Miracle-dz/UniFAR-FTUs huggingface dataset:
Diagnostic methods for measurement of peripheral blood flow during exercise in patients with type 2 diabetes and peripheral artery disease: a systematic review.

INTRODUCTION Measurement of systolic ankle and to some extent toe pressure in patients suffering from combined type 2 diabetes mellitus (T2DM) and peripheral arterial disease (PAD) face several obstacles due to complex changes in the vascular bed. The aim of this review was to address the current literature on blood flow during exercise in patients with PAD and T2DM and assess the feasibility of these methods to diagnose and grade arterial insufficiency. EVIDENCE ACQUISITION A systematic review of the PubMed and EMBASE databases, supplemented by hand searching was performed according to PRISMA guidelines. Clinical studies evaluating methods to investigate peripheral blood flow in patients with PAD and T2DM during exercise were included. EVIDENCE SYNTHESIS In total nine eligible studies consisting of 1105 non-diabetic PAD patients, 336 diabetic PAD patients, 161 diabetic patients without PAD and 69 healthy controls were included in the review. Near-infrared spectroscopy (NIRS) was described in three studies, transcutaneous oxygen pressure measurement (TcpO2) in two and the following methods described in single studies: thermodilution, contrast enhanced ultrasound (CEUS), scintigraphy and TcpO2 in combination with ultrasound. These studies shows that patients with PAD and T2DM compared with patients with only PAD suffers different atherosclerotic lesions characterised by increased arterial stiffness and microcirculation abnormalities, not well differentiated by pressure measurement alone. Investigating patients with PAD and T2DM during exercise reveals that NIRS, TcpO2, CEUS, and scintigraphy have distinct advantages over ankle and toe-pressure. CONCLUSIONS Using methods like NIRS, TcpO2, CEUS and scintigraphy, peripheral blood flow during exercise can be measured at a detailed level and potentially improve future severity grading in patients with combined T2DM and PAD.
Analysis of Sensing Spectral for Signal Recovery under a Generalized Linear Model

We consider a nonlinear inverse problem y = f ( Ax ) , where observations y ∈ R m are the componentwise nonlinear transformation of Ax ∈ R m , x ∈ R n is the signal of interest and A is a known linear mapping. By properly specifying the nonlinear processing function, this model can be particularized to many signal processing problems, including compressed sensing and phase retrieval. Our main goal in this paper is to understand the impact of sensing matrices, or more speciﬁcally the spectrum of sensing matrices, on the difﬁculty of recovering x from y . Towards this goal, we study the performance of one of the most successful recovery methods, i.e. the expectation propagation algorithm (EP). We deﬁne a notion for the spikiness of the spectrum of A and show the importance of this measure in the performance of the EP. Whether the spikiness of the spectrum can hurt or help the recovery performance of EP depends on f . We deﬁne certain quantities based on the function f that enables us to describe the impact of the spikiness of the spectrum on EP recovery. Based on our framework, we are able to show that for instance, in phase-retrieval problems, matrices with spikier spectrums are better for EP, while in 1-bit compressed sensing problems, less spiky (ﬂatter) spectrums offer better recoveries. Our results unify and substantially generalize the existing results that compare sub-Gaussian and orthogonal matrices, and provide a platform toward designing optimal sensing systems.
Hybrid Spectrogram and Waveform Source Separation

Source separation models either work on the spectrogram or waveform domain. In this work, we show how to perform end-to-end hybrid source separation, letting the model decide which domain is best suited for each source, and even combining both. The proposed hybrid version of the Demucs architecture (Défossez et al., 2019) won the Music Demixing Challenge 2021 organized by Sony. This architecture also comes with additional improvements, such as compressed residual branches, local attention or singular value regularization. Overall, a 1.4 dB improvement of the Signal-To-Distortion (SDR) was observed across all sources as measured on the MusDB HQ dataset (Raﬁi et al., 2019), an improvement conﬁrmed by human subjective evaluation, with an overall quality rated at 2.83 out of 5 (2.36 for the non hybrid Demucs), and absence of contamination at 3.04 (against 2.37 for the non hybrid Demucs and 2.44 for the second ranking model submitted at the competition).
AIM: Automatic Interaction Machine for Click-Through Rate Prediction

Feature embedding learning and feature interaction modeling are two crucial components of deep models for Click-Through Rate (CTR) prediction in recommender systems. Most existing deep CTR models suffer from the following three problems. First, feature interactions are either manually designed or simply enumerated. However, not all the feature interactions are useful for the prediction task and useless feature interactions may introduce noisy signals thus causing overfitting. Second, all the feature interactions are modeled with an identical interaction function, whereas different interaction functions introduce different inductive biases to better capture various feature interaction patterns. Third, in most existing models, different features share the same embedding size. However, model size can be further optimized without sacrificing performance by differentiating embedding sizes for individual features, as the amount of information contained in each feature varies much. To address the three issues mentioned above, we propose Automatic Interaction Machine (AIM) with three core components, namely, Feature Interaction Search (FIS), Interaction Function Search (IFS) and Embedding Dimension Search (EDS), respectively. To tackle the first problem, FIS component automatically identifies different orders of essential feature interactions with useless ones pruned. Taking care of the second problem, IFS component selects appropriate interaction functions for each individual feature interaction in a learnable way. Moreover, to avoid learning conflict among different interaction functions, IFS proposes function-wise embeddings via performing multiple embeddings for each feature, where each feature embedding corresponds to one possible interaction function. However, utilizing multiple embeddings for each feature may make the model size affordably large if we keep the same embedding size as utilizing shared embedding (i.e., each feature shares the same embedding for different interaction functions). To solve this third problem, EDS automatically selects proper embedding size for each feature. Such a flexible embedding size adaptation is able to reduce the large amount of embedding parameters introduced by function-wise embeddings. Offline experiments on three large-scale datasets (two public benchmarks, one private dataset) validate that AIM can significantly improve various FM-based models. AIM has been deployed in the recommendation service of a mainstream app market, where a three-week online A/B test demonstrated the superiority of AIM, improving DeepFM model by 4.4% in terms of CTR.
Cyber Risk Frequency, Severity and Insurance Viability

In this study an exploration of insurance risk transfer is undertaken for the cyber insurance industry in the United States of America, based on the leading industry dataset of cyber events provided by Advisen. We seek to address two core unresolved questions. First, what factors are the most significant covariates that may explain the frequency and severity of cyber loss events and are they heterogeneous over cyber risk categories? Second, is cyber risk insurable in regards to the required premiums, risk pool sizes and how would this decision vary with the insured companies industry sector and size? We address these questions through a combination of regression models based on the class of Generalised Additive Models for Location Shape and Scale (GAMLSS) and a class of ordinal regressions. These models will then form the basis for our analysis of frequency and severity of cyber risk loss processes. We investigate the viability of insurance for cyber risk using a utility modelling framework with premium calculated by classical certainty equivalence analysis utilising the developed regression models. Our results provide several new key insights into the nature of insurability of cyber risk and rigorously address the two insurance questions posed in a real data driven case study analysis.
Learning to Manipulate Tools by Aligning Simulation to Video Demonstration

A seamless integration of robots into human environments requires robots to learn how to use existing human tools. Current approaches for learning tool manipulation skills mostly rely on expert demonstrations provided in the target robot environment, for example, by manually guiding the robot manipulator or by teleoperation. In this work, we introduce an automated approach that replaces an expert demonstration with a Youtube video for learning a tool manipulation strategy. The main contributions are twofold. First, we design an alignment procedure that aligns the simulated environment with the real-world scene observed in the video. This is formulated as an optimization problem that finds a spatial alignment of the tool trajectory to maximize the sparse goal reward given by the environment. Second, we describe an imitation learning approach that focuses on the trajectory of the tool rather than the motion of the human. For this we combine reinforcement learning with an optimization procedure to find a control policy and the placement of the robot based on the tool motion in the aligned environment. We demonstrate the proposed approach on spade, scythe and hammer tools in simulation, and show the effectiveness of the trained policy for the spade on a real Franka Emika Panda robot demonstration.
Threat Scenarios and Monitoring Requirements for Cyber-Physical Systems of Energy Flexibility Markets

—The ongoing integration of renewable generation and distributed energy resources introduces new challenges to distribution grid operation. Due to the increasing volatility and uncertainty, distribution system operators are seeking concepts to enable more active management and control. Flexibility markets offer a platform for economically efﬁcient trading of electricity ﬂexibility between distribution system operators and other participants. The integration of cyber, physical and market domains of multiple participants makes ﬂexibility markets a system of cyber-physical systems. While cross-domain integration sets the foundation for efﬁcient deployment of ﬂexibility, it introduces new physical and cyber vulnerabilities to participants. This work provides a systematic overview of threat scenarios for the cyber-physical systems of ﬂexibility markets. Results reveal several remaining security challenges across all domains, that require more attention. Based on the threat scenarios and a novel generic monitoring requirements model, monitoring concepts for secure participation of distribution system operators in ﬂexibility markets are proposed. By highlighting approaches to tackle identiﬁed challenges, this work provides new directions for further investigations, and takes important steps towards secure implementation of ﬂexibility markets.
Dynamic Human-Robot Role Allocation based on Human Ergonomics Risk Prediction and Robot Actions Adaptation

Even though cobots have high potential in bringing several benefits in the manufacturing and logistic processes, their rapid (re-)deployment in changing environments is still limited. To enable fast adaptation to new product demands and to boost the fitness of the human workers to the allocated tasks, we propose a novel method that optimizes assembly strategies and distributes the effort among the workers in human-robot cooperative tasks. The cooperation model exploits AND/OR Graphs that we adapted to solve also the role allocation problem. The allocation algorithm considers quantitative measurements that are computed online to describe human operators' ergonomic status and task properties. We conducted preliminary experiments to demonstrate that the proposed approach succeeds in controlling the task allocation process to ensure safe and ergonomic conditions for the human worker.
Learning Large Neighborhood Search Policy for Integer Programming

We propose a deep reinforcement learning (RL) method to learn large neighborhood search (LNS) policy for integer programming (IP). The RL policy is trained as the destroy operator to select a subset of variables at each step, which is reoptimized by an IP solver as the repair operator. However, the combinatorial number of variable subsets prevents direct application of typical RL algorithms. To tackle this challenge, we represent all subsets by factorizing them into binary decisions on each variable. We then design a neural network to learn policies for each variable in parallel, trained by a customized actor-critic algorithm. We evaluate the proposed method on four representative IP problems. Results show that it can find better solutions than SCIP in much less time, and significantly outperform other LNS baselines with the same runtime. Moreover, these advantages notably persist when the policies generalize to larger problems. Further experiments with Gurobi also reveal that our method can outperform this state-of-the-art commercial solver within the same time limit.
A practical analysis of ROP attacks

Control Flow Hijacking attacks have posed a serious threat to the security of applications for a long time where an attacker can damage the control Flow Integrity of the program and execute arbitrary code. These attacks can be performed by injecting code in the program's memory or reusing already existing code in the program (also known as Code-Reuse Attacks). Code-Reuse Attacks in the form of Return-into-libc Attacks or Return-Oriented Programming Attacks are said to be Turing Complete, providing a guarantee that there will always exist code segments (also called ROP gadgets) within a binary allowing an attacker to perform any kind of function by building a suitable ROP chain (chain of ROP gadgets). Our goal is to study different techniques of performing ROP Attacks and find the difficulties encountered to perform such attacks. For this purpose, we have designed an automated tool which works on 64-bit systems and generates a ROP chain from ROP gadgets to execute arbitrary system calls.
Diagnosis methods for stator winding faults in three‐phase squirrel‐cage induction motors

SUMMARY Induction motors condition monitoring is a very important process for immediate detection of the incipient faults. This prevents the spread of the faults up to risky degrees and avoids catastrophic stop of the motor and relevant production line. The stator winding fault is one of the most commonly occurring faults in alternating current machines. This paper is a comprehensive survey on the stator winding faults causes, diagnosis methods, indexes, and modeling and simulation methods. An efficient method is used to model and simulate the squirrel-cage induction motor performance under inter-turn fault. The results are used to obtain the performance of some common indexes of the fault and recognize the most proper index. Finally, some simulation results are compared with the corresponding experimental results and good agreements are achieved. Copyright © 2013 John Wiley & Sons, Ltd.
Paradigm Shift in Language Modeling: Revisiting CNN for Modeling Sanskrit Originated Bengali and Hindi Language

Though there has been a large body of recent works in language modeling (LM) for high resource languages such as English and Chinese, the area is still unexplored for low resource languages like Bengali and Hindi. We propose an end to end trainable memory efficient CNN architecture named CoCNN to handle specific characteristics such as high inflection, morphological richness, flexible word order and phonetical spelling errors of Bengali and Hindi. In particular, we introduce two learnable convolutional sub-models at word and at sentence level that are end to end trainable. We show that state-of-the-art (SOTA) Transformer models including pretrained BERT do not necessarily yield the best performance for Bengali and Hindi. CoCNN outperforms pretrained BERT with 16X less parameters, and it achieves much better performance than SOTA LSTM models on multiple real-world datasets. This is the first study on the effectiveness of different architectures drawn from three deep learning paradigms - Convolution, Recurrent, and Transformer neural nets for modeling two widely used languages, Bengali and Hindi.
Hierarchically Integrated Models: Learning to Navigate from Heterogeneous Robots

Deep reinforcement learning algorithms require large and diverse datasets in order to learn successful policies for perception-based mobile navigation. However, gathering such datasets with a single robot can be prohibitively expensive. Collecting data with multiple different robotic platforms with possibly different dynamics is a more scalable approach to large-scale data collection. But how can deep reinforcement learning algorithms leverage such heterogeneous datasets? In this work, we propose a deep reinforcement learning algorithm with hierarchically integrated models (HInt). At training time, HInt learns separate perception and dynamics models, and at test time, HInt integrates the two models in a hierarchical manner and plans actions with the integrated model. This method of planning with hierarchically integrated models allows the algorithm to train on datasets gathered by a variety of different platforms, while respecting the physical capabilities of the deployment robot at test time. Our mobile navigation experiments show that HInt outperforms conventional hierarchical policies and single-source approaches.
Current State of Intellectual Property in Microfluidic Nucleic Acid Analysis

The development of novel fabrication methods, materials and surface chemistries to implement nucleic acid analysis brings reduced cost, reduced reagent consumption, increased analysis efficiency, portability, ease of use and reliability to today's genomic approach. This trend, as evident by the exponential growth in the number of patent applications, granted patents and commercialized systems, is motivated by the promise for significant breakthroughs and benefits of nucleic acid analysis to drug discovery and point-of-care diagnosis. This review paper aims at identifying the enabling technologies and key patents in microfluidics for nucleic acid analysis. In particular, it seeks to identify granted and pending patents for cell sorting and lysis, nucleic acid extraction and purification, followed by nucleic acid amplification, separation and detection. Additionally, it presents an overview of the current intellectual property environment and seeks to identify trends for the future development. Much of this development is geared increasingly toward fully integrated systems. The convergence of technology and interdisciplinary interests is expected to foster further breakthroughs and commercialization.
Segmentation of 2D Brain MR Images

Brain tumour segmentation is an essential task in medical image processing. Early diagnosis of brain tumours plays a crucial role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of the brain tumours for cancer diagnosis, from large number of MRI images, is both a difficult and time-consuming task. There is a need for automatic brain tumour image segmentation. The purpose of this project is to provide an automatic brain tumour segmentation method of MRI images to help locate the tumour accurately and quickly.
HOPE in action: A prospective multicenter pilot study of liver transplantation from donors with HIV to recipients with HIV

Liver transplantation (LT) from donors‐with‐HIV to recipients‐with‐HIV (HIV D+/R+) is permitted under the HOPE Act. There are only three international single‐case reports of HIV D+/R+ LT, each with limited follow‐up. We performed a prospective multicenter pilot study comparing HIV D+/R+ to donors‐without‐HIV to recipients‐with‐HIV (HIV D−/R+) LT. We quantified patient survival, graft survival, rejection, serious adverse events (SAEs), human immunodeficiency virus (HIV) breakthrough, infections, and malignancies, using Cox and negative binomial regression with inverse probability of treatment weighting. Between March 2016–July 2019, there were 45 LTs (8 simultaneous liver‐kidney) at 9 centers: 24 HIV D+/R+, 21 HIV D−/R+ (10 D− were false‐positive). The median follow‐up time was 23 months. Median recipient CD4 was 287 cells/µL with 100% on antiretroviral therapy; 56% were hepatitis C virus (HCV)‐seropositive, 13% HCV‐viremic. Weighted 1‐year survival was 83.3% versus 100.0% in D+ versus D− groups (p = .04). There were no differences in one‐year graft survival (96.0% vs. 100.0%), rejection (10.8% vs. 18.2%), HIV breakthrough (8% vs. 10%), or SAEs (all p > .05). HIV D+/R+ had more opportunistic infections, infectious hospitalizations, and cancer. In this multicenter pilot study of HIV D+/R+ LT, patient and graft survival were better than historical cohorts, however, a potential increase in infections and cancer merits further investigation.
A Scheme to Improve the Accuracy of Antenna Impedance Detection

The detection of antenna impedance is the core component of the Ultra-short-wave modulation System. The main scheme of current engineering application environment is vector impedance detection. This scheme is widely used because it has the advantages of high accuracy and wide range of measurable frequencies based on vector network and transmission line theory. However, the original vector impedance detection scheme has extremely high requirements on the performance of the circuit, and the system error in the high-frequency environment is difficult to restrain by calibration. In order to solve the above problems, this paper proposes a scheme based on Integrated Learning. The program builds a supervised machine learning model to suppress system errors through the data acquisition of standard devices thus improve the accuracy of impedance inspection The test results show that the impedance detection scheme based on Integrated Learning improves the accuracy significantly compared to the original vector impedance detection method.
Hysteresis current control of hybrid active power filters

This article presents a hysteresis pulse width modulation study for a inductor-capacitor (LC)-coupling hybrid active power filter (HAPF). As the coupling LC impedance yields a non-linear inverter current slope, this can affect the controllability of using the conventional hysteresis control method and generate unexpected trigger signals to the switching devices. This results in deteriorating the system operating performances. On the basis of the proposed modelling, the linearisation of the hysteresis control for the HAPF is firstly studied, investigated and compared with the linear active power filter. Two limits are proposed in this study that divides the HAPF into non-linear, quasi-linear and linear operation regions. The design criteria of hysteresis band and sampling time can then be derived. Single-phase simulation and experimental results are given to verify the hysteresis control study of HAPF compared with active power filter. Finally, representative simulation and experimental results of a three-phase four-wire centre-split HAPF for power quality compensation are presented to demonstrate the validity of the hysteresis linearisation study.