nils-herrmann/clean_scirepeval huggingface dataset:
Modeling multi-particle complexes in stochastic chemical systems

Large complexes of classical particles play central roles in biology, in polymer physics, and in other disciplines. However, physics currently lacks mathematical methods for describing such complexes in terms of component particles, interaction energies, and assembly rules. Here we describe a Fock space structure that addresses this need, as well as diagrammatic methods that facilitate the use of this formalism. These methods can dramatically simplify the equations governing both equilibrium and non-equilibrium stochastic chemical systems. A mathematical relationship between the set of all complexes and a list of rules for complex assembly is also identified.
Glauber modeling of high-energy nuclear collisions at the subnucleon level

Glauber models based on nucleon--nucleon interactions are commonly used to characterize the initial state in high-energy nuclear collisions, and the dependence of its properties on impact parameter or number of participating nucleons. In this paper, an extension to the Glauber model is presented, which accounts for an arbitrary number of effective sub-nucleon degrees of freedom, or active constituents, in the nucleons. Properties of the initial state, such as the number of constituent participants and collisions, as well as eccentricity and triangularity, are calculated and systematically compared for different assumptions of how to distribute the sub-nuclear degrees of freedom and for various collision systems. It is demonstrated that at high collision energy the number of produced particles scales with an average number of sub-nucleon degrees of freedom of between $3$ and $5$. The source codes for the constituent Monte Carlo Glauber extension, as well as for the calculation of the overlap area and participant density in a standard Glauber model, are made publicly available.
Predicting litigation likelihood and time to litigation for patents

An ability to forecast the likelihood of a patent litigation1 and time-to-litigation benefits companies in many aspects, such as in patent portfolio management, and strategic planning. Thus, we develop predictive models for estimating the likelihood of litigation for patents and the expected time to litigation. Our work focuses on improving the state-of-the-art by relying on a different set of features and employing more sophisticated algorithms with realistic data. Specifically, we consider potential factors influencing a patent to be litigated in the model. These features, collected at the issue date of the patent and thus prior to the actual litigation, include textual features, patent's general information as well as financial information of patent's assignee. Our proposed models are a combination of a clustering approach coupled with an ensemble classification method. With a very low litigation rate of 1 to 2 percent, the results from the models show promising predictability. Financial information and features related to referencing are important indicators to distinguish between litigated and non-litigated patents
Spatiotemporal Patterns and Predictability of Cyberattacks

A relatively unexplored issue in cybersecurity science and engineering is whether there exist intrinsic patterns of cyberattacks. Conventional wisdom favors absence of such patterns due to the overwhelming complexity of the modern cyberspace. Surprisingly, through a detailed analysis of an extensive data set that records the time-dependent frequencies of attacks over a relatively wide range of consecutive IP addresses, we successfully uncover intrinsic spatiotemporal patterns underlying cyberattacks, where the term “spatio” refers to the IP address space. In particular, we focus on analyzing macroscopic properties of the attack traffic flows and identify two main patterns with distinct spatiotemporal characteristics: deterministic and stochastic. Strikingly, there are very few sets of major attackers committing almost all the attacks, since their attack “fingerprints” and target selection scheme can be unequivocally identified according to the very limited number of unique spatiotemporal characteristics, each of which only exists on a consecutive IP region and differs significantly from the others. We utilize a number of quantitative measures, including the flux-fluctuation law, the Markov state transition probability matrix, and predictability measures, to characterize the attack patterns in a comprehensive manner. A general finding is that the attack patterns possess high degrees of predictability, potentially paving the way to anticipating and, consequently, mitigating or even preventing large-scale cyberattacks using macroscopic approaches.
Analyzing Complex Network User Arrival Patterns and Their Effect on Network Topologies

Complex networks have non-trivial characteristics and appear in many real-world systems. Due to their vital importance in a large number of research fields, various studies have offered explanations on how complex networks evolve, but the full underlying dynamics of complex networks are not completely understood. Many of the barriers to better understanding the evolution process of these networks can be removed with the emergence of new data sources. 
This study utilizes the recently published Reddit dataset, containing over 1.65 billion comments, to construct the largest publicly available social network corpus, which contains detailed information on the evolution process of 11,965 social networks. We used this dataset to study the effect of the patterns in which new users join a network (referred to as user arrival curves, or UACs) on the network topology. Our results present evidence that UACs are a central factor in molding a network's topology; that is, different arrival patterns create different topological properties. Additionally, we show that it is possible to uncover the types of user arrival patterns by analyzing a social network's topology. These results imply that existing complex network evolution models need to be revisited and modified to include user arrival patterns as input to the models, in order to create models that more accurately reflect real-world complex networks.
An Expressive Probabilistic Temporal Logic

This paper argues that a combined treatment of probabilities, time and actions is essential for an appropriate logical account of the notion of probability; and, based on this intuition, describes an expressive probabilistic temporal logic for reasoning about actions with uncertain outcomes. The logic is modal and higher-order: modalities annotated by actions are used to express possibility and necessity of propositions in the next states resulting from the actions, and a higher-order function is needed to express the probability operator. The proposed logic is shown to be an adequate extension of classical mathematical probability theory, and its expressiveness is illustrated through the formalization of the Monty Hall problem.
Algebraic method for constructing singular steady solitary waves: a case study

This article describes the use of algebraic methods in a phase plane analysis of ordinary differential equations. The method is illustrated by the study of capillary–gravity steady surface waves propagating in shallow water. We consider the (fully nonlinear, weakly dispersive) Serre–Green–Naghdi equation with surface tension, because it provides a tractable model that, at the same time, is not too simple, so interest in the method can be emphasized. In particular, we analyse a special class of solutions, the solitary waves, which play an important role in many fields of physics. In capillary–gravity regime, there are two kinds of localized infinitely smooth travelling wave solutions—solitary waves of elevation and of depression. However, if we allow the solitary waves to have an angular point, then the ‘zoology’ of solutions becomes much richer, and the main goal of this study is to provide a complete classification of such singular localized solutions using the methods of the effective algebraic geometry.
Multi-Photon Multi-Channel Interferometry for Quantum Information Processing

This thesis reports advances in the theory of design, characterization and simulation of multi-photon multi-channel interferometers. I advance the design of interferometers through an algorithm to realize an arbitrary discrete unitary transformation on the combined spatial and internal degrees of freedom of light. This procedure effects an arbitrary $n_{s}n_{p}\times n_{s}n_{p}$ unitary matrix on the state of light in $n_{s}$ spatial and $n_{p}$ internal modes. 
I devise an accurate and precise procedure for characterizing any multi-port linear optical interferometer using one- and two-photon interference. Accuracy is achieved by estimating and correcting systematic errors that arise due to spatiotemporal and polarization mode mismatch. Enhanced accuracy and precision are attained by fitting experimental coincidence data to a curve simulated using measured source spectra. The efficacy of our characterization procedure is verified by numerical simulations. 
I develop group-theoretic methods for the analysis and simulation of linear interferometers. I devise a graph-theoretic algorithm to construct the boson realizations of the canonical SU$(n)$ basis states, which reduce the canonical subgroup chain, for arbitrary $n$. The boson realizations are employed to construct $\mathcal{D}$-functions, which are the matrix elements of arbitrary irreducible representations, of SU$(n)$ in the canonical basis. I show that immanants of principal submatrices of a unitary matrix $T$ are a sum of the diagonal $\mathcal{D}(\Omega)$-functions of group element $\Omega$ over $t$ determined by the choice of submatrix and over the irrep $(\lambda)$ determined by the immanant under consideration. The algorithm for $\mathrm{SU}(n)$ $\mathcal{D}$-function computation and the results connecting these functions with immanants open the possibility of group-theoretic analysis and simulation of linear optics.
Density matrix of radiation of a black hole with a fluctuating horizon

The density matrix of Hawking radiation is calculated in the model of a black hole with a fluctuating horizon. Quantum fluctuations smear the classical horizon of a black hole and modify the density matrix of radiation producing the off-diagonal elements. The off-diagonal elements may store information on correlations between the radiation and the black hole. The smeared density matrix was constructed by convolution of the density matrix calculated with the instantaneous horizon with the Gaussian distribution over the instantaneous horizons. The distribution has the extremum at the classical radius of the black hole and the width of order of the Planck length. Calculations were performed in the model of a black hole formed by the thin collapsing shell which follows a trajectory that is a solution of the matching equations connecting the interior and exterior geometries.
Conic martingales from stochastic integrals

In this paper, we introduce the concept of conic martingales. This class refers to stochastic processes that have the martingale property but that evolve within given (possibly time‐dependent) boundaries. We first review some results about the martingale property of solution to driftless stochastic differential equations. We then provide a simple way to construct and handle such processes. Specific attention is paid to martingales in [0, 1]. One of these martingales proves to be analytically tractable. It is shown that up to shifting and rescaling constants, it is the only martingale (with the trivial constant, Brownian motion, and geometric Brownian motion) having a separable diffusion coefficient σ(t,y)=g(t)h(y) and that can be obtained via a time‐homogeneous mapping of Gaussian diffusions. The approach is exemplified by modeling stochastic conditional survival probabilities in the univariate and bivariate cases.
Dirac quantum time mirror

Both metaphysical and practical considerations related to time inversion have intrigued scientists for generations. Physicists have strived to devise and implement time-inversion protocols, in particular different forms of "time mirrors" for classical waves. Here we propose an instantaneous time mirror for quantum systems, i.e., a controlled time discontinuity generating wave function echoes with high fidelities. This concept exploits coherent particle -hole oscillations in a Dirac spectrum in order to achieve population reversal, and can be implemented in systems such as (real or artificial) graphene.
Entanglement and Coherence in Quantum State Merging.

Understanding the resource consumption in distributed scenarios is one of the main goals of quantum information theory. A prominent example for such a scenario is the task of quantum state merging, where two parties aim to merge their tripartite quantum state parts. In standard quantum state merging, entanglement is considered to be an expensive resource, while local quantum operations can be performed at no additional cost. However, recent developments show that some local operations could be more expensive than others: it is reasonable to distinguish between local incoherent operations and local operations which can create coherence. This idea leads us to the task of incoherent quantum state merging, where one of the parties has free access to local incoherent operations only. In this case the resources of the process are quantified by pairs of entanglement and coherence. Here, we develop tools for studying this process and apply them to several relevant scenarios. While quantum state merging can lead to a gain of entanglement, our results imply that no merging procedure can gain entanglement and coherence at the same time. We also provide a general lower bound on the entanglement-coherence sum and show that the bound is tight for all pure states. Our results also lead to an incoherent version of Schumacher compression: in this case the compression rate is equal to the von Neumann entropy of the diagonal elements of the corresponding quantum state.
STRANGE QUARK STARS AS A PROBE OF DARK MATTER

We demonstrate that the observation of old strange quark stars (SQSs) can set important limits on the scattering cross-sections between light quarks and non-interacting scalar dark matter (DM). By analyzing a set of 1403 solitary pulsar-like compact stars in the Milky Way, we find that the old solitary pulsar PSR J1801-0857D can set the most stringent upper limits on or the cross-sections between DM and protons. By converting into based on effective operator analyses, we show that the resulting limit, by assuming PSR J1801-0857D is an SQS, is comparable with that of the current direct detection experiments in terrestrial labs but weaker by several orders of magnitude than that obtained by assuming PSR J1801-0857D is a neutron star (NS), which requires an extremely small far beyond the limits of direct detection experiments. Our findings imply that old pulsars are more likely to be identified as SQSs than as NSs in future terrestrial experiments observing scalar DM.
A Short Note on P-Value Hacking

We present the expected values from p-value hacking as a choice of the minimum p-value among $m$ independents tests, which can be considerably lower than the "true" p-value, even with a single trial, owing to the extreme skewness of the meta-distribution. We first present an exact probability distribution (meta-distribution) for p-values across ensembles of statistically identical phenomena. We derive the distribution for small samples $2
The Interplay Between Conformity and Anticonformity and its Polarizing Effect on Society

Simmering debates leading to polarization are observed in many domains. Although empirical findings show a strong correlation between this phenomenon and modularity of a social network, still little is known about the actual mechanisms driving communities to conflicting opinions. In this paper, we used an agent-based model to check if the polarization may be induced by a competition between two types of social response: conformity and anticonformity. The proposed model builds on the q-voter model (Castellano et al. 2009b) and uses a double-clique topology in order to capture segmentation of a community. Our results indicate that the interplay between intra-clique conformity and inter-clique anticonformity may indeed lead to a polarized state of the entire system. We have found a dynamic phase transition controlled by the fraction $L$ of cross-links between cliques. In the regime of small values of $L$ system is able to reach the total positive consensus. If the values of $L$ are large enough, anticonformity takes over and the system always ends up in a polarized stated. Putting it the other way around, the segmentation of the network is not a sufficient condition for the polarization to appear. A suitable level of antagonistic interactions between segments is namely required to arrive at a polarized steady state within our model.
Black holes and information: A new take on an old paradox

Interest in the black hole information paradox has recently been catalyzed by the newer "firewall" argument. The crux of the updated argument is that previous solutions which relied on observer complementarity are in violation of the quantum condition of monogamy of entanglement; with the prescribed remedy being to discard the equivalence principle in favor of an energy barrier (or firewall) at the black hole horizon. Differing points of view have been put forward, including the "ER=EPR" counterargument and the final-state solution, both of which can be viewed as potential resolutions to the apparent conflict between quantum monogamy and Einstein equivalence. After reviewing these recent developments, this paper argues that the ER=EPR and final-state solutions can -- thanks to observer complementarity -- be seen as the same resolution of the paradox but from two different perspectives: inside and outside the black hole.
Gauge aspect of tetrad field in gravity

In general relativity, an inertial frame can only be established in a small region of spacetime, and a locally inertial frame is mathematically represented by a tetrad field in gravity. The tetrad field is not unique due to the freedom to perform a local Lorentz transformation in an inertial frame, and there exists freedom to choose the locally inertial frame at each spacetime. The local Lorentz transformations are known as non-Abelian gauge transformations for the tetrad field, and to fix the gauge freedom, corresponding to the Lorentz gauge $\partial^\mu\mathcal{A}_\mu=0$ and Coulomb gauge $\partial^i\mathcal{A}_i=0$ in electrodynamics, the Lorentz gauge and Coulomb gauge for the tetrad field are proposed in the present work. Moreover, properties of the Lorentz gauge and Coulomb gauge for tetrad field are discussed, which show the similarities to those in electromagnetic field.
Evaluating the Performance of Offensive Linemen in the NFL

How does one objectively measure the performance of an individual offensive lineman in the NFL? The existing literature proposes various measures that rely on subjective assessments of game film, but has yet to develop an objective methodology to evaluate performance. Using a variety of statistics related to an offensive lineman's performance, we develop a framework to objectively analyze the overall performance of an individual offensive lineman and determine specific linemen who are overvalued or undervalued relative to their salary. We identify eight players across the 2013-2014 and 2014-2015 NFL seasons that are considered to be overvalued or undervalued and corroborate the results with existing metrics that are based on subjective evaluation. To the best of our knowledge, the techniques set forth in this work have not been utilized in previous works to evaluate the performance of NFL players at any position, including offensive linemen.
Semantic Regularities in Document Representations

Recent work exhibited that distributed word representations are good at capturing linguistic regularities in language. This allows vector-oriented reasoning based on simple linear algebra between words. Since many different methods have been proposed for learning document representations, it is natural to ask whether there is also linear structure in these learned representations to allow similar reasoning at document level. To answer this question, we design a new document analogy task for testing the semantic regularities in document representations, and conduct empirical evaluations over several state-of-the-art document representation models. The results reveal that neural embedding based document representations work better on this analogy task than conventional methods, and we provide some preliminary explanations over these observations.
A note on the optimal dividends paid in a foreign currency

We consider an insurance entity endowed with an initial capital and a surplus process modelled as a Brownian motion with drift. It is assumed that the company seeks to maximise the cumulated value of expected discounted dividends, which are declared or paid in a foreign currency. The currency fluctuation is modelled as a Lévy process. We consider both cases: restricted and unrestricted dividend payments. It turns out that the value function and the optimal strategy can be calculated explicitly.
Dynamics of the diffusive DM-DE interaction – Dynamical system approach

We discuss dynamics of a model of an energy transfer between dark energy (DE) and dark matter (DM) . The energy transfer is determined by a non-conservation law resulting from a diffusion of dark matter in an environment of dark energy. The relativistic invariance defines the diffusion in a unique way. The system can contain baryonic matter and radiation which do not interact with the dark sector. We treat the Friedman equation and the conservation laws as a closed dynamical system. The dynamics of the model is examined using the dynamical systems methods for demonstration how solutions depend on initial conditions. We also fit the model parameters using astronomical observation: SNIa, H(z), BAO and Alcock-Paczynski test. We show that the model with diffuse DM-DE is consistent with the data.
Semantic Properties of Customer Sentiment in Tweets

An increasing number of people are using online social networking services (SNSs), and a significant amount of information related to experiences in consumption is shared in this new media form. Text mining is an emerging technique for mining useful information from the web. We aim at discovering in particular tweets semantic patterns in consumers' discussions on social media. Specifically, the purposes of this study are twofold: 1) finding similarity and dissimilarity between two sets of textual documents that include consumers' sentiment polarities, two forms of positive vs. negative opinions and 2) driving actual content from the textual data that has a semantic trend. The considered tweets include consumers' opinions on US retail companies (e.g., Amazon, Walmart). Cosine similarity and K-means clustering methods are used to achieve the former goal, and Latent Dirichlet Allocation (LDA), a popular topic modeling algorithm, is used for the latter purpose. This is the first study which discover semantic properties of textual data in consumption context beyond sentiment analysis. In addition to major findings, we apply LDA (Latent Dirichlet Allocations) to the same data and drew latent topics that represent consumers' positive opinions and negative opinions on social media.