nils-herrmann/clean_scirepeval huggingface dataset:
Broadband X-Ray Constraints on the Accreting Black Hole in Quasar 4C 74.26

X-ray data for quasar 4C 74.26 have previously been modeled with a broad Fe Kα emission line and reflection continuum originating in the inner part of the accretion disk around the central supermassive black hole (SMBH), i.e., the strong-gravity regime. We modeled broadband X-ray spectra from Suzaku and NuSTAR with mytorus, self-consistently accounting for Fe Kα line emission, as well as direct and reflected continuum emission, from matter with a finite column density. A narrow Fe Kα emission line originating in an X-ray reprocessor with solar Fe abundance far from the central SMBH is sufficient to produce excellent fits for all spectra. For the first time, we are able to measure the global column density, out of the line of sight, to be in the range ∼1.5 to ∼ , i.e., in the Compton-thick regime, while the line-of-sight column density is Compton-thin in all observations. The Fe Kα emission line is unresolved in all observations but one. The Compton-scattered continuum from distant matter removes the need for relativistic broadening of the Fe Kα emission line, which is required for SMBH spin measurements. The resolved line observation can alternatively be modeled with a relativistic model but we do not find evidence for a truncated accretion disk model. We conclude that the X-ray emission in these 4C 74.26 data is unlikely to originate in the inner accretion disk region and thus cannot be used to measure SMBH spin.
Finding Generalizable Evidence by Learning to Convince Q&A Models

We propose a system that finds the strongest supporting evidence for a given answer to a question, using passage-based question-answering (QA) as a testbed. We train evidence agents to select the passage sentences that most convince a pretrained QA model of a given answer, if the QA model received those sentences instead of the full passage. Rather than finding evidence that convinces one model alone, we find that agents select evidence that generalizes; agent-chosen evidence increases the plausibility of the supported answer, as judged by other QA models and humans. Given its general nature, this approach improves QA in a robust manner: using agent-selected evidence (i) humans can correctly answer questions with only ~20% of the full passage and (ii) QA models can generalize to longer passages and harder questions.
NIHAO-UHD: The properties of MW-like stellar disks in high resolution cosmological simulations

Simulating thin and extended galactic disks has long been a challenge in computational astrophysics. We introduce the NIHAO-UHD suite of cosmological hydrodynamical simulations of Milky Way mass galaxies and study stellar disk properties such as stellar mass, size and rotation velocity which agree well with observations of the Milky Way and local galaxies. In particular, the simulations reproduce the age-velocity dispersion relation and a multi-component stellar disk as observed for the Milky Way. Half of our galaxies show a double exponential vertical profile, while the others are well described by a single exponential model which we link to the disk merger history. In all cases, mono-age populations follow a single exponential whose scale height varies monotonically with stellar age and radius. The scale length decreases with stellar age while the scale height increases. The general structure of the stellar disks is already set at time of birth as a result of the inside-out and upside-down formation. Subsequent evolution modifies this structure by increasing both the scale length and height of all mono-age populations. Thus, our results put tight constraints on how much dynamical memory stellar disks can retain over cosmological timescales. Our simulations demonstrate that it is possible to form thin galactic disks in cosmological simulations provided there are no significant stellar mergers at low redshifts. Most of the stellar mass is formed in-situ with only a few percent ($\lesssim 5\%$) brought in by merging satellites at early times. Redshift zero snapshots and halo catalogues are publicly available.
The emotions that we perceive in music: the influence of language and lyrics comprehension on agreement

In the present study, we address the relationship between the emotions perceived in pop and rock music (mainly in Euro-American styles with English lyrics) and the language spoken by the listener. Our goal is to understand the influence of lyrics comprehension on the perception of emotions and use this information to improve Music Emotion Recognition (MER) models. Two main research questions are addressed: 1. Are there differences and similarities between the emotions perceived in pop/rock music by listeners raised with different mother tongues? 2. Do personal characteristics have an influence on the perceived emotions for listeners of a given language? Personal characteristics include the listeners' general demographics, familiarity and preference for the fragments, and music sophistication. Our hypothesis is that inter-rater agreement (as defined by Krippendorff's alpha coefficient) from subjects is directly influenced by the comprehension of lyrics.
WISDOM project – V. Resolving molecular gas in Keplerian rotation around the supermassive black hole in NGC 0383

As part of the mm-Wave Interferometric Survey of Dark Object Masses (WISDOM), we present a measurement of the mass of the supermassive black hole (SMBH) in the nearby early-type galaxy NGC 0383 (radio source 3C 031). This measurement is based on Atacama Large Millimeter/sub-millimeter Array (ALMA) cycle 4 and 5 observations of the 12CO(2–1) emission line with a spatial resolution of 58 × 32 pc2 (0.18 arcsec × 0.1 arcsec). This resolution, combined with a channel width of 10 km s−1, allows us to well resolve the radius of the black hole sphere of influence (measured as RSOI = 316 pc  =  0.98 arcsec), where we detect a clear Keplerian increase of the rotation velocities. NGC 0383 has a kinematically relaxed, smooth nuclear molecular gas disc with weak ring/spiral features. We forward model the ALMA data cube with the Kinematic Molecular Simulation (KinMS) tool and a Bayesian Markov Chain Monte Carlo method to measure an SMBH mass of (4.2 ± 0.7) × 109 M⊙, a F160W-band stellar mass-to-light ratio that varies from 2.8 ± 0.6 M⊙/L$_{\odot ,\, \mathrm{F160W}}$ in the centre to 2.4 ± 0.3 M⊙$/\rm L_{\odot ,\, \mathrm{F160W}}$ at the outer edge of the disc and a molecular gas velocity dispersion of 8.3 ± 2.1 km s−1(all 3σ uncertainties). We also detect unresolved continuum emission across the full bandwidth, consistent with synchrotron emission from an active galactic nucleus. This work demonstrates that low-J CO emission can resolve gas very close to the SMBH ($\approx 140\, 000$ Schwarzschild radii) and hence that the molecular gas method is highly complimentary to megamaser observations, as it can probe the same emitting material.
Analyzing machine-learned representations: A natural language case study

As modern deep networks become more complex, and get closer to human-like capabilities in certain domains, the question arises as to how the representations and decision rules they learn compare to the ones in humans. In this work, we study representations of sentences in one such artificial system for natural language processing. We first present a diagnostic test dataset to examine the degree of abstract composable structure represented. Analyzing performance on these diagnostic tests indicates a lack of systematicity in representations and decision rules, and reveals a set of heuristic strategies. We then investigate the effect of training distribution on learning these heuristic strategies, and we study changes in these representations with various augmentations to the training set. Our results reveal parallels to the analogous representations in people. We find that these systems can learn abstract rules and generalize them to new contexts under certain circumstances-similar to human zero-shot reasoning. However, we also note some shortcomings in this generalization behavior-similar to human judgment errors like belief bias. Studying these parallels suggests new ways to understand psychological phenomena in humans as well as informs best strategies for building artificial intelligence with human-like language understanding.
Near-Optimal Algorithms for Piecewise-Stationary Cascading Bandits

Cascading bandit (CB) is a popular model for web search and online advertising. However, the stationary CB model may be too simple to cope with real-world problems, where user preferences may change over time. Considering piecewise-stationary environments, two efficient algorithms, GLRT-CascadeUCB and GLRT-CascadeKL-UCB, are developed. Comparing with existing works, the proposed algorithms: i) are free of change-point-dependent information for choosing parameters; ii) have fewer tuning parameters; iii) improve regret upper bounds. We also show that the proposed algorithms are optimal up to logarithm terms by deriving a minimax lower bound $\Omega (\sqrt {NLT} )$ for piecewise-stationary CB. The efficiency of the proposed algorithms is validated through numerical tests on a real-world benchmark dataset.
Flavor Techniques for LFV Processes: Higgs Decays in a General Seesaw Model

Lepton flavor violating processes are optimal observables to test new physics, since they are forbidden in the Standard Model while they may be generated in new theories. The usual approach to these processes is to perform the computations in the physical basis; nevertheless this may lose track of the dependence on some of the fundamental parameters, in particular on those at the origin of the flavor violation. Consequently, in order to obtain analytical expressions directly in terms of these parameters, flavor techniques are often preferred. In this work, we focus on the mass insertion approximation technique, which works with the interaction states instead of the physical ones, and provides diagrammatic expansions of the observables. After reviewing the basics of this technique with two simple examples, we apply it to the lepton flavor violating Higgs decays in the framework of a general type-I seesaw model with an arbitrary number of right-handed neutrinos. We derive an effective vertex valid to compute these observables when the right-handed neutrino masses are above the electroweak scale and show that we recover previous results obtained for low scale seesaws. Finally, we apply current constraints on the model to conclude on maximum Higgs decay rates, which unfortunately are far from current experimental sensitivities.
Determining the Scale of Impact from Denial-of-Service Attacks in Real Time Using Twitter

Denial of Service (DoS) attacks are common in on-line and mobile services such as Twitter, Facebook and banking. As the scale and frequency of Distributed Denial of Service (DDoS) attacks increase, there is an urgent need for determining the impact of the attack. Two central challenges of the task are to get feedback from a large number of users and to get it in a timely manner. In this paper, we present a weakly-supervised model that does not need annotated data to measure the impact of DoS issues by applying Latent Dirichlet Allocation and symmetric Kullback-Leibler divergence on tweets. There is a limitation to the weakly-supervised module. It assumes that the event detected in a time window is a DoS attack event. This will become less of a problem, when more non-attack events twitter got collected and become less likely to be identified as a new event. Another way to remove that limitation, an optional classification layer, trained on manually annotated DoS attack tweets, to filter out non-attack tweets can be used to increase precision at the expense of recall. Experimental results show that we can learn weakly-supervised models that can achieve comparable precision to supervised ones and can be generalized across entities in the same industry.
Estimating differential latent variable graphical models with applications to brain connectivity

Differential graphical models are designed to represent the difference between the conditional dependence structures of two groups, thus are of particular interest for scientific investigation. Motivated by modern applications, this manuscript considers an extended setting where each group is generated by a latent variable Gaussian graphical model. Due to the existence of latent factors, the differential network is decomposed into sparse and low-rank components, both of which are symmetric indefinite matrices. We estimate these two components simultaneously using a two-stage procedure: (i) an initialization stage, which computes a simple, consistent estimator, and (ii) a convergence stage, implemented using a projected alternating gradient descent algorithm applied to a nonconvex objective, initialized using the output of the first stage. We prove that given the initialization, the estimator converges linearly with a nontrivial, minimax optimal statistical error. Experiments on synthetic and real data illustrate that the proposed nonconvex procedure outperforms existing methods.
A Note on Posterior Probability Estimation for Classifiers

One of the central themes in the classification task is the estimation of class posterior probability at a new point $\bf{x}$. The vast majority of classifiers output a score for $\bf{x}$, which is monotonically related to the posterior probability via an unknown relationship. There are many attempts in the literature to estimate this latter relationship. Here, we provide a way to estimate the posterior probability without resorting to using classification scores. Instead, we vary the prior probabilities of classes in order to derive the ratio of pdf's at point $\bf{x}$, which is directly used to determine class posterior probabilities. We consider here the binary classification problem.
Efficient 2.5D Hand Pose Estimation via Auxiliary Multi-Task Training for Embedded Devices

2D Key-point estimation is an important precursor to 3D pose estimation problems for human body and hands. In this work, we discuss the data, architecture, and training procedure necessary to deploy extremely efficient 2.5D hand pose estimation on embedded devices with highly constrained memory and compute envelope, such as AR/VR wearables. Our 2.5D hand pose estimation consists of 2D key-point estimation of joint positions on an egocentric image, captured by a depth sensor, and lifted to 2.5D using the corresponding depth values. Our contributions are two fold: (a) We discuss data labeling and augmentation strategies, the modules in the network architecture that collectively lead to $3\%$ the flop count and $2\%$ the number of parameters when compared to the state of the art MobileNetV2 architecture. (b) We propose an auxiliary multi-task training strategy needed to compensate for the small capacity of the network while achieving comparable performance to MobileNetV2. Our 32-bit trained model has a memory footprint of less than 300 Kilobytes, operates at more than 50 Hz with less than 35 MFLOPs.
Atomic Hydrogen in Star-forming Galaxies at Intermediate Redshifts

We have used the upgraded Giant Metrewave Radio Telescope to carry out a deep (117 on-source hours) L-band observation of the Extended Groth Strip, to measure the average neutral hydrogen (H i) mass and median star formation rate (SFR) of star-forming galaxies, as well as the cosmic H i mass density, at 0.2 < z < 0.4. This was done by stacking the H i 21 cm emission and the rest-frame 1.4 GHz radio continuum from 445 blue star-forming galaxies with MB ≤ −17 at zmean ≈ 0.34. The stacked H i 21 cm emission signal is detected at ≈7σ significance, implying an average H i mass of . We also stacked the rest-frame 1.4 GHz radio continuum emission of the same galaxies to obtain a median SFR of (0.54 ± 0.06) M⊙ yr−1; this implies an atomic gas depletion timescale of ΔtH i = (≈9) Gyr, consistent with values in star-forming galaxies in the local universe. This indicates that the star formation efficiency does not change significantly over the redshift range 0–0.4. We used the detection of the stacked H i 21 cm emission signal to infer the normalized cosmic H i mass density (ρH i/ρc,0) in star-forming galaxies at z ≈ 0.34. Assuming the local relation between H i mass and absolute B-magnitude, we obtain ρH i/ρc,0 = (4.81 ± 0.75) × 10−4, implying no significant evolution in ρH i/ρc,0 from z ≈ 0.4 to the present epoch.
Joint Inference of Reward Machines and Policies for Reinforcement Learning

Incorporating high-level knowledge is an effective way to expedite reinforcement learning (RL), especially for complex tasks with sparse rewards. We investigate an RL problem where the high-level knowledge is in the form of reward machines, i.e., a type of Mealy machine that encodes the reward functions. We focus on a setting in which this knowledge is a priori not available to the learning agent. We develop an iterative algorithm that performs joint inference of reward machines and policies for RL (more specifically, q-learning). In each iteration, the algorithm maintains a hypothesis reward machine and a sample of RL episodes. It derives q-functions from the current hypothesis reward machine, and performs RL to update the q-functions. While performing RL, the algorithm updates the sample by adding RL episodes along which the obtained rewards are inconsistent with the rewards based on the current hypothesis reward machine. In the next iteration, the algorithm infers a new hypothesis reward machine from the updated sample. Based on an equivalence relationship we defined between states of reward machines, we transfer the q-functions between the hypothesis reward machines in consecutive iterations. We prove that the proposed algorithm converges almost surely to an optimal policy in the limit if a minimal reward machine can be inferred and the maximal length of each RL episode is sufficiently long. The experiments show that learning high-level knowledge in the form of reward machines can lead to fast convergence to optimal policies in RL, while standard RL methods such as q-learning and hierarchical RL methods fail to converge to optimal policies after a substantial number of training steps in many tasks.
First constraint on coherent elastic neutrino-nucleus scattering in argon

Coherent elastic neutrino-nucleus scattering (CEvNS) is the dominant neutrino scattering channel for neutrinos of energy $E_\nu < 100$ MeV. We report a limit for this process using data collected in an engineering run of the 29 kg CENNS-10 liquid argon detector located 27.5 m from the Oak Ridge National Laboratory Spallation Neutron Source (SNS) Hg target with $4.2\times 10^{22}$ protons on target. The dataset yielded $< 7.4$ observed CEvNS events implying a cross section for the process, averaged over the SNS pion decay-at-rest flux, of $<3.4 \times 10^{-39}$ cm$^{2}$, a limit within twice the Standard Model prediction. This is the first limit on CEvNS from an argon nucleus and confirms the earlier CsI non-standard neutrino interaction constraints from the collaboration. This run demonstrated the feasibility of the ongoing experimental effort to detect CEvNS with liquid argon.
A method for Cloud Mapping in the Field of View of the Infra-Red Camera during the EUSO-SPB1 flight

EUSO-SPB1 was released on April 24$^{th}$, 2017, from the NASA balloon launch site in Wanaka (New Zealand) and landed on the South Pacific Ocean on May 7$^{th}$. The data collected by the instruments onboard the balloon were analyzed to search UV pulse signatures of UHECR (Ultra High Energy Cosmic Rays) air showers. Indirect measurements of UHECRs can be affected by cloud presence during nighttime, therefore it is crucial to know the meteorological conditions during the observation period of the detector. During the flight, the onboard EUSO-SPB1 UCIRC camera (University of Chicago Infra-Red Camera), acquired images in the field of view of the UV telescope. The available nighttime and daytime images, include information on meteorological conditions of the atmosphere observed in two infra-red bands. The presence of clouds has been investigated employing a method developed to provide a dense cloudiness map for each available infra-red image. The final masks are intended to give pixel cloudiness information at the IRcamera pixel resolution that is nearly 4-times higher than the one of the UV-camera. In this work 
cloudiness maps are obtained by using an expert system based on the analysis of different low level image features. Furthermore, an image enhancement step was needed to be applied as a preprocessing step to deal with uncalibrated data.
Towards Model-Agnostic Adversarial Defenses using Adversarially Trained Autoencoders

Adversarial machine learning is a well-studied field of research where an adversary causes predictable errors in a machine learning algorithm through precise manipulation of the input. Numerous techniques have been proposed to harden machine learning algorithms and mitigate the effect of adversarial attacks. Of these techniques, adversarial training, which augments the training data with adversarial samples, has proven to be an effective defense with respect to a certain class of attacks. However, adversarial training is computationally expensive and its improvements are limited to a single model. In this work, we take a first step toward creating a model-agnostic adversarial defense. We propose Adversarially-Trained Autoencoder Augmentation (AAA), the first model-agnostic adversarial defense that is robust against certain adaptive adversaries. We show that AAA allows us to achieve a partially model-agnostic defense by training a single autoencoder to protect multiple pre-trained classifiers; achieving adversarial performance on par or better than adversarial training without modifying the classifiers. Furthermore, we demonstrate that AAA can be used to create a fully model-agnostic defense for MNIST and Fashion MNIST datasets by improving the adversarial performance of a never before seen pre-trained classifier by at least 45% with no additional training. Finally, using a natural image corruption dataset, we show that our approach improves robustness to naturally corrupted images,which has been identified as strongly indicative of true adversarial robustness.