Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Deep Learning with a customizable GPU-like FPGA-based accelerator

An ever increasing number of challenging appli- cations are being approached using Deep Learning, obtaining impressive results in a variety of different domains. However, state-of-the-art accuracy requires deep neural networks with a larger number of layers and a huge number of different filters with millions of weights. GPU- and FPGA-based architectures have been proposed as a possible solution for facing this enormous demand of computing resources. In this paper, we investigate the adoption of different architectural features, i.e., SIMD paradigm, multithreading, and non-coherent on-chip memory for Deep Learning oriented FPGA-based accelerator designs. Experimental results on a Xilinx Virtex-7 FPGA show that the SIMD paradigm and multithreading can lead to an improvement in the execution time up to $5{\times }$and $3 . 5{\times }$, respectively. A further enhancement up to $1 . 75{\times }$can be obtained using a non-coherent on-chip memory.
Temporal correlations in social multiplex networks

Social interactions are composite, involve different communication layers and evolve in time. However, a rigorous analysis of the whole complexity of social networks has been hindered so far by lack of suitable data. Here we consider both the multi-layer and dynamic nature of social relations by analysing a diverse set of empirical temporal multiplex networks. We focus on the measurement and characterization of inter-layer correlations to investigate how activity in one layer affects social acts in another layer. We define observables able to detect when genuine correlations are present in empirical data, and single out spurious correlation induced by the bursty nature of human dynamics. We show that such temporal correlations do exist in social interactions where they act to depress the tendency to concentrate long stretches of activity on the same layer and imply some amount of potential predictability in the connection patterns between layers. Our work sets up a general framework to measure temporal correlations in multiplex networks, and we anticipate that it will be of interest to researchers in a broad array of fields.
Current-Source-Mode Single-Inductor Multiple-Output LED Driver With Single Closed-Loop Control Achieving Independent Dimming Function

Single-inductor multiple-output (SIMO) light-emitting diode (LED) drivers have the advantages of being compact, efficient, and low cost. However, the voltage-to-current transfer function of each output of the SIMO converter is not independent, with significant cross regulation issues that necessitate the use of complex closed-loop control for achieving independent dimming function. This paper proposes a current-source-mode (CSM) SIMO dc–dc converter from duality principle, which is shown to be more suited for LED driving due to widened control range of the duty cycle and inherently inductor-less LED driving topology. The outputs of the CSM SIMO dc–dc converter are inherently independent, resulting in very simple control requirement involving only one closed-loop controller for providing the constant current feeder, and several simple open-loop controllers for independent dimmable driving of LEDs. Simultaneous voltage step-up and step-down of multiple outputs is an added feature that simplifies the input power source requirement, especially for portable applications. The whole system is simple, reliable, and low cost. Taking the CSM single-inductor dual-output dc–dc converter as an example, we illustrate the circuit operation and establish a small-signal model for facilitating the design of the feedback control. A direct duty-cycle dimming method is described. Experimental tests are presented for verification.
Coverage-Based Heuristics for Selecting Assessment Items from Security Standards: A Core Set Proposal

In the realm of Internet of Things (IoT), information security is a critical issue. Security standards, including their assessment items, are essential instruments in the evaluation of systems security. However, a key question remains open: “Which test cases are most effective for security assessment? ” To create security assessment designs with suitable assessment items, we need to know the security properties and assessment dimensions covered by a standard. We propose an approach for selecting and analyzing security assessment items; its foundations come from a set of assessment heuristics and it aims to increase the coverage of assessment dimensions and security characteristics in assessment designs. The main contribution of this paper is the definition of a core set of security assessment heuristics. We systematize the security assessment process by means of a conceptual formalization of the security assessment area. Our approach can be applied to security standards to select or to prioritize assessment items with respect to 11 security properties and 6 assessment dimensions. The approach is flexible allowing the inclusion of dimensions and properties. Our proposal was applied to a well know security standard (ISO/IEC 27001) and its assessment items were analyzed. The proposal is meant to support: (i) the generation of high-coverage assessment designs, which include security assessment items with assured coverage of the main security characteristics, and (ii) evaluation of security standards with respect to the coverage of security aspects.
Wet-Spun Graphene Sheets as Flexible Heat Spreaders for Efficient Thermal Management

With the shrinkage of chip size and the increase of integration density, chip heat flux increases dramatically. Efficient heat dissipation becomes critical for the performance, reliability and service life of electronics. Therefore, advanced lateral heat spreader materials such as carbon allotropes and their derivatives are highly desirable in modern electronics. Graphene attracts great attention as a lateral heat spreader material due to its unique thermal transfer property (theoretical thermal conductivity ca. 5300 W m-1K-1) and its natural two-dimensional (2D) structure. However, an efficient method to accomplish large scale production and ordered assembly structures of graphene sheets is critical for real application in heat dissipation in electronics. Conventional production methods to produce graphene sheets such as filtration method, solvent evaporation method, chemical vapor deposition, electrostatic spray deposition etc. have some limitations–long production time, high energy consumption and great difficulties in controlling the sheet geometry, for instance. In this contribution, graphene sheets were fabricated by a wet-spinning method of graphene oxide (GO) solution followed by chemical reduction of GO to reduced graphene oxide (rGO). The wet-spinning method was able to produce graphene sheets in a high rate (~1.2m/min) and in different dimensions. Here sheets with different thickness (8µm and 16µm) are demonstrated. A series of characterizations are performed for the produced GO and rGO sheets including their Raman and infrared spectra, X-ray diffraction pattern, scanning microscopic pictures and atomic force microscopic pictures. These data show that the reduction is sufficient and the GO sheets are piled up parallel during the wet-spinning process, which is beneficial for the lateral heat transport. The rGO has an in-plane electrical conductivity ca. 6848.41 S/m and thermal conductivity ca. 1024.86 W m-1K-1. In addition, the film displayed excellent heat dissipation performance when attached on top of a light emitting diode (LED) light strip. The research results indicate our approach is facile and capable of fabricating scalable and controllable heat spreader materials with high performance.
Integrated Serum and Fecal Metabolomics Study of Collagen-Induced Arthritis Rats and the Therapeutic Effects of the Zushima Tablet

The Zushima tablet (ZT) has been used for decades in the clinical treatment of rheumatoid arthritis (RA) in China. However, its therapeutic mechanism is unclear. In this study, we aimed to explore the distinctive metabolic patterns in collagen-induced arthritis (CIA) rats and evaluate the therapeutic effects of ZT on RA using untargeted serum and fecal metabolomics approaches based on gas chromatography coupled with mass spectrometry. Body weight, hind paw swelling, TNF-α and IL-1β levels, arthritis scores, and histopathological parameters were assessed. In the metabolomics study, 31 altered metabolites in the serum and 30 in the feces were identified by comparing the model with the control group using statistical processing. These altered metabolites revealed that the tricarboxylic acid cycle, glycolysis metabolism, fatty acid metabolism, and purine metabolism were disturbed in CIA rats, and most of these altered metabolites including l-isoleucine, l-aspartic acid, pyruvic acid, cholic acid, and hypoxanthine, were rectified by ZT. Furthermore, short-chain fatty acids in feces were quantitatively determined, and the results showed that ZT could regulate the levels of propionate, butyrate, and valerate in CIA rats. Then, gut microbiota were analyzed by 16S rRNA analysis. Our results showed that Firmicutes and Bacteroidetes were the most abundant bacteria in rats. The levels of 19 types of bacteria at the family level were altered in RA rats, and most of them could be regulated by ZT. This study demonstrated that metabolomics analysis is a powerful tool for providing novel insight into RA and for elucidating the potential mechanism of ZT.
Direct Collocation for Dynamic Behaviors With Nonprehensile Contacts: Application to Flipping Burgers

To realize robotic systems in real-world settings, e.g., in restaurants, it will be necessary to achieve dynamic manipulation of nontrivial objects. In this context, this letter discusses methodologies used to realize trajectories in a robotic arm platform, specifically, applied to flipping burgers as an example of nonprehensile object manipulation. Flipping of burgers involves a series of tasks—going to the burger location, scooping, picking up, and flipping. Since the goal is to obtain these trajectories in a reasonably fast manner, we employ direct collocation based multisegmented trajectory optimization. We will first describe the problem setup, and then describe the constraints, decision variables employed, and then, to conclude, we will demonstrate these behaviors in a 6-DOF robot experimentally.
Plausible iris caustics and limbal arc rendering

In this paper, we apply anterior segment tomography measurements from contact lens research to photorealistic eye rendering. We improve on existing analytic rendering models by including a conical extension to the usual ellipsoidal corneal surface and we demonstrate the advantage of using a more accurate iris depth. We also introduce a practical method for automatically rendering the limbal arc as an intrinsic part of sclerotic scattering.
Automatic registration of 3D MR images with a computerized brain atlas

We present an automatic and unsupervised method for non-rigid registration of 3D magnetic resonance (MR) images with the Stockholm Computerized Brain Atlas (CBA). This method can be used in the context of multimodal medical image registration, fusion and automatic brain segmentation. In these applications anatomical images (MR) are coregistered with low spatial resolution functional imaging modalities (PET and SPECT) and fused with the neurological database of the CBA. The proposed matching method is based on the minimization of a 3D Chamfer distance function between the surface of the brain extracted from the MR image and the CBA brain surface. The surface-to-surface distance function is efficiently calculated by using a precomputed point-to-surface Euclidean distance map. The non-rigid inter-patient transformation of the CBA is modeled by a generalized 3D second order transformation. This transformation is easily differentiable and, as a consequence, fast and efficient minimization methods can be used. First, a quasi-rigid, first order transformation is computed. Then, the matching is improved by introducing the second order coefficients into the transformation. After this global matching, a local adaptation of the CBA is performed by a morphing method. The combination of a second order global transformation with a 3D local morphing allows the user to obtain a registration accuracy of one pixel, i.e. a mean distance between the surface of the brain in the MR image and the CBA of one pixel, which is significantly better than what can be expected from a human operator.
Elevated CO2 and Warming Altered Grassland Microbial Communities in Soil Top-Layers

As two central issues of global climate change, the continuous increase of both atmospheric CO2 concentrations and global temperature has profound effects on various terrestrial ecosystems. Microbial communities play pivotal roles in these ecosystems by responding to environmental changes through regulation of soil biogeochemical processes. However, little is known about the effect of elevated CO2 (eCO2) and global warming on soil microbial communities, especially in semiarid zones. We used a functional gene array (GeoChip 3.0) to measure the functional gene composition, structure, and metabolic potential of soil microbial communities under warming, eCO2, and eCO2 + warming conditions in a semiarid grassland. The results showed that the composition and structure of microbial communities was dramatically altered by multiple climate factors, including elevated CO2 and increased temperature. Key functional genes, those involved in carbon (C) degradation and fixation, methane metabolism, nitrogen (N) fixation, denitrification and N mineralization, were all stimulated under eCO2, while those genes involved in denitrification and ammonification were inhibited under warming alone. The interaction effects of eCO2 and warming on soil functional processes were similar to eCO2 alone, whereas some genes involved in recalcitrant C degradation showed no significant changes. In addition, canonical correspondence analysis and Mantel test results suggested that NO3-N and moisture significantly correlated with variations in microbial functional genes. Overall, this study revealed the possible feedback of soil microbial communities to multiple climate change factors by the suppression of N cycling under warming, and enhancement of C and N cycling processes under either eCO2 alone or in interaction with warming. These findings may enhance our understanding of semiarid grassland ecosystem responses to integrated factors of global climate change.
Anand Parameters for Modeling Prolonged Storage on High Strain Rate Mechanical Properties of SAC-Q Leadfree Solder at High Operating Temperature

Electronics in automotive underhood and downhole drilling applications may be subjected to sustained operation at high temperature in addition to high strain-rate loads. SAC solders used for second level interconnects have been shown to experience degradation in high strain-rate mechanical properties under sustained exposure to high temperatures. Industry search for solutions for resisting the high-temperature degradation of SAC solders has focused on the addition of dopants to the alloy. In this study, a doped SAC solder called SAC-Q solder have been studied. The high strain rate mechanical properties of SAC-Q solder have been studied under elevated temperatures up to 200°C. Samples with thermal aging at 50°C for up to 6-months have been used for measurements in uniaxial tensile tests. Measurements for SAC-Q have been compared to SAC105 and SAC305 for identical test conditions and sample geometry. Data from the SAC-Q measurements has been fit to the Anand Viscoplasticity model. In order to assess the predictive power of the model, the computed Anand parameters have been used to simulate the uniaxial tensile test and the model predictions compared with experimental data. Model predictions show good correlation with experimental measurements. The presented approach extends the Anand Model to include thermal aging effects.
Experimental and theoretical study on structure and spectroscopic properties of 2-bromo-3-N-(N',N'-dimethylformamidino) benzanthrone.

The goal of present research is a theoretical and experimental investigation of geometrical structure, electronic properties, absorption and fluorescence spectra prediction for 2-bromo-3-N-(N',N'-dimethylformamidino)benzanthrone. As a result of conformational analysis, two rotamers have been found with a rotational barrier of 5.45 kcal/mol. Absorption and fluorescence spectra maxima in the solvent (ethanol) have been calculated using the concepts of the Jablonsky diagram. The obtained values of the absorption and fluorescence maxima (437 and 679 nm, respectively) correspond to the experimental values (447 and 659 nm). The abnormally large Stokes shift is associated with the redistribution of electron density, as well as flattening of the structure of the molecule in the excited state. According to the frontal molecular orbital analysis data, the peak in the long-wave part of the absorption spectra is created by an electron transition from the highest occupied molecular orbital (HOMO) to the lowest occupied molecular orbital (LUMO) (π→π*). Substitute group does not participate in the formation of absorption and fluorescence spectra.
Quick Reconstruction of Arbitrary Pipeline Defect Profiles From MFL Measurements Employing Modified Harmony Search Algorithm

This paper proposes a quick and accurate method for reconstructing defect profiles in pipelines from magnetic flux leakage (MFL) signals employing a modified harmony search (MHS) algorithm. To improve the quality of the initial harmony memory, a multiple selection opposition-based learning strategy is presented in the MHS algorithm. Moreover, the pitch adjusting rate is set to increase linearly, and the distance bandwidth (bw) is designed to be dynamically adjusted to shorten the calculation time. A specially designed modified sum-squared error with weights is proposed and employed as the fitness function to improve the fitting degree between the reference and predicted signals in the defect response segment. The weights are determined by the characteristics of reference signals. We demonstrate the adequacy of the performance of the proposed model using both simulated and real MFL signals. We also present comparative results of the proposed model and other methods. The results prove that the proposed method can achieve higher efficiency while maintaining excellent accuracy.
A CMOS gate driver with ultra-fast dV/dt embedded control dedicated to optimum EMI and turn-on losses management for GaN power transistors

In this paper, a CMOS gate driver in $180\mathrm {n}\mathrm {m}$ technology is presented. The gate driver implements an integrated and independent ultra-fast $\mathrm {d}\mathrm {V}/\mathrm {d}\mathrm {t}$ control circuit dedicated to manage switch-on transients for $\mathrm {G}\mathrm {a}\mathrm {N}$ HEMT technology. In order to mitigate a detrimental effect in EMI spectrum for wide bandgap transistors, a novel method to reduce $\mathrm {d}\mathrm {V}/\mathrm {d}\mathrm {t}$ without increasing so much switching losses is proposed. A comprehensive benchmark with the classical method is also presented, where the gate driver resistance is typically adjusted. Simulations are conducted to show the feasibility of the proposed method and the amount of switching energy that can be saved. Time responses of a feedback loop lower than $200\mathrm {p}\mathrm {s}$ are expected. The preliminary characterization of the integrated CMOS circuit is shown.
Symbolic Execution for JavaScript

We present a framework for trustworthy symbolic execution of JavaScripts programs, whose aim is to assist developers in the testing of their code: the developer writes symbolic tests for which the framework provides concrete counter-models. We create the framework following a new, general methodology for designing compositional program analyses for dynamic languages. We prove that the underlying symbolic execution is sound and does not generate false positives. We establish additional trust by using the theory to precisely guide the implementation and by thorough testing. We apply our framework to whole-program symbolic testing of real-world JavaScript libraries and compositional debugging of separation logic specifications of JavaScript programs.
Loop‐mediated isothermal amplification for the rapid detection of Gardnerella vaginalis

The aim of this study was to develop a method for the rapid detection of Gardnerella vaginalis, which is proposed to play a key role in the pathogenesis of bacterial vaginosis. Specific loop‐mediated isothermal amplification (LAMP) primers were designed and used to detect target DNA within 45 min under isothermal conditions. Comparative screening indicated that the LAMP assay is superior to PCR in terms of rapidity, and is equivalent in sensitivity and specificity. This LAMP assay can be used for rapid screening and detection of G. vaginalis in vaginal samples; the limit of detection is 10 fg DNA.
Model Reduction with Memory and the Machine Learning of Dynamical Systems

The well-known Mori-Zwanzig theory tells us that model reduction leads to memory effect. For a long time, modeling the memory effect accurately and efficiently has been an important but nearly impossible task in developing a good reduced model. In this work, we explore a natural analogy between recurrent neural networks and the Mori-Zwanzig formalism to establish a systematic approach for developing reduced models with memory. Two training models-a direct training model and a dynamically coupled training model-are proposed and compared. We apply these methods to the Kuramoto-Sivashinsky equation and the Navier-Stokes equation. Numerical experiments show that the proposed method can produce reduced model with good performance on both short-term prediction and long-term statistical properties.
Cognitive reserve as an outcome predictor: first‐episode affective versus non‐affective psychosis

Cognitive reserve (CR) refers to the brain's capacity to cope with pathology in order to minimize the symptoms. CR is associated with different outcomes in severe mental illness. This study aimed to analyze the impact of CR according to the diagnosis of first‐episode affective or non‐affective psychosis (FEP).
IEEE Transactions on Instrumentation and Measurement

The ubiquitousness of location based services (LBS) has proven effective for many applications such as commercial, military, and emergency responders. Software-defined radio (SDR) has emerged as an adequate framework for development and testing of global navigational satellite systems (GNSS) such as the Global Position System (GPS). SDR receivers are constantly developing in terms of acceleration factors and accurate algorithms for precise user navigation. However, many SDR options for GPS receivers currently lack real-time operation or could be costly. This paper presents a LabVIEW (LV) and C/C++ based GPS L1 receiver platform with real-time capabilities. The system relies on LV acceleration factors as well as other C/C++ techniques such as dynamic link library (DLL) integration into LV and parallelizable loop structures, and single input multiple data (SIMD) methods which leverage host PC multi-purpose processors. A hardware testbed is presented for compactness and mobility, as well as software functionality and data flow handling inherent in LV environment. Benchmarks and other real-time results are presented as well as compared against other state-ofthe-art open-source GPS receivers.