Miracle-dz/UniFAR-FTUs huggingface dataset:
Mental health symptoms among those affected by Huntington's disease: A cross‐sectional study

Although cognitive and motor symptoms of Huntington's disease (HD) are associated with disease progression, the underlying causes of psychological symptoms are not as clearly understood. Recent evidence suggests that some mental health difficulties experienced by people with HD are shared by noncarriers within HD families. Accordingly, there is a need to evaluate potential systemic contributors to HD mental distress, to support meaningful interventions for psychological symptoms in people with HD and affected families.
Effect of the Tetravalent Dengue Vaccine TAK-003 on Sequential Episodes of Symptomatic Dengue

ABSTRACT. In the pivotal phase 3 efficacy trial (NCT02747927) of the TAK-003 dengue vaccine, 5 of 13,380 TAK-003 recipients and 13 of 6,687 placebo recipients experienced two episodes of symptomatic dengue between the first dose and the end of the study, ∼57 months later (patients received the second dose 3 months after the first dose). Two of these participants experienced repeat infection with the same serotype (i.e., homotypic reinfection). In comparison with placebo, the relative risk of a subsequent episode of symptomatic dengue was 0.19 (95% CI, 0.07–0.54) in TAK-003 recipients. Based on the small number of subsequent episodes, these data suggest a potential incremental effect of TAK-003 beyond prevention of the first episode of symptomatic dengue after vaccination.
Charge trapping and detrapping characteristics in hafnium silicate gate dielectric using an inversion pulse measurement technique

Threshold voltage (VTH) instability in metal oxide semiconductor field transistors (MOSFETs) with high dielectric constant (k) gate dielectrics has been investigated with an inversion pulse measurement technique, which can detect fast dielectric charging/discharging within microseconds (μs). The results indicate that VTH instability can be significantly underestimated by conventional VTH measurement techniques. Based on temperature-dependent stress data, it is suggested that charging and discharging are determined by direct tunneling and thermally assisted processes, respectively.
Distinct dorsal and ventral streams for binocular rivalry dominance and suppression revealed by magnetoencephalography

Binocular rivalry is an example of bistable visual perception extensively examined in neuroimaging. Magnetoencephalography can track brain responses to phasic visual stimulations of predetermined frequency and phase to advance our understanding of perceptual dominance and suppression in binocular rivalry. We used left and right eye stimuli that flickered at two tagging frequencies to track their respective oscillatory cortical evoked responses. We computed time‐resolved measures of coherence to track brain responses phase locked with stimulus frequencies and with respect to the participants' indications of alternations of visual rivalry they experienced. We compared the brain maps obtained to those from a non‐rivalrous control replay condition that used physically changing stimuli to mimic rivalry. We found stronger coherence within a posterior cortical network of visual areas during rivalry dominance compared with rivalry suppression and replay control. This network extended beyond the primary visual cortex to several retinotopic visual areas. Moreover, network coherence with dominant percepts in primary visual cortex peaked at least 50 ms prior to the suppressed percept nadir, consistent with the escape theory of alternations. Individual alternation rates were correlated with the rate of change in dominant evoked peaks, but not for the slope of response to suppressed percepts. Effective connectivity measures revealed that dominant (respectively, suppressed) percepts were expressed in dorsal (respectively ventral) streams. We thus demonstrate that binocular rivalry dominance and suppression engage distinct mechanisms and brain networks. These findings advance neural models of rivalry and may relate to more general aspects of selection and suppression in natural vision.
C-NODDI: a constrained NODDI model for axonal density and orientation determinations in cerebral white matter in normative aging

Purpose: Neurite orientation dispersion and density imaging (NODDI) provides measures of neurite density and dispersion through computation of the neurite density index (NDI) and the orientation dispersion index (ODI). However, NODDI overestimates the cerebrospinal fluid water fraction in white matter (WM) and provides physiologically unrealistic high NDI values. Furthermore, derived NDI values are echo time (TE)-dependent. In this work, we propose a modification of NODDI, named constrained NODDI (C-NODDI), for NDI and ODI mapping in WM. Methods: Using NODDI and C-NODDI, we investigated age-related alterations in WM in a cohort of 58 cognitively unimpaired adults. Further, NDI values derived using NODDI or C-NODDI were correlated with the neurofilament light chain (NfL) concentration levels, a plasma biomarker of axonal degeneration. Results: ODI derived values using both approaches were virtually identical. We confirm the previous finding that NDI estimation using NODDI is TE-dependent. In contrast, C-NODDI-NDI exhibit lower susceptibility to TE. Further, C-NODDI-NDI values were lower than NODDI-NDI. Further, our results indicate a quadratic relationship between NDI and age suggesting that axonal maturation continues until middle age followed by a decrease. This quadratic association was notably significant in several WM regions using C-NODDI, while limited to a few regions using NODDI. ODI exhibited overall constant trends with age. Finally, C-NODDI-NDI values exhibited a stronger correlation with NfL concentration levels as compared NODDI-NDI, with lower NDI values correspond to higher levels of NfL. Conclusions: C-NODDI provides a complementary method to NODDI for determination of NDI in white matter in normative aging.
Sparse Activations for Interpretable Disease Grading

Interpreting deep learning models typically relies on post-hoc saliency map techniques. However, these techniques often fail to serve as actionable feedback to clinicians, and they do not directly explain the decision mechanism. Here, we propose an inherently interpretable model that combines the feature extraction capabilities of deep neural networks with advantages of sparse linear models in interpretability. Our approach relies on straightforward but effective changes to a deep bag-of-local-features model (BagNet). These modifications lead to fine-grained and sparse class evidence maps which, by design, correctly reflect the model's decision mechanism. Our model is particularly suited for tasks which rely on characterising regions of interests that are very small and distributed over the image. In this paper, we focus on the detection of Diabetic Retinopathy, which is characterised by the progressive presence of small retinal lesions on fundus images. We observed good classification accuracy despite our added sparseness constraint. In addition, our model precisely highlighted retinal lesions relevant for the disease grading task and excluded irrelevant regions from the decision mechanism. The results suggest our sparse BagNet model can be a useful tool for clinicians as it allows efficient inspection of the model predictions and facilitates clinicians' and patients' trust.
Clinical Features of Genetic Resilience in Chronic Obstructive Pulmonary Disease

Introduction: In the personalized risk quantification of chronic obstructive pulmonary disease (COPD), genome-wide association studies and polygenic risk scores (PRS) complement traditional risk factors, such as age and cigarette smoking. However, despite being at considerable levels of risk, some individuals do not develop COPD. Research on COPD resilience remains largely unexplored. Methods: We applied the previously published COPD PRS to whole genome sequencing data from non-Hispanic white and African American individuals in the COPDGene study. We defined genetic resilience as individuals unaffected by COPD with a polygenic risk score above the 90th percentile. We defined risk-matched case individuals as those with COPD (i.e., FEV1/FVC < 0.70) and a PRS above the 90th percentile. We defined low risk individuals without COPD (i.e., FEV1/FVC > 0.70) as a polygenic risk score below the 10th percentile. We compared genetically resilient individuals to risk-matched individuals with COPD and low risk individuals by demographics, lung function, respiratory symptoms, co-morbidities, and chest CT scan measurements. We also performed survival analyses, differential expression analysis, and matching for sensitivity analyses. Results: We identified 211 resilient individuals without COPD, 605 genetic risk-matched individuals with COPD, and 527 low-risk individuals without COPD. Resilient individuals had higher FEV1 % predicted and lower percent emphysema. In contrast, resilient individuals had higher airway wall thickness compared to low-risk unaffected individuals. While there was no difference in survival between low-risk and resilient individuals, resilient individuals had higher survival compared to risk matched cases. We also identified two genes that were differentially expressed between low-risk unaffected individuals and resilient individuals. Conclusion: Genetically resilient individuals had a reduced burden of COPD disease-related measures compared to risk-matched cases but had subtly increased measures compared to low-risk unaffected individuals. Further genetic studies will be needed to illuminate the underlying pathobiology of our observations.
Benchmarking and Security Considerations of Wi-Fi FTM for Ranging in IoT Devices

The IEEE 802.11mc standard introduces fine time measurement (Wi-Fi FTM) for highly-accurate synchronization between peers and round-trip time calculation (Wi-Fi RTT) for location estimation – typically with a precision of one to two meters. This has considerable advantages over received signal strength (RSS)-based trilateration, which is prone to errors due to multipath reflections. We examine different commercial radios which support Wi-Fi RTT and benchmark Wi-Fi FTM ranging over different spectrums and bandwidths. Importantly, we find that while Wi-Fi FTM supports localization accuracy to within one to two meters in ideal conditions during outdoor line-of-sight experiments, for indoor environments at short ranges similar accuracy was only achievable on chipsets supporting Wi-Fi FTM on wider (VHT80) channel bandwidths rather than narrower (HT20) channel bandwidths. Finally, we explore the security implications of Wi-Fi FTM and use an on-air sniffer to demonstrate that FTM messages are unprotected. We consequently propose a threat model with possible mitigations and directions for further research.
Data Games: A Game-Theoretic Approach to Swarm Robotic Data Collection

Fleets of networked autonomous vehicles (AVs) collect terabytes of sensory data, which is often transmitted to central servers (the ''cloud'') for training machine learning (ML) models. Ideally, these fleets should upload all their data, especially from rare operating contexts, in order to train robust ML models. However, this is infeasible due to prohibitive network bandwidth and data labeling costs. Instead, we propose a cooperative data sampling strategy where geo-distributed AVs collaborate to collect a diverse ML training dataset in the cloud. Since the AVs have a shared objective but minimal information about each other's local data distribution and perception model, we can naturally cast cooperative data collection as an $N$-player mathematical game. We show that our cooperative sampling strategy uses minimal information to converge to a centralized oracle policy with complete information about all AVs. Moreover, we theoretically characterize the performance benefits of our game-theoretic strategy compared to greedy sampling. Finally, we experimentally demonstrate that our method outperforms standard benchmarks by up to $21.9\%$ on 4 perception datasets, including for autonomous driving in adverse weather conditions. Crucially, our experimental results on real-world datasets closely align with our theoretical guarantees.
Projection methods for the linear split feasibility problems

Some optimization problems can be reduced to finding a solution of a system of linear inequalities which belongs to a closed convex subset. In some optimization methods such a solution (or at least a better approximation of such a solution than the current one) should be found in each iteration. In the article, we present various projection methods to solve this problem. Furthermore, we show the relationship between these methods. We show that all presented methods can be reduced to the surrogate constraints method.
Land cover classification and height extraction experiments using Chinese airborne X-band PolInSAR system in China

In this paper, we describe our land cover classification and height extraction experiments with high-resolution X-band polarimetric interferometric synthetic aperture radar (PolInSAR) data acquired by Chinese airborne PolInSAR system over Linshui in southern China. The experimental area contains plenty of natural and artificial objects. We present two polarimetric synthetic aperture radar (PolSAR) classification schemes for different land cover types. An Oriented-Object classification scheme based on hierarchical segmentation is employed to the regular land cover classification experiment. The Entropy–PSD unsupervised classification method based on pixel is employed to the low backscattering targets classification experiment. Fully taking advantage of the characteristics of the multi-baseline data set, a dual-baseline polarimetric interferometry method is applied to the PolInSAR experiment of height extraction. Our PolSAR and PolInSAR experiment results demonstrate that high-resolution X-band PolInSAR imagery acquired by Chinese airborne SAR system have great application potential for land classification and height extraction.
Deep Learning for Inertial Positioning: A Survey

Inertial sensors are widely utilized in smartphones, drones, robots, and IoT devices, playing a crucial role in enabling ubiquitous and reliable localization. Inertial sensor-based positioning is essential in various applications, including personal navigation, location-based security, and human-device interaction. However, low-cost MEMS inertial sensors' measurements are inevitably corrupted by various error sources, leading to unbounded drifts when integrated doubly in traditional inertial navigation algorithms, subjecting inertial positioning to the problem of error drifts. In recent years, with the rapid increase in sensor data and computational power, deep learning techniques have been developed, sparking significant research into addressing the problem of inertial positioning. Relevant literature in this field spans across mobile computing, robotics, and machine learning. In this article, we provide a comprehensive review of deep learning-based inertial positioning and its applications in tracking pedestrians, drones, vehicles, and robots. We connect efforts from different fields and discuss how deep learning can be applied to address issues such as sensor calibration, positioning error drift reduction, and multi-sensor fusion. This article aims to attract readers from various backgrounds, including researchers and practitioners interested in the potential of deep learning-based techniques to solve inertial positioning problems. Our review demonstrates the exciting possibilities that deep learning brings to the table and provides a roadmap for future research in this field.
Benign Overfitting for Two-layer ReLU Networks

Modern deep learning models with great expressive power can be trained to overfit the training data but still generalize well. This phenomenon is referred to as benign overfitting. Recently, a few studies have attempted to theoretically understand benign overfitting in neural networks. However, these works are either limited to neural networks with smooth activation functions or to the neural tangent kernel regime. How and when benign overfitting can occur in ReLU neural networks remains an open problem. In this work, we seek to answer this question by establishing algorithm-dependent risk bounds for learning two-layer ReLU convolutional neural networks with label-flipping noise. We show that, under mild conditions, the neural network trained by gradient descent can achieve near-zero training loss and Bayes optimal test risk. Our result also reveals a sharp transition between benign and harmful overfitting under different conditions on data distribution in terms of test risk. Experiments on synthetic data back up our theory.
Efficient Symbolic Approaches for Quantitative Reactive Synthesis with Finite Tasks

This work introduces efficient symbolic algorithms for quantitative reactive synthesis. We consider resource-constrained robotic manipulators that need to interact with a human to achieve a complex task expressed in linear temporal logic. Our framework generates reactive strategies that not only guarantee task completion but also seek cooperation with the human when possible. We model the interaction as a two-player game and consider regret-minimizing strategies to encourage cooperation. We use symbolic representation of the game to enable scalability. For synthesis, we first introduce value iteration algorithms for such games with min-max objectives. Then, we extend our method to the regret-minimizing objectives. Our benchmarks reveal that our the symbolic framework not only significantly improves computation time (up to an order of magnitude) but also can scale up to much larger instances of manipulation problems with up to 2× number of objects and locations than the state of the art.
On Aspects of Spontaneous Symmetry Breaking in Rindler and Anti-de Sitter spacetimes for the $O(N)$ Linear Sigma Model

We investigate aspects of spontaneous breakdown of symmetry for $O(N)$ symmetric linear sigma model in the background of Rindler and Anti-de Sitter spacetimes respectively. In the large $N$ limit, by computing the one-loop effective action, we report that in three dimensional Rindler space, there is a phase transition from the disordered phase to an ordered phase past a certain critical Rindler acceleration parameter `$a$'. Connections with finite temperature field theory results are established, thereby further reinforcing the idea that Rindler space can indeed be a proxy for Minkowski spacetime with finite temperature. We extend our calculations to Anti-de Sitter space in various dimensions and observe that symmetry is broken in three dimensions, but not in four dimensions. We discuss the implications of our results.
Information geometry of Euclidean quantum fields

Information geometry provides differential geometric concepts like a Riemannian metric, connections and covariant derivatives on spaces of probability distributions. We discuss here how these concepts apply to quantum field theories in the Euclidean domain which can also be seen as statistical field theories. The geometry has a dual affine structure corresponding to sources and field expectation values seen as coordinates. A key concept is a new generating functional, which is a functional generalization of the Kullback-Leibler divergence. From its functional derivatives one can obtain connected as well as one-particle irreducible correlation functions. It also encodes directly the geometric structure, i. e. the Fisher information metric and the two dual connections, and it determines asymptotic probabilities for field configurations through Sanov's theorem.
Contact-Aware Non-prehensile Robotic Manipulation for Object Retrieval in Cluttered Environments

Non-prehensile manipulation methods usually use a simple end effector, e.g., a single rod, to manipulate the object. Compared to the grasping method, such an end effector is compact and flexible, and hence it can perform tasks in a constrained workspace; As a trade-off, it has relatively few degrees of freedom (DoFs), resulting in an under-actuation problem with complex constraints for planning and control. This paper proposes a new non-prehensile manipulation method for the task of object retrieval in cluttered environments, using a rod-like pusher. Specifically, a candidate trajectory in a cluttered environment is first generated with an improved Rapidly-Exploring Random Tree (RRT) planner; Then, a Model Predictive Control (MPC) scheme is applied to stabilize the slider's poses through necessary contact with obstacles. Different from existing methods, the proposed approach is with the contact-aware feature, which enables the synthesized effect of active removal of obstacles, avoidance behavior, and switching contact face for improved dexterity. Hence both the feasibility and efficiency of the task are greatly promoted. The performance of the proposed method is validated in a planar object retrieval task, where the target object, surrounded by many fixed or movable obstacles, is manipulated and isolated. Both simulation and experimental results are presented.
Identifying Misinformation Spreaders: A Graph-Based Semi-Supervised Learning Approach

In this paper we proposed a Graph-Based conspiracy source detection method for the MediaEval task 2022 FakeNews: Corona Virus and Conspiracies Multimedia Analysis Task. The goal of this study was to apply SOTA graph neural network methods to the problem of misinformation spreading in online social networks. We explore three different Graph Neural Network models: GCN, GraphSAGE and DGCNN. Experimental results demonstrate that DGCNN outperforms in terms of accuracy.
CLIP-Layout: Style-Consistent Indoor Scene Synthesis with Semantic Furniture Embedding

Indoor scene synthesis involves automatically picking and placing furniture appropriately on a floor plan, so that the scene looks realistic and is functionally plausible. Such scenes can serve as homes for immersive 3D experiences, or be used to train embodied agents. Existing methods for this task rely on labeled categories of furniture, e.g. bed, chair or table, to generate contextually relevant combinations of furniture. Whether heuristic or learned, these methods ignore instance-level visual attributes of objects, and as a result may produce visually less coherent scenes. In this paper, we introduce an auto-regressive scene model which can output instance-level predictions, using general purpose image embedding based on CLIP. This allows us to learn visual correspondences such as matching color and style, and produce more functionally plausible and aesthetically pleasing scenes. Evaluated on the 3D-FRONT dataset, our model achieves SOTA results in scene synthesis and improves auto-completion metrics by over 50%. Moreover, our embedding-based approach enables zero-shot text-guided scene synthesis and editing, which easily generalizes to furniture not seen during training.