nils-herrmann/clean_scirepeval huggingface dataset:
High-power liquid-lithium jet target for neutron production.

A compact liquid-lithium target (LiLiT) was built and tested with a high-power electron gun at the Soreq Nuclear Research Center. The lithium target, to be bombarded by the high-intensity proton beam of the Soreq Applied Research Accelerator Facility (SARAF), will constitute an intense source of neutrons produced by the (7)Li(p,n)(7)Be reaction for nuclear astrophysics research and as a pilot setup for accelerator-based Boron Neutron Capture Therapy. The liquid-lithium jet target acts both as neutron-producing target and beam dump by removing the beam thermal power (>5 kW, >1 MW/cm(3)) with fast transport. The target was designed based on a thermal model, accompanied by a detailed calculation of the (7)Li(p,n) neutron yield, energy distribution, and angular distribution. Liquid lithium is circulated through the target loop at ~200 °C and generates a stable 1.5 mm-thick film flowing at a velocity up to 7 m/s onto a concave supporting wall. Electron beam irradiation demonstrated that the liquid-lithium target can dissipate electron power areal densities of >4 kW/cm(2) and volume power density of ~2 MW/cm(3) at a lithium flow of ~4 m/s while maintaining stable temperature and vacuum conditions. The LiLiT setup is presently in online commissioning stage for high-intensity proton beam irradiation (1.91-2.5 MeV, 1-2 mA) at SARAF.
Hidden superconformal symmetry of the cosmological evolution

In the superconformal formulation of supergravity, the standard supergravity action appears as a result of spontaneous symmetry breaking when the conformal compensator scalar field, the conformon, acquires a nonzero value, giving rise to the Planck mass. After that, many symmetries of the original theory become well hidden, and therefore they are often ignored. However, recent developments demonstrated that superconformal invariance is more than just a tool: it plays an important role in generalizing previously existing formulations of supergravity and developing new classes of inflationary models. In this paper we describe hidden superconformal symmetry of the cosmological evolution. In this formulation, inflation can be equivalently described as the conformon instability, and creation of the universe `from nothing' can be interpreted as spontaneous symmetry breaking due to emergence of a classical conformon field. We develop a general formalism that allows to describe the cosmological evolution simultaneously with the evolution of the conformon. We find a set of gauge invariant physical observables, including the superconformally invariant generalizations of the square of the Weyl tensor, which are necessary for invariant description of the cosmological singularities.
Spectral dimension of quantum geometries

The spectral dimension is an indicator of geometry and topology of spacetime and a tool to compare the description of quantum geometry in various approaches to quantum gravity. This is possible because it can be defined not only on smooth geometries but also on discrete (e.g., simplicial) ones. In this paper, we consider the spectral dimension of quantum states of spatial geometry defined on combinatorial complexes endowed with additional algebraic data: the kinematical quantum states of loop quantum gravity (LQG). Preliminarily, the effects of topology and discreteness of classical discrete geometries are studied in a systematic manner. We look for states reproducing the spectral dimension of a classical space in the appropriate regime. We also test the hypothesis that in LQG, as in other approaches, there is a scale dependence of the spectral dimension, which runs from the topological dimension at large scales to a smaller one at short distances. While our results do not give any strong support to this hypothesis, we can however pinpoint when the topological dimension is reproduced by LQG quantum states. Overall, by exploring the interplay of combinatorial, topological and geometrical effects, and by considering various kinds of quantum states such as coherent states and their superpositions, we find that the spectral dimension of discrete quantum geometries is more sensitive to the underlying combinatorial structures than to the details of the additional data associated with them.
SUNNY: a Lazy Portfolio Approach for Constraint Solving

Within the context of constraint solving, a portfolio approach allows one to exploit the synergy between different solvers in order to create a globally better solver. In this paper we present SUNNY: a simple and flexible algorithm that takes advantage of a portfolio of constraint solvers in order to compute — without learning an explicit model — a schedule of them for solving a given Constraint Satisfaction Problem (CSP). Motivated by the performance reached by SUNNY vs. different simulations of other state of the art approaches, we developed sunny-csp, an effective portfolio solver that exploits the underlying SUNNY algorithm in order to solve a given CSP. Empirical tests conducted on exhaustive benchmarks of MiniZinc models show that the actual performance of sunny-csp conforms to the predictions. This is encouraging both for improving the power of CSP portfolio solvers and for trying to export them to fields such as Answer Set Programming and Constraint Logic Programming.
HINO: a BFO-aligned ontology representing human molecular interactions and pathways

Many database resources, such as Reactome, collect manually annotated reactions, interactions, and pathways from peer-reviewed publications. The interactors (e.g., a protein), interactions, and pathways in these data resources are often represented as instances in using BioPAX, a standard pathway data exchange format. However, these interactions are better represented as classes (or universals) since they always occur given appropriate conditions. This study aims to represent various human interaction pathways and networks as classes via a formal ontology aligned with the Basic Formal Ontology (BFO). Towards this goal, the Human Interaction Network Ontology (HINO) was generated by extending the BFO-aligned Interaction Network Ontology (INO). All human pathways and associated processes and interactors listed in Reactome and represented in BioPAX were first converted to ontology classes by aligning them under INO. Related terms and associated relations and hierarchies from external ontologies (e.g., CHEBI and GO) were also retrieved and imported into HINO. HINO ontology terms were resolved in the linked ontology data server Ontobee. The RDF triples stored in the RDF triple store are queryable through a SPARQL program. Such an ontology system supports advanced pathway data integration and applications.
Anytime Belief Propagation Using Sparse Domains

Belief Propagation has been widely used for marginal inference, however it is slow on problems with large-domain variables and high-order factors. Previous work provides useful approximations to facilitate inference on such models, but lacks important anytime properties such as: 1) providing accurate and consistent marginals when stopped early, 2) improving the approximation when run longer, and 3) converging to the fixed point of BP. To this end, we propose a message passing algorithm that works on sparse (partially instantiated) domains, and converges to consistent marginals using dynamic message scheduling. The algorithm grows the sparse domains incrementally, selecting the next value to add using prioritization schemes based on the gradients of the marginal inference objective. Our experiments demonstrate local anytime consistency and fast convergence, providing significant speedups over BP to obtain low-error marginals: up to 25 times on grid models, and up to 6 times on a real-world natural language processing task.
Scalar Green function of the Kerr spacetime

In this paper we study the scalar Green function in the Kerr spacetime using Wentzel-Kramers-Brillouin (WKB) methods. The Green function can be expressed by Fourier-transforming to its frequency-domain counterpart, and with the help of complex analysis it can be divided into parts: 1) the “direct part,” which propagates on the light cone and dominates at very early times; 2) the “quasinormal-mode part,” which represents the waves traveling around the photon sphere and is important at early and intermediate times; and 3) the “tail part,” which is due to scattering by the Coulomb-type potential and becomes more important at later times. We focus on the “quasinormal-mode part” of the Green function and derive an approximate analytical formula for it using WKB techniques. This approximate Green function diverges at points that are connected by null geodesics, and it recovers the fourfold singular structure of Green functions that are seen in Schwarzschild and other spacetimes. It also carries unique signatures of the Kerr spacetime such as frame dragging. Along the way, we also derive approximate quasinormal-mode wave functions and expressions for the black hole excitation factors in the Kerr spacetime. We expect this work to benefit the understanding of both wave propagation and the problem of self-force in the Kerr spacetime.
Integrated Expert Recommendation Model for Online Communities

Online communities have become vital places for Web 2.0 users to share knowledge and experiences. Recently, finding expertise user in community has become an important research issue. This paper proposes a novel cascaded model for expert recommendation using aggregated knowledge extracted from enormous contents and social network features. Vector space model is used to compute the relevance of published content with respect to a specific query while PageRank algorithm is applied to rank candidate experts. The experimental results show that the proposed model is an effective recommendation which can guarantee that the most candidate experts are both highly relevant to the specific queries and highly influential in corresponding areas.
Social Influence and the Collective Dynamics of Opinion Formation

Social influence is the process by which individuals adapt their opinion, revise their beliefs, or change their behavior as a result of social interactions with other people. In our strongly interconnected society, social influence plays a prominent role in many self-organized phenomena such as herding in cultural markets, the spread of ideas and innovations, and the amplification of fears during epidemics. Yet, the mechanisms of opinion formation remain poorly understood, and existing physics-based models lack systematic empirical validation. Here, we report two controlled experiments showing how participants answering factual questions revise their initial judgments after being exposed to the opinion and confidence level of others. Based on the observation of 59 experimental subjects exposed to peer-opinion for 15 different items, we draw an influence map that describes the strength of peer influence during interactions. A simple process model derived from our observations demonstrates how opinions in a group of interacting people can converge or split over repeated interactions. In particular, we identify two major attractors of opinion: (i) the expert effect, induced by the presence of a highly confident individual in the group, and (ii) the majority effect, caused by the presence of a critical mass of laypeople sharing similar opinions. Additional simulations reveal the existence of a tipping point at which one attractor will dominate over the other, driving collective opinion in a given direction. These findings have implications for understanding the mechanisms of public opinion formation and managing conflicting situations in which self-confident and better informed minorities challenge the views of a large uninformed majority.
Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation

Many machine learning approaches are characterized by information constraints on how they interact with the training data. These include memory and sequential access constraints (e.g. fast first-order methods to solve stochastic optimization problems); communication constraints (e.g. distributed learning); partial access to the underlying data (e.g. missing features and multi-armed bandits) and more. However, currently we have little understanding how such information constraints fundamentally affect our performance, independent of the learning problem semantics. For example, are there learning problems where any algorithm which has small memory footprint (or can use any bounded number of bits from each example, or has certain communication constraints) will perform worse than what is possible without such constraints? In this paper, we describe how a single set of results implies positive answers to the above, for several different settings.
Spectrum and entanglement of phonons in quantum fluids of light

We study the quantum state of phonons propagating on top of a fluid of light coherently generated in a planar microcavity device by a quasi-resonant incident laser beam. In the steady-state under a monochromatic pump, because of the finite radiative lifetime of photons, a sizable incoherent population of low frequency phonons is predicted to appear. Their mean occupation number differs from a Planck distribution and is independent on the photon lifetime. When the photon fluid is subjected to a sudden change of its parameters, additional phonon pairs are created in the fluid with remarkable two-mode squeezing and entanglement properties. Schemes to assess non-separability of the phonon state from measurements of the correlation functions of the emitted light are discussed.
Tangent bundle formulation of a charged gas

We discuss the relativistic kinetic theory for a simple, collisionless, charged gas propagating on an arbitrary curved spacetime geometry. Our general relativistic treatment is formulated on the tangent bundle of the spacetime manifold and takes advantage of its rich geometric structure. In particular, we point out the existence of a natural metric on the tangent bundle and illustrate its role for the development of the relativistic kinetic theory. This metric, combined with the electromagnetic field of the spacetime, yields an appropriate symplectic form on the tangent bundle. The Liouville vector field arises as the Hamiltonian vector field of a natural Hamiltonian. The latter also defines natural energy surfaces, called mass shells, which turn out to be smooth Lorentzian submanifolds. 
A simple, collisionless, charged gas is described by a distribution function which is defined on the mass shell and satisfies the Liouville equation. Suitable fibre integrals of the distribution function define observable fields on the spacetime manifold, such as the current density and stress-energy tensor. Finally, the geometric setting of this work allows us to discuss the relationship between the symmetries of the electromagnetic field, those of the spacetime metric, and the symmetries of the distribution function. Taking advantage of these symmetries, we construct the most general solution of the Liouville equation an a Kerr-Newman black hole background.
Evolutionary perspectives on collective decision making: Studying the implications of diversity and social network structure with agent-based simulations

Collective, especially group-based, managerial decision making is crucial in organizations. Using an evolutionary theory approach to collective decision making, agent-based simulations were conducted to investigate how collective decision making would be affected by the agents' diversity in problem understanding and/or behavior in discussion, as well as by their social network structure. Simulation results indicated that groups with consistent problem understanding tended to produce higher utility values of ideas and displayed better decision convergence, but only if there was no group-level bias in collective problem understanding. Simulation results also indicated the importance of balance between selection-oriented (i.e., exploitative) and variation-oriented (i.e., explorative) behaviors in discussion to achieve quality final decisions. Expanding the group size and introducing non-trivial social network structure generally improved the quality of ideas at the cost of decision convergence. Simulations with different social network topologies revealed that collective decision making on small-world networks with high local clustering tended to achieve highest decision quality more often than on random or scale-free networks. Implications of this evolutionary theory and simulation approach for future managerial research on collective, group, and multi-level decision making are discussed.
Exploiting Direct and Indirect Information for Friend Suggestion in ZingMe

Friend suggestion is a fundamental problem in social networks with the goal of assisting users in creating more relationships, and thereby enhances interest of users to the social networks. This problem is often considered to be the link prediction problem in the network. ZingMe is one of the largest social networks in Vietnam. In this paper, we analyze the current approach for the friend suggestion problem in ZingMe, showing its limitations and disadvantages. We propose a new efficient approach for friend suggestion that uses information from the network structure, attributes and interactions of users to create resources for the evaluation of friend connection amongst users. Friend connection is evaluated exploiting both direct communication between the users and information from other ones in the network. The proposed approach has been implemented in a new system version of ZingMe. We conducted experiments, exploiting a dataset derived from the users' real use of ZingMe, to compare the newly proposed approach to the current approach and some well-known ones for the accuracy of friend suggestion. The experimental results show that the newly proposed approach outperforms the current one, i.e., by an increase of 7% to 98% on average in the friend suggestion accuracy. The proposed approach also outperforms other ones for users who have a small number of friends with improvements from 20% to 85% on average. In this paper, we also discuss a number of open issues and possible improvements for the proposed approach.
Ensemble Relational Learning based on Selective Propositionalization

Dealing with structured data needs the use of expressive representation formalisms that, however, puts the problem to deal with the computational complexity of the machine learning process. Furthermore, real world domains require tools able to manage their typical uncertainty. Many statistical relational learning approaches try to deal with these problems by combining the construction of relevant relational features with a probabilistic tool. When the combination is static (static propositionalization), the constructed features are considered as boolean features and used offline as input to a statistical learner; while, when the combination is dynamic (dynamic propositionalization), the feature construction and probabilistic tool are combined into a single process. In this paper we propose a selective propositionalization method that search the optimal set of relational features to be used by a probabilistic learner in order to minimize a loss function. The new propositionalization approach has been combined with the random subspace ensemble method. Experiments on real-world datasets shows the validity of the proposed method.
MetaPar: Metagenomic sequence assembly via iterative reclassification

We introduce a parallel algorithmic architecture for metagenomic sequence assembly, termed MetaPar, which allows for significant reductions in assembly time and consequently enables the processing of large genomic datasets on computers with low memory usage. The gist of the approach is to iteratively perform read (re)classification based on phylogenetic marker genes and assembler outputs generated from random subsets of metagenomic reads. Once a sufficiently accurate classification within genera is performed, de novo metagenomic assemblers (such as Velvet or IDBA-UD) or reference based assemblers may be used for contig construction. We analyze the performance of MetaPar on synthetic data consisting of 15 randomly chosen species from the NCBI database [18] through the effective gap and effective coverage metrics.
Valence bond distribution and correlation in bipartite Heisenberg antiferromagnets

Every singlet state of a quantum spin 1/2 system can be decomposed into a linear combination of valence bond basis states. The range of valence bonds within this linear combination as well as the correlations between them can reveal the nature of the singlet state, and are key ingredients in variational calculations. In this work, we study the bipartite valence bond distributions and their correlations within the ground state of the Heisenberg antiferromagnet on bipartite lattices. In terms of field theory, this problem can be mapped to correlation functions near a boundary. In dimension d >= 2, a non-linear sigma model analysis reveals that at long distances the probability distribution P(r) of valence bond lengths decays as |r|^(-d-1) and that valence bonds are uncorrelated. By a bosonization analysis, we also obtain P(r) proportional to |r|^(-d-1) in d=1 despite the different mechanism. On the other hand, we find that correlations between valence bonds are important even at large distances in d=1, in stark contrast to d >= 2. The analytical results are confirmed by high-precision quantum Monte Carlo simulations in d=1, 2, and 3. We develop a single-projection loop variant of the valence bond projection algorithm, which is well-designed to compute valence bond probabilities and for which we provide algorithmic details.
Inferring Multilateral Relations from Dynamic Pairwise Interactions

Correlations between anomalous activity patterns can yield pertinent information about complex social processes: a significant deviation from normal behavior, exhibited simultaneously by multiple pairs of actors, provides evidence for some underlying relationship involving those pairs---i.e., a multilateral relation. We introduce a new nonparametric Bayesian latent variable model that explicitly captures correlations between anomalous interaction counts and uses these shared deviations from normal activity patterns to identify and characterize multilateral relations. We showcase our model's capabilities using the newly curated Global Database of Events, Location, and Tone, a dataset that has seen considerable interest in the social sciences and the popular press, but which has is largely unexplored by the machine learning community. We provide a detailed analysis of the latent structure inferred by our model and show that the multilateral relations correspond to major international events and long-term international relationships. These findings lead us to recommend our model for any data-driven analysis of interaction networks where dynamic interactions over the edges provide evidence for latent social structure.
Improving the performance of algorithms to find communities in networks

Most algorithms to detect communities in networks typically work without any information on the cluster structure to be found, as one has no a priori knowledge of it, in general. Not surprisingly, knowing some features of the unknown partition could help its identification, yielding an improvement of the performance of the method. Here we show that, if the number of clusters was known beforehand, standard methods, like modularity optimization, would considerably gain in accuracy, mitigating the severe resolution bias that undermines the reliability of the results of the original unconstrained version. The number of clusters can be inferred from the spectra of the recently introduced nonbacktracking and flow matrices, even in benchmark graphs with realistic community structure. The limit of such a two-step procedure is the overhead of the computation of the spectra.