nils-herrmann/clean_scirepeval huggingface dataset:
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks

We present ViLBERT (short for Vision-and-Language BERT), a model for learning task-agnostic joint representations of image content and natural language. We extend the popular BERT architecture to a multi-modal two-stream model, pro-cessing both visual and textual inputs in separate streams that interact through co-attentional transformer layers. We pretrain our model through two proxy tasks on the large, automatically collected Conceptual Captions dataset and then transfer it to multiple established vision-and-language tasks -- visual question answering, visual commonsense reasoning, referring expressions, and caption-based image retrieval -- by making only minor additions to the base architecture. We observe significant improvements across tasks compared to existing task-specific models -- achieving state-of-the-art on all four tasks. Our work represents a shift away from learning groundings between vision and language only as part of task training and towards treating visual grounding as a pretrainable and transferable capability.
Promoting Coordination through Policy Regularization in Multi-Agent Reinforcement Learning

In multi-agent reinforcement learning, discovering successful collective behaviors is challenging as it requires exploring a joint action space that grows exponentially with the number of agents. While the tractability of independent agent-wise exploration is appealing, this approach fails on tasks that require elaborate group strategies. We argue that coordinating the agents' policies can guide their exploration and we investigate techniques to promote such an inductive bias. We propose two policy regularization methods: TeamReg, which is based on inter-agent action predictability and CoachReg that relies on synchronized behavior selection. We evaluate each approach on four challenging continuous control tasks with sparse rewards that require varying levels of coordination. Our methodology allocates the same hyper-parameter search budget across our algorithms and baselines and we find that our approaches are more robust to hyper-parameter variations. Our experiments show that our methods significantly improve performance on cooperative multi-agent problems and scale well when the number of agents is increased. Finally, we quantitatively analyze the effects of our proposed methods on the policies that our agents learn and we show that our methods successfully enforce the qualities that we propose as proxies for coordinated behaviors.
Comyco: Quality-Aware Adaptive Video Streaming via Imitation Learning

Learning-based Adaptive Bit Rate~(ABR) method, aiming to learn outstanding strategies without any presumptions, has become one of the research hotspots for adaptive streaming. However, it is still suffering from several issues, i.e., low sample efficiency and lack of awareness of the video quality information. In this paper, we propose Comyco, a video quality-aware ABR approach that enormously improves the learning-based methods by tackling the above issues. Comyco trains the policy via imitating expert trajectories given by the instant solver, which can not only avoid redundant exploration but also make better use of the collected samples. Meanwhile, Comyco attempts to pick the chunk with higher perceptual video qualities rather than video bitrates. To achieve this, we construct Comyco's neural network architecture, video datasets and QoE metrics with video quality features. Using trace-driven and real world experiments, we demonstrate significant improvements of Comyco's sample efficiency in comparison to prior work, with 1700x improvements in terms of the number of samples required and 16x improvements on training time required. Moreover, results illustrate that Comyco outperforms previously proposed methods, with the improvements on average QoE of 7.5% - 16.79%. Especially, Comyco also surpasses state-of-the-art approach Pensieve by 7.37% on average video quality under the same rebuffering time.
Growth and disruption in the Lyra complex

Nearby clusters of galaxies, z ≲ 0.1, are cosmic structures still under formation. Understanding the thermodynamic properties of merging clusters can provide crucial information on how they grow in the local universe.
Aims. A detailed study of the intra-cluster medium (ICM) properties of un-relaxed systems is essential to understand the fate of in-falling structures and, more generally, the virialization process.
Methods. We analyzed a mosaic of XMM-Newton observations (240 ks) of the Lyra system (z ∼ 0.067) that shows a complex dynamical state.
Results. We find the main cluster RXC J1825.3+3026 to be in a late merger phase, whereas its companion CIZA J1824.1+3029 is a relaxed cool-core cluster. We estimate a mass ratio of ∼1 : 2 for the pair. No diffuse X-ray emission is found in the region between them, indicating that these clusters are in a pre-merger phase. We found evidence of a galaxy group infalling on RXC J1825.3+3026 in an advanced state of disruption. The SG, one of the brightest galaxies in the Lyra complex, was very likely at the center of the infalling group. This galaxy has a gaseous “corona” indicating that it was able to retain some of its gas after the ram-pressure stripping of the intra-group medium. In this scenario the diffuse emission excess observed southwest of RXC J1825.3+3026 could be due to gas once belonging to the group and/or to cluster ICM dislocated by the passage of the group. Finally, we identified three high-velocity galaxies aligned between RXC J1825.3+3026 and the SG, two of these showing evidence of gas stripped from them during infall. We estimate them to be currently falling onto the main cluster at an infall velocity of ∼3000 km s−1.
Conclusions. Our study of the Lyra complex provides important clues about the processes presiding over the virialization of massive clusters in the local Universe.
A Weakly-Supervised Attention-based Visualization Tool for Assessing Political Affiliation

In this work, we seek to finetune a weakly-supervised expert-guided Deep Neural Network (DNN) for the purpose of determining political affiliations. In this context, stance detection is used for determining political affiliation or ideology which is framed in the form of relative proximities between entities in a low-dimensional space. An attention-based mechanism is used to provide model interpretability. A Deep Neural Network for Natural Language Understanding (NLU) using static and contextual embeddings is trained and evaluated. Various techniques to visualize the projections generated from the network are evaluated for visualization efficiency. An overview of the pipeline from data ingestion, processing and generation of visualization is given here. A web-based framework created to faciliate this interaction and exploration is presented here. Preliminary results of this study are summarized and future work is outlined.
Triplet Based Embedding Distance and Similarity Learning for Text-independent Speaker Verification

Speaker embeddings become growing popular in the text-independent speaker verification task. In this paper, we propose two improvements during the training stage. The improvements are both based on triplet because the training stage and the evaluation stage of the baseline x-vector system focus on different aims. Firstly, we introduce triplet loss for optimizing the Euclidean distances between embeddings while minimizing the multi-class cross entropy loss. Secondly, we design an embedding similarity measurement network for controlling the similarity between the two selected embeddings. We further jointly train the two new methods with the original network and achieve state-of-the-art. The multi-task training synergies are shown with a 9% reduction equal error rate (EER) and detected cost function (DCF) on the 2016 NIST Speaker Recognition Evaluation (SRE) Test Set.
Clustering of Deep Contextualized Representations for Summarization of Biomedical Texts

In recent years, summarizers that incorporate domain knowledge into the process of text summarization have outperformed generic methods, especially for summarization of biomedical texts. However, construction and maintenance of domain knowledge bases are resource-intense tasks requiring significant manual annotation. In this paper, we demonstrate that contextualized representations extracted from the pre-trained deep language model BERT, can be effectively used to measure the similarity between sentences and to quantify the informative content. The results show that our BERT-based summarizer can improve the performance of biomedical summarization. Although the summarizer does not use any sources of domain knowledge, it can capture the context of sentences more accurately than the comparison methods. The source code and data are available at this https URL.
Prevalence of neutral gas in centres of merging galaxies−II: nuclear H i and multiwavelength properties

Using a sample of 38 radio-loud galaxy mergers at z ≤ 0.2, we confirm the high detection rate (∼84 per cent) of H i 21-cm absorption in mergers, which is significantly higher (∼4 times) than in non-mergers. The distributions of the H i column density [$N(\rm{H\,{\small I}}$)] and velocity shift of the absorption with respect to the systemic redshift of the galaxy hosting the radio source in mergers are significantly different from that in non-mergers. We investigate the connection of the nuclear H i gas with various multiwavelength properties of the mergers. While the inferred $N(\rm{H\,{\small I}}$) and gas kinematics do not show strong (i.e. ≥3σ level) correlation with galaxy properties, we find that the incidence and $N(\rm{H\,{\small I}}$) of absorption tend to be slightly higher at smaller projected separations between the galaxy pairs and among the lower stellar mass-radio galaxies. The incidence, $N(\rm{H\,{\small I}}$) and line width of H i absorption increase from the pre-merger to the post-merger stages. The 100 per cent detection rate in post-mergers indicates that the neutral gas in the circumnuclear regions survives the coalescence period and is not yet quenched by the nuclear radio activity.
Varstrometry for Off-nucleus and Dual Subkiloparsec AGN (VODKA): Methodology and Initial Results with Gaia DR2

Gaia’s precision astrometry allows systematic identification of optically selected subkiloparsec dual active galactic nuclei (AGNs), off-nucleus AGNs, and small-scale lensed quasars by “varstrometry”—where variability-induced astrometric jitter, i.e., temporal displacements of photocenter in unresolved sources, can be reasonably well detected or constrained. This approach extends systematic searches for small-scale (≳mas) dual and off-nucleus AGNs to the poorly explored regime between ∼10 pc and ∼1 kpc, with Gaia’s full sky coverage and depth to G ∼ 21. We outline the general principles of this method and calculate the expected astrometric signals from the full time series of photocenter measurements and light curves. We demonstrate the feasibility of varstrometry by using Gaia Data Release 2 (DR2) data on a sample of variable pre-main-sequence stars with known close companions. We find that extended host galaxies have a significant impact on the accuracy of astrometric and photometric variability in Gaia DR2, a situation to be improved in future Gaia releases. Using spectroscopically confirmed Sloan Digital Sky Survey quasars, we present several examples of candidate subkiloparsec off-nucleus or dual AGNs selected from Gaia DR2. We discuss the merits and limitations of this method and a follow-up strategy for promising candidates. We highlight Gaia’s potential of systematically discovering and characterizing the subkiloparsec off-nucleus and dual AGN population in the entire optical sky.
Spectral Properties of Populations Behind the Coherence in Spitzer Near-infrared and Chandra X-Ray Backgrounds

We study the coherence of the near-infrared and X-ray background fluctuations and the X-ray spectral properties of the sources producing it. We use data from multiple Spitzer and Chandra surveys, including the UDS/SXDF surveys, the Hubble Deep Field North, the EGS/AEGIS field, the Chandra Deep Field South, and the COSMOS surveys, comprising ∼2275 Spitzer/IRAC hours and ∼16 Ms of Chandra data collected over a total area of ∼1 deg2. We report an overall ∼5σ detection of a cross-power signal on large angular scales >20″ between the 3.6 and 4.5 μm and the X-ray bands, with the IR versus [1–2] keV signal detected at 5.2σ. The [0.5–1] and [2–4] keV bands are correlated with the infrared wavelengths at a ∼1–3σ significance level. The hardest X-ray band ([4–7] keV) alone is not significantly correlated with any infrared wavelengths due to poor photon and sampling statistics. We study the X-ray spectral energy distribution of the cross-power signal. We find that its shape is consistent with a variety of source populations of accreting compact objects, such as local unabsorbed active galactic nuclei or high-z absorbed sources. We cannot exclude that the excess fluctuations are produced by more than one population. Because of poor statistics, the current relatively broad photometric bands employed here do not allow distinguishing the exact nature of these compact objects or if a fraction of the fluctuations have instead a local origin.
An analysis of satellite planar configurations around the MW and M31: singling out new high quality planes

We present a detailed characterization of planes of satellites in the Milky Way (MW) and M31 systems. To this end we introduce an extension to the '4-galaxy-normal density plot' method \citep{Pawlowski13}, by which plot over-densities signal the normal direction to predominant planes of satellites within a given sample. For a given over-density, the extension provides a \textit{collection} of planes, each including a different number of objects $N_{\rm sat}$. We apply this method to the position data of confirmed MW and M31 satellites and quantify the quality of planes through the outputs of a Tensor of Inertia plane-fitting technique. Plane quality is quantified in terms of population ($N_{\rm sat}$) and flattening (the short-to-long axis ratio $c/a$ or the rms thickness normal to the plane). Therefore, planes with the same population or flattening can be compared with each other allowing us to single-out best-quality planes. For the first time, we study the second-most predominant planar configuration of satellites in M31, singling out a plane with 18 satellite members that shows a quality comparable to the Great Plane of Andromeda (GPoA, with $N_{\rm sat}=19$) despite it being more affected by distance uncertainties. This structure is viewed nearly face-on from the MW and is approximately normal to the GPoA. Overall, we find planes of satellites around the MW and M31 with higher qualities than those previously reported with a given $N_{\rm sat}$. We also show that mass plays no role in determining a satellite's membership or not to the respective best-quality planes.
3 things they don’t tell you about star clusters

Dense stellar systems in general and star clusters in particular have recently regained the interest of the extragalactic and even cosmology communities, due to the role they could play as actors and probes of re-ionization, galactic archeology and the dark matter content of galaxies, among many others. In the era of the exploitation and the preparation of large stellar surveys (Gaia, APOGEE, 4MOST, WEAVE), of the detection of gravitational waves mostly originating from dense regions like the cores of clusters (Ligo, LISA), and in an always more holistic view of galaxy formation (HARMONI, Euclid, LSST†), a complete theory on the formation and evolution of clusters is needed to interpret the on-going and forthcoming data avalanche. In this context, the community carries an effort to model the aspects of star cluster formation and evolution in galactic and even cosmological context. However, it is not always easy to understand the caveats and the shortcuts taken in theories and simulations, and their implications on the conclusions drawn. I take the opportunity of this document to highlight three of these topics and discuss why some shortcuts taken by the community are or could be misleading.
Resolved and Integrated Stellar Masses in the SDSS-iv/MaNGA Survey. I. PCA Spectral Fitting and Stellar Mass-to-light Ratio Estimates

We present a method of fitting optical spectra of galaxies using a basis set of six vectors obtained from principal-component analysis of a library of synthetic spectra of 40,000 star formation histories (SFHs). Using this library, we provide estimates of the resolved effective stellar mass-to-light ratio ( ) for thousands of galaxies from the SDSS-IV/MaNGA integral-field spectroscopic survey. Using a testing framework built on additional synthetic SFHs, we show that the estimates of are reliable (as are their uncertainties) at a variety of signal-to-noise ratios, stellar metallicities, and dust attenuation conditions. Finally, we describe the future release of the resolved stellar mass-to-light ratios as an SDSS-IV/MaNGA Value-Added Catalog and provide a link to the software used to conduct this analysis. The software can be found at https://github.com/zpace/pcay.
Predicted disease compositions of human gliomas estimated from multiparametric MRI can predict endothelial proliferation, tumor grade, and overall survival

Biopsy is the main determinants of glioma clinical management, but require invasive sampling that fail to detect relevant features because of tumor heterogeneity. The purpose of this study was to evaluate the accuracy of a voxel-wise, multiparametric MRI radiomic method to predict features and develop a minimally invasive method to objectively assess neoplasms. 
Methods: Multiparametric MRI were registered to T1-weighted gadolinium contrast-enhanced data using a 12 degree-of-freedom affine model. The retrospectively collected MRI data included T1-weighted, T1-weighted gadolinium contrast-enhanced, T2-weighted, fluid attenuated inversion recovery, and multi-b-value diffusion-weighted acquired at 1.5T or 3.0T. Clinical experts provided voxel-wise annotations for five disease states on a subset of patients to establish a training feature vector of 611,930 observations. Then, a k-nearest-neighbor (k-NN) classifier was trained using a 25% hold-out design. The trained k-NN model was applied to 13,018,171 observations from seventeen histologically confirmed glioma patients. Linear regression tested overall survival (OS) relationship to predicted disease compositions (PDC) and diagnostic age (alpha = 0.05). Canonical discriminant analysis tested if PDC and diagnostic age could differentiate clinical, genetic, and microscopic factors (alpha = 0.05). 
 The model predicted voxel annotation class with a Dice similarity coefficient of 94.34% +/- 2.98. Linear combinations of PDCs and diagnostic age predicted OS (p = 0.008), grade (p = 0.014), and endothelia proliferation (p = 0.003); but fell short predicting gene mutations for TP53BP1 and IDH1. 
 This voxel-wise, multi-parametric MRI radiomic strategy holds potential as a non-invasive decision-making aid for clinicians managing patients with glioma.
Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes

Gynaecologists and obstetricians visually interpret cardiotocography (CTG) traces using the International Federation of Gynaecology and Obstetrics (FIGO) guidelines to assess the wellbeing of the foetus during antenatal care. This approach has raised concerns among professionals with regards to inter- and intra-variability where clinical diagnosis only has a 30% positive predictive value when classifying pathological outcomes. Machine learning models, trained with FIGO and other user derived features extracted from CTG traces, have been shown to increase positive predictive capacity and minimise variability. This is only possible however when class distributions are equal which is rarely the case in clinical trials where case-control observations are heavily skewed in favour of normal outcomes. Classes can be balanced using either synthetic data derived from resampled case training data or by decreasing the number of control instances. However, this either introduces bias or removes valuable information. Concerns have also been raised regarding machine learning studies and their reliance on manually handcrafted features. While this has led to some interesting results, deriving an optimal set of features is considered to be an art as well as a science and is often an empirical and time consuming process. In this article, we address both of these issues and propose a novel CTG analysis methodology that a) splits CTG time-series signals into n-size windows with equal class distributions, and b) automatically extracts features from time-series windows using a one dimensional convolutional neural network (1DCNN) and multilayer perceptron (MLP) ensemble. Collectively, the proposed approach normally distributes classes and removes the need to handcrafted features from CTG traces. The 1DCNN-MLP models trained with several windowing strategies are evaluated to determine how well they can distinguish between normal and pathological birth outcomes. Our proposed method achieved good results using a window size of 200 with 80% (95% CI: 75%, 85%) for Sensitivity, 79% (95% CI: 73%, 84%) for Specificity and 86% (95% CI: 81%, 91%) for the Area Under the Curve. The 1DCNN approach is also compared with several traditional machine learning models, which all failed to improve on the windowing 1DCNN strategy proposed.
Debiasing Linear Prediction

Standard methods in supervised learning separate training and prediction: the model is fit independently of any test points it may encounter. However, can knowledge of the next test point $\mathbf{x}_{\star}$ be exploited to improve prediction accuracy? We address this question in the context of linear prediction, showing how debiasing techniques can be used transductively to combat regularization bias. We first lower bound the $\mathbf{x}_{\star}$ prediction error of ridge regression and the Lasso, showing that they must incur significant bias in certain test directions. Then, building on techniques from semi-parametric inference, we provide non-asymptotic upper bounds on the $\mathbf{x}_{\star}$ prediction error of two transductive, debiased prediction rules. We conclude by showing the efficacy of our methods on both synthetic and real data, highlighting the improvements test-point-tailored debiasing can provide in settings with distribution shift.
Online Planning for Decentralized Stochastic Control with Partial History Sharing

In decentralized stochastic control, standard approaches for sequential decision-making, e.g. dynamic programming, quickly become intractable due to the need to maintain a complex information state. Computational challenges are further compounded if agents do not possess complete model knowledge. In this paper, we take advantage of the fact that in many problems agents share some common information, or history, termed partial history sharing. Under this information structure the policy search space is greatly reduced. We propose a provably convergent, online tree-search based algorithm that does not require a closed-form model or explicit communication among agents. Interestingly, our algorithm can be viewed as a generalization of several existing heuristic solvers for decentralized partially observable Markov decision processes. To demonstrate the applicability of the model, we propose a novel collaborative intrusion response model, where multiple agents (defenders) possessing asymmetric information aim to collaboratively defend a computer network. Numerical results demonstrate the performance of our algorithm.