Miracle-dz/UniFAR-FTUs huggingface dataset:
UAV Target Tracking with A Boundary-Decision Network

The aspect ratio of a target changes frequently during UAV tracking task, which makes the aerial tracking very challenging. Traditional trackers struggle from such problem as they mainly focus on the scale variation issue by maintaining a certain aspect ratio. In this paper, we propose a novel tracker, named boundary-decision network (BDNet), to address the aspect ratio variation in UAV tracking. Unlike previous work, the proposed method aims at operating each boundary separately with a policy network. Given an initial estimate of the bounding box, a sequential actions are generated to tune the four boundaries with an optimization strategy including boundary proposal rejection, offline and online learning. Experimental results on the benchmark aerial dataset prove that the proposed approach outperforms existing trackers and produces significant accuracy gains in dealing with the aspect ratio variation in UAV tracking.
Towards Integrating Insurance Data into Information Security Investment Decision Making

Making security investment decisions involves giving consideration to a variety of risks. However, there is little robust empirical evidence that can be used to support this process. This paper builds a road-map for incorporating cyber insurance data into existing security investment models. We propose an approach for using this data as an input for one investment model and introduce three distinct methods for evaluating the effectiveness of a new investment. We then describe a road-map for improving the insurance data collection process that aims to improve data utility for researchers. This approach could benefit those trying to justify an investment at all levels by providing evidence for the return on security.
Nocturnal blood pressure decrease in patients with chronic kidney disease and in healthy controls – significance of obstructive sleep apnea and renal function

Background Chronic kidney disease (CKD) is often associated with a blunted nocturnal BP decrease and OSA. However, it is not fully clear whether a relationship exists between reduction in renal function and obstructive sleep apnea (OSA) on the one hand and relative nocturnal BP decrease in CKD patients on the other. The aim of this study was to investigate the association between nocturnal BP decrease and renal function, the degree of OSA, vasoactive hormones, and renal sodium handling in CKD3-4 patients and healthy age-matched controls. Methods We performed brachial and central 24-hour ambulatory BP measurement and CRM in 70 CKD3-4 patients and 56 controls. In plasma, we measured renin, AngII, aldosterone, and vasopressin. In urine, 24-hour excretion of sodium, protein fractions from the epithelial sodium channel (u-ENaCγ), and the AQP2 water channels (u-AQP2) were measured. Results CKD patients had lower relative nocturnal BP decrease than controls: brachial (10% vs 17%, P=0.001) and central (6% vs 10%, P=0.001). Moderate-to-severe OSA was more frequent in patients (15 vs 1%, P<0.0001). Neither the presence of OSA nor eGFR were predictors of either brachial or central nocturnal BP decrease. CKD3-4 nondippers were more obese, had higher HbA1c level, and more often a history of acute myocardial infarction than CKD3-4 dippers (P<0.05). Conclusion CKD3-4 patients had lower brachial and central nocturnal BP decrease than healthy controls. OSA and eGFR were not associated with nondipping in CKD patients or healthy controls. Nondipping in CKD3-4 was associated with obesity, diabetes, and cardiovascular disease. ClinicalTrials.gov ID NCT01951196.
Broadcasting protocols for coordinating nonlinear network systems

We propose a new methodology to design broadcasting protocols for coordinating nonlinear network systems. Our design of the scheduling of information transmission is based on the introduction of clock variables, whose dynamics are regulated through a suitable storage function. Required clock dynamics, ensuring stability, follow then elegantly from Lyapunov like arguments. For illustrative purposes, we first consider an example of a consensus algorithm, whereafter we discuss a distributed integral controller in feedback interconnection to a network composed of output strictly incrementally passive subsystems. Finally, we show how the proposed method can be used to redesign a popular distributed controller in power grids, enabling a sampled-data implementation.
Protective Efficacy Against Acute and Chronic Toxoplasma gondii Infection Induced by Immunization With the DNA Vaccine TgDOC2C

Toxoplasma gondii is a ubiquitous intracellular apicomplexan parasite that can cause zoonotic toxoplasmosis. Effective vaccines against T. gondii infection are necessary to prevent and control the spread of toxoplasmosis. The present study analyzed the B-linear epitopes of T. gondii DOC2 (TgDOC2) protein and then cloned the C-terminus of the TgDOC2 gene (TgDOC2C) to construct the pVAX-TgDOC2C eukaryotic vector. After intramuscular injection of pVAX-TgDOC2C, immune responses were monitored. Two weeks after the last immunization, the protective effects of pVAX-TgDOC2C against acute and chronic toxoplasmosis were evaluated by challenges with T. gondii RH tachyzoites (genotype I) and PRU cysts (genotype II). The DNA vaccine elicited strong humoral and cellular immune responses with high levels of IgG antibody, IL-2 and IFN-γ production compared to those of the controls. The percentage of CD4+ and CD8+ T cells in mice immunized with pVAX-TgDOC2C was significantly increased compared to that of mice injected with empty pVAX I or PBS. After acute infection with 103 lethal tachyzoites, mice immunized with pVAX-TgDOC2C survived longer (12.5 days) than mice treated with pVAX I (8 days) and PBS (7.5 days). Mice immunized with pVAX-TgDOC2C had significantly less brain cysts (1600.83 ± 284.61) compared to mice immunized with pVAX I (3016.67 ± 153.84) or PBS (3100 ± 246.98). Together, these results demonstrated that TgDOC2C confers protective immunity against T. gondii infection and may be a promising candidate antigen for further development of an effective multicomponent vaccine for veterinary use against toxoplasmosis in livestock animals.
Nonlinear Embedding of FET Devices for High Efficiency Power Amplifier Design

The nonlinear parasitic components of high-power FET devices require tremendous amount of device characterization efforts. The recent nonlinear embedding technique can significantly expedite the design process by removing the time-consuming source/loadpull measurements. This technique is demonstrated using the Angelov model for a 15 W peak power gallium nitride device. The power amplifier design starts from the intrinsic conceptual design and then the external conditions to maintain the intended operation are directly calculated using the nonlinear embedding. This technique is applied to a 9.54 dB back-off asymmetric Doherty power amplifier. 71 % drain efficiency at the peak power of 41.8 dBm and 62.7 % at 32.8 dBm (9 dB back-off) were measured. This technique is further investigated for the linear broadband design space identification. The linearization of the nonlinear intrinsic current source is proposed for the nonlinear embedding. The two dimensional design space is identified by applying the nonlinear embedding to the load modulated continuous-classF3 mode waveforms. The identified design space is applied to the manufacturer model and then the intrinsic waveforms are observed for the intended operation verification.
Detection of Exudates in Diabetic Retinopathy

Diabetic Retinopathy (DR) is an eye abnormality in which the human retina will get affected and is becoming one of the leading cause of preventable blindness. In the world, it is found that nearly 4.8% of blindness is caused due to DR. Preliminary symptoms include the formation of microaneurysms, exudates and hemorrhages. Early detection of DR can save the vision of diabetes patients and manual diagnosis takes time and effort for confirmation. In this paper, a Computer-aided Automated Diagnosis (CAD) is developed to solve this problem. The proposed approach uses edge-based segmentation method for segmenting the optic disc and blood vessels more accurately than region-based methods, followed by extraction of most probable exudates regions, feature extraction and the classifier stage to detect the presence of exudates. This system achieved sensitivity 82.61%, specificity 92.31% and moreover an accuracy of 87.75% for DIARETDB dataset.
Spatio-Temporal Interactive Laws Feature Correlation Method to Video Quality Assessment

In this work, we proposed a full-reference method to estimate video quality. First, we decompose the video into one spatial image and two spatiotemporal slice images. Then for each one of them, sixteen Laws texture filters are applied to generate nine different Laws feature maps. In order to compare the similarity degree of these feature maps obtained from both original and distorted videos, we compute the twodimensional correlation coefficients. Since the correlation coefficients are computed for each frame and spatiotemporal slice, we only choose four statistical values to represent them to reduce the complexity. Lastly, the regression approach is chosen to learn the mapping relationship between the selected features and subjective quality scores. The extensive experiments in the LIVE Video Quality Database suggest our proposed video quality assessment model has superior correlation performance with human visual perception than other state-of-the-art methods.
Deep Feature Learning for Disease Risk Assessment Based on Convolutional Neural Network With Intra-Layer Recurrent Connection by Using Hospital Big Data

This paper presents the analysis of real-life medical big data obtained from a hospital in central China from 2013 to 2015 for risk assessment of cerebral infarction disease. We propose a new recurrent convolutional neural network (RCNN)-based disease risk assessment multimodel by utilizing structured and unstructured text data from the hospital. In the proposed model, the convolutional layer becomes a bidirectional recurrent neural network by utilizing the intra-layer recurrent connection within the convolutional layer. Each neuron within convolutional layer receives feedforward and recurrent inputs from the previous unit and neighborhood, respectively. In addition to step-by-step recurrent operation, the region of context capture increases, thereby facilitating fine-grain feature extraction. Furthermore, we use a data parallelism approach over multimodel data during training and testing of the proposed model. Results show that the data parallelism approach leads to fast conversion speed. The RCNN-based model works differently from the traditional convolutional neural network and other typical methods. The proposed model exhibits a prediction accuracy of 96.02%, which is higher than those of typical existing methods.
Multi-Frequency and Multi-Polarization Synthetic Aperture Radar for the Larsen-C A-68 Iceberg Monitoring

Since 2016 the fracture of the Larsen-C ice shelf has been regularly observed in the Eastern Weddell Sea (68°S, 61°W, Antarctica). This process led to the final collapse in July 2017, when an area of about 6000 km2 (i. e., about 9-12% of the whole shelf) was lost. In this study the resulting calved iceberg, termed as “A-68” from the U. S. National Ice Center, is observed using multi-frequency and multi-polarization Synthetic Aperture Radar (SAR) satellite platforms that include L-band Alos PaISAR-2, C-band Sentinel-l and X-band COSMO-SkyMed. A large set of SAR scenes were considered, collected in ScanSAR imaging modes over a time span of about 1 year, to analyze the iceberg properties and its melting process and drifting.
Developing Word Sense Disambiguation Corpuses Using Word2vec and Wu Palmer for Disambiguation

In computational linguistics, meaning disambiguation is an open problem of natural language processing in the form of the process of identifying the meaning of the word polysemy used in a sentence. Resolving this problem, among others, has an impact on search engine relevance, anaphoric solving, coherence or cohesion, and inference or conclusion. Therefore, a study is needed that studies to find the meaning of a correct word on a topic. So that it affects the topics discussed in a sentence to find the true meaning. In this study, we focused on finding the meaning of words in a corpus-based sentence using word2vec and wu palmer. The word2vec algorithm is used to construct word vectors contained in sentences and wu palmer as an addition to new words that are not contained in the corpus, by assessing hypernym, meronym, and hyponym between words in sentences. The experimental results show that by adding a new word using wu palmer on corpus it can increase the precision value of 0.8232 in an introduction to a sentence contained in a topic, compared to not using the addition of a new word.
Hand Gesture Recognition Using Local Histogram Feature Descriptor

Hand gesture recognition system is widely used in the development of human-computer interaction. The vision based hand gesture recognition is achieved by the following steps: preprocessing, feature extraction and classification. The aim of preprocessing stage is to localize the hand region from the image frame. The Laplacian of Gaussian filtering technique along with zero crossing detector is applied on hand gesture images to detect the edges of hand region. This paper proposes a novel feature extraction technique, which is based on local histogram feature descriptor (LHFD). The proposed feature is extracted by finding the local histogram of the gray scale gesture image. This technique uses the whole region of the hand to extract the features. The proposed method is invariant to the scaling and illumination. Two standard datasets viz. Massey University gesture dataset (MUGD) and Jochen Triesch static hand posture database are used to evaluate the recognition performance of the proposed technique. The gesture recognition performance of the proposed technique is 99.5% and 95% on Massey University gesture dataset and Triesch dataset respectively, using multi-class support vector machine (SVM) classifier.
The effects of hydrogen plasma treatment of ZnO electron transport layers on VOC of polymer solar cell

The effects of surface modification of ZnO ETL by H-plasma treatment on the performance of bulk heterojunction (BHJ) solar cells were systemically investigated. The open circuit voltage (V}}$_{\mathbf{OC)}}$ of devices was dramatically improved after Hplasma treatment. Both Kelvin probe force microscopy (KPFM) and ultraviolet photoelectron spectroscopy (UPS) measurements indicate that the improved V}$_{\mathbf{OC}}$ is attributed to the reduced work function of ZnO caused by H-plasma treatment. After 2.5 min Hplasma treatment, the power conversion efficiency (PCE) of solar cells has been increased from 3.03% to 3.84% with a high V}$_{\mathbf{OC}}$of 0.670 V.
Regulation Characteristics of Hysteresis Clutches Based on the Fe-Cr-Co Material

The results of the analysis of the hysteresis electromagnetic clutches regulation characteristics are presented. Experimental data of the hysteresis material Fe-Cr-Co magnetic characteristics are given. It is shown that controllable hysteresis clutch based on the Fe-Cr-Co material can be an alternative to technical solutions of electric drives with torque limit control.
Nonnegative Sparse Representation of Signals With a Determinant-Type Sparsity Measure Based on the DC Programming

Nonnegative sparse representation has become highly popular in certain applications in the context of signals and corresponding dictionaries that have nonnegative limitations. Applying an adaptive dictionary for sparse representation of nonnegative signals has been shown to be very effective; but constructing adaptive dictionary, i.e., dictionary learning, remains a challenge. In this paper, we attempt to design an effective and tailored algorithm for the sparse representation of nonnegative signals. We consider the so-called determinant sparsity measure formed with the determinant of the Gram matrix of sparse coefficients. Based on the determinant measure, we formulate the nonnegative dictionary learning problem that is optimization of a non-convex function. For reducing the computational complexity of the optimization, the difference of convex functions (DC) programming is employed since the non-convex function can be set to the form of difference of two convex functions. Because there are two variables in the dictionary learning problem that cannot be solved directly by DC programming, an alternation scheme is employed to treat the sparse coding and dictionary update of the dictionary learning problem alternatively with the DC programming. Our proposed novel dictionary learning algorithm has three advantages: 1) the higher recovery ratio is achieved by the proposed algorithm, even when the signals are weakly sparse; 2) the generalization ability of the proposed algorithm is greater, more often converging to a global solution than to local optimal solutions; and 3) the obtained sparse solution has a low-overlapping property that enables the solutions to be sparse and makes the proposed algorithm robust. Numerical experiments and experiments using real-world data demonstrate the effectiveness and applicability of the proposed algorithm.
Controlled Consensus in a Social Network with “Simple” Agents

We consider the problem to find conditions, ensuring a required consensus of the agents’ beliefs about some situation parameter under a given structure of their interactions. We present the complete solution of this problem, showing that the desired weights of interactions can be calculated without the agents’ knowledge about the whole structure of the network-it suffices to use the local information about an agent and his neighbors.
Breast Segmentation in MRI via U-Net Deep Convolutional Neural Networks

Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) has demonstrated, in recent years, a great potential as a complementary diagnostic method for early detection and diagnosis of breast cancer. However, due to the large amount of data, DCE-MRI manual inspection is error prone and can hardly be handled without the use of a Computer Aided Diagnosis (CAD) system. In a typical CAD processing, the segmentation of the breast parenchyma is a crucial stage aimed to reduce computational effort and to increase reliability. In the last years, deep convolutional networks have outperformed the state-of-the-art in many visual tasks, such as image classification and object recognition. However, very few proposals based on a deep learning approach have been applied so far for segmentation tasks in the biomedical field. The aim of this work is to apply a suitably modified convolutional neural network for fully-automating the non-trivial breast tissues segmentation task in 3D MR data, in order to accurately segment breast parenchyma from the air and other tissues (such as chest-wall). The proposed approach has been validated over 42 DCE-MRI studies. The median segmentation accuracy and Dice similarity index were 98.93 (±0, 15) and 95.90 (±0, 74) respectively with p< 0.05, and 100% of neoplastic lesion coverage.
Short-Term Traffic Prediction with Vicinity Gaussian Process in the Presence of Missing Data

This paper considers the problem of short-term traffic flow prediction in the context of missing data and other measurement errors. These can be caused by many factors due to the complexity of the large scale city road network, such as sensors not being operational and communication failures. The proposed method called vicinity Gaussian Processes provides a flexible framework for dealing with missing data and prediction in vehicular traffic network. First, a weighted directed graph of the network is built up. Next, a dissimilarity matrix is derived that accounts for the selection of training subsets. A suitable cost function to find the best subsets is also defined. Experimental results show that with appropriately selected subsets, the prediction root mean square error of the traffic flow obtained by the vicinity Gaussian Processes method reaches 18.9% average improvement with lower costs, which is with comparison to inappropriately chosen training subsets.
Study on Microgrids and Its Control Using Conventional and Sliding Mode Control

The concept of Microgrids can be considered as an improvement to individual distributed generation. Microgrids comprises of a group of loads, microsources and their corresponding controllers to achieve the optimal output from the sources. The operational flexibility of MicroGrids to work with the grid and without the grid is one of their key characteristics. To accomplish appropriate functionality of microgrids due to various operating conditions and non steady nature of renewable microsources, sturdy control approach is must. This review paper deals with the architecture of microgrids and its control strategies using conventional control and a non linear sliding mode control.
Robust Polyphonic Sound Event Detection by Using Multi Frame Size Denoising Autoencoder

Over the past few years, lots of research has been done on polyphonic sound event detection. A main problem with sound event detection is that the detection performance sharply degrades in the presence of noise. As denoising autoencoder reportedly has superior performance in noisy environments, this paper proposes to use denoising autoencoder, which is trained by multi frame size information of audio signals, to extract robust features in a task of polyphonic sound event detection under noisy conditions. Performance of the extracted feature is evaluated by polyphonic sound event detection experiments with different noise levels, and compared with that of baseline features including Mel-band Energy (Mel), Log mel-band Energy (Logmel) and mel-frequency cepstral coefficients (MFCC). The experiemntal results show that the proposed feature has the best robustness among all features and achieves the best detection effect under noisy conditions.
FinFETs for RF Applications: A Literature review

Recent studies on FinFET pointed out the advantage of immunity from short channel effects (SCEs) and their performances at high frequencies need attention. In this paper, we review the potentiality of FinFET structure for high frequency applications. The challenges factors such as capacitance and noise are discussed. The development of analytical model for parasitic capacitance and noise are reviewed. It is necessary for accurate quantitative prediction of the parasitic and noise introduced by each part of the device structure. In addition generalized modeling approach based on artificial neural network is presented which helps in developing model with less computation time.
Robust Visual Tracking via Hierarchical Convolutional Features-Based Sparse Learning

In recent years, convolutional features have significantly advanced the Discriminative Correlation Filter (DCF) based trackers. In contrast to hand-crafted ones, features extracted from a CNN retain high spatial resolution while preserving semantic information. The improvements come at the risk of reduction in speed and over-fitting caused by the insufficiency of training data for tracking. In this paper, a novel Hierarchical Convolutional Features and Sparse learning based Tracker (HCFST) is proposed. We effectively tackle the issues of computational bottlenecks and over-fitting in the DCF formulation via the multi-task sparse learning. First, most of the noisy and irrelevant feature maps are safely removed for robust appearance modeling. Redundant features rejection effectively mitigates the redundancy among features from hierarchical layers of CNNs. Then a sparser updating scheme is further presented for conditional model update. Extensive experiments are performed on various challenging sequences from OTB50 and OTB100 datasets. The proposed HCFST performs favorably against state-of-the-art methods.