Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic Geometry Analysis of IRS-Assisted Downlink Cellular Networks

Using stochastic geometry tools, we develop a comprehensive framework to analyze the downlink performance of various types of users (e.g., users served by direct base station (BS) transmissions and indirect intelligent reflecting surface (IRS)-assisted transmissions) in a cellular network with multiple BSs and IRSs. For the proposed users, we provide the approximate expressions for the performance in terms of coverage probability, ergodic capacity, and energy efficiency (EE). The proposed stochastic geometry framework can capture the impact of channel fading, locations of BSs and IRSs, arbitrary phase-shifts and interference experienced by a typical user supported by direct transmission and/or IRS-assisted transmission. For IRS-assisted transmissions, we first model approximate the desired signal power from the nearest IRS as a sum of scaled generalized gamma (GG) random variables whose parameters are functions of the IRS phase shifts. Then, we derive the Laplace Transform (LT) of the received signal power in a closed form. Also, we approximate the aggregate interference from multiple IRSs as the sum of normal random variables. Then, we derive the LT of the aggregate interference from all IRSs and BSs. The derived LT expressions are used to calculate coverage probability, ergodic capacity, and EE for users served by direct BS transmissions as well as users served by IRS-assisted transmissions. Finally, we derive the overall network coverage probability, ergodic capacity, and EE based on the fraction of direct and IRS-assisted users, which is defined as a function of the deployment density of IRSs, as well as blockage probability of direct transmission links. Numerical results validate the derived analytical expressions and extract useful insights related to the number of IRS elements, large-scale deployment of IRSs and BSs, and the impact of IRS interference on direct transmissions.
DQ-GAT: Towards Safe and Efficient Autonomous Driving With Deep Q-Learning and Graph Attention Networks

Autonomous driving in multi-agent dynamic traffic scenarios is challenging: the behaviors of road users are uncertain and are hard to model explicitly, and the ego-vehicle should apply complicated negotiation skills with them, such as yielding, merging and taking turns, to achieve both safe and efficient driving in various settings. Traditional planning methods are largely rule-based and scale poorly in these complex dynamic scenarios, often leading to reactive or even overly conservative behaviors. Therefore, they require tedious human efforts to maintain workability. Recently, deep learning-based methods have shown promising results with better generalization capability but less hand engineering efforts. However, they are either implemented with supervised imitation learning (IL), which suffers from dataset bias and distribution mismatch issues, or are trained with deep reinforcement learning (DRL) but focus on one specific traffic scenario. In this work, we propose DQ-GAT to achieve scalable and proactive autonomous driving, where graph attention-based networks are used to implicitly model interactions, and deep Q-learning is employed to train the network end-to-end in an unsupervised manner. Extensive experiments in a high-fidelity driving simulator show that our method achieves higher success rates than previous learning-based methods and a traditional rule-based method, and better trades off safety and efficiency in both seen and unseen scenarios. Moreover, qualitative results on a trajectory dataset indicate that our learned policy can be transferred to the real world for practical applications with real-time speeds. Demonstration videos are available at https://caipeide.github. io/dq-gat/.
Are Negative Samples Necessary in Entity Alignment?: An Approach with High Performance, Scalability and Robustness

Entity alignment (EA) aims to find the equivalent entities in different KGs, which is a crucial step in integrating multiple KGs. However, most existing EA methods have poor scalability and are unable to cope with large-scale datasets. We summarize three issues leading to such high time-space complexity in existing EA methods: (1) Inefficient graph encoders, (2) Dilemma of negative sampling, and (3) "Catastrophic forgetting" in semi-supervised learning. To address these challenges, we propose a novel EA method with three new components to enable high Performance, high Scalability, and high Robustness (PSR): (1) Simplified graph encoder with relational graph sampling, (2) Symmetric negative-free alignment loss, and (3) Incremental semi-supervised learning. Furthermore, we conduct detailed experiments on several public datasets to examine the effectiveness and efficiency of our proposed method. The experimental results show that PSR not only surpasses the previous SOTA in performance but also has impressive scalability and robustness.
Semi-Supervised Domain Generalizable Person Re-Identification

Existing person re-identification (re-id) methods are stuck when deployed to a new unseen scenario despite the success in cross-camera person matching. Recent efforts have been substantially devoted to domain adaptive person re-id where extensive unlabeled data in the new scenario are utilized in a transductive learning manner. However, for each scenario, it is required to first collect enough data and then train such a domain adaptive re-id model, thus restricting their practical application. Instead, we aim to explore multiple labeled datasets to learn generalized domain-invariant representations for person re-id, which is expected universally effective for each new-coming re-id scenario. To pursue practicability in real-world systems, we collect all the person re-id datasets (20 datasets) in this field and select the three most frequently used datasets (i.e., Market1501, DukeMTMC, and MSMT17) as unseen target domains. In addition, we develop DataHunter that collects over 300K+ weak annotated images named YouTube-Human from YouTube street-view videos, which joins 17 remaining full labeled datasets to form multiple source domains. On such a large and challenging benchmark called FastHuman (~440K+ labeled images), we further propose a simple yet effective Semi-Supervised Knowledge Distillation (SSKD) framework. SSKD effectively exploits the weakly annotated data by assigning soft pseudo labels to YouTube-Human to improve models' generalization ability. Experiments on several protocols verify the effectiveness of the proposed SSKD framework on domain generalizable person re-id, which is even comparable to supervised learning on the target domains. Lastly, but most importantly, we hope the proposed benchmark FastHuman could bring the next development of domain generalizable person re-id algorithms.
Toward Top-Down Just Noticeable Difference Estimation of Natural Images

Just noticeable difference (JND) of natural images refers to the maximum pixel intensity change magnitude that typical human visual system (HVS) cannot perceive. Existing efforts on JND estimation mainly dedicate to modeling the diverse masking effects in either/both spatial or/and frequency domains, and then fusing them into an overall JND estimate. In this work, we turn to a dramatically different way to address this problem with a top-down design philosophy. Instead of explicitly formulating and fusing different masking effects in a bottom-up way, the proposed JND estimation model dedicates to first predicting a critical perceptual lossless (CPL) counterpart of the original image and then calculating the difference map between the original image and the predicted CPL image as the JND map. We conduct subjective experiments to determine the critical points of 500 images and find that the distribution of cumulative normalized KLT coefficient energy values over all 500 images at these critical points can be well characterized by a Weibull distribution. Given a testing image, its corresponding critical point is determined by a simple weighted average scheme where the weights are determined by a fitted Weibull distribution function. The performance of the proposed JND model is evaluated explicitly with direct JND prediction and implicitly with two applications including JND-guided noise injection and JND-guided image compression. Experimental results have demonstrated that our proposed JND model can achieve better performance than several latest JND models. In addition, we also compare the proposed JND model with existing visual difference predicator (VDP) metrics in terms of the capability in distortion detection and discrimination. The results indicate that our JND model also has a good performance in this task. The code of this work are available at https://github.com/Zhentao-Liu/KLT-JND.
Eliminating the Barriers: Demystifying Wi-Fi Baseband Design and Introducing the PicoScenes Wi-Fi Sensing Platform

The research on Wi-Fi sensing has been thriving over the past decade but the process has not been smooth. Three barriers always hamper the research: 1) unknown baseband design and its influence; 2) inadequate hardware; and 3) the lack of versatile and flexible measurement software. This article tries to eliminate these barriers through the following work. First, we present an in-depth study of the baseband design of the Qualcomm Atheros AR9300 (QCA9300) NIC. We identify a missing item of the existing channel state information (CSI) model, namely, the CSI distortion, and identify the baseband filter as its origin. We also propose a distortion removal method. Second, we reintroduce both the QCA9300 and software-defined radio (SDR) as powerful hardware for research. For the QCA9300, we unlock the arbitrary tuning of both the carrier frequency and bandwidth. For SDR, we develop a high-performance software implementation of the 802.11a/g/n/ac/ax baseband, allowing users to fully control the baseband and access the complete physical-layer information. Third, we release the PicoScenes software, which supports concurrent CSI measurement from multiple QCA9300, Intel Wireless Link (IWL5300), and SDR hardware. PicoScenes features rich low-level controls, packet injection, and software baseband implementation. It also allows users to develop their own measurement plugins. Finally, we report state-of-the-art results in the extensive evaluations of the PicoScenes system, such as the >2-GHz available spectrum on the QCA9300, concurrent CSI measurement, and up to 40 and 1 kHz CSI measurement rates achieved by the QCA9300 and SDR. PicoScenes is available at https://ps.zpj. io.
Application of the information-motivation-behavioral-skills model to validate a cognitive and behavioral intervention scale for tuberculosis patients in Jos, Nigeria

Psychometric scales can be used to assess the cognition of and behavior toward tuberculosis (TB) disease and anti-TB medicines in TB patients by applying the Information-Motivation-Behavioral-Skills model. This study validated a psychometric scale from the responses of 115 TB patients using principal component (PCA) and reliability analyses. The Cronbach’s-alpha was 0.864. The PCA identified 21 factors, with eigenvalues greater than one. Six factors, with factor loading >0.5 had more than two items loaded to a factor. Twenty-five items validated from six factors suggest that the scale can be used for cognitive and behavioral assessment and interventions in tuberculosis patients.
Evidence for genetic isolation and local adaptation in the field cricket Gryllus campestris

Understanding how species can thrive in a range of environments is a central challenge for evolutionary ecology. There is strong evidence for local adaptation along large‐scale ecological clines in insects. However, potential adaptation among neighbouring populations differing in their environment has been studied much less. We used RAD sequencing to quantify genetic divergence and clustering of ten populations of the field cricket Gryllus campestris in the Cantabrian Mountains of northern Spain, and an outgroup on the inland plain. Our populations were chosen to represent replicate high and low altitude habitats. We identified genetic clusters that include both high and low altitude populations indicating that the two habitat types do not hold ancestrally distinct lineages. Using common‐garden rearing experiments to remove environmental effects, we found evidence for differences between high and low altitude populations in physiological and life‐history traits. As predicted by the local adaptation hypothesis, crickets with parents from cooler (high altitude) populations recovered from periods of extreme cooling more rapidly than those with parents from warmer (low altitude) populations. Growth rates also differed between offspring from high and low altitude populations. However, contrary to our prediction that crickets from high altitudes would grow faster, the most striking difference was that at high temperatures, growth was fastest in individuals from low altitudes. Our findings reveal that populations a few tens of kilometres apart have independently evolved adaptations to their environment. This suggests that local adaptation in a range of traits may be commonplace even in mobile invertebrates at scales of a small fraction of species' distributions.
Monotone Quantifiers Emerge via Iterated Learning

Abstract Natural languages exhibit many semantic universals, that is, properties of meaning shared across all languages. In this paper, we develop an explanation of one very prominent semantic universal, the monotonicity universal. While the existing work has shown that quantifiers satisfying the monotonicity universal are easier to learn, we provide a more complete explanation by considering the emergence of quantifiers from the perspective of cultural evolution. In particular, we show that quantifiers satisfy the monotonicity universal evolve reliably in an iterated learning paradigm with neural networks as agents.
Histamine signaling and metabolism identify potential biomarkers and therapies for lymphangioleiomyomatosis

Inhibition of mTOR is the standard of care for lymphangioleiomyomatosis (LAM). However, this therapy has variable tolerability and some patients show progressive decline of lung function despite treatment. LAM diagnosis and monitoring can also be challenging due to the heterogeneity of symptoms and insufficiency of non‐invasive tests. Here, we propose monoamine‐derived biomarkers that provide preclinical evidence for novel therapeutic approaches. The major histamine‐derived metabolite methylimidazoleacetic acid (MIAA) is relatively more abundant in LAM plasma, and MIAA values are independent of VEGF‐D. Higher levels of histamine are associated with poorer lung function and greater disease burden. Molecular and cellular analyses, and metabolic profiling confirmed active histamine signaling and metabolism. LAM tumorigenesis is reduced using approved drugs targeting monoamine oxidases A/B (clorgyline and rasagiline) or histamine H1 receptor (loratadine), and loratadine synergizes with rapamycin. Depletion of Maoa or Hrh1 expression, and administration of an L‐histidine analog, or a low L‐histidine diet, also reduce LAM tumorigenesis. These findings extend our knowledge of LAM biology and suggest possible ways of improving disease management.
Extracellular matrix mediates moruloid-blastuloid morphodynamics in malignant ovarian spheroids

Expression dynamics of basement membrane-like extracellular matrix and fibronectin regulate transitions between solid (moruloid) and cavitational (blastuloid) phenotypes of ovarian cancer spheroids. Ovarian cancer metastasizes into peritoneum through dissemination of transformed epithelia as multicellular spheroids. Harvested from the malignant ascites of patients, spheroids exhibit startling features of organization typical to homeostatic glandular tissues: lumen surrounded by smoothly contoured and adhered epithelia. Herein, we demonstrate that cells of specific ovarian cancer lines in suspension, aggregate into dysmorphic solid “moruloid” clusters that permit intercellular movement, cell penetration, and interspheroidal coalescence. Moruloid clusters subsequently mature into “blastuloid” spheroids with smooth contours, a temporally dynamic lumen and immotile cells. Blastuloid spheroids neither coalesce nor allow cell penetration. Ultrastructural examination reveals a basement membrane-like extracellular matrix coat on the surface of blastuloid, but not moruloid, spheroids. Quantitative proteomics reveals down-regulation in ECM protein Fibronectin-1 associated with the moruloid-blastuloid transition; immunocytochemistry also confirms the relocalization of basement membrane ECM proteins: collagen IV and laminin to the surface of blastuloid spheroids. Fibronectin depletion accelerates, and enzymatic basement membrane debridement impairs, lumen formation, respectively. The regulation by ECM dynamics of the morphogenesis of cancer spheroids potentially influences the progression of the disease.
Toward proteome‐wide exploration of proteins in dried blood spots using liquid chromatography‐coupled mass spectrometry

Dried blood spot (DBS) sampling is a method with advantages over conventional blood sampling in relation to collection, cost, storage, and transportation. Such advantages have led to its wide use in newborn screening (NBS). Although target analysis of various biomolecules is conducted in NBS, protein quantification‐based NBS is still in its infancy. Thus, it is important to clarify how many proteins could be quantitatively detected in DBS samples using advanced liquid chromatography–mass spectrometry (LC–MS/MS) technologies; a catalogue of proteins detectable in DBSs by LC–MS/MS will enable us to judge which causative proteins in genetic diseases can be monitored at the protein level in NBS. In this review, we outline conventional proteome analyses of DBSs with a distinction between target and nontarget approaches. Additionally, we discuss the future perspectives for proteome analysis of DBSs in NBS of genetic diseases.
The challenges of achieving timely diagnosis and culturally appropriate care of people with dementia from minority ethnic groups in Europe

In a just society, everyone should have equal access to healthcare in terms of prevention, assessment, diagnosis, treatment and care. Europe is a multicultural society made up of people who identify with a wide range of ethnic groups. Many older people from minority ethnic groups also have a direct migration background. Several studies have shown that there is a lack of equity in relation to dementia diagnoses and care because equal opportunities do not necessarily translate into equal outcomes. An expert ethics working group led by Alzheimer Europe has produced an extensive report on this issue, a policy brief and a guide for health and social care workers. In this brief summary, the authors/members of the expert working group present some of the key challenges and recommendations for healthcare clinicians striving to provide timely diagnosis and good quality care and treatment to people with dementia from all ethnic groups.
Resilience of adolescents living with HIV in the Cape Metropole of the Western Cape

ABSTRACT Resilience shapes the experiences of adolescents living with HIV (ALWH), enabling them to come to terms with their diagnosis, have hope for the future and maintain meaningful relationships. Yet, little is known about contextual factors associated with resilience resources in South Africa. We aimed to describe individual, relational and community resilience resources, and identify contextual factors associated with resilience. We recruited 385 adolescents, aged 13–18, over a period of 5 months from 11 different public health HIV clinics. The Child and Youth Resilience Measure (CYRM-12) was used to measure resilience resources. Data on demographic variables, psychological attributes, and environmental factors such as HIV-related stigma and stressful life events were collected. ALWH lacked resilience in some aspects of the individual, relational and community domains. For every one-unit increase in the HIV-related stigma and stressful life events scores, resilience decreased by 0.29 (p = 0.01) and 0.37 (p = 0.04) units, respectively. Higher levels of resilience were associated with being virally suppressed (Mann–Whitney U, p = 0.028) although this association was no longer present in the regression model. Efforts to improve resilience amongst ALWH should be focused on fostering individual coping skills, interconnectedness, and positive relationships, to mitigate adverse environmental factors.
A Multimodal Framework for Improving in Silico Drug Repositioning With the Prior Knowledge From Knowledge Graphs

Drug repositioning/repurposing is a very important approach towards identifying novel treatments for diseases in drug discovery. Recently, large-scale biological datasets are increasingly available for pharmaceutical research and promote the development of drug repositioning, but efficiently utilizing these datasets remains challenging. In this paper, we develop a novel multimodal framework, termed GraphPK (Graph-based Prior Knowledge) for improving in silico drug repositioning via using the prior knowledge from a drug knowledge graph. First, we construct a knowledge graph by integrating relevant bio-entities (drugs, diseases, etc.) and associations/interactions among them, and apply the knowledge graph embedding technique to extract prior knowledge of drugs and diseases. Moreover, we make use of the known drug-disease association, and obtain known association-based features from an association bipartite graph through graph embedding, and also take into account biological domain features, i.e., drug chemical structures and disease semantic similarity. Finally, we design a multimodal neural network to combine three types of features from the knowledge graph, the known associations and the biological domain, and build the prediction model for predicting drug-disease associations. Massive experiments show that our method outperforms other state-of-the-art methods in terms of most metrics, and the ablation analysis regarding the three types of features reveals that prior knowledge from knowledge graphs can not only lift the predictive power of in silico drug repositioning, but also enhance the model's robustness to different scenarios. The results of case studies offer support that GraphPK has the potential for actual use.
Review and comparative analysis of machine learning-based predictors for predicting and analyzing of anti-angiogenic peptides.

Cancer is one of the leading causes of death worldwide and underlying this is angiogenesis that represents one of the hallmarks of cancer. Ongoing effort is already under way in the discovery of anti-angiogenic peptides (AAPs) as a promising therapeutic route by tackling the formation of new blood vessels. As such, the identification of AAPs constitutes a viable path for understanding their mechanistic properties pertinent for the discovery of new anti-cancer drugs. In spite of the abundance of peptide sequences in public databases, experimental efforts in the identification of anti-angiogenic peptides have progressed very slowly owing to its high expenditures and laborious nature. Owing to its inherent ability to make sense of large volumes of data, machine learning (ML) represents a lucrative technique that can be harnessed for peptide-based drug discovery. In this review, we conducted a comprehensive and comparative analysis of ML-based AAP predictors in terms of their employed feature descriptors, ML algorithms, cross-validation methods and prediction performance. Moreover, the common framework of these AAP predictors and their inherent weaknesses are also discussed. Particularly, we explore future perspectives for improving the prediction accuracy and model interpretability, which represents an interesting avenue for overcoming some of the inherent weaknesses of existing AAP predictors. We anticipate that this review would assist researchers in the rapid screening and identification of promising AAPs for clinical use.