Miracle-dz/UniFAR-FTUs huggingface dataset:
Prior-guided deep residual propagation for non-blind image deconvolution

Non-blind image deconvolution aims to recover clear images from observations corrupted by blur kernel, which plays a very important role in many signal processing and computer vision applications. In recent years, deep convolutional neural networks (CNNs) have achieved good performance on low-level image processing areas, such as denoising and superresolution. But unfortunately, till now only a few CNNs have been proposed for image deconvolution. It is mainly because this task is highly ill-posed and often with less high-quality training data. In this paper, we develop a novel prior-guided residual CNN to integrate task formulation, our cues on the incorporation of domain knowledge and data-dependent network for non-blind image deconvolution. The image propagation generated by our framework indeed gains advantages from both physical principles of the task and training data, thus can successfully recover more accurate deconvolution results. Experimental results on various challenging benchmarks demonstrate that our method can obtain excellent performance quantitatively and qualitatively.
Three Precise Spatial Signatures for Image Copy Recall

The literature is scarce on content-based copy detection and recall (CBCD). Methods on this categories attempt to exploit one or more feature(s) of images such as shape, color, or texture to recall a query image. The performance of these methods is satisfactory but not perfect. In this paper, we present three fast image copy recall algorithms from a database with perfect results. These algorithms are specifically developed to overcome the problems of copies of same image with different amounts of illumination intensities; similar images with trifling difference(s); and a limited image flipping cases in databases. The algorithms are based on spatial signatures that uniquely represent the images in the database as well as the query image to be recalled. We tested our algorithms on a set of 23, 443 images from LIVE, PIE databases, ORL Database of Faces (the AT&T laboratories Cambridge Database), Caltech-UCSD Birds database and our miscellany of images. Simulations results show that in each case, the query image was recalled with perfect accuracy. Finally, as our results show 100% accuracy, it was unnecessary to compare our results with previously published methods.
Predictive hand gesture classification for real time robot control

In this paper, we propose a vision-based hand gesture recognition system for human-computer interaction. The gesture recognition systems are employed in developing a rock-paper-scissors game between human and our robotic hands in realtime. Our task is to predict the gestures as soon as possible by using high-speed cameras. Due to the computational complexity, the standard long-term recurrent convolution network-based action classification system cannot be contented with classification tasks based on high-speed cameras. We propose to address this issue by employing a more efficient network architecture and using a threshold-based method to predict the gesture in advance. We validate our proposed method on the new gesture dataset for the rock-paper-scissors game. The model is able to successfully learn gestures varying in duration and complexity. A comparative analysis of CNN and long-term recurrent convolution network is performed. We report a gesture classification accuracy of 97% and report a near real-time computational complexity of 7 ms per frame.
Trimetazidine Attenuates Cardiac Dysfunction in Endotoxemia and Sepsis by Promoting Neutrophil Migration

Aims: Cardiac dysfunction can be a fatal complication during severe sepsis. The migration of neutrophils is significantly impaired during severe sepsis. We sought to determine the role of trimetazidine (TMZ) in regulation of neutrophil migration to the heart in a mouse model of sepsis and endotoxemia, and to identify the mechanism whereby TMZ confers a survival advantage. Methods and Results: C57/BL6 mice were (1) injected with LPS followed by 24-h TMZ administration, or (2) treated with TMZ (20 mg/kg/day) for 1 week post cecal ligation and puncture (CLP) operation. Echocardiography and Millar system detection showed that TMZ alleviated cardiac dysfunction and histological staining showed the failure of neutrophils migration to heart in both LPS- and CLP-induced mice. Bone marrow transplantation revealed that TMZ-pretreated bone marrow cells improved LPS- and CLP-induced myocardial dysfunction and enhanced neutrophil recruitment in heart. In CXCL2-mediated chemotaxis assays, TMZ increased neutrophils migration via AMPK/Nrf2-dependent up-regulation of CXCR2 and inhibition of GRK2. Furthermore, using luciferase reporter gene and chromatin immunoprecipitation assays, we found that TMZ promoted the binding of the Nrf2 and CXCR2 promoter regions directly. Application of CXCR2 inhibitor completely reversed the protective effects of TMZ in vivo. Co-culture of neutrophils and cardiomyocytes further validated that TMZ decreased LPS-induced cardiomyocyte pyroptosis by targeting neutrophils. Conclusion: Our findings suggested TMZ as a potential therapeutic agent for septic or endotoxemia associated cardiac dysfunction in mice. STUDY HIGHLIGHTS What is the current knowledge on the topic? Migration of neutrophils is significantly impaired during severe sepsis, but the underlying mechanisms remain unknown. What question did this study address? The effects of TMZ on cardiac dysfunction via neutrophils migration. What this study adds to our knowledge TMZ attenuated LPS-induced cardiomyocyte pyroptosis and cardiac dysfunction by promoting neutrophils recruitment to the heart tissues via CXCR2. How this might change clinical pharmacology or translational science Our findings suggested TMZ as a potential therapeutic agent for septic cardiac dysfunction.
Clinical value of chromosomal microarray analysis in prenatally diagnosed dextro-transposition of the great arteries

Abstract Objectives: To evaluate the usefulness of chromosomal microarray analysis (CMA) in fetuses with dextro-transposition of the great arteries (d-TGA). Methods: Thirty-two fetuses with d-TGA were examined for submicroscopic copy number variations (CNVs) using CMA. Results: Among the 32 d-TGA fetuses, 23 had isolated lesions (71.9%) and nine had other cardiac or extracardiac anomalies (28.1%). CNVs were detected in 16/32 (50%) of the fetuses, including benign CNVs detected in nine fetuses (28.1%), pathogenic CNVs detected in three fetuses (9.4%), and variants of unknown significance (VOUS) detected in four fetuses (12.5%). There was no significant difference in the detection rates of pathogenic CNVs between the isolated and nonisolated groups. All four VOUS were found in the nonisolated group. Conclusion: CMA might be an effective tool for identifying submicroscopic chromosomal aberrations in fetuses with d-TGA.
Augmented Reality Visualization of Joint Movements for Physical Examination and Rehabilitation

We present a visualization tool for human motion analysis in augmented reality. Our tool builds upon our previous work on joint biomechanical modelling for kinematic analysis, based on optical motion capture and personalized anatomical reconstruction of joint structures from medical imaging. It provides healthcare professionals with the in situ visualization of joint movements, where bones are accurately rendered as a holographic overlay on the subject - like if the user has an “X-ray vision” - and in real-time as the subject performs the movement. Currently, hip and knee joints are supported.
Restricted stochastic pooling for convolutional neural network

Deep learning has been demonstrated to be a universal and successful tool for classification, detection and segmentation. Convolutional neural network (CNN), as a representative model, achieves excellent performance in many image process areas due to its special structural design---shared weights, local receptive field and pooling layer. Large efforts are devoted to studying and improving model performance based on these three parts. In this paper, we proposed a novel pooling layer named restricted stochastic pooling layer. Restricted stochastic pooling randomly select activation from the top n maximum of each pooling region instead of the largest one (max pooling), or stochastic one (stochastic pooling). Our proposed method can not only obtain representative activation but also add randomness to the model. Through large amounts of experiments, we achieve good performance on SVHN and CIFAR-10 which demonstrates the effectiveness of restricted stochastic pooling.
A Performance Evaluation of General Traffic Systems by Machine Learning

When we design the Internet of Things (IoT) systems, it is important to evaluate general traffic systems. However, in general traffic systems, the exact solution is not available. Alternatively, we can evaluate it with simulation. However, simulation spends much time. In this paper, we propose evaluate general traffic systems by machine learning.
A Tactile Assistance for Improving Fingering Skill in Piano Performance

Playing a piano requires various techniques and properly acquiring fingering skills without expert's assistance is particularly difficult. While the expert tries to convey his skills through language and physical contacts to trainees, most ICT-based training systems visually present exercise information. We propose a method enabling trainees to easily improve their fingering skills via a tactile feedback technique. It indicates which finger to use by directly stimulating a target finger in parallel with presenting visual information. We made a tactile display for finely indicating fingering information and evaluated a prototype system to verify the effectiveness of the method.
A Configuration-Independent Software Architecture for Modular Robots

This paper presents a configuration-independent approach for setting up and running Thorvald II, a novel and reconfigurable robot for the agricultural domain. The module-based design of the Thorvald II platform allows for rapid design and construction of robots that can operate in various environments, such as open fields, greenhouses, and polytunnels. Modules can be put together in arbitrary configurations, and it is important that this modularity is supported by the robot's software. The presented approach allows for quick setup of module communication, kinematic modeling and odometry for arbitrary Thorvald II hardware configurations. It speeds up the process of creating new configurations of the robot, as new configurations will not require configuration-specific alterations to the robot's code. The approach also allows us to quickly rebuild robots, which means that they can be adapted to new environments, fast and efficiently. We verify the presented approach through a field experiment, and discuss further development. We also provide instructions on how to install ROS packages presented in this paper.
Restoration of Multilayered Single-Photon 3D Lidar Images

This paper proposes a new algorithm to restore 3D single-photon Lidar images obtained under challenging realistic scenarios which include imaging multilayered targets such as semi-transparent surfaces or imaging through obscurants such as scattering media (e.g., water, fog). The Data restoration and exploitation is achieved by minimising an appropriate cost-function accounting for the data Poisson statistics and the available prior knowledge regarding the depth and reflectivity estimates. The proposed algorithm takes into account (i) the non-local spatial correlations between pixels, by using a convex non-local total variation (TV) regularizer, and (ii) the clustered nature of the returned photons, by using a collaborative sparse prior. The resulting minimization problem is solved using the alternating direction method of multipliers (ADMM) that offers good convergence properties. The algorithm is validated using both synthetic and real data which show the benefit of the proposed strategy in the sparse regime due to a fast acquisition or in presence of a high background due to obscurants.
Global Perspectives on Nonalcoholic Fatty Liver Disease and Nonalcoholic Steatohepatitis

Over the past 2 decades, nonalcoholic fatty liver disease (NAFLD) has grown from a relatively unknown disease to the most common cause of chronic liver disease in the world. In fact, 25% of the world’s population is currently thought to have NAFLD. Nonalcoholic steatohepatitis (NASH) is the subtype of NAFLD that can progress to cirrhosis, hepatocellular carcinoma (HCC), and death. NAFLD and NASH are not only found in adults—there is also a high prevalence of these diseases in children and adolescents. Because of the close association of NAFLD with type 2 diabetes (T2DM) and obesity, the latest models predict that the prevalence of NAFLD and NASH will increase, causing a tremendous clinical and economic burden and poor patient‐reported outcomes. Nonetheless, there is no accurate noninvasive method to detect NASH, and treatment of this disease is limited to lifestyle modifications. To examine the state of NAFLD among different regions and understand the global trajectory of this disease, an international group of experts came together during the 2017 American Association for the Study of Liver Diseases Global NAFLD Forum. We provide a summary of this forum and an assessment of the current state of NAFLD and NASH worldwide.
Heterogeneous, Distributed Mixed Reality Applications. A Concept

This poster formulates the concept of heterogeneous distributed mixed reality (HDMR) applications in order to state some interesting research questions in this domain. HDMR applications give synchronous access to shared virtual worlds, from diverse mixed reality (MR) hardware, and at similar levels of functionality. We show the relationship between HDMR and previous concepts, state challenges in their development, and illustrate this concept and its challenges with an example.
Fatigue Detection System Using Enhanced So and Chan Method

The key objective of the ECG monitoring system is QRS complex detection. An Enhanced So and Chan Method is proposed in this paper to improve the So and Chan method. It can effectively reduce the fail detection rate. Compared to the original So and Chan method, the accuracy increases from 94.61% to 99.16%. A FPGA board is used to verify our algorithm. Then we take the RRI data into PC for power spectral density analysis to get LF / HF. The results can be used to determine the driver's state.
Performance of Caching in a Layered CoAP Proxy

In this paper we analyze the performance of a CoAP proxy with layered architecture and a built-in cache. The proxy acts as the gateway connected to the sensor nodes through an IEEE 802.15. 4 low data rate wireless personal area network (LR WPAN). We investigate the impact of various traffic and proxy parameters on the performance of the proxy, in particular the interplay of the data validity interval defined through the CoAP Max-Age parameter.
E-Band Multi-Phase LC Oscillators With Rotated-Phase-Tuning Using Implicit Phase Shifters

A rotated-phase-tuning (RPT) technique is proposed for millimeter-wave (mmW) LC-based oscillators. Without using varactors, the oscillation frequency is tuned by phase interpolation to vary the phase of the current flowing into the LC tank. Interestingly, the intrinsic delay of transistors is leveraged to rotate the phases for interpolation and thus to optimize the oscillator’s performance in terms of frequency tuning range and phase noise. Two E-band RPT oscillator prototypes are designed and implemented in a 65-nm CMOS process. The first one with four-phase output measures oscillation frequency from 67.8 to 81.4 GHz and phase noise of −108 to −113 dBc/Hz at 10-MHz offset, while consuming 13 to 25 mW from a 1-V supply. With similar power consumption, the second prototype featuring eight-phase output operates from 74.8 to 79 GHz and achieves phase noise of −116 to −118 dBc/Hz at 10-MHz offset. The four-phase and eight-phase oscillators occupy small core areas of 0.02 and 0.06 mm2, respectively.
Tractable Mobility Model for Multi-Connectivity in 5G User-Centric Ultra-Dense Networks

With the network densification, frequent handovers may degrade mobility reliability, cause heavy signaling load thus blocking the improvement of network capacity. This paper aims at reducing cost in terms of handover in dense networks, deriving compact expressions of network performance metric, including handover probabilities as well as the mobility-aware downlink data rate, under a multi-connectivity mobility model in user-centric ultra-dense networks (UUDNs). Specifically, the nearest <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> access points (APs) in mobile user’s vicinity serve the user for data transmission, while the best one among them is for control, which reduces handover probability and network overheads without a loss in the throughput performance gain. Based on the scheme, handover probabilities are derived for arbitrary movement trajectory in the UUDN, where the locations of APs are modeled as a homogeneous Poisson point process, and then is included in handover cost to derive the downlink rate for mobile users. It is shown that the handover probability in control-plane can considerably be decreased by at least 30% compared with the traditional single connectivity network. What is more, the downlink rate can be improved significantly by <inline-formula> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> compared with single connectivity.
Contextual Semantics for Radicalisation Detection on Twitter

Much research aims to detect online radical content mainly using radicalisation glossaries, i.e., by looking for terms and expressions associated with religion, war, offensive language, etc. However, such crude methods are highly inaccurate towards content that uses radicalisation terminology to simply report on current events, to share harmless religious rhetoric, or even to counter extremism. Language is complex and the context in which particular terms are used should not be disregarded. In this paper, we propose an approach for building a representation of the semantic context of the terms that are linked to radicalised rhetoric. We use this approach to analyse over 114K tweets that contain radicalisation-terms (around 17K posted by pro-ISIS users, and 97k posted by “general” Twitter users). We report on how the contextual information differs for the same radicalisation terms in the two datasets, which indicate that contextual semantics can help to better discriminate radical content from content that only uses radical terminology.The classifiers we built to test this hypothesis outperform those that disregard contextual information
An Overview of the Internet of Things Closed Source Operating Systems

The Internet of Things (IoT) attract a great deal of research and industry attention recently and are envisaged to support diverse emerging domains including intelligent trans- portation, smart cities, and health informatics. Operating system support for IoT plays a pivotal role in developing interoperable and scalable applications that are efficient and reliable. IoT is implemented by both high-end and low-end devices that require an operating system to run. Recently, we have witnessed a diversity of OSs emerging into IoT environment to facilitate IoT deployments and developments. In this paper, we present an overview of the common and existing closed source OSs for IoT. This paper is written in a tutorial style where each OS is described in details based on a set of designing and development aspects that we established. These aspects include architecture and kernel, memory management, scheduling, power consumption, networking protocols support, security, program- ming model, and multimedia support. The objective of this survey is to provide a well-structured guide to developers and researchers to determine the most appropriate OS for each specific IoT applications/devices based on their functional and non-functional requirements.
Integrated assessment of visual perception abnormalities in psychotic disorders and relationship with clinical characteristics

Abstract Background The visual system is recognized as an important site of pathology and dysfunction in schizophrenia. In this study, we evaluated different visual perceptual functions in patients with psychotic disorders using a potentially clinically applicable task battery and assessed their relationship with symptom severity in patients, and with schizotypal features in healthy participants. Methods Five different areas of visual functioning were evaluated in patients with schizophrenia and schizoaffective disorder (n = 28) and healthy control subjects (n = 31) using a battery that included visuospatial working memory (VSWM), velocity discrimination (VD), contour integration, visual context processing, and backward masking tasks. Results The patient group demonstrated significantly lower performance in VD, contour integration, and VSWM tasks. Performance did not differ between the two groups on the visual context processing task and did not differ across levels of interstimulus intervals in the backward masking task. Performances on VSWM, VD, and contour integration tasks were correlated with negative symptom severity but not with other symptom dimensions in the patient group. VSWM and VD performances were also correlated with negative sychizotypal features in healthy controls. Conclusion Taken together, these results demonstrate significant abnormalities in multiple visual processing tasks in patients with psychotic disorders, adding to the literature implicating visual abnormalities in these conditions. Furthermore, our results show that visual processing impairments are associated with the negative symptom dimension in patients as well as healthy individuals.
Automatic image cropping with a single fully convolutional network

Automatic image cropping is of great importance for improving visual quality of images. Existing methods typically repurpose classifiers to perform cropping. Instead, we frame image cropping as a regression problem to bounding boxes and associated visual quality scores. An end-to-end framework with fully convolutional network is proposed to predict bounding boxes and associated scores from full images in one evaluation, which accepts input images of arbitrary sizes. In this way, we circumvent the requirement of fixed-sized inputs from prevalent convolutional networks, and thus avoid impairing the visual quality of images. Additionally, the whole cropping pipeline is a single network, so it can be optimized end-to-end directly on cropping performance, which is different from existing methods requiring the processes of proposal generation and subsequent region feature extraction. Experiments on a public dataset confirm that our framework has competitive accuracy and speed to traditional approaches.
Robust PCA and Robust Subspace Tracking: A Comparative Evaluation

This paper provides a comparative theoretical and experimental evaluation of solutions for robust PCA and robust subspace tracking (dynamic RPCA) that rely on the sparse+lowrank matrix decomposition formulation. The emphasis is on simple and provably correct methods. Experimental comparisons are shown for video layering (separate a given video into foreground and background layer videos) which is a key first step in simplifying many video analytics and computer vision tasks, e.g., video denoising or activity recognition.