Miracle-dz/UniFAR-FTUs huggingface dataset:
When Weak Becomes Strong: Robust Quantification of White Matter Hyperintensities in Brain MRI scans

In clinical practice, as a first approximation, the severity of an abnormality on an image is often determined by measuring its volume. Researchers often first segment this abnormality with a neural network trained by voxel-wise labels and thereafter extract the volume. Instead of this indirect two steps approach, we propose to train neural networks directly using the volumes as image-level label and predict the volume directly. Using image-level labels to train automatic abnormality prediction could decrease the labeling burden for clinical experts, which is both expensive and time consuming. In this report, a neural network that consisted of a segmentation part and an appended regression part was compared with the indirect segmentation approach. It was investigated if networks trained with image-level labels have the same performance of image-level prediction as networks trained with voxel-wise labels. The neural networks were trained on a large local dataset to quantify white matter hyperintensity (WMH) burden from brain MRI, and their performance was evaluated on a held-out test set. Furthermore, generalization properties were compared by applying the trained networks on four independent public datasets. The networks trained with image-level labels achieved volume quantification that was slightly better than their counterpart on the held-out test set. The attention maps of these networks showed that the networks were able to focus on the surroundings of the WMH, and hence learned meaningful image features. Nevertheless, the attention maps were not suitable to achieve a compatible segmentation. In terms of generalization towards external datasets, the advantage of weak labels for volume quantification did not hold as there was no significant difference between the performance of the label types. The results suggest that neural networks optimized with image-level labels were able to directly predict WMH volume as well as neural networks trained with voxel-wise labels. Subsequently, we also studied networks that were optimized on both image-level and voxel-wise labels. Those networks reached a lower performance, which suggested that the tasks and their image features learned were not similar enough.
Signatures of Untwisting Magnetic Field in a Small Emerging Bipole in the Solar Photosphere

We perform a study of fluid motions and its temporal evolution in and around a small bipolar emerging flux region using observations made by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. We employ local correlation tracking of the Doppler observations to follow horizontal fluid motions and line-of-sight magnetograms to follow the flux emergence. Changes in vertical vorticity and horizontal divergence are used to derive signatures of evolving twists in the magnetic field. Our analysis reveals that the two polarities of the magnetic flux swirl in opposite directions in the early stages of flux emergence indicating an unwinding of the pre-emergence twists in the magnetic field. We further find that during the emergence, there is an increase in swirly motions in the neighboring nonmagnetic regions. We estimate the magnetic and kinetic energies and find that magnetic energy is about a factor of 10 larger than the kinetic energy. During the evolution, when the magnetic energy decreases, an increase in the kinetic energy is observed indicating transfer of energy from the unwinding of the magnetic flux tube to the surrounding fluid motions. Our results thus demonstrate the presence of pre-emergence twists in an emerging magnetic field that is important in the context of the hemispheric helicity rule warranting a detailed statistical study in this context. Further, our observations point to a possible widespread generation of torsional waves in emerging flux regions due to the untwisting magnetic field with implications for upward energy transport to the corona.
Understanding the Socio-Economic Disruption in the United States during COVID-19's Early Days

In this paper, we collect and study Twitter communications to understand the socio-economic impact of COVID-19 in the United States during the early days of the pandemic. Our analysis reveals that COVID-19 gripped the nation during this time as is evidenced by the significant number of trending hashtags. With infections soaring rapidly, users took to Twitter asking people to self isolate and quarantine themselves. Users also demanded closure of schools, bars, and restaurants as well as lockdown of cities and states. The communications reveal the ensuing panic buying and the unavailability of some essential goods, in particular toilet paper. We also observe users express their frustration in their communications as the virus spread continued. We methodically collect a total of 530,206 tweets by identifying and tracking trending COVID-related hashtags. We then group the hashtags into six main categories, namely 1) General COVID, 2) Quarantine, 3) Panic Buying, 4) School Closures, 5) Lockdowns, and 6) Frustration and Hope, and study the temporal evolution of tweets in these hashtags. We conduct a linguistic analysis of words common to all the hashtag groups and specific to each hashtag group. Our preliminary study presents a succinct and aggregated picture of people's response to the pandemic and lays the groundwork for future fine-grained linguistic and behavioral analysis.
Polluting the Pair-instability Mass Gap for Binary Black Holes through Super-Eddington Accretion in Isolated Binaries

The theory for single stellar evolution predicts a gap in the mass distribution of black holes (BHs) between approximately 45 and 130 , the so-called “pair-instability mass gap. ” We examine whether BHs can pollute the gap after accreting from a stellar companion. To this end, we simulate the evolution of isolated binaries using a population synthesis code, where we allow for super-Eddington accretion. Under our most extreme assumptions, we find that at most about 2% of all merging binary BH systems contains a BH with a mass in the pair-instability mass gap, and we find that less than 0.5% of the merging systems has a total mass larger than 90 . We find no merging binary BH systems with a total mass exceeding 100 . We compare our results to predictions from several dynamical pathways to pair-instability mass gap events and discuss the distinguishable features. We conclude that the classical isolated binary formation scenario will not significantly contribute to the pollution of the pair-instability mass gap. The robustness of the predicted mass gap for the isolated binary channel is promising for the prospective of placing constraints on (i) the relative contribution of different formation channels, (ii) the physics of the progenitors including nuclear reaction rates, and, tentatively, (iii) the Hubble parameter.
Online Initialization and Extrinsic Spatial-Temporal Calibration for Monocular Visual-Inertial Odometry

This paper presents an online initialization method for bootstrapping the optimization-based monocular visual-inertial odometry (VIO). The method can online calibrate the relative transformation (spatial) and time offsets (temporal) among camera and IMU, as well as estimate the initial values of metric scale, velocity, gravity, gyroscope bias, and accelerometer bias during the initialization stage. To compensate for the impact of time offset, our method includes two short-term motion interpolation algorithms for the camera and IMU pose estimation. Besides, it includes a three-step process to incrementally estimate the parameters from coarse to fine. First, the extrinsic rotation, gyroscope bias, and time offset are estimated by minimizing the rotation difference between the camera and IMU. Second, the metric scale, gravity, and extrinsic translation are approximately estimated by using the compensated camera poses and ignoring the accelerometer bias. Third, these values are refined by taking into account the accelerometer bias and the gravitational magnitude. For further optimizing the system states, a nonlinear optimization algorithm, which considers the time offset, is introduced for global and local optimization. Experimental results on public datasets show that the initial values and the extrinsic parameters, as well as the sensor poses, can be accurately estimated by the proposed method.
High-order well-balanced finite volume schemes for hydrodynamic equations with nonlocal free energy

We propose high-order well-balanced finite-volume schemes for a broad class of hydrodynamic systems with attractive-repulsive interaction forces and linear and nonlinear damping. Our schemes are suitable for free energies containing convolutions of an interaction potential with the density, which are essential for applications such as the Keller-Segel model, more general Euler-Poisson systems, or dynamic-density functional theory. Our schemes are also equipped with a nonnegative-density reconstruction which allows for vacuum regions during the simulation. We provide several prototypical examples from relevant applications highlighting the benefit of our algorithms elucidate also some of our analytical results.
Exploiting Interpretable Patterns for Flow Prediction in Dockless Bike Sharing Systems

Unlike the traditional dock-based systems, dockless bike-sharing systems are more convenient for users in terms of flexibility. However, the flexibility of these dockless systems comes at the cost of management and operation complexity. Indeed, the imbalanced and dynamic use of bikes leads to mandatory rebalancing operations, which impose a critical need for effective bike traffic flow prediction. While efforts have been made in developing traffic flow prediction models, existing approaches lack interpretability, and thus have limited value in practical deployment. To this end, we propose an Interpretable Bike Flow Prediction (IBFP) framework, which can provide effective bike flow prediction with interpretable traffic patterns. Specifically, by dividing the urban area into regions according to flow density, we first model the spatio-temporal bike flows between regions with graph regularized sparse representation, where graph Laplacian is used as a smooth operator to preserve the commonalities of the periodic data structure. Then, we extract traffic patterns from bike flows using subspace clustering with sparse representation to construct interpretable base matrices. Moreover, the bike flows can be predicted with the interpretable base matrices and learned parameters. Finally, experimental results on real-world data show the advantages of the IBFP method for flow prediction in dockless bike sharing systems. In addition, the interpretability of our flow pattern exploitation is further illustrated through a case study where IBFP provides valuable insights into bike flow analysis.
COVID-MobileXpert: On-Device COVID-19 Screening using Snapshots of Chest X-Ray

With the increasing demand for millions of COVID-19 screenings, Computed Tomography (CT) based test has emerged as a promising alternative to the gold standard RT-PCR test. However, it is primarily provided in hospital setting due to the need for expensive equipment and experienced radiologists. An accurate, rapid yet inexpensive test that is suitable for COVID-19 population screenings at mobile, urgent and primary care clinics is urgently needed. We present COVID-MobileXpert: a lightweight deep neural network (DNN) based mobile app that can use noisy snapshots of chest X-ray (CXR) for point-of-care COVID-19 screening. We design and implement a novel three-player knowledge transfer and distillation (KTD) framework including a pre-trained attending physician (AP) network that extracts CXR imaging features from large scale of lung disease CXR images, a fine-tuned resident fellow (RF) network that learns the essential CXR imaging features to discriminate COVID-19 from pneumonia and/or normal cases using a small amount of COVID-19 cases, and a trained lightweight medical student (MS) network that performs on-device COVID-19 screening. To accommodate the need for screening using noisy snapshots of CXR images, we employ novel loss functions and training schemes for the MS network to learn the robust imaging features for accurate on-device COVID-19 screening. We demonstrate the strong potential of COVID-MobileXpert for rapid deployment via extensive experiments with diverse MS network architecture, CXR imaging quality, and tuning parameter settings. The source code of cloud and mobile based models are available from this https URL.
A critique of the Covid-19 analysis for India by Singh and Adhikari

In a recent paper (arXiv:2003.12055), Singh and Adhikari present results of an analysis of a mathematical model of epidemiology based on which they argue that a $49$ day lockdown is required in India for containing the pandemic in India. We assert that as a model study with the stated assumptions, the analysis presented in the paper is perfectly valid, however, any serious comparison with real data and attempts at prediction from the model are highly problematic. The main point of the present paper is to convincingly establish this assertion while providing a warning that the results and analysis of such mathematical models should not be taken at face value and need to be used with great caution by policy makers.
Underwater Image Enhancement Based on Structure-Texture Reconstruction

Aiming at the problems of color distortion, blur and excessive noise of underwater image, an underwater image enhancement algorithm based on structure-texture reconstruction is proposed. Firstly, the color equalization of the degraded image is realized by the automatic color enhancement algorithm; Secondly, the relative total variation is introduced to decompose the image into the structure layer and texture layer; Then, the best background light point is selected based on brightness, gradient discrimination, and hue judgment, the transmittance of the backscatter component is obtained by the red dark channel prior, which is substituted into the imaging model to remove the fogging phenomenon in the structure layer. Enhancement of effective details in the texture layer by multi scale detail enhancement algorithm and binary mask; Finally, the structure layer and texture layer are reconstructed to get the final image. The experimental results show that the algorithm can effectively balance the hue, saturation, and clarity of underwater image, and has good performance in different underwater environments.
Monte Carlo Modeling of Terahertz Quantum Cascade Detectors

We demonstrate an Ensemble Monte Carlo (EMC) modeling approach for robust and rigorous simulations of photovoltaic quantum cascade detectors (QCDs) in the mid-infrared (mid-IR) and terahertz (THz) range. The existing EMC simulation tool for quantum cascade lasers (QCLs) was extended to simulate the photovoltaic transport effects in QCDs at thermal equilibrium under zero bias. Here, we present the results of the EMC study of a THz detector design with a detection wavelength of 84$\mu$m. The simulation results show good agreement with experimental data. For a temperature of 10 K we obtain a peak responsivity of 9.4 mA/W.
High-field depinned phase and planar Hall effect in the skyrmion host 
Gd2PdSi3

For the skyrmion-hosting intermetallic ${\mathrm{Gd}}_{2}{\mathrm{PdSi}}_{3}$ with centrosymmetric hexagonal lattice and triangular net of rare earth sites, we report a thorough investigation of the magnetic phase diagram. Our work reveals a magnetic phase with an isotropic value of the critical field for all orientations, where the magnetic ordering vector $\mathbf{q}$ is depinned from its preferred directions in the basal plane. The bulk nature of the skyrmion lattice and of other magnetic phases was evidenced by specific-heat measurements. Resistivity anisotropy, likely originating from partial gapping of the density of states along $\mathbf{q}$ in this Ruderman-Kittel-Kasuya-Yosida magnet, is picked up via the planar Hall effect (PHE). The PHE confirms the single-$\mathbf{q}$ nature of the magnetic order when the field is in the hexagonal plane, and allows one to detect the preferred directions of $\mathbf{q}$. Several scenarios for the depinned phase, such as tilted conical order, are discussed on the basis of the data.
Discriminative Learning via Adaptive Questioning

We consider the problem of designing an adaptive sequence of questions that optimally classify a candidate's ability into one of several categories or discriminative grades. A candidate's ability is modeled as an unknown parameter, which, together with the difficulty of the question asked, determines the likelihood with which s/he is able to answer a question correctly. The learning algorithm is only able to observe these noisy responses to its queries. We consider this problem from a fixed confidence-based $\delta$-correct framework, that in our setting seeks to arrive at the correct ability discrimination at the fastest possible rate while guaranteeing that the probability of error is less than a pre-specified and small $\delta$. In this setting we develop lower bounds on any sequential questioning strategy and develop geometrical insights into the problem structure both from primal and dual formulation. In addition, we arrive at algorithms that essentially match these lower bounds. Our key conclusions are that, asymptotically, any candidate needs to be asked questions at most at two (candidate ability-specific) levels, although, in a reasonably general framework, questions need to be asked only at a single level. Further, and interestingly, the problem structure facilitates endogenous exploration, so there is no need for a separately designed exploration stage in the algorithm.
Improving Disfluency Detection by Self-Training a Self-Attentive Model

Self-attentive neural syntactic parsers using contextualized word embeddings (e.g. ELMo or BERT) currently produce state-of-the-art results in joint parsing and disfluency detection in speech transcripts. Since the contextualized word embeddings are pre-trained on a large amount of unlabeled data, using additional unlabeled data to train a neural model might seem redundant. However, we show that self-training — a semi-supervised technique for incorporating unlabeled data — sets a new state-of-the-art for the self-attentive parser on disfluency detection, demonstrating that self-training provides benefits orthogonal to the pre-trained contextualized word representations. We also show that ensembling self-trained parsers provides further gains for disfluency detection.
Missed opportunities for HIV prevention: Results of a qualitative study on mother-daughter communication

Abstract This qualitative study examines the role of communication among African American mothers living with HIV and their daughters in HIV prevention. Multiple themes emerged from our analysis of semistructured interviews with mothers (n = 15), and their adult daughters, (n = 15) such as perceptions of HIV risk communication, HIV/AIDS knowledge, attitudes, and beliefs. The findings of the study revealed differences in communication between mothers and daughters. Daughters felt they did not receive adequate and frequent HIV prevention advice from their mothers. Implications include strengthening communication content between mother-daughter dyads in HIV prevention programs that can aid in reducing HIV risk.
Blind Adversarial Training: Balance Accuracy and Robustness

Adversarial training (AT) aims to improve the robustness of deep learning models by mixing clean data and adversarial examples (AEs). Most existing AT approaches can be grouped into restricted and unrestricted approaches. Restricted AT requires a prescribed uniform budget to constrain the magnitude of the AE perturbations during training, with the obtained results showing high sensitivity to the budget. On the other hand, unrestricted AT uses unconstrained AEs, resulting in the use of AEs located beyond the decision boundary; these overestimated AEs significantly lower the accuracy on clean data. These limitations mean that the existing AT approaches have difficulty in obtaining a comprehensively robust model with high accuracy and robustness when confronting attacks with varying strengths. Considering this problem, this paper proposes a novel AT approach named blind adversarial training (BAT) to better balance the accuracy and robustness. The main idea of this approach is to use a cutoff-scale strategy to adaptively estimate a nonuniform budget to modify the AEs used in the training, ensuring that the strengths of the AEs are dynamically located in a reasonable range and ultimately improving the overall robustness of the AT model. The experimental results obtained using BAT for training classification models on several benchmarks demonstrate the competitive performance of this method.
A sharp log-Sobolev inequality for the multislice

We determine the log-Sobolev constant of the multi-urn Bernoulli-Laplace diffusion model with arbitrary parameters, up to a small universal multiplicative constant. Our result extends a classical estimate of Lee and Yau (1998) and confirms a conjecture of Filmus, O'Donnell and Wu (2018). Among other applications, we completely quantify the "small-set expansion" phenomenon on the multislice, and obtain sharp mixing-time estimates for the colored exclusion process on various graphs.
Interpreting Deep Neural Networks for Single-Lead ECG Arrhythmia Classification

Cardiac arrhythmia is a prevalent and significant cause of morbidity and mortality among cardiac ailments. Early diagnosis is crucial in providing intervention for patients suffering from cardiac arrhythmia. Traditionally, diagnosis is performed by examination of the Electrocardiogram (ECG) by a cardiologist. This method of diagnosis is hampered by the lack of accessibility to expert cardiologists. For quite some time, signal processing methods had been used to automate arrhythmia diagnosis. However, these traditional methods require expert knowledge and are unable to model a wide range of arrhythmia. Recently, Deep Learning methods have provided solutions to performing arrhythmia diagnosis at scale. However, the black-box nature of these models prohibit clinical interpretation of cardiac arrhythmia. There is a dire need to correlate the obtained model outputs to the corresponding segments of the ECG. To this end, two methods are proposed to provide interpretability to the models. The first method is a novel application of Gradient-weighted Class Activation Map (Grad-CAM) for visualizing the saliency of the CNN model. In the second approach, saliency is derived by learning the input deletion mask for the LSTM model. The visualizations are provided on a model whose competence is established by comparisons against baselines. The results of model saliency not only provide insight into the prediction capability of the model but also aligns with the medical literature for the classification of cardiac arrhythmia.Clinical relevance— Adapts interpretability modules for deep learning networks in ECG arrhythmia classfication, allowing for better clinical interpretation