Miracle-dz/UniFAR-FTUs huggingface dataset:
GPU Acceleration of Adaptive Local Kriging Applied to Retrieving Slant-Range Surface Motion Maps

Differential interferometric synthetic aperture radar (DInSAR) is an effective technique to measure the surface displacement caused by strong earthquakes. However, in the area with respect to the most significant deformation along the fault zone, the coherence between pre- and post-earthquake SAR images is completely lost because of the earthquake-induced violent and chaotic destruction on the land surface and consequently, no surface displacement data can be measured. In our previous work, an adaptive local kriging (ALK) was proposed to solve this problem. However, according to the spatial distribution of the reference points for the interpolation procedure of ALK, using moving window with different sizes through all the pixels in the image is time consuming. Thus, a high-performance computing (HPC) is needed. As a result, a parallel image interpolation approach, referred as the graphics processing unit (GPU) based ALK method, to retrieving slant-range surface motion maps derived by DInSAR is proposed in this study. The proposed GPU-ALK makes use of the parallelism of the original ALK architectures to retrieve the loss of data through the fault zone. In this paper, an HPC GPU-ALK is proposed to speed up the interpolation. It makes use of the performance profiling to analyze the serial version of ALK and perform a parallel GPU computation for reducing the computation time efficiently. By employing one NVIDIA TITAN GPU, the proposed GPU-ALK achieves a speedup of 104.32× compared to its CPU counterpart.
A hybrid localization algorithm using Inertial Sensor, Satellite and Wi-Fi for Smartphone

The complex environment based on Intelligent mobile phone navigation technology has become a research hotspot. The traditional combination navigation technology based on satellite and inertial navigation system can effectively compensate for the effect of short satellite signal loss, but the sensors used in strapdown inertial navigation system generally need high precision and sensitivity, and the existing smart phone built-in MEMS sensor has relatively low precision and sensitivity. One the other hand, the smart phone’s WiFi signal can be considered for improving the positioning performance of inertial navigation system in the absence of satellite, but the fingerprint matching Iocalization results are scattered and there is noise because of its non-Gaussian noise and multipath interference in the outdoor scene. In order to improve the positioning and navigation accuracy and robustness of the smart phone in complex environment, a WiFi-aided fusion positioning algorithm for satellite and inertial navigation system is proposed, which includes two stages, namely, navigational solution and optimization fusion. In order to improve the positioning accuracy, in the navigation solution phase, in the absence of satellite, the system outputs the location result of the outdoor WiFi location algorithm and the combined navigation location result to the optimized fusion device. In order to improve the robustness of the positioning, in the optimization fusion stage, the paper makes a second fusion of two positioning results through an improved fusion weighted average mechanism. The experimental results show that the positioning accuracy of the satellite can be improved obviously in the time of the effective time and the failure moment after WiFi calibration.
A Reconfigurable Low Noise Amplifier for X/Ku Band

In this paper, a reconfigurable low noise amplifier (LNA) for X/Ku band is presented. The proposed LNA consists of two low noise input stages for X band and Ku band separately, and one output stage to achieve high gain and high reverse isolation. To achieve reconfiguration, a single-pole double-throw switch and a reconfigurable output matching network have been adopted. In X-band mode, the measured noise figure is 1.55dB with a small signal gain of 18dB. In Ku-band mode, the measured noise figure is 3.39dB, with a small signal gain of 20dB.
Effects of nanosecond pulse voltage parameters on characteristics of surface charge for epoxy resin

The surface charge accumulation on polymers can reduce the insulation performance of the materials and even cause surface flashover. The characteristics of surface charge accumulation and dissipation for epoxy resin (EP) exposed to various nanosecond pulse voltage parameters (i.e. pulse rise time, pulse width and voltage amplitude) are investigated. The results show that the fractional decay of surface charge increases with pulse rise time and decreases with pulse width and voltage amplitude. However, abnormal dissipation phenomenon appears when the pulse voltage reaches 9 kV. Analysis suggests that the abnormal dissipation phenomenon can be attributed to surface charge transport primarily along the surface. Furthermore, the surface charge fractional decay is less than 30% for a dissipation time of 30 min under different nanosecond pulse voltage parameters. In addition, the method of atmospheric pressure plasma jet (APPJ) is utilized to deposit SiCxHyOz thin film on the surface of epoxy resin (EP) samples. It is found that the fractional decay of surface charge for treated EP samples is promoted approximately by an average of 90%. The trap density distribution indicates that the shallow traps that are formed by APPJ treatment result in a decrease in the trap energy level.
KEH-Gait: Using Kinetic Energy Harvesting for Gait-based User Authentication Systems

With the rapid development of sensor networks and embedded computing technologies, miniaturized wearable healthcare monitoring devices have become practically feasible. For many of these devices, accelerometer-based user authentication systems by gait analysis are becoming a hot research topic. However, a major bottleneck of such system is it requires continuous sampling of accelerometer, which reduces battery life of wearable sensors. In this paper, we present KEH-Gait, which advocates use of output voltage signal from kinetic energy harvester (KEH) as the source for gait recognition. KEH-Gait is motivated by the prospect of significant power saving by not having to sample the accelerometer at all. Indeed, our measurements show that, compared to conventional accelerometer-based gait detection, KEH-Gait can reduce energy consumption by 82.15 percent. The feasibility of KEH-Gait is based on the fact that human gait has distinctive movement patterns for different individuals, which is expected to leave distinctive patterns for KEH as well. We evaluate the performance of KEH-Gait using two different types of KEH hardware on a data set of 20 subjects. Our experiments demonstrate that, although KEH-Gait yields slightly lower accuracy than accelerometer-based gait detection when single step is used, the accuracy problem can be overcome by the proposed Probability-based Multi-Step Sparse Representation Classification (PMSSRC). Moreover, the security analysis shows that the EER of KEH-Gait against an active spoofing attacker is 11.2 and 14.1 percent using two different types of KEH hardware, respectively.
RSU Controlled Named Data Networking for Traffic Information Dissemination in Vehicular Networks

Vehicular network is the infrastructure for intelligent transportation systems and Internet services. Due to high node mobility, vehicular networks always suffer from dynamic network topology and intermittent connectivity, which result in low data request success ratio and high communication cost. In this paper, we study how to disseminate traffic information to vehicles that will pass the associated road segments. Named Data Networking, a promising data-centric networking technology, is adopted to forward packets according to data content rather than IP addresses and avoid the overhead of address management. To cope with topology changes due to vehicles' movements, roadside units (RSUs) are used as controllers to store interest information of vehicles and determine alternate data paths upon disconnections. Vehicles are only responsible for carrying and forwarding packets to neighbor nodes. With such an RSU controlled mechanism, we design interest and data packets forwarding algorithms to realize traffic information dissemination. Our novel design can achieve high request success ratio and low communication cost simultaneously. Simulations via ndnSIM and SUMO confirm such advantages of our work.
SMT Solvers for Job-Shop Scheduling Problems: Models Comparison and Performance Evaluation

The optimal assignment of jobs to machines is a common problem when implementing automated production systems. A specific variant of this category is the job-shop scheduling problem (JSP) that is known to belong to the class of NP-hard problems. JSPs are typically either formulated as Mixed Integer Linear Programming (MILP) problems and solved by general-purpose-MILP solvers or approached using heuristic algorithms specifically designed for the purpose. During the last decade a new approach, satisfiability (SAT), led to develop solvers with incredible abilities in finding feasible solutions for hard combinatorial problems on Boolean variables. Moreover, an extension of SAT, Satisfability Modulo Theory (SMT), allows to formulate constraints involving linear operations over integers and reals and some SMT-solvers have been also extended with an optimizing tool. Since the JSP is a well-known hard combinatorial problem, it is interesting to evaluate how SMT-solvers perform in solving it and how they compare to traditional MILP-solvers. We therefore evaluate state-of-the-art MILP and SMT solvers on benchmark JSP instances and find that general-purpose open-source SMT-solvers are competitive against commercial MILP-solvers.
The Insurance Industry as a Complex Social System: Competition, Cycles and Crises

Insurance is critical to the fabric of modern societies and economies, but the insurance industry continues to suffer deep cycles and periodic crises. These have a great socio-economic cost as insurance cover can become prohibitively expensive or unavailable, damaging livelihoods, property, belongings and employment. These phenomena are poorly understood. A set of socio-anthropological and behavioral hypotheses have recently been posited. We investigate these explanations by means of an agent-based simulation model. The model is parameterized on actual property insurance industry data and is carefully validated. Our main result is that simple behavior and interaction at the individual level can result in complex cyclical industry-wide behavior. Heterogeneity and interaction at a micro level must therefore be understood if cycles and crises in the insurance industry are to be managed and prevented.
Modifying the Intracerebral Hemorrhage Score to Suit the Needs of the Developing World

Background: Published literature on intracerebral haemorrhage (ICH) from the Indian subcontinent is very scarce. The study aims to assess the prognostic factors influencing outcome and validating the ICH score which is widely used to prognosticate the disease in this financially constraint population. Prognosticating the outcome at the time of admission is important to customize treatment in a cost-effective manner. Materials and Methods: We conducted a prospective study of all Spontaneous ICH patients admitted from February 2015 to May 2016. Data pertaining to patient demographics, clinical findings, biochemical parameters and cranial computed tomography (CT) findings were recorded. mRS (modified Rankin score) was used to assess outcome at discharge and at three month follow up. Results: A total of 215 patients with hypertensive haemorrhage were analysed. The mean age of our cohort was 57.64 years and volume of bleed was 24.5ml. 73% pf patients with GCS<8, 46% with Intraventricular extension and 57% with hematoma volume >30 were died at the end of 3 months. Twenty eight patients succumbed during hospitalization while 38 died after their discharge. Mortality rates were 5%,16%, 33%, 54% and 93% for ICH Scores of 0, 1, 2, 3 and 4. The rICH score after modifying the age parameter in the ICH score to 70 years had mortality rates of 6%,15%,25%,51%,75% and 100%. Conclusion: ICH Score failed to accurately predict mortality in our cohort. ICH is predominately seen at a younger age group in our country and hence have better outcomes in comparison to the west. We propose a minor modification in the ICH score by reducing the age criteria by 10 years to prognosticate the disease better in our population.
Flood Event Recognition from Images Based on CNN and Semantics

Recognizing sensitive events in images, such as flood events, is significant for the maintenance of normal public opinion and social stability. By now, it is still a challenging problem. In this paper, we propose a novel method for recognizing flood events using semantics and CNN-based multi-label image classification. Our method utilizes the empirical information of people so as to improve the recognition result. We first define the semantic model of flood event and introduce it into a multi-label classification model which is based on CNN, and then use the event discrimination model to predict the results. Concretely, for the multi-label classification, we propose a novel end-to-end model, which takes the images as inputs and outputs the predictions of multi-labels. The model can be pre-trained with a single-label dataset, and no ground-truth bounding boxes are required for training. The experimental results demonstrate that our proposed flood event recognition method is superior to others.
An Ensemble Learning Framework for Credit Card Fraud Detection Based on Training Set Partitioning and Clustering

The popularity of credit card has greatly facilitated the transactions between merchants and cardholders. However, credit card fraud has been derived, which results in losses of billions of euros every year. In recent years, machine learning and data mining technology have been widely used in fraud detection and achieved favorable performances. Most of these studies use the technology of under-sampling to deal with the high imbalance of credit card data. However, it will potentially discard some relevant training samples which will weaken the ability of the classifier. In this paper, we propose an ensemble learning framework based on training set partitioning and clustering. It turns out that the proposed framework not only ensures the integrity of the sample features, but also solves the high imbalance of the dataset. A main feature of our framework is that every base estimator can be trained in parallel. This improves the efficiency of the framework. We show the effectiveness of our proposed ensemble framework by experimental results on a real credit card transaction dataset.
Intestinal microbiome and its potential functions in bighead carp (Aristichthys nobilis) under different feeding strategies

Bighead carps (Aristichthys nobilis) were divided into four groups with different feeding strategies: group A, nature live food only (fertiliser only, 200 g urea + 160 g ethylamine phosphate + 250 g Huangjintai bio-fertiliser); group B, nature live food + 1/2 formulated feed; group C, nature live food + formulated feed; and group D, formulated feed only. The intestinal microbiomes of the different groups were compared through the Illumina MiSeq sequencing of the bacterial 16S rRNA gene. The specific growth rate (SGR), survival and blood biochemical factors of the fish were also investigated. Results showed that feeding treatment influenced the intestinal communities in the fish. In specific, more bacterial phyla dominated in groups A and B (phyla Bacteroidetes, Fusobacteria, Firmicutes and Proteobacteria in group A, phyla Proteobacteria and Fusobacteria in group B) than in groups C and D (phylum Proteobacteria). The diversity was also lower in groups C and D than in groups A and B. Unweighted pair-group method analysis revealed a clear difference in intestinal microbiota among the different feeding treatments. No difference in survival rate was found among the treatment groups, but the SGR was significantly higher (P < 0.01) in groups B, C and D than in group A. Functional analysis showed that the intestinal bacteria correlated with fish glucose metabolism in group A but with lipid metabolic activity in groups B, C and D. In summary, the intestinal microbiomes and their potential functions vary in bighead carp under different feeding treatments. This study provides new insights into the gut microbiomes of filter-feeding and formulated diet-fed fish.
100 nm Gate-Length AlGaN/GaN FinFETs with High Linearity of Gm and fT/fmax

We report excellent dc and RF performance of high-linearity AIGaN/GaN FinFETs with 100 nm T-gate. Besides a high <tex>$f_{\mathrm{T}}$</tex> of 65 GHz enabled by scaled gate length, good linearity characteristics of <tex>$G_{\mathrm{m}}$</tex> and <tex>$f_{\mathrm{T}}/f_{\max}$</tex> are demonstrated. Loadpull measurements show that the proposed FinFETs achieve a maximum <tex>$P_{\mathrm{out}}$</tex> of 2.98 W/mm with an associated PAE of 64% at 4 GHz and <tex>$\mathrm{V}_{\mathrm{d}}=10\ \mathrm{V}$</tex>, At 18 GHz, <tex>$P_{\mathrm{out}}$</tex> and PAE turn out to 2.73 W/mm and 48%, respectively. To the best of our knowledge, this is the first demonstration of high-linearity GaN FinFETs which operate at Ku-band.
Pipelines for HDR Video Coding Based on Luminance Independent Chromaticity Preprocessing

We consider the chromaticity in high dynamic range (HDR) video coding and show the advantages of a constant luminance color space for encoding. For this, we introduce two constant luminance HDR video coding pipelines, which convert the source video to linear $Y u^\prime v^\prime $ . A content dependent scaling of the chromaticity components serves as color quality parameter. This reduces perceivable color artifacts while remaining fully compatible with core High Efficiency Video Coding or other video coding standards. One of the pipelines further combines the scaling with a dedicated chromaticity transform to optimize the representation of the chromaticity components for encoding. We validate both pipelines with subjective user studies in addition to an objective comparison to the other state-of-the-art methods. The user studies show a significant improvement in perceived color quality at medium to high compression rates without sacrificing luminance quality compared with current standard coding pipelines. The objective evaluation suggests that both pipelines perform at least comparable to the current state-of-the-art methods.
Negotiating digital spaces in everyday life: A case study of Indian women and their digital use

New developments in digital technology and better and affordable access allow women unprecedented access to information and communication networks — if they are able to use digital technologies to access networks and digital information. This study presents a case study of motivations of women to use information and communication technologies (ICT) in a ubiquitous information environment. This study follows an interpretive paradigm to explore an understanding of culture and gender in a middle-class Indian context. Women in this report varied in terms of heavy/light and mandatory/voluntary use. We discuss the implications of such variations in terms of technology adoption for digital inclusion. Social factors, such as the role of parents and social communities, were found to influence women to develop and maintain interests in ICT that were reflected in their professional and personal lives. Some women were slow to adapt to ICT use as they tended to adhere to normative expectations which hindered ICT use. Women tend to have greater ICT use if it is for fulfilling familial roles and for communication.
Distribution and evaluation of sense of coherence among older immigrants before and after a health promotion intervention – results from the RCT study promoting aging migrants’ capability

Background The migration process can be a threat to a person’s sense of coherence (SOC) and to their ability to experience life as comprehensible, manageable, and meaningful. Seen from a salutogenic perspective, this may have a negative impact on the experience of health. Purpose We describe the distribution of SOC and its components among older persons with an immigrant background now aging in Sweden. In addition, we evaluated whether a group-based health promotion program with a person-centered approach could support the SOC among older persons in this group. Materials and methods A randomized controlled trial with postintervention follow-ups at 6 and 12 months was conducted with 131 independently living persons aged ≥70 years from Finland and the Balkan Peninsula. Participants were randomly allocated to an intervention group (4 weeks of group intervention and one follow-up home visit) and a control group (no intervention but access to ordinary health care services). The outcome measure was the SOC measured by SOC-13. Chi-square and ORs were calculated. Results There was a significant improvement in total SOC scores for the intervention group at 6-month follow-up. Also, the ORs for the SOC components were higher in the person-centered intervention group. However, we found no significant between-group differences nor did the effect last until the 12-month follow-up. Conclusion Persons who have lived a long time in a host country after migration seem to have a SOC similar to native-born persons. Interventions with a person-centered approach could support the SOC by capturing individual life situations. Such interventions could support older persons by making everyday life more comprehensible and manageable and helping them to cope with challenges in daily life caused by aging.
Generating Underwater Images by GANs and Similarity Measurement

The complexity of the underwater environment makes it difficult to capture underwater images, therefore can not provide a large amount of training data required for image processing with deep learning. Traditional color transfer methods can only map the color distribution of source image to that of target image one by one, without considering the complexity of the underwater imaging environment, which results in the disadvantages of effectiveness and performance. In this paper, our proposed method, based on GANs(Generative Adversarial Networks), is superior in its consideration of taking underwater imaging model to train the generative network, the air images are converted into underwater images in batches, so as to achieve the purpose of data augment. Additionally, we describe several implementation details to improve the performance of GANs. Finally, we first utilize SIMILATION, SSIM(Structural Similarity Index) and MS-SSIM(Multi-Scale-Structural Similarity Index) to compute the color and structure similarity level for the purpose of evaluating the quality of generated samples. The results indicate that the proposed method performs well in data augment.