Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning in Football Betting: Prediction of Match Results Based on Player Characteristics

In recent times, football (soccer) has aroused an increasing amount of attention across continents and entered unexpected dimensions. In this course, the number of bookmakers, who offer the opportunity to bet on the outcome of football games, expanded enormously, which was further strengthened by the development of the world wide web. In this context, one could generate positive returns over time by betting based on a strategy which successfully identifies overvalued betting odds. Due to the large number of matches around the globe, football matches in particular have great potential for such a betting strategy. This paper utilizes machine learning to forecast the outcome of football games based on match and player attributes. A simulation study which includes all matches of the five greatest European football leagues and the corresponding second leagues between 2006 and 2018 revealed that an ensemble strategy achieves statistically and economically significant returns of 1.58% per match. Furthermore, the combination of different machine learning algorithms could neither be outperformed by the individual machine learning approaches nor by a linear regression model or naive betting strategies, such as always betting on the victory of the home team.
Neuropsychological function at first episode in treatment-resistant psychosis: findings from the ÆSOP-10 study

Background. Neuropsychological investigations can help untangle the aetiological and phe-nomenological heterogeneity of schizophrenia but have scarcely been employed in the context of treatment-resistant (TR) schizophrenia. No population-based study has examined neuropsychological function in the first-episode of TR psychosis. Methods. We report baseline neuropsychological findings from a longitudinal, population-based study of first-episode psychosis, which followed up cases from index admission to 10 years. At the 10-year follow up patients were classified as treatment responsive or TR after reconstructing their entire case histories. Of 145 cases with neuropsychological data at baseline, 113 were classified as treatment responsive, and 32 as TR at the 10-year follow-up. Results. Compared with 257 community controls, both case groups showed baseline deficits in three composite neuropsychological scores, derived from principal component analysis: verbal intelligence and fluency, visuospatial ability and executive function, and verbal memory and learning ( p values ⩽ 0.001). Compared with treatment responders, TR cases showed deficits in verbal intelligence and fluency, both in the extended psychosis sample ( t = − 2.32; p = 0.022) and in the schizophrenia diagnostic subgroup ( t = − 2.49; p = 0.017). Similar relative deficits in the TR cases emerged in sub-/sensitivity analyses excluding patients with delayed-onset treatment resistance ( p values<0.01 – 0.001) and those born outside the UK ( p values<0.05). Conclusions. Verbal intelligence and are in patients with TR psychosis compared with those who respond to treatment. This differential is already detectable – at a group level – at the first illness episode, supporting the conceptualisation of TR psychosis as a severe, pathogenically distinct variant, embedded in aberrant neurodevelopmental processes.
Dualization and Automatic Distributed Parameter Selection of Total Generalized Variation via Bilevel Optimization

Abstract Total Generalized Variation (TGV) regularization in image reconstruction relies on an infimal convolution type combination of generalized first- and second-order derivatives. This helps to avoid the staircasing effect of Total Variation (TV) regularization, while still preserving sharp contrasts in images. The associated regularization effect crucially hinges on two parameters whose proper adjustment represents a challenging task. In this work, a bilevel optimization framework with a suitable statistics-based upper level objective is proposed in order to automatically select these parameters. The framework allows for spatially varying parameters, thus enabling better recovery in high-detail image areas. A rigorous dualization framework is established, and for the numerical solution, a Newton type method for the solution of the lower level problem, i.e. the image reconstruction problem, and a bilevel TGV algorithm are introduced. Denoising tests confirm that automatically selected distributed regularization parameters lead in general to improved reconstructions when compared to results for scalar parameters.
Air-to-Air Communications Beyond 5G: A Novel 3D CoMP Transmission Scheme

In this paper, a novel 3D cellular model consisting of aerial base stations (aBSs) and aerial user equipments (aUEs) is proposed, by integrating the coordinated multi-point (CoMP) transmission technique with the theory of stochastic geometry. For this new 3D architecture, a tractable model for aBSs’ deployment based on the binomial-Delaunay tetrahedralization is developed, which ensures seamless coverage for a given space. In addition, a versatile and practical frequency allocation scheme is designed to eliminate the inter-cell interference effectively. Based on this model, performance metrics including the achievable data rate and coverage probability are derived for two types of aUEs: i) the general aUE (i.e., an aUE having distinct distances from its serving aBSs) and ii) the worst-case aUE (i.e., an aUE having equal distances from its serving aBSs). Simulation and numerical results demonstrate that the proposed approach emphatically outperforms the conventional binomial-Voronoi tessellation without CoMP. Insightfully, it provides a similar performance to the binomial-Voronoi tessellation which utilizes the conventional CoMP scheme; yet, introducing a considerably reduced computational complexity and backhaul/ signaling overhead.
Dilaton EFT from p-regime to RMT in the $\epsilon$-regime

New results are reported from tests of a low-energy effective field theory (EFT) that includes a dilaton field to describe the emergent light scalar with ${ 0^{++} }$ quantum numbers in the strongly coupled near-conformal gauge theory with a massless fermion flavor doublet in the two-index symmetric (sextet) representation of the SU(3) color gauge group. In the parlor of walking --- based on the observed light scalar, the small $\beta$-function at strong coupling, and the large anomalous scale dimension of the chiral condensate --- the dilaton EFT hypothesis is introduced to test if it explains the slowly changing nearly scale invariant physics that connects the asymptotically free UV fixed point and the far-infrared scale of chiral symmetry breaking. The characteristic dilaton EFT signatures of scale symmetry breaking are probed in this report in the small Compton wavelength limit of Goldstone bosons relative to the size of the lattice volume (p-regime) and in the limit when the Goldstone wavelength exceeds the size of the volume ($\epsilon$-regime). Random matrix theory (RMT) analysis of the dilaton EFT is applied to the lowest part of the Dirac spectrum in the $\epsilon$-regime to directly test predictions for the fundamental EFT parameters. The predictions, sensitive to the choice of the dilaton potential, were limited before to the p-regime, using extrapolations from far above the chiral limit with untested uncertainties. The dilaton EFT analysis of the $\epsilon$-regime was first suggested in \cite{Fodor:2019vmw}, with some results presented at this conference and with our continued post-conference analysis added to stimulate discussions.
Low-loss two-dimensional plasmon modes in antimonene

The effects of spin-orbit (SOC) and electron-phonon coupling on the collective excitation of doped monolayer ${\mathrm{Sb}}_{2}$ are investigated using density functional and many-body perturbation theories. The spin-orbit coupling is exclusively important for the monolayer ${\mathrm{Sb}}_{2}$ and it leads to the modification of the electronic band structure. In particular, plasmon modes of monolayer ${\mathrm{Sb}}_{2}$ are quite sensitive to the SOC and are characterized by very low damping rates, owing to small electron-phonon scatterings. Our results show how plasmons in antimonene are significantly less damped compared to monolayer graphene when plasmon energies are larger than 0.2 eV due to smaller plasmon-phonon coupling in the former material.
DeepSurf: A surface-based deep learning approach for the prediction of ligand binding sites on proteins

MOTIVATION The knowledge of potentially druggable binding sites on proteins is an important preliminary step towards the discovery of novel drugs. The computational prediction of such areas can be boosted by following the recent major advances in the deep learning field and by exploiting the increasing availability of proper data. RESULTS In this paper, a novel computational method for the prediction of potential binding sites is proposed, called DeepSurf. DeepSurf combines a surface-based representation, where a number of 3 D voxelized grids are placed on the protein's surface, with state-of-the-art deep learning architectures. After being trained on the large database of scPDB, DeepSurf demonstrates superior results on three diverse testing datasets, by surpassing all its main deep learning-based competitors, while attaining competitive performance to a set of traditional non-data-driven approaches. AVAILABILITY The source code of the method along with trained models are freely available at https://github.com/stemylonas/DeepSurf.git. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Keyphrase Extraction with Span-based Feature Representations

Keyphrases are capable of providing semantic metadata characterizing documents and producing an overview of the content of a document. Since keyphrase extraction is able to facilitate the management, categorization, and retrieval of information, it has received much attention in recent years. There are three approaches to address keyphrase extraction: (i) traditional two-step ranking method, (ii) sequence labeling and (iii) generation using neural networks. Two-step ranking approach is based on feature engineering, which is labor intensive and domain dependent. Sequence labeling is not able to tackle overlapping phrases. Generation methods (i.e., Sequence-to-sequence neural network models) overcome those shortcomings, so they have been widely studied and gain state-of-the-art performance. However, generation methods can not utilize context information effectively. In this paper, we propose a novelty Span Keyphrase Extraction model that extracts span-based feature representation of keyphrase directly from all the content tokens. In this way, our model obtains representation for each keyphrase and further learns to capture the interaction between keyphrases in one document to get better ranking results. In addition, with the help of tokens, our model is able to extract overlapped keyphrases. Experimental results on the benchmark datasets show that our proposed model outperforms the existing methods by a large margin.
The Fifth Candidate for an Intermediate-mass Black Hole in the Galactic Center

We report the results of high-resolution molecular line observations of the high-velocity compact cloud HCN–0.085–0.094 with the Atacama Large Millimeter/submillimeter Array. The HCN J = 4–3, HCO+ J = 4–3, and CS J = 7–6 line images reveal that HCN–0.085–0.094 consists mainly of three small clumps with extremely broad velocity widths. Each of the three clumps has a 5.5 GHz radio continuum counterpart in its periphery toward Sgr A*. The positional relationship indicates that their surfaces have been ionized by ultraviolet photons from young stars in the central cluster, suggesting the clumps are in close proximity to the Galactic nucleus. One of the three clumps has a ring-like structure with a very steep velocity gradient. This kinematical structure suggests an orbit around a point-like object with a mass of ∼104 M⊙. The absence of stellar counterparts indicates that the point-like object may be a quiescent black hole. This discovery adds another intermediate-mass black hole candidate in the central region of our Galaxy.
Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise

Stochastic gradient descent with momentum (SGDm) is one of the most popular optimization algorithms in deep learning. While there is a rich theory of SGDm for convex problems, the theory is considerably less developed in the context of deep learning where the problem is non-convex and the gradient noise might exhibit a heavy-tailed behavior, as empirically observed in recent studies. In this study, we consider a \emph{continuous-time} variant of SGDm, known as the underdamped Langevin dynamics (ULD), and investigate its asymptotic properties under heavy-tailed perturbations. Supported by recent studies from statistical physics, we argue both theoretically and empirically that the heavy-tails of such perturbations can result in a bias even when the step-size is small, in the sense that \emph{the optima of stationary distribution} of the dynamics might not match \emph{the optima of the cost function to be optimized}. As a remedy, we develop a novel framework, which we coin as \emph{fractional} ULD (FULD), and prove that FULD targets the so-called Gibbs distribution, whose optima exactly match the optima of the original cost. We observe that the Euler discretization of FULD has noteworthy algorithmic similarities with \emph{natural gradient} methods and \emph{gradient clipping}, bringing a new perspective on understanding their role in deep learning. We support our theory with experiments conducted on a synthetic model and neural networks.
Flipping A Statistics Classroom for Pre-Service English Language Teachers

Flipped classroom approach has gained attention for educational practitioners and researchers in recent years. In contrast with traditional classroom, in flipped classroom, students gather basic knowledge out of class, so that class time can be used by them to engage in active learning experience. While rich body of research has been investigated the effectiveness of flipped classroom, yet little is known about student's perspective towards the implementation of flipped classroom, especially for non-mathematics students that enroll statistics course. This study aims to describe pre-service English language teacher's perceptions with regard to the implementation of the learning approach in Statistics for Research course. Online questionnaire is employed in capturing student's perceptions. Students were generally positive regarding the implementation of flipped classroom. Although, the students still had difficulty with regard to online learning environment, they felt that many features of flipped classroom are helpful for their learning experiences.
BADGR: An Autonomous Self-Supervised Learning-Based Navigation System

Mobile robot navigation is typically regarded as a geometric problem, in which the robot's objective is to perceive the geometry of the environment in order to plan collision-free paths towards a desired goal. However, a purely geometric view of the world can be insufficient for many navigation problems. For example, a robot navigating based on geometry may avoid a field of tall grass because it believes it is untraversable, and will therefore fail to reach its desired goal. In this work, we investigate how to move beyond these purely geometric-based approaches using a method that learns about physical navigational affordances from experience. Our reinforcement learning approach, which we call BADGR, is an end-to-end learning-based mobile robot navigation system that can be trained with autonomously-labeled off-policy data gathered in real-world environments, without any simulation or human supervision. BADGR can navigate in real-world urban and off-road environments with geometrically distracting obstacles. It can also incorporate terrain preferences, generalize to novel environments, and continue to improve autonomously by gathering more data. Videos, code, and other supplemental material are available on our website https://sites.google. com/view/badgr
Fully Differentiable Procedural Content Generation through Generative Playing Networks

To procedurally create interactive content such as environments or game levels, we need agents that can evaluate the content; but to train such agents, we need content they can train on. Generative Playing Networks is a framework that learns agent policies and generates environments in tandem through a symbiotic process. Policies are learned using an actor-critic reinforcement learning algorithm so as to master the environment, and environments are created by a generator network which tries to provide an appropriate level of challenge for the agent. This is accomplished by the generator learning to make content based on estimates by the critic. Thus, this process provides an implicit curriculum for the agent, creating more complex environments over time. Unlike previous approaches to procedural content generation, Generative Playing Networks is end-to-end differentiable and does not require human-designed examples or domain knowledge. We demonstrate the capability of this framework by training an agent and level generator for a 2D dungeon crawler game.
High-resolution Spectroscopic Study of Dwarf Stars in the Northern Sky: Lithium, Carbon, and Oxygen Abundances

Abundances of lithium, carbon, and oxygen have been derived using spectral synthesis for a sample of 249 bright F, G, and K Northern Hemisphere dwarf stars from the high-resolution spectra acquired with the VUES spectrograph at the Molėtai Astronomical Observatory of Vilnius University. The sample stars have metallicities, effective temperatures, and ages between (–0.7 0.4) dex, (5000 6900) K, (1 12) Gyr, accordingly. We confirm a so far unexplained lithium abundance decrease at supersolar metallicities—A(Li) in our sample stars, which drop by 0.7 dex in the [Fe/H] range from +0.10 to +0.55 dex. Furthermore, we identified stars with similar ages, atmospheric parameters, and rotational velocities, but with significantly different lithium abundances, which suggests that additional specific evolutionary factors should be taken into account while interpreting the stellar lithium content. Nine stars with predominantly supersolar metallicities, i.e., about 12% among 78 stars with C and O abundances determined, have the C/O number ratios larger than 0.65, thus may form carbon-rich rocky planets. Ten planet-hosting stars, available in our sample, do not show a discernible difference from the stars with no planets detected regarding their lithium content.
Inner Boundary Condition in Quasi-Lagrangian Simulations of Accretion Disks

In simulations of viscously evolving accretion disks, the inner boundary condition is particularly important. If treated incorrectly, it induces incorrect behavior very quickly, because the viscous time is shortest near the inner boundary. Recent work has determined the correct inner boundary in Eulerian simulations. But in quasi-Lagrangian simulations (e.g., SPH, moving mesh, and meshless), where the inner boundary is modeled by removing mass within a finite zone, the inner density profile typically becomes anomalously depleted. Here we show how the boundary condition should be applied in such codes, via a simple modification of the usual approach: when one removes mass, one must speed up the remaining material so that the disk’s angular momentum is unchanged. We show with both 1D and 2D moving-mesh (AREPO) simulations that this scheme works as desired in viscously evolving disks. It produces no spurious density depletions and is independent of the mass removal rate, provided that the disk is adequately resolved and that the mass removal rate is not so extreme as to trigger instabilities. This “torque-free” mass removal technique permits the use of quasi-Lagrangian codes to simulate viscously evolving disks, while including a variety of additional effects. As an example, we apply our scheme to a 2D simulation of an accretion disk perturbed by a very massive planet, in which the disk is evolved to viscous steady state.
Sentiment Analysis Using Averaged Weighted Word Vector Features

People use the world wide web heavily to share their experience with entities such as products, services, or travel destinations. Texts that provide online feedback in the form of reviews and comments are essential to make consumer decisions. These comments create a valuable source that may be used to measure satisfaction related to products or services. Sentiment analysis is the task of identifying opinions expressed in such text fragments. In this work, we develop two methods that combine different types of word vectors to learn and estimate polarity of reviews. We develop average review vectors from word vectors and add weights to this review vectors using word frequencies in positive and negative sensitivity-tagged reviews. We applied the methods to several datasets from different domains that are used as standard benchmarks for sentiment analysis. We ensemble the techniques with each other and existing methods, and we make a comparison with the approaches in the literature. The results show that the performances of our approaches outperform the state-of-the-art success rates.
Multiscale Non-stationary Stochastic Bandits

Classic contextual bandit algorithms for linear models, such as LinUCB, assume that the reward distribution for an arm is modeled by a stationary linear regression. When the linear regression model is non-stationary over time, the regret of LinUCB can scale linearly with time. In this paper, we propose a novel multiscale changepoint detection method for the non-stationary linear bandit problems, called Multiscale-LinUCB, which actively adapts to the changing environment. We also provide theoretical analysis of regret bound for Multiscale-LinUCB algorithm. Experimental results show that our proposed Multiscale-LinUCB algorithm outperforms other state-of-the-art algorithms in non-stationary contextual environments.
Informed Machine Learning – A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems

Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process which leads to the notion of informed machine learning. In this paper, we present a structured overview of various approaches in this field. We provide a definition and propose a concept for informed machine learning which illustrates its building blocks and distinguishes it from conventional machine learning. We introduce a taxonomy that serves as a classification framework for informed machine learning approaches. It considers the source of knowledge, its representation, and its integration into the machine learning pipeline. Based on this taxonomy, we survey related research and describe how different knowledge representations such as algebraic equations, logic rules, or simulation results can be used in learning systems. This evaluation of numerous papers on the basis of our taxonomy uncovers key methods in the field of informed machine learning.
Cooperative Observation of Targets moving over a Planar Graph with Prediction of Positions

Consider a team with two types of agents: targets and observers. Observers are aerial UAVs that observe targets moving on land with their movements restricted to the paths that form a planar graph on the surface. Observers have limited range of vision and targets do not avoid observers. The objective is to maximize the integral of the number of targets observed in the observation interval. Taking advantage of the fact that the future positions of targets in the short term are predictable, we show in this article a modified hill climbing algorithm that surpasses its previous versions in this new setting of the CTO problem.