nils-herrmann/clean_scirepeval huggingface dataset:
Authorship detection of SMS messages using unigrams

SMS messaging is a popular media of communication. Because of its popularity and privacy, it could be used for many illegal purposes. Additionally, since they are part of the day to day life, SMSes can be used as evidence for many legal disputes. Since a cellular phone might be accessible to people close to the owner, it is important to establish the fact that the sender of the message is indeed the owner of the phone. For this purpose, the straight forward solutions seem to be the use of popular stylometric methods. However, in comparison with the data used for stylometry in the literature, SMSes have unusual characteristics making it hard or impossible to apply these methods in a conventional way. Our target is to come up with a method of authorship detection of SMS messages that could still give a usable accuracy. We argue that, considering the methods of author attribution, the best method that could be applied to SMS messages is an n-gram method. To prove our point, we checked two different methods of distribution comparison with varying number of training and testing data. We specifically try to compare how well our algorithms work under less amount of testing data and large number of candidate authors (which we believe to be the real world scenario) against controlled tests with less number of authors and selected SMSes with large number of words. To counter the lack of information in an SMS message, we propose the method of stacking together few SMSes.
Learning Soft Linear Constraints with Application to Citation Field Extraction

Accurately segmenting a citation string into fields for authors, titles, etc. is a challenging task because the output typically obeys various global constraints. Previous work has shown that modeling soft constraints, where the model is encouraged, but not require to obey the constraints, can substantially improve segmentation performance. On the other hand, for imposing hard constraints, dual decomposition is a popular technique for efficient prediction given existing algorithms for unconstrained inference. We extend the technique to perform prediction subject to soft constraints. Moreover, with a technique for performing inference given soft constraints, it is easy to automatically generate large families of constraints and learn their costs with a simple convex optimization problem during training. This allows us to obtain substantial gains in accuracy on a new, challenging citation extraction dataset.
Black Hole Atom as a Dark Matter Particle Candidate

We propose the new dark matter particle candidate—the “black hole atom,” which is an atom with the charged black hole as an atomic nucleus and electrons in the bound internal quantum states. As a simplified model we consider the the central Reissner-Nordstrom black hole with the electric charge neutralized by the internal electrons in bound quantum states. For the external observers these objects would look like the electrically neutral Schwarzschild black holes. We suppose the prolific production of black hole atoms under specific conditions in the early universe.
Mining Concurrent Topical Activity in Microblog Streams

Streams of user-generated content in social media exhibit patterns of collective attention across diverse topics, with temporal structures determined both by exogenous factors and endogenous factors. Teasing apart different topics and resolving their individual, concurrent, activity timelines is a key challenge in extracting knowledge from microblog streams. Facing this challenge requires the use of methods that expose latent signals by using term correlations across posts and over time. Here we focus on content posted to Twitter during the London 2012 Olympics, for which a detailed schedule of events is independently available and can be used for reference. We mine the temporal structure of topical activity by using two methods based on non-negative matrix factorization. We show that for events in the Olympics schedule that can be semantically matched to Twitter topics, the extracted Twitter activity timeline closely matches the known timeline from the schedule. Our results show that, given appropriate techniques to detect latent signals, Twitter can be used as a social sensor to extract topical-temporal information on real-world events at high temporal resolution.
Quantum cosmology, minimal length and holography

We study the effects of a generalized uncertainty principle on the classical and quantum cosmology of a closed Friedmann Universe filled with two fluids, namely, dust and radiation, non-interacting. More concretely, assuming the existence of a minimal length, we show that a corresponding minimal area will constitute a Dirac observable. In addition, ’t Hooft conjecture on the cosmological holographic principle is also investigated for either the case of dust or radiation being present. We describe how this holographic principle is satisfied for large values of a quantum number, n. This occurs when the entropy is computed in terms of the minimal area.
Real‐time classification of Twitter trends

In this work, we explore the types of triggers that spark trends on Twitter, introducing a typology with the following 4 types: news, ongoing events, memes, and commemoratives. While previous research has analyzed trending topics over the long term, we look at the earliest tweets that produce a trend, with the aim of categorizing trends early on. This allows us to provide a filtered subset of trends to end users. We experiment with a set of straightforward language‐independent features based on the social spread of trends and categorize them using the typology. Our method provides an efficient way to accurately categorize trending topics without need of external data, enabling news organizations to discover breaking news in real‐time, or to quickly identify viral memes that might inform marketing decisions, among others. The analysis of social features also reveals patterns associated with each type of trend, such as tweets about ongoing events being shorter as many were likely sent from mobile devices, or memes having more retweets originating from a few trend‐setters.
MODELLING THE BID AND ASK PRICES OF ILLIQUID CDSs

CDS (credit default swap) contracts that were initiated some time ago frequently have spreads and/or maturities that are not available on the current market of CDSs, and are thus illiquid. This article introduces an incomplete-market approach to valuing illiquid CDSs that, in contrast to the risk-neutral approach of current market practice, allows a dealer who buys an illiquid CDS from an investor to determine ask and bid prices (which differ) in such a way as to guarantee a minimum positive expected rate of return on the deal. An alternative procedure, which replaces the expected rate of return by an analogue of the Sharpe ratio, is also discussed. The approach to pricing just described belongs to the good-deal category of approaches, since the dealer decides what it would take to make an appropriate expected rate of return, and sets the bid and ask prices accordingly. A number of different hedges are discussed and compared within the general framework developed in the article. The approach is implemented numerically, and example plots of important quantities are given. The paper also develops a useful result in linear programming theory in the case that the cost vector is random.
Approximation Models of Combat in StarCraft 2

Real-time strategy (RTS) games make heavy use of artificial intelligence (AI), especially in the design of computerized opponents. Because of the computational complexity involved in managing all aspects of these games, many AI opponents are designed to optimize only a few areas of playing style. In games like StarCraft 2, a very popular and recently released RTS, most AI strategies revolve around economic and building efficiency: AI opponents try to gather and spend all resources as quickly and effectively as possible while ensuring that no units are idle. The aim of this work was to help address the need for AI combat strategies that are not computationally intensive. Our goal was to produce a computationally efficient model that is accurate at predicting the results of complex battles between diverse armies, including which army will win and how many units will remain. Our results suggest it may be possible to develop a relatively simple approximation model of combat that can accurately predict many battles that do not involve micromanagement. Future designs of AI opponents may be able to incorporate such an approximation model into their decision and planning systems to provide a challenge that is strategically balanced across all aspects of play.
Ya. B. Zeldovich (1914-1987) Chemist, Nuclear Physicist, Cosmologist

Yakov Borisovich Zeldovich was remarkably talented.† His active scientific career included major contributions in fields as diverse as chemical physics (adsorption & catalysis), the theory of shock waves, thermal explosions, the theory of flame propogation, the theory of combustion & detonation, nuclear & particle physics, and, during the latter part of his life: gravitation, astrophysics and cosmology (Zeldovich 1992).
Measuring and modelling correlations in multiplex networks

The interactions among the elementary components of many complex systems can be qualitatively different. Such systems are therefore naturally described in terms of multiplex or multilayer networks, i.e., networks where each layer stands for a different type of interaction between the same set of nodes. There is today a growing interest in understanding when and why a description in terms of a multiplex network is necessary and more informative than a single-layer projection. Here we contribute to this debate by presenting a comprehensive study of correlations in multiplex networks. Correlations in node properties, especially degree-degree correlations, have been thoroughly studied in single-layer networks. Here we extend this idea to investigate and characterize correlations between the different layers of a multiplex network. Such correlations are intrinsically multiplex, and we first study them empirically by constructing and analyzing several multiplex networks from the real world. In particular, we introduce various measures to characterize correlations in the activity of the nodes and in their degree at the different layers and between activities and degrees. We show that real-world networks exhibit indeed nontrivial multiplex correlations. For instance, we find cases where two layers of the same multiplex network are positively correlated in terms of node degrees, while other two layers are negatively correlated. We then focus on constructing synthetic multiplex networks, proposing a series of models to reproduce the correlations observed empirically and/or to assess their relevance.
Calculations of resonance enhancement factor in axion-search tube-experiments

It is pointed out that oscillating current density, produced due to the coupling between an external magnetic field and the cosmic axion field, can excite the TM resonant modes inside an open-ended cavity (tube). By systematically solving the field equations of axion-electrodynamics we obtain explicit expressions for the oscillating fields induced inside a cylindrical tube. We calculate the enhancement factor when a resonance condition is met. While the power obtained for TM modes replicates the previous result, we emphasize that the knowledge of explicit field configurations inside a tube opens up new ways to design axion experiments including a recent proposal to detect the induced fields using a superconducting LC circuit. In addition, as an example, we estimate the induced fields in a cylindrical tube in the presence of a static uniform magnetic field applied only to a part of its volume.
Predicting galaxy star formation rates via the co-evolution of galaxies and haloes

In this paper, we test the age matching hypothesis that the star formation rate (SFR) of a galaxy is determined by its dark matter halo formation history, and as such, that more quiescent galaxies reside in older halos. This simple model has been remarkably successful at predicting color-based galaxy statistics at low redshift as measured in the Sloan Digital Sky Survey (SDSS). To further test this method with observations, we present new SDSS measurements of the galaxy two-point correlation function and galaxy-galaxy lensing as a function of stellar mass and SFR, separated into quenched and star forming galaxy samples. We find that our age matching model is in excellent agreement with these new measurements. We also employ a galaxy group finder and show that our model is able to predict: (1) the relative SFRs of central and satellite galaxies, (2) the SFR-dependence of the radial distribution of satellite galaxy populations within galaxy groups, rich groups, and clusters and their surrounding larger scale environments, and (3) the interesting feature that the satellite quenched fraction as a function of projected radial distance from the central galaxy exhibits an approx r-.15 slope, independent of environment. The accurate prediction for the spatial distribution ofmore » satellites is intriguing given the fact that we do not explicitly model satellite-specific processes after infall, and that in our model the virial radius does not mark a special transition region in the evolution of a satellite, contrary to most galaxy evolution models. The success of the model suggests that present-day galaxy SFR is strongly correlated with halo mass assembly history.« less
EO-polymer waveguide based high dynamic range EM wave sensors

In this paper, we present the design and experimental demonstration of a high dynamic range electric field sensor based on electro-optic (EO) polymer directional coupler waveguides that offers the strong and ultra-fast EO response of EO polymer. As compared to conventional photonic electric field sensors, our directional coupler waveguide design offers several advantages such as bias-free operation, highly linear measurement response up to 70dB, and a wide electric field detection range from 16.7V/m to 750kV/m at a frequency of 1GHz.
Past incompleteness of a bouncing multiverse

According to classical GR, Anti-de Sitter (AdS) bubbles in the multiverse terminate in big crunch singularities. It has been conjectured, however, that the fundamental theory may resolve these singularities and replace them by nonsingular bounces. This may have important implications for the beginning of the multiverse. Geodesics in cosmological spacetimes are known to be past-incomplete, as long as the average expansion rate along the geodesic is positive, but it is not clear that the latter condition is satisfied if the geodesic repeatedly passes through crunching AdS bubbles. We investigate this issue in a simple multiverse model, where the spacetime consists of a patchwork of FRW regions. The conclusion is that the spacetime is still past-incomplete, even in the presence of AdS bounces.
Magneto-optic modulator with unit quantum efficiency.

We propose a device for the reversible and quiet conversion of microwave photons to optical sideband photons that can reach 100% quantum efficiency. The device is based on an erbium-doped crystal placed in both an optical and microwave resonator. We show that efficient conversion can be achieved so long as the product of the optical and microwave cooperativity factors can be made large. We argue that achieving this regime is feasible with current technology and we discuss a possible implementation.
Complete spectrum of the infinite-U Hubbard ring using group theory.

We present a full analytical solution of the multiconfigurational strongly correlated mixed-valence problem corresponding to the N-Hubbard ring filled with N-1 electrons, and infinite on-site repulsion. While the eigenvalues and the eigenstates of the model are known already, analytical determination of their degeneracy is presented here for the first time. The full solution, including degeneracy count, is achieved for each spin configuration by mapping the Hubbard model into a set of Hückel-annulene problems for rings of variable size. The number and size of these effective Hückel annulenes, both crucial to obtain Hubbard states and their degeneracy, are determined by solving a well-known combinatorial enumeration problem, the necklace problem for N-1 beads and two colors, within each subgroup of the CN-1 permutation group. Symmetry-adapted solution of the necklace enumeration problem is finally achieved by means of the subduction of coset representation technique [S. Fujita,  (1989)], which provides a general and elegant strategy to solve the one-hole infinite-U Hubbard problem, including degeneracy count, for any ring size. The proposed group theoretical strategy to solve the infinite-U Hubbard problem for N-1 electrons is easily generalized to the case of arbitrary electron count L, by analyzing the permutation group CL and all its subgroups.
Inside Money, Procyclical Leverage, and Banking Catastrophes

We explore a model of the interaction between banks and outside investors in which the ability of banks to issue inside money (short-term liabilities believed to be convertible into currency at par) can generate a collapse in asset prices and widespread bank insolvency. The banks and investors share a common belief about the future value of certain long-term assets, but they have different objective functions; changes to this common belief result in portfolio adjustments and trade. Positive belief shocks induce banks to buy risky assets from investors, and the banks finance those purchases by issuing new short-term liabilities. Negative belief shocks induce banks to sell assets in order to reduce their chance of insolvency to a tolerably low level, and they supply more assets at lower prices, which can result in multiple market-clearing prices. A sufficiently severe negative shock causes the set of equilibrium prices to contract (in a manner given by a cusp catastrophe), causing prices to plummet discontinuously and banks to become insolvent. Successive positive and negative shocks of equal magnitude do not cancel; rather, a banking catastrophe can occur even if beliefs simply return to their initial state. Capital requirements can prevent crises by curtailing the expansion of balance sheets when beliefs become more optimistic, but they can also force larger price declines. Emergency asset price supports can be understood as attempts by a central bank to coordinate expectations on an equilibrium with solvency.
Optimal Patching in Clustered Malware Epidemics

Studies on the propagation of malware in mobile networks have revealed that the spread of malware can be highly inhomogeneous. Platform diversity, contact list utilization by the malware, clustering in the network structure, etc., can also lead to differing spreading rates. In this paper, a general formal framework is proposed for leveraging such heterogeneity to derive optimal patching policies that attain the minimum aggregate cost due to the spread of malware and the surcharge of patching. Using Pontryagin's Maximum Principle for a stratified epidemic model, it is analytically proven that in the mean-field deterministic regime, optimal patch disseminations are simple single-threshold policies. These policies are amenable to implementation and can serve as benchmarks for policies that have less knowledge of the network. Through numerical simulations, the behavior of optimal patching policies is investigated in sample topologies, and their advantages are demonstrated.
Methods of executable code protection

The article deals with the problems in constructing a protection system of executable code. The techniques of breaking the integrity of executable code and ways to eliminate them are described. The adoption of virtual machine technology in the context of executable code protection from analysis is considered. The substantiation of the application of virtual machines as the best way to oppose the analysis of executable code is made. The protection of executable code by transferring the protected code in a virtual execution environment is considered. An efficient implementation of the method is proposed.
Do Google Trend Data Contain More Predictability than Price Returns?

Using non-linear machine learning methods and a proper backtest procedure, we critically examine the claim that Google Trends can predict future price returns. We first review the many potential biases that may influence backtests with this kind of data positively, the choice of keywords being by far the greatest culprit. We then argue that the real question is whether such data contain more predictability than price returns themselves: our backtest yields a performance of about 17bps per week which only weakly depends on the kind of data on which predictors are based, i.e. either past price returns or Google Trends data, or both.
PLANETS ON THE EDGE

Hot Jupiters formed through circularization of high-eccentricity orbits should be found at orbital separations a exceeding twice that of their Roche limit aR. Nevertheless, about a dozen giant planets have now been found well within this limit (aR < a < 2 aR), with one coming as close as 1.2 aR. In this Letter, we show that orbital decay (starting beyond 2 aR) driven by tidal dissipation in the star can naturally explain these objects. For a few systems (WASP-4 and 19), this explanation requires the linear reduction in convective tidal dissipation proposed originally by Zahn and verified by recent numerical simulations, but rules out the quadratic prescription proposed by Goldreich & Nicholson. Additionally, we find that WASP-19-like systems could potentially provide direct empirical constraints on tidal dissipation, as we could soon be able to measure their orbital decay through high precision transit timing measurements.
Chaotic behavior of the Compound Nucleus, open Quantum Dots and other nanostructures

It is well established that physical systems exhibit both ordered and chaotic behavior. The chaotic behavior of nanostructures such as open quantum dots has been confirmed experimentally and discussed exhaustively theoretically. This is manifested through random fluctuations in the electronic conductance. What useful information can be extracted from this noise in the conductance? In this contribution we shall address this question. In particular, we will show that the average maxima density in the conductance is directly related to the correlation function whose characteristic width is a measure of the energy- or applied magnetic field- correlation length. The idea behind the above was originally discovered in the context of the atomic nucleus, a mesoscopic system. Our findings are directly applicable to graphene.