Miracle-dz/UniFAR-FTUs huggingface dataset:
The efficacy of mifepristone combined with methotrexate for the treatment of ectopic pregnancy: a systematic review and meta-analysis*

Abstract Objective Systematically evaluate the clinical efficacy of mifepristone combined with methotrexate therapy for ectopic pregnancy (EP), analyze the experimental designs, put forward improvement ideas. Methods RCTs of mifepristone combined with mifepristone for EP until January 2022 in six databases were searched. The primary outcome indicator was the cure rate. RevMan 5.4 was used to analyse and the online GRADEpro tool was used to assess the certainty of the evidence. Results Twenty-five RCTs involved 2263 patients. The cure rate was higher in the investigational group (OR = 4.09, 95%CI: [3.20, 5.22]), time of vagina stopped bleeding (MD = −11.21, 95%CI: [−11.85, −10.57]) and time of abdominal pain disappeared (MD = −6.24, 95%CI: [−6.63, −5.86]) were shorter in the investigational group, β-HCG level (MD = −585.32, 95%CI: [−609.62, −561.03]) was lower and diameter of the mass (MD = −1.23, 95%CI: [−1.40, −106]) was smaller in the investigational group. The certainty of the evidence for most outcomes was moderate or high, and only one was low. Conclusions The combination of mifepristone and methotrexate can improve the efficacy of ectopic pregnancy without amplifying the toxic side effects. Larger scale and better design of the randomized controlled trials are needed. KEY MESSAGES In recent years, the increase in ectopic pregnancies and their impacts on female fertility makes physicians have to find an effective medical treatment as soon as possible that can avoid surgery. The mifepristone combined with methotrexate therapy for EP has better curative effects on improving the cure rate, lowering β-HCG level, reducing the mass, and alleviating symptoms of abdominal pain and bleeding, without amplifying the toxic side effects. Literature with high quality is lacking, and well-designed, large-scale and high-quality multicenter randomized controlled trials are needed.
Coronavirus statistics causes emotional bias: a social media text mining perspective

While COVID-19 has impacted humans for a long time, people search the web for pandemic-related information, causing anxiety. From a theoretic perspective, previous studies have confirmed that the number of COVID-19 cases can cause negative emotions, but how statistics of different dimensions, such as the number of imported cases, the number of local cases, and the number of government-designated lockdown zones, stimulate people's emotions requires detailed understanding. In order to obtain the views of people on COVID-19, this paper first proposes a deep learning model which classifies texts related to the pandemic from text data with place labels. Next, it conducts a sentiment analysis based on multi-task learning. Finally, it carries out a fixed-effect panel regression with outputs of the sentiment analysis. The performance of the algorithm shows a promising result. The empirical study demonstrates while the number of local cases is positively associated with risk perception, the number of imported cases is negatively associated with confidence levels, which explains why citizens tend to ascribe the protracted pandemic to foreign factors. Besides, this study finds that previous pandemic hits cities recover slowly from the suffering, while local governments' spending on healthcare can improve the situation. Our study illustrates the reasons for risk perception and confidence based on different sources of statistical information due to cognitive bias. It complements the knowledge related to epidemic information. It also contributes to a framework that combines sentiment analysis using advanced deep learning technology with the empirical regression method.
Auditing Algorithmic Fairness in Machine Learning for Health with Severity-Based LOGAN

Auditing machine learning-based (ML) healthcare tools for bias is critical to preventing patient harm, especially in communities that disproportionately face health inequities. General frameworks are becoming increasingly available to measure ML fairness gaps between groups. However, ML for health (ML4H) auditing principles call for a contextual, patient-centered approach to model assessment. Therefore, ML auditing tools must be (1) better aligned with ML4H auditing principles and (2) able to illuminate and characterize communities vulnerable to the most harm. To address this gap, we propose supplementing ML4H auditing frameworks with SLOGAN (patient Severity-based LOcal Group biAs detectioN), an automatic tool for capturing local biases in a clinical prediction task. SLOGAN adapts an existing tool, LOGAN (LOcal Group biAs detectioN), by contextualizing group bias detection in patient illness severity and past medical history. We investigate and compare SLOGAN's bias detection capabilities to LOGAN and other clustering techniques across patient subgroups in the MIMIC-III dataset. On average, SLOGAN identifies larger fairness disparities in over 75% of patient groups than LOGAN while maintaining clustering quality. Furthermore, in a diabetes case study, health disparity literature corroborates the characterizations of the most biased clusters identified by SLOGAN. Our results contribute to the broader discussion of how machine learning biases may perpetuate existing healthcare disparities.
Galactica: A Large Language Model for Science

Information overload is a major obstacle to scientific progress. The explosive growth in scientific literature and data has made it ever harder to discover useful insights in a large mass of information. Today scientific knowledge is accessed through search engines, but they are unable to organize scientific knowledge alone. In this paper we introduce Galactica: a large language model that can store, combine and reason about scientific knowledge. We train on a large scientific corpus of papers, reference material, knowledge bases and many other sources. We outperform existing models on a range of scientific tasks. On technical knowledge probes such as LaTeX equations, Galactica outperforms the latest GPT-3 by 68.2% versus 49.0%. Galactica also performs well on reasoning, outperforming Chinchilla on mathematical MMLU by 41.3% to 35.7%, and PaLM 540B on MATH with a score of 20.4% versus 8.8%. It also sets a new state-of-the-art on downstream tasks such as PubMedQA and MedMCQA dev of 77.6% and 52.9%. And despite not being trained on a general corpus, Galactica outperforms BLOOM and OPT-175B on BIG-bench. We believe these results demonstrate the potential for language models as a new interface for science. We open source the model for the benefit of the scientific community.
Structural Segmentation and Labeling of Tabla Solo Performances

Tabla is a North Indian percussion instrument used as an accompaniment and an exclusive instrument for solo performances. Tabla solo is intricate and elaborate, exhibiting rhythmic evolution through a sequence of homogeneous sections marked by shared rhythmic characteristics. Each section has a specific structure and name associated with it. Tabla learning and performance in the Indian subcontinent is based on stylistic schools called gharana-s. Several compositions by various composers from different gharana-s are played in each section. This paper addresses the task of segmenting the tabla solo concert into musically meaningful sections. We then assign suitable section labels and recognize gharana-s from the sections. We present a diverse collection of over 38 hours of solo tabla recordings for the task. We motivate the problem and present different challenges and facets of the tasks. Inspired by the distinct musical properties of tabla solo, we compute several rhythmic and timbral features for the segmentation task. This work explores the approach of automatically locating the significant changes in the rhythmic structure by analyzing local self-similarity in an unsupervised manner. We also explore supervised random forest and a convolutional neural network trained on hand-crafted features. Both supervised and unsupervised approaches are also tested on a set of held-out recordings. Segmentation of an audio piece into its structural components and labeling is crucial to many music information retrieval applications like repetitive structure finding, audio summarization, and fast music navigation. This work helps us obtain a comprehensive musical description of the tabla solo concert.
Multifrequency VLBI Observations of the M84 Inner Jet/Counterjet

Observational studies of the innermost regions of the edge-on jets in nearby active galactic nuclei (AGN) are crucial to understanding their kinematics and morphology. For the inner jet of the nearby low-luminosity AGN in M84, we present new high-sensitivity observations with very long baseline Interferometry since 2019, as well as archival Very Long Baseline Array observations in 2014. We find that the compact core in M84 has an inverted-to-flat spectrum from 1.5–88 GHz. Based on the turnover frequency of 4.2 ± 0.2 GHz in the spectrum, we estimated a magnetic field strength of 1 ∼ 10 mG and an electron number density of ∼105 cm−3 in the core region. Three inner jet components within ∼3 mas of the core are identified and traced in the images at 22 GHz, whose apparent speeds are 0.11 c, 0.27 c, and 0.32 c, respectively. We calculate the viewing angle of 58−18+17° for the inner jet based on the proper motion and the flux ratio of jet to counterjet. A propagating sinusoidal model with a wavelength of ∼3.4 mas is used to fit the helical morphology of the jet extended to 20 mas (∼2.2 × 104 Schwarzschild radii).
Linear stability analysis of purely elastic travelling-wave solutions in pressure-driven channel flows

Abstract Recent studies of pressure-driven flows of dilute polymer solutions in straight channels demonstrated the existence of two-dimensional coherent structures that are disconnected from the laminar state and appear through a subcritical bifurcation from infinity. These travelling-wave solutions were suggested to organise the phase-space dynamics of purely elastic and elasto-inertial chaotic channel flows. Here, we consider a wide range of parameters, covering the purely elastic and elasto-inertial cases, and demonstrate that the two-dimensional travelling-wave solutions are unstable when embedded in sufficiently wide three-dimensional domains. Our work demonstrates that studies of purely elastic and elasto-inertial turbulence in straight channels require three-dimensional simulations, and no reliable conclusions can be drawn from studying strictly two-dimensional channel flows.
Asynchronous Gradient Play in Zero-Sum Multi-agent Games

Finding equilibria via gradient play in competitive multi-agent games has been attracting a growing amount of attention in recent years, with emphasis on designing efficient strategies where the agents operate in a decentralized and symmetric manner with guaranteed convergence. While significant efforts have been made in understanding zero-sum two-player matrix games, the performance in zero-sum multi-agent games remains inadequately explored, especially in the presence of delayed feedbacks, leaving the scalability and resiliency of gradient play open to questions. In this paper, we make progress by studying asynchronous gradient plays in zero-sum polymatrix games under delayed feedbacks. We first establish that the last iterate of entropy-regularized optimistic multiplicative weight updates (OMWU) method converges linearly to the quantal response equilibrium (QRE), the solution concept under bounded rationality, in the absence of delays. While the linear convergence continues to hold even when the feedbacks are randomly delayed under mild statistical assumptions, it converges at a noticeably slower rate due to a smaller tolerable range of learning rates. Moving beyond, we demonstrate entropy-regularized OMWU -- by adopting two-timescale learning rates in a delay-aware manner -- enjoys faster last-iterate convergence under fixed delays, and continues to converge provably even when the delays are arbitrarily bounded in an average-iterate manner. Our methods also lead to finite-time guarantees to approximate the Nash equilibrium (NE) by moderating the amount of regularization. To the best of our knowledge, this work is the first that aims to understand asynchronous gradient play in zero-sum polymatrix games under a wide range of delay assumptions, highlighting the role of learning rates separation.
Light-induced phase crossovers in a quantum spin Hall system

In this work, we theoretically investigate the light-induced topological phases and finite-size crossovers in a paradigmatic quantum spin Hall (QSH) system with high-frequency pumping optics. Taking the HgTe quantum well for an example, our numerical results show that circularly polarized light can break time-reversal symmetry and induce the quantum anomalous Hall (QAH) phase. In particular, the coupling between the edge states is spin dependent and is related not only to the size of the system, but also to the strength of the polarized pumping optics. By tuning the two parameters (system width and optical pumping strength), we obtain four transport regimes, namely, QSH, QAH, edge conducting, and normal insulator. These four different transport regimes have contrasting edge conducting properties, which will feature prominently in transport experiments on various topological materials.
Unsupervised Domain Adaptation Based on the Predictive Uncertainty of Models

Unsupervised domain adaptation (UDA) aims to improve the prediction performance in the target domain under distribution shifts from the source domain. The key principle of UDA is to minimize the divergence between the source and the target domains. To follow this principle, many methods employ a domain discriminator to match the feature distributions. Some recent methods evaluate the discrepancy between two predictions on target samples to detect those that deviate from the source distribution. However, their performance is limited because they either match the marginal distributions or measure the divergence conservatively. In this paper, we present a novel UDA method that learns domain-invariant features that minimize the domain divergence. We propose model uncertainty as a measure of the domain divergence. Our UDA method based on model uncertainty (MUDA) adopts a Bayesian framework and provides an efficient way to evaluate model uncertainty by means of Monte Carlo dropout sampling. Empirical results on image recognition tasks show that our method is superior to existing state-of-the-art methods. We also extend MUDA to multi-source domain adaptation problems.
Shear-band cavitation determines the shape of the stress-strain curve of metallic glasses

Metallic glasses are known to have a remarkably robust yield strength, admitting Weibull moduli as high as for crystalline engineering alloys. However, their post-yielding behavior is strongly varying, with large scatter in both flow stress levels and strains at failure. Using x-ray tomography we reveal for the first time how a strain-dependent internal evolution of shear-band cavities underlies this unpredictable post yielding response. We demonstrate how macroscopic strain-softening coincides with the first detection of internal shear-band cavitation. Cavity growth during plastic flow is found to follow a power-law, which yields a fractal dimension and a roughness exponent in excellent agreement with self-similar surface properties obtained after fracture. These findings demonstrate how internal micro-cracking coexists with shear-band plasticity along the plastic part of a stress-strain curve, rationalizing the large variability of plastic flow behavior seen for metallic glasses.
Galileon Intermediate Inflation

We investigate intermediate inflation in the framework of a Galileon scalar field. To this aim, we first obtain the inflationary observables, including the scalar spectral index, the tensor-to-scalar ratio, the running of the scalar spectral index, as well as the non-Gaussianity parameters. Then, we examine the observational viability of the intermediate inflation within the framework of a Galileon scenario. Our results show that although the prediction of intermediate inflation in the standard framework is completely ruled out by the Planck 2015 observations, it can be put inside the allowed regions of the Planck 2015 TT,TE,EE+lowP data in the Galileon setting. Moreover, we determine the parameter space of the Galileon intermediate inflation for which the model is consistent with the Planck 2015 data. Besides, we derive the consistency relation in the Galileon scenario, and find that it differs from the standard inflation. We also estimate the running of the scalar spectral index and find that it is in well agreement with the 95% CL constraint of the Planck 2015 results. Finally, we evaluate the local, equilateral, orthogonal, and enfolded non-Gaussianity parameters, and conclude that not only is the shape of non-Gaussianity approximately close to the equilateral one, but that it also satisfies the 68% CL bound from the Planck 2015 data.
Online convex optimization for constrained control of linear systems using a reference governor

In this work, we propose a control scheme for linear systems subject to pointwise in time state and input constraints that aims to minimize time-varying and a priori unknown cost functions. The proposed controller is based on online convex optimization and a reference governor. In particular, we apply online gradient descent to track the time-varying and a priori unknown optimal steady state of the system. Moreover, we use a $\lambda$-contractive set to enforce constraint satisfaction and a sufficient convergence rate of the closed-loop system to the optimal steady state. We prove that the proposed scheme is recursively feasible, ensures that the state and input constraints are satisfied at all times, and achieves a dynamic regret that is linearly bounded by the variation of the cost functions. The algorithm's performance and constraint satisfaction is illustrated by means of a simulation example.
Discovery of GeV Gamma-Ray Emission from Pulsar Wind Nebula Kes 75 and PSR J1846–0258

We report the detection of gamma-ray emission from pulsar wind nebula (PWN) Kes 75 and PSR J1846−0258. Through modeling the spectral energy distribution incorporating the new Fermi-LAT data, we find that the observed gamma-ray emission is likely a combination of both the PWN and pulsar magnetosphere. The spectral shape of this magnetospheric emission is similar to the γ-ray spectrum of rotation-powered pulsars detected by Fermi-LAT, and the results from our best-fit model suggest that the pulsar’s magnetospheric emission accounts for 1% of the current spin-down luminosity. Prior works attempted to characterize the properties of this system and found a low supernova (SN) explosion energy and low SN ejecta mass. We reanalyze the broadband emission incorporating the new Fermi emission and compare the implications of our results to prior reports. The best-fit gamma-ray emission model suggests a second very hot photon field possibly generated by the stellar wind of a Wolf–Rayet star embedded within the nebula, which supports the low ejecta mass found for the progenitor in prior reports and here in the scenario of binary mass transfer.
High entropy and evidence for a merger in the low surface brightness cluster Abell 2399

We present results of the X-ray analyses of the nearby cluster of galaxies Abell 2399 ($z=0.058$) using the XMM-Newton and Suzaku satellites. This cluster is classified as a low surface brightness (LSB) cluster. It has a bimodal structure in X-rays, and the X-ray-emission peaks are significantly offset from the peaks in gas temperature. By de-projecting the annular spectra, the temperature and electron density in the central $r<0. 1r_{200}$ region are measured to be 3.6~keV and $1.4\times10^{-3}~{\rm cm^{-3}}$, respectively. This yields a very high gas entropy, $\sim 300~{\rm keV\,cm^{2}}$, in the central region, comparable to the values observed in other LSB clusters. The scaled entropy of Abell 2399 is the highest among the REXCESS cluster sample. The spatial distribution of the member galaxies exhibits multiple substructures, the locations of which are significantly different from those in the X-ray image. We discovered a clear discontinuity in the X-ray brightness and temperature distributions in the western gas clump, which provides evidence of the presence of a cold front. Therefore, our results strongly suggest that A2399 has experienced a merger and that the high central entropy originates from the merger activity.
Ionization-induced long-lasting orientation of symmetric-top molecules

We theoretically consider the phenomenon of field-free long-lasting orientation of symmetric-top molecules ionized by two-color laser pulses. The anisotropic ionization produces a significant long-lasting orientation of the surviving neutral molecules. The degree of orientation increases with both the pulse intensity and, counterintuitively, with the rotational temperature. The orientation may be enhanced even further by using multiple delayed two-color pulses. The long-lasting orientation may be probed by even harmonic generation or by Coulomb-explosion-based methods. The effect may enable the study of relaxation processes in dense molecular gases, and may be useful for molecular guiding and trapping by inhomogeneous fields.
VGFlow: Visibility guided Flow Network for Human Reposing

The task of human reposing involves generating a realistic image of a person standing in an arbitrary conceivable pose. There are multiple difficulties in generating perceptually accurate images, and existing methods suffer from limitations in preserving texture, maintaining pattern co-herence, respecting cloth boundaries, handling occlusions, manipulating skin generation, etc. These difficulties are further exacerbated by the fact that the possible space of pose orientation for humans is large and variable, the nature of clothing items is highly non-rigid, and the diversity in body shape differs largely among the population. To alle-viate these difficulties and synthesize perceptually accurate images, we propose VGFlow. Our model uses a visibility-guided flow module to disentangle the flow into visible and invisible parts of the target for simultaneous texture preser-vation and style manipulation. Furthermore, to tackle dis-tinct body shapes and avoid network artifacts, we also in-corporate a self-supervised patch-wise “realness” loss to improve the output. VGFlow achieves state-of-the-art results as observed qualitatively and quantitatively on different image quality metrics (SSIM, LPIPS, FID). Results can be downloaded from Project Webpage