nils-herrmann/clean_scirepeval huggingface dataset:
Astrophysical Limits on Very Light Axion-like Particles from Chandra Grating Spectroscopy of NGC 1275

Axions/axion-like particles (ALPs) are a well-motivated extension of the Standard Model and are generic within String Theory. The X-ray transparency of the intracluster medium (ICM) in galaxy clusters is a powerful probe of light ALPs (with mass ); as X-ray photons from an embedded or background source propagate through the magnetized ICM, they may undergo energy-dependent quantum mechanical conversion into ALPs (and vice versa), imprinting distortions on the X-ray spectrum. We present Chandra data for the active galactic nucleus NGC 1275 at the center of the Perseus cluster. Employing a 490 ks High Energy Transmission Gratings exposure, we obtain a high-quality 1–9 keV spectrum free from photon pileup and ICM contamination. Apart from iron-band features, the spectrum is described by a power-law continuum, with any spectral distortions at the <3% level. We compute photon survival probabilities as a function of ALP mass ma and ALP-photon coupling constant for an ensemble of ICM magnetic field models, and then use the NGC 1275 spectrum to constrain the -plane. Marginalizing over magnetic field realizations, the 99.7% credible region limits the ALP-photon coupling to (depending upon magnetic field model) for masses . These are the most stringent limit to date on for these light ALPs, and have already reached the sensitivity limits of next-generation helioscopes and light-shining-through-wall experiments. We highlight the potential of these studies with the next-generation X-ray observatories Athena and Lynx, but note the critical importance of advances in relative calibration of these future X-ray spectrometers.
Minimizers of the Empirical Risk and Risk Monotonicity

Plotting a learner's average performance against the number of training samples results in a learning curve. Studying such curves on one or more data sets is a way to get to a better understanding of the generalization properties of this learner. The behavior of learning curves is, however, not very well understood and can display (for most researchers) quite unexpected behavior. Our work introduces the formal notion of \emph{risk monotonicity}, which asks the risk to not deteriorate with increasing training set sizes in expectation over the training samples. We then present the surprising result that various standard learners, specifically those that minimize the empirical risk, can act \emph{non}monotonically irrespective of the training sample size. We provide a theoretical underpinning for specific instantiations from classification, regression, and density estimation. Altogether, the proposed monotonicity notion opens up a whole new direction of research.
Mobility restores the mechanism which supports cooperation in the voluntary prisoner’s dilemma game

It is generally believed that in a situation where individual and collective interests are in conflict, the availability of optional participation is a key mechanism to maintain cooperation. Surprisingly, this effect is sensitive to the use of microscopic dynamics and can easily be broken when agents make a fully rational decision during their strategy updates. In the framework of the celebrated prisoner’s dilemma game, we show that this discrepancy can be fixed automatically if we leave the strict and frequently artifact condition of a fully occupied interaction graph, and allow agents to change not just their strategies but also their positions according to their success. In this way, a diluted graph where agents may move offers a natural and alternative way to handle artifacts arising from the application of specific and sometimes awkward microscopic rules.
Using psychometric tools as a window into students’ quantitative reasoning in introductory physics

The Physics Inventory of Quantitative Literacy (PIQL), a reasoning inventory under development, aims to assess students' physics quantitative literacy at the introductory level. PIQL's design presents the challenge of isolating types of mathematical reasoning that are independent of each other in physics questions. In its current form, PIQL spans three principle reasoning subdomains previously identified in mathematics and physics education research: ratios and proportions, covariation, and signed (negative) quantities. An important psychometric objective is to test the orthogonality of these three reasoning subdomains. We present results from exploratory factor analysis, confirmatory factor analysis, and module analysis that inform interpretations of the underlying structure of PIQL from a student viewpoint, emphasizing ways in which these results agree and disagree with expert categorization. In addition to informing the development of existing and new PIQL assessment items, these results are also providing exciting insights into students' quantitative reasoning at the introductory level.
Online Learning to Estimate Warfarin Dose with Contextual Linear Bandits

Warfarin is one of the most commonly used oral blood anticoagulant agent in the world, the proper dose of Warfarin is difficult to establish not only because it is substantially variant among patients, but also adverse even severe consequences of taking an incorrect dose. Typical practice is to prescribe an initial dose, then doctor closely monitor patient response and adjust accordingly to the correct dosage. The three commonly used strategies for an initial dosage are the fixed-dose approach, the Warfarin Clinical algorithm, and the Pharmacogenetic algorithm developed by the IWPC (International Warfarin Pharmacogenetics Consortium). It is always best to prescribe correct initial dosage, motivated by this challenge, this work explores the performance of multi-armed bandit algorithms to best predict the correct dosage of Warfarin instead of trial-and-error procedure. Real data from the Pharmacogenetics and Pharmacogenomics Knowledge Base (PharmGKB) is used, with it a series of linear bandit algorithms and variants are developed and evaluated on Warfarin dataset. All proposed algorithms outperformed the fixed-dose baseline algorithm, and some even matched up the Warfarin Clinical Dosing Algorithm. In addition, a few promising future directions are given for further exploration and development.
Artificial Intelligence as a Service (AI-aaS) on Software-Defined Infrastructure

This paper investigates a paradigm for offering artificial intelligence as a service (AI-aaS) on software-defined infrastructures (SDIs). The increasing complexity of networking and computing infrastructures is already driving the introduction of automation in networking and cloud computing management systems. Here we consider how these automation mechanisms can be leveraged to offer AI-aaS. Use cases for AI-aaS are easily found in addressing smart applications in sectors such as transportation, manufacturing, energy, water, air quality, and emissions. We propose an architectural scheme based on SDIs where each AI-aaS application is comprised of a monitoring, analysis, policy, execution plus knowledge (MAPE-K) loop (MKL). Each application is composed as one or more specific service chains embedded in SDI, some of which will include a Machine Learning (ML) pipeline. Our model includes a new training plane and an AI-aaS plane to deal with the model-development and operational phases of AI applications. We also consider the role of an ML/MKL sandbox in ensuring coherency and consistency in the operation of multiple parallel MKL loops.
Collaborative Multi-Agent Dialogue Model Training Via Reinforcement Learning

Some of the major challenges in training conversational agents include the lack of large-scale data of real-world complexity, defining appropriate evaluation measures, and managing meaningful conversations across many topics over long periods of time. Moreover, most works tend to assume that the conversational agent’s environment is stationary, a somewhat strong assumption. To remove this assumption and overcome the lack of data, we take a step away from the traditional training pipeline and model the conversation as a stochastic collaborative game. Each agent (player) has a role (“assistant”, “tourist”, “eater”, etc.) and their own objectives, and can only interact via language they generate. Each agent, therefore, needs to learn to operate optimally in an environment with multiple sources of uncertainty (its own LU and LG, the other agent’s LU, Policy, and LG). In this work, we present the first complete attempt at concurrently training conversational agents that communicate only via self-generated language and show that they outperform supervised and deep learning baselines.
The Super Eight Galaxies: Properties of a Sample of Very Bright Galaxies at 7 < z < 8

We present the Super Eight galaxies—a set of very luminous, high-redshift (7.1 < z < 8.0) galaxy candidates found in the Brightest of Reionizing Galaxies (BoRG) Survey fields. The original sample includes eight galaxies that are Y-band dropout objects with H-band magnitudes of mH < 25.5. Four of these objects were originally reported in Calvi et al. Combining new Hubble Space Telescope (HST) WFC3/F814W imaging and Spitzer IRAC data with archival imaging from BoRG and other surveys, we explore the properties of these galaxies. Photometric redshift fitting places six of these galaxies in the redshift range of 7.1 < z < 8.0, resulting in three new high-redshift galaxies and confirming three of the four high-redshift galaxy candidates from Calvi et al. We calculate the half-light radii of the Super Eight galaxies using the HST F160W filter and find that the Super Eight sizes are in line with the typical evolution of size with redshift. The Super Eights have a mean mass of log (M*/M⊙) ∼10, which is typical for sources in this luminosity range. Finally, we place our sample on the UV z ∼ 8 luminosity function and find that the Super Eight number density is consistent with other surveys in this magnitude and redshift range.
HRAN: Hybrid Residual Attention Network for Single Image Super-Resolution

The extraction and proper utilization of convolutional neural network (CNN) features have a significant impact on the performance of image super-resolution (SR). Although CNN features contain both spatial and channel information, current deep learning techniques for SR often suffer to maximize the performance due to using either the spatial information or channel information. Moreover, they integrate such information within a deep or wide network rather than exploiting all the available features, eventually resulting in high computational complexity. To address these issues, we present a binarized feature fusion (BFF) structure that utilizes the extracted features from global residuals (GR) in an effective way. Each GR consists of multiple hybrid residual attention blocks (HRAB) that effectively integrates the multiscale feature extraction module and channel attention mechanism in a single block. Furthermore, to save computational power, instead of using a large filter size, we use convolutions with different dilation factors to extract multiscale features. We also propose to adopt global skip connections (GSC), short skip connections (SSC), long skip connections (LSC) and GR structure to ease the flow of information without losing important features details. In the paper, we call this overall network architecture as hybrid residual attention network (HRAN). In the experiment, we have observed the efficacy of our method against the state-of-the-art methods for both the quantitative and qualitative comparisons.
Probing observational bounds on scalar-tensor theories from standard sirens

Standard sirens are the gravitational wave (GW) analog of the astronomical standard candles, and can provide powerful information about the dynamics of the Universe. In this work, we simulate a catalog with 1000 standard siren events from binary neutron star mergers, within the sensitivity predicted for the third generation of the ground GW detector called Einstein telescope. After correctly modifying the propagation of GWs as input to generate the catalog, we apply our mock data set on scalar-tensor theories where the speed of GW propagation is equal to the speed of light. As a first application, we find new observational bounds on the running of the Planck mass, when considering appropriate values within the stability condition of the theory, and we discuss some consequences on the amplitude of the running of the Planck mass. In the second part, we combine our simulated standard sirens catalog with other geometric cosmological tests (Supernovae Ia and cosmic chronometers measurements) to constrain the Hu-Sawicki $f(R)$ gravity model. We thus find new and non-null deviations from the standard $\Lambda$CDM model, showing that in the future the $f(R)$ gravity can be tested up to 95\% confidence level. The results obtained here show that the statistical accuracy achievable by future ground based GW observations, mainly with the ET detector (and planed detectors with a similar sensitivity), can provide strong observational bounds on modified gravity theories.
The Landscape of Non-convex Empirical Risk with Degenerate Population Risk

The landscape of empirical risk has been widely studied in a series of machine learning problems, including low-rank matrix factorization, matrix sensing, matrix completion, and phase retrieval. In this work, we focus on the situation where the corresponding population risk is a degenerate non-convex loss function, namely, the Hessian of the population risk can have zero eigenvalues. Instead of analyzing the non-convex empirical risk directly, we first study the landscape of the corresponding population risk, which is usually easier to characterize, and then build a connection between the landscape of the empirical risk and its population risk. In particular, we establish a correspondence between the critical points of the empirical risk and its population risk without the strongly Morse assumption, which is required in existing literature but not satisfied in degenerate scenarios. We also apply the theory to matrix sensing and phase retrieval to demonstrate how to infer the landscape of empirical risk from that of the corresponding population risk.
A Federated Lightweight Authentication Protocol for the Internet of Things

Considering the world's IoT development and market, it is necessary to guarantee the security of the developed IoT applications as well as the privacy of their end users. In this sense, Federated Identity Management (FIdM) systems can be of great help as they improve user authentication and privacy. In this paper, we claim that traditional FIdM are mostly cumbersome and then ill-suited for IoT. As a solution to this problem, we come up with a federated identity authentication protocol exclusively tailored to IoT. Federated Lightweight Authentication of Things (FLAT), our solution, replaces weighty protocols and asymmetric cryptographic primitives used in traditional FIdM by lighter ones. For instance, FLAT synergistically combines symmetric cryptosystems and Implicit Certificates. The results show that FLAT can reduce the data exchange overhead by around 31% when compared to a baseline solution. FLAT's Client is also more efficient than the baseline solution in terms of data transmitted, data received, total data exchange, and computation time. Our results indicate that FLAT runs efficiently even on top of resource-constrained devices like Arduino.
Evolving complexity: how tinkering shapes cells, software and ecological networks

A common trait of complex systems is that they can be represented by means of a network of interacting parts. It is, in fact, the network organization (more than the parts) that largely conditions most higher-level properties, which are not reducible to the properties of the individual parts. Can the topological organization of these webs provide some insight into their evolutionary origins? Both biological and artificial networks share some common architectural traits. They are often heterogeneous and sparse, and most exhibit different types of correlations, such as nestedness, modularity or hierarchical patterns. These properties have often been attributed to the selection of functionally meaningful traits. However, a proper formulation of generative network models suggests a rather different picture. Against the standard selection–optimization argument, some networks reveal the inevitable generation of complex patterns resulting from reuse and can be modelled using duplication–rewiring rules lacking functionality. These give rise to the observed heterogeneous, scale-free and modular architectures. Here, we examine the evidence for tinkering in cellular, technological and ecological webs and its impact in shaping their architecture. Our analysis suggests a serious consideration of the role played by selection as the origin of network topology. Instead, we suggest that the amplification processes associated with reuse might shape these graphs at the topological level. In biological systems, selection forces would take advantage of emergent patterns. This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.
Neutrinos from Type Ia and Failed Core-Collapse Supernovae at Dark Matter Detectors.

Neutrinos produced in the hot and dense interior of the next galactic supernova would be visible at dark matter experiments in coherent elastic nuclear recoils. While studies on this channel have focused on successful core-collapse supernovae, a thermonuclear (type Ia) explosion, or a core collapse that fails to explode and forms a black hole, are as likely to occur as the next galactic supernova event. I show that generation-3 noble liquid-based dark matter experiments such as darwin and argo, operating at sub-keV thresholds with ionization-only signals, would distinguish between (a) leading hypotheses of type Ia explosion mechanisms by detecting an O(1)  s burst of O(1)  MeV neutrinos, and (b) progenitor models of failed supernovae by detecting an O(1)  s burst of O(10)  MeV neutrinos, especially by marking the instant of black hole formation from abrupt stoppage of neutrino detection. This detection is sensitive to all neutrino flavors and insensitive to neutrino oscillations, thereby making measurements complementary to neutrino experiments.
The Dynamic Embedded Topic Model

Topic modeling analyzes documents to learn meaningful patterns of words. For documents collected in sequence, dynamic topic models capture how these patterns vary over time. We develop the dynamic embedded topic model (D-ETM), a generative model of documents that combines dynamic latent Dirichlet allocation (D-LDA) and word embeddings. The D-ETM models each word with a categorical distribution parameterized by the inner product between the word embedding and a per-time-step embedding representation of its assigned topic. The D-ETM learns smooth topic trajectories by defining a random walk prior over the embedding representations of the topics. We fit the D-ETM using structured amortized variational inference with a recurrent neural network. On three different corpora---a collection of United Nations debates, a set of ACL abstracts, and a dataset of Science Magazine articles---we found that the D-ETM outperforms D-LDA on a document completion task. We further found that the D-ETM learns more diverse and coherent topics than D-LDA while requiring significantly less time to fit.
Canonical quantization of 1+1-dimensional Yang-Mills theory: An operator-algebraic approach

We present a mathematically rigorous canonical quantization of Yang-Mills theory in 1+1 dimensions (YM$_{1+1}$) by operator-algebraic methods. The latter are based on Hamiltonian lattice gauge theory and multi-scale analysis via inductive limits of $C^{*}$-algebras which are applicable in arbitrary dimensions. The major step, restricted to one spatial dimension, is the explicitly construction of the spatially-localized von Neumann algebras of time-zero fields in the time gauge in representations associated with scaling limits of Gibbs states of the Kogut-Susskind Hamiltonian. We relate our work to existing results about YM$_{1+1}$ and its counterpart in Euclidean quantum field theory (YM$_{2}$). In particular, we show that the operator-algebraic approach offers a unifying perspective on results about YM$_{1+1}$ obtained by Dimock as well as Driver and Hall, especially regarding the existence of dynamics. Although our constructions work for non-abelian gauge theory, we obtain the most explicit results in the abelian case by applying the results of our recent companion article. In view of the latter, we also discuss relations with the construction of unitary representations of Thompson's groups by Jones. To understand the scaling limits arising from our construction, we explain our findings via a rigorous adaptation of the Wilson-Kadanoff renormalization group, which connects our construction with the multi-scale entanglement renormalization ansatz (MERA). Finally, we discuss potential generalizations and extensions to higher dimensions ($d+1\geq 3$).
Faster Neural Network Training with Data Echoing

In the twilight of Moore's law, GPUs and other specialized hardware accelerators have dramatically sped up neural network training. However, earlier stages of the training pipeline, such as disk I/O and data preprocessing, do not run on accelerators. As accelerators continue to improve, these earlier stages will increasingly become the bottleneck. In this paper, we introduce "data echoing," which reduces the total computation used by earlier pipeline stages and speeds up training whenever computation upstream from accelerators dominates the training time. Data echoing reuses (or "echoes") intermediate outputs from earlier pipeline stages in order to reclaim idle capacity. We investigate the behavior of different data echoing algorithms on various workloads, for various amounts of echoing, and for various batch sizes. We find that in all settings, at least one data echoing algorithm can match the baseline's predictive performance using less upstream computation. We measured a factor of 3.25 decrease in wall-clock time for ResNet-50 on ImageNet when reading training data over a network.
Analytical representation for metrics of scalarized Einstein-Maxwell black holes and their shadows

Here we construct approximate analytical forms for the metric coefficients and fields representing the scalarized Einstein-Maxwell black holes with various couplings of the scalar field, once the parameters of the system are fixed. By increasing approximation order, one can obtain the analytic representation with any desired accuracy, what was tested via calculations of shadows for these black holes by using approximate analytical and accurate numerical metric functions. We share the Mathematica code which allows one to find an appropriate analytical form of the metric for any couplings and values of parameters. Scalarization increases the radius of the black-hole shadow for all the considered coupling functions.
Tiny-Inception-ResNet-v2: Using Deep Learning for Eliminating Bonded Labors of Brick Kilns in South Asia

This paper proposes to employ a Inception-ResNet inspired deep learning architecture called Tiny-Inception-ResNet-v2 to eliminate bonded labor by identifying brick kilns within "Brick-Kiln-Belt" of South Asia. The framework is developed by training a network on the satellite imagery consisting of 11 different classes of South Asian region. The dataset developed during the process includes the geo-referenced images of brick kilns, houses, roads, tennis courts, farms, sparse trees, dense trees, orchards, parking lots, parks and barren lands. The dataset is made publicly available for further research. Our proposed network architecture with very fewer learning parameters outperforms all state-of-the-art architectures employed for recognition of brick kilns. Our proposed solution would enable regional monitoring and evaluation mechanisms for the Sustainable Development Goals.