nils-herrmann/clean_scirepeval huggingface dataset:
A neural network based post-filter for speech-driven head motion synthesis

Despite the fact that neural networks are widely used for speech-driven head motion synthesis, it is well-known that the output of neural networks is noisy or discontinuous due to the limited capability of deep neural networks in predicting human motion. Thus, post-processing is required to obtain smooth head motion trajectories for animation. It is common to apply a linear filter or consider keyframes as post-processing. However, neither approach is optimal as there is always a trade-off between smoothness and accuracy. We propose to employ a neural network trained in a way that it is capable of reconstructing the head motions, in order to overcome this limitation. In the objective evaluation, this filter is proved to be good at de-noising data involving types of noise (dropout or Gaussian noise). Objective metrics also demonstrate the improvement of the joined head motion's smoothness after being processed by our proposed filter. A detailed analysis reveals that our proposed filter learns the characteristic of head motions. The subjective evaluation shows that participants were unable to distinguish the synthesised head motions with our proposed filter from ground truth, which was preferred over the Gaussian filter and moving average.
Synchronizing Geospatial Information for Personalized Health Monitoring

The health effects of air pollution have been subject to intense study in recent decades. Exposure to pollutants such as airborne particulate matter and ozone has been associated with increases in morbidity and mortality, especially with regards to respiratory and cardiovascular diseases. Unfortunately, individuals do not have readily accessible methods by which to track their exposure to pollution. This paper proposes how pollution parameters like CO, NO2, O3, PM2.5, PM10 and SO2 can be monitored for respiratory and cardiovascular personalized health during outdoor exercise events. Using location tracked activities, we synchronize them to public data sets of pollution sensors. For improved accuracy in estimation, we use heart rate data to understand breathing volume mapped with the local air quality sensors via constant GPS tracking.
EUDAQ2—A flexible data acquisition software framework for common test beams

The data acquisition software framework, EUDAQ, was originally developed to read out data from the EUDET-type pixel telescopes. This was successfully used in many test beam campaigns in which an external position and time reference were required. The software has recently undergone a significant upgrade, EUDAQ2, which is a generic, modern and modular system for use by many different detector types, ranging from tracking detectors to calorimeters. EUDAQ2 is suited as an overarching software that links individual detector readout systems and simplifies the integration of multiple detectors. The framework itself supports several triggering and event building modes. This flexibility makes test beams with multiple detectors significantly easier and more efficient, as EUDAQ2 can adapt to the characteristics of each detector prototype during testing. The system has been thoroughly tested during multiple test beams involving different detector prototypes. EUDAQii has now been released and is freely available under an open-source license.
Preheating in full general relativity

We investigate the importance of local gravity during preheating, the non-linear dynamics that may be responsible for starting the process of reheating the universe after inflation. We introduce three numerical methods that study a simple preheating scenario while relaxing gravitational assumptions, culminating in studying the process in full numerical relativity. We confirm that perturbation theory is no longer valid when one considers modes whose wavelengths are comparable to the size of the horizon at the end of inflation; however, this breakdown does not necessarily lead to a breakdown of the preheating process in non-linear gravity. For the specific model we test we find no evidence for the creation of primordial black holes from the instabilities in this model. Finally, we remark on the opportunity for future numerical study of non-linear gravitational dynamics in the early universe.
A 40 Billion Solar-mass Black Hole in the Extreme Core of Holm 15A, the Central Galaxy of Abell 85

Holm 15A, the brightest cluster galaxy of the galaxy cluster Abell 85, has an ultradiffuse central region, ∼ 2 mag fainter than the faintest depleted core of any early-type galaxy (ETG) that has been dynamically modeled in detail. We use orbit-based, axisymmetric Schwarzschild models to analyze the stellar kinematics of Holm 15A from new high-resolution, wide-field spectral observations obtained with the Multi-Unit Spectroscopic Explorer at the Very Large Telescope. We find a supermassive black hole with a mass of ( 4.0 ± 0.80 ) × 10 10 M ⊙ at the center of Holm 15A. This is the most massive black hole with a direct dynamical detection in the local universe. We find that the distribution of stellar orbits is increasingly biased toward tangential motions inside the core. However, the tangential bias is less than that in other cored elliptical galaxies. We compare Holm 15A with N-body simulations of mergers between galaxies with black holes and find that the observed amount of tangential anisotropy and the shape of the light profile are consistent with a formation scenario where Holm 15A is the remnant of a merger between two ETGs with pre-existing depleted cores. We find that black hole masses in cored galaxies, including Holm 15A, scale inversely with the central stellar surface brightness and mass density. These correlations are independent of a specific parameterization of the light profile.
The ASAS-SN catalogue of variable stars – V. Variables in the Southern hemisphere

The All-Sky Automated Survey for Supernovae (ASAS-SN) provides long baseline (∼4 yr) light curves for sources brighter than V ≲ 17 mag across the whole sky. As part of our effort to characterize the variability of all the stellar sources visible in ASAS-SN, we have produced ∼30.1 million V-band light curves for sources in the Southern hemisphere using the APASS DR9 (AAVSO Photometric All-Sky Survey Data Release) catalogue as our input source list. We have systematically searched these sources for variability using a pipeline based on random forest classifiers. We have identified ${\sim } 220\, 000$ variables, including ${\sim } 88\, 300$ new discoveries. In particular, we have discovered ${\sim }48\, 000$ red pulsating variables, ${\sim }23\, 000$ eclipsing binaries, ∼2200 δ-Scuti variables, and ${\sim }10\, 200$ rotational variables. The light curves and characteristics of the variables are all available through the ASAS-SN variable stars data base (https://asas-sn.osu.edu/variables). The pre-computed ASAS-SN V-band light curves for all the ∼30.1 million sources are available through the ASAS-SN photometry data base (https://asas-sn.osu.edu/photometry). This effort will be extended to provide ASAS-SN light curves for sources in the Northern hemisphere and for V ≲ 17 mag sources across the whole sky that are not included in APASS DR9.
Analytic description of primordial black hole formation from scalar field fragmentation

Primordial black hole (PBH) formation is a more generic phenomenon than was once thought. The dynamics of a scalar field in inflationary universe can produce PBHs under mild assumptions regarding the scalar potential. In the early universe, light scalar fields develop large expectation values during inflation and subsequently relax to the minimum of the effective potential at a later time. During the relaxation process, an initially homogeneous scalar condensate can fragment into lumps via an instability similar to the gravitational (Jeans) instability, where the scalar self-interactions, rather than gravity, play the leading role. The fragmentation of the scalar field into lumps (e.g. Q-balls or oscillons) creates matter composed of relatively few heavy “particles”, whose distribution is subject to significant fluctuations unconstrained by comic microwave background (CMB) observations and unrelated to the large-scale structure. If this matter component comes to temporarily dominate the energy density before the scalar lumps decay, PBHs can be efficiently produced during the temporary matter-dominated era. We develop a general analytic framework for description of PBH formation in this class of models. We highlight the differences between the scalar fragmentation scenario and other commonly considered PBH formation models. Given the existence of the Higgs field and the preponderance of scalar fields within supersymmetric and other models of new physics, PBHs constitute an appealing and plausible candidate for dark matter.
Chemical evolution of elliptical galaxies with a variable IMF

Growing evidence in recent years suggests a systematic variation of the stellar initial mass function (IMF), being top-heavy for starburst galaxies and possibly bottom-heavy for massive ellipticals. Galaxy chemical evolution simulations adopting an invariant canonical IMF face difficulty in simultaneously reproducing the metallicity and α-enhancement of the massive elliptical galaxies. Applying a variable IMF that changes with time is a promising solution, however, it is non-trivial to couple a variable IMF theory with the existing galaxy evolution codes. Here we present the first open source simulation code which recalculates the galaxy-wide IMF at each time step according to the integrated galactic IMF (IGIMF) theory where the galaxy-wide IMF depends on the galactic star formation rate and metallicity. The resulting galaxy-wide IMF and metal abundance evolve with time. With this pilot work, we explore the effect of the IGIMF theory on galaxy chemical evolution in comparison with an invariant IMF.
Quantum Solitons in any Dimension: Derrick's Theorem v. AQFT

A powerful tool for studying the behavior of classical field theories is Derrick's theorem: one may rule out the existence of localized inhomogeneous stable field configurations (solitons) by inspecting the Hamiltonian and making scaling arguments. For example, the theorem can be used to rule out compact domain wall configurations for the classic $\phi^4$ theory in $3+1$ dimensions and greater. We argue no such obstruction to constructing solitons exists in the framework of algebraic/axiomatic quantum field theory (AQFT), and that states like the example given lie in topologically trivial superselection sectors of the Hilbert space. A proof is presented making use of the relative entropy, and the implications are explored for a few common models of scalar fields and the pure Yang-Mills theory.
Not as big as a barn: Upper bounds on dark matter-nucleus cross sections

Critical probes of dark matter come from tests of its elastic scattering with nuclei. The results are typically assumed to be model-independent, meaning that the form of the potential need not be specified and that the cross sections on different nuclear targets can be simply related to the cross section on nucleons. For point-like spin-independent scattering, the assumed scaling relation is $\sigma_{\chi A} \propto A^2 \mu_A^2 \sigma_{\chi N}\propto A^4 \sigma_{\chi N}$, where the $A^2$ comes from coherence and the $\mu_A^2\simeq A^2 m_N^2$ from kinematics for $m_\chi\gg m_A$. Here we calculate where model independence ends, i.e., where the cross section becomes so large that it violates its defining assumptions. We show that the assumed scaling relations generically fail for dark matter-nucleus cross sections $\sigma_{\chi A} \sim 10^{-32}-10^{-27}\;\text{cm}^2$, significantly below the geometric sizes of nuclei, and well within the regime probed by underground detectors. Last, we show on theoretical grounds, and in light of existing limits on light mediators, that point-like dark matter cannot have $\sigma_{\chi N}\gtrsim10^{-25}\;\text{cm}^2$, above which many claimed constraints originate from cosmology and astrophysics. The most viable way to have such large cross sections is composite dark matter, which introduces significant additional model dependence through the choice of form factor. All prior limits on dark matter with cross sections $\sigma_{\chi N}>10^{-32}\;\text{cm}^2$ with $m_\chi\gtrsim 1\;\text{GeV}$ must therefore be re-evaluated and reinterpreted.
Global simulations of galactic discs: violent feedback from clustered supernovae during bursts of star formation

A suite of idealized, global, gravitationally unstable, star-forming galactic disc simulations with 2 pc spatial resolution, performed with the adaptive mesh refinement code ramses, is used in this paper to predict the emergent effects of supernova feedback. The simulations include a simplified prescription for the formation of single stellar populations of mass $\sim 100 \, {\rm M}_{\odot }$, radiative cooling, photoelectric heating, an external gravitational potential for a dark matter halo and an old stellar disc, self-gravity, and a novel implementation of supernova feedback. The results of these simulations show that gravitationally unstable discs can generate violent supersonic winds with mass-loading factors η ≳ 10, followed by a galactic fountain phase. These violent winds are generated by highly clustered supernovae exploding in dense environments created by gravitational instability, and they are not produced in simulation without self-gravity. The violent winds significantly perturb the vertical structure of the disc, which is later re-established during the galactic fountain phase. Gas resettles into a quasi-steady, highly turbulent disc with volume-weighted velocity dispersion $\sigma \gt 50 \, {\rm km\, s}^{-1}$. The new configuration drives weaker galactic winds with a mass-loading factor η ≤ 0.1. The whole cycle takes place in ≤10 dynamical times. Such high time variability needs to be taken into account when interpreting observations of galactic winds from starburst and post-starburst galaxies.
Curriculum based Dropout Discriminator for Domain Adaptation

Domain adaptation is essential to enable wide usage of deep learning based networks trained using large labeled datasets. Adversarial learning based techniques have shown their utility towards solving this problem using a discriminator that ensures source and target distributions are close. However, here we suggest that rather than using a point estimate, it would be useful if a distribution based discriminator could be used to bridge this gap. This could be achieved using multiple classifiers or using traditional ensemble methods. In contrast, we suggest that a Monte Carlo dropout based ensemble discriminator could suffice to obtain the distribution based discriminator. Specifically, we propose a curriculum based dropout discriminator that gradually increases the variance of the sample based distribution and the corresponding reverse gradients are used to align the source and target feature representations. The detailed results and thorough ablation analysis show that our model outperforms state-of-the-art results.
Dark Sectors from the Hidden Photon Perspective

The non-observation of dark matter (DM) by direct detection experiments suggests that any new interaction of DM with the Standard Model (SM) should be very weak. One of the simplest scenarios to achieve this is a dark sector that is charged under a new $U(1)_X$ symmetry, which is kinetically mixed with the SM hypercharge $U(1)_Y$. We briefly review the status of such a minimal setup and analyze in a second step how the picture is altered if also SM fields are charged under the new symmetry. We exemplify this for the case of a gauged $U(1)_{L_\mu-L_\tau}$ and show that this allows for a simultaneous explanation of the $(g-2)_\mu$ excess and the DM relic abundance $\Omega_{DM}$. Furthermore, we discuss the potential of four-lepton and two-lepton plus missing energy signatures to test such scenarios.
Semi-parametric Object Synthesis

We present a new semi-parametric approach to synthesize novel views of an object from a single monocular image. First, we exploit man-made object symmetry and piece-wise planarity to integrate rich a-priori visual information into the novel viewpoint synthesis process. An Image Completion Network (ICN) then leverages 2.5D sketches rendered from a 3D CAD as guidance to generate a realistic image. In contrast to concurrent works, we do not rely solely on synthetic data but leverage instead existing datasets for 3D object detection to operate in a real-world scenario. Differently from competitors, our semi-parametric framework allows the handling of a wide range of 3D transformations. Thorough experimental analysis against state-of-the-art baselines shows the efficacy of our method both from a quantitative and a perceptive point of view. Code and supplementary material are available at: this https URL
Sub-MeV dark matter and the Goldstone modes of superfluid helium

We show how the relativistic effective field theory for the superfluid phase of helium-4 can replace the standard methods used to compute the production rates of low momentum excitations due to the interaction with an external probe. This is done by studying the scattering problem of a light dark matter particle in the superfluid, and comparing to some existing results. We show that the rate of emission of two phonons, the Goldstone modes of the effective theory, gets strongly suppressed for sub-MeV dark matter particles due to a fine cancellation between two different tree-level diagrams in the limit of small exchanged momenta. This phenomenon is found to be a consequence of the particular choice of the potential felt by the dark matter particle in helium. The predicted rates can vary by orders of magnitude if this potential is changed. We prove that the dominant contribution to the total emission rate is provided by the phonons. Finally, we analyze the angular distributions for the emissions of one and two phonons, and discuss how they can be used to measure the mass of the hypothetical dark matter particle hitting the helium target.
WINOGRANDE: An Adversarial Winograd Schema Challenge at Scale

The Winograd Schema Challenge (WSC) (Levesque, Davis, and Morgenstern 2011), a benchmark for commonsense reasoning, is a set of 273 expert-crafted pronoun resolution problems originally designed to be unsolvable for statistical models that rely on selectional preferences or word associations. However, recent advances in neural language models have already reached around 90% accuracy on variants of WSC. This raises an important question whether these models have truly acquired robust commonsense capabilities or whether they rely on spurious biases in the datasets that lead to an overestimation of the true capabilities of machine commonsense.To investigate this question, we introduce WinoGrande, a large-scale dataset of 44k problems, inspired by the original WSC design, but adjusted to improve both the scale and the hardness of the dataset. The key steps of the dataset construction consist of (1) a carefully designed crowdsourcing procedure, followed by (2) systematic bias reduction using a novel AfLite algorithm that generalizes human-detectable word associations to machine-detectable embedding associations. The best state-of-the-art methods on WinoGrande achieve 59.4 – 79.1%, which are ∼15-35% (absolute) below human performance of 94.0%, depending on the amount of the training data allowed (2% – 100% respectively).Furthermore, we establish new state-of-the-art results on five related benchmarks — WSC (→ 90.1%), DPR (→ 93.1%), COPA(→ 90.6%), KnowRef (→ 85.6%), and Winogender (→ 97.1%). These results have dual implications: on one hand, they demonstrate the effectiveness of WinoGrande when used as a resource for transfer learning. On the other hand, they raise a concern that we are likely to be overestimating the true capabilities of machine commonsense across all these benchmarks. We emphasize the importance of algorithmic bias reduction in existing and future benchmarks to mitigate such overestimation.
Boundary of the pyramidal equisymmetric locus of M_g

The augmented moduli space is a compactification of moduli space M_n obtained by adding stable hyperbolic surfaces. The different topological types of the added stable surfaces produces a stratification of the boundary of M_n . Let P_n be the pyramidal locus in the moduli space, i.e., the set of hyperbolic surfaces of genus n such that the topological action of its preserving-orientation isometry group is the pyramidal action of the dihedral group Dn. The purpose of this paper is to state the complete list of strata in the boundary of Pn
SlugBot: Developing a Computational Model andFramework of a Novel Dialogue Genre

One of the most interesting aspects of the Amazon Alexa Prize competition is that the framing of the competition requires the development of new computational models of dialogue and its structure. Traditional computational models of dialogue are of two types: (1) task-oriented dialogue, supported by AI planning models,or simplified planning models consisting of frames with slots to be filled; or (2)search-oriented dialogue where every user turn is treated as a search query that may elaborate and extend current search results. Alexa Prize dialogue systems such as SlugBot must support conversational capabilities that go beyond what these traditional models can do. Moreover, while traditional dialogue systems rely on theoretical computational models, there are no existing computational theories that circumscribe the expected system and user behaviors in the intended conversational genre of the Alexa Prize Bots. This paper describes how UCSC's SlugBot team has combined the development of a novel computational theoretical model, Discourse Relation Dialogue Model, with its implementation in a modular system in order to test and refine it. We highlight how our novel dialogue model has led us to create a novel ontological resource, UniSlug, and how the structure of UniSlug determine show we curate and structure content so that our dialogue manager implements and tests our novel computational dialogue model.
Art: Abstraction Refinement-Guided Training for Provably Correct Neural Networks

Artificial Neural Networks (ANNs) have demonstrated remarkable utility in various challenging machine learning applications. While formally verified properties of their behaviors are highly desired, they have proven notoriously difficult to derive and enforce. Existing approaches typically formulate this problem as a post facto analysis process. In this paper, we present a novel learning framework that ensures such formal guarantees are enforced by construction. Our technique enables training provably correct networks with respect to a broad class of safety properties, a capability that goes well-beyond existing approaches, without compromising much accuracy. Our key insight is that we can integrate an optimization-based abstraction refinement loop into the learning process and operate over dynamically constructed partitions of the input space that considers accuracy and safety objectives synergistically. The refinement procedure iteratively splits the input space from which training data is drawn, guided by the efficacy with which such partitions enable safety verification. We have implemented our approach in a tool (ART) and applied it to enforce general safety properties on unmanned aviator collision avoidance system ACAS Xu dataset and the Collision Detection dataset. Importantly, we empirically demonstrate that realizing safety does not come at the price of much accuracy. Our methodology demonstrates that an abstraction refinement methodology provides a meaningful pathway for building both accurate and correct machine learning networks.