nils-herrmann/clean_scirepeval huggingface dataset:
An exact mapping between the Variational Renormalization Group and Deep Learning

Deep learning is a broad set of techniques that uses multiple layers of representation to automatically learn relevant features directly from structured data. Recently, such techniques have yielded record-breaking results on a diverse set of difficult machine learning tasks in computer vision, speech recognition, and natural language processing. Despite the enormous success of deep learning, relatively little is understood theoretically about why these techniques are so successful at feature learning and compression. Here, we show that deep learning is intimately related to one of the most important and successful techniques in theoretical physics, the renormalization group (RG). RG is an iterative coarse-graining scheme that allows for the extraction of relevant features (i.e. operators) as a physical system is examined at different length scales. We construct an exact mapping from the variational renormalization group, first introduced by Kadanoff, and deep learning architectures based on Restricted Boltzmann Machines (RBMs). We illustrate these ideas using the nearest-neighbor Ising Model in one and two-dimensions. Our results suggests that deep learning algorithms may be employing a generalized RG-like scheme to learn relevant features from data.
An Econophysical dynamical approach of expenditure and income distribution in the UK

We extend the exploration regarding dynamical approach of macroeconomic variables by tackling systematically expenditure using Statistical Physics models (for the first time to the best of our knowledge). Also, using polynomial distribution which characterizes the behavior of dynamical systems in certain situations, we extend also our analysis to mean income data from the UK that span for a time interval of 35 years. We find that most of the values for coefficient of determination obtained from fitting the data from consecutive years analysis to be above 80%. We used for our analysis first degree polynomial, but higher degree polynomials and longer time intervals between the years considered can dramatically increase goodness of the fit. As this methodology was applied successfully to income and wealth, we can conclude that macroeconomic systems can be treated similarly to dynamic systems from Physics. Subsequently, the analysis could be extended to other macroeconomic indicators.
Peer assessment enhances student learning

Feedback has a powerful influence on learning, but it is also expensive to provide. In large classes, it may even be impossible for instructors to provide individualized feedback. Peer assessment has received attention lately as a way of providing personalized feedback that scales to large classes. Besides these obvious benefits, some researchers have also conjectured that students learn by peer assessing, although no studies have ever conclusively demonstrated this effect. By conducting a randomized controlled trial in an introductory statistics class, we provide evidence that peer assessment causes significant gains in student achievement. The strength of our conclusions depends critically on the careful design of the experiment, which was made possible by a web-based platform that we developed. Hence, our study is also a proof of concept of the high-quality experiments that are possible with online tools.
Development and Uses of Upper-division Conceptual Assessment

The use of validated conceptual assessments alongside more standard course exams has become standard practice for the introductory courses in many physics departments. These assessments provide a more standard measure of certain learning goals, allowing for comparisons of student learning across instructors, semesters, and institutions. Researchers at the University of Colorado Boulder have developed several similar assessments designed to target the more advanced physics content of upper-division classical mechanics, electrostatics, quantum mechanics, and electrodynamics. Here, we synthesize the existing research on our upper-division assessments and discuss some of the barriers and challenges associated with developing, validating, and implementing these assessments as well as some of the strategies we have used to overcome these barriers.
Experimental realization of a one-way quantum computer algorithm solving Simon's problem.

We report an experimental demonstration of a one-way implementation of a quantum algorithm solving Simon's problem-a black-box period-finding problem that has an exponential gap between the classical and quantum runtime. Using an all-optical setup and modifying the bases of single-qubit measurements on a five-qubit cluster state, key representative functions of the logical two-qubit version's black box can be queried and solved. To the best of our knowledge, this work represents the first experimental realization of the quantum algorithm solving Simon's problem. The experimental results are in excellent agreement with the theoretical model, demonstrating the successful performance of the algorithm. With a view to scaling up to larger numbers of qubits, we analyze the resource requirements for an n-qubit version. This work helps highlight how one-way quantum computing provides a practical route to experimentally investigating the quantum-classical gap in the query complexity model.
An econophysical approach of polynomial distribution applied to income and expenditure

Polynomial distribution can be applied to dynamical systems in certain situations. Macroeconomic systems characterized by economic variables such as income and wealth can be modelled similarly using polynomials. We extend our previous work to data regarding income from a more diversified pool of countries, which contains developed countries with high income, developed countries with middle income, developing and underdeveloped countries. Also, for the first time we look at the applicability of polynomial distribution to expenditure (consumption). Using cumulative distribution function, we found that polynomials are applicable with a high degree of success to the distribution of income to all countries considered without significant differences. Moreover, expenditure data can be fitted very well by this polynomial distribution. We considered a distribution to be robust if the values for coefficient of determination are higher than 90%. Using this criterion, we decided the degree for the polynomials used in our analysis by trying to minimize the number of coefficients, respectively first or second degree. Lastly, we look at possible correlation between the values from coefficient of determination and Gini coefficient for disposable income.
A statistical physics analysis of expenditure in the UK

Most papers which explored so far macroeconomic variables took into account income and wealth. Equally important as the previous macroeconomic variables is the expenditure or consumption, which shows the amount of goods and services that a person or a household purchased. Using statistical distributions from Physics, such as Fermi-Dirac and polynomial distributions, we try to fit the data regarding the expenditure distribution divided in deciles of population according to their income (gross and disposable expenditure are taken into account). Using coefficient of determination as theoretical tool in order to assess the degree of success for these distributions, we find that both distributions are really robust in describing the expenditure distribution, regardless the data set or the methodology used to calculate the expenditure values for the deciles of income. This is the first paper to our knowledge which tackles expenditure, especially using a method to describe expenditure such as lower limit on expenditure. This is also relevant since it allows the approach of macroeconomic systems using more variables characterizing their activity, can help in the investigation of living standards and inequality, and points to more theoretical explorations which can be very useful for the Economics and business practice.
Black phosphorus radio-frequency transistors.

Few-layer and thin film forms of layered black phosphorus (BP) have recently emerged as a promising material for applications in high performance nanoelectronics and infrared optoelectronics. Layered BP thin films offer a moderate bandgap of around 0.3 eV and high carrier mobility, which lead to transistors with decent on-off ratios and high on-state current densities. Here, we demonstrate the gigahertz frequency operation of BP field-effect transistors for the first time. The BP transistors demonstrated here show respectable current saturation with an on-off ratio that exceeds 2 × 10(3). We achieved a current density in excess of 270 mA/mm and DC transconductance above 180 mS/mm for hole conduction. Using standard high frequency characterization techniques, we measured a short-circuit current-gain cutoff frequency fT of 12 GHz and a maximum oscillation frequency fmax of 20 GHz in 300 nm channel length devices. BP devices may offer advantages over graphene transistors for high frequency electronics in terms of voltage and power gain due to the good current saturation properties arising from their finite bandgap, thus can be considered as a promising candidate for the future high performance thin film electronics technology for operation in the multi-GHz frequency range and beyond.
Theory of force-extension curves for modular proteins and DNA hairpins.

We study a model describing the force-extension curves of modular proteins, nucleic acids, and other biomolecules made out of several single units or modules. At a mesoscopic level of description, the configuration of the system is given by the elongations of each of the units. The system free energy includes a double-well potential for each unit and an elastic nearest-neighbor interaction between them. Minimizing the free energy yields the system equilibrium properties whereas its dynamics is given by (overdamped) Langevin equations for the elongations, in which friction and noise amplitude are related by the fluctuation-dissipation theorem. Our results, both for the equilibrium and the dynamical situations, include analytical and numerical descriptions of the system force-extension curves under force or length control and agree very well with actual experiments in biomolecules. Our conclusions also apply to other physical systems comprising a number of metastable units, such as storage systems or semiconductor superlattices.
Tighter Low-rank Approximation via Sampling the Leveraged Element

In this work, we propose a new randomized algorithm for computing a low-rank approximation to a given matrix. Taking an approach different from existing literature, our method first involves a specific biased sampling, with an element being chosen based on the leverage scores of its row and column, and then involves weighted alternating minimization over the factored form of the intended low-rank matrix, to minimize error only on these samples. Our method can leverage input sparsity, yet produce approximations in spectral (as opposed to the weaker Frobenius) norm; this combines the best aspects of otherwise disparate current results, but with a dependence on the condition number κ = σ1/σr. In particular we require O(nnz(M)+nκ2r5/e2) computations to generate a rank-r approximation to M in spectral norm. In contrast, the best existing method requires O(nnz(M)+nr2/e4) time to compute an approximation in Frobenius norm. Besides the tightness in spectral norm, we have a better dependence on the error e. Our method is naturally and highly parallelizable. 
 
Our new approach enables two extensions that are interesting on their own. The first is a new method to directly compute a low-rank approximation (in efficient factored form) to the product of two given matrices; it computes a small random set of entries of the product, and then executes weighted alternating minimization (as before) on these. The sampling strategy is different because now we cannot access leverage scores of the product matrix (but instead have to work with input matrices). The second extension is an improved algorithm with smaller communication complexity for the distributed PCA setting (where each server has small set of rows of the matrix, and want to compute low rank approximation with small amount of communication with other servers).
Low-bias negative differential resistance effect in armchair graphene nanoribbon junctions

Graphene nanoribbons with armchair edges (AGNRs) have bandgaps that can be flexibly tuned via the ribbon width. A junction made of a narrower AGNR sandwiched between two wider AGNR leads was recently reported to possess two perfect transmission channels close to the Fermi level. Here, we report that by using a bias voltage to drive these transmission channels into the gap of the wider AGNR lead, we can obtain a negative differential resistance (NDR) effect. Owing to the intrinsic properties of the AGNR junctions, the on-set bias reaches as low as ∼0.2 V and the valley current almost vanishes. We further show that such NDR effect is robust against details of the atomic structure of the junction, substrate, and whether the junction is made by etching or by hydrogenation.
Thompson sampling with the online bootstrap

Thompson sampling provides a solution to bandit problems in which new observations are allocated to arms with the posterior probability that an arm is optimal. While sometimes easy to implement and asymptotically optimal, Thompson sampling can be computationally demanding in large scale bandit problems, and its performance is dependent on the model fit to the observed data. We introduce bootstrap Thompson sampling (BTS), a heuristic method for solving bandit problems which modifies Thompson sampling by replacing the posterior distribution used in Thompson sampling by a bootstrap distribution. We first explain BTS and show that the performance of BTS is competitive to Thompson sampling in the well-studied Bernoulli bandit case. Subsequently, we detail why BTS using the online bootstrap is more scalable than regular Thompson sampling, and we show through simulation that BTS is more robust to a misspecified error distribution. BTS is an appealing modification of Thompson sampling, especially when samples from the posterior are otherwise not available or are costly.
Complexity Issues and Randomization Strategies in Frank-Wolfe Algorithms for Machine Learning

Frank-Wolfe algorithms for convex minimization have recently gained considerable attention from the Optimization and Machine Learning communities, as their properties make them a suitable choice in a variety of applications. However, as each iteration requires to optimize a linear model, a clever implementation is crucial to make such algorithms viable on large-scale datasets. For this purpose, approximation strategies based on a random sampling have been proposed by several researchers. In this work, we perform an experimental study on the effectiveness of these techniques, analyze possible alternatives and provide some guidelines based on our results.
Tweets as impact indicators: Examining the implications of automated “bot” accounts on Twitter

This brief communication presents preliminary findings on automated Twitter accounts distributing links to scientific articles deposited on the preprint repository arXiv. It discusses the implication of the presence of such bots from the perspective of social media metrics (altmetrics), where mentions of scholarly documents on Twitter have been suggested as a means of measuring impact that is both broader and timelier than citations. Our results show that automated Twitter accounts create a considerable amount of tweets to scientific articles and that they behave differently than common social bots, which has critical implications for the use of raw tweet counts in research evaluation and assessment. We discuss some definitions of Twitter cyborgs and bots in scholarly communication and propose distinguishing between different levels of engagement—that is, differentiating between tweeting only bibliographic information to discussing or commenting on the content of a scientific work.
CHOLLA: A NEW MASSIVELY PARALLEL HYDRODYNAMICS CODE FOR ASTROPHYSICAL SIMULATION

We present Computational Hydrodynamics On ParaLLel Architectures (Cholla ), a new three-dimensional hydrodynamics code that harnesses the power of graphics processing units (GPUs) to accelerate astrophysical simulations. Cholla models the Euler equations on a static mesh using state-of-the-art techniques, including the unsplit Corner Transport Upwind algorithm, a variety of exact and approximate Riemann solvers, and multiple spatial reconstruction techniques including the piecewise parabolic method (PPM). Using GPUs, Cholla evolves the fluid properties of thousands of cells simultaneously and can update over 10 million cells per GPU-second while using an exact Riemann solver and PPM reconstruction. Owing to the massively parallel architecture of GPUs and the design of the Cholla code, astrophysical simulations with physically interesting grid resolutions (≳2563) can easily be computed on a single device. We use the Message Passing Interface library to extend calculations onto multiple devices and demonstrate nearly ideal scaling beyond 64 GPUs. A suite of test problems highlights the physical accuracy of our modeling and provides a useful comparison to other codes. We then use Cholla to simulate the interaction of a shock wave with a gas cloud in the interstellar medium, showing that the evolution of the cloud is highly dependent on its density structure. We reconcile the computed mixing time of a turbulent cloud with a realistic density distribution destroyed by a strong shock with the existing analytic theory for spherical cloud destruction by describing the system in terms of its median gas density.
Observation of interlayer phonon modes in van der Waals heterostructures

We have investigated the vibrational properties of van der Waals heterostructures of monolayer transition metal dichalcogenides (TMDs), specifically $\mathrm{Mo}{\mathrm{S}}_{2}/\mathrm{WS}{\mathrm{e}}_{2}$ and $\mathrm{MoS}{\mathrm{e}}_{2}/\mathrm{Mo}{\mathrm{S}}_{2}$ heterobilayers and twisted $\mathrm{Mo}{\mathrm{S}}_{2}$ bilayers, by means of ultralow-frequency Raman spectroscopy. We discovered Raman features (at $30\char21{}40\phantom{\rule{0.16em}{0ex}}{\mathrm{cm}}^{\ensuremath{-}1}$) that arise from the layer-breathing mode (LBM) vibration between the two incommensurate TMD monolayers in these structures. The LBM Raman intensity correlates strongly with the suppression of photoluminescence that arises from interlayer charge transfer. The LBM is generated only in bilayer areas with direct layer-layer contact and an atomically clean interface. Its frequency also evolves systematically with the relative orientation between the two layers. Our research demonstrates that the LBM can serve as a sensitive probe to the interface environment and interlayer interactions in van der Waals materials.
Quantum corrections in massive bigravity and new effective composite metrics

We compute the one-loop quantum corrections to the interactions between the two metrics of the ghost-free massive bigravity. When considering gravitons running in the loops, we show how the structure of the interactions gets destabilized at the quantum level, exactly in the same way as in its massive gravity limit. A priori one might have expected a better quantum behavior, however, the broken diffeomorphism invariance out of the two initial diffeomorphisms in bigravity has similar consequences at the quantum level as the broken diffeomorphism in massive gravity. From lessons of the generated quantum corrections through matter loops we propose yet other types of effective composite metrics to which the matter fields can couple. Among these new effective metrics there might be one or more that could provide interesting phenomenology and important cosmological implications.
Global dynamics of a Yang-Mills field on an asymptotically hyperbolic space

We consider a spherically symmetric (purely magnetic) SU(2) Yang-Mills field propagating on an ultrastatic spacetime with two asymptotically hyperbolic regions connected by a throat of radius $\alpha$. Static solutions in this model are shown to exhibit an interesting bifurcation pattern in the parameter $\alpha$. We relate this pattern to the Morse index of the static solution with maximal energy. Using a hyperboloidal approach to the initial value problem, we describe the relaxation to the ground state solution for generic initial data and unstable static solutions for initial data of codimension one, two, and three.
Multi-Level Anomaly Detection on Streaming Graph Data

As a natural structure for representing entities and interactions, graphs are commonly used in many domains. Because of inherent complexity, converting graph data to meaningful information through analysis or visualization is often challenging. Identifying patterns and aberrations in graph data can pinpoint areas of interest, provide context for deeper understanding, and enable discovery in many applications. This work presents a novel modeling and analysis framework for graph sequences. The framework addresses the issues of modeling, detecting anomalies at multiple scales, and enabling understanding of graph data. A new graph model, generalizing the BTER model of Seshadhri et al. by adding flexibility to community structure, is introduced and used to perform multi-scale graph anomaly detection. Specifically, probability models describing coarse subgraphs are built by aggregating probabilities at finer levels, and these closely related hierarchical models simultaneously detect deviations from expectation. This technique provides insight into the graph’s structure and internal context that may shed light on a detected event. Additionally, this multi-scale analysis facilitates intuitive visualizations by allowing users to narrow focus from an anomalous graph to particular subgraphs causing the anomaly. For evaluation, two hierarchical anomaly detectors are tested against a baseline on a series of sampled graphs. The superior hierarchical detector outperforms the baseline, and changes in community structure are accurately detected at the node, subgraph, and graph levels. To illustrate the accessibility of information made possible via this technique, a prototype visualization tool, informed by the multi-scale analysis is tested on NCAA football data. Teams and conferences exhibiting changes in membership are identified with greater than 92% precision and recall. Screenshots of an interactive visualization, allowing users to probe into selected communities, are given. ∗Lead author.  bridgesra@ornl.gov. †Oak Ridge National Laboratory ‡Dept. of Computer Science, North Carolina State University.