nils-herrmann/clean_scirepeval huggingface dataset:
Deformations and BBF form on non-Kahler holomorphically symplectic manifolds

In 1995, Dan Guan constructed examples of non-Kahler, simply-connected holomorphically symplectic manifolds. An alternative construction, using the Hilbert scheme of Kodaira-Thurston surface, was given by F. Bogomolov. We investigate topology and deformation theory of Bogomolov-Guan manifolds and show that it is similar to that of hyperkahler manifolds. We prove the local Torelli theorem, showing that holomorphically symplectic deformations of BG-manifolds are unobstructed, and the corresponding period map is locally a diffeomorphism. Using the local Torelli theorem, we prove the Fujiki formula for a BG-manifold $M$, showing that there exists a symmetric form q on the second cohomology such that for any $w\in H^2(M)$ one has $\int_M w^{2n}=q(w,w)^n$. This form is a non-Kahler version of the Beauville-Bogomolov-Fujiki form known in hyperkahler geometry.
Towards Debiasing Fact Verification Models

Fact verification requires validating a claim in the context of evidence. We show, however, that in the popular FEVER dataset this might not necessarily be the case. Claim-only classifiers perform competitively with top evidence-aware models. In this paper, we investigate the cause of this phenomenon, identifying strong cues for predicting labels solely based on the claim, without considering any evidence. We create an evaluation set that avoids those idiosyncrasies. The performance of FEVER-trained models significantly drops when evaluated on this test set. Therefore, we introduce a regularization method which alleviates the effect of bias in the training data, obtaining improvements on the newly created test set. This work is a step towards a more sound evaluation of reasoning capabilities in fact verification models.
New limits on charged dark matter from large-scale coherent magnetic fields

We study the interaction of an electrically charged component of the dark matter with a magnetized galactic interstellar medium (ISM) of (rotating) spiral galaxies. For the observed ordered component of the field, B∼ μG, we find that the accumulated Lorentz interactions between the charged particles and the ISM will extract an order unity fraction of the disk angular momentum over the few Gyr Galactic lifetime unless q/e ≲ 10−13±1 m c2/ GeV if all the dark matter is charged. The bound is weakened by factor fqdm−1/2 if only a mass fraction fqdm≳ 0.13 of the dark matter is charged. Here q and m are the dark matter particle mass and charge. If fqdm≈1 this bound excludes charged dark matter produced via the freeze-in mechanism for m ≲ TeV/c2. This bound on q/m, obtained from Milky Way parameters, is rough and not based on any precise empirical test. However this bound is extremely strong and should motivate further work to better model the interaction of charged dark matter with ordered and disordered magnetic fields in galaxies and clusters of galaxies; to develop precise tests for the presence of charged dark matter based on better estimates of angular momentum exchange; and also to better understand how charged dark matter might modify the growth of magnetic fields, and the formation and interaction histories of galaxies, galaxy groups, and clusters.
Changes in Solar Rotation over Two Solar Cycles

We use helioseismic data from ground- and space-based instruments to analyze how solar rotation has changed since the beginning of solar Cycle 23 with emphasis on studying the differences between Cycles 23 and 24. We find that the nature of solar rotation is indeed different for the two cycles. While the changes in the latitudinally independent component follows solar-cycle indices, some of the other components have a more complicated behavior. There is a substantial change in the behavior of the solar zonal flows and their spatial gradients too. While the zonal flows in Cycle 24 are weaker in general than those in Cycle 23, there are clear signs of the emergence of Cycle 25. We have also investigated the properties of the solar tachocline, in particular, its position, width, and the change (or jump) in the rotation rate across it. We find significant temporal variation in the change of the rotation rate across the tachocline. We also find that the changes in solar Cycle 24 were very different from those of Cycle 23. We do not find any statistically significant change in the position or the width of the tachocline.
Prospects and limitations for constraining light relics with primordial abundance measurements

The light relic density affects the thermal and expansion history of the early Universe leaving a number of observable imprints. We focus on the primordial abundances of light elements produced during the process of Big Bang nucleosynthesis which are influenced by the light relic density. Primordial abundances can be used to infer the density of light relics and thereby serve as a probe of physics beyond the Standard Model. We calculate the observational uncertainty on primordial light element abundances and associated quantities that would be required in order for these measurements to achieve sensitivity to the light relic density comparable to that anticipated from upcoming cosmic microwave background surveys. We identify the nuclear reaction rates that need to be better measured to maximize the utility of future observations. We show that improved measurements of the primordial helium-4 abundance can improve constraints on light relics, while more precise measurements of the primordial deuterium abundance are unlikely to be competitive with cosmic microwave background measurements of the light relic density.
Normalized symmetric cumulants as a measure of QCD phase transition: a viscous hydrodynamic study

Finding the existence and the location of the QCD critical point is one of the main goals of the RHIC beam energy scan program. To make theoretical predictions and corroborate with the experimental data requires modeling the space-time evolution of the matter created in heavy-ion collisions by dynamical models such as the relativistic hydrodynamics with an appropriate Equation of State (EoS). In the present exploratory study, we use a viscous 2+1 dimensional event-by-event (e-by-e) hydrodynamic code at ﬁnite baryon densities with two different EoSs (i) Lattice QCD + HRG with a crossover transition and (ii) EoS with a ﬁrst-order phase transition to studying the normalized symmetric cumulants of charged pions v n ( n = 2 − 4) . We show that the normalized symmetric cumulants can differentiate the two EoSs while all other conditions remain the same. The conclusion does not change for various initial conditions and shear viscosity. This indicates that these observables can be used to gain information about the QCD EoS from experimental data and can be used as an EoS meter.
On the contamination of the global 21 cm signal from polarized foregrounds

Global (i.e. sky-averaged) 21 cm signal experiments can measure the evolution of the universe from the Cosmic Dawn to the Epoch of Reionization. These measurements are challenged by the presence of bright foreground emission that can be separated from the cosmological signal if its spectrum is smooth. This assumption fails in the case of single polarization antennas as they measure linearly polarized foreground emission - which is inevitably Faraday rotated through the interstellar medium. We investigate the impact of Galactic polarized foregrounds on the extraction of the global 21 cm signal through realistic sky and dipole simulations both in a low frequency band from 50 to 100 MHz, where a 21 cm absorption profile is expected, and in a higher frequency band (100 − 200 MHz). We find that the presence of a polarized contaminant with complex frequency structure can bias the amplitude and the shape of the reconstructed signal parameters in both bands. We investigate if polarized foregrounds can explain the unexpected 21 cm Cosmic Dawn signal recently reported by the EDGES collaboration. We find that unaccounted polarized foreground contamination can produce an enhanced and distorted 21 cm absorption trough similar to the anomalous profile reported by Bowman et al. (2018), and whose amplitude is in mild tension with the assumed input Gaussian profile (at ∼1.5σ level). Moreover, we note that, under the hypothesis of contamination from polarized foreground, the amplitude of the reconstructed EDGES signal can be overestimated by around 30%, mitigating the requirement for an explanation based on exotic physics.
Predicting Eating Events in Free Living Individuals - A Technical Report

This technical report records the experiments of applying multiple machine learning algorithms for predicting eating and food purchasing behaviors of free-living individuals. Data was collected with accelerometer, global positioning system (GPS), and body-worn cameras called SenseCam over a one week period in 81 individuals from a variety of ages and demographic backgrounds. These data were turned into minute-level features from sensors as well as engineered features that included time (e.g., time since last eating) and environmental context (e.g., distance to nearest grocery store). Algorithms include Logistic Regression, RBF-SVM, Random Forest, and Gradient Boosting. Our results show that the Gradient Boosting model has the highest mean accuracy score (0.7289) for predicting eating events before 0 to 4 minutes. For predicting food purchasing events, the RBF-SVM model (0.7395) outperforms others. For both prediction models, temporal and spatial features were important contributors to predicting eating and food purchasing events.
Families of stable bundles on the fibres of the hyperkähler twistor projection

Given a holomorphic vector bundle $E$ on the twistor space $\mathrm{Tw}(M)$ of a simple hyperk\"ahler manifold $M$, we view it as a family of bundles $\left\{E_I\right\}$ on the fibres $\pi^{-1}(I)$ of the twistor projection $\pi : \mathrm{Tw}(M) \to \mathbb{CP}^1$, and study the relationship between stability of $E$ and its fibrewise stability. We verify that the argument of Teleman establishing the Zariski openness of stability and semi-stability in families of bundles applies in the case of the family $\left\{E_I\right\}$. We prove a partial converse to a result of Kaledin and Verbitsky, showing that an irreducible bundle $E$ on $\mathrm{Tw}(M)$ is generically fibrewise stable if the rank of $E$ is 2 or 3, or at least one element of the family $\left\{E_I\right\}$ is a simple bundle, in the sense that $\mathrm{Hom}(E_I, E_I) = \mathbb{C}$.
A Hierarchical Model for Estimating Exposure-Response Curves from Multiple Studies of Household Air Pollution

Cookstove replacement trials have found mixed results on their impact on respiratory health. The limited range of concentrations and small sample sizes of individual studies are important factors that may be limiting their statistical power. We present a hierarchical approach to modeling exposure concentrations and pooling data from multiple studies in order to estimate a common exposure-response curve. The exposure concentration model accommodates temporally sparse, clustered longitudinal observations. The exposure-response curve model provides a flexible, semi-parametric estimate of the exposure-response relationship while accommodating heterogeneous clustered data. We apply this model to data from three studies of cookstoves and respiratory infections in children in Nepal, which represent three study types: crossover trial, parallel trial, and case-control study. We find evidence of increased odds of disease for particulate matter concentrations between 50 and 200 $\mu$g/m$^3$ and a flattening of the exposure-response curve for higher exposure concentrations. The model we present can incorporate additional studies and be applied to other settings.
Evaluating Empathy in Artificial Agents

The novel research area of computational empathy is in its infancy and moving towards developing methods and standards. One major problem is the lack of agreement on the evaluation of empathy in artificial interactive systems. Even though the existence of well-established methods from psychology, psychiatry and neuroscience, the translation between these methods and computational empathy is not straightforward. It requires a collective effort to develop metrics that are more suitable for interactive artificial agents. This paper is aimed as an attempt to initiate the dialogue on this important problem. We examine the evaluation methods for empathy in humans and provide suggestions for the development of better metrics to evaluate empathy in artificial agents. We acknowledge the difficulty of arriving at a single solution in a vast variety of interactive systems and propose a set of systematic approaches that can be used with a variety of applications and systems.
Raw-to-End Name Entity Recognition in Social Media

Taking word sequences as the input, typical named entity recognition (NER) models neglect errors from pre-processing (e.g., tokenization). However, these errors can influence the model performance greatly, especially for noisy texts like tweets. Here, we introduce Neural-Char-CRF, a raw-to-end framework that is more robust to pre-processing errors. It takes raw character sequences as inputs and makes end-to-end predictions. Word embedding and contextualized representation models are further tailored to capture textual signals for each character instead of each word. Our model neither requires the conversion from character sequences to word sequences, nor assumes tokenizer can correctly detect all word boundaries. Moreover, we observe our model performance remains unchanged after replacing tokenization with string matching, which demonstrates its potential to be tokenization-free. Extensive experimental results on two public datasets demonstrate the superiority of our proposed method over the state of the art. The implementations and datasets are made available at: this https URL.
Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis

Multimodal signals are more powerful than unimodal data for emotion recognition since they can represent emotions more comprehensively. In this paper, we introduce deep canonical correlation analysis (DCCA) to multimodal emotion recognition. The basic idea behind DCCA is to transform each modality separately and coordinate different modalities into a hyperspace by using specified canonical correlation analysis constraints. We evaluate the performance of DCCA on five multimodal datasets: the SEED, SEED-IV, SEED-V, DEAP, and DREAMER datasets. Our experimental results demonstrate that DCCA achieves state-of-the-art recognition accuracy rates on all five datasets: 94.58% on the SEED dataset, 87.45% on the SEED-IV dataset, 84.33% and 85.62% for two binary classification tasks and 88.51% for a four-category classification task on the DEAP dataset, 83.08% on the SEED-V dataset, and 88.99%, 90.57%, and 90.67% for three binary classification tasks on the DREAMER dataset. We also compare the noise robustness of DCCA with that of existing methods when adding various amounts of noise to the SEED-V dataset. The experimental results indicate that DCCA has greater robustness. By visualizing feature distributions with t-SNE and calculating the mutual information between different modalities before and after using DCCA, we find that the features transformed by DCCA from different modalities are more homogeneous and discriminative across emotions.
Random sequential adsorption of spheres on a cylinder

Inspired by observations of beads packed on a thin string in such systems as sea-grapes and dental plaque, we study the random sequential adsorption of spheres on a cylinder. We determine the asymptotic fractional coverage of the cylinder as a function of the sole parameter in the problem, the ratio of the sphere radius to the cylinder radius (for a very long cylinder) using a combination of analysis and numerical simulations. Examining the asymptotic structures, we find weak chiral ordering on sufficiently small spatial scales. Experiments involving colloidal microspheres that can attach irreversibly to a silica wire via electrostatic forces or DNA hybridization allow us to verify our predictions for the asymptotic coverage.
Large-Scale-Exploit of GitHub Repository Metadata and Preventive Measures

When working with Git, a popular version-control system, email addresses are part of the metadata for each individual commit. When those commits are pushed to remote hosting services like GitHub, those email addresses become visible not only to fellow developers, but also to malicious actors aiming to exploit them. 
As a part of our research we created a tool that leverages the publicly available GitHub API to collect user data. Analysis of this data not only gives access to millions of email addresses in very little time, but is also powerful and dense enough to create targeted phishing attacks posing a great threat to all GitHub users and their private, potentially sensitive data. Even worse, existing countermeasures fail to effectively protect against such exploits. 
As a consequence and main conclusion of this paper, we suggest multiple preventive measures that should be implemented as soon as possible. We also consider it the duty of both companies like GitHub and well informed software engineers to inform fellow developers about the risk of exposing private email addresses in Git commits published publicly.
Sequential Computer Experimental Design for Estimating an Extreme Probability or Quantile

A computer code can simulate a system's propagation of variation from random inputs to output measures of quality. Our aim here is to estimate a critical output tail probability or quantile without a large Monte Carlo experiment. Instead, we build a statistical surrogate for the input-output relationship with a modest number of evaluations and then sequentially add further runs, guided by a criterion to improve the estimate. We compare two criteria in the literature. Moreover, we investigate two practical questions: how to design the initial code runs and how to model the input distribution. Hence, we close the gap between the theory of sequential design and its application.
Exploring galaxy dark matter haloes across redshifts with strong quasar absorbers

Quasar lines of sight intersect intervening galaxy discs or circumgalactic environments at random impact parameters and potential well depths. Absorption line velocity widths (Δv90) are known to scale with host galaxy stellar masses, and inversely with the projected separation from the quasar line of sight. Its dependence on stellar mass can be eliminated by normalizing with the emission-line widths of the host galaxies, σem, so that absorbers with a range of Δv90 values can be compared directly. Using a sample of DLA systems at 0.2 <z < 3.2 with spectroscopically confirmed host galaxies, we find that the velocity ratio Δv90/σem decreases with projected distances from the hosts. We compare the data with expectations of line-of-sight velocity dispersions derived for different dark matter halo mass distributions, and find that models with steeper radial dark matter profiles provide a better fit to the observations, although the scatter remains large. Gas outflows from the galaxies may cause an increased scatter, or scale radii of dark matter halo models may not be representative for the galaxies. We demonstrate by computing virial velocities, that metal-rich DLAs that belong to massive galaxy haloes (Mhalo ≈ 1012 M⊙) mostly remain gravitationally bound to the haloes.
End-to-End Learning from Complex Multigraphs with Latent Graph Convolutional Networks

We study the problem of end-to-end learning from complex multigraphs with potentially very large numbers of edges between two vertices, each edge labeled with rich information. Examples of such graphs include financial transactions, communication networks, or flights between airports. We propose Latent-Graph Convolutional Networks (L-GCNs), which can successfully propagate information from these edge labels to a latent adjacency tensor, after which further propagation and downstream tasks can be performed, such as node classification. We evaluate the performance of several variations of the model on two synthetic datasets simulating fraud in financial transaction networks, to ensure that the model must make use of edge labels in order to achieve good classification performance. We find that allowing for nonlinear interactions on a per-neighbor basis enhances performance significantly, while also showing promising results in an inductive setting.
Robust One-Bit Recovery via ReLU Generative Networks: Improved Statistical Rates and Global Landscape Analysis

We study the robust one-bit compressed sensing problem whose goal is to design an algorithm that faithfully recovers any sparse target vector θ0 ∈ R uniformly from m quantized noisy measurements. Under the assumption that the measurements are sub-Gaussian, to recover any k-sparse θ0 (k d) uniformly up to an error ε with high probability, the best known computationally tractable algorithm requires1 m ≥ Õ(k log d/ε). In this paper, we consider a new framework for the onebit sensing problem where the sparsity is implicitly enforced via mapping a low dimensional representation x0 through a known n-layer ReLU generative network G : R → R. Such a framework poses low-dimensional priors on θ0 without a known basis. We propose to recover the target G(x0) via an unconstrained empirical risk minimization (ERM) problem under a much weaker sub-exponential measurement assumption. For such a problem, we establish a joint statistical and computational analysis. In particular, we prove that the ERM estimator in this new framework achieves an improved statistical rate of m = Õ(kn log d/ ) recovering any G(x0) uniformly up to an error ε. Moreover, from the lens of computation, we prove that under proper conditions on the ReLU weights, our proposed empirical risk, despite non-convexity, has no stationary point outside of small neighborhoods around the true representation x0 and its negative multiple. Furthermore, we show that the global minimizer of the empirical risk stays within the neighborhood around x0 rather than its negative multiple. Our analysis sheds some light on the possibility of inverting a deep generative model under partial and quantized measurements, complementing the recent success of using deep generative models for inverse problems.
Uplift Modeling for Multiple Treatments with Cost Optimization

Uplift modeling is an emerging machine learning approach for estimating the treatment effect at an individual or subgroup level. It can be used for optimizing the performance of interventions such as marketing campaigns and product designs. Uplift modeling can be used to estimate which users are likely to benefit from a treatment and then prioritize delivering or promoting the preferred experience to those users. An important but so far neglected use case for uplift modeling is an experiment with multiple treatment groups that have different costs, such as for example when different communication channels and promotion types are tested simultaneously. In this paper, we extend standard uplift models to support multiple treatment groups with different costs. We evaluate the performance of the proposed models using both synthetic and real data. We also describe a production implementation of the approach.