nils-herrmann/clean_scirepeval huggingface dataset:
Computational Essays in the Physics Classroom

Writing and argumentation are critical to both professional physics and physics education. However, the skill of making an extended argument in writing is often overlooked in physics classrooms, apart from certain practices like lab notebooks or mathematical proofs. Computation is also critical to both professional physics and, increasingly, physics education. In recent years we have begun to develop a class of assignment, known as a computational essay, to both leverage the creative affordances of computation and help students build their argumentative writing skills. Computational essays are a type of essay or report that combine text and code to express an idea or make an argument, usually written in notebook software. In this article, we describe the motivation and philosophy behind computational essays, as well as initial results from a pilot implementation in an introductory undergraduate electricity and magnetism course.
MiniBooNE-DM: a dark matter search in a proton beam dump

In a dedicated run where protons from the Fermilab Booster were delivered directly to the steel beam dump of the Booster Neutrino Beamline (BNB), the MiniBooNE detector was used to search for the production of sub-GeV dark matter particles via vector-boson mediators. The signal searched for was the elastic scattering of dark matter particles off nucleons in the detector mineral oil, with neutrinos being an irreducible background. A review of the experiment, its analysis methods, its results and future perspectives are summarized, demonstrating that beam dump experiments provide a novel and promising approach to dark matter searches.
Interpretable Dynamics Models for Data-efficient Reinforcement Learning

In this paper, we present a Bayesian view on model-based reinforcement learning. We use expert knowledge to impose structure on the transition model and present an efficient learning scheme based on variational inference. This scheme is applied to a heteroskedastic and bimodal benchmark problem on which we compare our results to NFQ and show how our approach yields human-interpretable insight about the underlying dynamics while also increasing data-efficiency.
A resource-aware approach to collaborative loop-closure detection with provable performance guarantees

This paper presents resource-aware algorithms for distributed inter-robot loop-closure detection for applications such as collaborative simultaneous localization and mapping (CSLAM) and distributed image retrieval. In real-world scenarios, this process is resource-intensive as it involves exchanging many observations and geometrically verifying a large number of potential matches. This poses severe challenges for small-size and low-cost robots with various operational and resource constraints that limit, e.g., energy consumption, communication bandwidth, and computation capacity. This paper proposes a framework in which robots first exchange compact queries to identify a set of potential loop closures. We then seek to select a subset of potential inter-robot loop closures for geometric verification that maximizes a monotone submodular performance metric without exceeding budgets on computation (number of geometric verifications) and communication (amount of exchanged data for geometric verification). We demonstrate that this problem is, in general, NP-hard, and present efficient approximation algorithms with provable a priori performance guarantees. The proposed framework is extensively evaluated on real and synthetic datasets. A natural convex relaxation scheme is also presented to certify the near-optimal performance of the proposed framework a posteriori.
Topic Modeling in Embedding Spaces

Topic modeling analyzes documents to learn meaningful patterns of words. However, existing topic models fail to learn interpretable topics when working with large and heavy-tailed vocabularies. To this end, we develop the embedded topic model (etm), a generative model of documents that marries traditional topic models with word embeddings. More specifically, the etm models each word with a categorical distribution whose natural parameter is the inner product between the word’s embedding and an embedding of its assigned topic. To fit the etm, we develop an efficient amortized variational inference algorithm. The etm discovers interpretable topics even with large vocabularies that include rare words and stop words. It outperforms existing document models, such as latent Dirichlet allocation, in terms of both topic quality and predictive performance.
The special value $u=1$ of Artin-Ihara $L$-functions

We study the special value $u=1$ of Artin-Ihara $L$-functions associated to characters of the automorphism group of abelian covers of multigraphs. In particular, we show an annihilation statement analogous to a classical conjecture of Brumer on annihilation of class groups for abelian extensions of number fields and we also calculate the index of an ideal analogous to the classical Stickelberger ideal in algebraic number theory. Along the way, we make some observations about the number of spanning trees in abelian multigraph coverings that may be of independent interest.
Explaining an increase in predicted risk for clinical alerts

Much work aims to explain a model's prediction on a static input. We consider explanations in a temporal setting where a stateful dynamical model produces a sequence of risk estimates given an input at each time step. When the estimated risk increases, the goal of the explanation is to attribute the increase to a few relevant inputs from the past. While our formal setup and techniques are general, we carry out an in-depth case study in a clinical setting. The goal here is to alert a clinician when a patient's risk of deterioration rises. The clinician then has to decide whether to intervene and adjust the treatment. Given a potentially long sequence of new events since she last saw the patient, a concise explanation helps her to quickly triage the alert. We develop methods to lift static attribution techniques to the dynamical setting, where we identify and address challenges specific to dynamics. We then experimentally assess the utility of different explanations of clinical alerts through expert evaluation.
Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model

In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (Cc) is a key parameter in modeling the settlement of fine-grained soil layers. However, the estimation of this parameter is costly, time-consuming, and requires skilled technicians. To overcome these drawbacks, we aimed to predict Cc through other soil parameters, i.e., the liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Using these parameters is more convenient and requires substantially less time and cost compared to the conventional tests to estimate Cc. This study presents a novel prediction model for the Cc of fine-grained soils using gene expression programming (GEP). A database consisting of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of the developed GEP-based model was evaluated through the coefficient of determination (R2), the root mean squared error (RMSE), and the mean average error (MAE). The proposed model performed better in terms of R2, RMSE, and MAE compared to the other models.
Infer Implicit Contexts in Real-time Online-to-Offline Recommendation

Understanding users' context is essential for successful recommendations, especially for Online-to-Offline (O2O) recommendation, such as Yelp, Groupon, and Koubei. Different from traditional recommendation where individual preference is mostly static, O2O recommendation should be dynamic to capture variation of users' purposes across time and location. However, precisely inferring users' real-time contexts information, especially those implicit ones, is extremely difficult, and it is a central challenge for O2O recommendation. In this paper, we propose a new approach, called Mixture Attentional Constrained Denoise AutoEncoder (MACDAE), to infer implicit contexts and consequently, to improve the quality of real-time O2O recommendation. In MACDAE, we first leverage the interaction among users, items, and explicit contexts to infer users' implicit contexts, then combine the learned implicit-context representation into an end-to-end model to make the recommendation. MACDAE works quite well in the real system. We conducted both offline and online evaluations of the proposed approach. Experiments on several real-world datasets (Yelp, Dianping, and Koubei) show our approach could achieve significant improvements over state-of-the-arts. Furthermore, online A/B test suggests a 2.9% increase for click-through rate and 5.6% improvement for conversion rate in real-world traffic. Our model has been deployed in the product of "Guess You Like" recommendation in Koubei.
Statistical mechanics of time series

Countless natural and social multivariate systems are studied through sets of simultaneous and time-spaced measurements of the observables that drive their dynamics, i.e., through sets of time series. Typically, this is done via hypothesis testing: the statistical properties of the empirical time series are tested against those expected under a suitable null hypothesis. This is a very challenging task in complex interacting systems, where statistical stability is often poor due to lack of stationarity and ergodicity. Here, we describe an unsupervised, data-driven framework to perform hypothesis testing in such situations. This consists of a statistical mechanical theory - derived from first principles - for ensembles of time series designed to preserve, on average, some of the statistical properties observed on an empirical set of time series. We showcase its possible applications on a set of stock market returns from the NYSE.
Multiband Probabilistic Cataloging: A Joint Fitting Approach to Point-source Detection and Deblending

Probabilistic cataloging (PCAT) outperforms traditional cataloging methods on single-band optical data in crowded fields. We extend our work to multiple bands, achieving greater sensitivity (∼0.4 mag) and greater speed (500×) compared to previous single-band results. We demonstrate the effectiveness of multiband PCAT on mock data, in terms of both recovering accurate posteriors in the catalog space and directly deblending sources. When applied to Sloan Digital Sky Survey (SDSS) observations of M2, taking Hubble Space Telescope data as truth, our joint fit on r- and i-band data goes ∼0.4 mag deeper than single-band probabilistic cataloging and has a false discovery rate less than 20% for F606W ≤ 20. Compared to DAOPHOT, the two-band SDSS catalog fit goes nearly 1.5 mag deeper using the same data and maintains a lower false discovery rate down to F606W ∼ 20.5. Given recent improvements in computational speed, multiband PCAT shows promise in application to large-scale surveys and is a plausible framework for joint analysis of multi-instrument observational data.
Mathematical Analysis of Dynamic Risk Default in Microfinance

In this work we will develop a new approach to solve the non repayment problem in microfinance due to the problem of asymmetric information. This approach is based on modeling and simulation of ordinary differential systems where time remains a primordial component, they thus enable microfinance institutions to manage their risk portfolios by a prediction of numbers of solvent and insolvent borrowers ever a period, in order to define or redefine its development strategy, investment and management in an area, where the population is often poor and in need a mechanism of financial inclusion.
New constraints on neutrino electric millicharge from elastic neutrino-electron scattering and coherent elastic neutrino-nucleus scattering

The neutrino electromagnetic properties have been studied since this particle was first postulated by Wolfgang Pauli in 1930. In different extensions of the Standard Model of Particle Physics (SMPP), the neutrino has electric millicharge and charge radius, as well as electric and magnetic dipole moments, the latter being the most studied property in the literature. However, the possibility that neutrino be a millicharged particle has also been a subject of study in different theoretical and experimental works. Additionally, several experimental constraints on the neutrino electric millicharge (NEM) have been reported from reactors, accelerators, and astrophysical measurements. In this work, we first carry out a statistical analysis using data from reactor neutrino experiments, which include elastic neutrino-electron scattering (ENES) processes, in order to obtain both individual and combined limits on the neutrino electric millicharge. Then we perform a similar calculation, but this time employing data from several future experimental proposals involving coherent elastic neutrino-nucleus scattering (CE$\nu$NS). In the first case, the limit of NEM achieved from the combined results of different experiments is $q_{\nu} \lesssim 1.5\times 10^{-12} e$ at $90\%$ C.L., and in the second scenario the bound corresponds to $q_{\nu} \lesssim 3.5\times 10^{-13} e$ ($90\%$ C.L.). The outcomes indicate CE$\nu$NS experimental proposals might be a suitable alternative to improve the current limits of NEM.
Can Unconditional Language Models Recover Arbitrary Sentences?

Neural network-based generative language models like ELMo and BERT can work effectively as general purpose sentence encoders in text classification without further fine-tuning. Is it possible to adapt them in a similar way for use as general-purpose decoders? For this to be possible, it would need to be the case that for any target sentence of interest, there is some continuous representation that can be passed to the language model to cause it to reproduce that sentence. We set aside the difficult problem of designing an encoder that can produce such representations and instead ask directly whether such representations exist at all. To do this, we introduce a pair of effective complementary methods for feeding representations into pretrained unconditional language models and a corresponding set of methods to map sentences into and out of this representation space, the \textit{reparametrized sentence space}. We then investigate the conditions under which a language model can be made to generate a sentence through the identification of a point in such a space and find that it is possible to recover arbitrary sentences nearly perfectly with language models and representations of moderate size.
Physics Beyond the Standard Model With Pulsar Timing Arrays

Pulsar timing arrays (PTAs) will enable the detection of nanohertz gravitational waves (GWs) from a population of supermassive binary black holes (SMBBHs) in the next $\sim 3-7$ years. In addition, PTAs provide a rare opportunity to probe exotic physics. Potential sources of GWs in the nanohertz band include, cosmic strings and cosmic superstrings, inflation and, phase transitions in the early universe. GW observations will also make possible tests of gravitational theories that, by modifying Einstein's theory of general relativity, attempt to explain the origin of cosmic acceleration and reconcile quantum mechanics and gravity, two of the most profound challenges facing fundamental physics today. Finally, PTAs also provide a new means to probe certain dark matter models.
Comparison between DAMA/LIBRA and COSINE-100 in the light of quenching factors

There is a long standing debate about whether or not the annual modulation signal reported by the DAMA/LIBRA collaboration is induced by Weakly Interacting Massive Particles (WIMP) in the galaxy's dark matter halo scattering from nuclides in their NaI(Tl) crystal target/detector. This is because regions of WIMP-mass vs. WIMP-nucleon cross-section parameter space that can accommodate the DAMA/LIBRA-phase1 modulation signal in the context of the standard WIMP dark matter galactic halo and isospin-conserving (canonical), spin-independent (SI) WIMP-nucleon interactions have been excluded by many of other dark matter search experiments including COSINE-100, which uses the same NaI(Tl) target/detector material. Moreover, the recently released DAMA/LIBRA-phase2 results are inconsistent with an interpretation as WIMP-nuclide scattering via the canonical SI interaction and prefer, instead, isospin-violating or spin-dependent interactions. Dark matter interpretations of the DAMA/LIBRA signal are sensitive to the NaI(Tl) scintillation efficiency for nuclear recoils, which is characterized by so-called quenching factors (QF), and the QF values used in previous studies differ significantly from recently reported measurements, which may have led to incorrect interpretations of the DAMA/LIBRA signal. In this article, the compatibility of the DAMA/LIBRA and COSINE-100 results, in light of the new QF measurements is examined for different possible types of WIMP-nucleon interactions. The resulting allowed parameter space regions associated with the DAMA/LIBRA signal are explicitly compared with 90% confidence level upper limits from the initial 59.5 day COSINE-100 exposure. With the newly measured QF values, the allowed 3σ regions from the DAMA/LIBRA data are still generally excluded by the COSINE-100 data.
A Model-based Approach for Sample-efficient Multi-task Reinforcement Learning

The aim of multi-task reinforcement learning is two-fold: (1) efficiently learn by training against multiple tasks and (2) quickly adapt, using limited samples, to a variety of new tasks. In this work, the tasks correspond to reward functions for environments with the same (or similar) dynamical models. We propose to learn a dynamical model during the training process and use this model to perform sample-efficient adaptation to new tasks at test time. We use significantly fewer samples by performing policy optimization only in a "virtual" environment whose transitions are given by our learned dynamical model. Our algorithm sequentially trains against several tasks. Upon encountering a new task, we first warm-up a policy on our learned dynamical model, which requires no new samples from the environment. We then adapt the dynamical model with samples from this policy in the real environment. We evaluate our approach on several continuous control benchmarks and demonstrate its efficacy over MAML, a state-of-the-art meta-learning algorithm, on these tasks.
Diverse Trajectory Forecasting with Determinantal Point Processes

The ability to forecast a set of likely yet diverse possible future behaviors of an agent (e.g., future trajectories of a pedestrian) is essential for safety-critical perception systems (e.g., autonomous vehicles). In particular, a set of possible future behaviors generated by the system must be diverse to account for all possible outcomes in order to take necessary safety precautions. It is not sufficient to maintain a set of the most likely future outcomes because the set may only contain perturbations of a single outcome. While generative models such as variational autoencoders (VAEs) have been shown to be a powerful tool for learning a distribution over future trajectories, randomly drawn samples from the learned implicit likelihood model may not be diverse -- the likelihood model is derived from the training data distribution and the samples will concentrate around the major mode that has most data. In this work, we propose to learn a diversity sampling function (DSF) that generates a diverse and likely set of future trajectories. The DSF maps forecasting context features to a set of latent codes which can be decoded by a generative model (e.g., VAE) into a set of diverse trajectory samples. Concretely, the process of identifying the diverse set of samples is posed as a parameter estimation of the DSF. To learn the parameters of the DSF, the diversity of the trajectory samples is evaluated by a diversity loss based on a determinantal point process (DPP). Gradient descent is performed over the DSF parameters, which in turn move the latent codes of the sample set to find an optimal diverse and likely set of trajectories. Our method is a novel application of DPPs to optimize a set of items (trajectories) in continuous space. We demonstrate the diversity of the trajectories produced by our approach on both low-dimensional 2D trajectory data and high-dimensional human motion data.
My lips are concealed: Audio-visual speech enhancement through obstructions

Our objective is an audio-visual model for separating a single speaker from a mixture of sounds such as other speakers and background noise. Moreover, we wish to hear the speaker even when the visual cues are temporarily absent due to occlusion. To this end we introduce a deep audio-visual speech enhancement network that is able to separate a speaker's voice by conditioning on both the speaker's lip movements and/or a representation of their voice. The voice representation can be obtained by either (i) enrollment, or (ii) by self-enrollment -- learning the representation on-the-fly given sufficient unobstructed visual input. The model is trained by blending audios, and by introducing artificial occlusions around the mouth region that prevent the visual modality from dominating. The method is speaker-independent, and we demonstrate it on real examples of speakers unheard (and unseen) during training. The method also improves over previous models in particular for cases of occlusion in the visual modality.
Einstein’s equations in matter

Einstein's equations in matter are gravitational analogues of Maxwell's equations in matter, providing an effective classical description of gravitational fields. We derive Einstein's equations in matter for relativistic fluids, and use them to illustrate how the Tolman-Oppenheimer-Volkoff equations are modified by the matter's response to curvature. For a gas of massive fermions, we evaluate how the effective Newton's constant and other susceptibilities depend on the temperature and density. In anti--de Sitter space, we study the $O(1/(T\ensuremath{\ell}{)}^{2})$ corrections to the geometries sourced by perfect fluids, and illustrate the breakdown of hydrostatics in AdS at small temperatures.
Object Detection in Video with Spatial-temporal Context Aggregation

Recent cutting-edge feature aggregation paradigms for video object detection rely on inferring feature correspondence. The feature correspondence estimation problem is fundamentally difficult due to poor image quality, motion blur, etc, and the results of feature correspondence estimation are unstable. To avoid the problem, we propose a simple but effective feature aggregation framework which operates on the object proposal-level. It learns to enhance each proposal's feature via modeling semantic and spatio-temporal relationships among object proposals from both within a frame and across adjacent frames. Experiments are carried out on the ImageNet VID dataset. Without any bells and whistles, our method obtains 80.3\% mAP on the ImageNet VID dataset, which is superior over the previous state-of-the-arts. The proposed feature aggregation mechanism improves the single frame Faster RCNN baseline by 5.8% mAP. Besides, under the setting of no temporal post-processing, our method outperforms the previous state-of-the-art by 1.4% mAP.