nils-herrmann/clean_scirepeval huggingface dataset:
Kaggle LSHTC4 Winning Solution

Our winning submission to the 2014 Kaggle competition for Large Scale Hier-archical Text Classi cation (LSHTC) consists mostly of an ensemble of sparsegenerative models extending Multinomial Naive Bayes. The base-classi ers con-sist of hierarchically smoothed models combining document, label, and hierar-chy level Multinomials, with feature pre-processing using variants of TF-IDFand BM25. Additional diversi cation is introduced by di erent types of foldsand random search optimization for di erent measures. The ensemble algorithmoptimizes macroFscore by predicting the documents for each label, instead ofthe usual prediction of labels per document. Scores for documents are predictedby weighted voting of base-classi er outputs with a variant of Feature-WeightedLinear Stacking. The number of documents per label is chosen using label priorsand thresholding of vote scores.This document describes the models and software used to build our solution.Reproducing the results for our solution can be done by running the scripts in-cluded in the Kaggle package
Triangular constellations in fractal measures

The local structure of a fractal set is described by its dimension D, which is the exponent of a power-law relating the mass in a ball to its radius . It is desirable to characterise the shapes of constellations of points sampling a fractal measure, as well as their masses. The simplest example is the distribution of shapes of triangles formed by triplets of points, which we investigate for fractals generated by chaotic dynamical systems. The most significant parameter describing the triangle shape is the ratio z of its area to the radius of gyration squared. We show that the probability density of z has a phase transition: P(z) is independent of ε and approximately uniform below a critical flow compressibility , which we estimate. For \beta_{\text{c}}$ ?> the distribution appears to be described by two power laws: when , and when .
Evaluating gambles using dynamics.

Gambles are random variables that model possible changes in wealth. Classic decision theory transforms money into utility through a utility function and defines the value of a gamble as the expectation value of utility changes. Utility functions aim to capture individual psychological characteristics, but their generality limits predictive power. Expectation value maximizers are defined as rational in economics, but expectation values are only meaningful in the presence of ensembles or in systems with ergodic properties, whereas decision-makers have no access to ensembles, and the variables representing wealth in the usual growth models do not have the relevant ergodic properties. Simultaneously addressing the shortcomings of utility and those of expectations, we propose to evaluate gambles by averaging wealth growth over time. No utility function is needed, but a dynamic must be specified to compute time averages. Linear and logarithmic "utility functions" appear as transformations that generate ergodic observables for purely additive and purely multiplicative dynamics, respectively. We highlight inconsistencies throughout the development of decision theory, whose correction clarifies that our perspective is legitimate. These invalidate a commonly cited argument for bounded utility functions.
Optical phase locking of two infrared continuous wave lasers separated by 100  THz.

We report on phase locking of two continuous wave IR laser sources separated by 100 THz emitting around 1029 and 1544 nm, respectively. Our approach uses three independent harmonic generation processes of the IR laser frequencies in periodically poled MgO:LiNbO3 crystals to generate second and third harmonics of those two IR sources. The beat note between the two independent green radiations generated around 515 nm is used to phase lock one IR laser to the other, with tunable radio frequency offset. In this way, the whole setup operates as a mini-frequency comb emitting four intense optical radiations (1544, 1029, 772, and 515 nm), with output powers at least three orders of magnitude higher than the available power from each mode emitted by femtosecond lasers.
Multilayer Analysis and Visualization of Networks

Multilayer relationships among entities and information about entities must be accompanied by the means to analyse, visualize and obtain insights from such data. We present open-source software (muxViz) that contains a collection of algorithms for the analysis of multilayer networks, which are an important way to represent a large variety of complex systems throughout science and engineering. We demonstrate the ability of muxViz to analyse and interactively visualize multilayer data using empirical genetic, neuronal and transportation networks. Our software is available at https://github.com/manlius/muxViz.
Reasoning with Probabilistic Logics

The interest in the combination of probability with logics for modeling the world has rapidly increased in the last few years. One of the most effective approaches is the Distribution Semantics which was adopted by many logic programming languages and in Descripion Logics. In this paper, we illustrate the work we have done in this research field by presenting a probabilistic semantics for description logics and reasoning and learning algorithms. In particular, we present in detail the system TRILL P, which computes the probability of queries w.r.t. probabilistic knowledge bases, which has been implemented in Prolog.
Life on a tidally-locked planet

A tidally-locked planet in its orbit around a star keeps the same face towards the star. This happens when the rotation period of the planet around its own axis becomes equal to its revolution period around the star. Many questions then arise. What gives rise to a tidal locking? Are there any tidally-locked planets somewhere? What will be the atmospheric system of a tidally-locked planet? Could life be sustained on a tidally-locked planet? What will be a sort of DAY in the life of a denizen of a tidally-locked planet? We dwell on these questions.
K-NS: Section-Based Outlier Detection in High Dimensional Space

Finding rare information hidden in a huge amount of data from the Internet is a necessary but complex issue. Many researchers have studied this issue and have found effective methods to detect anomaly data in low dimensional space. However, as the dimension increases, most of these existing methods perform poorly in detecting outliers because of "high dimensional curse". Even though some approaches aim to solve this problem in high dimensional space, they can only detect some anomaly data appearing in low dimensional space and cannot detect all of anomaly data which appear differently in high dimensional space. To cope with this problem, we propose a new k-nearest section-based method (k-NS) in a section-based space. Our proposed approach not only detects outliers in low dimensional space with section-density ratio but also detects outliers in high dimensional space with the ratio of k-nearest section against average value. After taking a series of experiments with the dimension from 10 to 10000, the experiment results show that our proposed method achieves 100% precision and 100% recall result in the case of extremely high dimensional space, and better improvement in low dimensional space compared to our previously proposed method.
SEARCHING FOR DARK MATTER ANNIHILATION IN THE SMITH HIGH-VELOCITY CLOUD

Recent observations suggest that some high-velocity clouds may be confined by massive dark matter halos. In particular, the proximity and proposed dark matter content of the Smith Cloud make it a tempting target for the indirect detection of dark matter annihilation. We argue that the Smith Cloud may be a better target than some Milky Way dwarf spheroidal satellite galaxies and use γ-ray observations from the Fermi Large Area Telescope to search for a dark matter annihilation signal. No significant γ-ray excess is found coincident with the Smith Cloud, and we set strong limits on the dark matter annihilation cross section assuming a spatially extended dark matter profile consistent with dynamical modeling of the Smith Cloud. Notably, these limits exclude the canonical thermal relic cross section (∼ 3 × 10−26 cm3 s−1) for dark matter masses ≲ 30 GeV annihilating via the or τ+τ− channels for certain assumptions of the dark matter density profile; however, uncertainties in the dark matter content of the Smith Cloud may significantly weaken these constraints.
Detecting dark matter with imploding pulsars in the galactic center.

The paucity of old millisecond pulsars observed at the galactic center of the Milky Way could be the result of dark matter accumulating in and destroying neutron stars. In regions of high dark matter density, dark matter clumped in a pulsar can exceed the Schwarzschild limit and collapse into a natal black hole which destroys the pulsar. We examine what dark matter models are consistent with this hypothesis and find regions of parameter space where dark matter accumulation can significantly degrade the neutron star population within the galactic center while remaining consistent with observations of old millisecond pulsars in globular clusters and near the solar position. We identify what dark matter couplings and masses might cause a young pulsar at the galactic center to unexpectedly extinguish. Finally, we find that pulsar collapse age scales inversely with the dark matter density and linearly with the dark matter velocity dispersion. This implies that maximum pulsar age is spatially dependent on position within the dark matter halo of the Milky Way. In turn, this pulsar age spatial dependence will be dark matter model dependent.
Stars as resonant absorbers of gravitational waves.

Quadrupole oscillation modes in stars can resonate with incident gravitational waves (GWs), and grow non-linear at the expense of GW energy. Stars near massive black hole binaries (MBHBs) can act as GW-charged batteries, discharging radiatively. Mass-loss from these stars can prompt MBHB accretion at near-Eddington rates. GW opacity is independent of amplitude, so distant resonating stars can eclipse GW sources. Absorption by the Sun of GWs from Galactic white dwarf binaries may be detectable with second-generation space-based GW detectors as a shadow within a complex diffraction pattern.
How Community Feedback Shapes User Behavior

Social media systems rely on user feedback and rating mechanisms for personalization, ranking, and content filtering. However, when users evaluate content contributed by fellow users (e.g., by liking a post or voting on a comment), these evaluations create complex social feedback effects. This paper investigates how ratings on a piece of content affect its author's future behavior. By studying four large comment-based news communities, we find that negative feedback leads to significant behavioral changes that are detrimental to the community. Not only do authors of negatively-evaluated content contribute more, but also their future posts are of lower quality, and are perceived by the community as such. Moreover, these authors are more likely to subsequently evaluate their fellow users negatively, percolating these effects through the community. In contrast, positive feedback does not carry similar effects, and neither encourages rewarded authors to write more, nor improves the quality of their posts. Interestingly, the authors that receive no feedback are most likely to leave a community. Furthermore, a structural analysis of the voter network reveals that evaluations polarize the community the most when positive and negative votes are equally split.
Collapsing thin shells with rotation

We construct exact solutions describing the motion of rotating thin shells in a fully backreacted five-dimensional rotating black hole spacetime. The radial equation of motion follows from the Darmois-Israel junction conditions, where both interior and exterior geometries are taken to be equal angular momenta Myers-Perry solutions. We show that rotation generates anisotropic pressures and momentum along the shell. Gravitational collapse scenarios including rotation are analyzed and a new class of stationary solutions is introduced. Energy conditions for the matter shell are briefly discussed.
Training Restricted Boltzmann Machine by Perturbation

A new approach to maximum likelihood learning of discrete graphical models and RBM in particular is introduced. Our method, Perturb and Descend (PD) is inspired by two ideas (I) perturb and MAP method for sampling (II) learning by Contrastive Divergence minimization. In contrast to perturb and MAP, PD leverages training data to learn the models that do not allow efficient MAP estimation. During the learning, to produce a sample from the current model, we start from a training data and descend in the energy landscape of the "perturbed model", for a fixed number of steps, or until a local optima is reached. For RBM, this involves linear calculations and thresholding which can be very fast. Furthermore we show that the amount of perturbation is closely related to the temperature parameter and it can regularize the model by producing robust features resulting in sparse hidden layer activation.
The effect of wording on message propagation: Topic- and author-controlled natural experiments on Twitter

Consider a person trying to spread an important message on a social network. He/she can spend hours trying to craft the message. Does it actually matter? While there has been extensive prior work looking into predicting popularity of social-media content, the effect of wording per se has rarely been studied since it is often confounded with the popularity of the author and the topic. To control for these confounding factors, we take advantage of the surprising fact that there are many pairs of tweets containing the same url and written by the same user but employing different wording. Given such pairs, we ask: which version attracts more retweets? This turns out to be a more difficult task than predicting popular topics. Still, humans can answer this question better than chance (but far from perfectly), and the computational methods we develop can do better than both an average human and a strong competing method trained on non-controlled data.
Equitable random graphs

Random graph models have played a dominant role in the theoretical study of networked systems. The Poisson random graph of Erdős and Rényi, in particular, as well as the so-called configuration model, have served as the starting point for numerous calculations. In this paper we describe another large class of random graph models, which we call equitable random graphs and which are flexible enough to represent networks with diverse degree distributions and many nontrivial types of structure, including community structure, bipartite structure, degree correlations, stratification, and others, yet are exactly solvable for a wide range of properties in the limit of large graph size, including percolation properties, complete spectral density, and the behavior of homogeneous dynamical systems, such as coupled oscillators or epidemic models.
Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models

We present a machine learning framework for modeling protein dynamics. Our approach uses L1-regularized, reversible hidden Markov models to understand large protein datasets generated via molecular dynamics simulations. Our model is motivated by three design principles: (1) the requirement of massive scalability; (2) the need to adhere to relevant physical law; and (3) the necessity of providing accessible interpretations, critical for both cellular biology and rational drug design. We present an EM algorithm for learning and introduce a model selection criteria based on the physical notion of convergence in relaxation timescales. We contrast our model with standard methods in biophysics and demonstrate improved robustness. We implement our algorithm on GPUs and apply the method to two large protein simulation datasets generated respectively on the NCSA Bluewaters supercomputer and the Folding@Home distributed computing network. Our analysis identifies the conformational dynamics of the ubiquitin protein critical to cellular signaling, and elucidates the stepwise activation mechanism of the c-Src kinase protein.
Events and Controversies: Influences of a Shocking News Event on Information Seeking

It has been suggested that online search and retrieval contributes to the intellectual isolation of users within their preexisting ideologies, where people's prior views are strengthened and alternative viewpoints are infrequently encountered. This so-called "filter bubble" phenomenon has been called out as especially detrimental when it comes to dialog among people on controversial, emotionally charged topics, such as the labeling of genetically modified food, the right to bear arms, the death penalty, and online privacy. We seek to identify and study information-seeking behavior and access to alternative versus reinforcing viewpoints following shocking, emotional, and large-scale news events. We choose for a case study to analyze search and browsing on gun control/rights, a strongly polarizing topic for both citizens and leaders of the United States. We study the period of time preceding and following a mass shooting to understand how its occurrence, follow-on discussions, and debate may have been linked to changes in the patterns of searching and browsing. We employ information-theoretic measures to quantify the diversity of Web domains of interest to users and understand the browsing patterns of users. We use these measures to characterize the influence of news events on these web search and browsing patterns.
Electro-optical graphene plasmonic logic gates.

The versatile control of graphene's plasmonic modes via an external gate-voltage inspires us to design efficient electro-optical graphene plasmonic logic gates at the midinfrared wavelengths. We show that these devices are superior to the conventional optical logic gates because the former possess cut-off states and interferometric effects. Moreover, the designed six basic logic gates (i.e., NOR/AND, NAND/OR, XNOR/XOR) achieved not only ultracompact size lengths of less than λ/28 with respect to the operating wavelength of 10 μm, but also a minimum extinction ratio as high as 15 dB. These graphene plasmonic logic gates are potential building blocks for future nanoscale midinfrared photonic integrated circuits.
claspfolio 2: Advances in Algorithm Selection for Answer Set Programming

Building on the award-winning, portfolio-based ASP solver claspfolio, we present claspfolio 2, a modular and open solver architecture that integrates several different portfolio-based algorithm selection approaches and techniques. The claspfolio 2 solver framework supports various feature generators, solver selection approaches, solver portfolios, as well as solver-schedule-based pre-solving techniques. The default configuration of claspfolio 2 relies on a light-weight version of the ASP solver clasp to generate static and dynamic instance features. The flexible open design of claspfolio 2 is a distinguishing factor even beyond ASP. As such, it provides a unique framework for comparing and combining existing portfolio-based algorithm selection approaches and techniques in a single, unified framework. Taking advantage of this, we conducted an extensive experimental study to assess the impact of different feature sets, selection approaches and base solver portfolios. In addition to gaining substantial insights into the utility of the various approaches and techniques, we identified a default configuration of claspfolio 2 that achieves substantial performance gains not only over clasp's default configuration and the earlier version of claspfolio, but also over manually tuned configurations of clasp.
Planck data and ultralight axions

We examine the effects of photon-axion mixing on the CMB. We show that if there are very underdense regions between us and the last scattering surface which contain coherent magnetic fields (whose strength can be orders of magnitude weaker than the current bounds), then photon-axion mixing can induce observable deviations in the CMB spectrum. Specifically, we show that the mixing can give rise to non-thermal spots on the CMB sky. As an example we consider the well known CMB cold spot, which according to the Planck data has a weak distortion from a black body spectrum, that can be fit by our model. While this explanation of the non-thermality in the region of the cold spot is quite intriguing, photon-axion oscillations do not explain the temperature of the cold spot itself. Nevertheless we demonstrate the possible sensitivity of the CMB to ultralight axions which could be exploited by observers.
Deconfinement transition as black hole formation by the condensation of QCD strings

We argue that the deconfinement transition of large-N Yang-Mills theory is the condensation of very long and self-intersecting chromo-electric flux strings (QCD string), which is analogous to the formation of a black hole in string theory. We do this by using lattice gauge theory and matrix models. As evidence, we derive an analytic formula for the deconfinement temperature in the strong coupling limit and confirm it numerically. Dual gravity descriptions interpreted in this manner should make it possible to understand the details of the formation of black holes in terms of fundamental strings. We argue that very simple matrix models capture the essence of the formation of black holes.
Logic and Constraint Logic Programming for Distributed Constraint Optimization

The field of Distributed Constraint Optimization Problems (DCOPs) has gained momentum, thanks to its suitability in capturing complex problems (e.g., multi-agent coordination and resource allocation problems) that are naturally distributed and cannot be realistically addressed in a centralized manner. The state of the art in solving DCOPs relies on the use of ad-hoc infrastructures and ad-hoc constraint solving procedures. This paper investigates an infrastructure for solving DCOPs that is completely built on logic programming technologies. In particular, the paper explores the use of a general constraint solver (a constraint logic programming system in this context) to handle the agent-level constraint solving. The preliminary experiments show that logic programming provides benefits over a state-of-the-art DCOP system, in terms of performance and scalability, opening the doors to the use of more advanced technology (e.g., search strategies and complex constraints) for solving DCOPs.
Turkish text retrieval experiments using LEMUR toolkit

We used Lemur Toolkit, an open source toolkit designed for Information Retrieval (IR) research, for our automated indexing and retrieval experiments on a TREC-like test collection for Turkish. We study and compare three retrieval models Lemur supports, especially Language modeling approach to IR, combined with language specific preprocessing techniques. Our experiments show that all retrieval models benefits from language specific preprocessing in terms of retrieval quality.  Modeling approach is the best performing retrieval model when language specific preprocessing applied.