nils-herrmann/clean_scirepeval huggingface dataset:
Existence of outsiders as a characteristic of online communication networks

Online social networking services involve communication activities between large number of individuals over the public Internet and their crawled records are often regarded as proxies of real (i.e., offline) interaction structure. However, structure observed in these records might differ from real counterparts because individuals may behave differently online and non-human accounts may even participate. To understand the difference between online and real social networks, we investigate an empirical communication network between users on Twitter, which is perhaps one of the largest social networking services. We define a network of user pairs that send reciprocal messages. Based on the correlation between degree of adjacent nodes observed in this network, we hypothesize that this network differs from conventional understandings in the sense that there is a small number of distinctive users that we call outsiders. Outsiders do not belong to any user groups but they are connected with different groups, while not being well connected with each other. We identify outsiders by maximizing the degree assortativity coefficient of the network via node removal, thereby confirming that local structural properties of outsiders identified are consistent with our hypothesis. Our findings suggest that the existence of outsiders should be considered when using Twitter communication networks for social network analysis.
Consistency Relations for Large Field Inflation: Non-minimal Coupling

We derive the consistency relations for a chaotic inflation model with a non-minimal coupling to gravity. For a quadratic potential in the limit of a small non-minimal coupling parameter $\xi$ and for a quartic potential without assuming small $\xi$, we give the consistency relations among the spectral index $n_s$, the tensor-to-scalar ratio $r$ and the running of the spectral index $\alpha$. We find that unlike $r$, $\alpha$ is less sensitive to $\xi$. If $r<0.1$, then $\xi$ is constrained to $\xi<0$ and $\alpha$ is predicted to be $\alpha\simeq -8\times 10^{-4}$ for a quartic potential. For a general monomial potential, $\alpha$ is constrained in the range $-2.7\times 10^{-3}<\alpha< -6\times 10^{-4}$ as long as $|\xi|\leq 10^{-3}$ if $r<0.1$.
I-Love relations for incompressible stars and realistic stars

In spite of the diversity in the equations of state of nuclear matter, the recently discovered I-Love-Q relations [Yagi and Yunes, Science {\bf 341}, 365 (2013)], which relate the moment of inertia, tidal Love number (deformability) and the spin-induced quadrupole moment of compact stars, hold for various kinds of realistic neutron stars and quark stars. While the physical origin of such universality is still a current issue, the observation that the I-Love-Q relations of incompressible stars can well approximate those of realistic compact stars hints at a new direction to approach the problem. In this paper, by establishing recursive post-Minkowskian expansion for the moment of inertia and the tidal deformability of incompressible stars, we analytically derive the I-Love relation for incompressible stars and show that the so obtained formula can be used to accurately predict the behavior of realistic compact stars from the Newtonian limit to the maximum mass limit.
Post-firewall paradoxes

We derive two variations of the firewall paradox for evaporating black holes. Our approach sweeps away many unnecessary assumptions. There is no need: to assume ‘nice slices’ through the black hole spacetime; to decode Hawking radiation; to make any assumptions about the physics of Planck scale black holes; and other simplifications.  A contradiction exists between: 1.a) completely unitarily evaporating black holes, 1.b) a freely falling observer notices nothing special until they pass well within a large black hole’s horizon, and 1.c) the black hole interior Hilbert space dimensionality may be well approximated as the exponential of the Bekenstein-Hawking entropy.  A contradiction exists between: 2.a) completely unitarily evaporating black holes, 2.b) large black holes are described by local physics, and 2.c) externally, a large black hole should resemble its classical theoretical counterpart (aside from its slow evaporation). At least one of these conventionally accepted verities from each theorem must be false.
Localized Complexities for Transductive Learning

We show two novel concentration inequalities for suprema of empirical processes when sampling without replacement, which both take the variance of the functions into account. While these inequalities may potentially have broad applications in learning theory in general, we exemplify their significance by studying the transductive setting of learning theory. For which we provide the first excess risk bounds based on the localized complexity of the hypothesis class, which can yield fast rates of convergence also in the transductive learning setting. We give a preliminary analysis of the localized complexities for the prominent case of kernel classes.
Two-Phase Equilibrium Properties in Charged Topological Dilaton AdS Black Holes

We discuss phase transition of the charged topological dilaton AdS black holes by Maxwell equal area law. The two phases involved in the phase transition could coexist and we depict the coexistence region in diagrams. The two-phase equilibrium curves in diagrams are plotted, the Clapeyron equation for the black hole is derived, and the latent heat of isothermal phase transition is investigated. We also analyze the parameters of the black hole that could have an effect on the two-phase coexistence. The results show that the black holes may go through a small-large phase transition similar to that of a usual nongravity thermodynamic system.
On magnetized neutron stars

In this work we review the formalism normally used in the literature about the effects of density-dependent magnetic fields on the properties of neutron and quark stars, expose some ambiguities that arise and propose a way to solve the related problem. Our approach explores more deeply the concept of pressure, yielding the so called chaotic magnetic field formalism for the stress tensor. We also use a different way of introducing a variable magnetic field, which depends on the energy density rather than on the baryonic density, which allows us to build a parameter free model.
Holographic imaging of unlabelled sperm cells for semen analysis: a review

Male reproductive health in both humans and animals is an important research field in biological study. In order to characterize the morphology, the motility and the concentration of the sperm cells, which are the most important parameters to feature them, digital holography demonstrated to be an attractive technique. Indeed, it is a label‐free, non‐invasive and high‐resolution method that enables the characterization of live specimen. The review is intended both for summarizing the state‐of‐art on the semen analysis and recent achievement obtained by means of digital holography and for exploring new possible applications of digital holography in this field. Quantitative phase maps of living swimming spermatozoa.
Radio for hidden-photon dark matter detection

We propose a resonant electromagnetic detector to search for hidden-photon dark matter over an extensive range of masses. Hidden-photon dark matter can be described as a weakly coupled “hidden electric field,” oscillating at a frequency fixed by the mass, and able to penetrate any shielding. At low frequencies (compared to the inverse size of the shielding), we find that the observable effect of the hidden photon inside any shielding is a real, oscillating magnetic field. We outline experimental setups designed to search for hidden-photon dark matter, using a tunable, resonant LC circuit designed to couple to this magnetic field. Our “straw man” setups take into consideration resonator design, readout architecture and noise estimates. At high frequencies, there is an upper limit to the useful size of a single resonator set by 1/ν. However, many resonators may be multiplexed within a hidden-photon coherence length to increase the sensitivity in this regime. Hidden-photon dark matter has an enormous range of possible frequencies, but current experiments search only over a few narrow pieces of that range. As a result, we find the potential sensitivity of our proposal is many orders of magnitude beyond current limits over an extensive range of frequencies, from 100more » Hz up to 700 GHz and potentially higher.« less
Signal Recovery on Graphs: Variation Minimization

We consider the problem of signal recovery on graphs. Graphs model data with complex structure as signals on a graph. Graph signal recovery recovers one or multiple smooth graph signals from noisy, corrupted, or incomplete measurements. We formulate graph signal recovery as an optimization problem, for which we provide a general solution through the alternating direction methods of multipliers. We show how signal inpainting, matrix completion, robust principal component analysis, and anomaly detection all relate to graph signal recovery and provide corresponding specific solutions and theoretical analysis. We validate the proposed methods on real-world recovery problems, including online blog classification, bridge condition identification, temperature estimation, recommender system for jokes, and expert opinion combination of online blog classification.
$Υ$-DB: A system for data-driven hypothesis management and analytics

The vision of $\Upsilon$-DB introduces deterministic scientific hypotheses as a kind of uncertain and probabilistic data, and opens some key technical challenges for enabling data-driven hypothesis management and analytics. The $\Upsilon$-DB system addresses those challenges throughout a design-by-synthesis pipeline that defines its architecture. It processes hypotheses from their XML-based extraction to encoding as uncertain and probabilistic U-relational data, and eventually to their conditioning in the presence of observations. In this demo we present a first prototype of the $\Upsilon$-DB system. We showcase its core innovative features by means of use case scenarios in computational science in which the hypotheses are extracted from a model repository on the web and evaluated (rated/ranked) as probabilistic data.
Pattern Decomposition with Complex Combinatorial Constraints: Application to Materials Discovery

Identifying important components or factors in large amounts of noisy data is a key problem in machine learning and data mining. Motivated by a pattern decomposition problem in materials discovery, aimed at discovering new materials for renewable energy, e.g. for fuel and solar cells, we introduce CombiFD, a framework for factor based pattern decomposition that allows the incorporation of a-priori knowledge as constraints, including complex combinatorial constraints. In addition, we propose a new pattern decomposition algorithm, called AMIQO, based on solving a sequence of (mixed-integer) quadratic programs. Our approach considerably outperforms the state of the art on the materials discovery problem, scaling to larger datasets and recovering more precise and physically meaningful decompositions. We also show the effectiveness of our approach for enforcing background knowledge on other application domains.
Quantum energy teleportation across a three-spin Ising chain in a Gibbs state

In general, it is important to identify what is the informational resource for quantum tasks. Quantum energy teleportation (QET) is a quantum task, which attains energy transfer in an operational sense by local operations and classical communication, and is expected to play a role in future development of nano-scale smart grids. We consider QET protocols in a three-element Ising spin system with non-periodic boundary conditions coupled to a thermal bath. The open chain is the minimal model of QET between two edge spins that allows the measurement and operation steps of the QET protocol to be optimized without restriction. It is possible to analyze how two-body correlations of the system, such as mutual information, entanglement and quantum discord, can be resources of this QET at each temperature. In particular, we stress that quantum discord is not the QET resource in some cases, even if arbitrary measurements and operations are available.
Forecasting the Colorado River Discharge Using an Artificial Neural Network (ANN) Approach

Artificial Neural Network (ANN) based model is a computational approach commonly used for modeling the complex relationships between input and output parameters. Prediction of the flow rate of a river is a requisite for any successful water resource management and river basin planning. In the current survey, the effectiveness of an Artificial Neural Network was examined to predict the Colorado River discharge. In this modeling process, an ANN model was used to relate the discharge of the Colorado River to such parameters as the amount of precipitation, ambient temperature and snowpack level at a specific time of the year. The model was able to precisely study the impact of climatic parameters on the flow rate of the Colorado River.
Perspectives for Top Quark Physics at the (I)LC

Linear e+e- colliders provide a rich set of opportunities for top quark physics, crucial for the understanding of electroweak symmetry breaking and for the search for physics beyond the Standard Model. A ttbar threshold scan in e+e- annihilation enables a precise measurement in theoretically well-defined mass schemes with small experimental and theoretical systematic uncertainties. Above the production threshold, the efficient identification of top pair events combined with polarized beams provides the potential to extract the form factors for the top quark couplings with high precision and in a model-independent way, resulting in excellent sensitivity to physics beyond the Standard Model. This contribution provides an overview of top physics at linear colliders based on results from full-simulation studies of top quark pair production in the detectors proposed for ILC and CLIC. In addition, the influence of the luminosity spectrum of a 100 km circular e+e- collider (FCCee) compared to linear colliders on a top threshold scan is briefly discussed.
On the description of the GCR intensity in the last three solar minima

We discuss the main characteristic features in the heliospheric parameters important for the GCR intensity modulation for the last three solar minima (1986--1987, 1996--1997 and 2008--2009). The model for the GCR intensity modulation is considered and the set of the model parameters is chosen which allows the description of the observed GCR intensity distributions at the moments of the maximum GCR intensity in two solar minima (1987 and 1997) normal for the second half of the last century. Then we try to describe with the above model and set of parameters the unusually soft GCR energy spectra at the moments of the maximum GCR intensity in the last solar minimum between cycles 23 and 24 (2009). Our main conclusion is that the most simple way to do so is to reduce the size of the modulation region and, probably, change the rigidity dependence of the diffusion coefficient. The change of both parameters is substantiated by the observations of the solar wind and heliospheric magnetic field.
Temporal and energy behavior of cosmic ray fluxes in the periods of low solar activity

Modulation of galactic cosmic ray intensity is governed by several mechanisms including diffusion, convection, adiabatic energy losses and drift. Relative roles of these factors change in the course of an 11-year solar cycle. That can result in the changes in the energy dependence of the 11-year cosmic ray modulation. The minimum between the solar cycles 23 and 24 was extremely deep and long-lasting which led to the record high cosmic ray fluxes low-energy particles dominating. This was a signature of unusually soft energy spectrum of the cosmic rays. In this work we examine the energy dependence of the 11-year modulation during the last three solar cycles and argue that a soft energy spectrum was observed in the minimum of each cycle however only for particles below of energy around 10 GeV. From mid 1980s the energy dependence of cosmic rays became softer from minimum to minimum of solar activity. The work is based on the cosmic ray data of the spacecraft, balloon-borne and the ground-based observations.
Learning Stochastic Recurrent Networks

Leveraging advances in variational inference, we propose to enhance recurrent neural networks with latent variables, resulting in Stochastic Recurrent Networks (STORNs). The model i) can be trained with stochastic gradient methods, ii) allows structured and multi-modal conditionals at each time step, iii) features a reliable estimator of the marginal likelihood and iv) is a generalisation of deterministic recurrent neural networks. We evaluate the method on four polyphonic musical data sets and motion capture data.
On the Expressive Efficiency of Sum Product Networks

Sum Product Networks (SPNs) are a recently developed class of deep generative models which compute their associated unnormalized density functions using a special type of arithmetic circuit. When certain sufficient conditions, called the decomposability and completeness conditions (or "D&C" conditions), are imposed on the structure of these circuits, marginal densities and other useful quantities, which are typically intractable for other deep generative models, can be computed by what amounts to a single evaluation of the network (which is a property known as "validity"). However, the effect that the D&C conditions have on the capabilities of D&C SPNs is not well understood. 
In this work we analyze the D&C conditions, expose the various connections that D&C SPNs have with multilinear arithmetic circuits, and consider the question of how well they can capture various distributions as a function of their size and depth. Among our various contributions is a result which establishes the existence of a relatively simple distribution with fully tractable marginal densities which cannot be efficiently captured by D&C SPNs of any depth, but which can be efficiently captured by various other deep generative models. We also show that with each additional layer of depth permitted, the set of distributions which can be efficiently captured by D&C SPNs grows in size. This kind of "depth hierarchy" property has been widely conjectured to hold for various deep models, but has never been proven for any of them. Some of our other contributions include a new characterization of the D&C conditions as sufficient and necessary ones for a slightly strengthened notion of validity, and various state-machine characterizations of the types of computations that can be performed efficiently by D&C SPNs.
Classification with Noisy Labels by Importance Reweighting

In this paper, we study a classification problem in which sample labels are randomly corrupted. In this scenario, there is an unobservable sample with noise-free labels. However, before being observed, the true labels are independently flipped with a probability p E [0, 0.5), and the random label noise can be class-conditional. Here, we address two fundamental problems raised by this scenario. The first is how to best use the abundant surrogate loss functions designed for the traditional classification problem when there is label noise. We prove that any surrogate loss function can be used for classification with noisy labels by using importance reweighting, with consistency assurance that the label noise does not ultimately hinder the search for the optimal classifier of the noise-free sample. The other is the open problem of how to obtain the noise rate p. We show that the rate is upper bounded by the conditional probability P( ŶIX) of the noisy sample. Consequently, the rate can be estimated, because the upper bound can be easily reached in classification problems. Experimental results on synthetic and real datasets confirm the efficiency of our methods.
Password Cracking and Countermeasures in Computer Security: A Survey

With the rapid development of internet technologies, social networks, and other related areas, user authentication becomes more and more important to protect the data of the users. Password authentication is one of the widely used methods to achieve authentication for legal users and defense against intruders. There have been many password cracking methods developed during the past years, and people have been designing the countermeasures against password cracking all the time. However, we find that the survey work on the password cracking research has not been done very much. This paper is mainly to give a brief review of the password cracking methods, import technologies of password cracking, and the countermeasures against password cracking that are usually designed at two stages including the password design stage (e.g. user education, dynamic password, use of tokens, computer generations) and after the design (e.g. reactive password checking, proactive password checking, password encryption, access control). The main objective of this work is offering the abecedarian IT security professionals and the common audiences with some knowledge about the computer security and password cracking, and promoting the development of this area.
Elections with Few Voters: Candidate Control Can Be Easy

We study the computational complexity of candidate control in elections with few voters (that is, we take the number of voters as a parameter). We consider both the standard scenario of adding and deleting candidates, where one asks if a given candidate can become a winner (or, in the destructive case, can be precluded from winning) by adding/deleting some candidates, and a combinatorial scenario where adding/deleting a candidate automatically means adding/deleting a whole group of candidates. Our results show that the parameterized complexity of candidate control (with the number of voters as the parameter) is much more varied than in the setting with many voters.
Learning with Algebraic Invariances, and the Invariant Kernel Trick

When solving data analysis problems it is important to integrate prior knowledge and/or structural invariances. This paper contributes by a novel framework for incorporating algebraic invariance structure into kernels. In particular, we show that algebraic properties such as sign symmetries in data, phase independence, scaling etc. can be included easily by essentially performing the kernel trick twice. We demonstrate the usefulness of our theory in simulations on selected applications such as sign-invariant spectral clustering and underdetermined ICA.
Efficient inference of overlapping communities in complex networks

We discuss two views on extending existing methods for complex network modeling which we dub the communities first and the networks first view, respectively. Inspired by the networks first view that we attribute to White, Boorman, and Breiger (, we formulate the multiple-networks stochastic blockmodel (MNSBM), which seeks to separate the observed network into subnetworks of different types and where the problem of inferring structure in each subnetwork becomes easier. We show how this model is specified in a generative Bayesian framework where parameters can be inferred efficiently using Gibbs sampling. The result is an effective multiple-membership model without the drawbacks of introducing complex definitions of "groups" and how they interact. We demonstrate results on the recovery of planted structure in synthetic networks and show very encouraging results on link prediction performances using multiple-networks models on a number of real-world network data sets.