Miracle-dz/UniFAR-FTUs huggingface dataset:
Unsupervised Feature Learning With Winner-Takes-All Based STDP

We present a novel strategy for unsupervised feature learning in image applications inspired by the Spike-Timing-Dependent-Plasticity (STDP) biological learning rule. We show equivalence between rank order coding Leaky-Integrate-and-Fire neurons and ReLU artificial neurons when applied to non-temporal data. We apply this to images using rank-order coding, which allows us to perform a full network simulation with a single feed-forward pass using GPU hardware. Next we introduce a binary STDP learning rule compatible with training on batches of images. Two mechanisms to stabilize the training are also presented : a Winner-Takes-All (WTA) framework which selects the most relevant patches to learn from along the spatial dimensions, and a simple feature-wise normalization as homeostatic process. This learning process allows us to train multi-layer architectures of convolutional sparse features. We apply our method to extract features from the MNIST, ETH80, CIFAR-10, and STL-10 datasets and show that these features are relevant for classification. We finally compare these results with several other state of the art unsupervised learning methods.
Morphorider: Acquisition and reconstruction of 3D curves with mobile sensors

This paper introduces a new method for real-time shape sensing. Using a single inertial measurement unit (IMU), our method enables to scan physical objects and to reconstruct digital 3D models. By moving the IMU along the surface, a network of local orientation data is acquired together with traveled distances and network topology. We then reconstruct a consistent network of curves and fit these curves by a globally smooth surface. To demonstrate the feasibility of our approach, we have constructed a mobile device called the Morphorider, which is equipped with a 3A3M-sensor node and an odometer for distance tracking.
FINITE-ELEMENT MODELING OF DEFORMATION PROCESS OF PURE MAGNESIUM DURING ROTARY SWAGING

By using finite-element software program, MSC/Marc, and coupled thermo-mechanical model the rotary swaging process of pure magnesium was simulated. Distributions and changes of stress, strain and temperature are clarified and the critical conditions predicting two deformation defects are obtained. The deformation zone can be divided into the bulging head-end, the formed zone and the reduction zone. In the formed zone, the equivalent strain distributes inhomo-geneously along the radial direction. The equivalent strain in the center is about half of that at the edge. The critical conditions of preventing insufficient forging and edge-cracking are ec ≥ e0, ee eb respectively. The simulated height of spiral spine-like ridge is 0.0037 mm and the error is 7.5%. The residual stresses are between 18 MPa and 30 MPa. Temperature-rising owing to the heat translation from plastic deformation energy is not notable.
Early-life conditions and health at older ages: The mediating role of educational attainment, family and employment trajectories

Objectives We examine to what extent the effect of early-life conditions (health and socioeconomic status) on health in later life is mediated by educational attainment and life-course trajectories (fertility, partnership, employment). Methods Using data from the Survey of Health, Ageing and Retirement in Europe (N = 12,034), we apply, separately by gender, multichannel sequence analysis and cluster analysis to obtain groups of similar family and employment histories. The KHB method is used to disentangle direct and indirect effects of early-life conditions on health. Results Early-life-conditions indirectly impact on health in later life as result of their influence on education and family and employment trajectories. For example, between 22% and 42% of the effect of low parental socio-economic status at childhood on the three considered health outcomes at older age is explained by educational attainment for women. Even higher percentages are found for men (35% - 57%). On the contrary, the positive effect of poor health at childhood on poor health at older ages is not significantly mediated by education and life-course trajectories. Education captures most of the mediating effect of parental socio-economic status. More specifically, between 66% and 75% of the indirect effect of low parental socio-economic status at childhood on the three considered health outcomes at older age is explained by educational attainment for women. Again, higher percentages are found for men (86% - 93%). Early-life conditions, especially socioeconomic status, influence family and employment trajectories indirectly through their impact on education. We also find a persistent direct impact of early-life conditions on health at older ages. Conclusions Our findings demonstrate that early-life experiences influence education and life-course trajectories and health in later life, suggesting that public investments in children are expected to produce long lasting effects on people’s lives throughout the different phases of their life-course.
Fast Shadow Removal Using Adaptive Multi‐Scale Illumination Transfer

In this paper, we present a new method for removing shadows from images. First, shadows are detected by interactive brushing assisted with a Gaussian Mixture Model. Secondly, the detected shadows are removed using an adaptive illumination transfer approach that accounts for the reflectance variation of the image texture. The contrast and noise levels of the result are then improved with a multi‐scale illumination transfer technique. Finally, any visible shadow boundaries in the image can be eliminated based on our Bayesian framework. We also extend our method to video data and achieve temporally consistent shadow‐free results.
Influence of Single-Nucleotide Polymorphisms in PPAR-δ, PPAR-γ, and PRKAA2 on the Changes in Anthropometric Indices and Blood Measurements through Exercise-Centered Lifestyle Intervention in Japanese Middle-Aged Men

The purpose of the current study was to examine the influence of single-nucleotide polymorphisms (SNPs) in the peroxisome proliferator-activated receptor-δ (PPAR-δ), PPAR-γ, and α2 isoforms of the catalytic subunit of AMP-activated protein kinase (PRKAA2) on the extent of changes in anthropometric indices and blood measurements through exercise-centered lifestyle intervention in middle-aged men. A total of 109 Japanese middle-aged male subjects (47.0 ± 0.4 years) participated in the baseline health checkup, 6-month exercise-centered lifestyle intervention, and second checkup conducted several months after the subject completed the intervention. The body mass index (BMI), waist circumference, and clinical measurements, including hemoglobin Alc (HbA1c), triglyceride (TG), alanine aminotransferase (ALT), and γ-glutamyl-transpeptidase (γ-GTP), were measured at the baseline and second checkup. The three SNPs of PPAR-δ A/G (rs2267668), PPAR-γ C/G (rs1801282), and PRKAA2 A/G (rs1418442) were determined. Blunted responses in the reduction in the BMI and waist circumference were observed in A/A carriers of PPAR-δ SNP compared with G allele carriers (all p < 0.05). The A/A carriers also displayed less-marked improvements in HbA1c, TG, ALT, and γ-GTP (all p < 0.05). The current results suggest that A/A carriers of PPAR-δ SNP (rs2267668) may enjoy fewer beneficial effects of exercise-centered lifestyle intervention on anthropometric indices and blood measurements.
Impact of 2050 climate change on North American wildfire: consequences for ozone air quality

Abstract. We estimate future area burned in the Alaskan and Canadian forest by the mid-century (2046–2065) based on the simulated meteorology from 13 climate models under the A1B scenario. We develop ecoregion-dependent regressions using observed relationships between annual total area burned and a suite of meteorological variables and fire weather indices, and apply these regressions to the simulated meteorology. We find that for Alaska and western Canada, almost all models predict significant (p
Accelerating High-energy Physics Exploration with Deep Learning

very year, up to 30 petabytes of data are captured from the large hadron collider (LHC) at CERN, the European Centre for Nuclear Research. 1 petabyte of this data is offlineprocessed everyday using 11,000 servers with 100,000 processor cores. This huge amount of data represents only a very small fraction of the total amount of raw data generated by sensors in the collider’s trigger system at a rate of 40 million events per second. In order to adhere to the very strict experimental requirements, novel and efficient algorithms are needed to perform the required physics analytics very quickly during runtime. For the Compact Muon Solenoid (CMS) experiment [1], the design, construction, and processing of a large portion of the Level-1 trigger data for muon detection will become even more challenging with nearly 1 billion collisions occurring per second as a result of the increased luminosity from proposed LHC upgrades. Recently, machine learning has gained tremendous popularity in the research community and is used in many dataprocessing-intensive applications, ranging from recommendation engines [10, 11] and large-scale recognition applications to sequencing and identification systems [9]. Deep learning, which is a branch of machine learning, is based on a set of algorithms that model high-level data abstractions. While traditional machine learning algorithms (e.g., SVM, KNN, and BDT) [17] require a prior pre-processing step that manually extracts salient features from the data, deep learning obviates the need for this pre-processing step by dynamically performing feature extraction on the raw input data during runtime, which makes deep learning attractive for identifying physics particles based on raw detector readings from particle colliders [18]. An important aspect of data processing for the trigger system of LHC detectors is classifying collision events as signal (“an interesting event”) or noise (“an uninteresting event”). Interesting events are saved for later processing, and uninteresting events are discarded. This classification must be performed very quickly to keep up with the continuous stream of real-time data samples. In this work, we present our approach to using deep learning for identification of rarely produced physics particles (such as the Higgs Boson) out of a majority of uninteresting, background or noise-dominated data. A fast and efficient system to eliminate uninteresting data would result in much less data being stored, thus significantly reducing processing time and storage requirements. In this paper, we present a generalized preliminary version of our approach to motivate research interest in advancing the state-of-the-art in deep learning networks for other applications that can benefit from learning systems.
PrePrint Repository Coarse-Graining schemes for stochastic lattice systems with short and long-range interactions

We develop coarse-graining schemes for stochastic many-particle microscopic models with competing shortand long-range interactions on a d-dimensional lattice. We focus on the coarsegraining of equilibrium Gibbs states and using cluster expansions we analyze the corresponding renormalization group map. We quantify the approximation properties of the coarse-grained terms arising from different types of interactions and present a hierarchy of correction terms. We derive semi-analytical numerical schemes that are accompanied with a posteriori error estimates for coarsegrained lattice systems with short and long-range interactions.
Workaholism as a Mediator between Work-Related Stressors and Health Outcomes

It is currently unknown if unfavorable working conditions, reflected by the demand–control–support model and the effort–reward imbalance model, directly influence health or if the effects may be mediated by work-related attitudes and behaviors such as workaholism. In the present study, 988 employees (55.6% males, mean age 36.09, SD = 9.23) from a large consultant firm participated in a cross-sectional survey assessing work variables such as job demands, job control, social support, effort, reward, and overcommitment. Workaholism was also assessed together with eight different health-related outcomes. Although direct effects of the work stressors on health were found on most health outcomes, the work-related stressors were overall strongly related to workaholism (R2 = 0.522), which, in turn, was positively related to four (anxiety/insomnia, somatic symptoms, emotional exhaustion, and social dysfunction) of the eight outcome variables. Of a total of 40 relationships between work-related stressors and health outcomes, workaholism fully mediated three of these, and partly mediated 12. Overall, the study suggests that the effects of work-related stressors on health in many cases may be mediated by workaholism.
Fine-grained image classification based on the combination of artificial features and deep convolutional activation features

Fine-grained image classification is a challenging research topic in the field of computer vision, whose goal is to identify subclasses, such as distinguishing between different kinds of dogs. In order to overcome this problem, we combine the advantages of artificial feature with deep convolutional activation feature and design support vector machines (SVM) based on the importance of features. In this paper, we first use the bilinear neural network model to extract the deep convolutional activation feature of samples and combine it with artificial feature. The bilinear form simplifies gradient computation and allows end-to-end training. Then, multi-kernel SVM based on weighted features are trained to complete the image classification task. Finally, we present experiments and visualizations on FGVC-Aircraft and Stanford Dogs databases that analyze the effects of combined features and the multi-kernel SVM on the fine-grained object classification. The 83.8% and 66.1% accuracy proves the effectiveness of our strategy.
T-REx: A Large Scale Alignment of Natural Language with Knowledge Base Triples

Alignments between natural language and Knowledge Base (KB) triples are an essential prerequisite for training machine learning approaches employed in a variety of Natural Language Processing problems. These include Relation Extraction, KB Population, Question Answering and Natural Language Generation from KB triples. Available datasets that provide those alignments are plagued by significant shortcomings – they are of limited size, they exhibit a restricted predicate coverage, and/or they are of unreported quality. To alleviate these shortcomings, we present T-REx, a dataset of large scale alignments between Wikipedia abstracts and Wikidata triples. T-REx consists of 11 million triples aligned with 3.09 million Wikipedia abstracts (6.2 million sentences). T-REx is two orders of magnitude larger than the largest available alignments dataset and covers 2.5 times more predicates. Additionally, we stress the quality of this language resource thanks to an extensive crowdsourcing evaluation. T-REx is publicly available at: https://w3id.org/t-rex.
Understanding Is Key: An Analysis of Factors Pertaining to Trust in a Real-World Automation System

Objective: This paper aims to explore the role of factors pertaining to trust in real-world automation systems through the application of observational methods in a case study from the railway sector. Background: Trust in automation is widely acknowledged as an important mediator of automation use, but the majority of the research on automation trust is based on laboratory work. In contrast, this work explored trust in a real-world setting. Method: Experienced rail operators in four signaling centers were observed for 90 min, and their activities were coded into five mutually exclusive categories. Their observed activities were analyzed in relation to their reported trust levels, collected via a questionnaire. Results: The results showed clear differences in activity, even when circumstances on the workstations were very similar, and significant differences in some trust dimensions were found between groups exhibiting different levels of intervention and time not involved with signaling. Conclusion: Although the empirical, lab-based studies in the literature have consistently found that reliability and competence of the automation are the most important aspects of trust development, understanding of the automation emerged as the strongest dimension in this study. The implications are that development and maintenance of trust in real-world, safety-critical automation systems may be distinct from artificial laboratory automation. Application: The findings have important implications for emerging automation concepts in diverse industries including highly automated vehicles and Internet of things.
A Virtual Object-Location Task for Children: Gender and Videogame Experience Influence Navigation; Age Impacts Memory and Completion Time

The use of virtual reality-based tasks for studying memory has increased considerably. Most of the studies that have looked at child population factors that influence performance on such tasks have been focused on cognitive variables. However, little attention has been paid to the impact of non-cognitive skills. In the present paper, we tested 52 typically-developing children aged 5–12 years in a virtual object-location task. The task assessed their spatial short-term memory for the location of three objects in a virtual city. The virtual task environment was presented using a 3D application consisting of a 120″ stereoscopic screen and a gamepad interface. Measures of learning and displacement indicators in the virtual environment, 3D perception, satisfaction, and usability were obtained. We assessed the children’s videogame experience, their visuospatial span, their ability to build blocks, and emotional and behavioral outcomes. The results indicate that learning improved with age. Significant effects on the speed of navigation were found favoring boys and those more experienced with videogames. Visuospatial skills correlated mainly with ability to recall object positions, but the correlation was weak. Longer paths were related with higher scores of withdrawal behavior, attention problems, and a lower visuospatial span. Aggressiveness and experience with the device used for interaction were related with faster navigation. However, the correlations indicated only weak associations among these variables.
User association and backhaul bandwidth allocation for 5G heterogeneous networks in the millimeter-wave band

User association and wireless backhaul allocation in a two-tier heterogeneous network (HetNet) operating in the millimeter wave (mmWave) band is investigated. The macro cell tier base station (BS) is assumed to be equipped with large-scale antenna arrays, while the small cell BSs only have single-antenna capability and they rely on the wireless link to the macro BS for backhaul. To balance the throughput and the fairness, the sum of logarithmic user rate is chosen as the network utility for the optimization problem. A distributed algorithm based on hierarchical primal and dual decomposition is proposed. Firstly, the primal decomposition transforms the original problem into two sub-problems, the wireless backhaul bandwidth allocation subproblem and the user association subproblem. Secondly, biasing on the received signal-to-interference-plus-noise ratio (SINR) is introduced to the small cell tier to achieve traffic offloading. Finally, the optimal solution X∗ for the user association subproblem can be obtained from the Lagrange dual function through the dual decomposition when the Karush-Kuhn-Tucker condition or the Slater's condition holds. While achieving the same optimal performance, the proposed distributed algorithm converges much faster than the exhaustive search method. Simulation results reveal that the system throughput is improved as we increase the large-scale antenna array size and the number of users. The impact of changing the number of small cell BSs is insignificant. In addition, the system throughput achieved by the distributed algorithm outperforms that of the conventional maximum SINR based user association strategy.
Healing potential of mesenchymal stem cells cultured on a collagen-based scaffold for skin regeneration.

BACKGROUND Wound healing of burned skin remains a major goal in public health. Previous reports showed that the bone marrow stem cells were potent in keratinization and vascularization of full thickness skin wounds. METHODS In this study, mesenchymal stem cells were derived from rat adipose tissues and characterized by flowcytometry. Staining methods were used to evaluate their differentiation ability. A collagen-chitosan scaffold was prepared by freeze-drying method and crosslinked by carbodiimide-based crosslinker. RESULTS The results of immunecytochemistry and PCR experiments confirmed the adipose-derived stem cells (ASC) in differentiation to the keratinocytes under the treatment of keratinocyte growth factor. The isolated ASC were seeded on the scaffolds and implanted at the prepared wounds. The scaffolds without cells were considered as a control and implanted on the other side of the rat. Histopathological analyses confirmed the formation of new tissue on the scaffold-cell side after 14 days with the formation of dermis and epidermis. CONCLUSION These results indicated the capacity of ASC in differentiation to keratinocytes and also wound healing in vivo.
Electrochemical Impedance and Polarization Corrosion Studies of Tantalum Surface Modified by DC Plasma Electrolytic Oxidation

Tantalum has recently become an actively researched biomaterial for the bone reconstruction applications because of its excellent corrosion resistance and successful clinical records. However, a bare Ta surface is not capable of directly bonding to the bone upon implantation and requires some method of bioactivation. In this study, this was realized by direct current (DC) plasma electrolytic oxidation (PEO). Susceptibility to corrosion is a major factor determining the service-life of an implant. Therefore, herein, the corrosion resistance of the PEO coatings on Ta was investigated in Ringer’s solution. The coatings were formed by galvanostatic anodization up to 200, 300 and 400 V, after which the treatment was conducted potentiostatically until the total process time amounted to 5 min. Three solutions containing Ca(H2PO2)2, Ca(HCOO)2 and Mg(CH3COO)2 were used in the treatment. For the corrosion characterization, electrochemical impedance spectroscopy and potentiodynamic polarization techniques were chosen. The coatings showed the best corrosion resistance at voltages low enough so that the intensive sparking was absent, which resulted in the formation of thin films. The impedance data were fitted to the equivalent electrical circuits with two time constants, namely R(Q[R(QR)]) and R(Q[R(Q[RW])]). The inclusion of W in the circuit helped to fit the low-frequency part of the samples PEO-ed at 400 V, hinting at the important role of diffusion in the corrosion resistance of the PEO coatings described in the research.