nils-herrmann/clean_scirepeval huggingface dataset:
Context based Analysis of Lexical Semantics for Hindi Language

A word having multiple senses in a text introduces the lexical semantic task to find out which particular sense is appropriate for the given context. One such task is Word sense disambiguation which refers to the identification of the most appropriate meaning of the polysemous word in a given context using computational algorithms. The language processing research in Hindi, the official language of India, and other Indian languages is restricted by unavailability of the standard corpus. For Hindi word sense disambiguation also, the large corpus is not available. In this work, we prepared the text containing new senses of certain words leading to the enrichment of the sense-tagged Hindi corpus of sixty polysemous words. Furthermore, we analyzed two novel lexical associations for Hindi word sense disambiguation based on the contextual features of the polysemous word. The evaluation of these methods is carried out over learning algorithms and favorable results are achieved.
Constant Time Graph Neural Networks

The recent advancements in graph neural networks (GNNs) have led to state-of-the-art performances in various applications, including chemo-informatics, question-answering systems, and recommender systems. However, scaling up these methods to huge graphs, such as social networks and Web graphs, remains a challenge. In particular, the existing methods for accelerating GNNs either are not theoretically guaranteed in terms of the approximation error or incurred at least a linear time computation cost. In this study, we reveal the query complexity of the uniform node sampling scheme for Message Passing Neural Networks, including GraphSAGE, graph attention networks (GATs), and graph convolutional networks (GCNs). Surprisingly, our analysis reveals that the complexity of the node sampling method is completely independent of the number of the nodes, edges, and neighbors of the input and depends only on the error tolerance and confidence probability while providing a theoretical guarantee for the approximation error. To the best of our knowledge, this is the first article to provide a theoretical guarantee of approximation for GNNs within constant time. Through experiments with synthetic and real-world datasets, we investigated the speed and precision of the node sampling scheme and validated our theoretical results.
Predicting the Results of LTL Model Checking using Multiple Machine Learning Algorithms

In this paper, we study how to predict the results of LTL model checking using some machine learning algorithms. Some Kripke structures and LTL formulas and their model checking results are made up data set. The approaches based on the Random Forest (RF), K-Nearest Neighbors (KNN), Decision tree (DT), and Logistic Regression (LR) are used to training and prediction. The experiment results show that the predictive accuracy of the RF, KNN, DT and LR-based approaches are 97.9%, 98.2%, 97.1% and 98.2%, respectively, as well as the average computation efficiencies of the RF, KNN, DT and LR-based approaches are 7102500, 598, 4132364 and 5543415 times than that of the existing approach, respectively, if the length of each LTL formula is 500.
Navigability evaluation of complex networks by greedy routing efficiency

An important characteristic of any network is its navigability. The navigability of a network embedded in a geometrical space is associated with the fact that greedy routing (GR) performed using the node geometrical coordinates is efficient (1⇓⇓⇓–5). In PNAS, Seguin et al. (6) define a measure for quantifying the efficiency of brain network navigability in the Euclidean space, referred to as the efficiency ratio, whose formula exactly coincides with the GR-score previously introduced by Muscoloni et al. (7). However, in contrast to Muscoloni et al. (7), the study of Seguin et al. (6) ( i ) proposes the efficiency ratio without referring to GR at all, ( ii ) provides no rationale for the introduction of the efficiency ratio with respect to the two main GR measures already adopted in the literature, and ( iii ) does not discuss any advantage of the measure, nor does it explain that the efficiency ratio in fact integrates the two former measures. Therefore, we feel that, for … 

[↵][1]1To whom correspondence should be addressed.  kalokagathos.agon{at}gmail.com.

 : #xref-corresp-1-1
NLSC: Unrestricted Natural Language-based Service Composition through Sentence Embeddings

Current approaches for service composition (assemblies of atomic services) require developers to use: (a) domain-specific semantics to formalize services that restrict the vocabulary for their descriptions, and (b) translation mechanisms for service retrieval to convert unstructured user requests to strongly-typed semantic representations. In our work, we argue that effort to developing service descriptions, request translations, and matching mechanisms could be reduced using unrestricted natural language; allowing both: (1) end-users to intuitively express their needs using natural language, and (2) service developers to develop services without relying on syntactic/semantic description languages. Although there are some natural language-based service composition approaches, they restrict service retrieval to syntactic/semantic matching. With recent developments in Machine learning and Natural Language Processing, we motivate the use of Sentence Embeddings by leveraging richer semantic representations of sentences for service description, matching and retrieval. Experimental results show that service composition development effort may be reduced by more than 44% while keeping a high precision/recall when matching high-level user requests with low-level service method invocations.
The Junk News Aggregator: Examining junk news posted on Facebook, starting with the 2018 US Midterm Elections

In recent years, the phenomenon of online misinformation and junk news circulating on social media has come to constitute an important and widespread problem affecting public life online across the globe, particularly around important political events such as elections. At the same time, there have been calls for more transparency around misinformation on social media platforms, as many of the most popular social media platforms function as "walled gardens," where it is impossible for researchers and the public to readily examine the scale and nature of misinformation activity as it unfolds on the platforms. In order to help address this, we present the Junk News Aggregator, a publicly available interactive web tool, which allows anyone to examine, in near real-time, all of the public content posted to Facebook by important junk news sources in the US. It allows the public to gain access to and examine the latest articles posted on Facebook (the most popular social media platform in the US and one where content is not readily accessible at scale from the open Web), as well as organise them by time, news publisher, and keywords of interest, and sort them based on all eight engagement metrics available on Facebook. Therefore, the Aggregator allows the public to gain insights on the volume, content, key themes, and types and volumes of engagement received by content posted by junk news publishers, in near real-time, hence opening up and offering transparency in these activities as they unfold, at scale across the top most popular junk news publishers. In this way, the Aggregator can help increase transparency around the nature, volume, and engagement with junk news on social media, and serve as a media literacy tool for the public.
On 3-manifolds that are boundaries of exotic 4-manifolds

We give several criteria on a closed, oriented 3-manifold that will imply that it is the boundary of a (simply connected) 4-manifold that admits infinitely many distinct smooth structures. We also show that any weakly fillable contact 3-manifold, or contact 3-manifolds with non-vanishing Heegaard Floer invariant, is the boundary of a simply connected 4-manifolds that admits infinitely many distinct smooth structures each of which supports a symplectic structure with concave boundary, that is there are infinitely many exotic caps for any such contact manifold.
The quest for dark matter in dwarf spheroidal galaxies with the Cherenkov Telescope Array

Dwarf spheroidal galaxies are among the best environments that can be studied with Cherenkov telescopes for indirect searches of γ-ray signals coming from dark matter self-interaction (annihilation or decay), due to their proximity and negligible background emission. We present new determinations of the dark-matter amount – i.e. the astrophysical factors J and D – in dwarf-galaxy halos obtained through the MCMC Jeans analysis of their brightness and kinematic data. Such factors are of great importance to test the performances of the next-generation γ-ray instruments such as the Cherenkov Telescope Array in detecting dark-matter signals from astronomical environments, or constraining the limits to dark-matter physics parameters (particle mass and lifetime, annihilation cross section).
The PDS 110 observing campaign – photometric and spectroscopic observations reveal eclipses are aperiodic

PDS 110 is a young disc-hosting star in the Orion OB1A association. Two dimming events of similar depth and duration were seen in 2008 (WASP) and 2011 (KELT), consistent with an object in a closed periodic orbit. In this paper, we present data from a ground-based observing campaign designed to measure the star both photometrically and spectroscopically during the time of predicted eclipse in 2017 September. Despite high-quality photometry, the predicted eclipse did not occur, although coherent structure is present suggesting variable amounts of stellar flux or dust obscuration. We also searched for radial velocity (RV) oscillations caused by any hypothetical companion and can rule out close binaries to 0.1Ms. A search of Sonneberg plate archive data also enabled us to extend the photometric baseline of this star back more than 50 yr, and similarly does not re-detect any deep eclipses. Taken together, they suggest that the eclipses seen in WASP and KELT photometry were due to aperiodic events. It would seem that PDS 110 undergoes stochastic dimmings that are shallower and of shorter duration than those of UX Ori variables, but may have a similar mechanism.
Typed Graph Networks

Recently, the deep learning community has given growing attention to neural architectures engineered to learn problems in relational domains. Convolutional Neural Networks employ parameter sharing over the image domain, tying the weights of neural connections on a grid topology and thus enforcing the learning of a number of convolutional kernels. By instantiating trainable neural modules and assembling them in varied configurations (apart from grids), one can enforce parameter sharing over graphs, yielding models which can effectively be fed with relational data. In this context, vertices in a graph can be projected into a hyperdimensional real space and iteratively refined over many message-passing iterations in an end-to-end differentiable architecture. Architectures of this family have been referred to with several definitions in the literature, such as Graph Neural Networks, Message-passing Neural Networks, Relational Networks and Graph Networks. In this paper, we revisit the original Graph Neural Network model and show that it generalises many of the recent models, which in turn benefit from the insight of thinking about vertex \textbf{types}. To illustrate the generality of the original model, we present a Graph Neural Network formalisation, which partitions the vertices of a graph into a number of types. Each type represents an entity in the ontology of the problem one wants to learn. This allows - for instance - one to assign embeddings to edges, hyperedges, and any number of global attributes of the graph. As a companion to this paper we provide a Python/Tensorflow library to facilitate the development of such architectures, with which we instantiate the formalisation to reproduce a number of models proposed in the current literature.
Charged-particle angular correlations in XeXe collisions at 
sNN=5.44
 TeV

Azimuthal correlations of charged particles in xenon-xenon collisions at a center-of-mass energy per nucleon pair of root s(NN) = 5.44 TeV are studied. The data were collected by the CMS experiment at the LHC with a total integrated luminosity of 3.42 mu b(-1). The collective motion of the system formed in the collision is parametrized by a Fourier expansion of the azimuthal particle density distribution. The azimuthal anisotropy coefficients v(2), v(3), and v(4) are obtained by the scalar-product, two-particle correlation, and multiparticle correlation methods. Within a hydrodynamic picture, these methods have different sensitivities to noncollective and fluctuation effects. The dependence of the Fourier coefficients on the size of the colliding system is explored by comparing the xenon-xenon results with equivalent lead-lead data. Model calculations that include initial-state fluctuation effects are also compared to the experimental results. The observed angular correlations provide new constraints on the hydrodynamic description of heavy ion collisions.
Quantum and classical effects in a light-clock falling in Schwarzschild geometry

Quantum theory and relativity offer different conceptions of time. To explore the conflict between them, we study a quantum version of the light-clock commonly used to illustrate relativistic time dilation. This semiclassical model combines elements of both theories. We show for Gaussian states of the light field that the clock time is independent of the initial state. We calculate the discrepancy between two such clocks when one is held in a gravitational field and the other is left to fall a certain distance. Contrasting our results with the case of pointlike observers in general relativity, as well as classical light-clocks, we find both quantitative and qualitative differences. We find that the quantum contribution to the discrepancy between the two clocks increases with the gravitational field strength, and results in a minimum resolution of the dropped clock (distinct from the quantum uncertainty in its measurement).
Hierarchical Reinforcement Learning for Multi-agent MOBA Game

Real Time Strategy (RTS) games require macro strategies as well as micro strategies to obtain satisfactory performance since it has large state space, action space, and hidden information. This paper presents a novel hierarchical reinforcement learning model for mastering Multiplayer Online Battle Arena (MOBA) games, a sub-genre of RTS games. The novelty of this work are: (1) proposing a hierarchical framework, where agents execute macro strategies by imitation learning and carry out micromanipulations through reinforcement learning, (2) developing a simple self-learning method to get better sample efficiency for training, and (3) designing a dense reward function for multi-agent cooperation in the absence of game engine or Application Programming Interface (API). Finally, various experiments have been performed to validate the superior performance of the proposed method over other state-of-the-art reinforcement learning algorithms. Agent successfully learns to combat and defeat bronze-level built-in AI with 100% win rate, and experiments show that our method can create a competitive multi-agent for a kind of mobile MOBA game {\it King of Glory} in 5v5 mode.
Bottom-Up Object Detection by Grouping Extreme and Center Points

With the advent of deep learning, object detection drifted from a bottom-up to a top-down recognition problem. State of the art algorithms enumerate a near-exhaustive list of object locations and classify each into: object or not. In this paper, we show that bottom-up approaches still perform competitively. We detect four extreme points (top-most, left-most, bottom-most, right-most) and one center point of objects using a standard keypoint estimation network. We group the five keypoints into a bounding box if they are geometrically aligned. Object detection is then a purely appearance-based keypoint estimation problem, without region classification or implicit feature learning. The proposed method performs on-par with the state-of-the-art region based detection methods, with a bounding box AP of 43.7% on COCO test-dev. In addition, our estimated extreme points directly span a coarse octagonal mask, with a COCO Mask AP of 18.9%, much better than the Mask AP of vanilla bounding boxes. Extreme point guided segmentation further improves this to 34.6% Mask AP.
Hamiltonian Monte-Carlo for Orthogonal Matrices

We consider the problem of sampling from posterior distributions for Bayesian models where some parameters are restricted to be orthogonal matrices. Such matrices are sometimes used in neural networks models for reasons of regularization and stabilization of training procedures, and also can parameterize matrices of bounded rank, positive-definite matrices and others. In } authors have already considered sampling from distributions over manifolds using exact geodesic flows in a scheme similar to Hamiltonian Monte Carlo (HMC). We propose new sampling scheme for a set of orthogonal matrices that is based on the same approach, uses ideas of Riemannian optimization and does not require exact computation of geodesic flows. The method is theoretically justified by proof of symplecticity for the proposed iteration. In experiments we show that the new scheme is comparable or faster in time per iteration and more sample-efficient comparing to conventional HMC with explicit orthogonal parameterization and Geodesic Monte-Carlo. We also provide promising results of Bayesian ensembling for orthogonal neural networks and low-rank matrix factorization.
Beyond Topological Persistence: Starting from Networks

Persistent homology enables fast and computable comparison of topological objects. We give some instances of a recent extension of the theory of persistence, guaranteeing robustness and computability for relevant data types, like simple graphs and digraphs. We focus on categorical persistence functions that allow us to study in full generality strong kinds of connectedness—clique communities, k-vertex, and k-edge connectedness—directly on simple graphs and strong connectedness in digraphs.
Computing defects associated to bounded domain wall structures: the Z/pZ case

We discuss domain walls and defects in topological phases occurring as the Drinfeld center of some fusion category. Domain walls between such phases correspond to bimodules between the fusion categories. Point defects correspond to functors between the bimodules. A domain wall structure consists of a planar graph with faces labeled by fusion categories. Edges are labeled by bimodules. When the vertices are labeled by point defects we get a compound defect. We present an algorithm, called the domain wall structure algorithm, for computing the compound defect. We apply this algorithm to show that the bimodule associator, related to the O3 obstruction of Etingof et al (2010 ), is trivial for all domain walls of VecZ/pZ. In the language of this paper, the ground states of the Levin–Wen model are compound defects. We use this to define a generalization of the Levin–Wen model with domain walls and point defects. The domain wall structure algorithm can be used to compute the ground states of these generalized Levin–Wen type models.
Cooperative Online Learning: Keeping your Neighbors Updated

We study an asynchronous online learning setting with a network of agents. At each time step, some of the agents are activated, requested to make a prediction, and pay the corresponding loss. The loss function is then revealed to these agents and also to their neighbors in the network. Our results characterize how much knowing the network structure affects the regret as a function of the model of agent activations. When activations are stochastic, the optimal regret (up to constant factors) is shown to be of order $\sqrt{\alpha T}$, where $T$ is the horizon and $\alpha$ is the independence number of the network. We prove that the upper bound is achieved even when agents have no information about the network structure. When activations are adversarial the situation changes dramatically: if agents ignore the network structure, a $\Omega(T)$ lower bound on the regret can be proven, showing that learning is impossible. However, when agents can choose to ignore some of their neighbors based on the knowledge of the network structure, we prove a $O(\sqrt{\overline{\chi} T})$ sublinear regret bound, where $\overline{\chi} \ge \alpha$ is the clique-covering number of the network.
Model Function Based Conditional Gradient Method with Armijo-like Line Search

The Conditional Gradient Method is generalized to a class of non-smooth non-convex optimization problems with many applications in machine learning. The proposed algorithm iterates by minimizing so-called model functions over the constraint set. Complemented with an Amijo line search procedure, we prove that subsequences converge to a stationary point. The abstract framework of model functions provides great flexibility for the design of concrete algorithms. As special cases, for example, we develop an algorithm for additive composite problems and an algorithm for non-linear composite problems which leads to a Gauss--Newton-type algorithm. Both instances are novel in non-smooth non-convex optimization and come with numerous applications in machine learning. Moreover, we obtain a hybrid version of Conditional Gradient and Proximal Minimization schemes for free, which combines advantages of both. Our algorithm is shown to perform favorably on a sparse non-linear robust regression problem and we discuss the flexibility of the proposed framework in several matrix factorization formulations.
Constraining sterile neutrino interpretations of the LSND and MiniBooNE anomalies with coherent neutrino scattering experiments

Results from the LSND and MiniBooNE experiments have been interpreted as evidence for a sterile neutrino with a mass near the electronvolt scale. Here we propose to test such a scenario by measuring the coherent elastic scattering rate of neutrinos from a pulsed spallation source. Coherent scattering is universal across all active neutrino flavors, and thus can provide a measurement of the total Standard Model neutrino flux. By performing measurements over different baselines and making use of timing information, it is possible to significantly reduce the systematic uncertainties and to independently measure the fluxes of neutrinos that originate as $\nu_{\mu}$ or as either $\nu_e$ or $\bar{\nu}_{\mu}$. We find that a 100 kg CsI detector would be sensitive to the large fraction of the sterile neutrino parameter space that could potentially account for the LSND and MiniBooNE anomalies.
Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction

Despite the recent popularity of deep generative state space models, few comparisons have been made between network architectures and the inference steps of the Bayesian filtering framework – with most models simultaneously approximating both state transition and update steps with a single recurrent neural network (RNN). In this paper, we introduce the Recurrent Neural Filter (RNF), a novel recurrent autoencoder architecture that learns distinct representations for each Bayesian filtering step, captured by a series of encoders and decoders. Testing this on three real-world time series datasets, we demonstrate that the decoupled representations learnt improve the accuracy of one-step-ahead forecasts while providing realistic uncertainty estimates, and also facilitate multistep prediction through the separation of encoder stages.