Miracle-dz/UniFAR-FTUs huggingface dataset:
CFD simulations of electric motor end ring cooling for improved thermal management

Proper thermal management of an electric motor for vehicle applications extends its operating range. One cooling approach is to impinge Automatic Transmission Fluid (ATF) onto the rotor end ring. Increased ATF coverage correlates to enhanced heat transfer. Computational Fluid Dynamics (CFD) analytical tools provide a mechanism to assess motor thermal management prior to hardware fabrication. The complexity of the fluid flow (e.g., jet atomization, interface tracking, wall impingement) and heat transfer makes these simulations challenging. Computational costs are high when solving these flows on high-speed rotating meshes. Typically, a Volume-of Fluid (VOF) technique (i.e., two-fluid system) is used to resolve ATF dynamics within this rotating framework. Suitable numerical resolution of the relevant physics for thin films under strong inertial forces at high rotor speeds is computationally expensive, further increasing the run times. In this work, a numerical study of rotor-ring cooling by ATF is presented using a patent automated Cartesian cut-cell based method coupled with Automatic Mesh Refinement (AMR). This approach automatically creates the Cartesian mesh on-the-fly and can effectively handle complex rotating geometries by adaptively refining the mesh based on local gradients in the flow field which results in better resolution of the air-ATF interface. A Single non-inertial Reference Frame (SRF) approach is used to account for the rotating geometry and to further improve the overall computational efficiency. Quasi-steady state conditions are targeted in the analysis of the results. Important physics such as ATF jet structure, velocity detail near the air-jet interface, ATF coverage/accumulation on the ring surface, and cooling capacity are presented for a low-resolution Reynolds averaged Navier-Stokes (RANS), high-resolution RANS, and high-resolution Large-Eddy Simulation (LES) models. Computations are scaled over hundreds of cores on a supercomputer to maximize turnaround time. Each numerical approach is shown to capture the general trajectory of the oil jet prior to surface impingement. The high-resolution LES simulation, however, is superior in capturing small scale details and heat transfer between the free jet and surrounding air.
Live Demonstration: Efficient Deep Learning Algorithm for Alzheimer's Disease Diagnosis using Retinal Images

This live demonstration presents Alzheimer's Disease (AD) detection using deep learning based smartphone application. The deep learning model is based on MobileNetV3 as a backbone, and an Attention mechanism is applied. However, we modified the backbone structure into U-Net-like architecture to perform better. Furthermore, we modified the conventional Attention mechanism to the Weighted Attention mechanism. The masking-adding process has been applied in the training method of the model.
Nomogram Based on Inflammatory Biomarkers to Predict the Recurrence of Hepatocellular Carcinoma—A Multicentre Experience

Purpose Our study aimed to identify inflammatory biomarkers and develop a prediction model to stratify high-risk patients for hepatitis B virus-associated hepatocellular carcinoma (HBV-HCC) recurrence after curative resection. Patients and Methods A total of 583 eligible HBV-HCC patients with curative hepatectomy from Guangdong Provincial People’s Hospital (GDPH) and Sun Ya-sen University Cancer Centre (SYSUCC) were enrolled in our study. Cox proportional hazards regression was utilized to evaluate potential risk factors for disease-free survival (RFS). The area under the receiver operating characteristic (ROC) curve (AUC) was utilized to assess the discrimination performance. Calibration plots and decision curve analyses (DCA) were used to evaluate the calibration of the nomogram and the net benefit, respectively. Results Based on the systemic inflammation response index (SIRI), aspartate aminotransferase to neutrophil ratio index (ANRI), China Liver Cancer (CNLC) stage and microvascular invasion, a satisfactory nomogram was developed. The AUC of our nomogram for predicting 1-, 2-, and 3-year RFS was 0.767, 0.726, and 0.708 in the training cohort and 0.761, 0.716, and 0.715 in the validation cohort, respectively. Furthermore, our model demonstrated excellent stratification as well as clinical applicability. Conclusion The novel nomogram showed a higher prognostic power for the RFS of HCC patients with curative hepatectomy than the CNLC, AJCC 8th edition and BCLC staging systems and may help oncologists identify high-risk HCC patients.
The meaning of land and place for children born of war in northern Uganda

ABSTRACT The abduction and forced marriage of females was a key military strategy of the Lord’s Resistance Army (LRA) during northern Uganda’s civil war (1986–2007). Thousands of abducted girls became pregnant from sexual violence, giving birth to large numbers of children born of war. This article explores the experiences of 85 children born in LRA captivity, with a particular focus on the implications and meaning of both land and place. In the aftermath of the war, these children have been compelled to integrate into families and communities that often reject them because of their birth origins, identities, and former LRA affiliation. Facing multiple post-war challenges including poverty, and discrimination, many participants sought to retrace and reconnect with their paternal families to secure a better future. We highlight the important role of both land and place in the lives of children born in LRA captivity, particularly as it relates to belonging, social acceptance, and financial security. We conclude with a discussion of the potential risks and benefits of family tracing endeavors with children born of war in northern Uganda.
DGGCN: Dictionary based DGA detection method based on DomainGraph and GCN

Nowadays, malware uses Algorithmically Generated Domains (AGDs) to establish communication with Command and Control (C&C) servers. Dictionary based Domain Generation Algorithm (DGA) selects words from the frequently changed dictionaries to generate AGDs similar to benign domains, which degrades the accuracy of string based detection method. To combat this, we propose a DGA detection method based on DomainGraph and GCN (Graph Convolutional Network) which detects cross-dictionary AGDs based on the association relation between domains instead of lexical features. Starting from the association relation between domains rather than the lexical features of the domain itself, we can detect the unknown AGDs from a known AGD, regardless of the DGA dictionary they use. The proposed method exploits the fact that string association of benign domains is weak, while AGDs' association is strong. DGGCN composes a domain segmentation method, constructs a graph composed of domains (DomainGraph) based on segmentations and adopts GCN to detect AGDs. We conduct the experiments on public datasets under three settings: detecting AGDs generated by familiar dictionaries, unfamiliar dictionaries and confusing dictionaries. The results reveal that DGGCN can detect cross-dictionary AGDs similar to benign domains more accurately and robustly.
Representation Theory of The $W_{1+\infty}$ Algebra

We review the recent development in the representation theory of the $W_{1+\infty}$ algebra. The topics that we concern are, Quasifinite representation, Free field realizations, (Super) Matrix Generalization, Structure of subalgebras such as $W_\infty$ algebra, Determinant formula, Character formula. (Invited talk at ``Quantum Field Theory, Integrable Models and Beyond", YITP, 14-17 February 1994. To appear in Progress of Theoretical Physics Proceedings Supplement.)
A Simple Method to Calculate the Torque of Magnet-Rotating-Type Axial Magnetic Coupler Using a New Magnetic Equivalent Circuit Model

A magnet-rotating-type axial magnetic coupler (MAMC), namely, a novel axial magnetic coupler with rotatable monopole permanent magnets (PMs), is proposed. The speed of the MAMC can be adjusted by rotating the rotatable PMs mounted on the PM disk, or changing the axial air gap length between the PM disk and the conductor sheet (CS), or combining these methods. In order to analyze the torque performance, the magnetic circuit model of MAMC is established when the rotatable PMs are not parallel to the CS. Specifically, the influence of the induced eddy current is introduced into a new magnetic equivalent circuit (MEC) model. Combined with Kirchhoff’s law, the calculation formula of torque is derived. Moreover, according to Ampere’s law, the formula for calculating the induced eddy current is derived when the rotatable PMs after rotating are not parallel to the CS. Finally, the effectiveness of the proposed model is verified by the finite element method (FEM). The results obtained by the new MEC model proposed are consistent with those obtained by the FEM, and the formula derived is effective in the case of small rotational speed differences, small air gap lengths, and small thicknesses of the CS.
Understanding Air Quality Changes After Implementation of Mitigation Measures during a Pandemic: A Scoping Review of literature in the United States

Traffic-related emissions continue to be a significant source of air pollution in the United States (US) and around the globe. Evidence has shown that previous policies implemented to restrict -traffic flows have affected air pollution levels. Thus, mitigation strategies associated with the COVID-19 pandemic that modified population-level mobility patterns provide a unique opportunity to study air pollution change across the US. For instance, to slow the spread of the pandemic, state and local governments started implementing various mitigation actions, including stay-at-home directives, social distancing measures, school closures, and travel restrictions. This scoping review aimed to summarize the existing evidence about how air quality changed through mitigation practices throughout the pandemic in the US. We found 66 articles that fit our inclusion criteria. Generally, the consolidated results revealed that nitrogen dioxide (NO2) and carbon monoxide (CO) decreased across the country. Studies observed mixed directions and magnitudes of change for fine and coarse particulate matter (PM2.5, PM10), ozone (O3), and sulfur dioxide (SO2). Few articles tried to explain this notable heterogeneity in air quality changes by associating contextual factors, such as mobility, traffic flow, and demographic factors. However, all studies agreed that the change in air pollution was nonuniform across the US and even varied within a city.
Active Input Design to Balance Fault Diagnosis and Tracking Control Performance

The fault diagnosis module is usually integrated as a key component of active fault-tolerant control systems. However, the classical methods usually design fault diagnosis and control modules, separately, which ignore the existing interactions between the two key modules in active fault-tolerant control systems. In order to deal with this problem, this paper establishes a framework for integrated design of active fault diagnosis (AFD) and control while ensuring the performance of tracking control. In particular, the input is designed to balance the goal of AFD with the reference output tracking performance. The design of inputs is formulated to minimize the sum of a quadratic function and a non-convex quadratic fractional function at each step, whose global optimality can be obtained under the tighten condition of convex relaxation characterized by Frobenius norm of matrix. Furthermore, despite the global optimal condition is not always achieved, an enough satisfactory suboptimal solution could still be obtained, and the bound of suboptimality can be strictly guaranteed theoretically. At the end, a practical four-tank system example is used to verify the effectiveness of our proposed method.
An Attention-based Neural Network on Multiple Speaker Diarization

Speaker diarization is a task to label audio or video recordings with classes that correspond to speaker identity for each point in time, which can be used in a multi-speaker conversation environment, such as a meeting or interview. Moreover, speaker diarization can be used to improve the performance of auto speech recognition. This paper presents an end-to-end diarization model based on an attention mechanism with data augmentation, several data pre-processing, and post-processing. In the CALLHOME data set, the case of two speakers reached a 9.12% diarization error rate. We combine the speaker diarization model, and auto speech recognition model and implement the transcript conversion system on an edge device. By using proposed speaker diarization as preprocessing to segment recording according to different speakers, then get the transcript of each segmented utterance by ASR model to fulfill the transcript conversion on the edge device. Experiment shows that our model also performs well in the scenario with two people on edge devices with both accuracy and inference time.
Improving Deep-Learning-based Optical Music Recognition for Camera-based Inputs

Recently, convolutional neural networks (CNNs) have demonstrated promising performance in optical music recognition (OMR). However, the recognition accuracy of an OMR is significantly degraded for a noisy image input given by a camera. To address the problem, this study investigates the use of well-known preprocessed techniques and a state-of-the-art deep learning OMR method. The proposed framework is, then, trained with the Camera-PrIMuS dataset. The experimental results demonstrate that the proposed approach improves the accuracy by 11.1% on average. We conclude that developing OMR algorithms to read camera-based input music scores is plausible and should be further explored.
Control Vectorial de inversores trifásicos / y 2

En este articulo, se va a estudiar la tecnica de modulacion por ancho de pulso del vector espacio (SVPWM), generalmente llamada control vectorial. El Control Vectorial o SVPWM de inversores trifasicos alimentados por fuente de tension (VSI o voltage source inverter) constituye una poderosa herramienta para el desarrollo y control de los convertidores de continua-alterna. 66
On Distributed Sampling for Mismatched Estimation of Remote Sources

In this work, we study the problem of distributed sampling for the recovery of a remote source under information mismatch at the estimator. In particular, a centralized estimator seeks to estimate a remote Gaussian random signal, where unlike in the ‘classical’ estimation setup, we assume that the estimator has a fixed, unknown mismatch vis-à-vis source statistics, in particular, the source covariance matrix. Such a mismatched estimator deploys multiple samplers in the field, where each sampler observes an independently noise corrupted version of the remote source and then forwards its sampled version to the estimator. The estimator has a fixed limit on the number of samples it can concurrently process; given such a total sampling budget, it seeks to distribute these samples optimally among samplers so as to obtain a reasonably high fidelity sampled noisy observation of the remote source via the samplers. Using this sampled data, the mismatched estimator then outputs a source estimate which minimizes distortion (i.e., the overall mean squared error). Our principal goal in this work is to understand the distortion-versus-sampling rate trade-off for the mismatched Gaussian source estimation problem under general distributed configurations. In the high-rate sampling regime, where the estimator has a ‘large’ sampling budget and essentially every sampler can operate at ‘high’ sampling rate, we show the interesting result that for a wide range of parameters, the optimal distributed sampling strategy is a uniform sampling strategy but one which, interestingly, does not depend on the mismatch at the estimator. We also characterize the optimal distortion, which we show does indeed depend on the degree of mismatch. Our results also bring to the fore an interesting phenomenon where the optimal distortion behaves asymmetrically w.r. t. the nature of mismatch, i.e., even for identical mismatch magnitude, the distortion is significantly different depending on the sign of the mismatch.
Re-Identification on Korean Penicillium Sequences in GenBank Collected by Software GenMine

Abstract Penicillium species have been actively studied in various fields, and many new and unrecorded species continue to be reported in Korea. Moreover, unidentified and misidentified Korean Penicillium species still exist in GenBank. Therefore, it is necessary to revise the Korean Penicillium inventory based on accurate identification. We collected Korean Penicillium nucleotide sequence records from GenBank using the newly developed software, GenMine, and re-identified Korean Penicillium based on the maximum likelihood trees. A total of 1681 Korean Penicillium GenBank nucleotide sequence records were collected from GenBank. In these records, 1208 strains with four major genes (Internal Transcribed Spacer rDNA region, β-tubulin, Calmodulin and RNA polymerase II) were selected for Penicillium re-identification. Among 1208 strains, 927 were identified, 82 were identified as other genera, the rest remained undetermined due to low phylogenetic resolution. Identified strains consisted of 206 Penicillium species, including 156 recorded species and 50 new species candidates. However, 37 species recorded in the national list of species in Korea were not found in GenBank. Further studies on the presence or absence of these species are required through literature investigation, additional sampling, and sequencing. Our study can be the basis for updating the Korean Penicillium inventory.
Graph Segmentation-Based Pseudo-Labeling for Semi-Supervised Pathology Image Classification

Pathology image classification is an important step in cancer diagnosis and precision treatment. Training a pathology image classification model in a fully supervised manner requires exhaustive pixel-level manual annotations from pathologists, which may not be practical in real applications. Semi-supervised learning (SSL) has been widely used to exploit large amounts of unlabeled data to facilitate model training with a small set of labeled data. However, due to the limited annotations, it still suffers from the issue of inaccurate pseudo-labels of unlabeled data. In this paper, we propose a novel framework for semi-supervised pathology image classification, which incorporates graph-based segmentation to refine initial pseudo-labels of tissue regions by considering local and global contextual relationships of patches in whole-slide images (WSIs). Moreover, we define a new energy function for graph construction that allows the graph to take into account the uncertainty of network predictions on unlabeled data. Extensive experiments on two different pathology image datasets demonstrate the effectiveness of our method compared with state-of-the-art SSL baselines. In particular, when using 5% labeled data, our approach outperforms a strong baseline by 2.81% AUC.
Community Food Webs: Data and Theory

Food webs hold a central place in ecology. They describe which organisms feed on which others in natural habitats. This book describes recently discovered empirical regularities in real food webs: it proposes a novel theory unifying many of these regularities, as well as extensive empirical data. After a general introduction, reviewing the empirical and theoretical discoveries about food webs, the second portion of the book shows that community food webs obey several striking phenomenological regularities. Some of these unify, regardless of habitat. Others differentiate, showing that habitat significantly influences structure. The third portion of the book presents a theoretical analysis of some of the unifying empirical regularities. The fourth portion of the book presents 13 community food webs. Collected from scattered sources and carefully edited, they are the empirical basis for the results in the volume. The largest available set of data on community food webs provides a valuable foundation for future studies of community food webs. The book is intended for graduate students, teachers and researchers primarily in ecology. The theoretical portions of the book provide materials useful to teachers of applied combinatorics, in particular, random graphs. Researchers in random graphs will find here unsolved mathematical problems.
Visualizing Realistic Benchmarked IDS Dataset: CIRA-CIC-DoHBrw-2020

Intrusion Detection System (IDS) dataset is crucial to detect lateral movement of cyber-attacks. IDS dataset will help to train the IDS classifier model to achieve earliest detection. A good near-realism public dataset is essential to assist the development of advanced IDS classifier models. However, the available public IDS dataset has long been under scrutiny for its practicality to reflect real low-footprint cyber threats, render real-time network scenario, reflect recent malware attack over newly developed DoH protocol, disregard layer 3 information and finally publish contradictory results of classification and analysis between various studies which makes it non-reproducible and without shareable results. This problem can be resolved by sophisticatedly visualizing a new realistic, real-time, low footprint and up-to-date benchmarked dataset. Visualization helps to detect data deformation before designing the optimized and highly accurate classifier model. Therefore, this study aims to review a new realistic benchmarked IDS dataset and apply sophisticated technique to visualize them. The review starts by carefully examining production network features. These are then compared with various well-established public IDS datasets. Many of them are static, unrealistic meta-features and disregard source and destination Internet Protocol (IP) information except CIRA-CIC-DoHBrw-2020 dataset. The study then applies Eigen Centrality (EC) technique from the graph theory to visualize this layer 3 (L3) information. Finally, using various visualization techniques such as Principal Component Analysis (PCA) and Gaussian Mixture Model (GMM), the study further analyzes and subsequently visualizes the data. Results show that the CIRA-CIC-DoHBrw-2020 simulated recent malware attack and has a very imbalanced dataset which reflects the realistic low-footprint cyber-attacks. The centrality graph clearly visualizes IPs that are compromised by recent DoH attack in real-time, and the study concludes decisively that smaller packet length of size 1000 to 2000 bytes is to fit an attack trait.
Functional biomaterials for tendon/ligament repair and regeneration

Abstract With an increase in life expectancy and the popularity of high-intensity exercise, the frequency of tendon and ligament injuries has also increased. Owing to the specificity of its tissue, the rapid restoration of injured tendons and ligaments is challenging for treatment. This review summarizes the latest progress in cells, biomaterials, active molecules and construction technology in treating tendon/ligament injuries. The characteristics of supports made of different materials and the development and application of different manufacturing methods are discussed. The development of natural polymers, synthetic polymers and composite materials has boosted the use of scaffolds. In addition, the development of electrospinning and hydrogel technology has diversified the production and treatment of materials. First, this article briefly introduces the structure, function and biological characteristics of tendons/ligaments. Then, it summarizes the advantages and disadvantages of different materials, such as natural polymer scaffolds, synthetic polymer scaffolds, composite scaffolds and extracellular matrix (ECM)-derived biological scaffolds, in the application of tendon/ligament regeneration. We then discuss the latest applications of electrospun fiber scaffolds and hydrogels in regeneration engineering. Finally, we discuss the current problems and future directions in the development of biomaterials for restoring damaged tendons and ligaments.