nils-herrmann/clean_scirepeval huggingface dataset:
The SXS collaboration catalog of binary black hole simulations

Accurate models of gravitational waves from merging black holes are necessary for detectors to observe as many events as possible while extracting the maximum science. Near the time of merger, the gravitational waves from merging black holes can be computed only using numerical relativity. In this paper, we present a major update of the Simulating eXtreme Spacetimes (SXS) Collaboration catalog of numerical simulations for merging black holes. The catalog contains 2018 distinct configurations (a factor of 11 increase compared to the 2013 SXS catalog), including 1426 spin-precessing configurations, with mass ratios between 1 and 10, and spin magnitudes up to 0.998. The median length of a waveform in the catalog is 39 cycles of the dominant gravitational-wave mode, with the shortest waveform containing 7.0 cycles and the longest 351.3 cycles. We discuss improvements such as correcting for moving centers of mass and extended coverage of the parameter space. We also present a thorough analysis of numerical errors, finding typical truncation errors corresponding to a waveform mismatch of  ∼10−4. The simulations provide remnant masses and spins with uncertainties of 0.03% and 0.1% (90th percentile), about an order of magnitude better than analytical models for remnant properties. The full catalog is publicly available at www.black-holes.org/waveforms.
Minimal two-component scalar doublet dark matter with radiative neutrino mass

We propose a minimal extension of the Standard Model to accommodate two-component dark matter (DM) and light neutrino mass. The symmetry of the Standard Model is enhanced by an unbroken $\mathbb{Z}_2 \times \mathbb{Z}'_2$ such that being odd under each $\mathbb{Z}_2$, there exists one right handed neutrino and one inert scalar doublet. Therefore, each of the $\mathbb{Z}_2$ sectors contribute to ($i$) light neutrino masses radiatively similar to the scotogenic models while ($ii$) the two neutral CP even scalars present in two additional inert doublets play the role of dark matters. Focusing on the intermediate range of inert scalar doublet DM scenario: $M_W \leq M_{\rm DM} \lesssim 500 \; {\rm GeV}$, where one scalar doublet DM can not satisfy correct relic, we show that this entire range becomes allowed within this two-component scalar doublet DM, thanks to the inter-conversion between the two DM candidates in presence of neutrino Yukawa couplings with dark sector.
Too big to fail in light of Gaia

We point out an anticorrelation between the central dark matter (DM) densities of the bright Milky Way dwarf spheroidal galaxies (dSphs) and their orbital pericenter distances inferred from Gaia data. The dSphs that have not come close to the Milky Way centre (like Fornax, Carina and Sextans) are less dense in DM than those that have come closer (like Draco and Ursa Minor). The same anticorrelation cannot be inferred for the ultrafaint dSphs due to large scatter, while a trend that dSphs with more extended stellar distributions tend to have lower DM densities emerges with ultrafaints. We discuss how these inferences constrain proposed solutions to the Milky Way’s too-big-to-fail problem and provide new clues to decipher the nature of DM.
GraSPy: Graph Statistics in Python

We introduce GraSPy, a Python library devoted to statistical inference, machine learning, and visualization of random graphs and graph populations. This package provides flexible and easy-to-use algorithms for analyzing and understanding graphs with a scikit-learn compliant API. GraSPy can be downloaded from Python Package Index (PyPi), and is released under the Apache 2.0 open-source license. The documentation and all releases are available at this https URL.
Recurrent Event Network for Reasoning over Temporal Knowledge Graphs

Recently, there has been a surge of interest in learning representation of graph-structured data that are dynamically evolving. However, current dynamic graph learning methods lack a principled way in modeling temporal, multi-relational, and concurrent interactions between nodes---a limitation that is especially problematic for the task of temporal knowledge graph reasoning, where the goal is to predict unseen entity relationships (i.e., events) over time. Here we present Recurrent Event Network (RE-Net)---a novel neural architecture for modeling complex event sequences---which consists of a recurrent event encoder and a neighborhood aggregator. The event encoder employs an RNN to capture (subject, relation) or (object, relation)-specific patterns from historical, multi-relational interactions between entities. The neighborhood aggregator summarizes concurrent, multi-hop entity interactions within each time stamp. An output layer is designed for predicting forthcoming events. Extensive experiments on temporal link prediction over four public TKG datasets demonstrate the effectiveness and strength of RE-Net, especially on multi-step inference over future time stamps. Code and data are published at the this https URL {\text{GitHub repository}}.
Recurrent Space-time Graph Neural Networks

Learning in the space-time domain remains a very challenging problem in machine learning and computer vision. Current computational models for understanding spatio-temporal visual data are heavily rooted in the classical single-image based paradigm. It is not yet well understood how to integrate information in space and time into a single, general model. We propose a neural graph model, recurrent in space and time, suitable for capturing both the local appearance and the complex higher-level interactions of different entities and objects within the changing world scene. Nodes and edges in our graph have dedicated neural networks for processing information. Nodes operate over features extracted from local parts in space and time and previous memory states. Edges process messages between connected nodes at different locations and spatial scales or between past and present time. Messages are passed iteratively in order to transmit information globally and establish long range interactions. Our model is general and could learn to recognize a variety of high level spatio-temporal concepts and be applied to different learning tasks. We demonstrate, through extensive experiments and ablation studies, that our model outperforms strong baselines and top published methods on recognizing complex activities in video. Moreover, we obtain state-of-the-art performance on the challenging Something-Something human-object interaction dataset.
Load-dependent adaptation near zero load in the bacterial flagellar motor

The bacterial flagellar motor is an ion-powered transmembrane protein complex which drives swimming in many bacterial species. The motor consists of a cytoplasmic ‘rotor’ ring and a number of ‘stator’ units, which are bound to the cell wall of the bacterium. Recently, it has been shown that the number of functional torque-generating stator units in the motor depends on the external load, and suggested that mechanosensing in the flagellar motor is driven via a ‘catch bond’ mechanism in the motor’s stator units. We present a method that allows us to measure—on a single motor—stator unit dynamics across a large range of external loads, including near the zero-torque limit. By attaching superparamagnetic beads to the flagellar hook, we can control the motor’s speed via a rotating magnetic field. We manipulate the motor to four different speed levels in two different ion-motive force (IMF) conditions. This framework allows for a deeper exploration into the mechanism behind load-dependent remodelling by separating out motor properties, such as rotation speed and energy availability in the form of IMF, that affect the motor torque.
Guidelines for data analysis scripts

Unorganized heaps of analysis code are a growing liability as data analysis pipelines are getting longer and more complicated. This is worrying, as neuroscience papers are getting retracted due to programmer error. In this paper, some guidelines are presented that help keep analysis code well organized, easy to understand and convenient to work with: 
1. Each analysis step is one script 
2. A script either processes a single recording, or aggregates across recordings, never both 
3. One master script to run the entire analysis 
4. Save all intermediate results 
5. Visualize all intermediate results 
6. Each parameter and filename is defined only once 
7. Distinguish files that are part of the official pipeline from other scripts 
In addition to discussing the reasoning behind each guideline, an example analysis pipeline is presented as a case study to see how each guideline translates into code.
Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout

While the use of deep learning in drug discovery is gaining increasing attention, the lack of methods to compute reliable errors in prediction for Neural Networks prevents their application to guide decision making in domains where identifying unreliable predictions is essential, e.g., precision medicine. Here, we present a framework to compute reliable errors in prediction for Neural Networks using Test-Time Dropout and Conformal Prediction. Specifically, the algorithm consists of training a single Neural Network using dropout, and then applying it N times to both the validation and test sets, also employing dropout in this step. Therefore, for each instance in the validation and test sets an ensemble of predictions are generated. The residuals and absolute errors in prediction for the validation set are then used to compute prediction errors for the test set instances using Conformal Prediction. We show using 24 bioactivity data sets from ChEMBL 23 that Dropout Conformal Predictors are valid (i.e., the fraction of instances whose true value lies within the predicted interval strongly correlates with the confidence level) and efficient, as the predicted confidence intervals span a narrower set of values than those computed with Conformal Predictors generated using Random Forest (RF) models. Lastly, we show in retrospective virtual screening experiments that dropout and RF-based Conformal Predictors lead to comparable retrieval rates of active compounds. Overall, we propose a computationally efficient framework (as only N extra forward passes are required in addition to training a single network) to harness Test-Time Dropout and the Conformal Prediction framework, which is generally applicable to generate reliable prediction errors for Deep Neural Networks in drug discovery and beyond.
Chronic stress may disrupt covariant fluctuations of vitamin D and cortisol plasma levels in pregnant sheep during the last trimester: a preliminary report

Psychosocial stress during pregnancy is a known contributor to preterm birth, but also has been increasingly appreciated as an in utero insult acting long-term on prenatal and postnatal neurodevelopmental trajectories. These events impact many information molecules, including both vitamin D and cortisol. Both have been linked to low birth premature babies. Cortisol tends to be further elevated in women, while vitamin D tends to be decreased from their normal levels during pregnancy. One facilitates labor in part by elevating placental CRH, the other by limiting CRH in placental tissue. Both are linked to managing adversity. Studies in large animal models with high resemblance to human physiology are sparse to model the changes induced by such stress exposure. Using an established pregnant sheep model of stress during human development, here we focused on measuring the changes in maternal Vitamin D and cortisol responses due to chronic inescapable stress mimicking daily challenges in the last trimester of human pregnancy. The present pilot data show that chronic maternal stress during pregnancy results in endocrine and metabolic chronic habituation paralleled by sensitization to acute stress challenges. Chronic stress appears to disrupt a physiological relationship between oscillations of vitamin D and cortisol. These speculations need to be explored in future studies.
HAKE: Human Activity Knowledge Engine

Human activity understanding is crucial for building automatic intelligent system. With the help of deep learning, activity understanding has made huge progress recently. But some challenges such as imbalanced data distribution, action ambiguity, complex visual patterns still remain. To address these and promote the activity understanding, we build a large-scale Human Activity Knowledge Engine (HAKE) based on the human body part states. Upon existing activity datasets, we annotate the part states of all the active persons in all images, thus establish the relationship between instance activity and body part states. Furthermore, we propose a HAKE based part state recognition model with a knowledge extractor named Activity2Vec and a corresponding part state based reasoning network. With HAKE, our method can alleviate the learning difficulty brought by the long-tail data distribution, and bring in interpretability. Now our HAKE has more than 7 M+ part state annotations and is still under construction. We first validate our approach on a part of HAKE in this preliminary paper, where we show 7.2 mAP performance improvement on Human-Object Interaction recognition, and 12.38 mAP improvement on the one-shot subsets.
A quantum engineer's guide to superconducting qubits

The aim of this review is to provide quantum engineers with an introductory guide to the central concepts and challenges in the rapidly accelerating field of superconducting quantum circuits. Over the past twenty years, the field has matured from a predominantly basic research endeavor to one that increasingly explores the engineering of larger-scale superconducting quantum systems. Here, we review several foundational elements -- qubit design, noise properties, qubit control, and readout techniques -- developed during this period, bridging fundamental concepts in circuit quantum electrodynamics (cQED) and contemporary, state-of-the-art applications in gate-model quantum computation.
Dot-to-Dot: Explainable Hierarchical Reinforcement Learning for Robotic Manipulation

Robotic systems are ever more capable of automation and fulfilment of complex tasks, particularly with reliance on recent advances in intelligent systems, deep learning and artificial intelligence in general. However, as robots and humans come closer together in their interactions, the matter of interpretability, or explainability of robot decision-making processes for the human grows in importance. A successful interaction and collaboration would only be possible through mutual understanding of underlying representations of the environment and the task at hand. This is currently a challenge in deep learning systems. We present a hierarchical deep reinforcement learning system, consisting of a low-level agent handling the large actions/states space of a robotic system efficiently, by following the directives of a high-level agent which is learning the high-level dynamics of the environment and task. This high-level agent forms a representation of the world and task at hand that is interpretable for a human operator. The method, which we call Dot-to-Dot, is tested on a MuJoCo-based model of the Fetch Robotics Manipulator, as well as a Shadow Hand, to test its performance. Results show efficient learning of complex actions/states spaces by the low-level agent, and an interpretable representation of the task and decision-making process learned by the high-level agent.
Infinite Probabilistic Databases

Probabilistic databases (PDBs) model uncertainty in data in a quantitative
way. In the established formal framework, probabilistic (relational) databases
are finite probability spaces over relational database instances. This
finiteness can clash with intuitive query behavior (Ceylan et al., KR 2016),
and with application scenarios that are better modeled by continuous
probability distributions (Dalvi et al., CACM 2009).
 We formally introduced infinite PDBs in (Grohe and Lindner, PODS 2019) with a
primary focus on countably infinite spaces. However, an extension beyond
countable probability spaces raises nontrivial foundational issues concerned
with the measurability of events and queries and ultimately with the question
whether queries have a well-defined semantics.
 We argue that finite point processes are an appropriate model from
probability theory for dealing with general probabilistic databases. This
allows us to construct suitable (uncountable) probability spaces of database
instances in a systematic way. Our main technical results are measurability
statements for relational algebra queries as well as aggregate queries and
Datalog queries.
The Higgs profile in the standard model and beyond

We present a review of Higgs physics in the SM and beyond, including the tests of the Higgs boson properties that have been performed at LHC and have permitted to delineate its profile. After presenting the essential features of the BEH mechanism, and its implementation in the SM, we discuss how the Higgs mass limits developed over the years. These constraints in turn helped to classify the Higgs phenomenology (decays and production mechanisms), which provided the right direction to search for the Higgs particle, an enterprise that culminated with its discovery at LHC. So far, the constraints on the couplings of the Higgs particle, point towards a SM interpretation. However, the SM has open ends that suggest the need to look for extensions of the model. We discuss in general the connection of the Higgs sector with some new physics (e.g. supersymmetry, flavor and Dark matter), with special focus on a more flavored Higgs sector. Thus is realized in the most general 2HDM, and its textured version, which we study in general, and for its various limits, which contain distinctive flavor-violating signals that could be searched at current and future colliders.
Electron-interacting dark matter and implications from DAMA/LIBRA-phase2

We investigate the possibility for the direct detection of low mass (GeV scale) WIMP dark matter in scintillation experiments. Such WIMPs are typically too light to leave appreciable nuclear recoils, but may be detected via their scattering off atomic electrons. In particular, the DAMA Collaboration [R. Bernabei et al.,  (2018)] has recently presented strong evidence of an annual modulation in the scintillation rate observed at energies as low as 1 keV. Despite a strong enhancement in the calculated event rate at low energies, we find that an interpretation in terms of electron-interacting WIMPs cannot be consistent with existing constraints. We also demonstrate the importance of correct treatment of the atomic wavefunctions, and show the resulting event rate is very sensitive to the low-energy performance of the detectors, meaning it is crucial that the detector uncertainties be taken into account. Finally, we demonstrate that the potential scintillation event rate can be much larger than may otherwise be expected, meaning that competitive searches can be performed for m_chi ~ 1 GeV scale WIMPs using the conventional prompt (S1) scintillation signals. This is important given the recent and upcoming very large liquid xenon detectors.
Learning to Generate Unambiguous Spatial Referring Expressions for Real-World Environments

Referring to objects in a natural and unambiguous manner is crucial for effective human-robot interaction. Previous research on learning-based referring expressions has focused primarily on comprehension tasks, while generating referring expressions is still mostly limited to rule-based methods. In this work, we propose a two-stage approach that relies on deep learning for estimating spatial relations to describe an object naturally and unambiguously with a referring expression. We compare our method to the state of the art algorithm in ambiguous environments (e.g., environments that include very similar objects with similar relationships). We show that our method generates referring expressions that people find to be more accurate (~30% better) and would prefer to use (~32% more often).
Introduction to Multi-Armed Bandits

Multi-armed bandits a simple but very powerful framework for algorithms that make decisions over time under uncertainty. An enormous body of work has accumulated over the years, covered in several books and surveys. This book provides a more introductory, textbook-like treatment of the subject. Each chapter tackles a particular line of work, providing a self-contained, teachable technical introduction and a brief review of the further developments. The chapters are as follows: stochastic bandits, lower bounds; Bayesian bandits and Thompson Sampling; Lipschitz Bandits; full Feedback and adversarial costs; adversarial bandits; linear costs and semi-bandits; contextual Bandits; bandits and games; bandits with knapsacks; bandits and incentives.
A local Lagrangian for MOND as modified inertia

We propose a local Lagrangian for a point particle where its inertia part is modified in the regime of small accelerations. For the standard gravitational central force, it recovers the deep MOdified Newtonian Dynamics (MOND) (accelerations $\ll a_0\approx 10^{-10}$m/s$^{2}$) equations of motion in the case of a circular orbit. Perturbations to that turn on higher derivative terms, leading to exponentially unstable solutions that must vanish in order to account for the very small scattering of the Tully-Fisher relation. Unstable solutions linearly growing with time remain valid for a characteristic timescale of at least 3 billion years. We show that vertical perturbations recover similar results to dark matter for old galaxies, but deviations could be present for young ones. We also present ways to probe our approach and describe some of its subtleties, such as the strong equivalence principle (violated in general), the center of mass motion of a composite body, and how in some cases it could overcome Ostrogradsky's instabilities (with naturally occurring piecewise Lagrangians). Our main conclusions regarding our MOND-like proposal are: (i) it constitutes a possible recipe where Ostrogradsky instabilities could be "tamed"; (ii) it is a falsifiable approach in various contexts and (iii) it might explain simultaneously some of the issues usual modified gravity MOND and dark matter phenomenologies have difficulties individually. These aspects seem relevant to start addressing practical ways to differentiate modified gravity MOND from modified inertia and give insights into alternative ways to tackle some astrophysical and cosmological puzzles.
Counterfactual Visual Explanations

In this work, we develop a technique to produce counterfactual visual explanations. Given a 'query' image $I$ for which a vision system predicts class $c$, a counterfactual visual explanation identifies how $I$ could change such that the system would output a different specified class $c'$. To do this, we select a 'distractor' image $I'$ that the system predicts as class $c'$ and identify spatial regions in $I$ and $I'$ such that replacing the identified region in $I$ with the identified region in $I'$ would push the system towards classifying $I$ as $c'$. We apply our approach to multiple image classification datasets generating qualitative results showcasing the interpretability and discriminativeness of our counterfactual explanations. To explore the effectiveness of our explanations in teaching humans, we present machine teaching experiments for the task of fine-grained bird classification. We find that users trained to distinguish bird species fare better when given access to counterfactual explanations in addition to training examples.