Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Fundamental Limit of Private Information Retrieval for Coded Distributed Storage

We consider private information retrieval (PIR) for distributed storage systems (DSSs) with noncolluding nodes where data is stored using a non maximum distance separable (MDS) linear code. It was recently shown that if data is stored using a particular class of non-MDS linear codes, the MDS-PIR capacity, i.e., the maximum possible PIR rate for MDS-coded DSSs, can be achieved. For this class of codes, we prove that the PIR capacity is indeed equal to the MDS-PIR capacity, giving the first family of non-MDS codes for which the PIR capacity is known. For other codes, we provide asymmetric PIR protocols that achieve a strictly larger PIR rate compared to existing symmetric PIR protocols.
Evaluation of Organic Anion Transporter 1A2-knock-in Mice as a Model of Human Blood-brain Barrier

The present study aimed to establish a humanized mouse model with which to explore OATP1A2-mediated transcellular transport of drug substrates across the blood-brain barrier (BBB) and to evaluate the usefulness of the humanized mice in preclinical studies. Sulpiride, amisulpride, sultopride, and triptans were used as probes to discriminate OATP1A2 and Oatp1a4. We generated a mouse line humanized for OATP1A2 by introducing the coding region downstream of the Oatp1a4 promoter using the CRISPR/Cas9 technique. In the mice generated, OATP1A2 mRNA in the brain was increased corresponding to disappearance of Oatp1a4. OATP1A2 was localized on both the luminal and abluminal sides of the BBB. Unfortunately, study in vivo employing sulpiride, sumatriptan, and zolmitriptan as probes did not indicate any difference in their brain-to-plasma ratio between the control and humanized mice. Quantitative targeted absolute proteomic analysis of the BBB fraction from the humanized mice revealed that almost all analyzed transporters and membrane proteins were expressed at similar levels to those in control mice. The quantitative levels of OATP1A2 differed depending on the peptide quantified, which suggests that incomplete translation or posttranslational modification may occur. The blood-to-brain transport of zolmitriptan, determined by brain perfusion in situ, was 1.6-fold higher in the humanized mice than in the controls, whereas that of sulpiride was not significantly changed. To our knowledge, we established a mouse line humanized for a BBB uptake transporter for the first time. Unfortunately, because of limited impact, there is still room for improvement of the model system.
Automatic Keyword Extraction Using Word Embedding and Clustering

Existing word-frequency-based algorithms for keyword extraction do not consider the semantic relationships among words. Moreover, word-graph-based algorithms cannot distinguish multiple topics, and topic-model-based algorithms possess high time complexity. All of these keyword extraction algorithms exhibit limitations. This paper proposes a new word-embedding-based algorithm, namely, WEC, for keyword extraction. The algorithm incorporates word frequency, effects of word co-occurrence, and semantic relationship among contexts. The algorithm also estimates the final weights of words with cosine similarity and pointwise mutual information and extracts topics by clustering. Experimental results show that the WEC algorithm outperforms state-of-the-art keyword extraction methods on four datasets when tested under various evaluation metrics.
Cardiovascular Outcomes with Once-Weekly GLP-1 RAs: Clinical and Economic Implications

Type 2 diabetes mellitus (T2DM) is associated with an increased risk of atherosclerotic cardiovascular (ASCVD) disease, which is the largest contributor to the economic burden of diabetes. Minimization of disease morbidity through comprehensive management of ASCVD risk factors, including but not limited to hyperglycemia, is a key goal of T2DM therapy. Emerging evidence with some glucagon-like peptide-1 receptor agonists (GLP-1 RAs) points to beneficial effects across a range of atherosclerotic risk factors and possible improvement of some cardiovascular outcomes independent of these effects. Given these benefits, there has been substantial interest in evaluating the cardiovascular safety of GLP-1 RAs as well as their potential to reduce the risk of major adverse cardiac events (MACE). Following the superior clinical outcome with the once-daily GLP-1 RA liraglutide (Liraglutide Effect and Action in Diabetes: Evaluation of cardiovascular outcome Results [LEADER]), this review examines and summarizes the effects of once-weekly GLP-1 RAs, including exenatide extended release (ER), dulaglutide, and semaglutide, on reducing cardiovascular events in patients with T2DM. A phase 3 cardiovascular outcomes trial (EXSCEL) of exenatide ER found no significant difference between exenatide ER and placebo in reducing MACE in patients with T2DM. In a phase 3 premarketing trial in T2DM patients at high risk of cardiovascular disease (SUSTAIN-6), semaglutide significantly reduced the risks of MACE and non-fatal stroke compared with placebo. A phase 3 study (REWIND) is underway to evaluate the effects of dulaglutide on MACE. Considering the substantial costs of cardiovascular disease in patients with T2DM, it will be of interest to assess the impact of treatment with once-weekly GLP-1 RAs on cardiovascular disease-related costs among patients with T2DM.
Preference for Fractal-Scaling Properties Across Synthetic Noise Images and Artworks

A large number of studies support the notion that synthetic images within a certain intermediate fractal-scaling range possess an intrinsic esthetic value. Interestingly, the fractal-scaling properties that define this intermediate range have also been found to characterize a vast collection of representational, abstract, and graphic art. While some have argued that these statistic properties only serve to maximize the visibility of the artworks’ spatial structure, others argue that they are intrinsically tied to the artworks’ esthetic appeal. In this study, we bring together these two threads of research and make a direct comparison between visual preference for varying fractal-scaling characteristics in both synthetic images and artworks. Across two studies, viewers ranked and rated sets of synthetic noise images and artworks that systematically varied in fractal dimension for liking, pleasantness, complexity, and interestingness. We analyzed both average and individual patterns of preference between the two image classes. Average preference peaked for intermediate fractal dimension values for both categories, but individual patterns of preferences for both high and low values also emerged. Correlational analyses indicated that individual preferences between the two image classes remained moderately consistent and were improved when the fractal dimensions between synthetic images and artworks were more closely matched. Overall, these findings further support the role of fractal-scaling statistics both as a key determinant of an object’s esthetic value and as a valuable predictor of individual differences in esthetic preference.
CCAR: Caching and Content-Aware Routing for Content Oriented Networking

ICN (Information-Centric Networking) has been proposed for efficient content distribution to deal with rapid increase of the Internet traffic. This paper proposes a caching and content-aware routing method named CCAR. CCAR operates based on the ICN concept that users can obtain contents from anywhere in networks. CCAR is formulated as an optimization problem to coordinately perform content placement to caches and content download based on content-oriented traffic engineering. In this paper, after we formulate CCAR, we decompose the problem to a content placement sub-problem and a traffic engineering sub-problem. In the content placement sub-problem, contents are placed at routers by a local search algorithm. The traffic engineering sub-problem calculates routes according to the content placement. Our numerical results show that CCAR efficiently reduces the maximum link utilization and avoids congestion.
Collaborative Target-Localization and Information-Based Control in Networks of UAVs

In this paper, we study the capacity of UAV networks for high-accuracy localization of targets. We address the problem of designing a distributed control scheme for UAV navigation and formation based on an information-seeking criterion maximizing the target localization accuracy. Each UAV is assumed to be able to communicate and collaborate with other UAVs that are within a neighboring region, allowing for a feasible distributed solution which takes into account a trade-off between localization accuracy and speed of convergence to a suitable localization of the target. Such an investigation also considers communication latency constraints as well as safety requirements such as inter-UAV and obstacle collision avoidance.
Interference Management via User Clustering in Two-Stage Precoder Design

We consider a single cell downlink (DL) massive multiple-input multiple-output (MIMO) set-up with user clustering based on statistical information. The problem is to design a fully digital two-stage beamforming aiming to reduce the complexity involved in the conventional MIMO processing. The fully digital two-stage beamforming consists of a slow varying channel statistics based outer beamformer (OBF) and an inner beamformer (IBF) accounting for fast channel variations. Two different methods are presented to design the OBF matrix, so as to reduce the size of effective channel used for IBF design. A group specific two-stage optimization problem with weighted sum rate maximization (WSRM) objective is formulated to find the IBF for fixed OBF. We begin by proposing centralized IBF design were the optimization is carried out for all sub group jointly with user specific inter-group interference constraints. In order to further reduce the complexity, we also propose a group specific IBF design by fixing the inter group interference to a constant or by ignoring them from the problem altogether. In spite of incurring a small loss in performance, the computational complexity can be saved to a large extent with the group specific processing. Numerical experiments are used to demonstrate the performance of various proposed schemes by comparing the total sum rate of all users and the design complexity.
A High-Density Genetic Map of an Allohexaploid Brassica Doubled Haploid Population Reveals Quantitative Trait Loci for Pollen Viability and Fertility

A doubled haploid (DH) mapping population was obtained from microspore culture of an allohexaploid F1 from the cross between two recently-synthesized allohexaploid Brassica lines. We used single nucleotide polymorphism (SNP) genetic variation based on restriction-site associated DNA (RAD) sequencing to construct a high density genetic linkage map of the population. RAD libraries were constructed from the genomic DNA of both parents and 146 DH progenies. A total of 2.87 G reads with an average sequencing depth of 2.59 × were obtained in the parents and of 1.41 × in the progeny. A total of 290,422 SNPs were identified from clustering of RAD reads, from which we developed 7,950 high quality SNP markers that segregated normally (1:1) in the population. The linkage map contained all 27 chromosomes from the parental A, B and C genomes with a total genetic distance of 5725.19 cM and an average of 0.75 cM between adjacent markers. Genetic distance on non-integrated linkage groups was 1534.23 cM, or 21% of total genetic distance. Out of 146 DH progenies, 91 had a complete set of 27 chromosomes as expected of a hexaploid species, and 21 out of 27 chromosomes showed high collinearity between the physical and linkage maps. The loss of chromosome(s) or chromosome segment(s) in the DH population was associated with a reduction in pollen viability. Twenty-five additive QTL were associated with pollen viability and fertility-related traits (seed number, seed yield, pod length, plant height, 1000-seed weight). In addition, 44 intra-genomic and 18 inter-genomic epistatic QTL pairs were detected for 4 phenotypic traits. This provides confidence that the DH population may be selected for improved pollen viability and fertility in a future allohexaploid Brassica species.
A Unified Approach of Multitemporal SAR Data Filtering Through Adaptive Estimation of Complex Covariance Matrix

Speckle inherent in synthetic aperture radar (SAR) images usually complicates visual interpretation and brings difficulty to information extraction for applications. Current speckle filters are mainly developed for single SAR image or an image pair (InSAR or PolInSAR). Although some multichannel filters are proposed, they only exploit pixel intensity to identify statistically homogeneous pixels (SHPs). In this paper, we present a new unified approach to filter multitemporal SAR images by adaptively estimating complex covariance matrix-based multitemporal filtering, named CCM-MTF. The key idea is to employ generalized likelihood ratio (GLR) test on the Wishart distributed initial CCM to evaluate the similarity between two pixels. A special design is given to the initial CCM estimation, in which temporal samples are used instead of spatially neighboring samples. Then, a threshold determined by the asymptotic distribution of the logarithm of GLR test statistics at a fixed significance level is used to select spatial SHPs for the reference pixel. Subsequently, the filtering is implemented by estimation of the final CCM from original SAR scattering vector over SHP pixels, and all filtered target information channels including intensity, interferometric phase, and coherence can be explicitly derived from the final CCM. The effectiveness of the proposed CCM-MTF method is validated by experiments on both simulated and real multitemporal SAR images. Both qualitative and quantitative comparisons between CCM-MTF and four state-of-the-art SAR filters are carried out to demonstrate its advantages in terms of speckle suppression as well as detail preservation for all the three information channels.
Design of LLC converter as start-up power supply for automotive applications

This article deals with the design, simulation and practical realization of LLC converter as start-up power supply for automotive applications. The main topology of the power supply is realized as full-bridge resonant LLC converter in connection with two-phase interleaved PFC converter. Initially, the calculation of main circuit components is done. The main focus was given to the proper design of the transformer because high power levels are required. Transfer characteristic was modified in the way that required operational characteristics for the proposed application can be achieved. Consequently, the simulation verification of the operation is realized. At the end of the paper, a physical sample of the power supply is shown together with the verification of its operation within given operational regions.
Efficient Learning of Grasp Selection for Five-Finger Dexterous Hand

This paper proposes a novel learn-to-search method to efficiently learn grasp configuration for five-finger dexterous hand. The framework is divided into three stages. Firstly, a rectangle is learned for locating an approximate grasp region; secondly, the thumb position is searched within this rectangle; thirdly, the position of other four fingers is learned. In three different stages, different coordinate systems are established and different features are extracted, applying SVM-rank algorithm to train the three-stage learning model. The experimental results show that, the proposed method can generate feasible grasp configurations for the five-finger dexterous hand and avoid excessive computation when searching five grasp points directly within the entire image.
Strategies for disaster risk reduction education: A systematic review

INTRODUCTION: For many years, numerous researches and risk reduction activists have emphasized the importance of public awareness and education for disaster risk reduction (DRR). These needs, due to human natural manipulation, have increased. The present study was aimed to assess and determine the evidence on the strategies for education of DRR. MATERIALS AND METHODS: This study was a systematic review of publications and gray literatures regarding to strategies for education of DRR conducted in December 2016. Fifteen articles and dissertations published during January 1, 2000 to December 31, 2016 were extracted through PubMed, Scopus, ProQuest, Web of Science, ScienceDirect, Ovid, Google Scholar, Scientific Information Database, Magiran, and Irandoc with the inclusion criteria of English and Persian language. Thematic analysis technique was used to analyze the articles. RESULTS: The thematic analyses revealed eight major categories of DRR educational strategies such as raising knowledge, educational needs assessment, educational planning, educational approaches, educational content, educational tools, involved organizations, and educational learning barriers and challenges. CONCLUSIONS: Most countries have launched DRR education activities, but these actions are not enough, and there are some gaps between what is it and what should be. More effective and efficient teaching and learning strategies are needed to increase the effectiveness of preparedness and DRR activities at all levels of community.
A Real-time Hand Postures Estimation Method

Hand postures can be used to achieve friendly human-machine interaction (HMI) for the advantages of simple expressions, informative instructions and unconstrained operations. However, most previous postures estimation methods have problems such as poor real time performance, low estimation accuracy, being sensitive to the varying illumination and relying on marks on hands or special assistant devices. In this paper, we will introduce an efficient real-time method to estimate hand postures with an ordinary monocular camera. This method contains two stages, which are hand segmentation and postures recognition. Rather segment objects from the whole image, we locate the hand within a local area first and then adapt motion, color and contour informations to segment the hand region accurately. After that, template matching algorithm is used to recognize hand postures with fingertips and shape features. The experimental results demonstrated that the introduced method realizes high estimation accuracy and fast processing speed. Furthermore, the method is robust to varying illumination conditions.
Experimental Study of Micromanipulation System with Multi-Fingered Structure

With developments of micro-manipulation and micro-assembly, there is a growing demand for micro-grippers which are able to overcome the adhesion forces to pick up and release micro-objects effectively. This paper presents a separate-structured triple-finger gripper. Each finger is driven by piezoelectric (PZT) transducer. The gripper's end effectors are replaceable, so different end effectors can be used to adjust various targets with different shapes and sizes. The three fingers were assembled on an adjustable pedestal, so this gripper is able to achieve a precise clamping range. In the experiments, microspheres of different materials with diameters ranging from $10\ \boldsymbol{\mu} \mathbf{m}$ to $800\ \boldsymbol{\mu} \mathbf{m}$ were picked and released with more than 85% success rate. The applications of micro-assembly and super-resolution imaging were shown in the gripping experiments, which demonstrate important practical significance of this gripper. This triple-finger gripper has simple machining processes, high flexibility, and low cost.
Ground Moving Target Tracking for a Patrol Robot Based on Monocular Vision

This paper investigates the problem of monocular-vision based ground moving target tracking for a patrol robot. Considering the patrol robot and the tracking target are both moving, we cannot obtain enough features from image data in real time to ensure the reliability of object detection and tracking. To solve the above problem, we design a ground target tracking system which is based on TLD (Tracking Learning Detection) algorithm. The TLD algorithm integrates tracking, detection and learning modules, which can improve the performance and the robustness of moving target tracking. To show the practicability of this system, a series of experiments were conducted on a real micro patrol robot. Experimental results show the effectiveness of our ground moving target tracking method for a patrol robot.
Stacked Nonnegative Sparse Autoencoders for Robust Hyperspectral Unmixing

As an unsupervised learning tool, autoencoder has been widely applied in many fields. In this letter, we propose a new robust unmixing algorithm that is based on stacked nonnegative sparse autoencoders (NNSAEs) for hyperspectral data with outliers and low signal-to-noise ratio. The proposed stacked autoencoders network contains two main steps. In the first step, a series of NNSAE is used to detect the outliers in the data. In the second step, a final autoencoder is performed for unmixing to achieve the endmember signatures and abundance fractions. By taking advantage from nonnegative sparse autoencoding, the proposed approach can well tackle problems with outliers and low noise-signal ratio. The effectiveness of the proposed method is evaluated on both synthetic and real hyperspectral data. In comparison with other unmixing methods, the proposed approach demonstrates competitive performance.
Performance of multihop detect‐and‐forward relaying system over fluctuating two‐ray fading channels

The performance of multihop detect‐and‐forward relaying system is analyzed over fluctuating two‐ray fading channels for 28 GHz millimeter wave communications. Exact closed‐form expressions for outage probability and average symbol error rate of various modulation schemes over nonidentically distributed fluctuating two‐ray fading channels have been obtained. The derived expressions are given in terms of the Lauricella hypergeometric functions Φ2(n)(·) and FD(n)(·) . Moreover, the derived expressions also accommodate several others reported results in the literature for two‐wave diffuse power, Rician, Rician shadowed, Nakagami‐m, and Hoyt fading channels. Asymptotic outage probability and average symbol error rate expressions are also derived, which provide useful insights on the factors governing the performance of the considered system. The numerical results have been verified with Monte‐Carlo simulation results.
Monitor Pupils' Attention by Image Super-Resolution and Anomaly Detection

Academic success at young age substantially lays on in-class behavior. Looking at the right spatial element - teacher or board for instance - can be considered as the first and main step towards mental focus. In this paper, we propose a novel method for monitoring pupils' attention from classic surveillance videos, based on focus points determination and classification. This approach raises two main challenges: the low-resolution of children's faces and the categorization of the focus points itself. Indeed, the gaze point of each student is deduced from his facial landmarks, which allow us to know his head pose (by solving the PnP problem). But the resolutions of the students' faces are usually 40x40 or even smaller. Therefore, we propose to combine the super-resolution algorithm to more accurately estimate the facial landmarks of the pupils in the last few rows. To achieve the second hard task - classification - and detect the children who do not pay attention to the proper spatial area, we consider the distribution of the focus points in two dimensions. After what we discriminate the anomalous points on a density criterion. Experiments on several real videos demonstrate the effectiveness and efficiency of this process.