nils-herrmann/clean_scirepeval huggingface dataset:
First M87 Event Horizon Telescope Results. I. The Shadow of the Supermassive Black Hole

When surrounded by a transparent emission region, black holes are expected to reveal a dark shadow caused by gravitational light bending and photon capture at the event horizon. To image and study this phenomenon, we have assembled the Event Horizon Telescope, a global very long baseline interferometry array observing at a wavelength of 1.3 mm. This allows us to reconstruct event-horizon-scale images of the supermassive black hole candidate in the center of the giant elliptical galaxy M87. We have resolved the central compact radio source as an asymmetric bright emission ring with a diameter of 42 ± 3 μas, which is circular and encompasses a central depression in brightness with a flux ratio ≳10:1. The emission ring is recovered using different calibration and imaging schemes, with its diameter and width remaining stable over four different observations carried out in different days. Overall, the observed image is consistent with expectations for the shadow of a Kerr black hole as predicted by general relativity. The asymmetry in brightness in the ring can be explained in terms of relativistic beaming of the emission from a plasma rotating close to the speed of light around a black hole. We compare our images to an extensive library of ray-traced general-relativistic magnetohydrodynamic simulations of black holes and derive a central mass of M = (6.5 ± 0.7) × 109 M⊙. Our radio-wave observations thus provide powerful evidence for the presence of supermassive black holes in centers of galaxies and as the central engines of active galactic nuclei. They also present a new tool to explore gravity in its most extreme limit and on a mass scale that was so far not accessible.
First M87 Event Horizon Telescope Results. V. Physical Origin of the Asymmetric Ring

The Event Horizon Telescope (EHT) has mapped the central compact radio source of the elliptical galaxy M87 at 1.3 mm with unprecedented angular resolution. Here we consider the physical implications of the asymmetric ring seen in the 2017 EHT data. To this end, we construct a large library of models based on general relativistic magnetohydrodynamic (GRMHD) simulations and synthetic images produced by general relativistic ray tracing. We compare the observed visibilities with this library and confirm that the asymmetric ring is consistent with earlier predictions of strong gravitational lensing of synchrotron emission from a hot plasma orbiting near the black hole event horizon. The ring radius and ring asymmetry depend on black hole mass and spin, respectively, and both are therefore expected to be stable when observed in future EHT campaigns. Overall, the observed image is consistent with expectations for the shadow of a spinning Kerr black hole as predicted by general relativity. If the black hole spin and M87’s large scale jet are aligned, then the black hole spin vector is pointed away from Earth. Models in our library of non-spinning black holes are inconsistent with the observations as they do not produce sufficiently powerful jets. At the same time, in those models that produce a sufficiently powerful jet, the latter is powered by extraction of black hole spin energy through mechanisms akin to the Blandford-Znajek process. We briefly consider alternatives to a black hole for the central compact object. Analysis of existing EHT polarization data and data taken simultaneously at other wavelengths will soon enable new tests of the GRMHD models, as will future EHT campaigns at 230 and 345 GHz.
A Tractable Algorithm for Finite-Horizon Continuous Reinforcement Learning

We consider the finite horizon continuous reinforcement learning problem. Our contribution is three-fold. First,we give a tractable algorithm based on optimistic value iteration for the problem. Next,we give a lower bound on regret of order Ω(T^2/3) for any algorithm discretizes the state space, improving the previous regret bound of Ω(T^1/2) of Ortner and Ryabko  for the same problem. Next,under the assumption that the rewards and transitions are Hölder Continuous we show that the upper bound on the discretization error is const.Ln^-α T. Finally,we give some simple experiments to validate our propositions.
Morpheus: A Deep Learning Framework for the Pixel-level Analysis of Astronomical Image Data

We present Morpheus, a new model for generating pixel-level morphological classifications of astronomical sources. Morpheus leverages advances in deep learning to perform source detection, source segmentation, and morphological classification pixel-by-pixel via a semantic segmentation algorithm adopted from the field of computer vision. By utilizing morphological information about the flux of real astronomical sources during object detection, Morpheus shows resiliency to false-positive identifications of sources. We evaluate Morpheus by performing source detection, source segmentation, morphological classification on the Hubble Space Telescope data in the five CANDELS fields with a focus on the GOODS South field, and demonstrate a high completeness in recovering known GOODS South 3D-HST sources with H < 26 AB. We release the code publicly, provide online demonstrations, and present an interactive visualization of the Morpheus results in GOODS South.
An Empirically Derived Formula for the Shape of Planet-induced Gaps in Protoplanetary Disks

This study uses numerical hydrodynamics calculations and a novel method for densely sampling parameter space to measure the precise shape of a gap opened by a planet in a gaseous disk, as a function of planet-to-star mass ratio, disk Mach number, and disk viscosity. Formulas for gap depth and width are determined, which are combined to form a complete formula for surface density as a function of radius in the disk. This new analytical formula is compared with numerically derived gaps opened by planets ranging from very low masses up to a few times Jupiter’s mass, and excellent agreement is found over a wide range of parameter space relevant to planet-disk interactions. A simple-to-use code is presented to rapidly generate synthetic disk profiles.
Reachability Deficits in Quantum Approximate Optimization

The quantum approximate optimization algorithm (QAOA) has rapidly become a cornerstone of contemporary quantum algorithm development. Despite a growing range of applications, only a few results have been developed towards understanding the algorithm's ultimate limitations. Here we report that QAOA exhibits a strong dependence on a problem instances constraint to variable ratio-this problem density places a limiting restriction on the algorithms capacity to minimize a corresponding objective function (and hence solve optimization problem instances). Such reachability deficits persist even in the absence of barren plateaus and are outside of the recently reported level-1 QAOA limitations. These findings are among the first to determine strong limitations on variational quantum approximate optimization.
Relic density of dark matter in the inert doublet model beyond leading order: The heavy mass case

A full renormalisation of the Inert Doublet Model (IDM) is presented and exploited for a precise calculation of the relic density of Dark Matter (DM) at one-loop. In this first paper, we study the case of a DM candidate with $m_{\rm DM} \sim 500$ GeV. In this regime, the co-annihilation channels are important. We therefore compute, for a wide range of relative velocities, the full Next-to-Leading-Order (NLO) electroweak corrections to 7 annihilation/co-annihilation processes that contribute $\sim$ 70\% to the relic density of DM. These corrected cross-sections are interfaced with {\tt micrOMEGAs} to obtain the one-loop correction to the freeze-out relic density. Due to the accurate measurement of this observable, the one-loop corrections are relevant. We discuss the one-loop renormalisation scheme dependence and point out the influence, at one-loop, of a parameter that solely describes the scattering in the dark sector. A tree-level computation of the relic density is not sensitive to this parameter.
Cosmological simulations of massive black hole seeds: predictions for next-generation electromagnetic and gravitational wave observations

We study how statistical properties of supermassive black holes depend on the frequency and conditions for massive seed formation in cosmological simulations of structure formation. We develop a novel method to re-calculate detailed growth histories and merger trees of black holes within the framework of the Illustris simulation for several seed formation models, including a physically motivated model where black hole seeds only form in progenitor galaxies that conform to the conditions for direct collapse black hole formation. While all seed models considered here are in a broad agreement with present observational constraints on black hole populations from optical, UV and X-ray studies, we find they lead to widely different black hole number densities and halo occupation fractions which are currently observationally unconstrained. In terms of future electromagnetic spectrum observations, the faint-end quasar luminosity function and the low mass-end black hole-host galaxy scaling relations are very sensitive to the specific massive seed prescription. Specifically, the direct collapse model exhibits a seeding efficiency which decreases rapidly with cosmic time and produces much fewer black holes in low mass galaxies, in contrast to the original Illustris simulation. We further find that the total black hole merger rate varies by more than one order of magnitude for different seed models, with the redshift evolution of the chirp mass changing as well. Supermassive black hole merger detections with LISA and International Pulsar Timing Array may hence provide the most direct means of constraining massive black hole seed formation in the early Universe.
Do reverberation mapping analyses provide an accurate picture of the broad-line region?

Reverberation mapping (RM) is a powerful approach for determining the nature of the broad-line region (BLR) in active galactic nuclei. However, inferring physical BLR properties from an observed spectroscopic time series is a difficult inverse problem. Here, we present a blind test of two widely used RM methods: MEMEcho (developed by Horne) and CARAMEL (developed by Pancoast and collaborators). The test data are simulated spectroscopic time series that track the Hα emission line response to an empirical continuum light curve. The underlying BLR model is a rotating, biconical accretion disc wind, and the synthetic spectra are generated via self-consistent ionization and radiative transfer simulations. We generate two mock data sets, representing Seyfert galaxies and QSOs. The Seyfert model produces a largely negative response, which neither method can recover. However, both fail ‘gracefully', neither generating spurious results. For the QSO model both CARAMEL and expert interpretation of MEMEchoś output both capture the broadly annular, rotation-dominated nature of the line-forming region, though MEMEcho analysis overestimates its size by 50 per cent, but CARAMEL is unable to distinguish between additional inflow and outflow components. Despite fitting individual spectra well, the CARAMEL velocity-delay maps and RMS line profiles are strongly inconsistent with the input data. Finally, since the Hα line-forming region is rotation dominated, neither method recovers the disc wind nature of the underlying BLR model. Thus considerable care is required when interpreting the results of RM analyses in terms of physical models.
A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatry

Drawing an inspiration from behavioral studies of human decision making, we propose here a more general and flexible parametric framework for reinforcement learning that extends standard Q-learning to a two-stream model for processing positive and negative rewards, and allows to incorporate a wide range of reward-processing biases -- an important component of human decision making which can help us better understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems, as well as various neuropsychiatric conditions associated with disruptions in normal reward processing. From the computational perspective, we observe that the proposed Split-QL model and its clinically inspired variants consistently outperform standard Q-Learning and SARSA methods, as well as recently proposed Double Q-Learning approaches, on simulated tasks with particular reward distributions, a real-world dataset capturing human decision-making in gambling tasks, and the Pac-Man game in a lifelong learning setting across different reward stationarities.
Near Optimal Stratified Sampling

The performance of a machine learning system is usually evaluated by using i.i.d.\ observations with true labels. However, acquiring ground truth labels is expensive, while obtaining unlabeled samples may be cheaper. Stratified sampling can be beneficial in such settings and can reduce the number of true labels required without compromising the evaluation accuracy. Stratified sampling exploits statistical properties (e.g., variance) across strata of the unlabeled population, though usually under the unrealistic assumption that these properties are known. We propose two new algorithms that simultaneously estimate these properties and optimize the evaluation accuracy. We construct a lower bound to show the proposed algorithms (to log-factors) are rate optimal. Experiments on synthetic and real data show the reduction in label complexity that is enabled by our algorithms.
User-Oriented Summaries Using a PSO Based Scoring Optimization Method

Automatic text summarization tools have a great impact on many fields, such as medicine, law, and scientific research in general. As information overload increases, automatic summaries allow handling the growing volume of documents, usually by assigning weights to the extracted phrases based on their significance in the expected summary. Obtaining the main contents of any given document in less time than it would take to do that manually is still an issue of interest. In this article, a new method is presented that allows automatically generating extractive summaries from documents by adequately weighting sentence scoring features using Particle Swarm Optimization. The key feature of the proposed method is the identification of those features that are closest to the criterion used by the individual when summarizing. The proposed method combines a binary representation and a continuous one, using an original variation of the technique developed by the authors of this paper. Our paper shows that using user labeled information in the training set helps to find better metrics and weights. The empirical results yield an improved accuracy compared to previous methods used in this field.
A sharp upper bound for the size of Lusztig series

The paper is concerned with the character theory of finite groups of Lie type. The irreducible characters of a group G of Lie type are partitioned in Lusztig series. We provide a simple formula for an upper bound of the maximal size of a Lusztig series for classical groups with connected center; this is expressed for each group G in terms of its Lie rank and defining characteristic. When G is specified as G⁢(q){G(q)} and q is large enough, we determine explicitly the maximum of the sizes of the Lusztig series of G.
One Size Does Not Fit All: Quantifying and Exposing the Accuracy-Latency Trade-Off in Machine Learning Cloud Service APIs via Tolerance Tiers

Today's cloud service architectures follow a “one size fits all” deployment strategy where the same service version instantiation is provided to the end users. However, consumers are broad and different applications have different accuracy and responsiveness requirements, which as we demonstrate renders the “one size fits all” approach inefficient in practice. We use a production grade speech recognition engine, which serves several thousands of users, and an open source computer vision based system, to explain our point. To overcome the limitations of the “one size fits all” approach, we recommend Tolerance Tiers where each MLaaS tier exposes an accuracy/responsiveness characteristic, and consumers can programmatically select a tier. We evaluate our proposal on the CPU-based automatic speech recognition (ASR) engine and cutting-edge neural networks for image classification deployed on both CPUs and GPUs. The results show that our proposed approach provides a MLaaS cloud service architecture that can be tuned by the end API user or consumer to outperform the conventional “one size fits all” approach.
A Corpus for Modeling User and Language Effects in Argumentation on Online Debating

Existing argumentation datasets have succeeded in allowing researchers to develop computational methods for analyzing the content, structure and linguistic features of argumentative text. They have been much less successful in fostering studies of the effect of “user” traits — characteristics and beliefs of the participants — on the debate/argument outcome as this type of user information is generally not available. This paper presents a dataset of 78,376 debates generated over a 10-year period along with surprisingly comprehensive participant profiles. We also complete an example study using the dataset to analyze the effect of selected user traits on the debate outcome in comparison to the linguistic features typically employed in studies of this kind.
Determining Relative Argument Specificity and Stance for Complex Argumentative Structures

Systems for automatic argument generation and debate require the ability to (1) determine the stance of any claims employed in the argument and (2) assess the specificity of each claim relative to the argument context. Existing work on understanding claim specificity and stance, however, has been limited to the study of argumentative structures that are relatively shallow, most often consisting of a single claim that directly supports or opposes the argument thesis. In this paper, we tackle these tasks in the context of complex arguments on a diverse set of topics. In particular, our dataset consists of manually curated argument trees for 741 controversial topics covering 95,312 unique claims; lines of argument are generally of depth 2 to 6. We find that as the distance between a pair of claims increases along the argument path, determining the relative specificity of a pair of claims becomes easier and determining their relative stance becomes harder.
Generalization to Novel Objects using Prior Relational Knowledge

To solve tasks in new environments involving objects unseen during training, agents must reason over prior information about those objects and their relations. We introduce the Prior Knowledge Graph network, an architecture for combining prior information, structured as a knowledge graph, with a symbolic parsing of the visual scene, and demonstrate that this approach is able to apply learned relations to novel objects whereas the baseline algorithms fail. Ablation experiments show that the agents ground the knowledge graph relations to semantically-relevant behaviors. In both a Sokoban game and the more complex Pacman environment, our network is also more sample efficient than the baselines, reaching the same performance in 5-10x fewer episodes. Once the agents are trained with our approach, we can manipulate agent behavior by modifying the knowledge graph in semantically meaningful ways. These results suggest that our network provides a framework for agents to reason over structured knowledge graphs while still leveraging gradient based learning approaches.
A Stricter Heap Separating Points-To Logic

Dynamic memory issues are hard to locate and may cost much of a development project's efforts and was repeatedly reported similarly afterwards independently by different persons. Verification as one formal method may proof a given program's heap matches a specified dynamic behaviour. Dynamic (or heap) memory, is the region within main memory that is manipulated by program statements like alloc, free and pointer manipulation during program execution. Usually, heap memory is allocated for problems where the amount of used memory is unknown prior to execution. Regions within the heap may be related "somehow" with each other, often, but not always, by pointers containing absolute addresses of related heap cells. The data structure described by all valid pointer variables manifests heap graphs. 
A heap graph is a directed connected simple graph within the dynamic memory which may contain cycles, and where each vertex represents an unique memory address and every edge links two heap vertices. The heap graph must be pointed by at least one variable from the local stack or a chain of other heap graphs which is finally pointed by at least one stacked variable. Heap vertices may not overlap. A heap formula expresses the assertion on dynamic memory and can either be a heaplet, or a recursively defined heap-spatial or logical formula.
Correlators of Polynomial Processes

In the setting of polynomial jump-diffusion dynamics, we provide a formula for computing correlators, namely, cross-moments of the process at different time points along its path. The formula involves only linear combinations of the exponential of the so-called generator matrix, extending the well-known moment formula for polynomial processes. The developed framework allows to replace costly simulations with more accurate estimates, and it may be used for increasing the accuracy in financial pricing, such as for path-dependent options or in a stochastic volatility models context.
Adversarial FDI Attack against AC State Estimation with ANN

Artificial neural network (ANN) provides superior accuracy for nonlinear alternating current (AC) state estimation (SE) in smart grid over traditional methods. However, research has discovered that ANN could be easily fooled by adversarial examples. In this paper, we initiate a new study of adversarial false data injection (FDI) attack against AC SE with ANN: by injecting a deliberate attack vector into measurements, the attacker can degrade the accuracy of ANN SE while remaining undetected. We propose a population-based algorithm and a gradient-based algorithm to generate attack vectors. The performance of these algorithms is evaluated through simulations on IEEE 9-bus, 14-bus and 30-bus systems under various attack scenarios. Simulation results show that DE is more effective than SLSQP on all simulation cases. The attack examples generated by DE algorithm successfully degrade the ANN SE accuracy with high probability.
Fairness criteria through the lens of directed acyclic graphical models

A substantial portion of the literature on fairness in algorithms proposes, analyzes, and operationalizes simple formulaic criteria for assessing fairness. Two of these criteria, Equalized Odds and Calibration by Group, have gained significant attention for their simplicity and intuitive appeal, but also for their incompatibility. This chapter provides a perspective on the meaning and consequences of these and other fairness criteria using graphical models which reveals Equalized Odds and related criteria to be ultimately misleading. An assessment of various graphical models suggests that fairness criteria should ultimately be case-specific and sensitive to the nature of the information the algorithm processes.