nils-herrmann/clean_scirepeval huggingface dataset:
Ellipsis and Coreference Resolution as Question Answering

Coreference and many forms of ellipsis are similar to reading comprehension questions, in that in order to resolve these, we need to identify an appropriate text span in the previous discourse. This paper exploits this analogy and proposes to use an architecture developed for machine comprehension for ellipsis and coreference resolution. We present both single-task and joint models and evaluate them across standard benchmarks, outperforming the current state of the art for ellipsis by up to 48.5% error reduction -- and for coreference by 37.5% error reduction.
SGX-LKL: Securing the Host OS Interface for Trusted Execution

Hardware support for trusted execution in modern CPUs enables tenants to shield their data processing workloads in otherwise untrusted cloud environments. Runtime systems for the trusted execution must rely on an interface to the untrusted host OS to use external resources such as storage, network, and other functions. Attackers may exploit this interface to leak data or corrupt the computation. 
We describe SGX-LKL, a system for running Linux binaries inside of Intel SGX enclaves that only exposes a minimal, protected and oblivious host interface: the interface is (i) minimal because SGX-LKL uses a complete library OS inside the enclave, including file system and network stacks, which requires a host interface with only 7 calls; (ii) protected because SGX-LKL transparently encrypts and integrity-protects all data passed via low-level I/O operations; and (iii) oblivious because SGX-LKL performs host operations independently of the application workload. For oblivious disk I/O, SGX-LKL uses an encrypted ext4 file system with shuffled disk blocks. We show that SGX-LKL protects TensorFlow training with a 21% overhead.
Origin of the galaxy H i size–mass relation

We analytically derive the observed size–mass relation of galaxies’ atomic hydrogen (H i), including limits on its scatter, based on simple assumptions about the structure of H i discs. We trial three generic profiles for H i surface density as a function of radius. First, we assert that H i surface densities saturate at a variable threshold, and otherwise fall off exponentially with radius or, secondly, radius squared. Our third model assumes the total gas surface density is exponential, with the H i fraction at each radius depending on local pressure. These are tested against a compilation of 110 galaxies from the THINGS, LITTLE THINGS, LVHIS, and Bluedisk surveys, whose H i surface density profiles are well resolved. All models fit the observations well and predict consistent size–mass relations. Using an analytical argument, we explain why processes that cause gas disc truncation – such as ram-pressure stripping – scarcely affect the H i size–mass relation. This is tested with the IllustrisTNG(100) cosmological, hydrodynamic simulation and the Dark Sage semi-analytic model of galaxy formation, both of which capture radially resolved disc structure. For galaxies with $m_* \ge 10^9\, {\rm M}_{\odot }$ and $m_{\rm H\, {\small {I}}} \ge 10^8\, {\rm M}_{\odot }$, both simulations predict H i size–mass relations that align with observations, show no difference between central and satellite galaxies, and show only a minor, second-order dependence on host halo mass for satellites. Ultimately, the universally tight H i size–mass relation is mathematically inevitable and robust. Only by completely disrupting the structure of H i discs, e.g. through overly powerful feedback, could a simulation predict the relation poorly.
Wikidata from a Research Perspective - A Systematic Mapping Study of Wikidata

Wikidata is one of the most edited knowledge bases which contains structured data. It serves as the data source for many projects in the Wikimedia sphere and beyond. Since its inception in October 2012, it has been increasingly growing in term of both its community and its content. This growth is reflected by an expanding number of research focusing on Wikidata. Our study aims to provide a general overview of the research performed on Wikidata through a systematic mapping study in order to identify the current topical coverage of existing research as well as the white spots which need further investigation. In this study, 67 peer-reviewed research from journals and conference proceedings were selected, and classified into meaningful categories. We describe this data set descriptively by showing the publication frequency, the publication venue and the origin of the authors and reveal current research focuses. These especially include aspects concerning data quality, including questions related to language coverage and data integrity. These results indicate a number of future research directions, such as, multilingualism and overcoming language gaps, the impact of plurality on the quality of Wikidata's data, Wikidata's potential in various disciplines, and usability of user interface.
A guide to LIGO–Virgo detector noise and extraction of transient gravitational-wave signals

The LIGO Scientific Collaboration and the Virgo Collaboration have cataloged eleven confidently detected gravitational-wave events during the first two observing runs of the advanced detector era. All eleven events were consistent with being from well-modeled mergers between compact stellar-mass objects: black holes or neutron stars. The data around the time of each of these events have been made publicly available through the gravitational-wave open science center. The entirety of the gravitational-wave strain data from the first and second observing runs have also now been made publicly available. There is considerable interest among the broad scientific community in understanding the data and methods used in the analyses. In this paper, we provide an overview of the detector noise properties and the data analysis techniques used to detect gravitational-wave signals and infer the source properties. We describe some of the checks that are performed to validate the analyses and results from the observations of gravitational-wave events. We also address concerns that have been raised about various properties of LIGO–Virgo detector noise and the correctness of our analyses as applied to the resulting data.
Analysis of photoproduction data on $\gamma p\to pf_1(1285)$ and $\gamma p\to p\eta(958)$ from the reaction $\gamma p\to p\eta\pi^+\pi^-$

We investigate the reaction $\gamma p\to pf_1(1285)\to p\eta\pi^+\pi^-$ in a simple $\rho+\omega$ Reggeon exchanges. As the decay mode $\eta(1295)\to \eta\pi^+\pi^-$ is potentially overlapped with the former reaction, a combined analysis of $\gamma p\to p\eta'\to p\eta\pi^+\pi^-$ reaction including $\eta(1295)$ is performed to find that the measured cross sections for $\eta'$ and $f_1$ are well explained by the vector meson exchanges with a correction by the branching fraction to $\eta'\to\eta\pi^+\pi^-$ and $f_1\to\eta\pi^+\pi^-$, respectively. The numerical consequences show that $\eta(1295)$ cross section is negligible in the reconstruction of $f_1$ cross section from $\gamma p\to p\eta\pi^+\pi^-$.
New Model of Density Distribution for Fermionic Dark Matter Halos

We formulate a new model of density distribution for halos made of warm dark matter (WDM) particles. The model is described by a single microphysical parameter – the mass (or, equivalently, the maximal value of the initial phase-space density distribution) of dark matter particles. Given the WDM particle mass and the parameters of a dark matter density profile at the halo periphery, this model predicts the inner density profile. In the case of initial Fermi–Dirac distribution, we successfully reproduce cored dark matter profiles from N-body simulations. We calculate also the core radii of warm dark matter halos of dwarf spheroidal galaxies for particle masses mFD = 100, 200, 300, and 400 eV.
A comparative study for interpreting deep learning prediction of the Parkinson's disease diagnosis from SPECT imaging

The application of deep learning to single-photon emission computed tomography (SPECT) imaging in Parkinson's disease shows effectively high diagnosis accuracy. However, difficulties in model interpretation were occurred due to the complexity of the deep learning model. Although several interpretation methods were created to show the attention map that contains important features of the input data, it is still uncertain whether these methods can be applied in PD diagnosis. Four different models of the deep learning approach based on 3-dimensional convolution neural network (3D-CNN) of well-established architectures have been trained with an accuracy up to 95-96% in classification performance. These four models have been used as the comparative study for well-known interpretation methods. Generally, radiologists interpret SPECT images by confirming the shape of the I123-Ioflupane uptake in the striatal nuclei. To evaluate the interpretation performance, the segmented striatal nuclei of SPECT images are chosen as the ground truth. Results suggest that guided backpropagation and SHAP which were developed recently, provided the best interpretation performance. Guided backpropagation has the best performance to generate the attention map that focuses on the location of striatal nuclei. On the other hand, SHAP surpasses other methods in suggesting the change of the striatal nucleus uptake shape from healthy to PD subjects. Results from both methods confirm that 3D-CNN focuses on the striatal nuclei in the same way as the radiologist, and both methods should be suggested to increase the credibility of the model.
The Grover search as a naturally occurring phenomenon

We provide first evidence that under certain conditions, 1/2-spin fermions may naturally behave like a Grover search, looking for topological defects in a material. The theoretical framework is that of discrete-time quantum walks (QWs), i.e., local unitary matrices that drive the evolution of a single particle on the lattice. Some QWs are well known to recover the (2+1)-dimensional Dirac equation in continuum limit, i.e., the free propagation of the 1/2-spin fermion. We study two such Dirac QWs, one on the square grid and the other on a triangular grid reminiscent of graphenelike materials. The numerical simulations show that the walker localizes around the defects in O(sqrt[N]) steps with probability O(1/logN), in line with previous QW search on the grid. The main advantage brought by those of this Letter is that they could be implemented as "naturally occurring" freely propagating particles over a surface featuring topological defects-without the need for a specific oracle step. From a quantum computing perspective, however, this hints at novel applications of QW search: instead of using them to look for "good" solutions within the configuration space of a problem, we could use them to look for topological properties of the entire configuration space.
Global Reasoning over Database Structures for Text-to-SQL Parsing

State-of-the-art semantic parsers rely on auto-regressive decoding, emitting one symbol at a time. When tested against complex databases that are unobserved at training time (zero-shot), the parser often struggles to select the correct set of database constants in the new database, due to the local nature of decoding. %since their decisions are based on weak, local information only. In this work, we propose a semantic parser that globally reasons about the structure of the output query to make a more contextually-informed selection of database constants. We use message-passing through a graph neural network to softly select a subset of database constants for the output query, conditioned on the question. Moreover, we train a model to rank queries based on the global alignment of database constants to question words. We apply our techniques to the current state-of-the-art model for Spider, a zero-shot semantic parsing dataset with complex databases, increasing accuracy from 39.4% to 47.4%.
From the Token to the Review: A Hierarchical Multimodal approach to Opinion Mining

The task of predicting fine grained user opinion based on spontaneous spoken language is a key problem arising in the development of Computational Agents as well as in the development of social network based opinion miners. Unfortunately, gathering reliable data on which a model can be trained is notoriously difficult and existing works rely only on coarsely labeled opinions. In this work we aim at bridging the gap separating fine grained opinion models already developed for written language and coarse grained models developed for spontaneous multimodal opinion mining. We take advantage of the implicit hierarchical structure of opinions to build a joint fine and coarse grained opinion model that exploits different views of the opinion expression. The resulting model shares some properties with attention-based models and is shown to provide competitive results on a recently released multimodal fine grained annotated corpus.
Environment Sound Classification using Multiple Feature Channels and Deep Convolutional Neural Networks

In this paper, we propose a model for the Environment Sound Classification Task (ESC) that consists of multiple feature channels given as input to a Deep Convolutional Neural Network (CNN). The novelty of the paper lies in using multiple feature channels consisting of Mel-Frequency Cepstral Coefficients (MFCC), Gammatone Frequency Cepstral Coefficients (GFCC), the Constant Q-transform (CQT) and Chromagram. Such multiple features have never been used before for signal or audio processing. Also, we employ a deeper CNN (DCNN) compared to previous models, consisting of 2D separable convolutions working on time and feature domain separately. The model also consists of max pooling layers that downsample time and feature domain separately. We use some data augmentation techniques to further boost performance. Our model is able to achieve state-of-the-art performance on all three benchmark environment sound classification datasets, i.e. the UrbanSound8K (98.60%), ESC-10 (97.25%) and ESC-50 (95.50%). To the best of our knowledge, this is the first time that a single environment sound classification model is able to achieve state-of-the-art results on all three datasets and by a considerable margin over the previous models. For ESC-10 and ESC-50 datasets, the accuracy achieved by the proposed model is beyond human accuracy of 95.7% and 81.3% respectively.
Multi-Channel Deep Networks for Block-Based Image Compressive Sensing

Incorporating deep neural networks in image compressive sensing (CS) receives intensive attentions in multimedia technology and applications recently. As deep network approaches learn the inverse mapping directly from the CS measurements, the reconstruction speed is significantly faster than the conventional CS algorithms. However, for existing network-based approaches, a CS sampling procedure has to map a separate network model. This may potentially degrade the performance of image CS with block-wise sampling because of blocking artifacts, especially when multiple sampling rates are assigned to different blocks within an image. In this paper, we develop a multi-channel deep network for block-based image CS by exploiting inter-block correlation with performance significantly exceeding the current state-of-the-art methods. The significant performance improvement is attributed to block-wise approximation but full-image removal of blocking artifacts. Specifically, with our multi-channel structure, the image blocks with a variety of sampling rates can be reconstructed in a single model. The initially reconstructed blocks are then capable of being reassembled into a full image to improve the recovered images by unrolling a hand-designed block-based CS recovery algorithm. Experimental results demonstrate that the proposed method outperforms the state-of-the-art CS methods by a large margin in terms of objective metrics and subjective visual image quality. Our source codes are available at https://github.com/siwangzhou/DeepBCS.
Structure formation in the new Deser-Woodard nonlocal gravity model

We consider the structure formation in the nonlocal gravity model proposed recently by Deser and Woodard (DW-2019 model), which can not only reproduce the ΛCDM cosmology without fine-tuning puzzle but also may provide a screening mechanism for free. By using the direct numerical method of the reconstructing technique, the nonlocal distortion function f(Y) is fixed as f(Y)≃e2.153(Y−16.97) which has a small deviation with the fitted function proposed by Deser and Woodard. Based on the numerical results, we plotted the curve of the growth rate fσ8(z) under the DW-2019 model, which shows this model is not ruled out by the fσ8 data from the Redshift-space distortions measurements. The evolving curve of the growth rate has a distinct plummet at z≃0.39, which is a great difference with the DW model. The possible reason is that the DW-2019 model provides the strong nonlocal effect, which behaves as the anisotropic effect to influence the matter perturbation in the low-redshift range. Finally the qualitative analysis of the screening mechanism is discussed by considering the spacial dependence of the nonlocal modification in the small-scale range.
Enabling Simulation-Based Optimization through Machine Learning: A Case Study on Antenna Design

Complex phenomena are generally modeled with sophisticated simulators that, depending on their accuracy, can be very demanding in terms of computational resources and simulation time. Their time-consuming nature, together with a typically vast parameter space to be explored, make simulation- based optimization often infeasible. In this work, we present a method that enables the optimization of complex systems through Machine Learning (ML) techniques. We show how well-known learning algorithms are able to reliably emulate a complex simulator with a modest dataset obtained from it. The trained emulator is then able to yield values close to the simulated ones in virtually no time. Therefore, it is possible to perform a global numerical optimization over the vast multi-dimensional parameter space, in a fraction of the time that would be required by a simple brute-force search. As a testbed for the proposed methodology, we used a network simulator for next-generation mmWave cellular systems. After simulating several antenna configurations and collecting the resulting network-level statistics, we feed it into our framework. Results show that, even with few data points, extrapolating a continuous model makes it possible to estimate the global optimum configuration almost instantaneously. The very same tool can then be used to achieve any further optimization goal on the same input parameters in negligible time.
White-box vs Black-box: Bayes Optimal Strategies for Membership Inference

Membership inference determines, given a sample and trained parameters of a machine learning model, whether the sample was part of the training set. In this paper, we derive the optimal strategy for membership inference with a few assumptions on the distribution of the parameters. We show that optimal attacks only depend on the loss function, and thus black-box attacks are as good as white-box attacks. As the optimal strategy is not tractable, we provide approximations of it leading to several inference methods, and show that existing membership inference methods are coarser approximations of this optimal strategy. Our membership attacks outperform the state of the art in various settings, ranging from a simple logistic regression to more complex architectures and datasets, such as ResNet-101 and Imagenet.
New CTEQ global analysis with high precision data from the LHC

We present the new CTEQ-TEA global analysis of quantum chromodynamics (QCD). In this analysis, parton distribution functions (PDFs) of the nucleon are determined within the Hessian method at the next-to-next-to leading order (NNLO) in perturbative QCD, based on the most recent measurements from the Large Hadron Collider (LHC) and a variety of world collider data. Because of difficulties in fitting both the ATLAS 7 and 8 TeV $W$ and $Z$ vector boson production cross section data, we present two families of PDFs, named CT18 and CT18$Z$ PDFs, respectively, without and with the ATLAS 7 TeV $W$ and $Z$ measurements. We study the impact of the CT18 family of PDFs on the theoretical predictions of standard candle cross sections at the LHC.
Visualizing biomolecular electrostatics in virtual reality with UnityMol‐APBS

Virtual reality is a powerful tool with the ability to immerse a user within a completely external environment. This immersion is particularly useful when visualizing and analyzing interactions between small organic molecules, molecular inorganic complexes, and biomolecular systems such as redox proteins and enzymes. A common tool used in the biomedical community to analyze such interactions is the Adaptive Poisson‐Boltzmann Solver (APBS) software, which was developed to solve the equations of continuum electrostatics for large biomolecular assemblages. Numerous applications exist for using APBS in the biomedical community including analysis of protein ligand interactions and APBS has enjoyed widespread adoption throughout the biomedical community. Currently, typical use of the full APBS toolset is completed via the command line followed by visualization using a variety of two‐dimensional external molecular visualization software. This process has inherent limitations: visualization of three‐dimensional objects using a two‐dimensional interface masks important information within the depth component. Herein, we have developed a single application, UnityMol‐APBS, that provides a dual experience where users can utilize the full range of the APBS toolset, without the use of a command line interface, by use of a simple graphical user interface (GUI) for either a standard desktop or immersive virtual reality experience.
Using Mobile Devices to Augment Inquiry-Based Learning Processes with Multiple Representations

Technological advances offer the possibility of using mobile devices to enrich learning environments with multimedia content. Although physical experiments play a key role in science learning, little is known about integrating multimedia learning in physical experimental processes, especially in real classroom settings. In our approach, students use tablet PCs to investigate motions with an application providing multiple representations of the measurement data. We present the results of a cluster-randomized controlled trial (N=262 matched samples) that covered the topic uniform motion. The results show that the treatment not only leads to a significant reduction of extraneous cognitive load (F(1,240)=27.01, p<10-3, ${\eta}$2=0.101, $1-{\beta}$=1.000), but also to greater conceptual knowledge (F(1,259)=10.82, p=0.001, ${\eta}$2=0.048, $1-{\beta}$=0.953). Structure equation modeling statistically supports the assumption of a causal relationship between the reduction of extraneous cognitive load and the increase in conceptual understanding.
HARE: a Flexible Highlighting Annotator for Ranking and Exploration

Exploration and analysis of potential data sources is a significant challenge in the application of NLP techniques to novel information domains. We describe HARE, a system for highlighting relevant information in document collections to support ranking and triage, which provides tools for post-processing and qualitative analysis for model development and tuning. We apply HARE to the use case of narrative descriptions of mobility information in clinical data, and demonstrate its utility in comparing candidate embedding features. We provide a web-based interface for annotation visualization and document ranking, with a modular backend to support interoperability with existing annotation tools. Our system is available online at https://github.com/OSU-slatelab/HARE.