Miracle-dz/UniFAR-FTUs huggingface dataset:
THE ULTIMATE LIGHT CURVE OF SN 1998bw/GRB 980425

We present multicolor light curves of SN 1998bw which appeared in ESO184-G82 in close temporal and spatial association with GRB 980425. The light curves are based on observations conducted at Cerro Tololo Inter-American Observatory (CTIO) and data from the literature. The CTIO photometry reaches ∼86 days after the gamma-ray burst (GRB) in U and ∼160 days after the GRB in BV(RI)C. The observations in U extend the previously known coverage by about 30 days and determine the slope of the early exponential tail. We calibrate a large set of local standards in common with those of previous studies and use them to transform published observations of the supernova (SN) to our realization of the standard photometric system. We show that the photometry from different sources merges smoothly and we provide a unified set of 300 observations of the SN in five bands. Using the extensive set of spectra in the public domain, we compute extinction and K-corrections and build quasi-bolometric unreddened rest-frame light curves. We provide low-degree piecewise spline fits to these light curves with daily sampling. They reach ∼86 rest-frame days after the GRB with U-band coverage and ∼498 rest-frame days after the GRB without U.
MOSNet: Deep Learning based Objective Assessment for Voice Conversion

Existing objective evaluation metrics for voice conversion (VC) are not always correlated with human perception. Therefore, training VC models with such criteria may not effectively improve naturalness and similarity of converted speech. In this paper, we propose deep learning-based assessment models to predict human ratings of converted speech. We adopt the convolutional and recurrent neural network models to build a mean opinion score (MOS) predictor, termed as MOSNet. The proposed models are tested on large-scale listening test results of the Voice Conversion Challenge (VCC) 2018. Experimental results show that the predicted scores of the proposed MOSNet are highly correlated with human MOS ratings at the system level while being fairly correlated with human MOS ratings at the utterance level. Meanwhile, we have modified MOSNet to predict the similarity scores, and the preliminary results show that the predicted scores are also fairly correlated with human ratings. These results confirm that the proposed models could be used as a computational evaluator to measure the MOS of VC systems to reduce the need for expensive human rating.
Risk factors for suicidal ideation in a chronic illness

Abstract Suicide is an urgent concern for people with chronic illnesses, particularly for those with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Prior work has examined suicide risk in this illness, but few studies have examined specific risk factors. This study uses binary logistic regression to identify physical, social, and demographic risk and protective factors for suicidal ideation in ME/CFS (N = 559). The results indicate sleep-related symptoms, stigma, disability status, physical functioning, and marital status as risk factors for suicidal ideation in this group. These findings highlight the importance of education, de-stigmatization, and the search for effective disease-modifying treatments for ME/CFS.
Ketone Body Infusion abrogates Growth Hormone Induced Lipolysis and Insulin Resistance.

BACKGROUND Exogenous ketone body administration lowers circulating glucose levels but the underlying mechanisms are uncertain. We tested the hypothesis that administration of the ketone body β-hydroxybutyrate (βOHB) acutely increases insulin sensitivity via feedback suppression of circulating free fatty acid (FFA) levels. DESIGN In a randomized, single-blinded crossover design, eight healthy men were studied twice with a GH infusion to induce lipolysis in combination with infusion of either βOHB or saline. Each study day comprised a basal period and a hyperinsulinemic-euglycemic clamp combined with a glucose tracer and adipose tissue and skeletal muscle biopsies. RESULTS βOHB administration profoundly suppressed FFA levels concomitantly with a significant increase in glucose disposal and energy expenditure. This was accompanied by a many-fold increase in skeletal muscle content of both βOHB and its derivative acetoacetate. CONCLUSION Our data unravel an insulin sensitizing effect of βOHB, which we suggest is mediated by concomitant suppression of lipolysis.
Segmentation of Vestibular Schwannomas on Postoperative Gadolinium-Enhanced T1-Weighted and Noncontrast T2-Weighted Magnetic Resonance Imaging Using Deep Learning

Objective Surveillance of postoperative vestibular schwannomas currently relies on manual segmentation and measurement of the tumor by content experts, which is both labor intensive and time consuming. We aimed to develop and validate deep learning models for automatic segmentation of postoperative vestibular schwannomas on gadolinium-enhanced T1-weighted magnetic resonance imaging (GdT1WI) and noncontrast high-resolution T2-weighted magnetic resonance imaging (HRT2WI). Study Design A supervised machine learning approach using a U-Net model was applied to segment magnetic resonance imaging images into pixels representing vestibular schwannoma and background pixels. Setting Tertiary care hospital. Patients Our retrospective data set consisted of 122 GdT1WI and 122 HRT2WI studies in 82 postoperative adult patients with a vestibular schwannoma treated with subtotal surgical resection between September 1, 2007, and April 17, 2018. Forty-nine percent of our cohort was female, the mean age at the time of surgery was 49.8 years, and the median time from surgery to follow-up scan was 2.26 years. Intervention(s) N/A. Main Outcome Measure(s) Tumor areas were manually segmented in axial images and used as ground truth for training and evaluation of the model. We measured the Dice score of the predicted segmentation results in comparison to manual segmentations from experts to assess the model's accuracy. Results The GdT1WI model achieved a Dice score of 0.89, and the HRT2WI model achieved a Dice score of 0.85. Conclusion We demonstrated that postoperative vestibular schwannomas can be accurately segmented on GdT1WI and HRT2WI without human intervention using deep learning. This artificial intelligence technology has the potential to improve the postoperative surveillance and management of patients with vestibular schwannomas.
Dipole modes with depressed amplitudes in red giants are mixed modes

Seismic observations have shown that a number of evolved stars exhibit low-amplitude dipole modes, which are referred to as depressed modes. Recently, these low amplitudes have been attributed to the presence of a strong magnetic field in the stellar core of those stars. We intend to study the properties of depressed modes in evolved stars, which is a necessary condition before concluding on the physical nature of the mechanism responsible for the reduction of the dipole mode amplitudes. We perform a thorough characterization of the global seismic parameters of depressed dipole modes and show that these modes have a mixed character. The observation of stars showing dipole mixed modes that are depressed is especially useful for deriving model-independent conclusions on the dipole mode damping. Observations prove that depressed dipole modes in red giants are not pure pressure modes but mixed modes. This result invalidates the hypothesis that the depressed dipole modes result from the suppression of the oscillation in the radiative core of the stars. Observations also show that, except for the visibility, the seismic properties of the stars with depressed modes are equivalent to those of normal stars. The mixed nature of the depressed modes in red giants and their unperturbed global seismic parameters carry strong constraints on the physical mechanism responsible for the damping of the oscillation in the core. This mechanism is able to damp the oscillation in the core but cannot fully suppress it. Moreover, it cannot modify the radiative cavity probed by the gravity component of the mixed modes. The recent mechanism involving high magnetic field proposed for explaining depressed modes is not compliant with the observations and cannot be used to infer the strength and the prevalence of high magnetic fields in red giants.
Cosmology with clustering anisotropies: disentangling dynamic and geometric distortions in galaxy redshift surveys

We investigate the impact of different observational effects affecting a precise and accurate measurement of the growth rate of fluctuations from the anisotropy of clustering in galaxy redshift surveys. We focus here on redshift measurement errors, on the reconstruction of the underlying real-space clustering and, most importantly, on the apparent degeneracy existing with the geometrical distortions induced by the cosmology-dependent conversion of redshifts into distances. We use a suite of mock catalogues extracted from large N-body simulations, focusing on the analysis of intermediate, mildly non-linear scales (r < 50 h−1 Mpc) and apply the standard ‘dispersion model’ to fit the anisotropy of the observed correlation function ξ(r⊥, r∥) . We first verify that redshift errors up to δz ∼ 0.2 per cent (i.e. σz ∼ 0.002 at z = 1) have a negligible impact on the precision with which the specific growth rate β can be measured. Larger redshift errors introduce a positive systematic error, which can be alleviated by adopting a Gaussian distribution function of pairwise velocities. This is, in any case, smaller than the systematic error of up to 10 per cent due to the limitations of the dispersion model, which is studied in a separate paper. We then show that 50 per cent of the statistical error budget on β depends on the deprojection procedure through which the real-space correlation function, needed for the modelling process, is obtained. Finally, we demonstrate that the degeneracy with geometric distortions can in fact be circumvented. This is obtained through a modified version of the Alcock–Paczynski test in redshift space, which successfully recovers the correct cosmology by searching for the solution that optimizes the description of dynamical redshift distortions. For a flat cosmology, we obtain largely independent, robust constraints on β and on the mass density parameter, ΩM. In a volume of 2.4 (h−1 Gpc)3, the correct ΩM is obtained with ∼12 per cent error and negligible bias, once the real-space correlation function is properly reconstructed.
Differential deposition correction of segmented glass x-ray optics

One of the challenges faced within the astronomical X-ray community is how to produce lightweight high angular resolution optics for a future X-ray mission capable of probing the early X-ray universe. To this end, the differential deposition project at NASA Marshall Space flight Center (MSFC) is looking to improve current X-ray optic technology by applying a corrective coating with a goal of achieving arc-second-level resolution. This paper will focus on the correction of segmented glass optics fabricated at NASA Goddard Space Flight Center (GSFC) and the paper will highlight: the design of the vacuum chamber and internal mechanics; the algorithm used to perform the correction; metrology of the glass segments; and the improvement post correction that has been achieved to date.
Molecular insights on the crystalline cellulose-water interfaces via three-dimensional atomic force microscopy

Cellulose, a renewable structural biopolymer, is ubiquitous in nature and is the basic reinforcement component of the natural hierarchical structures of living plants, bacteria, and tunicates. However, a detailed picture of the crystalline cellulose surface at the molecular level is still unavailable. Here, using atomic force microscopy (AFM) and molecular dynamics (MD) simulations, we revealed the molecular details of the cellulose chain arrangements on the surfaces of individual cellulose nanocrystals (CNCs) in water. Furthermore, we visualized the three-dimensional (3D) local arrangement of water molecules near the CNC surface using 3D AFM. AFM experiments and MD simulations showed anisotropic water structuring, as determined by the surface topologies and exposed chemical moieties. These findings provide important insights into our understanding of the interfacial interactions between CNCs and water at the molecular level. This may allow the establishment of the structure-property relationship of CNCs extracted from various biomass sources.
Prioritizing Small Sets of Molecules for Synthesis through in‐silico Tools: A Comparison of Common Ranking Methods

Prioritizing molecules for synthesis is a key role of computational methods within medicinal chemistry. Multiple tools exist for ranking molecules, from the cheap and popular molecular docking methods to more computationally expensive molecular‐dynamics (MD)‐based methods. It is often questioned whether the accuracy of the more rigorous methods justifies the higher computational cost and associated calculation time. Here, we compared the performance on ranking the binding of small molecules for seven scoring functions from five docking programs, one end‐point method (MM/GBSA), and two MD‐based free energy methods (PMX, FEP+). We investigated 16 pharmaceutically relevant targets with a total of 423 known binders. The performance of docking methods for ligand ranking was strongly system dependent. We observed that MD‐based methods predominantly outperformed docking algorithms and MM/GBSA calculations. Based on our results, we recommend the application of MD‐based free energy methods for prioritization of molecules for synthesis in lead optimization, whenever feasible.
Search for Standard Model Higgs boson in the two-photon final state in ATLAS

We report on the search for the Standard Model Higgs boson decaying into two photons based on proton-proton collision data with a center-of-mass energy of 7 TeV recorded by the ATLAS experiment at the LHC. The dataset has an integrated luminosity of about 1:08 fb 1 . The expected cross section exclusion at 95% confidence level varies between 2:0 and 5:8 times the Standard Model cross section over the diphoton mass range 110 150 GeV. The maximum deviations from the background-only expectation are consistent with statistical fluctuations.
A new extension of the beta generator of distributions

Abstract In this paper, we introduced a new extension of the beta generator of distributions. Some important properties of the model are discussed, such as the series representation, quantile function, moments, Rényi entropy, and order statistics. Three special members are discussed, namely, the new extended beta exponential (NEBE), new extended beta uniform (NEBU), and new extended beta half logistic (NEBH). The model parameters estimation was achieved by the method of maximum likelihood and the Bayes estimation under square error loss function (SEL); the estimators were examined by simulation studies and it was a very good result as discussed by the bias and mean square error (MSE) of the estimators. Two real data applications are provided to illustrate the importance of the new model in practice.
Recursive formulas for the overlaps between Bethe states and product states in XXZ Heisenberg chains

We consider the problem of computing the overlaps between the Bethe states of the XXZ spin-1/2 chain and generic states. We derive recursive formulas for the overlaps between some simple product states and off-shell Bethe states within the framework of the algebraic Bethe ansatz. These recursive formulas can be used to prove in a simple and straightforward way the recently obtained results for the overlaps of the Bethe states with the Néel state, the dimer state, and the q-deformed dimer state. However, these recursive formulas are derived for a broader class of states and represent a concrete starting point for the computation of rather general overlaps. Our approach can be easily extended to other one-dimensional Bethe ansatz integrable models.
The Upside of Negative: Social Distance in Online Reviews of Identity-Relevant Brands

Conventional wisdom in marketing emphasizes the detrimental effects of negative online reviews for brands. An important question is whether some firms could more effectively manage negative reviews to increase brand preference and improve outcomes. To address the question, this research examines how customers respond to online reviews of identity-relevant brands in particular, which have been overlooked in the online reviews literature. Eight studies (field data and experiments featuring consequential and hypothetical behaviors) show that negative online reviews may not be so detrimental for identity-relevant brands, especially when those reviews originate from socially distant (vs. socially close) reviewers. This occurs because a negative review of an identity-relevant brand can pose a threat to a customer's identity, prompting the customer to strengthen their relationship with the identity-relevant brand. To document the underlying process, the authors show that this effect does not emerge when the review is positive or the brand is identity-irrelevant. Importantly, the authors identify circumstances when negative reviews can actually produce positive outcomes (higher preference) for identity-relevant brands over no reviews or even positive reviews. By demonstrating the upside of negative reviews for identity-relevant brands, the findings have important implications for marketing theory and practice.
Effective Neural Team Formation via Negative Samples

Forming teams of experts who collectively hold a set of required skills and can successfully cooperate is challenging due to the vast pool of feasible candidates with diverse backgrounds, skills, and personalities. Neural models have been proposed to address scalability while maintaining efficacy by learning the distributions of experts and skills from successful teams in the past in order to recommend future teams. However, such models are prone to overfitting when training data suffers from a long-tailed distribution, i.e., few experts have most of the successful collaborations, and the majority has participated sparingly. In this paper, we present an optimization objective that leverages both successful and virtually unsuccessful teams to overcome the long-tailed distribution problem. We propose three negative sampling heuristics that can be seamlessly employed during the training of neural models. We study the synergistic effects of negative samples on the performance of neural models compared to lack thereof on two large-scale benchmark datasets of computer science publications and movies, respectively. Our experiments show that neural models that take unsuccessful teams (negative samples) into account are more efficient and effective in training and inference, respectively.
Can We Have Both Fish and Bear's Paw?: Improving Performance, Reliability, and both of them for Relation Extraction under Label Shift

Neural Relation Extraction (RE) models need large amounts of labeled data for effective training, which mainly comes from automatically labeling by Distant Supervision (DS). Though fast and easy, the label shift problem inevitably happens, i.e., the label distribution of DS-generated training set is quite different from that of the real world (i.e. test set). According to our observations, label shift not only leads to performance diminishment, but also hinders the reliability of DS-RE models by causing bad confidence estimation. In this paper, we make contributions by answering the following three questions: 1) How to improve performance of DS-RE models under label shift? 2) How to make sure their reliability under label shift? 3) How to improve both performance and reliability for DS-RE models under label shift? To the best of our knowledge, this is the first paper to study the performance as well as reliability of DS-RE models under label shift. Experiment results show significant improvements on two real-world datasets and six popular neural RE models, making a step further towards high-performance and reliable RE system under real-world label-shift conditions.
Ledgit: A Service to Diagnose Illicit Addresses on Blockchain using Multi-modal Unsupervised Learning

Distributed ledger technology benefits society by enabling an ecosystem of decentralised finance. However the pseudo-anonymised nature of transactions has also been an enabler of new routes for illicit activities ranging from individual scams to organised crimes. Current solutions for identifying addresses involved in illicit activities (illicit addresses) rely on commercial intelligence services, which are costly due to the intensive investigative efforts required. We propose Ledgit, an automatic real-time service for diagnosing illicit addresses on the Bitcoin blockchain. Ledgit is based solely on publicly available data, and uses an unsupervised clustering method that combines information from textual reports and the blockchain graph to assign a risk score that a Bitcoin address is involved in illicit activities. We verify the system with labeled addresses, showing high performance in identifying illicit addresses. Finally, we provide an intuitive user interface that provides accessible risk assessment with graph and report analytics.
NEST: Simulating Pandemic-like Events for Collaborative Filtering by Modeling User Needs Evolution

We outline a simulation-based study of the effect rapid population-scale concept drifts have on Collaborative Filtering (CF) models. We create a framework for analyzing the effects of macro-trends in population dynamics on the behavior of such models. Our framework characterizes population-scale concept drifts in item preferences and provides a lens to understand the influence events, such as a pandemic, have on CF models. Our experimental results show the initial impact on CF performance at the initial stage of such events, followed by an aggravated population herding effect during the event. The herding introduces a popularity bias that may benefit affected users, but which comes at the expense of a normal user experience. We propose an adaptive ensemble method that can effectively apply optimal algorithms to cope with the change brought about by different stages of the event.
Learning Pyramid-Context Encoder Network for High-Quality Image Inpainting

High-quality image inpainting requires filling missing regions in a damaged image with plausible content. Existing works either fill the regions by copying high-resolution patches or generating semantically-coherent patches from region context, while neglecting the fact that both visual and semantic plausibility are highly-demanded. In this paper, we propose a Pyramid-context Encoder Network (denoted as PEN-Net) for image inpainting by deep generative models. The proposed PEN-Net is built upon a U-Net structure with three tailored components, ie. , a pyramid-context encoder, a multi-scale decoder, and an adversarial training loss. First, we adopt a U-Net as backbone which can encode the context of an image from high-resolution pixels into high-level semantic features, and decode the features reversely. Second, we propose a pyramid-context encoder, which progressively learns region affinity by attention from a high-level semantic feature map, and transfers the learned attention to its adjacent high-resolution feature map. As the missing content can be filled by attention transfer from deep to shallow in a pyramid fashion, both visual and semantic coherence for image inpainting can be ensured. Third, we further propose a multi-scale decoder with deeply-supervised pyramid losses and an adversarial loss. Such a design not only results in fast convergence in training, but more realistic results in testing. Extensive experiments on a broad range of datasets shows the superior performance of the proposed network.