Miracle-dz/UniFAR-FTUs huggingface dataset:
Recovery of Block-Structured Sparse Signal Using Block-Sparse Adaptive Algorithms via Dynamic Grouping

A key point for the recovery of a block-sparse signal is how to treat the different sparsity distributed on the different parts of the considered signal. It has been shown recently that grouping the signal, i.e., partitioning the original signal into different groups or segments, and conducting the recovery for these groups separately provides an effective method to deal with the block-structured sparsity and can generate much better performance than the conventional sparse signal recovery (SSR) algorithms. In order to further improve the recovery performance, instead of the fixed grouping method used in the recent results, a novel dynamic grouping method will be first proposed in this paper, which classifies the segments due to the different levels of sparsity in a dynamic way. Then, by incorporating this technique into the block version of adaptive SSR algorithms. we developed recently, i.e., the block zero-attracting least-mean-square (BZA-LMS) algorithm and the block <inline-formula> <tex-math notation="LaTeX">$\ell _{0}$ </tex-math></inline-formula>-norm LMS (<inline-formula> <tex-math notation="LaTeX">$\text{B}\ell _{0}$ </tex-math></inline-formula>-LMS) algorithm, the corresponding new algorithms, i.e., the BZA-LMS-D and <inline-formula> <tex-math notation="LaTeX">$\text{B}\ell _{0}$ </tex-math></inline-formula>-LMS-D algorithms, will be established. The performance superiorities and the robustness against different block-sparsity and/or noise interference for the new algorithms based on dynamic grouping will be demonstrated by both analytic discussions and numerical simulations for a variety of scenarios.
‘I want what every other woman has’: reasons for wanting clitoral reconstructive surgery after female genital cutting – a qualitative study from Sweden

Abstract Female genital cutting (FGC) involves the removal of women’s external genitalia for non-therapeutic reasons. An estimated 38,000 women living in Sweden have undergone some form of the procedure. These women often belong to marginalised minorities of immigrant women from countries where FGC is widespread. Clitoral reconstructive surgery following FGC has recently been introduced in Sweden. This study investigates women’s perceptions of FGC and clitoral reconstructive surgery with a particular focus on: (1) reasons for requesting reconstructive surgery, and (2) FGC-affected women’s expectations of the surgery. Seventeen women referred for clitoral reconstructive surgery at the Department of Plastic and Reconstructive Surgery, Karolinska University Hospital, Stockholm, participated in the study. Findings revealed five factors motivating women’s request for clitoral reconstruction (CR): (1) symbolic restitution – undoing the harm of FGC; (2) repairing the visible stigma of FGC; (3) improving sex and intimacy through physical, aesthetic and symbolic recovery; (4) eliminating physical pain; (5) and CR as a personal project offering hope. These factors were highly interconnected, suggesting that the reasons for seeking surgery were often multiple and complex.
Discriminative Latent Semantic Regression for Cross-Modal Hashing of Multimedia Retrieval

Due to large heterogeneity gaps between image, text, and video, finding content similarities of multimedia data is a challenging problem yet to be resolved. In this paper, we propose to integrate high-level feature extractions and learning of the common data representations based on latent semantic regression for hashing multimedia data in a unified optimization framework. We show that the proposed latent semantic regression approach results in a discriminative solution maximizing the inter-modal correlation while preserving the intra-modal similarity of high-level features. In this way, the heterogeneous data are embedded into their common label space more effectively. The embedded feature representations have a natural interpretation as being proportional to the probabilities of classes that each sample belongs to. The training time complexity of the proposed learning scheme is linear with the data size, i.e., O(N) and its experimental results on popular multimodal datasets of Wiki and NUS-WIDE can significantly improve cross-modal hashing by up to 28% in term of the mean average precision (mAP) value.
Precomputation Methods for UOV Signature on Energy-Harvesting Sensors

Wireless sensor networks (WSNs) are increasingly gaining impact on our daily lives. They are finding a wide range of applications in various domains, such as health-care, environmental monitoring, and so on. In future, WSNs are expected to be integrated into the Internet of Things (IoT). The integrity of the sensed data is of primary importance in WSN and IoT applications. However, WSN platforms always have very limited resources in terms of battery power, computing, and memory. Therefore, it is a key challenge to design an energy-friendly, lightweight digital signature algorithm for WSN platforms. In this paper, we present precomputation methods for unbalanced oil–vinegar (UOV) signature scheme by exploiting the energy-harvesting capabilities of WSN to enhance the performance of UOV signature. In addition, we combine a circulant method with precomputation in the UOV signature to further reduce the energy cost. Meanwhile, the circulant method reduces the size and the memory overhead of the precomputation tuple. This increases the availability of precomputation and greatly enhances the availability of the overflow energy. By integrating the above-mentioned optimization methods, the cost of UOV signature in a WSN node can be reduced by 93%. This greatly enhances the performance of UOV signature, making it feasible for the practical deployment on resource-constrained wireless sensor platforms.
The CODATA 2017 Special Adjustment for the Revision of the SI

This paper summarizes the 2017 special leastsquares adjustment of the values of the fundamental constants performed by the Committee on Data for Science and Technology (CODATA). Based on relevant data available by 1 July 2017, exact values of the Planck constant h, elementary charge e, Boltzmann constant k, and Avogadro constant NA are presented. These values are recommended to the 26th General Conference on Weights and Measures (GCPM) to form the foundation of the revised SI.
Role of Prefrontal Cortex Anti- and Pro-inflammatory Cytokines in the Development of Abnormal Behaviors Induced by Disconnection of the Ventral Hippocampus in Neonate Rats

Neonatal disconnection of ventral hippocampus (VH) outputs in rats has been reported to lead to post-pubertal behavioral and synaptic changes of relevance to schizophrenia. Increased oxidative and inflammatory load in the prefrontal cortex (PFC) has been suggested to mediate some of the effects of neonatal VH lesion (NVHL). In this study, we hypothesized that developmental imbalance of anti- and pro-inflammatory factors within the PFC might affect synaptic development thus contributing to the adult NVHL-induced behavioral deficits. Ibotenic acid-induced excitotoxic NVHL was performed in postnatal day (PD) 7 male Sprague-Dawley rats and the mRNA levels of select pro- and anti-inflammatory cytokines were measured in the medial PFC (mPFC) at two developmental time points (PD15 and PD60). We observed a development-specific increase of pro-inflammatory cytokine, interleukin (IL)-1β mRNA at PD15, and an overall reduction in the expression and signaling of transforming growth factor beta 1 (TGF-β1), an anti-inflammatory cytokine, at both PD15 and PD60 in the NVHL animals. These cytokine changes were not seen in the somatosensory cortex (SSC) or tissue surrounding the lesion site. Daily administration of systemic recombinant TGF-β1 from PD7-14 prevented the appearance of hyperlocomotion, deficits in prepulse inhibition (PPI) of startle and social interaction (SI) in post-pubertal (PD60) NVHL rats. Neonatal supplementation of TGF-β1 was also able to attenuate dendritic spine loss in the layer 3 mPFC pyramidal neurons of NVHL animals. These results suggest that early damage of the VH has long-lasting inflammatory consequences in distant connected structures, and that TGF-β1 has potential to confer protection against the deleterious effects of developmental hippocampal damage.
A Full-Range Analytical Current Model for Heterojunction TFET With Dual Material Gate

In this brief, based on the Gaussian quadrature method, we develop an analytical drain current model for the dual-material-gate heterojunction tunnel field-effect transistor (DMG-H-TFET) due to its advantages over the H-TFET and DMG-TFET. This model accounts for the effects of source depletion, drain depletion, ambipolar behavior, and mobile charge both in inversion and accumulation states. The accuracy of the model is validated against TCAD simulation and published data. The dependences of the surface potential, electric field, and drain current on biases and drain doping concentrations are well predicted. Besides, in comparison with the conventional H-TFET, the improvement in drain current and the alleviation of delayed saturation in DMG-H-TFET can also be observed through the proposed model.
Adaptive Optimization with Nested Prior Navigation for Blind Image Deblurring

Image deblurring is a typical inverse problem and has many applications in vision and multimedia areas. It involves the estimation of blur kernel and sharp image given only a blurry observation. Due to the fundamentally ill-posed nature, most existing works design different priors within the maximum a posteriori (MAP) framework to regularize their solution space. However, due to the unknown image distribution, complex kernel structure and non-uniform noises, it is indeed challenging to explicitly design a fixed prior for blurry images in realworld scenarios. Different from these conventional strategies to integrate sophisticated priors into optimization model, this work only formulates the necessary constraints on latent image and blur kernel as a lightweight MAP model. Then we develop an inexact projected gradient scheme to incorporate flexible sparse structure control for MAP inference. We demonstrate that this adaptive scheme can successfully avoid degenerate solutions and is universally suitable for different blurry scenarios, such as low-illumination, face and text. Extensive experiments on both synthetic data and real-world images demonstrate the effectiveness and robustness of the proposed method against other state-of-the-art methods.
The Influence of a Memory Delay on Spatial Coding in the Superior Colliculus: Is Visual Always Visual and Motor Always Motor?

The memory-delay saccade task is often used to separate visual and motor responses in oculomotor structures such as the superior colliculus (SC), with the assumption that these same responses would sum with a short delay during immediate “reactive” saccades to visual stimuli. However, it is also possible that additional signals (suppression, delay) alter visual and/or motor response in the memory delay task. Here, we compared the spatiotemporal properties of visual and motor responses of the same SC neurons recorded during both the reactive and memory-delay tasks in two head-unrestrained monkeys. Comparing tasks, visual (aligned with target onset) and motor (aligned on saccade onset) responses were highly correlated across neurons, but the peak response of visual neurons and peak motor responses (of both visuomotor (VM) and motor neurons) were significantly higher in the reactive task. Receptive field organization was generally similar in both tasks. Spatial coding (along a Target-Gaze (TG) continuum) was also similar, with the exception that pure motor cells showed a stronger tendency to code future gaze location in the memory delay task, suggesting a more complete transformation. These results suggest that the introduction of a trained memory delay alters both the vigor and spatial coding of SC visual and motor responses, likely due to a combination of saccade suppression signals and greater signal noise accumulation during the delay in the memory delay task.
Decentralized Power Sharing Control for Parallel-Connected Inverters in Islanded Single-Phase Micro-Grids

In this paper, a Lyapunov function-based decentralized control scheme is proposed for a single-phase parallel-connected inverters system within islanded micro-grid. With this novel method, a proper voltage regulation (e.g., amplitude and waveform quality) and excellent power and current sharing performance can be well ensured even in the presence of unknown line impedance and unbalanced LC filter parameters. In addition, high reliability and robustness can be guaranteed since no inter-communication among inverters as well as no centralized controller is demanded, which also delivers advantages of scalability and redundancy. The stability of the closed-loop system is ensured by standard Lyapunov method. The designation of the decentralized controller is simple to implement but rather effective. Detailed simulation and hardware-in-the-loop experimental results are presented to validate the feasibility of the proposed scheme under different operating conditions.
Image Retargeting with Robust Multi-Scale Epitomic Resizing and Seamless Merging

Image retargeting by summarizing image using bidirectional similarity does not take matching error into account in M-step, causing noticeable blurriness when resizing ratio is small. To handle this problem, we take the following modifications to improve the visual quality of the resized image: Firstly, the matching errors in E-step are taken into account for M-step, which means the patch with larger matching error contributes less to the resized image, and vice versa; Secondly, multi-scale strategy is applied to accelerate the EM convergence; To highlight regions of interest (ROI), we integrate the resized background with the original foreground (ROI) or less resized one by seamless merging through Poisson editing. Experimental results show that new retargeting algorithm can get resized images with perceptually good quality. Considering the patch-based features, it's also suitable for the resizing in spatial scalable video coding.
Video Captioning with Semantic Guiding

Video captioning is to generate descriptions of videos. Most existing approaches adopt the encoder-decoder architecture, which usually use different kinds of visual features, such as temporal features and motion features, but they neglect the abundant semantic information in the video. To address this issue, we propose a framework that jointly explores visual features and semantic attributions named Semantic Guiding Long Short-Term Memory (SG-LSTM). The proposed SG-LSTM has two semantic guiding layers, both of them use three types of semantic - global semantic, object semantic and verb semantic - attributes to guide language model to use the most relevant representation to generate sentences. We evaluate our method on the public available challenging Youtube2Text dataset. Experimental results shown that our framework outperforms the state-of-the-art methods.
Dynamic Droop Control for Wind Turbines Participating in Primary Frequency Regulation in Microgrids

Wind power will provide a significant portion of electricity generation in the near future. This significant role requires wind power generators to contribute to the system frequency regulation. The droop method is one of the most popular methods to be implemented in these generators to mimic the governors of conventional generators and contribute to both transient and steady-state frequency regulation. However, the unpredictability of the variable wind speed complicates this implementation. In the present wind-based droop methods, the maximum allowable droop gain is a function of the wind speed. This dependency means either that the entire available capacity of the wind generator will not be used or that instability will threaten the implemented droop wind generators. This paper proposes the efficiency droop, a new droop-based method, which can be tuned regardless of the wind speed. Small-signal analyses are used to study the method in depth and compare its influences on both the transient and steady-state frequency performance to the influence of the present methods while adopting minimum approximation. Detailed time domain analyses are used to verify the analytical results.
Real-Time Fine-Grained Air Quality Sensing Networks in Smart City: Design, Implementation, and Optimization

Driven by the increasingly serious air pollution problem, the monitoring of air quality has gained much attention in both theoretical studies and practical implementations. In this paper, we present the architecture, implementation, and optimization of our own air quality sensing system, which provides real-time and fine-grained air quality map of the monitored area. As the major component, the optimization problem of our system is studied in detail. Our objective is to minimize the average joint error of the established real-time air quality map, which involves data inference for the unmeasured data values. A deep ${Q}$ -learning solution has been proposed for the power control problem to reasonably plan the sensing tasks of the power-limited sensing devices online. A genetic algorithm has been designed for the location selection problem to efficiently find the suitable locations to deploy limited number of sensing devices. The performance of the proposed solutions are evaluated by simulations, showing a significant performance gain when adopting both strategies.
First Demonstration of Vertically Stacked Gate-All-Around Highly Strained Germanium Nanowire pFETs

This paper reports on 45-nm fin pitch strained p-type Ge gate-all-around devices fabricated on 300-mm SiGe strain-relaxed-buffers (SRB). By improving the process integration flow, excellent electrical performance is demonstrated: the <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> factor is increased to 25 as compared to our previous work, <inline-formula> <tex-math notation="LaTeX">$I_{ \mathrm{\scriptscriptstyle ON}} = \textsf {500}~\mu \text{A}/ \mu \text{m}$ </tex-math></inline-formula> at <inline-formula> <tex-math notation="LaTeX">$I_{ \mathrm{\scriptscriptstyle OFF}} = \textsf {100}$ </tex-math></inline-formula> nA/<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> is achieved, approaching the best published results on Ge finFETs. Good negative-bias temperature instability reliability is also maintained, thanks to the use of Si-cap passivation. The process flow developed for the fabrication of the single Ge nanowire (NW) is adapted and vertically stacked strained Ge NWs featuring 8-nm channel diameter are successfully demonstrated. A systematic analysis of the strain evolution is conducted on both single and double Ge NWs after the most challenging steps of the process integration flow: 1.7-GPa uniaxial-stress is demonstrated along the Ge wire, which originates from the lattice mismatch between the Ge source/drain and the Si<sub>0.3</sub>Ge<sub>0.7</sub> SRB.
Feasibility of utilizing autism navigator® for primary care in South Africa

There is a significant research‐to‐practice gap in early detection of young children with autism spectrum disorder (ASD) worldwide but particularly in low‐ and middle‐income countries (LMICs) where expertise is limited and high‐quality training is difficult to access. Autism Navigator® for Primary Care is a web‐based course designed to increase awareness of red flags of ASD in the second year of life and thus promote earlier detection and referral for intervention. It contains extensive video illustrations that offer rapid access to multiple exemplars of ASD red flags. This study examined aspects of feasibility of the Autism Navigator® for Primary Care in one LMIC, South Africa. A mixed‐methods quasi‐experimental design was used to examine relevant professionals' implementation of the course and measure changes in their knowledge of red flags after training. Perceptions of the acceptability, demand, and practicality of the course were explored in focus groups. Sixty‐two providers completed the course online with a 94% completion rate. Built‐in learner assessment pass rates ranged from 88% to 100%. Second‐language English speakers took longer to complete the learner assessments, and professionals with less access to the Internet spent less time in the course. Participants' perceptions of the acceptability, demand, and practicality of the course were mostly positive with some suggestions made for local conditions. Results supported the feasibility of the course in this LMIC with some supports required pertaining to language and Internet access. We propose that this training has the potential to lower the age of detection of ASD in South Africa and other LMICs. Autism Research 2018, 11: 1511–1521. © 2018 International Society for Autism Research, Wiley Periodicals, Inc.
Heterogeneous-Aware Distributed Clustering for Wireless Sensor Networks

With rapidly increasing data, clustering techniques are becoming very useful tools for analyzing data in modern research. Clustering has proven to be an effective technique for data aggregation in wireless sensor networks (WSNs). Classical clustering algorithms perform all computations in one central location such as the sink, which has a full knowledge of the network. These algorithms lead to excessive energy consumption as they rely on broadcast communication. This also leads to high data delivery latency since the sink is the only node responsible for handling all network decisions. In this paper, we propose a heterogeneous-aware distributed clustering (HADC) algorithm for WSNs. The proposed algorithm takes advantage of the presence of node heterogeneity in terms of energy in order to prolong the network lifetime. We select cluster heads (CHs) based on an effective cost function that considers residual energy and node load. In addition, HADC aims to balance load in the network and reduce energy consumption of sensor nodes by making a trade-off between node degree and distance towards potential CHs. Simulations demonstrate that HADC can achieve efficient performance in terms of several metrics as compared with HEED and LEACH. Furthermore, we conduct experiments to study the effect of nodes' heterogeneity in clustered WSNs.
A Watermarking Method for 3D Models Based on Feature Vertex Localization

This paper presents a new blind watermarking scheme for 3-D point cloud models by using feature vertices to locate the embedded watermark. The vertices with larger mean curvature are considered to be feature vertices carrying watermarking information; non-feature vertices are used to build a new coordinate system in which the 3-D point cloud model is classified into bins containing several longitudes. Each bit of the watermarking information is repeatedly embedded into a bin by altering azimuth angles of feature vertices lying in the bin. The non-feature vertices are not exploited to embed the watermarking information, which enables the built coordinate system to avoid the impacts of the embedding of watermark. The choice of the embedding position and redundant embedding provide an optimal balance between imperceptibility and robustness of watermark. Simulation results testify that our watermarking method for 3-D point cloud models has desired robustness against common attack and geometric attack while demonstrating good transparency.