Miracle-dz/UniFAR-FTUs huggingface dataset:
Waveform Design for Joint Communication and SAR Imaging Under Random Signaling

Conventional synthetic aperture radar (SAR) imaging systems typically employ deterministic signal designs, which lack the capability to convey communication information and are thus not suitable for integrated sensing and communication (ISAC) scenarios. In this letter, we propose a joint communication and SAR imaging (JCASAR) system based on orthogonal frequency-division multiplexing (OFDM) signal with cyclic prefix (CP), which is capable of reconstructing the target profile while serving a communication user. In contrast to traditional matched filters, we propose a least squares (LS) estimator for range profiling. Then the SAR image is obtained followed by range cell migration correction (RCMC) and azimuth processing. By minimizing the mean squared error (MSE) of the proposed LS estimator, we investigate the optimal waveform design for SAR imaging, and JCASAR under random signaling, where power allocation strategies are conceived for Gaussian-distributed ISAC signals, in an effort to strike a flexible performance tradeoff between the communication and SAR imaging tasks. Numerical results are provided to validate the effectiveness of the proposed ISAC waveform design for JCASAR systems.
Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucination

This paper presents a self-supervised learning method to safely learn a motion planner for ground robots to navigate environments with dense and dynamic obstacles. When facing highly-cluttered, fast-moving, hard-to-predict obstacles, classical motion planners may not be able to keep up with limited onboard computation. For learning-based planners, high-quality demonstrations are difficult to acquire for imitation learning while reinforcement learning becomes inefficient due to the high probability of collision during exploration. To safely and efficiently provide training data, the Learning from Hallucination (LfH) approaches synthesize difficult navigation environments based on past successful navigation experiences in relatively easy or completely open ones, but unfortunately cannot address dynamic obstacles. In our new Dynamic Learning from Learned Hallucination (Dyna-LfLH), we design and learn a novel latent distribution and sample dynamic obstacles from it, so the generated training data can be used to learn a motion planner to navigate in dynamic environments. Dyna-LfLH is evaluated on a ground robot in both simulated and physical environments and achieves up to 25% better success rate compared to baselines.
Phosphoinositide 5- and 3-phosphatase activities of a voltage-sensing phosphatase in living cells show identical voltage dependence

Significance Phosphatase and tensin homolog (PTEN) is a phosphatidylinositol 3,4-bisphosphate [PI(3,4)P2] and phosphatidylinositol 3,4,5-trisphosphate [PI(3,4,5)P3] 3-phosphatase that plays important roles in cell polarization, division, and development as well as in tumor suppression. Voltage-sensing phosphatases (VSPs) are PTEN family members but have catalytic activity in response to membrane depolarization with a wider range of substrates: They cleave the 5-phosphate of PI(3,4,5)P3 and phosphatidylinositol 4,5-bisphosphate as well as the 3-phosphate of PI(3,4)P2 and perhaps PI(3,4,5)P3. Using in-cell observations accompanied by quantitative analysis, we demonstrated that VSPs clearly cleave 3-phosphate from PI(3,4,5)P3 as PTEN does. Our results suggest that VSPs might act as surrogates for PTEN in several biological events related to membrane depolarization in addition to their inherent role. Voltage-sensing phosphatases (VSPs) are homologs of phosphatase and tensin homolog (PTEN), a phosphatidylinositol 3,4-bisphosphate [PI(3,4)P2] and phosphatidylinositol 3,4,5-trisphosphate [PI(3,4,5)P3] 3-phosphatase. However, VSPs have a wider range of substrates, cleaving 3-phosphate from PI(3,4)P2 and probably PI(3,4,5)P3 as well as 5-phosphate from phosphatidylinositol 4,5-bisphosphate [PI(4,5)P2] and PI(3,4,5)P3 in response to membrane depolarization. Recent proposals say these reactions have differing voltage dependence. Using Förster resonance energy transfer probes specific for different PIs in living cells with zebrafish VSP, we quantitate both voltage-dependent 5- and 3-phosphatase subreactions against endogenous substrates. These activities become apparent with different voltage thresholds, voltage sensitivities, and catalytic rates. As an analytical tool, we refine a kinetic model that includes the endogenous pools of phosphoinositides, endogenous phosphatase and kinase reactions connecting them, and four exogenous voltage-dependent 5- and 3-phosphatase subreactions of VSP. We show that apparent voltage threshold differences for seeing effects of the 5- and 3-phosphatase activities in cells are not due to different intrinsic voltage dependence of these reactions. Rather, the reactions have a common voltage dependence, and apparent differences arise only because each VSP subreaction has a different absolute catalytic rate that begins to surpass the respective endogenous enzyme activities at different voltages. For zebrafish VSP, our modeling revealed that 3-phosphatase activity against PI(3,4,5)P3 is 55-fold slower than 5-phosphatase activity against PI(4,5)P2; thus, PI(4,5)P2 generated more slowly from dephosphorylating PI(3,4,5)P3 might never accumulate. When 5-phosphatase activity was counteracted by coexpression of a phosphatidylinositol 4-phosphate 5-kinase, there was accumulation of PI(4,5)P2 in parallel to PI(3,4,5)P3 dephosphorylation, emphasizing that VSPs can cleave the 3-phosphate of PI(3,4,5)P3.
Imitating Cost-Constrained Behaviors in Reinforcement Learning

Complex planning and scheduling problems have long been solved using various optimization or heuristic approaches. In recent years, imitation learning that aims to learn from expert demonstrations has been proposed as a viable alternative to solving these problems. Generally speaking, imitation learning is designed to learn either the reward (or preference) model or directly the behavioral policy by observing the behavior of an expert. Existing work in imitation learning and inverse reinforcement learning has focused on imitation primarily in unconstrained settings (e.g., no limit on fuel consumed by the vehicle). However, in many real-world domains, the behavior of an expert is governed not only by reward (or preference) but also by constraints. For instance, decisions on self-driving delivery vehicles are dependent not only on the route preferences/rewards (depending on past demand data) but also on the fuel in the vehicle and the time available. In such problems, imitation learning is challenging as decisions are not only dictated by the reward model but are also dependent on a cost-constrained model. In this paper, we provide multiple methods that match expert distributions in the presence of trajectory cost constraints through (a) Lagrangian-based method; (b) Meta-gradients to find a good trade-off between expected return and minimizing constraint violation; and (c) Cost-violation-based alternating gradient. We empirically show that leading imitation learning approaches imitate cost-constrained behaviors poorly and our meta-gradient-based approach achieves the best performance.
Identifying relationships between weather variables and domestic water consumption using smart metering

Satisfying the water supply-demand balance is a major problem of modern societies due to water scarcity, which is expected to be amplified by changes in climate. In order to fulfil future demands, accurate predictions of water consumption are essential. This paper investigates the relationship between water consumption and air temperature, using a combination of smart demand metering data, household characteristics, and socio-economic data. Results indicate that the correlation between water consumption and temperature increases during working days, evenings as well as during the summer and spring. In addition, a positive correlation was identified for households that are metered, have bigger gardens and medium occupancy, as well as residents with higher socio-economic status and high seasonal variations in water consumption.
LASIL: Learner-Aware Supervised Imitation Learning For Long-term Microscopic Traffic Simulation

Microscopic traffic simulation plays a crucial role in transportation engineering by providing insights into individual vehicle behavior and overall traffic flow. However, creating a realistic simulator that accurately replicates human driving behaviors in various traffic conditions presents significant challenges. Traditional simulators relying on heuristic models often fail to deliver accurate simulations due to the complexity of real-world traffic environments. Due to the covariate shift issue, existing imitation learning-based simulators often fail to generate stable long-term simulations. In this paper, we propose a novel approach called learner-aware supervised imitation learning to address the covariate shift problem in multi-agent imitation learning. By leveraging a variational autoencoder simultaneously modeling the expert and learner state distribution, our approach augments expert states such that the augmented state is aware of learner state distribution. Our method, applied to urban traffic simulation, demonstrates significant improvements over existing state-of-the-art baselines in both short-term microscopic and long-term macroscopic realism when evaluated on the real-world dataset pNEUMA.
The Pursuit of Fairness in Artificial Intelligence Models: A Survey

Artificial Intelligence (AI) models are now being utilized in all facets of our lives such as healthcare, education and employment. Since they are used in numerous sensitive environments and make decisions that can be life altering, potential biased outcomes are a pressing matter. Developers should ensure that such models don't manifest any unexpected discriminatory practices like partiality for certain genders, ethnicities or disabled people. With the ubiquitous dissemination of AI systems, researchers and practitioners are becoming more aware of unfair models and are bound to mitigate bias in them. Significant research has been conducted in addressing such issues to ensure models don't intentionally or unintentionally perpetuate bias. This survey offers a synopsis of the different ways researchers have promoted fairness in AI systems. We explore the different definitions of fairness existing in the current literature. We create a comprehensive taxonomy by categorizing different types of bias and investigate cases of biased AI in different application domains. A thorough study is conducted of the approaches and techniques employed by researchers to mitigate bias in AI models. Moreover, we also delve into the impact of biased models on user experience and the ethical considerations to contemplate when developing and deploying such models. We hope this survey helps researchers and practitioners understand the intricate details of fairness and bias in AI systems. By sharing this thorough survey, we aim to promote additional discourse in the domain of equitable and responsible AI.
Deepfake Generation and Detection: A Benchmark and Survey

In addition to the advancements in deepfake generation, corresponding detection technologies need to continuously evolve to regulate the potential misuse of deepfakes, such as for privacy invasion and phishing attacks. This survey comprehensively reviews the latest developments in deepfake generation and detection, summarizing and analyzing the current state of the art in this rapidly evolving field. We first unify task definitions, comprehensively introduce datasets and metrics, and discuss the development of generation and detection technology frameworks. Then, we discuss the development of several related sub-fields and focus on researching four mainstream deepfake fields: popular face swap, face reenactment, talking face generation, and facial attribute editing, as well as foreign detection. Subsequently, we comprehensively benchmark representative methods on popular datasets for each field, fully evaluating the latest and influential works published in top conferences/journals. Finally, we analyze the challenges and future research directions of the discussed fields. We closely follow the latest developments in https://github.com/flyingby/Awesome-Deepfake-Generation-and-Detection.
Assessment of Multimodal Large Language Models in Alignment with Human Values

Large Language Models (LLMs) aim to serve as versatile assistants aligned with human values, as defined by the principles of being helpful, honest, and harmless (hhh). However, in terms of Multimodal Large Language Models (MLLMs), despite their commendable performance in perception and reasoning tasks, their alignment with human values remains largely unexplored, given the complexity of defining hhh dimensions in the visual world and the difficulty in collecting relevant data that accurately mirrors real-world situations. To address this gap, we introduce Ch3Ef, a Compreh3ensive Evaluation dataset and strategy for assessing alignment with human expectations. Ch3Ef dataset contains 1002 human-annotated data samples, covering 12 domains and 46 tasks based on the hhh principle. We also present a unified evaluation strategy supporting assessment across various scenarios and different perspectives. Based on the evaluation results, we summarize over 10 key findings that deepen the understanding of MLLM capabilities, limitations, and the dynamic relationships between evaluation levels, guiding future advancements in the field.
A computational model of STP and LTP for gene level signaling cascade in human episodic memory

In this paper a computational model of gene level signaling for STP (Short Term Potentiation) and LTP (Long Term Potentiation) have been proposed. STP and LTP are prime signaling cascade for establishment of Episodic Memory in human brain. In various previous works receptor level signaling cascades are presented for STP and LTP. In this work signaling cascade at gene level through gene-gene interaction of various receptor gene clusters are presented and simulated. The signaling cascade computational model would be helpful in estimating the activity of various genes for episodic memory, related disorders and their management through drugs and gene therapy.
Vascular Endothelial Growth Factor in Tuberculous Meningitis

ABSTRACT Vascular endothelial growth factor (VEGF) is linked to brain edema and infarction, but there is paucity of studies correlating VEGF level with magnetic resonance imaging (MRI) changes in tuberculous meningitis (TBM). The aim of this study was to measure serum VEGF level in TBM and correlate it with clinical, laboratory, and MRI findings. Forty patients with TBM underwent cranial evaluation, cranial MRI, and MR angiography (MRA). Presence of exudates, hydrocephalous, infarction, tuberculoma, and MRA abnormalities was noted. Serum VEGF level was measured by enzyme-linked immunosorbent assay and compared in patients and controls. The VEGF level was also correlated with clinical, MRI, and MRA findings. The median age of the patients was 26.5 years. There was a trend towards higher serum VEGF level in TBM patients (100.7 ± 110.6 pg/ml) compared to the controls (60.6 ± 20.3 pg/ml). There was also a trend towards higher VEGF level in patients with shorter duration of illness (127.5 ± 152.4 pg/ml vs 76.5 ± 40.9 pg/ml), MRI evidence of infarction (131.4 ± 150.7 pg/ml vs 73.0 ± 41.4 pg/ml), and paradoxical response (122.3 ± 157.6 pg/ml vs 88.8 ± 50.8 pg/ml). Five patients died, and death was not related to VEGF level. It can be concluded that serum VEGF level in TBM patients is insignificantly higher in those with shorter duration of illness and infarction.
A Sociotechnical Framework For Addressing Stigma and Designing Personalized Digital Health Products

Stigma, a recognized global barrier to effective disease management, impacts social interactions, resource access, and psychological well-being. In this study, we developed a patient-centered framework for deriving design requirements and interventions for health conditions subject to social stigma. This study introduces a patient-centered framework, grounded in sociotechnical systems theory, to create tailored interventions and design requirements for health conditions influenced by social stigma. We tested this framework through a mixed-method study on chronic pelvic pain patients. Our approach led to the identification of ten design requirements that encompass behavioral and psychological support and strategies for day-to-day living. The findings reveal a preference among CPP patients for priming and social support interventions. This study underscores the value of a systems-based perspective in healthcare, advocating for a nuanced, patient-centered approach that addresses the complex nature of health conditions affected by social stigma. It contributes to the ongoing discourse on integrating STS theory into healthcare frameworks, highlighting the need for targeted strategies to combat the complexities of stigma in patient care.
FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids

Predicting and classifying faults in electricity networks is crucial for uninterrupted provision and keeping maintenance costs at a minimum. Thanks to the advancements in the field provided by the smart grid, several data-driven approaches have been proposed in the literature to tackle fault prediction tasks. Implementing these systems brought several improvements, such as optimal energy consumption and quick restoration. Thus, they have become an essential component of the smart grid. However, the robustness and security of these systems against adversarial attacks have not yet been extensively investigated. These attacks can impair the whole grid and cause additional damage to the infrastructure, deceiving fault detection systems and disrupting restoration. In this paper, we present FaultGuard, the first framework for fault type and zone classification resilient to adversarial attacks. To ensure the security of our system, we employ an Anomaly Detection System (ADS) leveraging a novel Generative Adversarial Network training layer to identify attacks. Furthermore, we propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. We comprehensively evaluate the framework's performance against various adversarial attacks using the IEEE13-AdvAttack dataset, which constitutes the state-of-the-art for resilient fault prediction benchmarking. Our model outclasses the state-of-the-art even without considering adversaries, with an accuracy of up to 0.958. Furthermore, our ADS shows attack detection capabilities with an accuracy of up to 1.000. Finally, we demonstrate how our novel training layers drastically increase performances across the whole framework, with a mean increase of 154% in ADS accuracy and 118% in model accuracy.
Quantile Regression Analysis of the Distributional Effects of Air Pollution on Blood Pressure, Heart Rate Variability, Blood Lipids, and Biomarkers of Inflammation in Elderly American Men: The Normative Aging Study

Background: Previous studies have observed associations between air pollution and heart disease. Susceptibility to air pollution effects has been examined mostly with a test of effect modification, but little evidence is available whether air pollution distorts cardiovascular risk factor distribution. Objectives: This paper aims to examine distributional and heterogeneous effects of air pollution on known cardiovascular biomarkers. Methods: A total of 1,112 men from the Normative Aging Study and residents of the greater Boston, Massachusetts, area with mean age of 69 years at baseline were included in this study during the period 1995–2013. We used quantile regression and random slope models to investigate distributional effects and heterogeneity in the traffic-related responses on blood pressure, heart rate variability, repolarization, lipids, and inflammation. We considered 28-day averaged exposure to particle number, PM2.5 black carbon, and PM2.5 mass concentrations (measured at a single monitor near the site of the study visits). Results: We observed some evidence suggesting distributional effects of traffic-related pollutants on systolic blood pressure, heart rate variability, corrected QT interval, low density lipoprotein (LDL) cholesterol, triglyceride, and intercellular adhesion molecule-1 (ICAM-1). For example, among participants with LDL cholesterol below 80 mg/dL, an interquartile range increase in PM2.5 black carbon exposure was associated with a 7-mg/dL (95% CI: 5, 10) increase in LDL cholesterol, while among subjects with LDL cholesterol levels close to 160 mg/dL, the same exposure was related to a 16-mg/dL (95% CI: 13, 20) increase in LDL cholesterol. We observed similar heterogeneous associations across low versus high percentiles of the LDL distribution for PM2.5 mass and particle number. Conclusions: These results suggest that air pollution distorts the distribution of cardiovascular risk factors, and that, for several outcomes, effects may be greatest among individuals who are already at high risk. Citation: Bind MA, Peters A, Koutrakis P, Coull B, Vokonas P, Schwartz J. 2016. Quantile regression analysis of the distributional effects of air pollution on blood pressure, heart rate variability, blood lipids, and biomarkers of inflammation in elderly American men: the Normative Aging Study. Environ Health Perspect 124:1189–1198; http://dx.doi. org/10.1289/ehp.1510044
A Real-Time Smart Display Detection System

A smart display detection system is proposed that allows users to connect with displays using mobile cameras. The smart displays dynamically update a server with information about the current screen content and the system matches captured images from mobile devices with the screen information. A synchronized timestamped matching strategy is employed to achieve high performance in detecting screens playing motion intensive video and an aggressive feature selection method is used to minimize bandwidth requirements.
Reverse Kron reduction of Multi-phase Radial Network

We consider the problem of identifying the admittance matrix of a three-phase radial network from voltage and current measurements at a subset of nodes. These measurements are used to estimate a virtual network represented by the Kron reduction (Schur complement) of the full admittance matrix. We focus on recovering exactly the full admittance matrix from its Kron reduction, i.e., computing the inverse of Schur complement. The key idea is to decompose Kron reduction into a sequence of iterations that maintains an invariance structure, and exploit this structure to reverse each step of the iterative Kron reduction.
Acceleration trajectory analysis in remote gait monitoring

The study demonstrates part of an ambient assisted living system developed for the remote care of the elderly. Described methods and experiments involve acceleration-based trajectories analysis that yields a feature vector to be subjected to an expert system able to create an individual patient's model by learning high-level features of her/his motion. At this stage we have implemented a footstep detector that permits each foot movement to be analyzed separately and described in terms of predefined features. By mounting the sensors at five various locations on the subjects body, we have indicated areas that feature a high sensitivity to the measurement of abnormal step incidents. Our experiments demonstrate also features able to distinguish abnormal patient motion.