nils-herrmann/clean_scirepeval huggingface dataset:
Flavor and Collider Signatures of Asymmetric Dark Matter

We consider flavor constraints on, and collider signatures of, asymmetric dark matter (ADM) via higher dimension operators. In the supersymmetric models we consider, $R$-parity-violating (RPV) operators carrying $B\ensuremath{-}L$ interact with $n$ dark matter particles $X$ through an interaction of the form $W={X}^{n}{\mathcal{O}}_{B\ensuremath{-}L}$, where ${\mathcal{O}}_{B\ensuremath{-}L}=q\ensuremath{\ell}{d}^{c}$, ${u}^{c}{d}^{c}{d}^{c}$, $\ensuremath{\ell}\ensuremath{\ell}{e}^{c}$. This interaction ensures that the lightest ordinary supersymmetric particle is unstable to decay into the $X$ sector, leading to a higher multiplicity of final state particles and reduced missing energy at a collider. Flavor-violating processes place constraints on the scale of the higher dimension operator, impacting whether the LOSP decays promptly. While the strongest limitations on RPV from $n\ensuremath{-}\overline{n}$ oscillations and proton decay do not apply to ADM, we analyze the constraints from meson mixing, $\ensuremath{\mu}\ensuremath{-}e$ conversion, $\ensuremath{\mu}\ensuremath{\rightarrow}3e$ and $b\ensuremath{\rightarrow}s{\ensuremath{\ell}}^{+}{\ensuremath{\ell}}^{\ensuremath{-}}$. We show that these flavor constraints, even in the absence of flavor symmetries, allow parameter space for prompt decay to the $X$ sector, with additional jets and leptons in exotic flavor combinations. We study the constraints from existing 8 TeV LHC Supersymmetry (SUSY) searches with (i) 2--6 jets plus missing energy and (ii) 1--2 leptons, 3--6 jets plus missing energy, comparing the constraints on ADM-extended supersymmetry with the usual supersymmetric simplified models.
Information Relaxation is Ultradiffusive

We investigate how the overall response to a piece of information (a story or an article) evolves and relaxes as a function of time in social networks like Reddit, Digg and Youtube. This response or popularity is measured in terms of the number of votes/comments that the story (or article) accrued over time. We find that the temporal evolution of popularity can be described by a universal function whose parameters depend upon the system under consideration. Unlike most previous studies, which empirically investigated the dynamics of voting behavior, we also give a theoretical interpretation of the observed behavior using ultradiffusion. 
Whether it is the inter-arrival time between two consecutive votes on a story on Reddit or the comments on a video shared on Youtube, there is always a hierarchy of time scales in information propagation. One vote/comment might occur almost simultaneously with the previous, whereas another vote/comment might occur hours after the preceding one. This hierarchy of time scales leads us to believe that the dynamical response of users to information is ultradiffusive in nature. We show that a ultradiffusion based stochastic process can be used to rationalize the observed temporal evolution.
A Sparse and Adaptive Prior for Time-Dependent Model Parameters

We consider the scenario where the parameters of a probabilistic model are expected to vary over time. We construct a novel prior distribution that promotes sparsity and adapts the strength of correlation between parameters at successive timesteps, based on the data. We derive approximate variational inference procedures for learning and prediction with this prior. We test the approach on two tasks: forecasting nancial quantities from relevant text, and modeling language contingent on time-varying nancial measurements.
The small-world effect is a modern phenomenon

The “small-world effect” is the observation that one can find a short chain of acquaintances, often of no more than a handful of individuals, connecting almost any two people on the planet. It is often expressed in the language of networks, where it is equivalent to the statement that most pairs of individuals are connected by a short path through the acquaintance network. Although the small-world effect is well-established empirically for contemporary social networks, we argue here that it is a relatively recent phenomenon, arising only in the last few hundred years: for most of mankind’s tenure on Earth the social world was large, with most pairs of individuals connected by relatively long chains of acquaintances, if at all. Our conclusions are based on observations about the spread of diseases, which travel over contact networks between individuals and whose dynamics can give us clues to the structure of those networks even when direct network measurements are not available. As an example we consider the spread of the Black Death in 14th-century Europe, which is known to have traveled across the continent in well-defined waves of infection over the course of several years. Using established epidemiological models, we show that such wave-like behavior can occur only if contacts between individuals living far apart are exponentially rare. We further show that if long-distance contacts are exponentially rare, then the shortest chain of contacts between distant individuals is on average a long one. The observation of the wave-like spread of a disease like the Black Death thus implies a network without the small-world effect.
Quantum oscillations in non-Fermi liquids: Implications for high-temperature superconductors

We address quantum oscillation experiments in high Tc superconductors and the evidence from these experiments for a pseudogap versus a Fermi liquid phase at high magnetic fields. As a concrete alternative to a Fermi liquid phase, the pseudogap state we consider derives from earlier work within a Gor'kov-based Landau level approach. Here the normal state pairing gap in the presence of high fields is spatially non-uniform, incorporating small gap values. These, in addition to d-wave gap nodes, are responsible for the persistence of quantum oscillations. Important here are methodologies for distinguishing different scenarios. To this end we examine the temperature dependence of the oscillations. Detailed quantitative analysis of this temperature dependence demonstrates that a high field pseudogap state in the cuprates may well "masquerade" as a Fermi liquid.
Mechanism for enhancement of superconductivity in multi-band systems with odd parity hybridization

The study of multi-band superconductivity is relevant for a variety of systems, from ultra-cold atoms with population imbalance to particle physics and condensed matter. As a consequence, this problem has been widely investigated and has brought to light many new and interesting phenomena. In this work we point out and explore a correspondence between a two-band metal with a k-dependent hybridization and a uniformly polarized fermionic system in the presence of spin–orbit coupling (SOC). We study the ground state phase diagram of this metal in the presence of an attractive interaction. We find remarkable superconducting properties whenever hybridization mixes orbitals of different parities in neighboring sites. We show that in this case hybridization enhances superconductivity and drives the crossover from weak to strong coupling which is analogous with the SOC in cold atoms. We obtain the quantum phase transitions between the normal and superfluid states, as the intensities of different parameters characterizing the metal are varied, including Lifshitz transitions, with no symmetry breaking associated with the appearance of soft modes in the Fermi surface.
Clustering memes in social media

The increasing pervasiveness of social media creates new opportunities to study human social behavior, while challenging our capability to analyze their massive data streams. One of the emerging tasks is to distinguish between different kinds of activities, for example engineered misinformation campaigns versus spontaneous communication. Such detection problems require a formal definition of meme, or unit of information that can spread from person to person through the social network. Once a meme is identified, supervised learning methods can be applied to classify different types of communication. The appropriate granularity of a meme, however, is hardly captured from existing entities such as tags and keywords. Here we present a framework for the novel task of detecting memes by clustering messages from large streams of social data. We evaluate various similarity measures that leverage content, metadata, network features, and their combinations. We also explore the idea of pre-clustering on the basis of existing entities. A systematic evaluation is carried out using a manually curated dataset as ground truth. Our analysis shows that pre-clustering and a combination of heterogeneous features yield the best trade-off between number of clusters and their quality, demonstrating that a simple combination based on pairwise maximization of similarity is as effective as a non-trivial optimization of parameters. Our approach is fully automatic, unsupervised, and scalable for real-time detection of memes in streaming data.
Traveling trends: social butterflies or frequent fliers?

Trending topics are the online conversations that grab collective attention on social media. They are continually changing and often reflect exogenous events that happen in the real world. Trends are localized in space and time as they are driven by activity in specific geographic areas that act as sources of traffic and information flow. Taken independently, trends and geography have been discussed in recent literature on online social media; although, so far, little has been done to characterize the relation between trends and geography. Here we investigate more than eleven thousand topics that trended on Twitter in 63 main US locations during a period of 50 days in 2013. This data allows us to study the origins and pathways of trends, how they compete for popularity at the local level to emerge as winners at the country level, and what dynamics underlie their production and consumption in different geographic areas. We identify two main classes of trending topics: those that surface locally, coinciding with three different geographic clusters (East coast, Midwest and Southwest); and those that emerge globally from several metropolitan areas, coinciding with the major air traffic hubs of the country. These hubs act as trendsetters, generating topics that eventually trend at the country level, and driving the conversation across the country. This poses an intriguing conjecture, drawing a parallel between the spread of information and diseases: Do trends travel faster by airplane than over the Internet?
Analyzing Big Data with Dynamic Quantum Clustering

How does one search for a needle in a multi-dimensional haystack without knowing what a needle is and without knowing if there is one in the haystack? This kind of problem requires a paradigm shift - away from hypothesis driven searches of the data - towards a methodology that lets the data speak for itself. Dynamic Quantum Clustering (DQC) is such a methodology. DQC is a powerful visual method that works with big, high-dimensional data. It exploits variations of the density of the data (in feature space) and unearths subsets of the data that exhibit correlations among all the measured variables. The outcome of a DQC analysis is a movie that shows how and why sets of data-points are eventually classified as members of simple clusters or as members of - what we call - extended structures. This allows DQC to be successfully used in a non-conventional exploratory mode where one searches data for unexpected information without the need to model the data. We show how this works for big, complex, real-world datasets that come from five distinct fields: i.e., x-ray nano-chemistry, condensed matter, biology, seismology and finance. These studies show how DQC excels at uncovering unexpected, small - but meaningful - subsets of the data that contain important information. We also establish an important new result: namely, that big, complex datasets often contain interesting structures that will be missed by many conventional clustering techniques. Experience shows that these structures appear frequently enough that it is crucial to know they can exist, and that when they do, they encode important hidden information. In short, we not only demonstrate that DQC can be flexibly applied to datasets that present significantly different challenges, we also show how a simple analysis can be used to look for the needle in the haystack, determine what it is, and find what this means.
Vacuum Energy Accretion and Primordial Black Holes in Brans-Dicke theory

In this paper, we investigate the evolution of primordial black hole (PBH) in vacuum-dominated era within Brans-Dicke cosmology. We consider the accretion of vacuum energy by PBHs and found that vacuum energy accretion efficiency should be less than 0.61. We also study the evaporation of PBHs where we conclude that larger the value of accretion efficiency longer live the PBHs. We also find that PBHs evaporate at a quicker rate in Brans-Dicke theory compared with Standard Cosmology.
RADIOACTIVELY POWERED EMISSION FROM BLACK HOLE–NEUTRON STAR MERGERS

Detection of the electromagnetic counterparts of gravitational wave (GW) sources is important to unveil the nature of compact binary coalescences. We perform three-dimensional, time-dependent, multi-frequency radiative transfer simulations for radioactively powered emission from the ejecta of black hole (BH)–neutron star (NS) mergers. Depending on the BH to NS mass ratio, spin of the BH, and equations of state of dense matter, BH–NS mergers can eject more material than NS–NS mergers. In such cases, radioactively powered emission from the BH–NS merger ejecta can be more luminous than that from NS–NS mergers. We show that, in spite of the expected larger distances to BH–NS merger events, the observed brightness of BH–NS mergers can be comparable to or even higher than that of NS–NS mergers. We find that, when the tidally disrupted BH–NS merger ejecta are confined to a small solid angle, the emission from BH–NS merger ejecta tends to be bluer than that from NS–NS merger ejecta for a given total luminosity. Thanks to this property, we might be able to distinguish BH–NS merger events from NS–NS merger events by multi-band observations of the radioactively powered emission. In addition to the GW observations, such electromagnetic observations can potentially provide independent information on the progenitors of GW sources and the nature of compact binary coalescences.
Feature Selection with Annealing for Computer Vision and Big Data Learning

Many computer vision and medical imaging problems are faced with learning from large-scale datasets, with millions of observations and features. In this paper we propose a novel efficient learning scheme that tightens a sparsity constraint by gradually removing variables based on a criterion and a schedule. The attractive fact that the problem size keeps dropping throughout the iterations makes it particularly suitable for big data learning. Our approach applies generically to the optimization of any differentiable loss function, and finds applications in regression, classification and ranking. The resultant algorithms build variable screening into estimation and are extremely simple to implement. We provide theoretical guarantees of convergence and selection consistency. In addition, one dimensional piecewise linear response functions are used to account for nonlinearity and a second order prior is imposed on these functions to avoid overfitting. Experiments on real and synthetic data show that the proposed method compares very well with other state of the art methods in regression, classification and ranking while being computationally very efficient and scalable.
Evidence for Water in the Rocky Debris of a Disrupted Extrasolar Minor Planet

Remnants of a Water-Bearing World Stars like the Sun end their lives as white dwarfs. Farihi et al. (p. 218) used detailed spectroscopic analysis of a debris-accreting white dwarf, along with knowledge that such systems accrete this debris from remnants of rocky planetary bodies, to derive the water content in a disrupted extrasolar body. The findings suggest that the white dwarf contains the signature of a rocky minor planet composed of 26% water by mass. Spectroscopic analysis of a debris-accreting star in its latest stage of life reveals the remnants of a water-bearing world. The existence of water in extrasolar planetary systems is of great interest because it constrains the potential for habitable planets and life. We have identified a circumstellar disk that resulted from the destruction of a water-rich and rocky extrasolar minor planet. The parent body formed and evolved around a star somewhat more massive than the Sun, and the debris now closely orbits the white dwarf remnant of the star. The stellar atmosphere is polluted with metals accreted from the disk, including oxygen in excess of that expected for oxide minerals, indicating that the parent body was originally composed of 26% water by mass. This finding demonstrates that water-bearing planetesimals exist around A- and F-type stars that end their lives as white dwarfs.
Supernova enrichment and dynamical histories of solar-type stars in clusters

We use N-body simulations of star cluster evolution to explore the hypothesis that short-lived radioactive isotopes found in meteorites, such as Al-26, were delivered to the Sun's protoplanetary disc from a supernova at the epoch of Solar system formation. We cover a range of star cluster formation parameter space and model both clusters with primordial substructure and those with smooth profiles. We also adopt different initial virial ratios - from cool, collapsing clusters to warm, expanding associations. In each cluster, we place the same stellar population; the clusters each have 2100 stars and contain one massive 25 M-circle dot star which is expected to explode as a supernova at about 6.6Myr. We determine the number of solar (G)-type stars that are within 0.1-0.3 pc of the 25 M-circle dot star at the time of the supernova, which is the distance required to enrich the protoplanetary disc with the 26Al abundances found in meteorites. We then determine how many of these G-dwarfs are unperturbed 'singletons'; stars which are never in close binaries, nor suffer sub-100 au encounters, and which also do not suffer strong dynamical perturbations. The evolution of a suite of 20 initially identical clusters is highly stochastic, with the supernova enriching over 10 G-dwarfs in some clusters, and none at all in others. Typically, only similar to 25 per cent of clusters contain enriched, unperturbed singletons, and usually only one to two per cluster (from a total of 96 G-dwarfs in each cluster). The initial conditions for star formation do not strongly affect the results, although a higher fraction of supervirial (expanding) clusters would contain enriched G-dwarfs if the supernova occurred earlier than 6.6Myr. If we sum together simulations with identical initial conditions, then similar to 1 per cent of all G-dwarfs in our simulations are enriched, unperturbed singletons.
Quantum Nucleodynamics

The core ideas underlying a quantitative, bottom-up theory of nuclear structure, i.e., quantum nucleodynamics, are introduced. The replacement of the fictitious"mean-field"approximation of the nuclear force with the empirically-known nuclear potential-well is the essential first step. From there, calculation of short-range nucleon-nucleon effects can be achieved on the basis of a unique lattice representation of nuclear quantum space, as introduced by Wigner in 1937. The lattice reproduces all of the n-shells and j- and m-subshells of the well-established independent-particle model on a geometrical basis, and represents a return to a comprehensible, coordinate-space depiction of the atomic nucleus. Multiple regression results for the binding energies of 273 stable isotopes using 1st-3rd neighbor interactions in the lattice are presented as"in principle"demonstration of the utility of the nuclear lattice. Noteworthy is the fact that all near-neighbor nuclear binding effects fall between -1.2 and 3.0 MeV.
Regulatory-Optimal Funding

Funding is a cost to trading desks that they see as an input. Current FVA-related literature reflects this by also taking funding costs as an input, usually constant, and always risk-neutral. However, this funding curve is the output from a Treasury point of view. Treasury must consider Regulatory-required liquidity buffers, and both risk-neutral (Q) and physical measures (P). We describe the Treasury funding problem and optimize against both measures, using the Regulatory requirement as a constraint. We develop theoretically optimal strategies for Q and P, then demonstrate a combined approach in four markets (USD, JPY, EUR, GBP). Since we deal with physical measures we develop appropriate statistical tests, and demonstrate highly significant (p
Impact of order three cycles in complex network spectra

The asymptotic behaviour of dynamical processes in networks can be expressed as a function of spectral properties of the Adjacency and Laplacian matrices. Although many theoretical results are known for the spectra of traditional configuration models, networks generated through these models fail to describe many topological features of real-world networks, in particular non-null values for the clustering coefficient. Here we study the effects of cycles or order three (triangles) in network spectra. By using recent advances in random matrix theory, we determine the spectrum distribution of the network Adjacency matrix as a function of the average number of triangles attached to each node for networks without modular structure and degree-degree correlations. Furthermore we show that cycles of order three have a weak influence on the Laplacian eigenvalues, fact that explains the recent controversy on the dynamics of clustered networks. Our findings can shed light in the study of how particular kinds of subgraphs influence network dynamics.
On the reliability of seasonal climate forecasts

Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: how good are seasonal forecasts on a scale of 1–5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal forecasts are made from ensembles of integrations of numerical models of climate. We argue that ‘goodness’ should be assessed first and foremost in terms of the probabilistic reliability of these ensemble-based forecasts; reliable inputs are essential for any forecast-based decision-making. We propose that a ‘5’ should be reserved for systems that are not only reliable overall, but where, in particular, small ensemble spread is a reliable indicator of low ensemble forecast error. We study the reliability of regional temperature and precipitation forecasts of the current operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts, universally regarded as one of the world-leading operational institutes producing seasonal climate forecasts. A wide range of ‘goodness’ rankings, depending on region and variable (with summer forecasts of rainfall over Northern Europe performing exceptionally poorly) is found. Finally, we discuss the prospects of reaching ‘5’ across all regions and variables in 30 years time.
Sublinear Column-wise Actions of the Matrix Exponential on Social Networks

We consider stochastic transition matrices from large social and information networks. For these matrices, we describe and evaluate three fast methods to estimate one column of the matrix exponential. The methods are designed to exploit the properties inherent in social networks, such as a power-law degree distribution. Using only this property, we prove that one of our three algorithms has a sublinear runtime. We present further experimental evidence showing that all three of them run quickly on social networks with billions of edges, and they accurately identify the largest elements of the column.