Miracle-dz/UniFAR-FTUs huggingface dataset:
The relativistic Scott correction for atoms and molecules

We prove the first correction to the leading Thomas-Fermi energy for the ground state energy of atoms and molecules in a model where the kinetic energy of the electrons is treated relativistically. The leading Thomas-Fermi energy, established in [Sorensen], as well as the correction given here are of semi-classical nature. Our result on atoms and molecules is proved from a general semi-classical estimate for relativistic operators with potentials with Coulomb-like singularities. This semi-classical estimate is obtained using the coherent state calculus introduced in [Solovej-Spitzer]. The paper contains a unified treatment of the relativistic as well as the non-relativistic case.
Caveolin-1 Protects B6129 Mice against Helicobacter pylori Gastritis

Caveolin-1 (Cav1) is a scaffold protein and pathogen receptor in the mucosa of the gastrointestinal tract. Chronic infection of gastric epithelial cells by Helicobacter pylori (H. pylori) is a major risk factor for human gastric cancer (GC) where Cav1 is frequently down-regulated. However, the function of Cav1 in H. pylori infection and pathogenesis of GC remained unknown. We show here that Cav1-deficient mice, infected for 11 months with the CagA-delivery deficient H. pylori strain SS1, developed more severe gastritis and tissue damage, including loss of parietal cells and foveolar hyperplasia, and displayed lower colonisation of the gastric mucosa than wild-type B6129 littermates. Cav1-null mice showed enhanced infiltration of macrophages and B-cells and secretion of chemokines (RANTES) but had reduced levels of CD25+ regulatory T-cells. Cav1-deficient human GC cells (AGS), infected with the CagA-delivery proficient H. pylori strain G27, were more sensitive to CagA-related cytoskeletal stress morphologies (“humming bird”) compared to AGS cells stably transfected with Cav1 (AGS/Cav1). Infection of AGS/Cav1 cells triggered the recruitment of p120 RhoGTPase-activating protein/deleted in liver cancer-1 (p120RhoGAP/DLC1) to Cav1 and counteracted CagA-induced cytoskeletal rearrangements. In human GC cell lines (MKN45, N87) and mouse stomach tissue, H. pylori down-regulated endogenous expression of Cav1 independently of CagA. Mechanistically, H. pylori activated sterol-responsive element-binding protein-1 (SREBP1) to repress transcription of the human Cav1 gene from sterol-responsive elements (SREs) in the proximal Cav1 promoter. These data suggested a protective role of Cav1 against H. pylori-induced inflammation and tissue damage. We propose that H. pylori exploits down-regulation of Cav1 to subvert the host's immune response and to promote signalling of its virulence factors in host cells.
Distance in spatial interpolation of daily rain gauge data

Abstract. Spatial interpolation of rain gauge data is important in forcing of hydrological simulations or evaluation of weather predictions, for example. This paper investigates the application of statistical distance, like one minus common variance of observation time series, between data sites instead of geographical distance in interpolation. Here, as a typical representative of interpolation methods the inverse distance weighting interpolation is applied and the test data is daily precipitation observed in Austria. Choosing statistical distance instead of geographical distance in interpolation of available coarse network observations to sites of a denser network, which is not reporting for the interpolation date, yields more robust interpolation results. The most distinct performance enhancement is in or close to mountainous terrain. Therefore, application of statistical distance in the inverse distance weighting interpolation or in similar methods can parsimoniously densify the currently available observation network. Additionally, the success further motivates search for conceptual rain-orography interaction models as components of spatial rain interpolation algorithms in mountainous terrain.
Enhanced Ca2 Release and Na/Ca Exchange Activity in Hypertrophied Canine Ventricular Myocytes: Potential Link Between Contractile Adaptation and Arrhythmogenesis

BackgroundVentricular arrhythmias are a major cause of sudden death in patients with heart failure and hypertrophy. The dog with chronic complete atrioventricular block (CAVB) has biventricular hypertrophy and ventricular arrhythmias and is a useful model to study underlying cellular mechanisms. We investigated whether changes in Ca2+ homeostasis are part of the contractile adaptation to CAVB and might contribute to arrhythmogenesis. Methods and ResultsIn enzymatically isolated myocytes, cell shortening, Ca2+ release from the sarcoplasmic reticulum (SR), and SR Ca2+ content were enhanced at low stimulation frequencies. Ca2+ influx through L-type Ca2+ channels was unchanged, but Ca2+ influx via the Na/Ca exchanger was increased and contributed to Ca2+ loading of the SR. Inward Na/Ca exchange currents were also larger. Changes in Ca2+ fluxes were less pronounced in the right versus left ventricle. ConclusionsEnhanced Na/Ca exchange activity may improve contractile adaptation to CAVB but at the same time facilitate arrhythmias by (1) increasing the propensity to Ca2+ overload, (2) providing more inward current leading to (nonhomogeneous) action potential prolongation, and (3) enhancing (arrhythmogenic) currents during spontaneous Ca2+ release.
Advanced system on a chip design based on controllable-polarity FETs

Field-Effect Transistors (FETs) with on-line controllable-polarity are promising candidates to support next generation System-on-Chip (SoC). Thanks to their enhanced functionality, controllable-polarity FETs enable a superior design of critical components in a SoC, such as processing units and memories, while also providing native solutions to control power consumption. In this paper, we present the efficient design of a SoC core with controllable-polarity FET. Processing units are speeded-up at the datapath level, as arithmetic operations require fewer physical resources than in standard CMOS. Power consumption is decreased via embedded power-gating techniques and tunable high-performance/low-power devices operation. Memory cells are made smaller by merging the access interface with the storage circuitry. We foresee the advantages deriving from these techniques, by evaluating their impact on the design of SoC for a contemporary telecommunication application. Using a 22-nm vertically-stacked silicon nanowire technology, a coarse-grain evaluation at the block level estimates a delay and power reduction of 20% and 19% respectively, at a cost of a moderate area overhead of 15%, with respect to a state-of-art FinFET technology.
Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm

This paper presents an algorithm for automatically computing the planar patterns of custom-made assistive medical braces, which are employed to restrict the motion of the joints (such as wrist and knee) that suffer from musculoskeletal disorders caused by repetitive strain injuries. An elastic brace is manufactured by warping a planar elastic fabric pattern. With a specified material, different shapes of planar patterns for producing a brace will generate different biomechanical effects on the joint. As an assistive medical device, an elastic brace is often requested to provide certain normal pressures at certain specific locations on the joint. Traditionally the planar pattern of a brace respecting the prescribed normal pressure requirement is designed through empirical tests by trial-and-error. We develop a woven fitting based method in this paper to automate this geometric design process.
Finding Recurrent Copy Number Alteration Regions: A Review of Methods

Copy number alterations (CNA) in genomic DNA are linked to a variety of human diseases. Although many methods have been developed to analyze data from a single subject, disease-critical genes are more likely to be found in regions that are common or recurrent among diseased subjects. Unfortunately, finding recurrent CNA regions remains a challenge. We review existing methods for the identification of recurrent CNA regions. Methods differ in their working definition of " recurrent region " , the type of input data, the statistical and computational methods used to identify recurrence , and the biological considerations they incorporate (which play a role in the identification of " interesting " regions and in the details of null models used to assess statistical significance). Very few approaches use and/or return probabilities , and code is not easily available for several methods. We emphasize that, when analyzing data from complex diseases with significant among-subject heterogeneity, methods should be able to identify CNAs that affect only a subset of subjects. We suggest that finding recurrent CNAs would benefit from clearly specifying the types of pattern to be detected and the intended usage of the regions found (CNA association with disease, CNA effects on gene expression, clustering of subjects). We finish with suggestions for further methodological research.
3D-Model Retrieval Using Bag-of-Features Based on Closed Curves

Bag-of-feature technique is a popular approach in areas of computer vision and pattern recognition. Recently, it plays an important role in shape analysis community and especially in 3D-model retrieval. We present our approach for partial 3D-model retrieval using this technique based on closed curves. We define an invariant scalar function on the surface based on the commute-time distance. Our mapping function respects important properties in order to compute robust closed curves. Each scale of our scalar function detects a small region. The form of these regions are encoded in the form of the closed curves. We generate a collection of closed curves from a source point detected automatically. Based on the collection of all closed curves extracted, we construct our bag-of-features. Then we cluster the bag-of-features in the sense in accurate categorization. The centres of classes are defined as keyshapes. This method is particularly interesting in the sense of quantifying the 3D-model by its keyshapes that are accumulated into an histogram. The results shows the robustness of our method (BOF) compared to a method based on indexed closed curves (ICC) on various 3D-models with different poses.
Action-Oriented Population Nutrition Research: High Demand but Limited Supply

Action-oriented research in nutrition, vital to guiding effective policies and programs at scale, is greatly underrepresented in public health journals and, even more so, in nutrition journals. Action-oriented research in nutrition, vital to guiding effective policies and programs at scale, is greatly underrepresented in public health journals and, even more so, in nutrition journals. Background: The relatively rapid ascendancy of nutrition and health on policy agendas, along with greater emphasis on accountability and results, has stimulated interest in new forms of research to guide the development and implementation of effective policies, programs, and interventions—what we refer to as action-oriented research. To date, action-oriented research in the nutrition field is thought to be the exception rather than the rule, but empirical evidence to support this claim is lacking. Methods: We conducted a survey of selected journals in nutrition and public health to assess the extent and nature of population nutrition research published in 2012 that embodied 5 defined characteristics of action-oriented research in relation to: (1) topic(s) of study, (2) processes/influences, (3) actors, (4) methods, and (5) approaches. We identified 762 articles from the 6 selected nutrition journals and 77 nutrition-related articles from the 4 selected public health journals that met our search criteria. Results: Only 7% of the 762 papers in nutrition journals had at least 1 of the 5 action-oriented research characteristics, compared with 36% of the 77 nutrition-related papers in the public health journals. Of all 80 articles that had at least 1 action-oriented research characteristic, only 5 articles (6.25%) embodied all 5 characteristics. Articles with action-oriented research covered a broad range of topics and processes/influences, including policy, workforce development, and schools, as well as actors, such as program staff, store owners, parents, and school staff. In addition, various research methods were used, such as stakeholder analysis, ethnographic narrative, iterative action research, and decision tree modeling, as well as different approaches, including participant-observer and community-based participatory research. Conclusions: Action-oriented research represents a small fraction of articles published in nutrition journals, especially compared with public health journals. This reinforces recent calls to expand population nutrition research agendas to more effectively inform and guide the initiation, development, implementation, and governance of policies, programs, and interventions to address the varied forms of nutrition-related problems. With heightened attention to the magnitude and importance of nutrition problems worldwide, there are substantial reasons and opportunities to incentivize and support such expansion.
Occurrence of dsRNA Mycovirus (LeV-FMRI0339) in the Edible Mushroom Lentinula edodes and Meiotic Stability of LeV-FMRI0339 among Monokaryotic Progeny

dsRNA was found in malformed cultures of Lentinula edodes strain FMRI0339, one of the three most popular sawdust cultivated commercial strains of shiitake, and was also found in healthy-looking fruiting bodies and actively growing mycelia. Cloning of the partial genome of the dsRNA revealed the presence of the RdRp sequence of a novel L. edodes mycovirus (LeV), and sequence comparison of the cloned amplicon showed identical sequences sequence to known RNA-dependent RNA polymerase genes of LeV found in strain HKA. The meiotic stability of dsRNA was examined by measuring the ratio of the presence of dsRNA among sexual monokaryotic progeny. More than 40% of the monokaryotic progeny still contained the dsRNA, indicating the persistence of dsRNA during sexual reproduction. Comparing the mycelia growth of monokaryotic progeny suggested that there appeared to be a tendency toward a lower frequency of virus incidence in actively growing progeny.
A ROTTERDAM APPLICATION TO INTERNATIONAL TRADE IN FRESH APPLES: A DIFFERENTIAL APPROACH

A Rotterdam import allocation model is used to fit import data for fresh apples in four importing markets important to U.S. apple exporters. Nested tests rejected homotheticity but could not reject homogeneity, symmetry, or separability among import suppliers. A Monte Carlo test rejected first-order autocorrelation in each market. Expenditure and price elasticities are calculated and reported.
Intrusion Detection Based on Device-Free Localization in the Era of IoT

Device-free localization (DFL) locates targets without being equipped with the attached devices, which is of great significance for intrusion detection or monitoring in the era of the Internet-of-Things (IoT). Aiming at solving the problems of low accuracy and low robustness in DFL approaches, in this paper, we first treat the RSS signal as an RSS-image matrix and conduct a process of eliminating the background to dig out the variation component with distinguished features. Then, we make use of these feature-rich images by formulating DFL as an image classification problem. Furthermore, a deep convolutional neural network (CNN) is designed to extract features automatically for classification. The localization performance of the proposed background elimination-based CNN (BE-CNN) scheme is validated with a real-world dataset of outdoor DFL. In addition, we also validate the robust performance of the proposal by conducting numerical experiments with different levels of noise. Experimental results demonstrate that the proposed scheme has an obvious advantage in terms of improving localization accuracy and robustness for DFL. Particularly, the BE-CNN can maintain the highest localization accuracy of 100%, even in noisy conditions when the SNR is over −5 dB. The BE-based methods can outperform all the corresponding raw data-based methods in terms of the localization accuracy. In addition, the proposed method can outperform the comparison methods, deep neural network with autoencoder, K-nearest-neighbor (KNN), support vector machines (SVM), etc., in terms of the localization accuracy and robustness.
MASA+: An Improved MAC Scheme Based on MASA Algorithm

Multiple Access with Salvation Army Protocol (MASA) achieves the higher aggregate throughput than IEEE802.11DCF via packet salvaging at the MAC layer. The implementation of MASA is based on DCF specification. But the back off timer of SACK in MASA is different from that of DATA in DCF. The MASA selects (0, NAV) as the back off range of SACK, whereas DCF thinks the channel is busy in this duration. In Ad Hoc network, DCF is the most widely used MAC protocol, to the full extent of keeping compatible with DCF and retaining the merit of MASA, this paper proposes an Improved MAC scheme called MASA+. The simulation based on NS-2 shows that new scheme outperforms DCF in aggregate throughput.
A spatially explicit reinforcement learning model for geographic knowledge graph summarization

Web‐scale knowledge graphs such as the global Linked Data cloud consist of billions of individual statements about millions of entities. In recent years, this has fueled the interest in knowledge graph summarization techniques that compute representative subgraphs for a given collection of nodes. In addition, many of the most densely connected entities in knowledge graphs are places and regions, often characterized by thousands of incoming and outgoing relationships to other places, actors, events, and objects. In this article, we propose a novel summarization method that incorporates spatially explicit components into a reinforcement learning framework in order to help summarize geographic knowledge graphs, a topic that has not been considered in previous work. Our model considers the intrinsic graph structure as well as the extrinsic information to gain a more comprehensive and holistic view of the summarization task. By collecting a standard data set and evaluating our proposed models, we demonstrate that the spatially explicit model yields better results than non‐spatial models, thereby demonstrating that spatial is indeed special as far as summarization is concerned.
The novel reconstruction strategy for signals with uncertain dynamic properties

For the reconstruction of signals based on a sequence of observations governed by uncertain dynamic properties, we propose a new method by using Taylor's formula and tracking differentiators. The method can be summarized as follows: Firstly, for a sequence of observations with uncertain dynamic properties, and/or the measurement contains noise, we use tracking-differentiators to smooth the observation results or filter the noise, and obtain the estimation of their differential values. Secondly, for the reconstruction of the signal nearby the given points, we use Taylor's formula to construct the values. Theoretical estimation is provided to guarantee the precision of the relevant reconstruction results. Comparative analysis is also provided to show the advantages of the method. And simulation results for reconstructing certain kinds of observations indicate the effectiveness of the method.
A hysteretic buck converter with fast transient response

A current-mode hysteretic buck converter is described in which inductor current is sensed by an RC network. The inductor current sensing RC network is reset multiple times per switching clock period. The multiple reset allows the RC network to have time constant much smaller than switching clock period and therefore occupy small silicon area. The multiple reset also removes DC-output offset which is dependent on load current and improves the load transient property. The current-mode hysteretic buck converter is implemented in a 65-nm CMOS process and the converter operates in 0.6~2.0-V output from 3.3-V input with 1.5-A maximum load current. The recovery time for step-up load transient is 3.4-μs under 0.9-A/0.1-μs load change and the measured peak power efficiency is 96.3-%.
A precision-improved processing architecture of physical computing for energy-efficient SIFT feature extraction

A precision-improved processing architecture of physical computing for energy-efficient SIFT feature extraction algorithm has been proposed in this paper. With the novel physical computing technology of active resistor network (PC: ARN), the SIFT algorithm could be processed in analog signal domain without synchronizing clock signals, which means the complex algorithm could be completed within the setup time of the circuit. Especially for the multi-scale Gaussian convolution of SIFT algorithm, an architecture of two-layer 1-dimension PC: ARN has been adopted to compute the horizontal and vertical 1-dimension gaussian filter, in which way higher accuracy can be obtained when compared with the results processed by a 2D active circuit network. A circuit-level simulation with 65nm CMOS technology has been carried out, which shows the energy consumption of gaussian pyramid multi-scale-filtering hardware architecture is about 25.3pJ, where the size of input frame is assigned as 256×256 pixels. Additionally, the average matching ratio in different image pairs is around 80%. Moreover, integrated into the dominating CMOS image sensor with column-parallel readout technology of analog-to-digital convertor, about 20× speedup can be achieved comparing with previous implementations with FPGA, GPU, etc.
A data locality-aware design framework for reconfigurable sparse matrix-vector multiplication kernel

Sparse matrix-vector multiplication (SpMV) is an important computational kernel in many applications. For performance improvement, software libraries designated for SpMV computation have been introduced, e.g., MKL library for CPUs and cuSPARSE library for GPUs. However, the computational throughput of these libraries is far below the peak floating-point performance offered by hardware platforms, because the efficiency of SpMV kernel is greatly constrained by the limited memory bandwidth and irregular data access patterns. In this work, we propose a data locality-aware design framework for FPGA-based SpMV acceleration. We first include the hardware constraints in sparse matrix compression at software level to regularize the memory allocation and accesses. Moreover, a distributed architecture composed of processing elements is developed to improve the computation parallelism. We implement the reconfigurable SpMV kernel on Convey HC-2ex and conduct the evaluation by using the University of Florida sparse matrix collection. The experiments demonstrate an average computational efficiency of 48.2%, which is a lot better than those of CPU and GPU implementations. Our FPGA-based kernel has a comparable runtime as GPU, and achieves 2.1× reduction than CPU. Moreover, our design obtains substantial saving in energy consumption, say, 9.3× and 5.6× better than the implementations on CPU and GPU, respectively.