Miracle-dz/UniFAR-FTUs huggingface dataset:
Computation Rate Maximization for Wireless Powered Mobile-Edge Computing With Binary Computation Offloading

Finite battery lifetime and low computing capability of size-constrained wireless devices (WDs) have been longstanding performance limitations of many low-power wireless networks, e.g., wireless sensor networks and Internet of Things. The recent development of radio frequency-based wireless power transfer (WPT) and mobile edge computing (MEC) technologies provide a promising solution to fully remove these limitations so as to achieve sustainable device operation and enhanced computational capability. In this paper, we consider a multi-user MEC network powered by the WPT, where each energy-harvesting WD follows a binary computation offloading policy, i.e., the data set of a task has to be executed as a whole either locally or remotely at the MEC server via task offloading. In particular, we are interested in maximizing the (weighted) sum computation rate of all the WDs in the network by jointly optimizing the individual computing mode selection (i.e., local computing or offloading) and the system transmission time allocation (on WPT and task offloading). The major difficulty lies in the combinatorial nature of the multi-user computing mode selection and its strong coupling with the transmission time allocation. To tackle this problem, we first consider a decoupled optimization, where we assume that the mode selection is given and propose a simple bi-section search algorithm to obtain the conditional optimal time allocation. On top of that, a coordinate descent method is devised to optimize the mode selection. The method is simple in implementation but may suffer from high computational complexity in a large-size network. To address this problem, we further propose a joint optimization method based on the alternating direction method of multipliers (ADMM) decomposition technique, which enjoys a much slower increase of computational complexity as the networks size increases. Extensive simulations show that both the proposed methods can efficiently achieve a near-optimal performance under various network setups, and significantly outperform the other representative benchmark methods considered.
The effect of mechanics on migration, morphology and matrix production by primary human corneal fibroblasts: long-term dynamic observation.

Doctor of Philosophy Mechanical load is believed to play a critical role in tissue formation, growth, maintenance, and disease in vertebrate animals. Although many studies have investigated the effect of mechanical stimuli on biological processes, the dynamics of these processes have not been closely explored. Live long-term observation of the collective behavior of Primary Human Corneal Fibroblasts (PHCFs) exposed to mechanical load could provide important insight into the mechanisms used by fibroblasts while they migrate, produce multilayered constructs and manage collagen deposition during the synthesis of organized ExtraCellular Matrix (ECM). Previous studies have shown that PHCFs produce locally organized and aligned ECM similar to developing corneas when cultured in the presence of stabilized ascorbic acid. In this study, DIC imaging, live cell fluorescent labeling, traction force microscopy and our previously designed mechanobioreactor were employed to locally and globally capture PHCF's behavior under cell culture conditions for up to two weeks. We have directly tracked and observed the morphology and migration of PHCFs under physiological conditions in the presence of uniaxial mechanical load directly on the microscope. The behavior of human corneal fibroblasts is observed from initial seeding through matrix production. The orientation of cells, the magnitude and direction of their velocity as well as forces exerted by PHCFs were tracked on substrates every 6 minutes. The analyzed data demonstrates that corneal fibroblasts are induced to align on loaded substrates at a relatively fixed angle to the applied force while corneal fibroblasts on unloaded substrates exhibit only local alignment. We conclude that applied external mechanical loads significantly affect the patterns of cell migration and culture morphology and that load abrogates local guidance signals. Tissue engineers should be able to take advantage of such mechanical guidance to produce highly organized tissues such as the corneal stromal matrix.
Dynamic behavior analysis of android applications for malware detection

Android is most popular operating system for smartphones and small devices with 86.6% market share (Chau 2016). Its open source nature makes it more prone to attacks creating a need for malware analysis. Main approaches for detecting malware intents of mobile applications are based on either static analysis or dynamic analysis. In static analysis, apps are inspected for suspicious patterns of code to identify malicious segments. However, several obfuscation techniques are available to provide a guard against such analysis. The dynamic analysis on the other hand is a behavior-based detection method that involves investigating the run-time behavior of the suspicious app to uncover malware. The present study extracts the system call behavior of 216 malicious apps and 278 normal apps to construct a feature vector for training a classifier. Seven data classification techniques including decision tree, random forest, gradient boosting trees, k-NN, Artificial Neural Network, Support Vector Machine and deep learning were applied on this dataset. Three feature ranking techniques were usedto select appropriate features from the set of 337 attributes (system calls). These techniques of feature ranking included information gain, Chi-square statistic and correlation analysis by determining weights of the features. After discarding select features with low ranks the performances of the classifiers were measured using accuracy and recall. Experiments show that Support Vector Machines (SVM) after selecting features through correlation analysis outperformed other techniques where an accuracy of 97.16% is achieved with recall 99.54% (for malicious apps). The study also contributes by identifying the set of systems calls that are crucial in identifying malicious intent of android apps.
NLEVP: A Collection of Nonlinear Eigenvalue Problems

We present a collection of 52 nonlinear eigenvalue problems in the form of a MATLAB toolbox. The collection contains problems from models of real-life applications as well as ones constructed specifically to have particular properties. A classification is given of polynomial eigenvalue problems according to their structural properties. Identifiers based on these and other properties can be used to extract particular types of problems from the collection. A brief description of each problem is given. NLEVP serves both to illustrate the tremendous variety of applications of nonlinear eigenvalue problems and to provide representative problems for testing, tuning, and benchmarking of algorithms and codes.
Implicit sequence learning in people with Parkinson’s disease

Implicit sequence learning involves learning about dependencies in sequences of events without intent to learn or awareness of what has been learned. Sequence learning is related to striatal dopamine levels, striatal activation, and integrity of white matter connections. People with Parkinson’s disease (PD) have degeneration of dopamine-producing neurons, leading to dopamine deficiency and therefore striatal deficits, and they have difficulties with sequencing, including complex language comprehension and postural stability. Most research on implicit sequence learning in PD has used motor-based tasks. However, because PD presents with motor deficits, it is difficult to assess whether learning itself is impaired in these tasks. The present study used an implicit sequence learning task with a reduced motor component, the Triplets Learning Task (TLT). People with PD and age- and education-matched healthy older adults completed three sessions (each consisting of 10 blocks of 50 trials) of the TLT. Results revealed that the PD group was able to learn the sequence, however, when learning was examined using a Half Blocks analysis (Nemeth et al., 2013), which compared learning in the 1st 25/50 trials of all blocks to that in the 2nd 25/50 trials, the PD group showed significantly less learning than Controls in the 2nd Half Blocks, but not in the 1st. Nemeth et al. (2013) hypothesized that the 1st Half Blocks involve recall and reactivation of the sequence learned, thus reflecting hippocampal-dependent learning, while the 2nd Half Blocks involve proceduralized behavior of learned sequences, reflecting striatal-based learning. The present results suggest that the PD group had intact hippocampal-dependent implicit sequence learning, but impaired striatal-dependent learning. Thus, sequencing deficits in PD are likely due to striatal impairments, but other brain systems, such as the hippocampus, may be able to partially compensate for striatal decline to improve performance.
Advancements in measuring bed load transport with a magnetic detection system

A large number of bed load measuring devices have been developed in order to estimate sediment transport in rivers. In spite of that, the geomorphic and engineering communities do not have a reliable method to estimate bed material load. Available techniques suffer from insufficient temporal and spatial resolution to capture the variability inherent in bed load movement. This work involves the design and construction of an in situ magnetic detection device, which shows promise as a method capable of overcoming these limitations. The sensors work by inducing a magnetic dipole in naturally magnetic stones via magnets installed in the bed of the channel. These stones then pass over a coil of wire, inducing a small voltage, which is recorded. The system is installed in a five meter flume and is calibrated using video based particle tracking as well as manual sediment collection and sieving. Initial results indicate the new design performs significantly better than its predecessor.
Virtual Travel Training for Autism Spectrum Disorder: Proof-of-Concept Interventional Study

Background Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social interaction and repetitive patterns of behavior, which can lead to deficits in adaptive behavior. In this study, a serious game was developed to train individuals with ASD for an important type of outdoor activity, which is the use of buses as a means of transportation. Objective The aim of this study was to develop a serious game that defines a “safe environment” where the players became familiar with the process of taking a bus and to validate if it could be used effectively to teach bus-taking routines and adaptive procedures to individuals with ASD. Methods In the game, players were placed in a three-dimensional city and were submitted to a set of tasks that involved taking buses to reach specific destinations. Participants with ASD (n=10) underwent between 1 to 3 training sessions. Participants with typical development (n=10) were also included in this study for comparison purposes and received 1 control session. Results We found a statistically significant increase in the measures of knowledge of the process of riding a bus, a reduction in the electrodermal activity (a metric of anxiety) measured inside the bus environments, and a high success rate of their application within the game (93.8%). Conclusions The developed game proved to be potentially useful in the context of emerging immersive virtual reality technologies, of which use in therapies and serious games is still in its infancy. Our findings suggest that serious games, using these technologies, can be used effectively in helping people with ASD become more independent in outdoor activities, specifically regarding the use of buses for transportation.
Wafer-level ordered arrays of aligned carbon nanotubes with controlled size and spacing on silicon

Aligned carbon nanotubes with uniform outer diameters, ordered over wafer-scale areas were fabricated using porous alumina templates on silicon. Anodization of aluminium films deposited on corrugated silicon surfaces, patterned using interference lithography, led to periodic pore structures over wafer-scale areas with controlled spacing and symmetry, and with pore sizes independently controlled through the anodization conditions. Nickel deposited at the base of the pores catalysed CNT growth during PECVD. These results demonstrate a wafer-scale approach to the control of the size, pitch, ordering symmetry, and position of nanotubes and nanowires in a rigid insulating scaffold.
Evaluation of in vitro degradation of PCL scaffolds fabricated via BioExtrusion – Part 2: Influence of pore size and geometry

The in vivo degradation processes by which scaffolds degrade and are replaced by neo-tissue are complex and may be influenced by many factors, including environmental conditions, material properties, porosity and 3D architecture. The present study is focused on the influence of design parameters, filament distance (FD) and lay-down pattern, on the degradation kinetics of Polycaprolactone (PCL) scaffolds obtained via BioExtrusion. Through the variation of design parameters it was possible to obtain two groups of scaffolds with distinct pore geometry and size. The in vitro degradation was performed in simulated body fluid (SBF) and in phosphate buffer solution (PBS) for six months. Our results highlight a more complex degradation pattern of the scaffolds in SBF than in PBS, probably related to a mineral deposition. Significant statistical differences in weight loss values at month 6, allowed us to conclude that degradation kinetics of PCL scaffolds is strongly influenced by the pore size.
Mixed f‐divergence and inequalities for log‐concave functions

Mixed f ‐divergences, a concept from information theory and statistics, measure the difference between multiple pairs of distributions. We introduce them for log‐concave functions and establish some of their properties. Among them are affine invariant vector entropy inequalities, like new Alexandrov–Fenchel‐type inequalities and an affine isoperimetric inequality for the vector form of the Kullback Leibler divergence for log‐concave functions.
Opportunities for exercise during pullet rearing, Part I: Effect on the musculoskeletal characteristics of pullets

&NA; Increased load‐bearing exercise improves bone quality characteristics in a variety of species, including laying hens. Providing increased opportunities for exercise during the pullet rearing phase, a period of substantial musculoskeletal growth, offers a proactive approach to reducing osteoporosis by improving bone composition. The main objective of this study was to determine whether differing opportunities for exercise during rearing influences pullet musculoskeletal characteristics. Two flock replicates of 588 Lohmann Selected Leghorn‐Lite pullets were reared in either standard, conventional cages (Conv) or an aviary rearing system (Avi) from day‐old chicks until 16 wk of age. The keel bone and the muscles and long bones of the wings and legs were collected at 16 wk to measure muscle growth differences between rearing treatments and quantify bone quality characteristics using quantitative computed tomography (QCT) and bone breaking strength (BBS) assessment. Keel bone characteristics and muscle weights were adjusted for BW and analyses for QCT and BBS included BW as a covariate. At 16 wk of age, rearing system had an effect on the majority of keel bone characteristics (P < 0.05). The length of the keel metasternum, caudal tip cartilage length, and the overall percentage of cartilage present on the keel at 16 wk was greater in the Avi pullets compared to the Conv pullets (P < 0.01). Wing and breast muscle weights of the Avi pullets were greater than the Conv pullets (P < 0.001), but leg muscle weights were greater in the Conv pullets (P = 0.026). Avi pullets had greater total bone density, total cross‐sectional area, cortical cross‐sectional area, total bone mineral content, and cortical bone mineral content than Conv pullets for the radius, humerus, and tibia (P < 0.001). Avi pullets had greater BBS compared to the Conv pullets for the radius, humerus, and tibia (P < 0.01). Increased opportunities for exercise offered by the aviary rearing system increased muscle and bone growth characteristics in pullets at 16 wk of age.
Low-complexity receiver for uplink massive MIMO systems

This paper focuses on the uplink (UL) of a single-cell massive multiple-input multiple-output (MIMO) systems in which the base station (BS) equipped with M antennas communicates with K single-antenna user equipments (UEs). Due to the finer spatial resolution achieved by a huge number of antennas at the base stations, these systems can substantially reduce intra-cell interference with simple signal processing. However, most precoding schemes exhibit prohibitively high computation complexity as the system dimensions increase. For example, the near-optimal minimum mean squared error (MMSE) precoding requires the inversion of a large matrix. To reduce further the complexity, the truncated polynomial expansion (TPE) is applied to approximate the inverse of large matrices that appear on the expressions of the optimal linear transceivers. A key requirement to achieve good detection performance is to find good polynomial coefficients (J), where the polynomial coefficients (J) are optimized to maximize the system performance. The simulation results show that the proposed scheme provides very close to optimal performance while reducing significantly the computational complexity.
Study on Woven Fabric Texture Based on Fourier Transform and Gabor Transform

Woven fabric texture was periodic and complex, the woven fabric texture analysis method was based on Fourier transform and Gabor transform. Firstly the frequency range of woven fabric texture was obtained by using the Fourier Transform method, and the influence on fabric frequency of image resolution and fabric density was analyzed. Then the main parameters of Gabor filter was confirmed by the woven fabric texture frequency, and the sub-images which contain different texture information were obtained after the woven fabric images were decomposed and fused in different scales and directions using the Gabor filters. Finally the main texture enhancement method, the main texture elimination method, the direntional texture analysis method and extraessential texture enhancement method were discussed. The experiment proved that this method would be a powerful tool in the application of texture analysis.
Comparative Transcriptome Analysis of Two Oysters, Crassostrea gigas and Crassostrea hongkongensis Provides Insights into Adaptation to Hypo-Osmotic Conditions

Environmental salinity creates a key barrier to limit the distribution of most aquatic organisms. Adaptation to osmotic fluctuation is believed to be a factor facilitating species diversification. Adaptive evolution often involves beneficial mutations at more than one locus. Bivalves hold great interest, with numerous species living in waters, as osmoconformers, who maintain the osmotic pressure balance mostly by free amino acids. In this study, 107,076,589 reads from two groups of Crassostrea hongkongensis were produced and the assembled into 130,629 contigs. Transcripts putatively involved in stress-response, innate immunity and cell processes were identified according to Gene ontology and KEGG pathway analyses. Comparing with the transcriptome of C. gigas to characterize the diversity of transcripts between species with osmotic divergence, we identified 182,806 high-quality single nucleotide polymorphisms (SNPs) for C. hongkongensis, and 196,779 SNPs for C. gigas. Comparison of 11,602 pairs of putative orthologs allowed for identification of 14 protein-coding genes that experienced strong positive selection (Ka/Ks>1). In addition, 45 genes that may show signs of moderate positive selection (1≥Ka/Ks>0.5) were also identified. Based on Ks ratios and divergence time between the two species published previously, we estimated a neutral transcriptome-wide substitution mutation rate of 1.39×10−9 per site per year. Several genes were differentially expressed across the control and treated groups of each species. This is the first time to sequence the transcriptome of C. hongkongensis and provide the most comprehensive transcriptomic resource available for it. The increasing amount of transcriptome data on Crassostrea provides an excellent resource for phylogenetic analysis. A large number of SNPs identified in this work are expected to provide valuable resources for future marker and genotyping assay development. The analysis of natural selection provides an innovative view on the adaptation within species and sets the basis for future genetic and evolutionary studies.
The Influence of Electromagnatic Field Microwave on Physical and Mechanical Characteristics of CFRP (Carbon Fiber Reinforced Polymer) Structural

Based on the analysis of the development trends of modern transport equipment the prospect of structural composite materials usage, for example CFRP structural, is shown. In this regard, the expediency of conducting research to improve the strength characteristics of these materials is established. It is shown that, despite the large amount of scientific publications on the application of electro-technological methods and microwave technologies for modification of composite materials, applied to heterogeneous materials, such as carbon fiber, in Russia and abroad there are still some unsolved theoretical and practical questions. The studies of the effect of microwave electromagnetic field power density on different physical and mechanical characteristics of laminated composite materials in order to establish the opportunities to improve their performance, in particular their strength, were made. The example of carbon is used to show that the microwave electromagnetic field power density of 17.5 W/cm3 at a frequency of 2450 MHz exposure time of 2 minutes causes an increase in the shear strength of 38 – 40 % and an increase in the duration of the construction operation of carbon under the action of the peak load of 1.5 up to 4.5 times, wherein the change in flexural strength and modulus of elasticity was 2.6 %.
Stretchable, Skin‐Mountable, and Wearable Strain Sensors and Their Potential Applications: A Review

There is a growing demand for flexible and soft electronic devices. In particular, stretchable, skin‐mountable, and wearable strain sensors are needed for several potential applications including personalized health‐monitoring, human motion detection, human‐machine interfaces, soft robotics, and so forth. This Feature Article presents recent advancements in the development of flexible and stretchable strain sensors. The article shows that highly stretchable strain sensors are successfully being developed by new mechanisms such as disconnection between overlapped nanomaterials, crack propagation in thin films, and tunneling effect, different from traditional strain sensing mechanisms. Strain sensing performances of recently reported strain sensors are comprehensively studied and discussed, showing that appropriate choice of composite structures as well as suitable interaction between functional nanomaterials and polymers are essential for the high performance strain sensing. Next, simulation results of piezoresistivity of stretchable strain sensors by computational models are reported. Finally, potential applications of flexible strain sensors are described. This survey reveals that flexible, skin‐mountable, and wearable strain sensors have potential in diverse applications while several grand challenges have to be still overcome.