Miracle-dz/UniFAR-FTUs huggingface dataset:
RecVAE: A New Variational Autoencoder for Top-N Recommendations with Implicit Feedback

Recent research has shown the advantages of using autoencoders based on deep neural networks for collaborative filtering. In particular, the recently proposed Mult-VAE model, which used the multinomial likelihood variational autoencoders, has shown excellent results for top-N recommendations. In this work, we propose the Recommender VAE (RecVAE) model that originates from our research on regularization techniques for variational autoencoders. RecVAE introduces several novel ideas to improve Mult-VAE, including a novel composite prior distribution for the latent codes, a new approach to setting the beta hyperparameter for the beta-VAE framework, and a new approach to training based on alternating updates. In experimental evaluation, we show that RecVAE significantly outperforms previously proposed autoencoder-based models, including Mult-VAE and RaCT, across classical collaborative filtering datasets, and present a detailed ablation study to assess our new developments. Code and models are available at https://github.com/ilya-shenbin/RecVAE.
Performance analysis of a 2.45 GHz microwave plasma torch for CO2 decomposition in gas swirl configuration

Microwave plasmas are a promising technology for energy-efficient CO2 valorization via conversion of CO2 into CO and O2 using renewable energies. A 2.45 GHz microwave plasma torch with swirling CO2 gas flow is studied in a large pressure (20–1000 mbar) and flow (1–100 L min−1) range. Two different modes of the plasma torch, depending on the operating pressure and microwave input power, are described: at pressures below 120 mbar the plasma fills most of the plasma torch volume whereas at pressures of about 120 mbar an abrupt contraction of the plasma in the center of the resonator is observed along with an increase of the gas temperature from 3000 K to 6000 K. The CO outflow is generally found to be proportional to the plasma effective power and exhibits no significant dependence on the actual CO2 flow injected into the reactor but only on the input power at certain pressure. Thermal dissociation calculations show that, even at the lowest pressures of this study, the observed conversion and energy efficiency are compatible with a thermal dissociation mechanism.
Robustness of brain tumor segmentation

Abstract. Purpose: The segmentation of brain tumors is one of the most active areas of medical image analysis. While current methods perform superhuman on benchmark data sets, their applicability in daily clinical practice has not been evaluated. In this work, we investigate the generalization behavior of deep neural networks in this scenario. Approach: We evaluate the performance of three state-of-the-art methods, a basic U-Net architecture, and a cascadic Mumford–Shah approach. We also propose two simple modifications (which do not change the topology) to improve generalization performance. Results: In these experiments, we show that a well-trained U-network shows the best generalization behavior and is sufficient to solve this segmentation problem. We illustrate why extensions of this model in a realistic scenario can be not only pointless but even harmful. Conclusions: We conclude from these experiments that the generalization performance of deep neural networks is severely limited in medical image analysis especially in the area of brain tumor segmentation. In our opinion, current topologies are optimized for the actual benchmark data set but are not directly applicable in daily clinical practice.
Fluctuation dynamo in collisionless and weakly collisional, magnetized plasmas

In weakly collisional astrophysical plasmas, such as the intracluster medium of galaxy clusters, the amplification of cosmic magnetic fields by chaotic fluid motions is hampered by the adiabatic production of magnetic-field-aligned pressure anisotropy. This anisotropy drives a viscous stress parallel to the field that inhibits the plasma's ability to stretch magnetic-field lines. I demonstrate through the use of kinetic simulations that in high-$\beta$ plasmas, kinetic ion-Larmor scale instabilities sever the adiabatic link between the thermal and magnetic pressures, reducing this viscous stress and thereby allowing the dynamo to operate. Two distinct regimes of the fluctuation dynamo in a magnetized plasma are identified: one in which these instabilities efficiently regulate the pressure anisotropy and one in which this regulation is imperfect. I elucidate the role of these kinetic instabilities on the plasma viscosity and determine how the fields and flows self-organize to allow the dynamo to operate in the face of parallel viscous stresses. In the case of efficient pressure-anisotropy regulation, the plasma dynamo closely resembles its more traditional Pm ~ 1 MHD counterpart. When the regulation is imperfect, the dynamo exhibits characteristics remarkably similar to those found in the saturated state of the MHD dynamo. A novel set of microphysical closures for fluid simulations that bridges these two regimes are constructed, exhibiting explosive magnetic-field growth caused by a field-strength-dependent viscosity. The dynamos in both collisionless and weakly collisional plasmas are then closely compared to each other, revealing substantial differences in how sub-parallel viscous motions behave. The former (collisionless) scenario experiences a cascade of stretching motions to sub-Larmor scales that lead to increasingly fast dynamo as the magnetic Reynolds number is increased.
An error bound for Lasso and Group Lasso in high dimensions

We leverage recent advances in high-dimensional statistics to derive new L2 estimation upper bounds for Lasso and Group Lasso in high-dimensions. For Lasso, our bounds scale as $(k^*/n) \log(p/k^*)$---$n\times p$ is the size of the design matrix and $k^*$ the dimension of the ground truth $\boldsymbol{\beta}^*$---and match the optimal minimax rate. For Group Lasso, our bounds scale as $(s^*/n) \log\left( G / s^* \right) + m^* / n$---$G$ is the total number of groups and $m^*$ the number of coefficients in the $s^*$ groups which contain $\boldsymbol{\beta}^*$---and improve over existing results. We additionally show that when the signal is strongly group-sparse, Group Lasso is superior to Lasso.
Local versions of the Wiener–Lévy theorem

Let $h$ be a real-analytic function on the neighborhood of some compact set $K$ on the plane, and let $f(y)$ be the Fourier--Stieltjes transform of a complex measure of a finite total variation without singular components on the Euclidean space. Then there exists another measure of a finite total variation with the Fourier--Stieltjes transform $g(y)$ such that $g(y)=h(f(y))$ whenever the value $f(y)$ belongs to $K$.
Structural defects in MBE-grown CdTe-based heterojunctions for photovoltaic applications

Structural defects in the p-ZnTe/i-CdTe/n-CdTe single-crystalline heterojunctions designed for photovoltaic applications have been investigated by transmission electron microscopy (TEM) and deep-level transient spectroscopy (DLTS). Lattice parameters and misfit strain in the undoped CdTe absorber layers of the heterojunctions, grown by the molecular-beam epitaxy technique on two different substrates, GaAs and CdTe, have been determined with high-resolution X-ray diffractometry. A dense network of misfit dislocations at the lattice-mismatched CdTe/GaAs and ZnTe/CdTe interfaces and numerous threading dislocations and stacking faults have been shown by the cross-sectional TEM imaging of the heterojunctions. The DLTS measurements revealed five deep-level traps in the heterojunctions grown on the GaAs substrates and only three of them in the heterojunctions grown on CdTe. One of the traps, showing the exponential capture kinetics of charge carriers, has been identified as associated with the double acceptor level of Cd vacancies in the CdTe absorber layers. All the other traps have been attributed to the electronic states of extended defects, presumably dislocations, on the grounds of their logarithmic capture kinetics. Two of these traps, displaying the largest values of their capture cross-section and the properties characteristic of bandlike electronic states, have been ascribed to the core states of dislocations. It is argued that they are most likely responsible for decreased lifetime of photo-excited carriers resulting in a low energy conversion efficiency of solar cells based on similarly grown heterojunctions.
Non-small-bowel lesions detected by capsule endoscopy in patients with obscure GI bleeding

Background: Approximately two thirds of patients undergoing capsule endoscopy for obscure GI bleeding will have an abnormality found in the small intestine. This report describes 9 patients (4 men, 5 women) of 140 with obscure bleeding in whom a source of their blood loss was found in the stomach or the colon at capsule endoscopy. Methods: A review was made of a prospective database of 140 consecutive patients undergoing capsule endoscopy for obscure GI bleeding at a single center. Patients with a definite or likely cause of bleeding within reach of conventional upper or lower GI endoscopy were identified. Results: Three patients had gastric antral vascular ectasia and another an inflamed pyloric canal polyp. Two patients had actively bleeding cecal carcinoma, missed at previous colonoscopies. Two others had bleeding cecal angiodysplasia. The final patient had severe nonspecific cecal inflammation. The identification of these lesions was aided by the suspected blood indicator. All patients underwent endoscopic therapy or surgery for their non-small-bowel lesions. Conclusions: Like push enteroscopy, capsule endoscopy also can identify lesions within reach of conventional endoscopy and colonoscopy. These subsequently can be treated successfully. The reasons why these lesions have been missed are unclear.
Geometry-aware Generation of Adversarial and Cooperative Point Clouds

Machine learning models have been shown to be vulnerable to adversarial examples. While most of the existing methods for adversarial attack and defense work on the 2D image domain, a few recent attempts have been made to extend them to 3D point cloud data. However, adversarial results obtained by these methods typically contain point outliers, which are both noticeable and easy to defend against using the simple techniques of outlier removal. Motivated by the different mechanisms by which humans perceive 2D images and 3D shapes, in this paper we propose the new design of \emph{geometry-aware objectives}, whose solutions favor (the discrete versions of) the desired surface properties of smoothness and fairness. To generate adversarial point clouds, we use a targeted attack misclassification loss that supports continuous pursuit of increasingly malicious signals. Regularizing the targeted attack loss with our proposed geometry-aware objectives results in our proposed method, Geometry-Aware Adversarial Attack ($GeoA^3$). The results of $GeoA^3$ tend to be more harmful, arguably harder to defend against, and of the key adversarial characterization of being imperceptible to humans. While the main focus of this paper is to learn to generate adversarial point clouds, we also present a simple but effective algorithm termed $Geo_{+}A^3$-IterNormPro, with Iterative Normal Projection (IterNorPro) that solves a new objective function $Geo_{+}A^3$, towards surface-level adversarial attacks via generation of adversarial point clouds. We quantitatively evaluate our methods on both synthetic and physical objects in terms of attack success rate and geometric regularity. For a qualitative evaluation, we conduct subjective studies by collecting human preferences from Amazon Mechanical Turk. Comparative results in comprehensive experiments confirm the advantages of our proposed methods.
Wigner crystals in two-dimensional transition-metal dichalcogenides: Spin physics and readout

Wigner crystals are prime candidates for the realization of regular electron lattices under minimal requirements on external control and electronics. However, several technical challenges have prevented their detailed experimental investigation and applications to date. We propose an implementation of two-dimensional electron lattices for quantum simulation of Ising spin systems based on self-assembled Wigner crystals in transition-metal dichalcogenides. We show that these semiconductors allow for minimally invasive all-optical detection schemes of charge ordering and total spin. For incident light with optimally chosen beam parameters and polarization, we predict a strong dependence of the transmitted and reflected signals on the underlying lattice periodicity, thus revealing the charge order inherent in Wigner crystals. At the same time, the selection rules in transition-metal dichalcogenides provide direct access to the spin degree of freedom via Faraday rotation measurements.
BETANAS: BalancEd TrAining and selective drop for Neural Architecture Search

Automatic neural architecture search techniques are becoming increasingly important in machine learning area. Especially, weight sharing methods have shown remarkable potentials on searching good network architectures with few computational resources. However, existing weight sharing methods mainly suffer limitations on searching strategies: these methods either uniformly train all network paths to convergence which introduces conflicts between branches and wastes a large amount of computation on unpromising candidates, or selectively train branches with different frequency which leads to unfair evaluation and comparison among paths. To address these issues, we propose a novel neural architecture search method with balanced training strategy to ensure fair comparisons and a selective drop mechanism to reduce conflicts among candidate paths. The experimental results show that our proposed method can achieve a leading performance of 79.0% on ImageNet under mobile settings, which outperforms other state-of-the-art methods in both accuracy and efficiency.
Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview

An increasing number of natural language processing papers address the effect of bias on predictions, introducing mitigation techniques at different parts of the standard NLP pipeline (data and models). However, these works have been conducted individually, without a unifying framework to organize efforts within the field. This situation leads to repetitive approaches, and focuses overly on bias symptoms/effects, rather than on their origins, which could limit the development of effective countermeasures. In this paper, we propose a unifying predictive bias framework for NLP. We summarize the NLP literature and suggest general mathematical definitions of predictive bias. We differentiate two consequences of bias: outcome disparities and error disparities, as well as four potential origins of biases: label bias, selection bias, model overamplification, and semantic bias. Our framework serves as an overview of predictive bias in NLP, integrating existing work into a single structure, and providing a conceptual baseline for improved frameworks.
How to Train Your Deep Multi-Object Tracker

The recent trend in vision-based multi-object tracking (MOT) is heading towards leveraging the representational power of deep learning to jointly learn to detect and track objects. However, existing methods train only certain sub-modules using loss functions that often do not correlate with established tracking evaluation measures such as Multi-Object Tracking Accuracy (MOTA) and Precision (MOTP). As these measures are not differentiable, the choice of appropriate loss functions for end-to-end training of multi-object tracking methods is still an open research problem. In this paper, we bridge this gap by proposing a differentiable proxy of MOTA and MOTP, which we combine in a loss function suitable for end-to-end training of deep multi-object trackers. As a key ingredient, we propose a Deep Hungarian Net (DHN) module that approximates the Hungarian matching algorithm. DHN allows estimating the correspondence between object tracks and ground truth objects to compute differentiable proxies of MOTA and MOTP, which are in turn used to optimize deep trackers directly. We experimentally demonstrate that the proposed differentiable framework improves the performance of existing multi-object trackers, and we establish a new state of the art on the MOTChallenge benchmark. Our code is publicly available from https://github.com/yihongXU/deepMOT.
Characteristics of Traumatic Urogenital Injuries in Emergency Department; a 10-year Cross-sectional Study

Introduction: Urogenital system injuries (UGIs) are seen in 10% of adult cases with multiple trauma. Although UGIs are rarely life threatening, they can cause major long-term morbidities. This study aimed to evaluate the characteristics of traumatic UGIs in patients who were referred to emergency department following multiple traumas. Methods: This retrospective cross-sectional study was conducted on multiple trauma patients who were presented to emergency department during a 10-year period (2008-2017). All patients with kidney, ureter, bladder, urethra, or external genitalia injuries were studied. The patients’ data were extracted from their clinical profiles. Results: Out of the 13598 admitted patients in our trauma center, UGIs were seen in 267 (1.9%) cases. The mean age of patients with UGIs was 27.3 ± 6.1 years (74.15% male). The highest incidence of UGI was seen in those aged between 21 and 30 years (39.7%) and motorcycle accidents (49%) was the most frequent cause of trauma. 221 patients had an unstable situation and were emergently transferred to operation room (13.57% with traumatic kidney injury). The most common injured sites of urogenital system were kidney with 155 (58%) cases, followed by external genitalia with 91 (34.1%) cases. 77.5% of cases were managed conservatively and the rest (22.5%) underwent surgical procedures. Conclusion: UGIs comprise a low percentage (2%) of traumatic injuries, which are mostly caused by blunt trauma due to road traffic accidents. Kidney is the most common injured organ and UGIs mostly happen in young ages.
Crowdsourcing visual perception experiments: a case of contrast threshold

Crowdsourcing has commonly been used for psychological research but not for studies on sensory perception. A reason is that in online experiments, one cannot ensure that the rigorous settings required for the experimental environment are replicated. The present study examined the suitability of online experiments on basic visual perception, particularly the contrast threshold. We conducted similar visual experiments in the laboratory and online, employing three experimental conditions. The first was a laboratory experiment, where a small sample of participants (n = 24; laboratory condition) completed a task with 10 iterations. The other two conditions were online experiments: participants were either presented with a task without repetition of trials (n = 285; online non-repetition condition) or one with 10 iterations (n = 166; online repetition condition). The results showed significant equivalence in the contrast thresholds between the laboratory and online repetition conditions, although a substantial amount of data needed to be excluded from the analyses in the latter condition. The contrast threshold was significantly higher in the online non-repetition condition compared with the laboratory and online repetition conditions. To make crowdsourcing more suitable for investigating the contrast threshold, ways to reduce data wastage need to be formulated.
Incidence of Corrective Procedures After Nonoperatively Managed Distal Radius Fractures in the Elderly

Introduction: Although the majority distal radius fractures in the elderly are initially managed nonoperatively, the true incidence of subsequent corrective surgery is unknown. The purpose of this study was to determine the incidence and predictors of corrective surgery after conservative management. Methods: ICD-9 and Current Procedural Terminology codes were queried from the Medicare 5% sample to select patients aged 65 years and older undergoing nonsurgical treatment of distal radius fractures with a minimum 5-year follow-up. Rates of subsequent ipsilateral wrist surgery were correlated against patient age, sex, geographic region, and initial closed reduction. Results: Five thousand eighty patients with a mean age of 78.3 years were included. Fifty-five patients (1.1%) had undergone subsequent wrist surgery at a median time of 182 days after injury. The youngest cohort (65 to 69 years) had a significantly higher operation rate (1.9%, P = 0.007) than the oldest cohort (80+ years) (0.5%, P = 0.004). There was no notable difference in corrective procedures between sex, geographic region, and initial closed reduction. Discussion: Once surgical intervention is deemed unnecessary per standard guidelines, the data support successful nonsurgical management in a large majority of patients but highlight a small subset of younger patients who remain at increased risk of requiring additional surgery.
PIRO, SOFA and MEDS Scores in Predicting One-Month Mortality of Sepsis Patients; a Diagnostic Accuracy Study

Introduction: Different scoring systems based on clinical and laboratory findings are designed for prediction of short-term mortality of patients with severe sepsis and septic shock. This study aimed to compare the screening performance characteristics of PIRO, SOFA and MEDS Scores in predicting one-month mortality of sepsis patients. Methods: This diagnostic accuracy study was performed on septic shock and severe sepsis patients referring to emergency department of Loghmane Hakim Hospital, Tehran, Iran, from 2017 to 2018. The performance of MEDS, SOFA, and PIRO models in predicting 30-day mortality of patients was evaluated using discrimination and calibration indices. Results: 200 patients with the mean age of 71.03±15.59 years were studied (61% male). During the 30 days, 66 patients died (mortality rate=33%). The area under the ROC curve of PIRO, MEDS, and SOFA scores were 0.83 (95% CI=0.78-0.89), 0.94 (95% CI=0.91-0.97) and 0.87 (95% CI=0.81-0.92), respectively. Based on Brier, BrierScaled and Nagelkerke’s R2 of the models, the best performance in predicting one-month mortality belonged to MEDS score. C-statistic showed that MEDS score had the highest value in the differentiation between the survived and non-survived cases. Conclusion: This study showed that MEDS score performs better than PIRO and SOFA scores in predicting one-month mortality of patients with severe sepsis and septic shock.