Miracle-dz/UniFAR-FTUs huggingface dataset:
Geolocation of Unknown Emitters Using Tdoa of Path Rays Through the Ionosphere by Multiple Coordinated Distant Receivers

We consider the problem of unknown emitter geolocation using the time difference of arrival (TDOA) of the path rays through the ionosphere by multiple coordinated distant receivers. We formulate the geolocation in the sense of maximum likelihood with the exact ray expressions for the quasi-parabolic (QP) ionosphere, which is a highly nonlinear and non-convex optimization problem. By carefully studying the characteristic of the group path ray, we propose an efficient procedure to approach the optimal solution of the geolocation. Simulation results show that the geolocation error approaches the associated Cramer-Rao bound when the knowledge of the ionosphere is available. We also performed Monte Carlo runs to evaluate the performance of the geolocation when the knowledge of the ionosphere is not exactly known, e.g., the QP model parameters are perturbed. Simulation results show that the geolocation performance under the perturbation within a given certain range is acceptable.
How Eye Movements Stabilize Posture in Patients With Bilateral Vestibular Hypofunction

Chronic patients with bilateral vestibular hypofunction (BVH) complain of oscillopsia and great instability particularly when vision is excluded and on irregular surfaces. The real nature of the visual input substituting to the missing vestibular afferents and improving posture control remains however under debate. Is retinal slip involved? Do eye movements play a substantial role? The present study tends to answer this question in BVH patients by investigating their posture stability during quiet standing in four different visual conditions: total darkness, fixation of a stable space-fixed target, and pursuit of a visual target under goggles delivering visual input rate at flicker frequency inducing either slow eye movements (4.5 Hz) or saccades (1.2 Hz). Twenty one chronic BVH patients attested by both the caloric and head impulse test were examined by means of static posturography, and compared to a control group made of 21 sex-and age-matched healthy participants. The posturography data were analyzed using non-linear computation of the center of foot pressure (CoP) by means of the wavelet transform (Power Spectral Density in the visual frequency part, Postural Instability Index) and the fractional Brownian-motion analysis (stabilogram-diffusion analysis, Hausdorff fractal dimension). Results showed that posture stability was significantly deteriorated in darkness in the BVH patients compared to the healthy controls. Strong improvement of BVH patients' posture stability was observed during fixation of a visual target, pursuit with slow eye movements, and saccades, whereas the postural performance of the control group was less affected by the different visual conditions. It is concluded that BVH patients improve their posture stability by (1) using extraocular signals from eye movements (efference copy, muscle re-afferences) much more than the healthy participants, and (2) shifting more systematically than the controls to a more automatic mode of posture control when they are in dual-task conditions associating the postural task and a concomitant visuo- motor task.
Hand-Raising Gesture Detection in Real Classroom

This paper proposes a novel method for hand-raising detection in the real classroom environment. Different from traditional motion detection, the hand-raising detection is quite challenging in the real classroom due to complex scenarios, various gestures, and low resolutions. To solve these challenges, we first build up a large-scale hand-raising data set from thirty primary schools and middle schools of Shanghai, China. Then we propose an improved R-FCN to solve the above-mentioned challenges. Specifically, we first design an automatic detection templates algorithm for various gestures of hand-raising detection. Second, for better detection of the small-size hands, we present a feature pyramid to simultaneously capture the detail and highly semantic features. Incorporating these two strategies into a basic R-FCN architecture, our model achieves impressive results on real classroom scenarios. After a wide test, the accuracy of the hand-raising detection achieves 85% on average, which can satisfy the real application.
Improving LoRa Network Capacity Using Multiple Spreading Factor Configurations

LoRa networks enable long range communications for Internet of Things (IoT) applications. The current LoRa technology provides a wide range of communication settings whereas many combination settings are orthogonal and, thus, they can be successfully decoded at the gateway when the signals are transmitted simultaneously. Previous simulation results showed that the LoRa network capacity can be improved when multiple communication parameters are applied. In this paper, we model a LoRa network consisting of nodes with different communication settings in terms of bandwidth and spreading factor. We compute the average success probability per configuration as a function of density taking into account both intra and inter-spreading factor collisions. We, also, formulate and solve an optimization problem to maximize the node capacity for a given deployment area and frequency by optimizing the number of nodes having different spreading factor configurations. We present numerical results and we show that solutions close to the optimal can increase the maximum number of nodes by more than 700% compared to case where equal number of users per spreading factor are considered.
Uneven balance of power between hypothalamic peptidergic neurons in the control of feeding

Significance The interplay between the anorexigenic and orexigenic neurons in the arcuate nucleus that contributes to the control of feeding remains elusive. Using optogenetic stimulation, we show that activation of POMC neurons rapidly inhibits feeding behavior in fasted animals. However, simultaneous stimulation of both POMC neurons and a subset of the orexigenic neurons that express AgRP is sufficient to reverse that inhibition and trigger intense feeding behavior. We used 3D imaging and functional studies to illuminate the anatomical underpinning of both the inhibitory and excitatory events. Our work suggests that translational applications that aim to control appetite need to target the activation rather than the inhibition mechanisms. Two classes of peptide-producing neurons in the arcuate nucleus (Arc) of the hypothalamus are known to exert opposing actions on feeding: the anorexigenic neurons that express proopiomelanocortin (POMC) and the orexigenic neurons that express agouti-related protein (AgRP) and neuropeptide Y (NPY). These neurons are thought to arise from a common embryonic progenitor, but our anatomical and functional understanding of the interplay of these two peptidergic systems that contribute to the control of feeding remains incomplete. The present study uses a combination of optogenetic stimulation with viral and transgenic approaches, coupled with neural activity mapping and brain transparency visualization to demonstrate the following: (i) selective activation of Arc POMC neurons inhibits food consumption rapidly in unsated animals; (ii) activation of Arc neurons arising from POMC-expressing progenitors, including POMC and a subset of AgRP neurons, triggers robust feeding behavior, even in the face of satiety signals from POMC neurons; (iii) the opposing effects on food intake are associated with distinct neuronal projection and activation patterns of adult hypothalamic POMC neurons versus Arc neurons derived from POMC-expressing lineages; and (iv) the increased food intake following the activation of orexigenic neurons derived from POMC-expressing progenitors engages an extensive neural network that involves the endogenous opioid system. Together, these findings shed further light on the dynamic balance between two peptidergic systems in the moment-to-moment regulation of feeding behavior.
Preexisting Simian Immunodeficiency Virus Infection Increases Susceptibility to Tuberculosis in Mauritian Cynomolgus Macaques

Tuberculosis (TB), caused by Mycobacterium tuberculosis, is the leading cause of death among human immunodeficiency virus (HIV)-positive patients. The precise mechanisms by which HIV impairs host resistance to a subsequent M. tuberculosis infection are unknown. ABSTRACT Tuberculosis (TB), caused by Mycobacterium tuberculosis, is the leading cause of death among human immunodeficiency virus (HIV)-positive patients. The precise mechanisms by which HIV impairs host resistance to a subsequent M. tuberculosis infection are unknown. We modeled this coinfection in Mauritian cynomolgus macaques (MCM) using simian immunodeficiency virus (SIV) as an HIV surrogate. We infected seven MCM with SIVmac239 intrarectally and 6 months later coinfected them via bronchoscope with ∼10 CFU of M. tuberculosis. Another eight MCM were infected with M. tuberculosis alone. TB progression was monitored by clinical parameters, by culturing bacilli in gastric and bronchoalveolar lavages, and by serial [18F]fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) imaging. The eight MCM infected with M. tuberculosis alone displayed dichotomous susceptibility to TB, with four animals reaching humane endpoint within 13 weeks and four animals surviving >19 weeks after M. tuberculosis infection. In stark contrast, all seven SIV+ animals exhibited rapidly progressive TB following coinfection and all reached humane endpoint by 13 weeks. Serial PET/CT imaging confirmed dichotomous outcomes in MCM infected with M. tuberculosis alone and marked susceptibility to TB in all SIV+ MCM. Notably, imaging revealed a significant increase in TB granulomas between 4 and 8 weeks after M. tuberculosis infection in SIV+ but not in SIV-naive MCM and implies that SIV impairs the ability of animals to contain M. tuberculosis dissemination. At necropsy, animals with preexisting SIV infection had more overall pathology, increased bacterial loads, and a trend towards more extrapulmonary disease than animals infected with M. tuberculosis alone. We thus developed a tractable MCM model in which to study SIV-M. tuberculosis coinfection and demonstrate that preexisting SIV dramatically diminishes the ability to control M. tuberculosis coinfection.
LSTM knowledge transfer for HRV-based sleep staging

Automated sleep stage classification using heart-rate variability is an active field of research. In this work limitations of the current state-of-the-art are addressed through the use of deep learning techniques and their efficacy is demonstrated. First, a temporal model is proposed for the inference of sleep stages from electrocardiography using a deep long- and short-term (LSTM) classifier and it is shown that this model outperforms previous approaches which were often limited to non-temporal or Markovian classifiers on a comprehensive benchmark data set (292 participants, 541214 samples) comprising a wide range of ages and pathological profiles, achieving a Cohen's $\kappa$ of $0.61\pm0.16$ and accuracy of $76.30\pm10.17$ annotated according to the Rechtschaffen & Kales annotation standard. Subsequently, it is demonstrated how knowledge learned on this large benchmark data set can be re-used through transfer learning for the classification of photoplethysmography (PPG) data. This is done using a smaller data set (60 participants, 91479 samples) that is annotated with the more recent American Association of Sleep Medicine annotation standard, achieving a Cohen's $\kappa$ of $0.63\pm0.13$ and accuracy of $74.65\pm8.63$ for wrist-mounted PPG-based sleep stage classification, higher than any previously reported performance using this sensor modality. This demonstrates the feasibility of knowledge transfer in sleep staging to adapt models for new sensor modalities as well as different annotation strategies.
Robust Adversarial Perturbation on Deep Proposal-based Models

Adversarial noises are useful tools to probe the weakness of deep learning based computer vision algorithms. In this paper, we describe a robust adversarial perturbation (R-AP) method to attack deep proposal-based object detectors and instance segmentation algorithms. Our method focuses on attacking the common component in these algorithms, namely Region Proposal Network (RPN), to universally degrade their performance in a black-box fashion. To do so, we design a loss function that combines a label loss and a novel shape loss, and optimize it with respect to image using a gradient based iterative algorithm. Evaluations are performed on the MS COCO 2014 dataset for the adversarial attacking of 6 state-of-the-art object detectors and 2 instance segmentation algorithms. Experimental results demonstrate the efficacy of the proposed method.
Altered oligomeric states in pathogenic ALS2 variants associated with juvenile motor neuron diseases cause loss of ALS2-mediated endosomal function

Familial amyotrophic lateral sclerosis type 2 (ALS2) is a juvenile autosomal recessive motor neuron disease caused by the mutations in the ALS2 gene. The ALS2 gene product, ALS2/alsin, forms a homophilic oligomer and acts as a guanine nucleotide–exchange factor (GEF) for the small GTPase Rab5. This oligomerization is crucial for both Rab5 activation and ALS2-mediated endosome fusion and maturation in cells. Recently, we have shown that pathogenic missense ALS2 mutants retaining the Rab5 GEF activity fail to properly localize to endosomes via Rac1-stimulated macropinocytosis. However, the molecular mechanisms underlying dysregulated distribution of ALS2 variants remain poorly understood. Therefore, we sought to clarify the relationship between intracellular localization and oligomeric states of pathogenic ALS2 variants. Upon Rac family small GTPase 1 (Rac1) activation, all mutants tested moved from the cytosol to membrane ruffles but not to macropinosomes and/or endosomes. Furthermore, most WT ALS2 complexes were tetramers. Importantly, the sizes of an ALS2 complex carrying missense mutations in the N terminus of the regulator of chromosome condensation 1–like domain (RLD) or in-frame deletion in the pleckstrin homology domain were shifted toward higher molecular weight, whereas the C-terminal vacuolar protein sorting 9 (VPS9) domain missense mutant existed as a smaller dimeric or trimeric smaller form. Furthermore, in silico mutagenesis analyses using the RLD protein structure in conjunction with a cycloheximide chase assay in vitro disclosed that these missense mutations led to a decrease in protein stability. Collectively, disorganized higher structures of ALS2 variants might explain their impaired endosomal localization and the stability, leading to loss of the ALS2 function.
Deep Feature Embedding Learning for Person Re-Identification Using Lifted Structured Loss

In this paper, we propose deep feature embedding learning for person re-identification (re-id) using lifted structured loss. Although triplet loss has been commonly used in deep neural networks for person re-id, the triplet loss-based framework is not effective in fully using the batch information. Thus, it needs to choose hard negative samples manually that is very time-consuming. To address this problem, we adopt lifted structured loss for deep neural networks that makes the network learn better feature embedding by minimizing intra-class variation and maximizing inter-class variation. Extensive experiments on CUHK03, CUHKOI and VIPeR datasets demonstrate the superior performance of the proposed method over state-of-the-arts in terms of the cumulative match curve (CMC) metric.
Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment

Facial landmark localisation in images captured in-the-wild is an important and challenging problem. The current state-of-the-art revolves around certain kinds of Deep Convolutional Neural Networks (DCNNs) such as stacked U-Nets and Hourglass networks. In this work, we innovatively propose stacked dense U-Nets for this task. We design a novel scale aggregation network topology structure and a channel aggregation building block to improve the model's capacity without sacrificing the computational complexity and model size. With the assistance of deformable convolutions inside the stacked dense U-Nets and coherent loss for outside data transformation, our model obtains the ability to be spatially invariant to arbitrary input face images. Extensive experiments on many in-the-wild datasets, validate the robustness of the proposed method under extreme poses, exaggerated expressions and heavy occlusions. Finally, we show that accurate 3D face alignment can assist pose-invariant face recognition where we achieve a new state-of-the-art accuracy on CFP-FP.
Sparse Laplacian Component Analysis for Internet Traffic Anomalies Detection

We consider the problem of anomaly detection in network traffic. It is a challenging problem because of high-dimensional and noisy nature of network traffic. A popularly used technique is subspace analysis . In particular, subspace analysis aims to separate the high-dimensional space of traffic signals into disjoint subspaces corresponding to normal and anomalous network conditions. Principal component analysis (PCA) and its improvements have been applied for this analysis. In this work, we take a different approach to determine the subspaces, and propose to capture the essence of the data using the eigenvectors of graph Laplacian, which we refer as Laplacian components (LCs). Our main contribution is to propose a regression framework to compute LCs followed by its application in anomaly detection. This framework provides much flexibility in incorporating different properties into the LCs, notably LCs with sparse loadings, which we exploit in detail. In other words, our contribution is a new framework to compute the graph Fourier transform (GFT). The proposed framework enables sparse loadings and potentially other properties to be incorporated into the analysis components of GFT to suit different tasks. Furthermore, different from previous work that uses a sample graph to preserve local structure, we advocate modeling with a dual-input feature graph that encodes the correlation of the time series data and prior information. Therefore, the proposed model can readily incorporate the “physics” of some applications as prior information to improve the analysis. We perform experiments on volume anomaly detection using three real data sets. We demonstrate that the proposed model can correctly uncover the essential low-dimensional principal subspace containing the normal Internet traffic and achieve outstanding detection performance.
Attacking Object Detectors via Imperceptible Patches on Background

Deep neural networks have been proven vulnerable against adversarial perturbations. Recent works succeeded to generate adversarial perturbations on either the entire image or on the target of interests to corrupt object detectors. In this paper, we investigate the vulnerability of object detectors from a new perspective — adding minimal perturbations on small background patches outside of targets to fail the detection re-sults. Our work focuses on attacking the common component in the state-of-the-art detectors (e.g. Faster R-CNN), Region Proposal Networks (RPNs). As the receptive ﬁelds generated by RPN is often larger than the proposals themselves, we propose a novel method to generate background perturbation patches, and show that the perturbations solely outside of the targets can severely damage the performance of multiple types of detectors by simultaneously decreasing the true positives and increasing the false positives. We demonstrate the efﬁcacy of our method on 5 different state-of-the-art object detectors on MS COCO 2014 dataset.
How bacterioplankton community can go with cascade damming in the highly regulated Lancang–Mekong River Basin

Rivers make vital contributions to the transport of water, sediment and nutrients from terrestrial to marine ecosystems. However, many large rivers worldwide are suffering from dam regulation. Increasing attention has been paid to bacterioplankton communities since they are highly responsive to river alterations and may influence biogeochemical processes. Here, a comprehensive study was conducted in the highly regulated Lancang–Mekong River Basin to address the question of how bacterioplankton communities respond to cascade damming. The results showed that dam constructions increased nutrient concentrations and threatened water quality in cascade reservoirs. Bacterioplankton cell abundance was reduced by damming, and α‐diversity was inhibited in cascade reservoirs. Fortunately, however, river ecosystems were resilient after the remarkable disturbance caused by damming. Moreover, bacterioplankton community composition was significantly altered by cascade dams, including a shift in the dominant phylum from r‐strategists to k‐strategists. Meanwhile, according to GeoChip analysis, the functional composition of bacterioplankton was less affected than taxonomic composition. In addition, geographic and environmental features both followed a distance–decay relationship with community and functional composition, but the local environment condition was the dominant driver in the Lancang River. Therefore, the impoundments of cascade dams had significant impacts on bacterioplankton communities and more attention should be paid to the potential ecological consequences of river regulation.
Tip-Apex Distance Is Most Important of Six Predictors of Screw Cutout After Internal Fixation of Intertrochanteric Fractures in Women

Background: Six risk factors for screw cutout after internal fixation of intertrochanteric fractures have been reported. The purpose of the present study was to evaluate and compare the impact of the 6 risk factors of screw cutout to clarify the most important one. Methods: We enrolled 8 consecutive patients who had screw cutout and 48 random control subjects after internal fixation of intertrochanteric fractures treated with proximal femoral nail antirotation systems at our institution. All of the patients were female. The group that had screw cutout and the control group were retrospectively evaluated and compared with respect to the OTA/AO classification, presence of a posterolateral fragment, types of reduction pattern on anteroposterior and lateral radiographic images, position of the screw, and the presence of a tip-apex distance (TAD) of ≥20 mm. The impact of each factor on screw cutout was assessed using backward stepwise multivariable logistic regression analysis with the Akaike information criterion. Risk stratification was assessed using classification and regression tree (CART) analysis. Results: Among 6 risk factors, only a TAD of ≥20 mm had a significant impact on screw cutout, with an adjusted odds ratio of 12.4 (95% confidence interval, 1.6 to 129.0; p = 0.019). CART analysis also demonstrated that a TAD of ≥20 mm was the most important risk stratification factor (p < 0.001). Conclusions: Among the 6 previously reported screw cutout-related factors, only a TAD of ≥20 mm was associated with screw cutout after internal fixation of intertrochanteric fractures with proximal femoral nail antirotation systems. Level of Evidence: Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.