate their chewing behaviour using haptic feedback. An experiment study was carried out with 20 additional participants to evaluate the effectiveness of eating monitoring and haptic interface in promoting mindful eating.
 
 
 RESULTS
 Our proposed algorithm is able to automatically assess eating behaviour accurately using the EMG-extracted features and a Support Vector Machine (SVM): F1-Score=0.94 for chewing classification, and F1-Score=0.86 for swallowing classification. The experimental study showed that that participants exhibited a lower rate of chewing when haptic feedback delivered in forms of wristband vibration was used compared to a baseline and non-haptic condition (F (2,38) = 58.243, p <.001).
 
 
 CONCLUSIONS
 These findings may have major implications for research in eating behaviour, providing key insights into the impacts of automatic chewing detection and haptic feedback systems on moderating eating behaviour with the aim to improve health outcomes.
Analysis of the axial vector Bc-like tetraquark states with the QCD sum rules

In this paper, we construct the diquark-antidiquark–type current operators to study the axial vector Bc-like tetraquark states with the QCD sum rules. In the calculations, we take the energy scale formula as a powerful constraint to choose the ideal energy scales of the QCD spectral densities and add detailed discussions to illustrate why we take the energy scale formula to improve the QCD sum rules for the doubly heavy tetraquark states. The predicted masses and lie between the conventional 2P and 3P Bc states with from the potential quark models, which is a typical feature of the diquark-antidiquark–type Bc-like tetraquark states.
On Uniform Equivalence of Epistemic Logic Programs

Epistemic Logic Programs (ELPs) extend Answer Set Programming (ASP) with epistemic negation and have received renewed interest in recent years. This led to the development of new research and efficient solving systems for ELPs. In practice, ELPs are often written in a modular way, where each module interacts with other modules by accepting sets of facts as input, and passing on sets of facts as output. An interesting question then presents itself: under which conditions can such a module be replaced by another one without changing the outcome, for any set of input facts? This problem is known as uniform equivalence, and has been studied extensively for ASP. For ELPs, however, such an investigation is, as of yet, missing. In this paper, we therefore propose a characterization of uniform equivalence that can be directly applied to the language of state-of-the-art ELP solvers. We also investigate the computational complexity of deciding uniform equivalence for two ELPs, and show that it is on the third level of the polynomial hierarchy.
Polylogarithmic-time deterministic network decomposition and distributed derandomization

We present a simple polylogarithmic-time deterministic distributed algorithm for network decomposition. This improves on a celebrated 2 O(√logn)-time algorithm of Panconesi and Srinivasan [STOC’92] and settles a central and long-standing question in distributed graph algorithms. It also leads to the first polylogarithmic-time deterministic distributed algorithms for numerous other problems, hence resolving several well-known and decades-old open problems, including Linial’s question about the deterministic complexity of maximal independent set [FOCS’87; SICOMP’92]—which had been called the most outstanding problem in the area. The main implication is a more general distributed derandomization theorem: Put together with the results of Ghaffari, Kuhn, and Maus [STOC’17] and Ghaffari, Harris, and Kuhn [FOCS’18], our network decomposition implies that P-RLOCAL = P-LOCAL. That is, for any problem whose solution can be checked deterministically in polylogarithmic-time, any polylogarithmic-time randomized algorithm can be derandomized to a polylogarithmic-time deterministic algorithm. Informally, for the standard first-order interpretation of efficiency as polylogarithmic-time, distributed algorithms do not need randomness for efficiency. By known connections, our result leads also to substantially faster randomized distributed algorithms for a number of well-studied problems including (Δ+1)-coloring, maximal independent set, and Lovász Local Lemma, as well as massively parallel algorithms for (Δ+1)-coloring.
Cross Attention Network for Semantic Segmentation

In this paper, we address the semantic segmentation task with a deep network that combines contextual features and spatial information. The proposed Cross Attention Network is composed of two branches and a Feature Cross Attention (FCA) module. Specifically, a shallow branch is used to preserve low-level spatial information and a deep branch is employed to extract high-level contextual features. Then the FCA module is introduced to combine these two branches. Different from most existing attention mechanisms, the FCA module obtains spatial attention map and channel attention map from two branches separately, and then fuses them. The contextual features are used to provide global contextual guidance in fused feature maps, and spatial features are used to refine localizations. The proposed network outperforms other real-time methods with improved speed on the Cityscapes and CamVid datasets with lightweight backbones, and achieves state-of-the-art performance with a deep backbone.
Families of rational curves on holomorphic symplectic varieties and applications to zero-cycles

We study families of rational curves on irreducible holomorphic symplectic varieties. We give a necessary and sufficient condition for a sufficiently ample linear system on a holomorphic symplectic variety of $K3^{[n]}$-type to contain a uniruled divisor covered by rational curves of primitive class. In particular, for any fixed $n$, we show that there are only finitely many polarization types of holomorphic symplectic variety of $K3^{[n]}$-type that do not contain such a uniruled divisor. As an application we provide a generalization of a result due to Beauville-Voisin on the Chow group of 0-cycles on such varieties.
Dynamic Input for Deep Reinforcement Learning in Autonomous Driving

In many real-world decision making problems, reaching an optimal decision requires taking into account a variable number of objects around the agent. Autonomous driving is a domain in which this is especially relevant, since the number of cars surrounding the agent varies considerably over time and affects the optimal action to be taken. Classical methods that process object lists can deal with this requirement. However, to take advantage of recent high-performing methods based on deep reinforcement learning in modular pipelines, special architectures are necessary. For these, a number of options exist, but a thorough comparison of the different possibilities is missing. In this paper, we elaborate limitations of fully-connected neural networks and other established approaches like convolutional and recurrent neural networks in the context of reinforcement learning problems that have to deal with variable sized inputs. We employ the structure of Deep Sets in off-policy reinforcement learning for high-level decision making, highlight their capabilities to alleviate these limitations, and show that Deep Sets not only yield the best overall performance but also offer better generalization to unseen situations than the other approaches.
Online Event Recognition from Moving Vehicles: Application Paper

We present a system for online composite event recognition over streaming positions of commercial vehicles. Our system employs a data enrichment module, augmenting the mobility data with external information, such as weather data and proximity to points of interest. In addition, the composite event recognition module, based on a highly optimised logic programming implementation of the Event Calculus, consumes the enriched data and identifies activities that are beneficial in fleet management applications. We evaluate our system on large, real-world data from commercial vehicles, and illustrate its efficiency.
Stellar Representation of Non-Gaussian Quantum States.

The so-called stellar formalism allows us to represent the non-Gaussian properties of single-mode quantum states by the distribution of the zeros of their Husimi Q function in phase space. We use this representation in order to derive an infinite hierarchy of single-mode states based on the number of zeros of the Husimi Q function: the stellar hierarchy. We give an operational characterization of the states in this hierarchy with the minimal number of single-photon additions needed to engineer them, and derive equivalence classes under Gaussian unitary operations. We study in detail the topological properties of this hierarchy with respect to the trace norm, and discuss implications for non-Gaussian state engineering, and continuous variable quantum computing.
Experimentation on the motion of an obstacle avoiding robot

An intelligent robot can be used for applications where a human is at significant risk (like nuclear, space, military), the economics or menial nature of the application result in inefficient use of human workers (service industry, agriculture), for humanitarian uses where there is great risk (demining an area of land mines, urban search and rescue). This paper implements an experiment on one of important fields of AI Searching Algorithms, to find shortest possible solution by searching the produced tree. We will concentrate on Hill climbing algorithm, which is one of simplest searching algorithms in AI. This algorithm is one of most suitable searching methods to help expert system to make decision at every state, at every node. The experimental robot will traverse the maze by using sensors plugged on it. The robot used is E.V.3 Lego Mind storms, with native software for programming LabView. The reason we chose this robot is that it interacts quickly with sensors and can be reconstructed in many ways. This programmed robot will calculate the best possibilities to find way out of maze. The maze is made of wood, and it is adjustable, as robot should be able to leave the maze in any design.
Grammatical Sequence Prediction for Real-Time Neural Semantic Parsing

While sequence-to-sequence (seq2seq) models achieve state-of-the-art performance in many natural language processing tasks, they can be too slow for real-time applications. One performance bottleneck is predicting the most likely next token over a large vocabulary; methods to circumvent this bottleneck are a current research topic. We focus specifically on using seq2seq models for semantic parsing, where we observe that grammars often exist which specify valid formal representations of utterance semantics. By developing a generic approach for restricting the predictions of a seq2seq model to grammatically permissible continuations, we arrive at a widely applicable technique for speeding up semantic parsing. The technique leads to a 74% speed-up on an in-house dataset with a large vocabulary, compared to the same neural model without grammatical restrictions
Optimal Make-Take Fees in a Multi Market-Maker Environment

Following the recent literature on make take fees policies, we consider an exchange wishing to set a suitable contract with several market makers in order to improve trading quality on its platform. To do so, we use a principal-agent approach, where the agents (the market makers) optimise their quotes in a Nash equilibrium fashion, providing best response to the contract proposed by the principal (the exchange). This contract aims at attracting liquidity on the platform. This is because the wealth of the exchange depends on the arrival of market orders, which is driven by the spread of market makers. We compute the optimal contract in quasi explicit form and also derive the optimal spread policies for the market makers. Several new phenomena appears in this multi market maker setting. In particular we show that it is not necessarily optimal to have a large number of market makers in the presence of a contracting scheme.
Resolution of the St. Petersburg Paradox Using Von Mises’ Axiom of Randomness

In this article we will propose a completely new point of view for solving one of the most important paradoxes concerning game theory. The solution develop shifts the focus from the result to the strategy s ability to operate in a cognitive way by exploiting useful information about the system. In order to determine from a mathematical point of view if a strategy is cognitive, we use Von Mises' axiom of randomness. Based on this axiom, the knowledge of useful information consequently generates results that cannot be reproduced randomly. Useful information in this case may be seen as a significant datum for the recipient, for their present or future decision-making process. Finally, by resolving the paradox from this new point of view, we will demonstrate that an expected gain that tends toward infinity is not always a consequence of a cognitive and non-random strategy. Therefore, this result leads us to define a hierarchy of values in decision-making, where the cognitive aspect, whose statistical consequence is a divergence from random behaviour, turns out to be more important than the expected gain.
Special Drawing Rights in a New Decentralized Century

Unfulfilled expectations from macro-economic initiatives during the Great Recession and the massive shift into globalization echo today with political upheaval, anti-establishment propaganda, and looming trade/currency wars that threaten domestic and international value chains. Once stable entities like the EU now look fragile and political instability in the US presents unprecedented challenges to an International Monetary System (IMS) that predominantly relies on the USD and EUR as reserve currencies. In this environment, it is critical for an international organization mandated to ensure stability to plan and act ahead. This paper argues that Decentralized Ledger-based technology (DLT) is key for the International Monetary Fund (IMF) to mitigate some of those risks, promote stability and safeguard world prosperity. Over the last two years, DLT has made headline news globally and created a worldwide excitement not seen since the internet entered the mainstream. The rapid adoption and open-to-all philosophy of DLT has already redefined global socioeconomics, promises to shake up the world of commerce/finance and challenges the workings of central governments/regulators. This paper examines DLT core premises and proposes a two-step approach for the IMF to expand Special Drawing Rights (SDR) into that sphere so as to become the originally envisioned numeraire and reserve currency for cross-border transactions in this new decentralized century.
An Empirical Analysis of the Python Package Index (PyPI)

In this research, we provide a comprehensive empirical summary of the Python Package Repository, PyPI, including both package metadata and source code covering 178,592 packages, 1,745,744 releases, 76,997 contributors, and 156,816,750 import statements. We provide counts and trends for packages, releases, dependencies, category classifications, licenses, and package imports, as well as authors, maintainers, and organizations. As one of the largest and oldest software repositories as of publication, PyPI provides insight not just into the Python ecosystem today, but also trends in software development and licensing more broadly over time. Within PyPI, we find that the growth of the repository has been robust under all measures, with a compound annual growth rate of 47% for active packages, 39% for new authors, and 61% for new import statements over the last 15 years. As with many similar social systems, we find a number of highly right-skewed distributions, including the distribution of releases per package, packages and releases per author, imports per package, and size per package and release. However, we also find that most packages are contributed by single individuals, not multiple individuals or organizations. The data, methods, and calculations herein provide an anchor for public discourse on PyPI and serve as a foundation for future research on the Python software ecosystem.
The Virtual Patch Clamp: Imputing C. elegans Membrane Potentials from Calcium Imaging

We develop a stochastic whole-brain and body simulator of the nematode roundworm Caenorhabditis elegans (C. elegans) and show that it is sufficiently regularizing to allow imputation of latent membrane potentials from partial calcium fluorescence imaging observations. This is the first attempt we know of to "complete the circle," where an anatomically grounded whole-connectome simulator is used to impute a time-varying "brain" state at single-cell fidelity from covariates that are measurable in practice. The sequential Monte Carlo (SMC) method we employ not only enables imputation of said latent states but also presents a strategy for learning simulator parameters via variational optimization of the noisy model evidence approximation provided by SMC. Our imputation and parameter estimation experiments were conducted on distributed systems using novel implementations of the aforementioned techniques applied to synthetic data of dimension and type representative of that which are measured in laboratories currently.
Info Intervention

Pearl asserts that all three main obstacles for strong AI can be overcome using causal modeling tools, in particular, causal diagrams and their associated logic. We point out issues of existing definition of Pearl's do intervention (also known as "surgical" or "atomatic" or "perfect" intervention) and other notations for causation. To highlight the view of causality as information transfer we propose the info intervention, which intervening the sending out information. It turns out that info intervention $\sigma(\cdot)$ is very competitive in formalizing causal queries, identification of cause-effect, and it can solve (or alleviate) some issues in the existing causal notations. We conclude that $\sigma(\cdot)$-operator is a necessary and useful tool for causal inference.