nils-herrmann/clean_scirepeval huggingface dataset:
The tale of the Milky Way globular cluster NGC 6362 – I. The orbit and its possible extended star debris features as revealed by Gaia DR2

We report the identification of possible extended star debris candidates beyond the cluster tidal radius of NGC 6362 based on the second Gaia data release (Gaia DR2). We found 259 objects possibly associated with the cluster lying in the vicinity of the giant branch and 1–2 magnitudes fainter/brighter than the main-sequence turn-off in the cluster colour–magnitude diagram and which cover an area on the sky of ∼4.1 deg2 centred on the cluster. We traced back the orbit of NGC 6362 in a realistic Milky Way potential, using the gravpot16 package, for 3 Gyr. The orbit shows that the cluster shares similar orbital properties as the inner disc, having peri-/apogalactic distances, and maximum vertical excursion from the Galactic plane inside the corotation radius (CR), moving inwards from CR radius to visit the inner regions of the Milky Way. The dynamical history of the cluster reveals that it has crossed the Galactic disc several times in its lifetime and has recently undergone a gravitational shock, ∼15.9 Myr ago, suggesting that less than 0.1 per cent of its mass has been lost during the current disc-shocking event. Based on the cluster’s orbit and position in the Galaxy, we conclude that the possible extended star debris candidates are a combined effect of the shocks from the Galactic disc and evaporation from the cluster. Lastly, the evolution of the vertical component of the angular momentum shows that the cluster is strongly affected dynamically by the Galactic bar potential.
A rogues gallery of Andromeda’s dwarf galaxies – II. Precise distances to 17 faint satellites

We present new horizontal branch (HB) distance measurements to 17 of the faintest known M31 satellites (−6 ≲ MV ≲ −13) based on deep Hubble Space Telescope (HST) imaging. The colour–magnitude diagrams extend ∼1–2 mag below the HB, which provides for well-defined HBs, even for faint galaxies in which the tip of the red giant branch (TRGB) is sparsely populated. We determine distances across the sample to an average precision of 4 per cent (∼30 kpc at 800 kpc). We find that the majority of these galaxies are in good agreement, though slightly farther (0.1–0.2 mag) when compared to recent ground-based TRGB distances. Two galaxies (And IX and And XVII) have discrepant HST and ground-based distances by ∼0.3 mag (∼150 kpc), which may be due to contamination from Milky Way foreground stars and/or M31 halo stars in sparsely populated TRGB regions. We use the new distances to update the luminosities and structural parameters for these 17 M31 satellites. The new distances do not substantially change the spatial configuration of the M31 satellite system. We comment on future prospects for precise and accurate HB distances for faint galaxies in the Local Group and beyond.
Gravitational waves as a probe of left-right symmetry breaking

Left-right symmetry at high energy scales is a well-motivated extension of the Standard Model. In this paper we consider a typical minimal scenario in which it gets spontaneously broken by scalar triplets. Such a realization has been scrutinized over the past few decades chiefly in the context of collider studies. In this work we take a complementary approach and investigate whether the model can be probed via the search for a stochastic gravitational wave background induced by the phase transition in which SU(3)C × SU(2)L × SU(2)R × U(1)B−L is broken down to the Standard Model gauge symmetry group. A prerequisite for gravitational wave production in this context is a first-order phase transition, the occurrence of which we find in a significant portion of the parameter space. Although the produced gravitational waves are typically too weak for a discovery at any current or future detector, upon investigating correlations between all relevant terms in the scalar potential, we have identified values of parameters leading to observable signals. This indicates that, given a certain moderate fine-tuning, the minimal left-right symmetric model with scalar triplets features another powerful probe which can lead to either novel constraints or remarkable discoveries in the near future. Let us note that some of our results, such as the full set of thermal masses, have to the best of our knowledge not been presented before and might be useful for future studies, in particular in the context of electroweak baryogenesis.
Simple supergravity model of inflation constrained with 
Planck
 2018 data

We study a model of inflation based on $\mathcal{N}=1$ supergravity essentially depending on one effective parameter. Under a field transformation we show that this model turns out to be equivalent to a previously studied supergravity model known to be ruled out with the original choice of the parameter. Such parameter measures the slope of the potential at observable scales. Through a Bayesian parameter estimation, it is shown how this model is compatible with recent CMB temperature measurements by {\it Planck 2018} giving rise to a simple, viable, single field model of inflation. The tensor to scalar ratio constraint is found to be $r_{0.002}<0.065$ with negative running. We discuss how observables are invariant under the field transformation which leaves unaltered the slow-roll parameters. As a consequence the use of one presentation of the model or its field-transformed version is purely a matter of convenience.
Gauging the accidental symmetries of the standard model, and implications for the flavor anomalies

We explore the possibility that lepton family numbers and baryon number are such good symmetries of Nature because they are the global remnant of a spontaneously broken gauge symmetry. We find that an almost arbitrary linear combination of these symmetries (together with a component of global hypercharge) can be consistently gauged, if the Standard Model (SM) fermion content is augmented by three chiral SM singlet states. Within this framework of $U(1)$ extensions of the SM one generically expects flavour non-universality to emerge in the charged leptons, in such a way that naturally prevents flavour violation, by aligning the mass and weak eigenbases. For quarks, all the SM Yukawa couplings responsible for their observed masses and mixings arise at the renormalisable level. We perform fits to show that models in this class can explain $R_{K^{(\ast)}}$ and the other neutral current $B$ anomaly data if we introduce a heavy vector-like quark to mediate the required quark flavour violation, while simultaneously satisfying other constraints from direct $Z^\prime$ searches at the LHC, $B_s$ meson mixing, a number of electroweak precision observables, and neutrino trident production. Within this symmetry-motivated framework of models, we find interesting implications for the flavour anomalies; notably, any axial couplings of the $Z^\prime$ to electrons and muons must be flavour universal, with the flavour universality violation arising solely from the vector-like couplings. We also comment on the generation of neutrino masses in these models.
Deep transfer learning for star cluster classification: I. application to the PHANGS–HST survey

We present the results of a proof-of-concept experiment which demonstrates that deep learning can successfully be used for production-scale classification of compact star clusters detected in HST UV-optical imaging of nearby spiral galaxies in the PHANGS-HST survey. Given the relatively small and unbalanced nature of existing, human-labelled star cluster datasets, we transfer the knowledge of neural network models for real-object recognition to classify star clusters candidates into four morphological classes. We show that human classification is at the 66%:37%:40%:61% agreement level for the four classes considered. Our findings indicate that deep learning algorithms achieve 76%:63%:59%:70% for a star cluster sample within 4Mpc < D <10Mpc. We tested the robustness of our deep learning algorithms to generalize to different cluster images using the first data obtained by PHANGS-HST of NGC1559, which is more distant at D = 19Mpc, and found that deep learning produces classification accuracies 73%:42%:52%:67%. We furnish evidence for the robustness of these analyses by using two different neural network models for image classification, trained multiple times from the ground up to assess the variance and stability of our results. We quantified the importance of the NUV, U, B, V and I images for morphological classification with our deep learning models, and find that the V-band is the key contributor as human classifications are based on images taken in that filter. This work lays the foundations to automate classification for these objects at scale, and the creation of a standardized dataset.
A Lower Limit on the Mass of Our Galaxy from the H3 Survey

The timing argument provides a lower limit on the mass of the Milky Way. Using a sample of 32 stars at R > 60 kpc drawn from the H3 Spectroscopic Survey and mock catalogs created from published numerical simulations, we find that M200 > 0.91 × 1012 M⊙ with 90% confidence. We recommend using this limit to refine the allowed prior mass range in more complex and sophisticated statistical treatments of Milky Way dynamics. The use of such a prior would have significantly reduced many previously published uncertainty ranges. Our analysis suggests that the most likely value of M200 is ≈1.5 × 1012 M⊙, but establishing this as the Milky Way mass requires a larger sample of outer halo stars and a more complete analysis of the inner halo stars in H3. The imminent growth in the sample of outer halo stars due to ongoing and planned surveys will make this possible.
An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction

Task-oriented dialog systems need to know when a query falls outside their range of supported intents, but current text classification corpora only define label sets that cover every example. We introduce a new dataset that includes queries that are out-of-scope—i.e., queries that do not fall into any of the system’s supported intents. This poses a new challenge because models cannot assume that every query at inference time belongs to a system-supported intent class. Our dataset also covers 150 intent classes over 10 domains, capturing the breadth that a production task-oriented agent must handle. We evaluate a range of benchmark classifiers on our dataset along with several different out-of-scope identification schemes. We find that while the classifiers perform well on in-scope intent classification, they struggle to identify out-of-scope queries. Our dataset and evaluation fill an important gap in the field, offering a way of more rigorously and realistically benchmarking text classification in task-driven dialog systems.
Majoron at two loops

We present singlet-majoron couplings to Standard Model particles through two loops at leading order in the seesaw expansion, including couplings to gauge bosons as well as flavor-changing quark interactions. We discuss and compare the relevant phenomenological constraints on majoron production as well as decaying majoron dark matter. A comparison with standard seesaw observables in low-scale settings highlights the importance of searches for lepton-flavor-violating two-body decays $\ell \to \ell' +$majoron both in the muon and tau sector.
Online Regularization by Denoising with Applications to Phase Retrieval

Regularization by denoising (RED) is a powerful framework for solving imaging inverse problems. Most RED algorithms are iterative batch procedures, which limits their applicability to very large datasets. In this paper, we address this limitation by introducing a novel online RED (On-RED) algorithm, which processes a small subset of the data at a time. We establish the theoretical convergence of On-RED in convex settings and empirically discuss its effectiveness in non-convex ones by illustrating its applicability to phase retrieval. Our results suggest that On-RED is an effective alternative to the traditional RED algorithms when dealing with large datasets.
Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning

Machine learning (ML) in the representation of molecular-orbital-based (MOB) features has been shown to be an accurate and transferable approach to the prediction of post-Hartree-Fock correlation energies. Previous applications of MOB-ML employed Gaussian Process Regression (GPR), which provides good prediction accuracy with small training sets; however, the cost of GPR training scales cubically with the amount of data and becomes a computational bottleneck for large training sets. In the current work, we address this problem by introducing a clustering/regression/classification implementation of MOB-ML. In a first step, regression clustering (RC) is used to partition the training data to best fit an ensemble of linear regression (LR) models; in a second step, each cluster is regressed independently, using either LR or GPR; and in a third step, a random forest classifier (RFC) is trained for the prediction of cluster assignments based on MOB feature values. Upon inspection, RC is found to recapitulate chemically intuitive groupings of the frontier molecular orbitals, and the combined RC/LR/RFC and RC/GPR/RFC implementations of MOB-ML are found to provide good prediction accuracy with greatly reduced wall-clock training times. For a dataset of thermalized (350 K) geometries of 7211 organic molecules of up to seven heavy atoms (QM7b-T), both RC/LR/RFC and RC/GPR/RFC reach chemical accuracy (1 kcal/mol prediction error) with only 300 training molecules, while providing 35000-fold and 4500-fold reductions in the wall-clock training time, respectively, compared to MOB-ML without clustering. The resulting models are also demonstrated to retain transferability for the prediction of large-molecule energies with only small-molecule training data. Finally, it is shown that capping the number of training datapoints per cluster leads to further improvements in prediction accuracy with negligible increases in wall-clock training time.
TIGEr: Text-to-Image Grounding for Image Caption Evaluation

This paper presents a new metric called TIGEr for the automatic evaluation of image captioning systems. Popular metrics, such as BLEU and CIDEr, are based solely on text matching between reference captions and machine-generated captions, potentially leading to biased evaluations because references may not fully cover the image content and natural language is inherently ambiguous. Building upon a machine-learned text-image grounding model, TIGEr allows to evaluate caption quality not only based on how well a caption represents image content, but also on how well machine-generated captions match human-generated captions. Our empirical tests show that TIGEr has a higher consistency with human judgments than alternative existing metrics. We also comprehensively assess the metric’s effectiveness in caption evaluation by measuring the correlation between human judgments and metric scores.
A note on the volume of ∇-Einstein manifolds with skew-torsion

We study the volume of compact Riemannian manifolds which are Einstein with respect to a metric connection with (parallel) skew-torsion. We provide a result for the sign of the first variation of the volume in terms of the corresponding scalar curvature. This generalizes a result of M. Ville  related with the first variation of the volume on a compact Einstein manifold.
An Entity-Driven Framework for Abstractive Summarization

Abstractive summarization systems aim to produce more coherent and concise summaries than their extractive counterparts. Popular neural models have achieved impressive results for single-document summarization, yet their outputs are often incoherent and unfaithful to the input. In this paper, we introduce SENECA, a novel System for ENtity-drivEn Coherent Abstractive summarization framework that leverages entity information to generate informative and coherent abstracts. Our framework takes a two-step approach: (1) an entity-aware content selection module first identifies salient sentences from the input, then (2) an abstract generation module conducts cross-sentence information compression and abstraction to generate the final summary, which is trained with rewards to promote coherence, conciseness, and clarity. The two components are further connected using reinforcement learning. Automatic evaluation shows that our model significantly outperforms previous state-of-the-art based on ROUGE and our proposed coherence measures on New York Times and CNN/Daily Mail datasets. Human judges further rate our system summaries as more informative and coherent than those by popular summarization models.
Performance Analysis and Comparison of Distributed Machine Learning Systems

Deep learning has permeated through many aspects of computing/processing systems in recent years. While distributed training architectures/frameworks are adopted for training large deep learning models quickly, there has not been a systematic study of the communication bottlenecks of these architectures and their effects on the computation cycle time and scalability. In order to analyze this problem for synchronous Stochastic Gradient Descent (SGD) training of deep learning models, we developed a performance model of computation time and communication latency under three different system architectures: Parameter Server (PS), peer-to-peer (P2P), and Ring allreduce (RA). To complement and corroborate our analytical models with quantitative results, we evaluated the computation and communication performance of these system architectures of the systems via experiments performed with Tensorflow and Horovod frameworks. We found that the system architecture has a very significant effect on the performance of training. RA-based systems achieve scalable performance as they successfully decouple network usage from the number of workers in the system. In contrast, 1PS systems suffer from low performance due to network congestion at the parameter server side. While P2P systems fare better than 1PS systems, they still suffer from significant network bottleneck. Finally, RA systems also excel by virtue of overlapping computation time and communication time, which PS and P2P architectures fail to achieve.
The Diversity–Innovation Paradox in Science

By analyzing data from nearly all US PhD recipients and their dissertations across three decades, this paper finds demographically underrepresented students innovate at higher rates than majority students, but their novel contributions are discounted and less likely to earn them academic positions. The discounting of minorities’ innovations may partly explain their underrepresentation in influential positions of academia. Prior work finds a diversity paradox: Diversity breeds innovation, yet underrepresented groups that diversify organizations have less successful careers within them. Does the diversity paradox hold for scientists as well? We study this by utilizing a near-complete population of ∼1.2 million US doctoral recipients from 1977 to 2015 and following their careers into publishing and faculty positions. We use text analysis and machine learning to answer a series of questions: How do we detect scientific innovations? Are underrepresented groups more likely to generate scientific innovations? And are the innovations of underrepresented groups adopted and rewarded? Our analyses show that underrepresented groups produce higher rates of scientific novelty. However, their novel contributions are devalued and discounted: For example, novel contributions by gender and racial minorities are taken up by other scholars at lower rates than novel contributions by gender and racial majorities, and equally impactful contributions of gender and racial minorities are less likely to result in successful scientific careers than for majority groups. These results suggest there may be unwarranted reproduction of stratification in academic careers that discounts diversity’s role in innovation and partly explains the underrepresentation of some groups in academia.
Motion of objects embedded in lipid bilayer membranes: Advection and effective viscosity.

An interfacial regularized Stokeslet scheme is presented to predict the motion of solid bodies (e.g., proteins or gel-phase domains) embedded within flowing lipid bilayer membranes. The approach provides a numerical route to calculate velocities and angular velocities in complex flow fields that are not amenable to simple Faxén-like approximations. Additionally, when applied to shearing motions, the calculations yield predictions for the effective surface viscosity of dilute rigid-body-laden membranes. In the case of cylindrical proteins, effective viscosity calculations are compared to two prior analytical predictions from the literature. Effective viscosity predictions for a dilute suspension of rod-shaped objects in the membrane are also presented.
ApproxNet: Content and Contention-Aware Video Analytics System for Embedded Clients.

Videos take lot of time to transport over the network, hence running analytics on the live video at the embedded or mobile devices has become an important system driver. Considering such devices, e.g., surveillance cameras or AR/VR gadgets, are resource constrained, althrough there has been significant work in creating lightweight deep neural networks (DNNs) for such clients, none of these can adapt to changing runtime conditions, e.g. changes in resource availability on the device, the content characteristics, or requirements from the user. In this paper we introduce ApproxNet, a video analytics system for embedded or mobile clients (which we collectively refer to as ``sensor devices''). It enables novel dynamic approximation techniques to achieve desired inference latency and accuracy trade-off under changing runtime conditions. It achieves this by enabling two approximation knobs within a single DNN model, rather than creating and maintaining an ensemble of models (such as MCDNN [MobiSys-16]). We show that ApproxNet can adapt seamlessly at runtime to these changes, provide low and stable latency for the image and video frame classification problems, and show the improvement in accuracy and latency over ResNet [CVPR-16], MCDNN [MobiSys-16], and MobileNets [Google-17].
Conversational Product Search Based on Negative Feedback

Intelligent assistants change the way people interact with computers and make it possible for people to search for products through conversations when they have purchase needs. During the interactions, the system could ask questions on certain aspects of the ideal products to clarify the users' needs. For example, previous work proposed to ask users the exact characteristics of their ideal items before showing results. However, users may not have clear ideas about what an ideal item looks like, especially when they have not seen any item. So it is more feasible to facilitate the conversational search by showing example items and asking for feedback instead. In addition, when the users provide negative feedback for the presented items, it is easier to collect their detailed feedback on certain properties (aspect-value pairs) of the non-relevant items. By breaking down the item-level negative feedback to fine-grained feedback on aspect-value pairs, more information is available to help clarify users' intents. So in this paper, we propose a conversational paradigm for product search driven by non-relevant items, based on which fine-grained feedback is collected and utilized to show better results in the next iteration. We then propose an aspect-value likelihood model to incorporate both positive and negative feedback on fine-grained aspect-value pairs of the non-relevant items. Experimental results show that our model is significantly better than state-of-the-art product search baselines without using feedback and those baselines using item-level negative feedback.