Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast and Robust LRSD-based SAR/ISAR Imaging and Decomposition

The earlier works in the context of low-rank-sparse-decomposition (LRSD)-driven stationary SAR imaging have shown significant improvement in the reconstruction-decomposition process. Neither of the proposed frameworks, however, can achieve a satisfactory performance when facing a platform residual phase error (PRPE) arising from the instability of airborne platforms. More importantly, in spite of the significance of real-time processing requirement in the remote sensing applications, these prior works have only focused on enhancing the quality of the formed image, not reducing the computational burden. To address these two concerns, this paper presents a fast and unified joint SAR imaging framework where the dominant sparse objects and low-rank features of the image background are decomposed and enhanced through a robust LRSD. In particular, our unified algorithm circumvents the tedious task of computing the inverse of large matrices for image formation and takes advantages of the recent advances in constrained quadratic programming to handle the unimodular constraint imposed due to the PRPE. Furthermore, we extend our approach to ISAR autofocusing and imaging. Specifically, due to the intrinsic sparsity of ISAR images, the LRSD framework is essentially tasked with the recovery of an sparse image. Several experiments based on synthetic and real data are presented to validate the superiority of the proposed method in terms of imaging quality and computational cost as compared to the state-of-the-art methods.
Safety and Cost Assessment of Connected and Automated Vehicles

As the automobile industry transitions to partial vehicle automation, newer crash avoidance technologies are beginning to appear more frequently in non-luxury vehicles such as the Honda Accord and Mazda CX-9. The availability of Forward Collision Warning (FCW), Lane Departure Warning (LDW), and Blind Spot Monitoring (BSM) technologies could reach 95% of the registered vehicle fleet anywhere between the years 2032 and 2048 (HLDI, 2014a). This paper estimates the costs and benefits of large-scale deployment of BSM, LDW, and FCW crash avoidance systems within the light-duty vehicle fleet. Two estimates are made to provide insight on current trends and technology potential. First, an upper bound of relevant crashes that potentially could be avoided or made less severe by the three technologies is estimated, assuming 100% technology effectiveness. Next, a lower bound in crash reduction is estimated using current changes in observed insurance collision claim frequency and severity (average loss payment per claim) in motor vehicles with these technologies. After these estimates are made, an annualized cost to equip each vehicle with the technologies enables a cost benefit analysis for the lower bound and upper bound estimates.
Brain network modulation in Alzheimer’s disease: clinical phenotypes and windows of opportunity

Dementia, for which there is no cure or effective treatment, is the leading cause of disability and death worldwide. Due to the high global prevalence and economic impact on families, caregivers, and communities, this condition represents one of the most significant public health challenges of our time. Dementia is an umbrella term describing a range of progressive neurodegenerative diseases, including Alzheimer’s disease (AD), which is the most common cause of cognitive and functional impairment among older adults. The presence of misfolded protein aggregates characterizes neurodegenerative disorders (e.g., amyloid-beta (Aβ) and tau in AD). Stemming from this, advancements in the molecular imaging field paved the way to new experimental AD treatments targeting Aβ. However, the results have been disappointing so far, and there is an ongoing debate about the emerging role and efficacy of anti-Aβ monoclonal antibodies (Musiek and Bennett, 2021), stressing the need for alternative biomarkers to guide new, effective preventive, and therapeutic interventions. Here, we report recent advancements in the field of functional connectivity in AD, underscoring the link with the underlying molecular pathology. We then discuss the meaning of the interplay between AD phenotypes, disease stage, and brain stimulation interventions.
Doctoral Writers’ Resiliency in the COVID-19 Pandemic

Aim/Purpose: The COVID-19 pandemic and subsequent lockdown required doctoral writers to demonstrate resiliency to continue their culminating projects. This study examines the socioecological factors that fostered that resiliency. Background: Resiliency is a key factor in determining whether doctoral writers continue with their culminating projects. Thus far, studies on doctoral student experiences during the pandemic have yet to investigate doctoral students’ adaptive strategies to continue with their projects. Methodology: The qualitative study uses in-depth interviews to document the narrative journeys of four research participants pre-pandemic and in-pandemic. Those narratives are analyzed using an infectious disease resilience framework as a metaphor to highlight the resilience within each participant’s writing ecology. Contribution: The study seeks to reframe the approach to doctoral writing beyond the individual student toward a broader ecological system to better serve those students and the knowledge produced, regardless of a disruptive crisis. Findings: The disruptions that the four participants experienced are documented through their narratives. The participants described their coping strategies related to their workspace, technology, loss of connection, and their breaking point. Recommendations for Practitioners: The resilience shown by the four participants demonstrates areas where institutions can provide assistance to alleviate the pressures placed on doctoral writers. Reframing the dissertation writing process as a socioecological system rather than a cognitive one allows for solutions to problems that are not limited to individual writers. Recommendation for Researchers: Extending the socioecological systems metaphor, further research should investigate other stakeholders in a writer’s ecology to obtain different perspectives on a particular system. Impact on Society: The pandemic has presented an opportunity for educational institutions to reassess how they can cultivate students’ resilience to positively impact their socioecological balance. Future Research: It would be worthwhile to document the post-pandemic experiences of doctoral writers to find out how they seek balance in their ecology as they continue to deal with the post-pandemic fallout.
Driver’s Face Pose Estimation Using Fine-Grained Wi-Fi Signals for Next-Generation Internet of Vehicles

Driver’s behavior and gesture recognition are most significant in the emerging next-generation vehicular technology. Driver’s face may provide important cues about his/her attention and fatigue behavior. Therefore, driver’s face pose is one of the key indicators to be considered for automatic driver monitoring system in next-generation Internet of Vehicles (IoV) technology. Driver behavior monitoring is most significant in order to reduce road accidents. This paper aims to address the problem of driver’s attentiveness monitoring using face pose estimation in a nonintrusive manner. The proposed system is based on wireless sensing, leveraging channel state information (CSI) of WiFi signals. In this paper, we present a novel classification algorithm that is based on the combination of support vector machine (SVM) and K nearest neighbor (KNN) to enhance the classification accuracy. Experimental results demonstrate that the proposed device-free wireless implementation can localize a driver’s face very accurately with an average recognition rate of 91.8 % .
Meta-Analytically Exploring the Learning Outcomes Assisted With Twitter in the Pandemic Time

The use of social media such as Twitter has gained popularity in education during the COVID-19 pandemic. This study included 22 high-quality peer-reviewed journal articles for the meta-analysis. We reveal that there are no significant differences in teaching effectiveness between the Twitter and non-Twitter-assisted learning approaches; Twitter-assisted learning outcomes are significantly higher than the non-Twitter-assisted whether Twitter is used as a supplementary or an integrated tool; Twitter-assisted learning can lead to significantly higher learning outcomes than non-Twitter-assisted learning in the USA, Greece, and Sweden but no significant difference is revealed in Spain; Sweden users hold significantly positive attitudes towards the use of Twitter in education but no significant difference is found in the USA; Twitter-assisted learning can cause significantly more engagement than non-Twitter-assisted in the USA; and Male learners have significantly higher learning outcomes than females in both the USA and Spain.
Población reproductora de milano real Milvus milvus L., 1758 en Gipuzkoa en 2020

A lo largo de lo que va de siglo se viene observando en Gipuzkoa un posible aumento de la población nidificante de milano real Milvus milvus. Si bien parcialmente esta realidad se ha asociado a un incremento en el esfuerzo de muestreo, el proceso de colonización y aumento poblacional de la especie en la provincia parece evidente. Así, de la ausencia de aves reproductoras dada según el censo de 2004 se pasó a algo más de 20 parejas durante el periodo 2014-2015. En un contexto de declive en muchas zonas de España, la realidad de la especie en Gipuzkoa supone, hasta cierto punto, un caso anómalo. En este contexto, durante los últimos años se ha invertido un esfuerzo notable en el seguimiento de la especie. Este artículo resume los resultados del seguimiento de milano real en Gipuzkoa, actualizado para la temporada de cría de 2020. Se detectaron 32 territorios (parejas). La distribución de estos nidos en la provincia no es uniforme, ya que se detecta una concentración más alta de ellos en el sector más sudoriental del territorio. Se registró un éxito de cría alto (85,2%), así como una productividad de 2,0 pollos/pareja (rango: 1-3; 1,4 pollos/pareja se sumamos, también, las parejas que no llegaron a reproducirse), lo que confirma que Gipuzkoa es una zona buena para la reproducción de la especie. Esto puede jugar un papel clave no sólo en un posible proceso de consolidación de la población en la región, sino de colonización y reforzamiento poblacional en regiones limítrofes. Siendo Gipuzkoa una provincia muy forestal, el milano real tiende a ocupar zonas con mayor superficie relativa de hábitats abiertos, lo cual pone de relieve el valor de la campiña en la conservación de la especie. En este contexto, es importante impulsar el desarrollo de estudios orientados a determinar el uso del territorio así como las características que definen qué tipo de campiña es óptima para el milano real.
Major moisture source patterns for extreme precipitation events over the Chinese Loess Plateau

Extreme precipitation (EP) has significant hydrological and environmental influences on the Chinese Loess Plateau (CLP) during the rainy season (June–September). In this study, we analysed the moisture sources for the EP events over the CLP during the rainy season. Three major moisture source patterns were identified through analysing the moisture source variability across the EP events with a newly developed method combining the Lagrangian moisture diagnostic and a clustering algorithm. South China and North China were determined as the most critical moisture source regions for the EP events over the CLP. The three moisture source patterns corresponded to three EP types, including the CLP‐A type with a high contribution from North China (23.55%, the relative moisture contribution) and a relatively low contribution from South China (13.77%), and the CLP‐B and CLP‐C types, which had limited contributions (10.64% and 13.28%) from North China in common, and exhibited moderately high (19.30%) and extremely high (30.59%) contributions from South China, respectively. The moisture transport pathways for the three EP types were quantitatively analysed in detail. The favourable circulation patterns for the three EP types were further illustrated to reveal the atmospheric conditions for the spatial variations in moisture source patterns. In addition, this study provides a new strategy to analyse the moisture source variability, especially when it is difficult to find an easily manipulated criterion to classify the precipitation events.
Cardiovascular Fitness Improves Cardiac Interoceptive Accuracy

Many of the sensory receptors encoding touch, temperature, pain, and visual or auditory cues are known; these exteroceptive receptors allow us to respond to external threats. The interoceptive sensory receptors that identify stimuli within the internal milieu, e.g. changes in our heart rate, gastric fullness, or breathing difficulty are not known. We postulated mechanosensitive piezo channels in the heart are responsible for cardiac interoception. We found that individuals with lower resting heart rates and resting rate‐pressure products, and higher heart rate variability and stroke volumes have greater interoceptive accuracy. These characteristics often result from exercise training.
Classification of images using EfficientNet CNN model with convolutional block attention module (CBAM) and spatial group-wise enhance module (SGE)

Classification of images is highly useful in medical, agriculture, industry, and other fields. Improving the accuracy of classification with a small quality of parameters is a challenging problem. This paper performs a study that relied on the use of EfficientNet and convolutional block attention module (CBAM). Especially, Spatial Group-wise Enhance (SGE) module is used to adjust the importance of sub features and suppress possible noise. Stochastic gradient 1. descent (SGD) is chosen as the optimizer. After experiments, the EfficientNet model with CBAM module and SGE module can achieve higher accuracy in image classification. This module has achieved high accuracy on Flowers (98.53%).
Question answering over knowledge graphs using BERT based relation mapping

A knowledge graph (KG) is a structured form of knowledge describing real‐world entities, properties and relationships as a graph. Question answering over knowledge graphs (KGQA) allows people to ask questions in natural language and extract answers from KG accurately and more quickly. The main task of a KGQA is to convert a natural language query to the corresponding structured query form like SPARQL. However, generating the precise SPARQL query from a question is challenging and highly error‐prone. Here we propose a question‐answering framework that uses KG to answer simple questions without using SPARQL. Question classification, dependency parsing, entity linking, BERT‐based relation finding and answer extraction constitute the main modules of the approach. We have used the DBpedia as the KG and tested the end‐to‐end system with a subset of QALD‐4, LC‐QuAD and SimpleQuestions datasets. Results show considerable improvement compared to other approaches in terms of F1‐score.
Metaheuristics for the optimisation and management of Transport and Logistics

IDSIA, Istituto Dalle Molle di Studi sull'Intelligenza Artificiale is a research institute active in both theoretical and applied research in the field of Operations Research and Artificial Intelli- gence. Since 1996 IDSIA has capitalized on the results obtained in theoretical research fields applying them to real world logistic and transport problems. In this short communication we outline the major results we have obtained in combinatorial optimisation problems and their ap- plications to real-world problems. We also show how the real world provides a source of con- tinuous inspiration to define new theoretical problems whose solutions can later have an impact on real problems.
Extracting Salient Facts from Company Reviews with Scarce Labels

In this paper, we propose the task of extracting salient facts from online company reviews. Salient facts present unique and distinctive information about a company, which helps the user in deciding whether to apply to the company. We formulate the salient fact extraction task as a text classification problem, and leverage pretrained language models to tackle the problem. However, the scarcity of salient facts in company reviews causes a serious label imbalance issue, which hinders taking full advantage of pretrained language models. To address the issue, we developed two data enrichment methods: first, representation enrichment, which highlights uncommon tokens by appending special tokens, and second, label propagation, which interactively creates pseudopositive examples from unlabeled data. Experimental results on an online company review corpus show that our approach improves the performance of pretrained language models by up to an F1 score of 0.24. We also confirm that our approach competitively performs well against the state-of-the-art data augmentation method on the SemEval 2019 benchmark even when trained with only 20% of
Enhancing Software Quality Using Quality Assurance Practices in the Project Life-Cycle

Due to the widespread acceptance, and use of software systems in various areas like ecommerce, banking, medical etc software bugs are proving to be costly and sometimes fatal. To avoid software bugs Software Quality Assurance (SQA) involves in the entire software development process - monitoring and improving the process, making sure that any agreed-upon standards and ensuring that problems are found and removed. This paper emphasizes the importance of Software Quality Assurance in the product life cycle and also discuss how it improves the Software Quality.
Brachial Plexopathy Due to Upper-Extremity Deep Vein Thrombosis: A Case Report

Upper-extremity deep vein thrombosis (DVT) accounts for 1% to 4% of DVT cases. Brachial plexus injury can result from venous distention due to thrombosis in the upper extremity, leading to neurologic deficits. We report a patient diagnosed with brachial plexopathy caused by venous thrombosis in the upper extremity. A 66-year-old female patient with a medical history of Parkinson disease and dementia presented to the emergency department with drowsy mental status. She was diagnosed with pre-renal acute kidney injury with multi-organ dysfunction. On the second hospital day, right upper extremity edema and muscle weakness were observed. On the sixth hospital day, the patient’s overall medical condition improved after conservative treatment, but severe edema was noticed, and muscle weakness did not show significant improvement in the right upper extremity. Computed tomography showed multifocal pulmonary thromboembolism and DVT in the pulmonary arteries, superior vena cava, and popliteal veins. After anticoagulant administration, the swelling subsided, but the weakness in the entire right upper extremity did not improve. Electromyography demonstrated right brachial pan-plexopathy involving the upper, middle, and lower trunks. A follow-up examination showed no significant improvement of muscle strength and function in the right upper extremity at 3 months after the first hospital day.
HDHL-INTIMIC: A European Knowledge Platform on Food, Diet, Intestinal Microbiomics, and Human Health

Studies indicate that the intestinal microbiota influences general metabolic processes in humans, thereby modulating the risk of chronic diseases such as type 2 diabetes, allergy, cardiovascular disease, and colorectal cancer (CRC). Dietary factors are also directly related to chronic disease risk, and they affect the composition and function of the gut microbiota. Still, detailed knowledge on the relation between diet, the microbiota, and chronic disease risk is limited. The overarching aim of the HDHL-INTIMIC (INtesTInal MICrobiomics) knowledge platform is to foster studies on the microbiota, nutrition, and health by assembling available knowledge of the microbiota and of the other aspects (e.g., food science and metabolomics) that are relevant in the context of microbiome research. The goal is to make this information findable, accessible, interoperable, and reusable (FAIR) to the scientific community, and to share information with the various stakeholders. Through these efforts a network of transnational and multidisciplinary collaboration has emerged, which has contributed to further develop and increase the impact of microbiome research in human health. The roles of microbiota in early infancy, during ageing, and in subclinical and clinically manifested disease are identified as urgent areas of research in this knowledge platform.
China’s Fair Competition Review System: A Single Case Study

The case study methodology has proved to be a useful empirical tool for competition policy evaluation. However, as far as China’s Fair Competition Review System (FCRS) is concerned, empirical studies are scarce. This article aims to partly fill this gap by thoroughly studying the first litigation case in light of three questions: (1) does China’s FCRS contribute to a competitive market? ; (2) does it face challenges regarding implementation, including judicial proceedings? ; and (3) how to tackle these challenges? We find that China’s FCRS promotes a competitive market to some extent, but diverse issues need to be tackled in the coming years. Some policymakers still lack understanding of the system. Public antitrust enforcement also faces understanding and capability problems to fully implement the FCRS. The review standards are not specific enough. Regarding judicial scrutiny of the FCRS, we note that also judges lack knowledge of the FCRS, especially in primary courts. Court jurisdictions for filing administrative monopoly litigation are not of high enough rank. In addition, the nature of the FCRS brings up doubts when entering into litigation as the case has to be connected with the Anti-Monopoly Law (AML). Consequently, we formulate several suggestions for improvement: First, strengthening competition advocacy and FCRS training for policymakers, antitrust enforcement officials, and judges. Second, establishing disciplinary and incentive mechanisms. Third, increasing enforcement capacity. Fourth, specifying industry-specific review standards. In terms of judicial scrutiny, in addition to the training for judges, we also propose to reform the administrative proceeding system, adding corresponding clauses connected to Chapter V of the AML and the FCRS into the Administrative Procedure Law. Abstract administrative actions should also have the possibility to initiate litigation in the near future, and administrative monopoly cases should be filed at least to an intermediate court or intellectual property court, rather than to a primary court. The establishment of a dedicated competition court could also add value. Last, we recommend upgrading the FCRS to proper law. China, Fair Competition Review System, Evaluation, Case study, Administrative monopoly