Miracle-dz/UniFAR-FTUs huggingface dataset:
EVIDENCE-BASED MEDICINE IN THE TIMES OF COVID-19: A CROSS-SECTIONAL SURVEY TO UNDERSTAND THE INDIAN PERSPECTIVE

Introduction: During the COVID-19 pandemic, there is a tremendous amount of literature published regularly. In a country like India, historically, where there is a paternalistic approach to practicing medicine, there is a lot of hindrance to evidence-based medicine (EBM). Doctors have always weighed one's clinical experience superior over any other form of decision-making. This system of practice has made decision-making difficult for the physicians during this pandemic as COVID-19 is a reasonably new disease entity and the physicians lack enough 'prior experience' dealing with such a situation. Our survey tries to address the common barriers to evidence-based medical practices especially during the COVID-19 pandemic in India. We also try to explore the various source of information used by the doctors. Methods: It is a descriptive cross-sectional survey. The questions were provided in multiple-choice question format. An online survey comprising of 10 questions entitled “Hurdles faced by physicians to assimilate evidence-based guidelines on COVID-19” was made using Google Forms (Google Inc, California, US) and circulated through email to medical practitioners in the Ghatkopar (Mumbai, India) Medical Association's register from 17 th June 2020 to 1 st September 2020. Results: Our survey collected 213 responses, out of which 80.3% (n=171) of doctors were involved in care, counseling, or management of COVID-19 patients. The most opted primary sources for evidencebased information during this pandemic were teachings of/discussions with medical colleagues (71.4%, n=152), followed by online webinars (59.6%, n= 127) and social media (41.8%, n=89). When questioned about the main obstacles faced by them to obtain evidence-based information, the responses were as follows: Overload of medical literature (53.5%, n=114), limited access to quality resources (40.8%, n=87), unfamiliarity with the bio-statistics analysis (39%, n= 83), difficulty in locating relevant medical literature (38%, n=81), unfamiliarity with the research methodology (37.1%, n=79), lack of time (30%, n=64). Our respondents' perspective concerning EBM attributes: 57.3% (n=122) think evidence-based practice takes their clinical experience into account. 93.4% (n=199) of them have shown an interest in broadening their skills. There was no significant difference between doctors' attitudes with less than 10 years and more than 10 years of experience (chi-square value = 0.857, p = 0.65). Conclusion: Our survey results highlight the balance maintained between evidence-based medicine and experience-based medicine by Indian physicians. They identify the importance of EBM while Karan B. Bhanushali et al International Journal of Medical Science and Diagnosis Research (IJMSDR) 2 | P a g e acknowledging its shortcomings. They realize the significance of developing their repertoire to understand, appraise, and practice EBM.
Improving Nighttime Retrieval-Based Localization

Outdoor visual localization is a crucial component to many computer vision systems. We propose an approach to localization from images that is designed to explicitly handle the strong variations in appearance happening between daytime and nighttime. As revealed by recent long-term localization benchmarks, both traditional feature-based and retrieval-based approaches still struggle to handle such changes. Our novel localization method combines a state-of-the-art image retrieval architecture with condition-specific sub-networks allowing the computation of global image descriptors that are explicitly dependent of the capturing conditions. We show that our approach improves localization by a factor of almost 300\% compared to the popular VLAD-based methods on nighttime localization.
Party goals and interest group influence on parties

Abstract Although research on interest group influence has had a revival in recent decades, little is known about interest group influence on political parties. This study considers how parties’ goals can affect interest groups’ ability to influence these actors. Interest groups are expected to be more likely to influence parties that are ideologically approximate to the interest groups as well as parties that are more willing to make policy compromises; the access that interest groups have to parties mediates these effects. Results from the empirical analysis of 5000 party-interest group observations from Denmark, France, Germany, the Netherlands, Norway and the United Kingdom support these expectations. In addition to contributing to the literature on parties and interest groups, the findings shed light on party position taking and when the link between citizens and parties is likely to be affected by interest groups. Supplemental data for this article can be accessed online at: https://doi.org/10.1080/01402382.2021. 1921496 .
Mild deficiency of mitochondrial Complex III in a mouse model of Alzheimer’s disease decreases amyloid beta plaque formation

Background: For decades, mitochondrial dysfunctions and the generation of reactive oxygen species have been proposed to promote the development and progression of the amyloid pathology in Alzheimer’s disease, but this association is still debated. In particular, it is still unclear if mitochondrial dysfunctions are a trigger or rather a consequence of the formation of amyloid aggregates, and in particular, the role of the different mitochondrial oxidative phosphorylation complexes in Alzheimer’s patients’ brain remains poorly understood. Methods: To study how mitochondrial Complex III defects affect amyloid beta pathology in vivo, we partially knocked out mitochondrial Complex III (CIIIKO) in mature forebrain neurons of an Alzheimer’s mouse model that develops plaque pathology (APP/PS1). Results: We found that Complex III dysfunction in adult neurons induced mild oxidative stress which did not correlate with increased amyloid beta accumulation. In fact, CIIIKO-AD mice showed decreased plaque number, decreased Aβ42 toxic fragment and altered amyloid precursor protein cleavage pathway. Conclusions: Our results support a model in which mitochondrial dysfunction is not the cause of amyloid oligomer accumulation but rather a consequence of amyloid beta toxicity.
Association between microsatellite markers and rice response to the rice blast pathogen Magnaporthe oryzae in selected Zambian rice genotypes

Magnaporthe oryzae, the rice blast disease pathogen is estimated to cause losses of up to 30% in rice production worldwide, yet its impact on rice production in Zambia remains unknown. Symptomatic rice leaf tissue collected from rice fields in Mongu District of Zambia was cultured on oatmeal agar medium and fungal colonies were purified and identified through leaf lesions and conidia using published keys. An M. oryzae isolate from this study was used to challenge twenty-eight selected Zambian rice genotypes to determine their responses to infection with the pathogen estimated by lesion numbers and sizes. Twelve of the genotypes (42.8%) were highly resistant (HR) with 1 to 2 lesions per cm2, 6 (21.4%) were moderately resistant with mean lesion numbers of 3 to 4 per cm2 while 10 (35.7%) were susceptible with lesion numbers above 5 per cm2 of leaf tissue. Polymerase chain reaction (PCR)-amplification of rice genomic DNA extracted from the 28 genotypes using selected microsatellite marker primers revealed a moderate genetic diversity with three of them showing significant genetic diversity. Allelic diversity among the selected genotypes was also confirmed from a calculated mean polymorphic information content of 0.8. A correlation between 8 of the microsatellite markers in some of the rice genotypes and their resistance response to M. oryzae was also established. Results show that some of the analysed rice genotypes have a significant potential for use as a genetic resource in breeding for varieties that are resistant to the rice blast pathogen for Zambian farmers.
Pushing the right boundaries matters! Wasserstein Adversarial Training for Label Noise

Noisy labels often occur in vision datasets, especially when they are issued from crowdsourcing or Web scraping. In this paper, we propose a new regularization method which enables one to learn robust classifiers in presence of noisy data. To achieve this goal, we augment the virtual adversarial loss with a Wasserstein distance. This distance allows us to take into account specific relations between classes by leveraging on the geometric properties of this optimal transport distance. Notably, we encode the class similarities in the ground cost that is used to compute the Wasserstein distance. As a consequence, we can promote smoothness between classes that are very dissimilar, while keeping the classification decision function sufficiently complex for similar classes. While designing this ground cost can be left as a problem-specific modeling task, we show in this paper that using the semantic relations between classes names already leads to good results.Our proposed Wasserstein Adversarial Training (WAT) outperforms state of the art on four datasets corrupted with noisy labels: three classical benchmarks and one real case in remote sensing image semantic segmentation.
Information Aggregation for Multi-Head Attention with Routing-by-Agreement

Multi-head attention is appealing for its ability to jointly extract different types of information from multiple representation subspaces. Concerning the information aggregation, a common practice is to use a concatenation followed by a linear transformation, which may not fully exploit the expressiveness of multi-head attention. In this work, we propose to improve the information aggregation for multi-head attention with a more powerful routing-by-agreement algorithm. Specifically, the routing algorithm iteratively updates the proportion of how much a part (i.e. the distinct information learned from a specific subspace) should be assigned to a whole (i.e. the final output representation), based on the agreement between parts and wholes. Experimental results on linguistic probing tasks and machine translation tasks prove the superiority of the advanced information aggregation over the standard linear transformation.
Deep Predictive Video Compression with Bi-directional Prediction

Recently, deep image compression has shown a big progress in terms of coding efficiency and image quality improvement. However, relatively less attention has been put on video compression using deep learning networks. In the paper, we first propose a deep learning based bi-predictive coding network, called BP-DVC Net, for video compression. Learned from the lesson of the conventional video coding, a B-frame coding structure is incorporated in our BP-DVC Net. While the bi-predictive coding in the conventional video codecs requires to transmit to decoder sides the motion vectors for block motion and the residues from prediction, our BP-DVC Net incorporates optical flow estimation networks in both encoder and decoder sides so as not to transmit the motion information to the decoder sides for coding efficiency improvement. Also, a bi-prediction network in the BP-DVC Net is proposed and used to precisely predict the current frame and to yield the resulting residues as small as possible. Furthermore, our BP-DVC Net allows for the compressive feature maps to be entropy-coded using the temporal context among the feature maps of adjacent frames. The BP-DVC Net has an end-to-end video compression architecture with newly designed flow and prediction losses. Experimental results show that the compression performance of our proposed method is comparable to those of H.264, HEVC in terms of PSNR and MS-SSIM.
Discovery of a Compact Companion to a Nearby Star

Radial velocity (RV) searches for exoplanets have surveyed many of the nearest and brightest stars for long-term velocity variations indicative of a companion body. Such surveys often detect high-amplitude velocity signatures of objects that lie outside the planetary mass regime, most commonly those of a low-mass star. Such stellar companions are frequently discarded as false-alarms to the main science goals of the survey, but high-resolution imaging techniques can be employed to either directly detect or place significant constraints on the nature of the companion object. Here, we present the discovery of a compact companion to the nearby star HD 118475. Our Anglo-Australian Telescope RV data allow the extraction of the full Keplerian orbit of the companion, which is found to have a minimum mass of 0.445 M⊙. Follow-up speckle imaging observations at the predicted time of maximum angular separation rule out a main-sequence star as the source of the RV signature at the 3. 3σ significance level, implying that the companion must be a low-luminosity compact object, most likely a white dwarf. We provide an isochrone analysis combined with our data that constrain the possible inclinations of the binary orbit. We discuss the eccentric orbit of the companion in the context of tidal circularization timescales and show that non-circular orbit was likely inherited from the progenitor. Finally, we emphasize the need for utilizing such an observation method to further understand the demographics of white dwarf companions around nearby stars.
A weak convergence result for sequential empirical processes under weak dependence

ABSTRACT The purpose of this paper is to prove a weak convergence result for empirical processes indexed in general classes of functions and with an underlying α-mixing triangular array of random variables. In particular, the uniformly boundedness assumption on the function class, which is required in most of the existing literature, is spared. Furthermore, under strict stationarity a weak convergence result for the sequential empirical process indexed in function classes is obtained as a direct consequence. Two examples in mathematical statistics, that cannot be treated with existing results, are given as possible applications.
A survey on Deep Learning Advances on Different 3D Data Representations

3D data is a valuable asset the computer vision filed as it provides rich information about the full geometry of sensed objects and scenes. Recently, with the availability of both large 3D datasets and computational power, it is today possible to consider applying deep learning to learn specific tasks on 3D data such as segmentation, recognition and correspondence. Depending on the considered 3D data representation, different challenges may be foreseen in using existent deep learning architectures. In this work, we provide a comprehensive overview about various 3D data representations highlighting the difference between Euclidean and non-Euclidean ones. We also discuss how Deep Learning methods are applied on each representation, analyzing the challenges to overcome.
Adaptively Connected Neural Networks

This paper presents a novel adaptively connected neural network (ACNet) to improve the traditional convolutional neural networks (CNNs) {in} two aspects. First, ACNet employs a flexible way to switch global and local inference in processing the internal feature representations by adaptively determining the connection status among the feature nodes (e.g., pixels of the feature maps). Note that in a computer vision domain, a node refers to a pixel of a feature map, while in the graph domain, a node denotes a graph node. We can show that existing CNNs, the classical multilayer perceptron (MLP), and the recently proposed non-local network (NLN) are all special cases of ACNet. Second, ACNet is also capable of handling non-Euclidean data. Extensive experimental analyses on a variety of benchmarks (i.e., ImageNet-1k classification, COCO 2017 detection and segmentation, CUHK03 person re-identification, CIFAR analysis, and Cora document categorization) demonstrate that ACNet cannot only achieve state-of-the-art performance but also overcome the limitation of the conventional MLP and CNN. The code is available at https://github.com/wanggrun/Adaptively-Connected-Neural-Networks.
Fast and slow decaying solutions for $H^{1}$-supercritical quasilinear Schr\"{o}dinger equations.

We consider the following quasilinear Schr\"{o}dinger equations of the form \begin{equation*} \triangle u-\varepsilon V(x)u+u\triangle u^2+u^{p}=0,\ u>0\ \mbox{in}\ \mathbb{R}^N\ \mbox{and}\ \underset{|x|\rightarrow \infty}{\lim} u(x)=0, \end{equation*} where $N\geq 3,$ $p>\frac{N+2}{N-2},$ $\varepsilon>0$ and $V(x)$ is a positive function. By imposing appropriate conditions on $V(x),$ we prove that, for $\varepsilon=1,$ the existence of infinity many positive solutions with slow decaying $O(|x|^{-\frac{2}{p-1}})$ at infinity if $p>\frac{N+2}{N-2}$ and, for $\varepsilon$ sufficiently small, a positive solution with fast decaying $O(|x|^{2-N})$ if $\frac{N+2}{N-2}<p<\frac{3N+2}{N-2}. $ The proofs are based on perturbative approach. To this aim, we also analyze the structure of positive solutions for the zero mass problem.
Accelerating AGN jets to parsec scales using general relativistic MHD simulations

Accreting black holes produce collimated outflows, or jets, that traverse many orders of magnitude in distance, accelerate to relativistic velocities, and collimate into tight opening angles. Of these, perhaps the least understood is jet collimation due to the interaction with the ambient medium. In order to investigate this interaction, we carried out axisymmetric general relativistic magnetohydrodynamic simulations of jets produced by a large accretion disc, spanning over 5 orders of magnitude in time and distance, at an unprecedented resolution. Supported by such a disc, the jet attains a parabolic shape, similar to the M87 galaxy jet, and the product of the Lorentz factor and the jet half-opening angle, γθ ≪ 1, similar to values found from very long baseline interferometry (VLBI) observations of active galactic nuclei (AGNs) jets; this suggests extended discs in AGNs. We find that the interaction between the jet and the ambient medium leads to the development of pinch instabilities, which produce significant radial and lateral variability across the jet by converting magnetic and kinetic energy into heat. Thus pinched regions in the jet can be detectable as radiating hotspots and may provide an ideal site for particle acceleration. Pinching also causes gas from the ambient medium to become squeezed between magnetic field lines in the jet, leading to enhanced mass loading and deceleration of the jet to non-relativistic speeds, potentially contributing to the spine-sheath structure observed in AGN outflows.
Camera Lens Super-Resolution

Existing methods for single image super-resolution (SR) are typically evaluated with synthetic degradation models such as bicubic or Gaussian downsampling. In this paper, we investigate SR from the perspective of camera lenses, named as CameraSR, which aims to alleviate the intrinsic tradeoff between resolution (R) and field-of-view (V) in realistic imaging systems. Specifically, we view the R-V degradation as a latent model in the SR process and learn to reverse it with realistic low- and high-resolution image pairs. To obtain the paired images, we propose two novel data acquisition strategies for two representative imaging systems (i.e., DSLR and smartphone cameras), respectively. Based on the obtained City100 dataset, we quantitatively analyze the performance of commonly-used synthetic degradation models, and demonstrate the superiority of CameraSR as a practical solution to boost the performance of existing SR methods. Moreover, CameraSR can be readily generalized to different content and devices, which serves as an advanced digital zoom tool in realistic imaging systems.
Human Intracranial EEG Quantitative Analysis and Automatic Feature Learning for Epileptic Seizure Prediction

Objective: The aim of this study is to develop an efficient and reliable epileptic seizure prediction system using intracranial EEG (iEEG) data, especially for people with drug-resistant epilepsy. The prediction procedure should yield accurate results in a fast enough fashion to alert patients of impending seizures. Methods: We quantitatively analyze the human iEEG data to obtain insights into how the human brain behaves before and between epileptic seizures. We then introduce an efficient pre-processing method for reducing the data size and converting the time-series iEEG data into an image-like format that can be used as inputs to convolutional neural networks (CNNs). Further, we propose a seizure prediction algorithm that uses cooperative multi-scale CNNs for automatic feature learning of iEEG data. Results: 1) iEEG channels contain complementary information and excluding individual channels is not advisable to retain the spatial information needed for accurate prediction of epileptic seizures. 2) The traditional PCA is not a reliable method for iEEG data reduction in seizure prediction. 3) Hand-crafted iEEG features may not be suitable for reliable seizure prediction performance as the iEEG data varies between patients and over time for the same patient. 4) Seizure prediction results show that our algorithm outperforms existing methods by achieving an average sensitivity of 87.85% and AUC score of 0.84. Conclusion: Understanding how the human brain behaves before seizure attacks and far from them facilitates better designs of epileptic seizure predictors. Significance: Accurate seizure prediction algorithms can warn patients about the next seizure attack so they could avoid dangerous activities. Medications could then be administered to abort the impending seizure and minimize the risk of injury.
Context-Aware Human Motion Prediction

The problem of predicting human motion given a sequence of past observations is at the core of many applications in robotics and computer vision. Current state-of-the-art formulates this problem as a sequence-to-sequence task, in which a historical of 3D skeletons feeds a Recurrent Neural Network (RNN) that predicts future movements, typically in the order of 1 to 2 seconds. However, one aspect that has been obviated so far, is the fact that human motion is inherently driven by interactions with objects and/or other humans in the environment. In this paper, we explore this scenario using a novel context-aware motion prediction architecture. We use a semantic-graph model where the nodes parameterize the human and objects in the scene and the edges their mutual interactions. These interactions are iteratively learned through a graph attention layer, fed with the past observations, which now include both object and human body motions. Once this semantic graph is learned, we inject it to a standard RNN to predict future movements of the human/s and object/s. We consider two variants of our architecture, either freezing the contextual interactions in the future of updating them. A thorough evaluation in the Whole-Body Human Motion Database shows that in both cases, our context-aware networks clearly outperform baselines in which the context information is not considered.