nils-herrmann/clean_scirepeval huggingface dataset:
Radially stabilized inflating cosmic strings

In General Relativity, cosmic strings are well known to produce a static, locally flat spacetime with a wedge removed. If the tension exceeds a critical value, the deficit angle becomes larger than $ 2\pi $, leading to a compact exterior that ends in a conical singularity. In this work, we investigate dynamical solutions for cosmic strings with super-critical tensions. To this end, we model the string as a cylindrical shell of finite and stabilized transverse width and show that there is a marginally super-critical regime in which the stabilization can be achieved by physically reasonable matter. 
We show numerically that the static deficit angle solution is unstable for super-critical string tensions. Instead, the geometry starts expanding in axial direction at an asymptotically constant rate, and a horizon is formed in the exterior, which has the shape of a growing cigar. We are able to find the analytic form of the attractor solution describing the interior of the cosmic string. In particular, this enables us to analytically derive the relation between the string tension and the axial expansion rate. Furthermore, we show that the exterior conical singularity can be avoided for dynamical solutions. 
Our results might be relevant for theories with two extra dimensions, modeling our universe as a cosmic string with a three-dimensional axis. We derive the corresponding Friedmann equation, relating the on-brane Hubble parameter to the string tension or, equivalently, brane cosmological constant.
Plan or not: Remote Human-robot Teaming with Incomplete Task Information

Human-robot interaction can be divided into two categories based on the physical distance between the human and robot: remote and proximal. In proximal interaction, the human and robot often engage in close coordination; in remote interaction, the human and robot are less coupled due to communication constraints. As a result, providing automation for the robot in remote interaction becomes more important. Thus far, human factor studies on automation in remote human-robot interaction have been restricted to various forms of supervision, in which the robot is essentially being used as a smart mobile manipulation platform with sensing capabilities. In this paper, we investigate the incorporation of general planning capability into the robot to facilitate peer-to-peer human-robot teaming, in which the human and robot are viewed as teammates that are physically separated. The human and robot share the same global goal and collaborate to achieve it. Note that humans may feel uncomfortable at such robot autonomy, which can potentially reduce teaming performance. One important difference between peer-to-peer teaming and supervised teaming is that an autonomous robot in peer-to-peer teaming can achieve the goal alone when the task information is completely specified. However, incompleteness often exists, which implies information asymmetry. While information asymmetry can be desirable sometimes, it may also lead to the robot choosing improper actions that negatively influence the teaming performance. We aim to investigate the various trade-offs, e.g., mental workload and situation awareness, between these two types of remote human-robot teaming.
Entanglement negativity in the multiverse

We explore quantum entanglement between two causally disconnected regions in the multiverse. We first consider a free massive scalar field, and compute the entanglement negativity between two causally separated open charts in de Sitter space. The qualitative feature of it turns out to be in agreement with that of the entanglement entropy. We then introduce two observers who determine the entanglement between two causally disconnected de Sitter spaces. When one of the observers remains constrained to a region of the open chart in a de Sitter space, we find that the scale dependence enters into the entanglement. We show that a state which is initially maximally entangled becomes more entangled or less entangled on large scales depending on the mass of the scalar field and recovers the initial entanglement in the small scale limit. We argue that quantum entanglement may provide some evidence for the existence of the multiverse.
Score Function Features for Discriminative Learning: Matrix and Tensor Framework

Author(s): Janzamin, Majid; Sedghi, Hanie; Anandkumar,  Feature learning forms the cornerstone for tackling challenging learning problems in domains such as speech, computer vision and natural language processing. In this paper, we consider a novel class of matrix and tensor-valued features, which can be pre-trained using unlabeled samples. We present efficient algorithms for extracting discriminative information, given these pre-trained features and labeled samples for any related task. Our class of features are based on higher-order score functions, which capture local variations in the probability density function of the input. We establish a theoretical framework to characterize the nature of discriminative information that can be extracted from score-function features, when used in conjunction with labeled samples. We employ efficient spectral decomposition algorithms (on matrices and tensors) for extracting discriminative components. The advantage of employing tensor-valued features is that we can extract richer discriminative information in the form of an overcomplete representations. Thus, we present a novel framework for employing generative models of the input for discriminative learning.
Bayesian Fisher's Discriminant for Functional Data

We propose a Bayesian framework of Gaussian process in order to extend Fisher's discriminant to classify functional data such as spectra and images. The probability structure for our extended Fisher's discriminant is explicitly formulated, and we utilize the smoothness assumptions of functional data as prior probabilities. Existing methods which directly employ the smoothness assumption of functional data can be shown as special cases within this framework given corresponding priors while their estimates of the unknowns are one-step approximations to the proposed MAP estimates. Empirical results on various simulation studies and different real applications show that the proposed method significantly outperforms the other Fisher's discriminant methods for functional data.
Towards a Model of Systemic Change in University STEM Education

Despite numerous calls for the transformation of undergraduate STEM education, there is still a lack of successful models for creating large-scale, systemic cultural changes in STEM departments. To date, change efforts have generally focused on one of three areas: developing reflective teachers, disseminating curricula and pedagogy, or enacting institutional policy. These efforts illustrate many of the challenges of departmental change; in particular, they highlight the need for a holistic approach that integrates across all three of these levels: individual faculty, whole departments, and university policymakers. To address these challenges, as part of our campus-wide AAU-sponsored effort in STEM education transformation, we import and integrate models of change from multiple perspectives. We draw from models in organizational change, from departmental and disciplinary change in STEM education, and from efforts to support individual efforts such as the development and dissemination model. As a result, our departmental cultural change efforts are an attempt at holistic reform. We will discuss our theoretical underpinnings and ground this theory in a sample of approaches in two departments.
Provable Tensor Methods for Learning Mixtures of Classifiers

We consider the problem of learning associative mixtures for classification and regression problems, where the output is modeled as a mixture of conditional distributions, conditioned on the input. In contrast to approaches such as expectation maximization (EM) or variational Bayes, which can get stuck in bad local optima, we present a tensor decomposition method which is guaranteed to correctly recover the parameters. The key insight is to learn score function features of the input, and employ them in a moment-based approach for learning associative mixtures. Specifically, we construct the cross-moment tensor between the label and higher order score functions of the input. We establish that the decomposition of this tensor consistently recovers the components of the associative mixture under some simple non-degeneracy assumptions. Thus, we establish that feature learning is the critical ingredient for consistent estimation of associative mixtures using tensor decomposition approaches.
Hierarchical Mixture-of-Experts Model for Large-Scale Gaussian Process Regression

We propose a practical and scalable Gaussian process model for large-scale nonlinear probabilistic regression. Our mixture-of-experts model is conceptually simple and hierarchically recombines computations for an overall approximation of a full Gaussian process. Closed-form and distributed computations allow for efficient and massive parallelisation while keeping the memory consumption small. Given sufficient computing resources, our model can handle arbitrarily large data sets, without explicit sparse approximations. We provide strong experimental evidence that our model can be applied to large data sets of sizes far beyond millions. Hence, our model has the potential to lay the foundation for general large-scale Gaussian process research.
Computoser - rule-based, probability-driven algorithmic music composition

This paper presents the Computoser hybrid probability/rule based algorithm for music composition (this http URL) and provides a reference implementation. It addresses the issues of unpleasantness and lack of variation exhibited by many existing approaches by combining the two methods (basing the parameters of the rules on data obtained from preliminary analysis). 
A sample of 500+ musical pieces was analyzed to derive probabilities for musical characteristics and events (e.g. scale, tempo, intervals). The algorithm was constructed to produce musical pieces using the derived probabilities combined with a large set of composition rules, which were obtained and structured after studying established composition practices. Generated pieces were published on the Computoser website where evaluation was performed by listeners. The feedback was positive (58.4% approval), asserting the merits of the undertaken approach. 
The paper compares this hybrid approach to other approaches to algorithmic composition and presents a survey of the pleasantness of the resulting music.
Quantum Monte Carlo methods for nuclear physics

Quantum Monte Carlo methods have proved very valuable to study the structure and reactions of light nuclei and nucleonic matter starting from realistic nuclear interactions and currents. These ab-initio calculations reproduce many low-lying states, moments and transitions in light nuclei, and simultaneously predict many properties of light nuclei and neutron matter over a rather wide range of energy and momenta. We review the nuclear interactions and currents, and describe the continuum Quantum Monte Carlo methods used in nuclear physics. These methods are similar to those used in condensed matter and electronic structure but naturally include spin-isospin, tensor, spin-orbit, and three-body interactions. We present a variety of results including the low-lying spectra of light nuclei, nuclear form factors, and transition matrix elements. We also describe low-energy scattering techniques, studies of the electroweak response of nuclei relevant in electron and neutrino scattering, and the properties of dense nucleonic matter as found in neutron stars. A coherent picture of nuclear structure and dynamics emerges based upon rather simple but realistic interactions and currents.
THE X-RAY FLUX DISTRIBUTION OF SAGITTARIUS A* AS SEEN BY CHANDRA

We present a statistical analysis of the X-ray flux distribution of Sgr A* from the Chandra X-Ray Observatory's 3 Ms Sgr A* X-ray Visionary Project in 2012. Our analysis indicates that the observed X-ray flux distribution can be decomposed into a steady quiescent component, represented by a Poisson process with rate Q = (5.24  ±  0.08) × 10−3 counts s−1, and a variable component, represented by a power law process (dN/dF∝F−ξ, ). This slope matches our recently reported distribution of flare luminosities. The variability may also be described by a log-normal process with a median unabsorbed 2–8 keV flux of erg s−1 cm−2 and a shape parameter σ = 2.4 ± 0.2, but the power law provides a superior description of the data. In this decomposition of the flux distribution, all of the intrinsic X-ray variability of Sgr A* (spanning at least three orders of magnitude in flux) can be attributed to flaring activity, likely in the inner accretion flow. We confirm that at the faint end, the variable component contributes ∼10% of the apparent quiescent flux, as previously indicated by our statistical analysis of X-ray flares in these Chandra observations. Our flux distribution provides a new and important observational constraint on theoretical models of Sgr A*, and we use simple radiation models to explore the extent to which a statistical comparison of the X-ray and infrared can provide insights into the physics of the X-ray emission mechanism.
Rule reasoning for legal norm validation of FSTP facts

An invention should be at an adequate distance beyond its prior art in order to be patented. Fulfilling a minimum measurement limit would enable a patent applicant to have its invention patented. Based on this fact, we proposed a method for nonobviousness analysis of a patent over its prior arts, Hased oﾐ highest Iou�t’s p�eIedeﾐts, Ialled the F“TP
Notes on the firewall paradox, complexity, and quantum theory

We investigate what it means to apply the solution, proposed to the firewall paradox by Harlow and Hayden, to the famous quantum paradoxes of Sch\"odinger's Cat and Wigner's Friend if ones views these as posing a thermodynamic decoding problem (as does Hawking radiation in the firewall paradox). The implications might point to a relevance of the firewall paradox for the axiomatic and set theoretic foundations underlying mathematics. We reconsider in this context the results of Benioff on the foundational challenges posed by the randomness postulate of quantum theory. A central point in our discussion is that one can mathematically not naturally distinguish between computational complexity (as central to the approach of Harlow and Hayden and further developed by Susskind) and proof theoretic complexity (since they represent the same concept on a Turing machine), with the latter being related to a finite bound on Kolmogorov entropy (due to Chaitin incompleteness).
Bach in 2014: Music Composition with Recurrent Neural Network

We propose a framework for computer music composition that uses resilient propagation (RProp) and long short term memory (LSTM) recurrent neural network. In this paper, we show that LSTM network learns the structure and characteristics of music pieces properly by demonstrating its ability to recreate music. We also show that predicting existing music using RProp outperforms Back propagation through time (BPTT).
Magnetic fields in mixed neutron-star-plus-wormhole systems

We consider mixed configurations consisting of a wormhole filled by a strongly magnetized isotropic or anisotropic neutron fluid. The nontrivial topology of the spacetime is allowed by the presence of exotic matter. By comparing these configurations with ordinary magnetized neutron stars, we clarify the question of how the presence of the nontrivial topology influences the magnetic field distribution inside the fluid. In the case of an anisotropic fluid, we find new solutions describing configurations, where the maximum of the fluid density is shifted from the center. A linear stability analysis shows that these mixed configurations are unstable.
Single-enzyme kinetics with branched pathways: exact theory and series expansion

The progress of the successive rounds of catalytic conversion of substrates into product(s) by a single enzyme is characterized by the distribution of turnover times. Establishing the most general form of dependence of this distribution on the substrate concentration [S] is one of the fundamental challenges in single molecule enzymology. The distribution of the times of dwell of a molecular motor at the successive positions on its track is an analogous quantity. We derive approximate series expansions for the [ATP]-dependence of the first two moments of the dwell time distributions of motors that catalyze hydrolysis of ATP to draw input energy. Comparison between our results for motors with branched pathways and the corresponding expressions reported earlier for linear enzymatic pathways provides deep insight into the effects of the branches. Such insight is likely to help in discovering the most general form of [S]-dependence of these fundamental distributions.
Avoiding Extinction by Migration: the Case of the Head Louse

The possibility of spreading by migration, colonizing new spatial domains suitable for development and reproduction, can substantially relieve a biological population from the risk of extinction. By means of a realistic computational model based on empirical data, we study this phenomenon for the human head louse, Pediculus humanus capitis. In particular, we show that a lice colony infesting a single isolated host is prone to extinction by stochastic population fluctuations within an interval of several months, while migration over a relatively small group of hosts in contact with each other is enough to insure the prevalence of the infestation for indefinitely long periods. We characterize the interplay of the size of the host group with the host-to-host contagion probability, which controls a transition between extinction of the lice population and a situation where the infestation is endemic.
Memory Vectors for Similarity Search in High-Dimensional Spaces

We study an indexing architecture to store and search in a database of high-dimensional vectors from the perspective of statistical signal processing and decision theory. This architecture is composed of several memory units, each of which summarizes a fraction of the database by a single representative vector. The potential similarity of the query to one of the vectors stored in the memory unit is gauged by a simple correlation with the memory unit's representative vector. This representative optimizes the test of the following hypothesis: the query is independent from any vector in the memory unit versus the query is a simple perturbation of one of the stored vectors. Compared to exhaustive search, our approach finds the most similar database vectors significantly faster without a noticeable reduction in search quality. Interestingly, the reduction of complexity is provably better in high-dimensional spaces. We empirically demonstrate its practical interest in a large-scale image search scenario with off-the-shelf state-of-the-art descriptors.
High School Students' Understandings and Representations of the Electric Field

This study investigates the representations and understandings of electric fields expressed by Chinese high school students 15 to 16 years old who have not received high school level physics instruction. The physics education research literature has reported students conceptions of electric fields post-instruction as indicated by students performance on textbook-style questions. It has, however, inadequately captured student ideas expressed in other situations yet informative to educational research. In this study, we explore students ideas of electric fields pre-instruction as shown by students representations produced in open-ended activities. 92 participant students completed a worksheet that involved drawing comic strips about electric charges as characters of a cartoon series. Three students who had spontaneously produced arrow diagrams were interviewed individually after class. We identified nine ideas related to electric fields that these three students spontaneously leveraged in the comic strip activity. In this paper, we describe in detail each idea and its situated context. As most research in the literature has understood students as having relatively fixed conceptions and mostly identified divergences in those conceptions from canonical targets, this study shows students reasoning to be more variable in particular moments, and that variability includes common sense resources that can be productive for learning about electric fields.
Training Deep Convolutional Neural Networks to Play Go

Mastering the game of Go has remained a longstanding challenge to the field of AI. Modern computer Go programs rely on processing millions of possible future positions to play well, but intuitively a stronger and more 'humanlike' way to play the game would be to rely on pattern recognition rather than brute force computation. Following this sentiment, we train deep convolutional neural networks to play Go by training them to predict the moves made by expert Go players. To solve this problem we introduce a number of novel techniques, including a method of tying weights in the network to 'hard code' symmetries that are expected to exist in the target function, and demonstrate in an ablation study they considerably improve performance. Our final networks are able to achieve move prediction accuracies of 41.1% and 44.4% on two different Go datasets, surpassing previous state of the art on this task by significant margins. Additionally, while previous move prediction systems have not yielded strong Go playing programs, we show that the networks trained in this work acquired high levels of skill. Our convolutional neural networks can consistently defeat the well known Go program GNU Go and win some games against state of the art Go playing program Fuego while using a fraction of the play time.
GP-Select: Accelerating EM Using Adaptive Subspace Preselection

We propose a nonparametric procedure to achieve fast inference in generative graphical models when the number of latent states is very large. The approach is based on iterative latent variable preselection, where we alternate between learning a selection function to reveal the relevant latent variables and using this to obtain a compact approximation of the posterior distribution for EM. This can make inference possible where the number of possible latent states is, for example, exponential in the number of latent variables, whereas an exact approach would be computationally infeasible. We learn the selection function entirely from the observed data and current expectation-maximization state via gaussian process regression. This is in contrast to earlier approaches, where selection functions were manually designed for each problem setting. We show that our approach performs as well as these bespoke selection functions on a wide variety of inference problems. In particular, for the challenging case of a hierarchical model for object localization with occlusion, we achieve results that match a customized state-of-the-art selection method at a far lower computational cost.