nils-herrmann/clean_scirepeval huggingface dataset:
Embedding Time Expressions for Deep Temporal Ordering Models

Data-driven models have demonstrated state-of-the-art performance in inferring the temporal ordering of events in text. However, these models often overlook explicit temporal signals, such as dates and time windows. Rule-based methods can be used to identify the temporal links between these time expressions (timexes), but they fail to capture timexes’ interactions with events and are hard to integrate with the distributed representations of neural net models. In this paper, we introduce a framework to infuse temporal awareness into such models by learning a pre-trained model to embed timexes. We generate synthetic data consisting of pairs of timexes, then train a character LSTM to learn embeddings and classify the timexes’ temporal relation. We evaluate the utility of these embeddings in the context of a strong neural model for event temporal ordering, and show a small increase in performance on the MATRES dataset and more substantial gains on an automatically collected dataset with more frequent event-timex interactions.
Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks

The MAPF problem is the fundamental problem of planning paths for multiple agents, where the key constraint is that the agents will be able to follow these paths concurrently without colliding with each other. Applications of MAPF include automated warehouses and autonomous vehicles. Research on MAPF has been flourishing in the past couple of years. Different MAPF research papers make different assumptions, e.g., whether agents can traverse the same road at the same time, and have different objective functions, e.g., minimize makespan or sum of agents' actions costs. These assumptions and objectives are sometimes implicitly assumed or described informally. This makes it difficult to establish appropriate baselines for comparison in research papers, as well as making it difficult for practitioners to find the papers relevant to their concrete application. This paper aims to fill this gap and support researchers and practitioners by providing a unifying terminology for describing common MAPF assumptions and objectives. In addition, we also provide pointers to two MAPF benchmarks. In particular, we introduce a new grid-based benchmark for MAPF, and demonstrate experimentally that it poses a challenge to contemporary MAPF algorithms.
Traversable ℓ-wormholes supported by ghost scalar fields

We present new, asymptotically flat, static, spherically symmetric and traversable wormhole solutions in general relativity which are supported by a family of ghost scalar fields with quartic potential. This family consists of a particular composition of the scalar field modes, in which each mode is characterized by the same value of the angular momentum number , yet the composition yields a spherically symmetric stress–energy–momentum and metric tensor. For our solutions reduce to wormhole configurations which had been reported previously in the literature. We discuss the effects of the new parameter on the wormhole geometry including the motion of free-falling test particles.
Measurement of 
ν¯μ
 charged-current single 
π−
 production on hydrocarbon in the few-GeV region using MINERvA

T. Le, 2 F. Akbar, L. Aliaga, 5 D.A. Andrade, M. V. Ascencio, A. Bashyal, A. Bercellie, M. Betancourt, A. Bodek, J. L. Bonilla, A. Bravar, H. Budd, G. Caceres, T. Cai, M.F. Carneiro, D. Coplowe, S.A. Dytman, G.A. Dı́az, 5 J. Felix, L. Fields, 14 A. Filkins, R. Fine, N. Fiza, A.M. Gago, H. Gallagher, A. Ghosh, 11 R. Gran, D.A. Harris, S. Henry, S. Jena, J. Kleykamp, M. Kordosky, D. Last, X.-G. Lu, E. Maher, S. Manly, W.A. Mann, C. Mauger, K.S. McFarland, 9 A.M. McGowan, B. Messerly,  Perdue, 8 M.A. Ramı́rez, R.D. Ransome, H. Ray, D. Rimal, D. Ruterbories, H. Schellman, 14 J.T. Sobczyk, C.J. Solano Salinas, H. Su, M. Sultana, V.S. Syrotenko, E. Valencia, 6 M.Wospakrik, B. Yaeggy, and L. Zazueta
Measurements of $\gamma$ from tree-level decays

The LHCb approach for a precise determination of the CKM angle $\gamma$ from tree--level decays is presented. Up to 16 independent determinations, using a variety of beauty and charm mesons decay modes, are available at LHCb. Not all of them have the same sensitivity to $\gamma$. The best accuracy is reached by combining all of the available results. These proceedings review some of the independent determinations used in the combination and present the latest combined result available, as of Summer 2018.
SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures

Designing neural architectures for edge devices is subject to constraints of accuracy, inference latency, and computational cost. Traditionally, researchers manually craft deep neural networks to meet the needs of mobile devices. Neural Architecture Search (NAS) was proposed to automate the neural architecture design without requiring extensive domain expertise and significant manual efforts. Recent works utilized NAS to design mobile models by taking into account hardware constraints and achieved state-of-the-art accuracy with fewer parameters and less computational cost measured in Multiply-accumulates (MACs). To find highly compact neural architectures, existing works relies on predefined cells and directly applying width multiplier, which may potentially limit the model flexibility, reduce the useful feature map information, and cause accuracy drop. To conquer this issue, we propose GRAM(GRAph propagation as Meta-knowledge) that adopts fine-grained (node-wise) search method and accumulates the knowledge learned in updates into a meta-graph. As a result, GRAM can enable more flexible search space and achieve higher search efficiency. Without the constraints of predefined cell or blocks, we propose a new structure-level pruning method to remove redundant operations in neural architectures. SwiftNet, which is a set of models discovered by GRAM, outperforms MobileNet-V2 by 2.15x higher accuracy density and 2.42x faster with similar accuracy. Compared with FBNet, SwiftNet reduces the search cost by 26x and achieves 2.35x higher accuracy density and 1.47x speedup while preserving similar accuracy. SwiftNetcan obtain 63.28% top-1 accuracy on ImageNet-1K with only 53M MACs and 2.07M parameters. The corresponding inference latency is only 19.09 ms on Google Pixel 1.
A hybrid deep learning approach to vertexing

During the LHC Run 3, the instantaneous luminosity received by LHCb will be increased going from 1.1 to 5.6 expected visible Primary Vertices (PVs) per event. To face this challenge, the LHCb detector will be upgraded and will adopt a purely software trigger. This has fueled increased interest in alternative highly-parallel and GPU friendly algorithms for tracking and reconstruction. We will present a novel prototype algorithm for vertexing in the LHCb upgrade conditions. We use a custom kernel to transform the sparse 3D space of hits and tracks into a dense 1D dataset, and then apply Deep Learning techniques to find PV locations. By training networks on our kernels using several Convolutional Neural Network layers, we have achieved better than 90% efficiency with no more than 0.2 False Positives (FPs) per event. Beyond its physics performance, this algorithm also provides a rich collection of possibilities for visualization and study of 1D convolutional networks. We will discuss the design, performance, and future potential areas of improvement and study, such as possible ways to recover the full 3D vertex information.
A public relativistic transfer function model for X-ray reverberation mapping of accreting black holes

We present the publicly available model reltrans that calculates the light-crossing delays and energy shifts experienced by X-ray photons originally emitted close to the black hole when they reflect from the accretion disc and are scattered into our line of sight, accounting for all general relativistic effects. Our model is fast and flexible enough to be simultaneously fit to the observed energy-dependent cross-spectrum for a large range of Fourier frequencies, as well as to the time-averaged spectrum. This not only enables better geometric constraints than only modelling the relativistically broadened reflection features in the time-averaged spectrum, but additionally enables constraints on the mass of supermassive black holes in active galactic nuclei and stellar-mass black holes in X-ray binaries. We include a self-consistently calculated radial profile of the disc ionization parameter and properly account for the effect that the telescope response has on the predicted time lags. We find that a number of previous spectral analyses have measured artificially low source heights due to not accounting for the former effect and that timing analyses have been affected by the latter. In particular, the magnitude of the soft lags in active galactic nuclei may have been underestimated, and the magnitude of lags attributed to thermal reverberation in X-ray binaries may have been overestimated. We fit reltrans to the lag-energy spectrum of the Seyfert galaxy Mrk 335, resulting in a best-fitting black hole mass that is smaller than previous optical reverberation measurements (∼7 million compared with ∼14–26 million M⊙).
REflex: Flexible Framework for Relation Extraction in Multiple Domains

Systematic comparison of methods for relation extraction (RE) is difficult because many experiments in the field are not described precisely enough to be completely reproducible and many papers fail to report ablation studies that would highlight the relative contributions of their various combined techniques. In this work, we build a unifying framework for RE, applying this on three highly used datasets (from the general, biomedical and clinical domains) with the ability to be extendable to new datasets. By performing a systematic exploration of modeling, pre-processing and training methodologies, we find that choices of preprocessing are a large contributor performance and that omission of such information can further hinder fair comparison. Other insights from our exploration allow us to provide recommendations for future research in this area.
Scalable and Differentially Private Distributed Aggregation in the Shuffled Model

Federated learning promises to make machine learning feasible on distributed, private datasets by implementing gradient descent using secure aggregation methods. The idea is to compute a global weight update without revealing the contributions of individual users. Current practical protocols for secure aggregation work in an "honest but curious" setting where a curious adversary observing all communication to and from the server cannot learn any private information assuming the server is honest and follows the protocol. A more scalable and robust primitive for privacy-preserving protocols is shuffling of user data, so as to hide the origin of each data item. Highly scalable and secure protocols for shuffling, so-called mixnets, have been proposed as a primitive for privacy-preserving analytics in the Encode-Shuffle-Analyze framework by Bittau et al., which was later analytically studied by Erlingsson et al. and Cheu et al.. The recent papers by Cheu et al., and Balle et al. have given protocols for secure aggregation that achieve differential privacy guarantees in this "shuffled model". Their protocols come at a cost, though: Either the expected aggregation error or the amount of communication per user scales as a polynomial $n^{\Omega(1)}$ in the number of users $n$. In this paper we propose simple and more efficient protocol for aggregation in the shuffled model, where communication as well as error increases only polylogarithmically in $n$. Our new technique is a conceptual "invisibility cloak" that makes users' data almost indistinguishable from random noise while introducing zero distortion on the sum.
The Influence of the Void Environment on the Ratio of Dark Matter Halo Mass to Stellar Mass in SDSS MaNGA Galaxies

We study how the void environment affects the formation and evolution of galaxies in the universe by comparing the ratio of dark matter halo mass to stellar mass of galaxies in voids with galaxies in denser regions. Using spectroscopic observations from the Sloan Digital Sky Survey MaNGA DR15, we estimate the dark matter halo mass of 641 void galaxies and 937 galaxies in denser regions. We use the relative velocities of the Hα emission line across the galaxy’s surface to measure the rotation curve of each galaxy because the kinematics of the interstellar medium is smoother than the stellar kinematics. We find that neither the stellar-to-halo-mass relation nor the relationship between the gas-phase metallicity and the ratio of dark matter halo mass to stellar mass is affected by the void environment. We also observe no difference in the distribution of the ratio of dark matter halo mass to stellar mass between void galaxies and galaxies in denser regions, implying that the shape of the dark matter halo profile is independent of a galaxy’s environment.
Light Field Saliency Detection With Deep Convolutional Networks

Light field imaging presents an attractive alternative to RGB imaging because of the recording of the direction of the incoming light. The detection of salient regions in a light field image benefits from the additional modeling of angular patterns. For RGB imaging, methods using CNNs have achieved excellent results on a range of tasks, including saliency detection. However, it is not trivial to use CNN-based methods for saliency detection on light field images because these methods are not specifically designed for processing light field inputs. In addition, current light field datasets are not sufficiently large to train CNNs. To overcome these issues, we present a new Lytro Illum dataset, which contains 640 light fields and their corresponding ground-truth saliency maps. Compared to current publicly available light field saliency datasets , , our new dataset is larger, of higher quality, contains more variation and more types of light field inputs. This makes our dataset suitable for training deeper networks and benchmarking. Furthermore, we propose a novel end-to-end CNN-based framework for light field saliency detection. Specifically, we propose three novel MAC (Model Angular Changes) blocks to process light field micro-lens images. We systematically study the impact of different architecture variants and compare light field saliency with regular 2D saliency. Our extensive comparisons indicate that our novel network significantly outperforms state-of-the-art methods on the proposed dataset and has desired generalization abilities on other existing datasets.
A Strong Baseline and Batch Normalization Neck for Deep Person Re-Identification

This study proposes a simple but strong baseline for deep person re-identification (ReID). Deep person ReID has achieved great progress and high performance in recent years. However, many state-of-the-art methods design complex network structures and concatenate multi-branch features. In the literature, some effective training tricks briefly appear in several papers or source codes. The present study collects and evaluates these effective training tricks in person ReID. By combining these tricks, the model achieves 94.5% rank-1 and 85.9% mean average precision on Market1501 with only using the global features of ResNet50. The performance surpasses all existing global- and part-based baselines in person ReID. We propose a novel neck structure named as batch normalization neck (BNNeck). BNNeck adds a batch normalization layer after global pooling layer to separate metric and classification losses into two different feature spaces because we observe they are inconsistent in one embedding space. Extended experiments show that BNNeck can boost the baseline, and our baseline can improve the performance of existing state-of-the-art methods. Our codes and models are available at: https://github.com/michuanhaohao/reid-strong-baseline
Spatial Pyramid Encoding with Convex Length Normalization for Text-Independent Speaker Verification

In this paper, we propose a new pooling method called spatial pyramid encoding (SPE) to generate speaker embeddings for text-independent speaker verification. We first partition the output feature maps from a deep residual network (ResNet) into increasingly fine sub-regions and extract speaker embeddings from each sub-region through a learnable dictionary encoding layer. These embeddings are concatenated to obtain the final speaker representation. The SPE layer not only generates a fixed-dimensional speaker embedding for a variable-length speech segment, but also aggregates the information of feature distribution from multi-level temporal bins. Furthermore, we apply deep length normalization by augmenting the loss function with ring loss. By applying ring loss, the network gradually learns to normalize the speaker embeddings using model weights themselves while preserving convexity, leading to more robust speaker embeddings. Experiments on the VoxCeleb1 dataset show that the proposed system using the SPE layer and ring loss-based deep length normalization outperforms both i-vector and d-vector baselines.
Bosonic interactions in a nonlocal theory in (
2+1
) dimensions

Pseudo-Quantum Electrodynamics (PQED) provides an excellent description of the interaction between charged particles confined to a plane. When we couple pseudo-gauge field with a bosonic matter field, we obtain the so-called Scalar Pseudo-Quantum Electrodynamics (SPQED). In this work, we make a perturbative analysis of SPQED via Feynman diagrams. We compute the one loop Green functions: bosonic field self-energy, electromagnetic field self-energy, and vertex corrections. Finally, we consider the non-relativistic interaction potential between two bosonic particles. We compute the radiative corrections to the usual Coulomb potential and comment on the analogies and the differences with the fermionic case.
Learning Patient Engagement in Care Management: Performance vs. Interpretability

The health outcomes of high-need patients can be substantially influenced by the degree of patient engagement in their own care. The role of care managers includes that of enrolling patients into care programs and keeping them sufficiently engaged in the program, so that patients can attain various goals. The attainment of these goals is expected to improve the patients' health outcomes. In this paper, we present a real world data-driven method and the behavioral engagement scoring pipeline for scoring the engagement level of a patient in two regards: (1) Their interest in enrolling into a relevant care program, and (2) their interest and commitment to program goals. We use this score to predict a patient's propensity to respond (i.e., to a call for enrollment into a program, or to an assigned program goal). Using real-world care management data, we show that our scoring method successfully predicts patient engagement. We also show that we are able to provide interpretable insights to care managers, using prototypical patients as a point of reference, without sacrificing prediction performance.
Learning Compressed Sentence Representations for On-Device Text Processing

Vector representations of sentences, trained on massive text corpora, are widely used as generic sentence embeddings across a variety of NLP problems. The learned representations are generally assumed to be continuous and real-valued, giving rise to a large memory footprint and slow retrieval speed, which hinders their applicability to low-resource (memory and computation) platforms, such as mobile devices. In this paper, we propose four different strategies to transform continuous and generic sentence embeddings into a binarized form, while preserving their rich semantic information. The introduced methods are evaluated across a wide range of downstream tasks, where the binarized sentence embeddings are demonstrated to degrade performance by only about 2% relative to their continuous counterparts, while reducing the storage requirement by over 98%. Moreover, with the learned binary representations, the semantic relatedness of two sentences can be evaluated by simply calculating their Hamming distance, which is more computational efficient compared with the inner product operation between continuous embeddings. Detailed analysis and case study further validate the effectiveness of proposed methods.
Precision measurement of the 
Λc+
, 
Ξc+
, and 
Ξc0
 baryon lifetimes

We report measurements of the lifetimes of the $\Lambda_c^+$, $\Xi_c^+$ and $\Xi_c^0$ charm baryons using proton-proton collision data at center-of-mass energies of 7 and 8\tev, corresponding to an integrated luminosity of 3.0 fb$^{-1}$, collected by the LHCb experiment. The charm baryons are reconstructed through the decays $\Lambda_c^+\to pK^-\pi^+$, $\Xi_c^+\to pK^-\pi^+$ and $\Xi_c^0\to pK^-K^-\pi^+$, and originate from semimuonic decays of beauty baryons. The lifetimes are measured relative to that of the $D^+$ meson, and are determined to be \begin{align*} \tau_{\Lambda_c^+} &= 203.5\pm1.0\pm1.3\pm1.4~{\rm fs}, \newline \tau_{\Xi_c^+} &= 456.8\pm3.5\pm2.9\pm3.1~{\rm fs}, \newline \tau_{\Xi_c^0} &= 154.5\pm1.7\pm1.6\pm1.0~{\rm fs}, \end{align*} where the uncertainties are statistical, systematic, and due to the uncertainty in the $D^+$ lifetime. The measurements are approximately 3--4 times more precise than the current world average values. The $\Lambda_c^+$ and $\Xi_c^+$ lifetimes are in agreement with previous measurements; however, the $\Xi_c^0$ baryon lifetime is approximately 3.3 standard deviations larger than the world average value.
Transformation of XML Documents with Prolog

Transforming XML documents with conventional XML languages, like XSL-T, is disadvantageous because there is too lax abstraction on the target language and it is rather difficult to recognize rule-oriented transformations. Prolog as a programming language of declarative paradigm is especially good for implementation of analysis of formal languages. Prolog seems also to be good for term manipulation, complex schema-transformation and text retrieval. In this report an appropriate model for XML documents is proposed, the basic transformation language for Prolog LTL is defined and the expressiveness power compared with XSL-T is demonstrated, the implementations used throughout are multi paradigmatic.
Trepan Reloaded: A Knowledge-driven Approach to Explaining Artificial Neural Networks

Explainability in Artificial Intelligence has been revived as a topic of active research by the need of conveying safety and trust to users in the `how' and `why' of automated decision-making. Whilst a plethora of approaches have been developed for post-hoc explainability, only a few focus on how to use domain knowledge, and how this influences the understandability of global explanations from the users' perspective. In this paper, we show how ontologies help the understandability of global post-hoc explanations, presented in the form of symbolic models. In particular, we build on Trepan, an algorithm that explains artificial neural networks by means of decision trees, and we extend it to include ontologies modeling domain knowledge in the process of generating explanations. We present the results of a user study that measures the understandability of decision trees using a syntactic complexity measure, and through time and accuracy of responses as well as reported user confidence and understandability. The user study considers domains where explanations are critical, namely, in finance and medicine. The results show that decision trees generated with our algorithm, taking into account domain knowledge, are more understandable than those generated by standard Trepan without the use of ontologies.