Miracle-dz/UniFAR-FTUs huggingface dataset:
Monte Carlo calculations for simulating electron scattering in gas phase

The measurement method X-ray photoemission spectroscopy (XPS) is often used to determine the chemical state (such as oxidation state and chemical bonding environment) of solid surfaces. During a measurement, ionizing X-ray radiation is projected onto a sample, causing the electrons to be emitted from the sample's surface. The emitted electrons have a distribution of kinetic energies. By analyzing the kinetic energy distribution–using devices like hemispherical analyzers–one can determine the chemical composition and oxidation state of the sample's surface atoms. Due to the need to preserve the kinetic energy distribution of the emitted electrons, such measurements are typically performed with the sample housed in a vacuum environment (pressure ca. 5E-9 mbar); otherwise atmospheric gas molecules would inelastically scatter the emitted electrons, changing their kinetic energy distribution.
Contrastive Learning for Recommender System

Recommender systems, which analyze users' preference patterns to suggest potential targets, are indispensable in today's society. Collaborative Filtering (CF) is the most popular recommendation model. Specifically, Graph Neural Network (GNN) has become a new state-of-the-art for CF. In the GNN-based recommender system, message dropout is usually used to alleviate the selection bias in the user-item bipartite graph. However, message dropout might deteriorate the recommender system's performance due to the randomness of dropping out the outgoing messages based on the user-item bipartite graph. To solve this problem, we propose a graph contrastive learning module for a general recommender system that learns the embeddings in a self-supervised manner and reduces the randomness of message dropout. Besides, many recommender systems optimize models with pairwise ranking objectives, such as the Bayesian Pairwise Ranking (BPR) based on a negative sampling strategy. However, BPR has the following problems: suboptimal sampling and sample bias. We introduce a new debiased contrastive loss to solve these problems, which provides sufficient negative samples and applies a bias correction probability to alleviate the sample bias. We integrate the proposed framework, including graph contrastive module and debiased contrastive module with several Matrix Factorization(MF) and GNN-based recommendation models. Experimental results on three public benchmarks demonstrate the effectiveness of our framework.
pyUPMASK: an improved unsupervised clustering algorithm

Aims. We present pyUPMASK, an unsupervised clustering method for stellar clusters that builds upon the original UPMASK package. Its general approach makes it plausible to be applied to analyses that deal with binary classes of any kind, as long as the fundamental hypotheses are met. The code is written entirely in Python and is made available through a public repository. Methods.The core of the algorithm follows the method developed in UPMASK but introducing several key enhancements. These enhancements not only make pyUPMASK more general, they also improve its performance considerably. Results. We thoroughly tested the performance of pyUPMASK on 600 synthetic clusters, affected by varying degrees of contamination by field stars. To assess the performance we employed six different statistical metrics that measure the accuracy of probabilistic classification. Conclusions. Our results show that pyUPMASK is better performant than UPMASK for every statistical performance metric, while still managing to be many times faster.
Rapid and robust parameter inference for binary mergers

The detection rate for compact binary mergers has grown as the sensitivity of the global network of ground based gravitational wave detectors has improved, now reaching the stage where robust automation of the analyses is essential. Automated low-latency algorithms have been developed that send out alerts when candidate signals are detected. The alerts include sky maps to facilitate electromagnetic follow up observations, along with probabilities that the system might contain a neutron star, and hence be more likely to generate an electromagnetic counterpart. Data quality issues, such as loud noise transients (glitches), can adversely affect the low-latency algorithms, causing false alarms and throwing off parameter estimation. Here a new analysis method is presented that is robust against glitches, and capable of producing fully Bayesian parameter inference, including sky maps and mass estimates, in a matter of minutes. Key elements of the method are wavelet-based de-noising, penalized maximization of the likelihood during the initial search, rapid sky localization using pre-computed inner products, and heterodyned likelihoods for full Bayesian inference.
Test of the superdiffusion model in the interstellar medium around the Geminga pulsar

The TeV $\gamma$-ray halo around the Geminga pulsar is an important indicator of cosmic-ray (CR) propagation in the local zone of the Galaxy as it reveals the spatial distribution of the electrons and positrons escaping from the pulsar. Considering the intricate magnetic field in the interstellar medium (ISM), it is proposed that superdiffusion model could be more realistic to describe the CR propagation than the commonly used normal diffusion model. In this work, we test the superdiffusion model in the ISM around the Geminga pulsar by fitting to the surface brightness profile of the Geminga halo measured by HAWC. Our results show that the chi-square statistic monotonously increases as $\alpha$ decreases from 2 to 1, where $\alpha$ is the characteristic index of superdiffusion describing the degree of fractality of the ISM and $\alpha=2$ corresponds to the normal diffusion model. We find that model with $\alpha<1.32$ (or $<1.4$, depending on the data used in fit) is disfavored at 95\% confidence level. Superdiffusion model with $\alpha$ close to 2 can well explain the morphology of the Geminga halo, while it predicts much higher positron flux on the Earth than the normal diffusion model. This has important implication for the interpretation of the CR positron excess.
Convergence and finite sample approximations of entropic regularized Wasserstein distances in Gaussian and RKHS settings

This work studies the convergence and finite sample approximations of entropic regularized Wasserstein distances in the Hilbert space setting. Our first main result is that for Gaussian measures on an infinite-dimensional Hilbert space, convergence in the 2-Sinkhorn divergence is strictly weaker than convergence in the exact 2-Wasserstein distance. Specifically, a sequence of centered Gaussian measures converges in the 2-Sinkhorn divergence if the corresponding covariance operators converge in the Hilbert-Schmidt norm. This is in contrast to the previous known result that a sequence of centered Gaussian measures converges in the exact 2-Wasserstein distance if and only if the covariance operators converge in the trace class norm. In the reproducing kernel Hilbert space (RKHS) setting, the kernel Gaussian-Sinkhorn divergence, which is the Sinkhorn divergence between Gaussian measures defined on an RKHS, defines a semi-metric on the set of Borel probability measures on a Polish space, given a characteristic kernel on that space. With the Hilbert-Schmidt norm convergence, we obtain dimension-independent convergence rates for finite sample approximations of the kernel Gaussian-Sinkhorn divergence, with the same order as the Maximum Mean Discrepancy. These convergence rates apply in particular to Sinkhorn divergence between Gaussian measures on Euclidean and infinite-dimensional Hilbert spaces. The sample complexity for the 2-Wasserstein distance between Gaussian measures on Euclidean space, while dimension-dependent and larger than that of the Sinkhorn divergence, is exponentially faster than the worst case scenario in the literature. RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 1030027, JAPAN E-mail: minh.haquang@riken.jp ar X iv :2 10 1. 01 42 9v 1 [ st at . M L ] 5 J an 2 02 1
GeCo: Quality Counterfactual Explanations in Real Time

Machine learning is increasingly applied in high-stakes decision making that directly affect people's lives, and this leads to an increased demand for systems to explain their decisions. Explanations often take the form of counterfactuals, which consists of conveying to the end user what she/he needs to change in order to improve the outcome. Computing counterfactual explanations is challenging, because of the inherent tension between a rich semantics of the domain, and the need for real time response. In this paper we present GeCo, the first system that can compute plausible and feasible counterfactual explanations in real time. At its core, GeCo relies on a genetic algorithm, which is customized to favor searching counterfactual explanations with the smallest number of changes. To achieve real-time performance, we introduce two novel optimizations: $\Delta$-representation of candidate counterfactuals, and partial evaluation of the classifier. We compare empirically GeCo against five other systems described in the literature, and show that it is the only system that can achieve both high quality explanations and real time answers.
Generalized RNN beamformer for target speech separation

Recently we proposed an all-deep-learning minimum variance distortionless response (ADL-MVDR) method where the unstable matrix inverse and principal component analysis (PCA) operations in the MVDR were replaced by recurrent neural networks (RNNs). However, it is not clear whether the success of the ADL-MVDR is owed to the calculated covariance matrices or following the MVDR formula. In this work, we demonstrate the importance of the calculated covariance matrices and propose three types of generalized RNN beamformers (GRNN-BFs) where the beamforming solution is beyond the MVDR and optimal. The GRNN-BFs could predict the frame-wise beamforming weights by leveraging on the temporal modeling capability of RNNs. The proposed GRNN-BF method obtains better performance than the state-of-the-art ADL-MVDR and the traditional mask-based MVDR methods in terms of speech quality (PESQ), speech-to-noise ratio (SNR), and word error rate (WER).
Bose-Einstein Condensation of pions in Proton-Proton collisions at the Large Hadron Collider

The possibility of formation of Bose-Einstein Condensation (BEC) is studied in pp collisions at √ s = 7 TeV at the Large Hadron Collider. A thermodynamically consistent form of Tsallis non-extensive distribution is used to estimate the transition temperature required to form BEC of pions, which are the most abundant species in a multi-particle production process in hadronic and nuclear collisions. The obtained results have been contrasted with the systems produced in Pb-Pb collisions to have a better picture. We observe an explicit dependency of BEC on the non-extensive parameter q, which is a measure of degree of non-equilibrium – as q decreases, the transition temperature increases and approaches to the transition temperature obtained from Bose-Einstein statistics without nonextensivity. Studies are performed on the final state multiplicity dependence of number of particles in the pion condensates in a wide range of multiplicity covering hadronic and heavy-ion collisions, using the inputs from experimental transverse momentum spectra.
Distinct high-energy cutoff variation patterns in two Seyfert galaxies

Investigating how the cutoff energy Ecut varies with X-ray flux and photon index Γ in individual AGNs opens a new window to probe the yet unclear coronal physics. So far Ecut variations have only been detected in several AGNs but different patterns have been reported. Here we report new detections of Ecut variations in two Seyfert galaxies with multiple NuSTAR exposures. While in NGC 3227 Ecut monotonically increases with Γ, the Ecut–Γ relation exhibits a Λ shape in SWIFT J2127.4+5654 (Ecut increasing with Γ at Γ . 2.05, but reversely decreasing at Γ & 2.05), indicating more than a single underlying mechanism is involved. Meanwhile both galaxies show softer spectra while they brighten in X-ray, a common phenomenon in Seyfert galaxies. Plotting all 7 AGNs with Ecut variations ever reported with NuSTAR observations in the Ecut–Γ diagram, we find they could be unified with the Λ pattern. Although the sample is small and SWIFT J2127.4+5654 is the only source with Γ varying across the break point thus the only one exhibiting the complete Λ pattern in a single source, the discoveries shed new light on the coronal physics in AGNs. Possible underlying physical mechanisms are discussed.
Measurements of the 
E
-mode polarization and temperature-
E
-mode correlation of the CMB from SPT-3G 2018 data

We present measurements of the $E$-mode ($EE$) polarization power spectrum and temperature-$E$-mode ($TE$) cross-power spectrum of the cosmic microwave background using data collected by SPT-3G, the latest instrument installed on the South Pole Telescope. This analysis uses observations of a 1500 deg$^2$ region at 95, 150, and 220 GHz taken over a four month period in 2018. We report binned values of the $EE$ and $TE$ power spectra over the angular multipole range $300 \le \ell<3000$, using the multifrequency data to construct six semi-independent estimates of each power spectrum and their minimum-variance combination. These measurements improve upon the previous results of SPTpol across the multipole ranges $300 \le \ell \le 1400$ for $EE$ and $300 \le \ell \le 1700$ for $TE$, resulting in constraints on cosmological parameters comparable to those from other current leading ground-based experiments. We find that the SPT-3G dataset is well-fit by a $\Lambda$CDM cosmological model with parameter constraints consistent with those from Planck and SPTpol data. From SPT-3G data alone, we find $H_0 = 68.8 \pm 1.5 \mathrm{km\,s^{-1}\,Mpc^{-1}}$ and $\sigma_8 = 0.789 \pm 0.016$, with a gravitational lensing amplitude consistent with the $\Lambda$CDM prediction ($A_L = 0.98 \pm 0.12$). We combine the SPT-3G and the Planck datasets and obtain joint constraints on the $\Lambda$CDM model. The volume of the 68% confidence region in six-dimensional $\Lambda$CDM parameter space is reduced by a factor of 1.5 compared to Planck-only constraints, with only slight shifts in central values. We note that the results presented here are obtained from data collected during just half of a typical observing season with only part of the focal plane operable, and that the active detector count has since nearly doubled for observations made with SPT-3G after 2018.
To do or not to do: cost-sensitive causal decision-making

Causal classification models are adopted across a variety of operational business processes to predict the effect of a treatment on a categorical business outcome of interest depending on the process instance characteristics. This allows optimizing operational decision-making and selecting the optimal treatment to apply in each specific instance, with the aim of maximizing the positive outcome rate. While various powerful approaches have been presented in the literature for learning causal classification models, no formal framework has been elaborated for optimal decision-making based on the estimated individual treatment effects, given the cost of the various treatments and the benefit of the potential outcomes. In this article, we therefore extend upon the expected value framework and formally introduce a cost-sensitive decision boundary for double binary causal classification, which is a linear function of the estimated individual treatment effect, the positive outcome probability and the cost and benefit parameters of the problem setting. The boundary allows causally classifying instances in the positive and negative treatment class to maximize the expected causal profit, which is introduced as the objective at hand in cost-sensitive causal classification. We introduce the expected causal profit ranker which ranks instances for maximizing the expected causal profit at each possible threshold for causally classifying instances and differs from the conventional ranking approach based on the individual treatment effect. The proposed ranking approach is experimentally evaluated on synthetic and marketing campaign data sets. The results indicate that the presented ranking method effectively outperforms the cost-insensitive ranking approach and allows boosting profitability.
Gap evolution in nanographene assemblies

Recently cycloarene has been experimentally obtained in a self-assembled structure, forming graphene-like monoatomic layered systems. Here, we establish the bandgap engineering/prediction in cycloarene assemblies within a combination of density functional theory and tight-binding Hamiltonians. Our results show a weak dependence of the gap with the assembly geometry, contrasting a strong dependence with the inter-molecule bond density. We derived a effective model that allows the interpretation of the arising energy gap for general particle-hole symmetric molecular arranges based on inter-molecular bond strength.
Forward Modeling Helioseismic Signatures of One- and Two-cell Meridional Circulation

Using a 3D global solver of the linearized Euler equations, we model acoustic oscillations over background velocity flow fields of single-cell meridional circulation with deep and shallow return flows as well as double-cell meridional circulation with strong and weak reversals. The velocities are generated using a mean-field hydrodynamic and dynamo model—moving through the regimes with minimal parameter changes, counterrotation near the base of the tachocline is induced by sign inversion of the nondiffusive action of turbulent Reynolds stresses (Λ-effect) due to the radial inhomogeneity of the Coriolis number. By mimicking the stochastic excitation of resonant modes in the convective interior, we simulate realization noise present in solar observations. Using deep-focusing to analyze differences in travel-time signatures between the four regimes, as well as comparing to solar observations, we show that current helioseismology techniques may offer important insights about the location and strength of the return flow; however, it may not currently be possible to definitively distinguish between profiles of single-cell or double-cell meridional circulation.
Lyman-α emission from a WISE-selected optically faint powerful radio galaxy M151304.72-252439.7 at z = 3.132

We report the detection of a large (∼90 kpc) and luminous Lyα nebula [LLyα = (6.80 ± 0.08) × 1044$\rm {\, erg\, s^{-1}}$] around an optically faint (r > 23 mag) radio galaxy M1513-2524 at zem = 3.132. The double-lobed radio emission has an extent of 184 kpc, but the radio core, i.e. emission associated with the active galactic nucleus (AGN) itself, is barely detected. This object was found as part of our survey to identify high-z quasars based on Wide-field Infrared Survey Explorer (WISE) colours. The optical spectrum has revealed Lyα, N v, C iv, and He ii emission lines with a very weak continuum. Based on long-slit spectroscopy and narrow band imaging centred on the Lyα emission, we identify two spatial components: a ‘compact component’ with high velocity dispersion (∼1500 km s−1) seen in all three lines, and an ‘extended component’, having low velocity dispersion (i.e. 700–1000 km s−1). The emission line ratios are consistent with the compact component being in photoionization equilibrium with an AGN. We also detect spatially extended associated Lyα absorption, which is blue-shifted within 250–400 km s−1 of the Lyα peak. The probability of Lyα absorption detection in such large radio sources is found to be low (∼10 per cent) in the literature. M1513-2524 belongs to the top few percent of the population in terms of Lyα and radio luminosities. Deep integral field spectroscopy is essential for probing this interesting source and its surroundings in more detail.
Recent Trends in Food Intake Monitoring using Wearable Sensors

Obesity and being over-weight add to the risk of some major life threatening diseases. According to W.H. O. , a considerable population suffers from these disease whereas poor nutrition plays an important role in this context. Traditional food activity monitoring systems like Food Diaries allow manual record keeping of eating activities over time, and conduct nutrition analysis. However, these systems are prone to the problems of manual record keeping and biased-reporting. Therefore, recently, the research community has focused on designing automatic food monitoring systems since the last decade which consist of one or multiple wearable sensors. These systems aim at providing different macro and micro activity detections like chewing, swallowing, eating episodes, and food types as well as estimations like food mass and eating duration. Researchers have emphasized on high detection accuracy, low estimation errors, un-intrusive nature, low cost and real life implementation while designing these systems, however a comprehensive automatic food monitoring system has yet not been developed. Moreover, according to the best of our knowledge, there is no comprehensive survey in this field that delineates the automatic food monitoring paradigm, covers a handful number of research studies, analyses these studies against food intake monitoring tasks using various parameters, enlists the limitations and sets up future directions. In this research work, we delineate the automatic food intake monitoring paradigm and present a survey of research studies. With special focus on studies with wearable sensors, we analyze these studies against food activity monitoring tasks. We provide brief comparison of these studies along with shortcomings based upon experimentation results conducted under these studies. We setup future directions at the end to facilitate the researchers working in this domain.
Double Dirac nodal line semimetal with a torus surface state

We propose a class of nodal line semimetals that host an eightfold-degenerate double Dirac nodal line (DDNL) with negligible spin-orbit coupling. We find only 5 of the 230 space groups host the DDNL. The DDNL can be considered as a combination of two Dirac nodal lines, and has a trivial Berry phase. This leads to two possible but completely different surface states, namely, a torus surface state covering the whole surface Brillouin zone and no surface state at all. Based on first-principles calculations, we predict that the hydrogen storage material LiBH is an ideal DDNL semimetal, where the line resides at Fermi level, is relatively flat in energy, and exhibits a large linear energy range. Interestingly, both the two novel surface states of DDNL can be realized in LiBH. Further, we predict that with a magnetic field parallel to DDNL, the Landau levels of DDNL are doubly degenerate due to a Kramers-like degeneracy and have a doubly degenerate zero mode.
CASS: Towards Building a Social-Support Chatbot for Online Health Community

Chatbots systems, despite their popularity in today's HCI and CSCW research, fall short for one of the two reasons: 1) many of the systems use a rule-based dialog flow, thus they can only respond to a limited number of pre-defined inputs with pre-scripted responses; or 2) they are designed with a focus on single-user scenarios, thus it is unclear how these systems may affect other users or the community. In this paper, we develop a generalizable chatbot architecture (CASS) to provide social support for community members in an online health community. The CASS architecture is based on advanced neural network algorithms, thus it can handle new inputs from users and generate a variety of responses to them. CASS is also generalizable as it can be easily migrate to other online communities. With a follow-up field experiment, CASS is proven useful in supporting individual members who seek emotional support. Our work also contributes to fill the research gap on how a chatbot may influence the whole community's engagement.