Miracle-dz/UniFAR-FTUs huggingface dataset:
Development and Implementation of Spline-based Path Planning Algorithm in ROS/Gazebo Environment

Path planning for autonomous mobile robots is an important task within robotics field. It is common to use one of the two classical approaches in path planning: a global approach when an entire map of a working environment is available for a robot or local methods, which require the robot to detect obstacles with a variety of onboard sensors as the robot traverses the environment. In our previous work, a multi-criteria spline algorithm prototype for a global path construction was developed and tested in Matlab environment. The algorithm used the Voronoi graph for computing an initial path that serves as a starting point of the iterative method. This approach allowed finding a path in all map configurations whenever the path existed. During the iterative search, a cost function with a number of different criteria and associated weights was guiding further path optimization. A potential field method was used to implement some of the criteria. This paper describes an implementation of a modified spline-based algorithm that could be used with real autonomous mobile robots. Equations of the characteristic criteria of a path optimality were further modified. The obstacle map was previously presented as intersections of a finite number of circles with various radii. However, in real world environments, obstacles’ data is a dynamically changing probability map that could be based on an occupancy grid. Moreover, the robot is no longer a geometric point. To implement the spline algorithm and further use it with real robots, the source code of the Matlab environment prototype was transferred into C++ programming language. The testing of the method and the multi criteria cost function optimality was carried out in ROS/Gazebo environment, which recently has become a standard for programming and modeling robotic devices and algorithms. The resulting spline-based path planning algorithm could be used on any real robot, which is equipped with a laser rangefinder. The algorithm operates in real time and the influence of the objective function criteria parameters are available for dynamic tuning during a robot motion.
Optimizing Mobile Edge Computing Multi-Level Task Offloading via Deep Reinforcement Learning

In a mobile edge computing (MEC) network, mobile devices could selectively offload tasks to the edge server(s) to save time and energy. However, we should consider many dynamic factors in task offloading optimization, which increases the complexity of this problem. Instead of executing the traditional optimization algorithm repeatedly, a well-trained empirical model such as an artificial neural network could be more efficient in decision making. In this research, considering the potential uneven spatial distribution of mobile devices in an MEC network with multiple wireless edge gateways, we allow an edge gateway to offload tasks to a nearby edge gateway further. We propose a deep reinforcement learning-based joint optimization approach for both device-level and edge-level task offloading. Experimental results show that the proposed approach achieves a near-optimal task delay performance and a better trade-off between the task delay and the energy consumption on tasks.
Image Recommendation Algorithm Based on Deep Learning

With the rapid development of network technology, the number of digital images is growing at an alarming rate, people’s demand for information gradually shift from text into images. However, it is very difficult for users to quickly find the images they are interested in from the large number of image libraries. The purpose of this paper is to study the image recommendation algorithm based on deep learning. In this paper, image classification algorithm is firstly studied. LReLU - Softplus activation function is formed by combining LReLU function and Softplus function, and CNN is improved. Then, an image retrieval model based on local sensitive hash algorithm is proposed in this paper. This model calculates the distance in hamming space for the binary hash code generated by mapping. Euclidean distance is calculated inside the result set after similarity measurement to improve the accuracy, and the image retrieval model is constructed. Finally, an image recommendation model based on implicit support vector machine (SVM) is proposed in this paper. This image recommendation method combines image text information and image content information. The experimental results show that the proposed image recommendation model can meet the practical needs. In this paper, the overlap rate between the CNN-based recommendation model and the human recommendation algorithm was tested, and the coincidence degree of the two recommended images reached 88%.
Structure and mechanism of the Mrp complex, an ancient cation/proton antiporter

Multiple resistance and pH adaptation (Mrp) antiporters are multi-subunit Na+ (or K+)/H+ exchangers representing an ancestor of many essential redox-driven proton pumps, such as respiratory complex I. The mechanism of coupling between ion or electron transfer and proton translocation in this large protein family is unknown. Here, we present the structure of the Mrp complex from Anoxybacillus flavithermus solved by cryo-EM at 3.0 Å resolution. It is a dimer of seven-subunit protomers with 50 trans-membrane helices each. Surface charge distribution within each monomer is remarkably asymmetric, revealing probable proton and sodium translocation pathways. On the basis of the structure we propose a mechanism where the coupling between sodium and proton translocation is facilitated by a series of electrostatic interactions between a cation and key charged residues. This mechanism is likely to be applicable to the entire family of redox proton pumps, where electron transfer to substrates replaces cation movements.
On Performance Prediction of Big Data Transfer in High-performance Networks

Big data generated by large-scale scientific and industrial applications need to be transferred between different geographical locations for remote storage, processing, and analysis. High-speed dedicated connections provisioned in High-performance Networks (HPNs) are increasingly utilized to carry out such big data transfer. HPN management highly relies on an important capability of performance (mainly throughput) prediction to reserve sufficient bandwidth and meanwhile avoid over-provisioning that may result in unnecessary resource waste. This capability is critical to improving the resource (mainly bandwidth) utilization of dedicated connections and meeting various user requests for data transfer. Conventional methods conduct performance prediction by fitting prior observed transfer history with predefined loss functions, without considering unobservable latent factors such as competing loads on end hosts. Such latent factors also have a significant impact on the application-level data transfer performance, which may result in an inaccurate prediction model. In this paper, we first investigate the impact of latent factors and propose a clustering-based method to eliminate their negative impact on performance prediction. We then develop a robust machine learning-based performance predictor by: i) incorporating the proposed latent factor elimination method into data preprocessing, and ii) adopting a customized domain guided loss function. Extensive experimental results show that our predictor achieves significantly higher prediction accuracy than several other state-of-the-art methods.
Influence of Sulfonamide Contamination Derived from Veterinary Antibiotics on Plant Growth and Development

Veterinary antibiotics such as sulfonamides are widely used to increase feed efficiency and to protect against disease in livestock production. The sulfonamide antimicrobial mechanism involves the blocking of folate biosynthesis by inhibiting bacterial dihydropteroate synthase (DHPS) activity competitively. Interestingly, most treatment antibiotics can be released into the environment via manure and result in significant diffuse pollution in the environment. However, the physiological effects of sulfonamide during plant growth and development remain elusive because the plant response is dependent on folate biosynthesis and the concentration of antibiotics. Here, we present a chemical interaction docking model between Napa cabbage (Brassica campestris) DHPS and sulfamethoxazole and sulfamethazine, which are the most abundant sulfonamides detected in the environment. Furthermore, seedling growth inhibition was observed in lentil bean (Lens culinaris), rice (Oryza sativa), and Napa cabbage plants upon sulfonamide exposure. The results revealed that sulfonamide antibiotics target plant DHPS in a module similar to bacterial DHPS and affect early growth and the development of crop seedlings. Taking these results together, we suggest that sulfonamides act as pollutants in crop fields.
Calcium‐dependent association of annexin VI, protein kinase Cα, and neurocalcinα on the raft fraction derived from the synaptic plasma membrane of rat brain

A membrane microdomain enriched in cholesterol and sphingolipids or so called “raft” region was found to contain many signal transducing proteins such as GPI‐anchored proteins, trimeric G proteins and protein tyrosine kinases. Because brain‐derived raft contains two calmodulin‐binding proteins, GAP‐43 and NAP‐22 as the major protein components, the raft domain is assumed to be important in the Ca2+‐signaling. In this study, we analyzed protein components showing Ca2+‐dependent binding to the raft of synaptic plasma membrane from rat brain. SDS‐PAGE analysis of the protein components in the EGTA eluate from the raft prepared in the presence of Ca2+‐ions showed the elution of 80 kDa, 68 kDa, 22 kDa, and 21 kDa proteins. These proteins were identified as protein kinase Cα (80 kDa) and annexin VI (68 kDa) from the partial amino‐acid sequencing, and neurocalcinα (22 kDa) and calmodulin (21 kDa) with western blotting and electrophoretic mobilities in the presence or absence of Ca2+ ions. Further immunoblotting experiments showed the Ca2+‐dependent association of conventional, but not non‐conventional, subtypes of PKC to the raft. J. Neurosci. Res. 64:235–241, 2001. © 2001 Wiley‐Liss, Inc.
16-Bit Fixed-Point Number Multiplication With CNT Transistor Dot-Product Engine

Resistive crossbar arrays can carry out energy-efficient vector–matrix multiplication, which is a crucial operation in most machine learning applications. However, practical computing tasks that require high precision remain challenging to implement in such arrays because of intrinsic device variability. Herein, we experimentally demonstrate a precision-extension technique whereby high precision can be attained through the combined operation of multiple devices, each of which stores a portion of the required bit width. Additionally, designed analog-to-digital converters are used to remove the unpredictable effects from noise sources. An $8\times 15$ carbon nanotube transistor array can perform multiplication operation, where operands have up to 16 valid bits, without any error, making in-memory computing approaches attractive for high-throughput energy-efficient machine learning accelerators.
A benchmark of heart sound classification systems based on sparse decompositions

Background: Nowadays, cardiovascular diseases (CVD) remain the main cause of death worldwide. A heart sound signal or phonocardiogram (PCG) is the most simple, economical and non-invasive tool to detect CVDs. Advances in technology and signal processing allow the design of computer-aided systems for heart illnesses detection from PCG signals. Purpose: The paper proposes a pipeline and benchmark for binary heart sounds classification. The features extraction architecture is focused on the use of Matching Pursuit time-frequency decomposition using Gabor dictionaries and the Linear Predictive Coding method of a residual. We compare seven classifiers with two different approaches: feature averaging and cycle averaging. Methods: We test our proposal on the PhysioNet/CinC challenge 2016 database, which comprises a wide variety of heart sounds recorded from patients with normal and different pathological heart conditions. We conduct a 10-fold stratified cross-validation method to evaluate the performance of different classification algorithms. The feature sets were also tested when using an oversampling method for balancing. Results: The benchmark identified systems showing a satisfying performance in terms of accuracy, sensitivity, and Matthews correlation coefficient. Results can be improved when using feature averaging and an oversampling strategy.
Eco-evolutionary perspectives on emergence, dispersion and dissolution of historical Dutch commons

Historical commons represent self-governed governance regimes that regulate the use and management of natural and man-made shared resources. Despite growing scientific interests, analyses of commons evolution and temporal dynamics are rare and drivers of change (birth, adaptation, dissolution) remain obscure. We apply an interdisciplinary approach and address these issues from an eco-evolutionary perspective. Analyses of > 400 Dutch commons over more than a millennium (between the 9th and the 20th century) uncovered that most commons originated between 1200 and 1700, and that there was a particularly high rate of evolution during 1300–1550, a pattern intermediate to gradualism and punctuated equilibrium in biological evolution. Dissolutions of commons were rare prior to 1800 and peaked around 1850, comparable to a mass extinction in biology. Temporal trends in number, spatial distribution, density, and dispersion of historical commons were distinctive and resembled developments seen at the levels of species and individuals in the growth of biological communities and populations, in that they showed signs of saturation determined by the abundance and distribution of resources. The spatiotemporal dynamics of commons also pointed to important roles of social, economic and political factors, such as new reclamations of resources and pressure on resources due to population growth. Despite internal and external pressures, the self-governing commons studied here were very successful, in the sense that they persisted for on average >350 years. There was a weak positive relationship between the use of multiple resources and the lifespan of commons, resembling associations between diversity and persistence seen in biological systems. It is argued that eco-evolutionary perspectives can further the understanding of the long-term dynamics of commons as institutions for collective action, vitalize future research, improve management of shared goods, and advise about sustainable utilization of finite resources.
Broadening our understanding of the genetics of Juvenile Idiopathic Arthritis (JIA): Interrogation of three dimensional chromatin structures and genetic regulatory elements within JIA-associated risk loci

Objective The risk loci for juvenile idiopathic arthritis (JIA) consist of extended haplotypes that include functional elements in addition to canonical coding genes. As with most autoimmune diseases, the risk haplotypes for JIA are highly enriched for H3K4me1/H3K27ac histone marks, epigenetic signatures that typically identify poised or active enhancers. In this study, we test the hypothesis that genetic risk for JIA is exerted through altered enhancer-mediated gene regulation. Methods We mined publically available HiC and other chromatin conformation data to determine whether H3K27ac-marked regions in 25 JIA risk loci showed physical evidence of contact with gene promoters. We also used in vitro reporter assays to establish as proof-of-concept the idea that genetic variants in linkage disequilibrium with GWAS-identified tag SNPs alter enhancer function. Results All 25 loci examined showed multiple contact sites in the 4 different cell lines that we queried. These regions were characterized by HiC-defined loop structures that included 237 immune-related genes. Using in vitro assays, we found that a 657 bp, H3K4me1/H3K27-marked region within the first intron of IL2RA shows enhancer activity in reporter assays, and this activity is attenuated by SNPs on the IL2RA haplotype that we identified using whole genome sequencing of children with JIA. Similarly, we identified a 1,669 bp sequence in an intergenic region of the IL6R locus where SNPs identified in children with JIA increase enhancer function in reporter assays. Conclusions These studies provide evidence that altered enhancer function contributes to genetic risk in JIA. Further studies to identify the specific target genes of genetically altered enhancers are warranted.
SmoothMix: a Simple Yet Effective Data Augmentation to Train Robust Classifiers

Data augmentation has been proven effective which, by preventing overfitting, not only enhances the performance of a deep neural network but also leads to a better generalization even with limited dataset. Recently introduced regional dropout based data augmentation strategies remove (or replace) some parts of an input image with a desideratum to make the network focus on less discriminative portions of an image, which results in an improved performance. However, such approaches usually possess’ strong-edge’ problem caused by an obvious change in the pixels at the positions where the image is manipulated. It may not only impact on the local convolution operation but can also provide clues for the network to latch on to, which do not align well with the fundamental philosophy of augmentation. In order to minimize such peculiarities, we introduce Smoothmix in which blending of images is done based on soft edges and the training labels are computed accordingly. Extensive analysis performed on CIFAR-10, CIFAR- 100 and ImageNet for image classification demonstrates state-of-the-art results. Furthermore, Smoothmix significantly increases the robustness of a network against image corruption which is validated by the experiments carried out on CIFAR-100-C & ImageNet-C corruption datasets.
Evaluation of genetic variations at glutenin loci in Korean wheat landraces using allele-specific DNA markers

The allelic variations at glutenin loci could significantly affect the bread baking quality, and specific glutenin alleles might be closely associated with greater gluten strength, which, in turn, is related to superior bread baking quality. In this study, allelic variations at Glu-1, Glu-A3 and Glu-B3 loci were evaluated in 222 Korean wheat landraces using gene-specific polymerase chain reaction (PCR) markers. Ten alleles were identified at Glu-1 loci. Glu-A1c, Glu-B1b, and Glu-D1a or Glu-D1f alleles were predominantly found at the respective loci and their frequencies were 86.5, 87.8 and 96.9 %, respectively. Seven Korean wheat landraces carried the Glu-D1d allele, and only one Korean wheat landrace (IT173162) achieved 10 points for the Glu-1 score. Fifteen alleles were identified at Glu-A3 and Glu-B3 loci; Glu-A3c and Glu-B3d or Glu-B3i alleles were commonly found at the respective loci and their frequencies were 77.0, 33.3 and 37.8 %, respectively. Glu-B3 alleles exhibited the highest genetic diversity than other alleles, while Glu-B1 and Glu-A1 alleles exhibited the lowest genetic diversity than other alleles. Twenty Korean wheat landraces had the Glu-A3d and Glu-B3b, Glu-B3d, Glu-B3f, Glu-B3g or Glu-B3i alleles, which were correlated with superior bread baking quality. Among these wheat lines, two (IT59787 and IT236544) carried the Glu-D1d allele.
Subchannel Assignment and Power Optimization in Caching based UAV Networks With NOMA

This paper intends to study the energy efficiency in caching based UAV networks, where fog radio access network (FRAN) and non-orthogonal multiple access (NOMA) are considered meanwhile. Taking full account of the impact of caching, subchannel assignment, and power allocation in UAV enabled wireless networks, we formulate the problem of maximizing energy efficiency. In order to better solve the proposed non-convex problem, we propose a subchannel assignment algorithm and a power allocation algorithm applying alternating direction method of multipliers (ADMM). The final simulation section verifies the fast convergence of the algorithm and compares the advantages with existing algorithms.
Linear Algebraic Louvain Method in Python

We show that a linear algebraic formulation of the Louvain method for community detection can be derived systematically from the linear algebraic definition of modularity. Using the pygraphblas interface, a high-level Python wrapper for the GraphBLAS C Application Programming Interface (API), we demonstrate that the linear algebraic formulation of the Louvain method can be rapidly implemented.
A passive fingerprinting approach for device-free surveillance and localization applications using a Bluetooth Low Energy infrastructure

Passive, device-free indoor localization is a research field that has drawn the attention of many researchers in the last years. Existing solutions that do not require an active device at the target, do not fulfil the requirements of reliability, cost and power efficiency and accurate localization at the same time. In order to tackle this challenge, we propose a passive, device-free fingerprinting method based on Bluetooth Low Energy. This method takes advantage of the highly configurable and fully connected mesh network of a custom sensor platform. The measurements of the Received Signal Strength values are carried out within a pre-defined network topology. It will be shown that the proposed passive fingerprinting approach does not need recalibration and its median localization error of 1.2 m is constant while using both old and updated calibration data. This is verified by extensive measurements using an experimental setup.
Optimal Battery Dispatch and Real-Time State of Charge Tracking for Microgrid Applications

This work studies the grid-connected microgrid of a medical facility that is equipped with photovoltaic (PV) energy generation and a local battery energy storage system (BESS). Following a systematic approach in which power flow and state of charge (SoC) dynamics of the BESS is modeled and estimated from experiments, an optimal SoC reference profile is computed given electricity price, predicted solar generation, predicted load, and power and energy constraints. With SoC measurements subjected to errors, a Kalman filter is designed and used in a feedback controller that uses information on the Kalman filtered SoC, PV power and measured power flow at the PCC to compute the desired power demand signal for the DER to ensure the SoC of the BESS remains within operational conditions and tracks the optimal SoC reference profile. Experimental results on SoC scheduling and power control are provided to demonstrate the implementation of the approach.
On the Robust Implementation of Projected Dynamical Systems with Anti-Windup Controllers

While projected dynamical systems (PDS) are mathematically well-defined and useful for modeling dynamics for constrained optimization and variational inequalities, their physical realization is not immediate. In this paper, we study how anti-windup schemes, which are ubiquitous in feedback control to deal with saturation, can be used to approximate PDS. Namely, we show that a particular class of anti-windup control loops are perturbations of PDS. This allows us to establish results about uniform convergence and robust stability of high-gain anti-windup schemes. We also establish how these insights apply in the context of feedback optimization, popularized recently for power systems and communication networks, and how our results introduce a new class of optimization dynamics that is particularly suited for this application.