nils-herrmann/clean_scirepeval huggingface dataset:
3-SAT Problem A New Memetic-PSO Algorithm

3-SAT problem is of great importance to many technical and scientific applications. This paper presents a new hybrid evolutionary algorithm for solving this satisfiability problem. 3-SAT problem has the huge search space and hence it is known as a NP-hard problem. So, deterministic approaches are not applicable in this context. Thereof, application of evolutionary processing approaches and especially PSO will be very effective for solving these kinds of problems. In this paper, we introduce a new evolutionary optimization technique based on PSO, Memetic algorithm and local search approaches. When some heuristics are mixed, their advantages are collected as well and we can reach to the better outcomes. Finally, we test our proposed algorithm over some benchmarks used by some another available algorithms. Obtained results show that our new method leads to the suitable results by the appropriate time. Thereby, it achieves a better result in compared with the existent approaches such as pure genetic algorithm and some verified types
Non-Equivalent Beliefs and Subjective Equilibrium Bubbles

This paper develops a dynamic equilibrium model where agents exhibit a strong form of belief heterogeneity: they disagree about zero probability events. It is shown that, somewhat surprisingly, equilibrium exists in this setting, and that the disagreement about nullsets naturally leads to equilibrium asset pricing bubbles. The bubbles are subjective in the sense that they are perceived by some but not necessarily all agents. In contrast to existing models, bubbles arise with no restrictions on trade beyond a standard solvency constraint.
How well do we know the halo mass function

The parameters governing the standardCold Dark Matter cosmological model have been constrained with unprecedented accuracy by precise measurements of the cosmic microwave background by the Wilkinson Microwave Anisotropy Probe (WMAP) and Planck satellites. Each new data release has refined further our knowledge of quan- tities - such as the matter density parameter M - that are imprinted on the dark matter halo mass function (HMF), a powerful probe of dark matter and dark energy models. In this letter we trace how changes in the cosmological parameters over the last decade have influenced uncertainty in our knowledge of the HMF. We show that this uncertainty has reduced significantly since the 3 rd WMAP data release, but the rate of this reduction is slowing. This is limited by uncertainty in the normalisation σ8, whose influence is most pronounced at the high mass end of the mass function. Interestingly, we find that the accuracy with which we can constrain the HMF in terms of the cosmological parameters has now reached the point at which it is comparable to the scatter in HMF fitting functions. This suggests that the power of the HMF as a precision probe of dark matter and dark energy hinges on more accurate deter- mination of the theoretical HMF. Finally, we assess prospects of using the HMF to differentiate between Cold and Warm Dark Matter models based on ongoing improve- ments in measurements of M, and we comment briefly on optimal survey strategies for constraining dark matter and dark energy models using the HMF.
SOCIAL DISCOUNTING AND THE LONG RATE OF INTEREST

The well‐known theorem of Dybvig, Ingersoll, and Ross shows that the long zero‐coupon rate can never fall. This result, which, although undoubtedly correct, has been regarded by many as surprising, stems from the implicit assumption that the long‐term discount function has an exponential tail. We revisit the problem in the setting of modern interest rate theory, and show that if the long “simple” interest rate (or Libor rate) is finite, then this rate (unlike the zero‐coupon rate) acts viably as a state variable, the value of which can fluctuate randomly in line with other economic indicators. New interest rate models are constructed, under this hypothesis and certain generalizations thereof, that illustrate explicitly the good asymptotic behavior of the resulting discount bond systems. The conditions necessary for the existence of such “hyperbolic” and “generalized hyperbolic” long rates are those of so‐called social discounting, which allow for long‐term cash flows to be treated as broadly “just as important” as those of the short or medium term. As a consequence, we are able to provide a consistent arbitrage‐free valuation framework for the cost‐benefit analysis and risk management of long‐term social projects, such as those associated with sustainable energy, resource conservation, and climate change.
Clinical Relationships Extraction Techniques from Patient Narratives

The Clinical E-Science Framework (CLEF) project was used to extract important information from medical texts by building a system for the purpose of clinical research, evidence-based healthcare and genotype-meets-phenotype informatics. The system is divided into two parts, one part concerns with the identification of relationships between clinically important entities in the text. The full parses and domain-specific grammars had been used to apply many approaches to extract the relationship. In the second part of the system, statistical machine learning (ML) approaches are applied to extract relationship. A corpus of oncology narratives that hand annotated with clinical relationships can be used to train and test a system that has been designed and implemented by supervised machine learning (ML) approaches. Many features can be extracted from these texts that are used to build a model by the classifier. Multiple supervised machine learning algorithms can be applied for relationship extraction. Effects of adding the features, changing the size of the corpus, and changing the type of the algorithm on relationship extraction are examined.  Text mining; information extraction; NLP; entities; and relations.
Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose

Twitter is a social media giant famous for the exchange of short, 140-character messages called "tweets". In the scientific community, the microblogging site is known for openness in sharing its data. It provides a glance into its millions of users and billions of tweets through a "Streaming API" which provides a sample of all tweets matching some parameters preset by the API user. The API service has been used by many researchers, companies, and governmental institutions that want to extract knowledge in accordance with a diverse array of questions pertaining to social media. The essential drawback of the Twitter API is the lack of documentation concerning what and how much data users get. This leads researchers to question whether the sampled data is a valid representation of the overall activity on Twitter. In this work we embark on answering this question by comparing data collected using Twitter's sampled API service with data collected using the full, albeit costly, Firehose stream that includes every single published tweet. We compare both datasets using common statistical metrics as well as metrics that allow us to compare topics, networks, and locations of tweets. The results of our work will help researchers and practitioners understand the implications of using the Streaming API.
Effects of spin-dependent interactions on polarization of bright polariton solitons.

We report on the spin properties of bright polariton solitons supported by an external pump to compensate losses. We observe robust circularly polarized solitons when a circularly polarized pump is applied, a result attributed to phase synchronization between nondegenerate TE and TM polarized polariton modes at high momenta. For the case of a linearly polarized pump, either σ+ or σ- circularly polarized bright solitons can be switched on in a controlled way by a σ+ or σ- writing beam, respectively. This feature arises directly from the widely differing interaction strengths between co- and cross-circularly polarized polaritons. In the case of orthogonally linearly polarized pump and writing beams, the soliton emission on average is found to be unpolarized, suggesting strong spatial evolution of the soliton polarization. The observed results are in agreement with theory, which predicts stable circularly polarized solitons and unstable linearly polarized solitons.
How to Demonstrate the Voltage on a charged object in Physics Laboratory

Common Objects like a comb or a pen get charged when rubbed against something like human hair or garment clothing. Charged objects exhibit noticeable attractive or repulsive force lifting small pieces of paper or pushing/pulling a suspended light object charged with the same/opposite(uncharged) polarity respectively. This indicates the strong electrical nature of charged objects. Flashes due to spark between oppositely charged objects can be seen in total darkness. Implying a large potential difference between these charged objects which is not possible at lower voltages. This article describes a method to measure the voltage on commonly charged objects with respect to earth using simple instrumentation based on capacitors and CMOS voltmeter. Once the potential difference is known the average charge on the object can be calculated as well. The article also suggests a simple femto-farad capacitance meter for electrostatics work.
The Impact of Population Demography and Selection on the Genetic Architecture of Complex Traits

Population genetic studies have found evidence for dramatic population growth in recent human history. It is unclear how this recent population growth, combined with the effects of negative natural selection, has affected patterns of deleterious variation, as well as the number, frequency, and effect sizes of mutations that contribute risk to complex traits. Because researchers are performing exome sequencing studies aimed at uncovering the role of low-frequency variants in the risk of complex traits, this topic is of critical importance. Here I use simulations under population genetic models where a proportion of the heritability of the trait is accounted for by mutations in a subset of the exome. I show that recent population growth increases the proportion of nonsynonymous variants segregating in the population, but does not affect the genetic load relative to a population that did not expand. Under a model where a mutation's effect on a trait is correlated with its effect on fitness, rare variants explain a greater portion of the additive genetic variance of the trait in a population that has recently expanded than in a population that did not recently expand. Further, when using a single-marker test, for a given false-positive rate and sample size, recent population growth decreases the expected number of significant associations with the trait relative to the number detected in a population that did not expand. However, in a model where there is no correlation between a mutation's effect on fitness and the effect on the trait, common variants account for much of the additive genetic variance, regardless of demography. Moreover, here demography does not affect the number of significant associations detected. These findings suggest recent population history may be an important factor influencing the power of association tests and in accounting for the missing heritability of certain complex traits.
Static and Dynamic Aspects of Scientific Collaboration Networks

Collaboration networks arise when we map the connections between scientists which are formed through joint publications. These networks thus display the social structure of academia, and also allow conclusions about the structure of scientific knowledge. Using the computer science publication database DBLP, we compile relations between authors and publications as graphs and proceed with examining and quantifying collaborative relations with graph-based methods. We review standard properties of the network and rank authors and publications by centrality. Additionally, we detect communities with modularity-based clustering and compare the resulting clusters to a ground-truth based on conferences and thus topical similarity. In a second part, we are the first to combine DBLP network data with data from the Dagstuhl Seminars: We investigate whether seminars of this kind, as social and academic events designed to connect researchers, leave a visible track in the structure of the collaboration network. Our results suggest that such single events are not influential enough to change the network structure significantly. However, the network structure seems to influence a participant's decision to accept or decline an invitation.
EXPLICIT IMPLIED VOLATILITIES FOR MULTIFACTOR LOCAL‐STOCHASTIC VOLATILITY MODELS

We consider an asset whose risk‐neutral dynamics are described by a general class of local‐stochastic volatility models and derive a family of asymptotic expansions for European‐style option prices and implied volatilities. We also establish rigorous error estimates for these quantities. Our implied volatility expansions are explicit; they do not require any special functions nor do they require numerical integration. To illustrate the accuracy and versatility of our method, we implement it under four different model dynamics: constant elasticity of variance local volatility, Heston stochastic volatility, three‐halves stochastic volatility, and SABR local‐stochastic volatility.
The Geospatial Characteristics of a Social Movement Communication Network

Social movements rely in large measure on networked communication technologies to organize and disseminate information relating to the movements’ objectives. In this work we seek to understand how the goals and needs of a protest movement are reflected in the geographic patterns of its communication network, and how these patterns differ from those of stable political communication. To this end, we examine an online communication network reconstructed from over 600,000 tweets from a thirty-six week period covering the birth and maturation of the American anticapitalist movement, Occupy Wall Street. We find that, compared to a network of stable domestic political communication, the Occupy Wall Street network exhibits higher levels of locality and a hub and spoke structure, in which the majority of non-local attention is allocated to high-profile locations such as New York, California, and Washington D.C. Moreover, we observe that information flows across state boundaries are more likely to contain framing language and references to the media, while communication among individuals in the same state is more likely to reference protest action and specific places and times. Tying these results to social movement theory, we propose that these features reflect the movement’s efforts to mobilize resources at the local level and to develop narrative frames that reinforce collective purpose at the national level.
Cross-linked structure of network evolution.

We study the temporal co-variation of network co-evolution via the cross-link structure of networks, for which we take advantage of the formalism of hypergraphs to map cross-link structures back to network nodes. We investigate two sets of temporal network data in detail. In a network of coupled nonlinear oscillators, hyperedges that consist of network edges with temporally co-varying weights uncover the driving co-evolution patterns of edge weight dynamics both within and between oscillator communities. In the human brain, networks that represent temporal changes in brain activity during learning exhibit early co-evolution that then settles down with practice. Subsequent decreases in hyperedge size are consistent with emergence of an autonomous subgraph whose dynamics no longer depends on other parts of the network. Our results on real and synthetic networks give a poignant demonstration of the ability of cross-link structure to uncover unexpected co-evolution attributes in both real and synthetic dynamical systems. This, in turn, illustrates the utility of analyzing cross-links for investigating the structure of temporal networks.
Physical principles for scalable neural recording

Simultaneously measuring the activities of all neurons in a mammalian brain at millisecond resolution is a challenge beyond the limits of existing techniques in neuroscience. Entirely new approaches may be required, motivating an analysis of the fundamental physical constraints on the problem. We outline the physical principles governing brain activity mapping using optical, electrical, magnetic resonance, and molecular modalities of neural recording. Focusing on the mouse brain, we analyze the scalability of each method, concentrating on the limitations imposed by spatiotemporal resolution, energy dissipation, and volume displacement. Based on this analysis, all existing approaches require orders of magnitude improvement in key parameters. Electrical recording is limited by the low multiplexing capacity of electrodes and their lack of intrinsic spatial resolution, optical methods are constrained by the scattering of visible light in brain tissue, magnetic resonance is hindered by the diffusion and relaxation timescales of water protons, and the implementation of molecular recording is complicated by the stochastic kinetics of enzymes. Understanding the physical limits of brain activity mapping may provide insight into opportunities for novel solutions. For example, unconventional methods for delivering electrodes may enable unprecedented numbers of recording sites, embedded optical devices could allow optical detectors to be placed within a few scattering lengths of the measured neurons, and new classes of molecularly engineered sensors might obviate cumbersome hardware architectures. We also study the physics of powering and communicating with microscale devices embedded in brain tissue and find that, while radio-frequency electromagnetic data transmission suffers from a severe power–bandwidth tradeoff, communication via infrared light or ultrasound may allow high data rates due to the possibility of spatial multiplexing. The use of embedded local recording and wireless data transmission would only be viable, however, given major improvements to the power efficiency of microelectronic devices.
The imprint of inhomogeneous He ii reionization on the H i and He ii Lyα forest

We use a set of AMR hydrodynamic simulations post-processed with the radiative-transfer code RADAMESH to study how inhomogeneous HeII reionization affects the intergalactic medium (IGM). We propagate radiation from active galactic nuclei (AGNs) considering two scenarios for the time evolution of the ionizing sources. We find that HeII reionization takes place in a very inhomogeneous fashion, through the production of well separated bubbles of the ionized phase that subsequently percolate. Overall, the reionization process is extended in time and lasts for a redshift interval Delta z>1. At fixed gas density, the temperature distribution is bimodal during the early phases of HeII reionization and cannot be described by a simple effective equation of state. When HeII reionization is complete, the IGM is characterized by a polytropic equation of state with index gamma~1.20. This relation is appreciably flatter than at the onset of the reionization process (gamma=1.56) and also presents a much wider dispersion around the mean. We extract HI and HeII Ly-alpha absorption spectra from the simulations and fit Voigt profiles to them. We find that the regions where helium is doubly ionized are characterized by different probability density functions of the curvature and of the Doppler b parameters of the HI Ly-alpha forest as a consequence of the bimodal temperature distribution during the early phases of HeII reionization. The column-density ratio in HeII and HI shows a strong spatial variability. Its probability density function rapidly evolves with time reflecting the increasing volume fraction in which ionizing radiation is harder due to the AGN contribution. Finally we show that the number density of the flux-transmission windows per unit redshift and the mean size of the dark gaps in the HeII spectra have the potential to distinguish between different reionization scenarios.
Luminosity of young Jupiters revisited Massive cores make hot planets

The intrinsic luminosity of young Jupiters is of high interest for planet formation theory. It is an observable quantity that is determined by important physical mechanisms during formation, namely, the structure of the accretion shock, and even more fundamentally, the basic formation mechanism (core accretion or gravitational instability).  Our aim is to study the impact of the core mass on the post-formation entropy and luminosity of young giant planets forming via core accretion with a supercritical accretion shock that radiates all accretion shock energy (cold accretion).  For this, we conduct self-consistently coupled formation and evolution calculations of giant planets with masses between 1 and 12 Jovian masses and core masses between 20 and 120 Earth masses in the 1D spherically symmetric approximation.  As the main result, it is found that the post-formation luminosity of massive giant planets is very sensitive to the core mass. An increase of the core mass by a factor 6 results in an increase of the post-formation luminosity of a 10 Jovian mass planet by a factor 120, indicating a dependency as M 2 3 core. Due to this dependency, there is no single well defined post-formation luminosity for core accretion, but a wide range, even for completely cold accretion. For massive cores ( 100 Earth masses), the post-formation luminosities of core accretion planets become so high that they approach those in the hot start scenario that is often associated with gravitational instability. For the mechanism to work, it is necessary that the solids are accreted before or during gas runaway accretion, and that they sink during this time deep into the planet.  We make no claims whether or not such massive cores can actually form in giant planets especially at large orbital distances. But if they can form, it becomes di cult to rule out core accretion as formation mechanism based solely on luminosity for directly imaged planets that are more luminous than predicted for low core masses. Instead of invoking gravitational instability as the consequently necessary formation mode, the high luminosity can also be caused, at least in principle, simply by a more massive core.
Note on gauge invariance and causal propagation

Interactions of gauge-invariant systems are severely constrained by several consistency requirements.One is the preservation of the number of gauge symmetries, another is causal propagation. For lower-spinfields, the emphasis is usually put on gauge invariance that happens to be very selective by itself. Wedemonstrate with an explicit example, however, that gauge invariance, albeit indispensable for constructinginteractions, may not suffice as a consistency condition. The chosen example that exhibits this featureis the theory of a massless spin-3=2 field coupled to electromagnetism.We show that this system admits anelectromagnetic background in which the spin-3=2 gauge field may move faster than light. Requiringcausal propagation rules out otherwise allowed gauge-invariant couplings. This emphasizes the importanceof causality analysis as an independent test for a system of interacting gauge fields. We comment onthe implications of allowing new degrees of freedom and nonlocality in a theory, on higher-derivativegravity and Vasiliev’s higher-spin theories.