nils-herrmann/clean_scirepeval huggingface dataset:
Shaping the X-ray spectrum of galaxy clusters with AGN feedback and turbulence

The hot plasma filling galaxy clusters emits copious X-ray ra diation. The classic unheated and unperturbed cooling flow model predicts dramatic cooling ra tes and an isobaric X-ray spectrum with constant differential luminosity distribution. The observed cores of clusters (and groups) show instead a strong deficit of soft X-ray emission: dLx/dT ∝ (T/Thot) �=2±1 . Using 3D hydrodynamic simulations, we show that such deficit arises from the tight selfregulation between thermal instability condensation and AGN outflow injection: condensing clouds boost the AGN outflows, which quench cooling as they th ermalize through the core. The resultant average distribution slope is �≃ 2, oscillating within the observed 1 8), while unopposed cooling drives a too shallow slope, � < 1. AGN outflows deposit their energy inside-out, releasing more heat in the inner cooler phase; r adially distributed heating alone induces a declining spectrum, 1 <�< 2. Turbulence further steepens the spectrum and increases the scatter: the turbulent Mach number in the hot phase is subsonic, while it becomes transonic in the cooler phase, making perturbations to depart from the isobaric mode. Such increase in dlnP/dlnT leads to � ≈ 3. Self-regulated AGN outflow feedback can address the soft X-ray problem through the interplay of heating and turbulence.
What does a binary black hole merger look like?

We present a method of calculating the strong-field gravitational lensing caused by many analytic and numerical spacetimes. We use this procedure to calculate the distortion caused by isolated black holes (BHs) and by numerically evolved BH binaries. We produce both demonstrative images illustrating details of the spatial distortion and realistic images of collections of stars taking both lensing amplification and redshift into account. On large scales the lensing from inspiraling binaries resembles that of single BHs, but on small scales the resulting images show complex and in some cases self-similar structure across different angular scales.
Can Collimated Extraterrestrial Signals be Intercepted

The Optical Search for Extraterrestrial Intelligence (OSETI) attempts to detect collimated, narrowband pulses of electromagnetic radiation. These pulses may either consist of signals intentionally directed at the Earth, or signals between two star systems with a vector that unintentionally intersects the Solar System, allowing Earth to intercept the communication. But should we expect to be able to intercept these unintentional signals? And what constraints can we place upon the frequency of intelligent civilisations if we do? 
We carry out Monte Carlo Realisation simulations of interstellar communications between civilisations in the Galactic Habitable Zone (GHZ) using collimated beams. We measure the frequency with which beams between two stars are intercepted by a third. The interception rate increases linearly with the fraction of communicating civilisations, and as the cube of the beam opening angle, which is somewhat stronger than theoretical expectations, which we argue is due to the geometry of the GHZ. We find that for an annular GHZ containing 10,000 civilisations, intersections are unlikely unless the beams are relatively uncollimated. 
These results indicate that optical SETI is more likely to find signals deliberately directed at the Earth than accidentally intercepting collimated communications. Equally, civilisations wishing to establish a network of communicating species may use weakly collimated beams to build up the network through interception, if they are willing to pay a cost penalty that is lower than that meted by fully isotropic beacons. Future SETI searches should consider the possibility that communicating civilisations will attempt to strike a balance between optimising costs and encouraging contact between civilisations, and look for weakly collimated pulses as well as narrow-beam pulses directed deliberately at the Earth.
Beta-Negative Binomial Process and Exchangeable Random Partitions for Mixed-Membership Modeling

The beta-negative binomial process (BNBP), an integer-valued stochastic process, is employed to partition a count vector into a latent random count matrix. As the marginal probability distribution of the BNBP that governs the exchangeable random partitions of grouped data has not yet been developed, current inference for the BNBP has to truncate the number of atoms of the beta process. This paper introduces an exchangeable partition probability function to explicitly describe how the BNBP clusters the data points of each group into a random number of exchangeable partitions, which are shared across all the groups. A fully collapsed Gibbs sampler is developed for the BNBP, leading to a novel nonparametric Bayesian topic model that is distinct from existing ones, with simple implementation, fast convergence, good mixing, and state-of-the-art predictive performance.
Sources of Gravitational Waves: Theory and Observations

Historical perspective James Clerk Maxwell discovered in 1865 that electromagnetic phenomena satisfied wave equations and found that the velocity of these waves in vacuum was numerically the same as the speed of light . Maxwell was puzzled at this coincidence between the speed of light and his theoretical prediction for the speed of electromagnetic phenomena and proposed that “light is electromagnetic disturbance propagated through the field according to electromagnetic laws” . Because any theory of gravitation consistent with special relativity cannot be an action-at-a-distance theory, in many ways, Maxwell's theory, being the first relativistic physical theory, implied the existence of gravitational waves (GWs) in general relativity (GR). Indeed, years before Einstein derived the wave equation in the linearised version of his field equations and discussed the generation of GWs as one of the first consequences of his new theory of gravity  Henri Poincare proposed the existence of les ondes gravifiques purely based on consistency of gravity with special relativity [4]. However, for many years GWs caused much controversy and a lot of doubt was cast on their existence [5-8]. The year 1959 was, in many ways, the turning point – it was the year of publication of a seminal paper by Bondi, Pirani and Robinson  on the exact plane wave solution with cylindrical symmetry and the energy carried by the waves [9]. This paper proved that wave solutions exist not just in the weak-field approximation and that GWs in GR carry energy and angular momentum away from their sources. These results cleared the way for Joseph Weber [10] to start pioneering experimental efforts. The discovery of the Hulse–Taylor binary [11], a system of two neutron stars in orbit around each other, led to the first observational evidence for the existence of gravitational radiation . The loss of energy and angular momentum to GWs causes the two stars in this system to slowly spiral in towards each other.
Walk, Not Wait: Faster Sampling Over Online Social Networks

In this paper, we introduce a novel, general purpose, technique for faster sampling of nodes over an online social network. Specifically, unlike traditional random walks which wait for the convergence of sampling distribution to a predetermined target distribution - a waiting process that incurs a high query cost - we develop WALK-ESTIMATE, which starts with a much shorter random walk, and then proactively estimate the sampling probability for the node taken before using acceptance-rejection sampling to adjust the sampling probability to the predetermined target distribution. We present a novel backward random walk technique which provides provably unbiased estimations for the sampling probability, and demonstrate the superiority of WALK-ESTIMATE over traditional random walks through theoretical analysis and extensive experiments over real world online social networks.
A new multivariate dependence measure based on comonotonicity

In this paper we introduce a new multivariate dependence measure based on comonotonicity by means of product moment which motivated by the recent papers of Koch and Schepper (ASTIN Bulletin 41 (2011) 191-213) and Dhaene et al. (Journal of Computational and Applied Mathematics 263 (2014) 78-87). Some differences and relations between the new dependence measure and other multivariate measures are an- alyzed. We also give several characteristics of this measure and estimations based on the definitions and its property are presented.
A mutli-thread tabu search algorithm

This paper describes a novel refinement to a Tabu search algorithm that has been implemented in an attempt to improve the robustness of the search when applied to particularly complex problems. In this approach, two Tabu searches are carried out in parallel. Each search thread is characterised by it's own short term memory which forces that point out of local optima. However, the two search threads share an intermediate term memory so allowing a degree of information to be passed between them. Results are presented for both unconstrained and constrained numerical functions as well as a problem in the field of hydraulic circuit optimization. Simulation of hydraulic circuit performance is achieved by linking the optimization algorithm to the commercial simulation package Bathfp.
Efficient optimisation of structures using tabu search

This paper presents a novel approach to the optimisation of structures using a Tabu search (TS) method. TS is a metaheuristic which is used to guide local search methods towards a globally optimal solution by using flexible memory cycles of differing time spans. Results are presented for the well established ten bar truss problem and compared to results published in the literature. In the first example a truss is optimised to minimise mass and the results compared to results obtained using an alternative TS implementation. In the second example, the problem has multiple objectives that are compounded into a single objective function value using game theory. In general the results demonstrate that the TS method is capable of solving structural optimisation problems at least as efficiently as other numerical optimisation approaches.
A Statistical Decision-Theoretic Framework for Social Choice

In this paper, we take a statistical decision-theoretic viewpoint on social choice, putting a focus on the decision to be made on behalf of a system of agents. In our framework, we are given a statistical ranking model, a decision space, and a loss function defined on (parameter, decision) pairs, and formulate social choice mechanisms as decision rules that minimize expected loss. This suggests a general framework for the design and analysis of new social choice mechanisms. We compare Bayesian estimators, which minimize Bayesian expected loss, for the Mallows model and the Condorcet model respectively, and the Kemeny rule. We consider various normative properties, in addition to computational complexity and asymptotic behavior. In particular, we show that the Bayesian estimator for the Condorcet model satisfies some desired properties such as anonymity, neutrality, and monotonicity, can be computed in polynomial time, and is asymptotically different from the other two rules when the data are generated from the Condorcet model for some ground truth parameter.
Faster graphical model identification of tandem mass spectra using peptide word lattices

Liquid chromatography coupled with tandem mass spectrometry, also known as shotgun proteomics, is a widely-used high-throughput technology for identifying proteins in complex biological samples. Analysis of the tens of thousands of fragmentation spectra produced by a typical shotgun proteomics experiment begins by assigning to each observed spectrum the peptide hypothesized to be responsible for generating the spectrum, typically done by searching each spectrum against a database of peptides. We have recently described a machine learning method---Dynamic Bayesian Network for Rapid Identification of Peptides (DRIP)---that not only achieves state-of-the-art spectrum identification performance on a variety of datasets but also provides a trainable model capable of returning valuable auxiliary information regarding specific peptide-spectrum matches. In this work, we present two significant improvements to DRIP. First, we describe how to use word lattices, which are widely used in natural language processing, to significantly speed up DRIP's computations. To our knowledge, all existing shotgun proteomics search engines compute independent scores between a given observed spectrum and each possible candidate peptide from the database. The key idea of the word lattice is to represent the set of candidate peptides in a single data structure, thereby allowing sharing of redundant computations among the different candidates. We demonstrate that using lattices in conjunction with DRIP leads to speedups on the order of tens across yeast and worm data sets. Second, we introduce a variant of DRIP that uses a discriminative training framework, performing maximum mutual entropy estimation rather than maximum likelihood estimation. This modification improves DRIP's statistical power, enabling us to increase the number of identified spectrum at a 1% false discovery rate on yeast and worm data sets.
Using Synchronous Boolean Networks to Model Several Phenomena of Collective Behavior

In this paper, we propose an approach for modeling and analysis of a number of phenomena of collective behavior. By collectives we mean multi-agent systems that transition from one state to another at discrete moments of time. The behavior of a member of a collective (agent) is called conforming if the opinion of this agent at current time moment conforms to the opinion of some other agents at the previous time moment. We presume that at each moment of time every agent makes a decision by choosing from the set (where 1-decision corresponds to action and 0-decision corresponds to inaction). In our approach we model collective behavior with synchronous Boolean networks. We presume that in a network there can be agents that act at every moment of time. Such agents are called instigators. Also there can be agents that never act. Such agents are called loyalists. Agents that are neither instigators nor loyalists are called simple agents. We study two combinatorial problems. The first problem is to find a disposition of instigators that in several time moments transforms a network from a state where the majority of simple agents are inactive to a state with the majority of active agents. The second problem is to find a disposition of loyalists that returns the network to a state with the majority of inactive agents. Similar problems are studied for networks in which simple agents demonstrate the contrary to conforming behavior that we call anticonforming. We obtained several theoretical results regarding the behavior of collectives of agents with conforming or anticonforming behavior. In computational experiments we solved the described problems for randomly generated networks with several hundred vertices. We reduced corresponding combinatorial problems to the Boolean satisfiability problem (SAT) and used modern SAT solvers to solve the instances obtained.
The hipster effect: When anti-conformists all look the same

In such different domains as neurosciences, spin glasses, social science, economics and finance, large ensemble of interacting individuals following (mainstream) or opposing (hipsters) to the majority are ubiquitous. In these systems, interactions generally occur after specific delays associated to transport, transmission or integration of information. We investigate here the impact of anti-conformism combined to delays in the emergent dynamics of large populations of mainstreams and hipsters. To this purpose, we introduce a class of simple statistical systems of interacting agents composed of (i) mainstreams and anti-conformists in the presence of (ii) delays, possibly heterogeneous, in the transmission of information. In this simple model, each agent can be in one of two states, and can change state in continuous time with a rate depending on the state of others in the past. We express the thermodynamic limit of these systems as the number of agents diverge, and investigate the solutions of the limit equation, with a particular focus on synchronized oscillations induced by delayed interactions. We show that when hipsters are too slow in detecting the trends, they will consistently make the same choice, and realizing this too late, they will switch, all together to another state where they remain alike. Similar synchronizations arise when the impact of mainstreams on hipsters choices (and reciprocally) dominate the impact of other hipsters choices, and we show that these may emerge only when the randomness in the hipsters decisions is sufficiently large. Beyond the choice of the best suit to wear this winter, this study may have important implications in understanding synchronization of nerve cells, investment strategies in finance, or emergent dynamics in social science, domains in which delays of communication and the geometry of information accessibility are prominent.
Physical properties of Zener tunnelling nano‐devices in graphene

By considering the direction of charge carriers and the conservation of probablity current the transmission properties of graphene Zener tunnelling nano‐devices were obtained. The scattering properties were then used with an adaptation of the Landauer formalism to calculate an analytical expression for current and conductance. The numerical results of the IV characteristics were then briefly discussed for the graphene step and Zener barrier. A comparison between the theoretical model and experimental results shows the similarities of graphene nanoribbons and infinite sheet graphene.
Towards a Visual Turing Challenge

As language and visual understanding by machines progresses rapidly, we are observing an increasing interest in holistic architectures that tightly interlink both modalities in a joint learning and inference process. This trend has allowed the community to progress towards more challenging and open tasks and refueled the hope at achieving the old AI dream of building machines that could pass a turing test in open domains. In order to steadily make progress towards this goal, we realize that quantifying performance becomes increasingly difficult. Therefore we ask how we can precisely define such challenges and how we can evaluate different algorithms on this open tasks? In this paper, we summarize and discuss such challenges as well as try to give answers where appropriate options are available in the literature. We exemplify some of the solutions on a recently presented dataset of question-answering task based on real-world indoor images that establishes a visual turing challenge. Finally, we argue despite the success of unique ground-truth annotation, we likely have to step away from carefully curated dataset and rather rely on 'social consensus' as the main driving force to create suitable benchmarks. Providing coverage in this inherently ambiguous output space is an emerging challenge that we face in order to make quantifiable progress in this area.
Latent Feature Based FM Model For Rating Prediction

Rating Prediction is a basic problem in Recommender System, and one of the most widely used method is Factorization Machines(FM). However, traditional matrix factorization methods fail to utilize the benefit of implicit feedback, which has been proved to be important in Rating Prediction problem. In this work, we consider a specific situation, movie rating prediction, where we assume that watching history has a big influence on his/her rating behavior on an item. We introduce two models, Latent Dirichlet Allocation(LDA) and word2vec, both of which perform state-of-the-art results in training latent features. Based on that, we propose two feature based models. One is the Topic-based FM Model which provides the implicit feedback to the matrix factorization. The other is the Vector-based FM Model which expresses the order info of watching history. Empirical results on three datasets demonstrate that our method performs better than the baseline model and confirm that Vector-based FM Model usually works better as it contains the order info.
High-Performance Distributed ML at Scale through Parameter Server Consistency Models

As Machine Learning (ML) applications embrace greater data size and model complexity, practitioners turn to distributed clusters to satisfy the increased computational and memory demands. Effective use of clusters for ML programs requires considerable expertise in writing distributed code, but existing highly-abstracted frameworks like Hadoop that pose low barriers to distributed-programming have not, in practice, matched the performance seen in highly specialized and advanced ML implementations. The recent Parameter Server (PS) paradigm is a middle ground between these extremes, allowing easy conversion of single-machine parallel ML programs into distributed ones, while maintaining high throughput through relaxed ``consistency models" that allow asynchronous (and, hence, inconsistent) parameter reads. However, due to insufficient theoretical study, it is not clear which of these consistency models can really ensure correct ML algorithm output; at the same time, there remain many theoretically-motivated but undiscovered opportunities to maximize computational throughput. Inspired by this challenge, we study both the theoretical guarantees and empirical behavior of iterative-convergent ML algorithms in existing PS consistency models. We then use the gleaned insights to improve a consistency model using an "eager" PS communication mechanism, and implement it as a new PS system that enables ML programs to reach their solution more quickly.
Impacts of Terraces on Phylogenetic Inference.

Terraces are sets of trees with precisely the same likelihood or parsimony score, which can be induced by missing sequences in partitioned multi-locus phylogenetic data matrices. The potentially large set of trees on a terrace can be characterized by enumeration algorithms or consensus methods that exploit the pattern of partial taxon coverage in the data, independent of the sequence data themselves. Terraces can add ambiguity and complexity to phylogenetic inference, particularly in settings where inference is already challenging: data sets with many taxa and relatively few loci. In this article we present five new findings about terraces and their impacts on phylogenetic inference. First, we clarify assumptions about partitioning scheme model parameters that are necessary for the existence of terraces. Second, we explore the dependence of terrace size on partitioning scheme and indicate how to find the partitioning scheme associated with the largest terrace containing a given tree. Third, we highlight the impact of terrace size on bootstrap estimates of confidence limits in clades, and characterize the surprising result that the bootstrap proportion for a clade, as it is usually calculated, can be entirely determined by the frequency of bipartitions on a terrace, with some bipartitions receiving high support even when incorrect. Fourth, we dissect some effects of prior distributions of edge lengths on the computed posterior probabilities of clades on terraces, to understand an example in which long edges "attract" each other in Bayesian inference. Fifth, we describe how assuming relationships between edge-lengths of different loci, as an attempt to avoid terraces, can also be problematic when taxon coverage is partial, specifically when heterotachy is present. Finally, we discuss strategies for remediation of some of these problems. One promising approach finds a minimal set of taxa which, when deleted from the data matrix, reduces the size of a terrace to a single tree.