Miracle-dz/UniFAR-FTUs huggingface dataset:
An Intelligent Control of Electronic Limited Slip Differential for Improving Vehicle Yaw Stability

This paper investigates the problem of improving vehicle stability using an electronic limited slip differential (ELSD). Unlike brake-based vehicle stability control systems, electronic limited slip differentials have the potential to control a vehicle's yaw stability without slowing down its speed. In this study, first, an ELSD model is designed to properly predict ELSD torque distribution. The model's accuracy is evaluated using experimental test data. Then, an intelligent ELSD control design is proposed using the developed ELSD model, capable of working in both off-throttle and on-throttle scenarios. The proposed design, which is based on a model predictive control approach, offers control of the ELSD differential by directly controlling the ELSD clutch pressure. This control design enables the controller to prevent unwanted oversteering yaw moments and avoid chattering in the ELSD clutch. The controller is implemented in a Cadillac CTS vehicle to evaluate its performance experimentally in various driving maneuvers.
Audience expansion based on user browsing history

A huge number of display advertising campaigns are launched every day by advertisers in order to promote their products or services. The main objective of each advertiser is to display ads to specific groups of users, i.e. users who meet specific criteria or their interests are related to the promoted products or services. Audience expansion, also known as audience look-alike targeting, is one of the major display advertising techniques that helps advertisers to discover audiences with similar attributes to a target audience who is interested in advertisers' products or services. In this paper, we present different audience expansion schemes able to identify users with similar browsing interests to those of the seed users provided by the advertiser. The proposed audience expansion schemes are based on different unsupervised representation models that are able to capture the interests of the users according to their browsing history. We have conducted an extensive empirical study on a real data collected from an advertising platform to analyse the effectiveness of the proposed schemes to expand the audiences of five different advertisers.
Theta Autoregressive Neural Network: A Hybrid Time Series Model for Pandemic Forecasting

Forecasting time series present a perpetual topic of research in statistical machine learning for the last five decades. Due to the unprecedented outbreak of the novel coronavirus (COVID-19), forecasting the COVID-19 pandemic became a key research interest for both epidemiologists and statisticians. These future predictions are useful for the effective allocation of health care resources, stockpiling, and help in strategic planning for clinicians, government authorities, and public-health policymakers. This paper develops an effective forecasting model that can generate real-time short-term (ten days) and long-term (fifty days) out-of-sample forecasts of COVID-19 outbreaks for eight profoundly affected countries, namely the United States of America, Brazil, India, Russia, South Africa, Mexico, Spain, and Iran. A novel hybrid approach based on the Theta method and Autoregressive neural network (ARNN) model, named Theta-ARNN (TARNN) model, is proposed. The proposed method outperforms previously available single and hybrid forecasting models for COVID-19 predictions in most data sets. The ergodicity and asymptotic stationarity of the TARNN model are also studied.
Context encoder self-supervised approaches for eye fundus analysis

The broad availability of medical images in current clinical practice provides a source of large image datasets. In order to use these datasets for training deep neural networks in detection and segmentation tools, it is necessary to provide pixel-wise annotations associated to each image. However, the image annotation is a tedious, time consuming and error prone process that requires the participation of experienced specialists. In this work, we propose different complementary context encoder self-supervised approaches to learn relevant characteristics for the restricted medical imaging domain of retinographies. In particular, we propose a patch-wise approach, inspired in the previous proposal of broad domain context encoders, and complementary fully convolutional approaches. These approaches take advantage of the restricted application domain to learn the relevant features of the eye fundus, situation that can be extrapolated to many medical imaging issues. Different representative experiments were conducted in order to evaluate the performance of the trained models, demonstrating the suitability of the proposed approaches in the understanding of the eye fundus characteristics. The proposed self-supervised models can serve as reference to support other domain-related issues through transfer or multi-task learning paradigms, like the detection and evaluation of the retinal structures or anomaly detections in the context of pathological analysis.
Modulated Motion Blur-Based Vehicle Body Velocity and Pose Estimation Using an Optical Image Modulator

This paper presents a new velocity and pose sensor for a ground vehicle using modulated motion blur generated from an optical image modulator (OIM). The OIM repeatedly shakes the lens of a camera in the lateral direction of the vehicle body, which creates patterns of motion blur that are recorded in an image frame. The relationship between the blur pattern and vehicle velocity is determined using camera dynamics. The 2D velocity of the vehicle body is estimated by analyzing the motion blur pattern. The vehicle roll and pitch angle with respect to the ground are calculated by comparing the estimated average depths (heights) of the sections of a scene. We propose a signal processing and control system to precisely modulate the OIM, which is an electro-mechanical system consisting of a coil, magnet, spring and damper. In addition, we validate the influence of the control reference waveform through various experiments and confirm the robustness of the proposed system. The proposed system is suitable for many applications because it is insensitive to environmental changes and has a relatively low computation time while accurately measuring the speed.
Retrieval Augmentation for Deep Neural Networks

Deep neural networks have achieved state-of-the-art results in various vision and/or language tasks. Despite the use of large training datasets, most models are trained by iterating over single input-output pairs, discarding the remaining examples for the current prediction. In this work, we actively exploit the training data, using the information from nearest training examples to aid the prediction both during training and testing. Specifically, our approach uses the target of the most similar training example to initialize the memory state of an LSTM model, or to guide attention mechanisms. We apply this approach to image captioning and sentiment analysis, respectively through image and text retrieval. Results confirm the effectiveness of the proposed approach for the two tasks, on the widely used Flickr8 and IMDB datasets. Our code is publicly available11htttp://github.com/RitaRamo/retrieval-augmentation-nn.
Learning Disentangled Representation for Mixed- Reality Human Activity Recognition With a Single IMU Sensor

Together with the rapid development of the sensors technology in recent years, sensor-based human activity recognition (HAR) has shown promising performance using well-known supervised deep learning methods. However, it remains challenging in a realistic scenario, i.e., limited number of labeled samples and sensors. This article proposes a novel deep learning method to achieve accurate and robust HAR with only a single inertial measurement unit (IMU) sensor. Our contributions are twofold. First, based on the skinned multiperson linear (SMPL) model, we build a large synthetic HAR dataset containing multimodal measurements: acceleration and angular velocity, which were generated according to the forward kinematics. Second, We propose a multiple-level domain adaptive learning model with information-theoretically stimulated constraints to simultaneously align the distributions of low- and high-level representations of virtual and real HAR data. The proposed mutual information constraints encourage the neural network to learn a disentangled representation for the multimodal sensing data. Comprehensive experimental results on three publicly available datasets demonstrate that the proposed method compares favorably with competing ones and has robust performance with variable labeled samples.
Biden vs Trump: Modeling US General Elections Using BERT Language Model

Social media plays a crucial role in shaping the worldview during election campaigns. Social media has been used as a medium for political campaigns and a tool for organizing protests; some of which have been peaceful, while others have led to riots. Previous research indicates that understanding user behaviour, particularly in terms of sentiments expressed during elections can give an indication of the election outcome. Recently, there has been tremendous progress in language modelling with deep learning via long short-term memory (LSTM) models and variants known as bidirectional encoder representations from transformers (BERT). Motivated by these innovations, we develop a framework to model the US general elections. We investigate if sentiment analysis can provide a means to predict election outcomes. We use the LSTM and BERT language models for Twitter sentiment analysis leading to the US 2020 presidential elections. Our results indicate that sentiment analysis can provide a general basis for modelling election outcomes where the BERT model indicates Biden winning the elections.
An Improved Fast-Transient-Response Low-Transient-Voltage Boost Converter With Pseudo-Current Hysteresis-Controlled Techniques

This paper proposes a new and improved fast-transient-response low-transient-voltage boost converter with pseudo-current hysteresis-controlled techniques (PCHC). The converter uses the rail-to-rail current-sensing circuit to provide a hysteresis-controlled signal, helping to speed up the transient response and improve the overall efficiency. The proposed boost converter has been fabricated with TSMC <inline-formula> <tex-math notation="LaTeX">$0.18~\mu \text{m}$ </tex-math></inline-formula> CMOS 1P6M technology, and the chip area is <inline-formula> <tex-math notation="LaTeX">$1.18\times 1.18$ </tex-math></inline-formula> mm<sup>2</sup> (include PADs). The measured results show that the input voltage range is from 0.5 V to 1 V, the output voltage is set as 1.8 V, and the output load current range is from 10 mA to 100 mA. When the output load current changes from 10 mA to 100mA and 100 mA to 10 mA, the transient responses are both 2 <inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula>, and peak power efficiency is 88.5% at the output load current of 75 mA.
Coarray MUSIC-Group Delay: High-Resolution Source Localization Using Non-Uniform Arrays

Non-uniform linear arrays (NULA) operate in the difference coarray domain to localize more sources than the number of physical sensors. Usually, the spectral magnitude of the subspace-based coarray MUSIC algorithm is used for this task of underdetermined source localization. However, the phase spectrum of coarray MUSIC, which has not yet been utilized, exhibits sharp transitions at the direction of arrival (DOA) of the respective sources. As a result, the negative differential of this phase called the coarray group delay shows peaks at the DOAs. In this correspondence, a new source localization technique called the coarray MUSIC-Group delay (coarray MGD) described as the product of the coarray-MUSIC magnitude and the coarray group delay function is introduced. It is shown that as long as the difference coarray of the NULA is a uniform linear array (ULA), the group delay function displays an additive property in the coarray spatial domain. Owing to this property, the proposed coarray MGD technique is able to localize and resolve more closely-spaced sources than the coarray MUSIC magnitude method. Numerical simulations are performed to validate the high resolution property of the proposed method.
Meta-Reinforcement Learning Based Resource Allocation for Dynamic V2X Communications

This paper studies the allocation of shared resources between vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) links in vehicle-to-everything (V2X) communications. In existing algorithms, dynamic vehicular environments and quantization of continuous power become the bottlenecks for providing an effective and timely resource allocation policy. In this paper, we develop two algorithms to deal with these difficulties. First, we propose a deep reinforcement learning (DRL)-based resource allocation algorithm to improve the performance of both V2I and V2V links. Specifically, the algorithm uses deep Q-network (DQN) to solve the sub-band assignment and deep deterministic policy-gradient (DDPG) to solve the continuous power allocation problem. Second, we propose a meta-based DRL algorithm to enhance the fast adaptability of the resource allocation policy in the dynamic environment. Numerical results demonstrate that the proposed DRL-based algorithm can significantly improve the performance compared to the DQN-based algorithm that quantizes continuous power. In addition, the proposed meta-based DRL algorithm can achieve the required fast adaptation in the new environment with limited experiences.
Label Propagation Through Optimal Transport

In this paper, we tackle the transductive semi-supervised learning problem that aims to obtain label predictions for the given unlabeled data points according to Vapnik's principle. Our proposed approach is based on optimal transport, a mathematical theory that has been successfully used to address various machine learning problems, and is starting to attract renewed interest in semi-supervised learning community. The proposed approach, Optimal Transport Propagation (OTP), performs in an incremental process, label propagation through the edges of a complete bipartite edge-weighted graph, whose affinity matrix is constructed from the optimal transport plan between empirical measures defined on labeled and unlabeled data. OTP ensures a high degree of predictions certitude by controlling the propagation process using a certainty score based on Shannon's entropy. We also provide a convergence analysis of our algorithm. Experiments task show the superiority of the proposed approach over the state-of-the-art. We make our code publicly available.11Code is available at: https://github.com/MouradElHamri/OTP
TSA-GAN: A Robust Generative Adversarial Networks for Time Series Augmentation

Time series classification (TSC) is widely used in various real-world applications such as human activity recognition, smart city governance, etc. Unfortunately, due to different reasons, only part of time series could be collected which may obviously degrade the performance of time series classifiers. To alleviate this problem, time series augmentation aims to generate synthetic time series by learning useful features from collected time series. As the popular generative model, generative adversarial networks (GAN) is regarded as a promising model for time series augmentation. However, applying GAN to the time series data suffers from a challenge in which the generated instances hold low quality but the model has gotten saturation. In this paper, for time series augmentation, we proposed TSA-GAN which is a robust GAN model with a self-adaptive recovering strategy to solve this problem. On 85 datasets of the UCR 2015 archive, our proposed TSA-GAN helps time series classifiers achieve performance improvements ranging from 8.3% to 12.5%, which is far better than the baseline.
How Much Pigment Should Flowers Have? Flowers With Moderate Pigmentation Have Highest Color Contrast

Floral pigments are a core component of flower colors, but how much pigment a flower should have to yield a strong visual signal to pollinators is unknown. Using an optical model and taking white, blue, yellow and red flowers as case studies, I investigate how the amount of pigment determines a flower’s color contrast. Modeled reflectance spectra are interpreted using established insect color vision models. Contrast as a function of the amount of pigment shows a pattern of diminishing return. Low pigment amounts yield pale colors, intermediate amounts yield high contrast, and extreme amounts of pigment do not further increase, and sometimes even decrease, a flower’s color contrast. An intermediate amount of floral pigment thus yields the highest visibility, a finding that is corroborated by previous behavioral experiments on bees. The implications for studies on plant-pollinator signaling, intraspecific flower color variation and the costs of flower color are discussed.
An Update on Molecular Pathways Regulating Vasculogenic Mimicry in Human Osteosarcoma and Their Role in Canine Oncology

Canine tumors are valuable comparative models for human counterparts, especially to explore novel biomarkers and to understand pathways and processes involved in metastasis. Vasculogenic mimicry (VM) is a unique property of malignant cancer cells which promote metastasis. Thus, it represents an opportunity to investigate both the molecular mechanisms and the therapeutic targets of a crucial phenotypic malignant switch. Although this biological process has been largely investigated in different human cancer types, including osteosarcoma, it is still largely unknown in veterinary pathology, where it has been mainly explored in canine mammary tumors. The presence of VM in human osteosarcoma is associated with poor clinical outcome, reduced patient survival, and increased risk of metastasis and it shares the main pathways involved in other type of human tumors. This review illustrates the main findings concerning the VM process in human osteosarcoma, search for the related current knowledge in canine pathology and oncology, and potential involvement of multiple pathways in VM formation, in order to provide a basis for future investigations on VM in canine tumors.
Distribution of Ocular Anterior and Posterior Segment Lengths Among a Cataract Surgical Population in Shanghai

Purpose: To investigate the distributions of the ocular anterior and posterior segment lengths among a cataract surgical population in Shanghai. Design: Cross-sectional study. Methods: Ocular biometric parameters of 23,462 eyes of 23,462 cataract surgery candidates were reviewed. Axial length (AL), anterior chamber depth (ACD), and lens thickness (LT) were obtained using IOL Master. Anterior segment length (ASL = ACD + LT), posterior segment length (PSL = AL – ASL) and the ratio of ASL to PSL (ASL/PSL) were calculated. Results: The mean ASL was 7.58 ± 0.39 mm, the mean PSL was 17.12 ± 2.64 mm. As the age grew, the ASL increased, and PSL increased firstly then decreased. Male subjects tended to have significantly longer ASL and shorter PSL than female subjects. With the increasing AL, the ASL was firstly decreased to trough at 20–22 mm AL group, then increased gradually, while the PSL increased rapidly. The ASL correlated positively with AL in normal, moderate and highly myopic eyes, negatively in short eyes. The PSL correlated positively with AL across the entire study population. The ASL/PSL was not constant in the eyes with different AL but had a relatively steep downward trend with the increasing AL in the short eyes, then decreased smoothly in normal, moderate and highly myopic eyes. Conclusions: In Chinese cataractous eyes, longer ASL and shorter PSL were associated with elder age and male gender. The change of ASL over AL was not linear, and the ASL was smallest in the eyes with AL of 20–22 mm. The elongation of the eyeball was mainly due to the extension of the posterior segment.
The Role of Trauma in Early Onset Borderline Personality Disorder: A Biopsychosocial Perspective

The role of childhood trauma in the development of borderline personality disorder (BPD) in young age has long been studied. The most accurate theoretical models are multifactorial, taking into account a range of factors, including early trauma, to explain evolutionary pathways of BPD. We reviewed studies published on PubMed in the last 20 years to evaluate whether different types of childhood trauma, like sexual and physical abuse and neglect, increase the risk and shape the clinical picture of BPD. BPD as a sequela of childhood traumas often occurs with multiple comorbidities (e.g. mood, anxiety, obsessive-compulsive, eating, dissociative, addictive, psychotic, and somatoform disorders). In such cases it tends to have a prolonged course, to be severe, and treatment-refractory. In comparison with subjects who suffer from other personality disorders, patients with BPD experience childhood abuse more frequently. Adverse childhood experiences affect different biological systems (HPA axis, neurotransmission mechanisms, endogenous opioid systems, gray matter volume, white matter connectivity), with changes persisting into adulthood. A growing body of evidence is emerging about interaction between genes (e.g. FKBP5 polymorphisms and CRHR2 variants) and environment (physical and sexual abuse, emotional neglect).
Hepatic Injury Induced by Dietary Energy Level via Lipid Accumulation and Changed Metabolites in Growing Semi-Fine Wool Sheep

Liver injury threatens the overall health of an organism, as it is the core organ of the animal body. Liver metabolism is affected by numerous factors, with dietary energy level being a crucial one. Therefore, the present study aimed to evaluate hepatic injury and to describe its metabolic mechanism in ruminants fed diets with different dietary energy levels. A total of 25 Yunnan semi-fine wool sheep were fed diets with five dietary metabolic energy levels and were randomly assigned to five groups as follows: low energy (LE), medium–low energy (MLE), medium energy (ME), medium–high energy (MHE), and high energy (HE). The results revealed that the average optical density (AOD) of lipid droplets in the LE, MLE, and HE groups was higher than that in the ME and MHE groups. The enzyme activity of alanine aminotransferase (ALT) was the lowest in the ME group. An increase in dietary energy level promoted the superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) activity and altered the malondialdehyde (MDA) and protein carbonyl (PCO) concentration quadratically. In addition, both high and low dietary energy levels upregulated the mRNA abundance of proinflammatory cytokine interleukin (IL)-1β, nuclear factor-kappa B (NF-κB), and tumor necrosis factor (TNF)-α. Metabonomic analysis revealed that 142, 77, 65, and 108 differential metabolites were detected in the LE, MLE, MHE, and HE groups, compared with ME group respectively. These metabolites were involved in various biochemical pathways, such as glycolipid, bile acid, and lipid metabolism. In conclusion, both high and low dietary energy levels caused hepatic injury. Section staining and metabonomic results revealed that hepatic injury might be caused by altered metabolism and lipid accumulation induced by lipid mobilization.