Verification of Identity and Syntax Check of Verilog and LEF Files

The Verilog and LEF files are units of the digital design flow [1][2]. They are being developed in different stages. Before the development of the LEF file, the Verilog file passes through numerous steps during which partial losses of information are possible. The identity check allows to make sure that during the flow the information has not been lost. The syntax accuracy of the Verilog and LEF files is checked as well. nbspnbspnbspnbspnbspnbspnbspnbspnbspnbspnbsp The scripting language Perl is selected for the program. The language is flexible to work with text files [3]. nbspnbspnbspnbspnbspnbspnbspnbspnbspnbspnbsp The method developed in the present paper is substantial as the application of integrated circuits today is actual in different scientific, technical and many other spheres which gradually finds wider application bringing about large demand

Regulatory feedback on receptor and non-receptor synthesis for robust signaling.

Elaborate regulatory feedback processes are thought to make biological development robust, that is, resistant to changes induced by genetic or environmental perturbations. How this might be done is still not completely understood. Previous numerical simulations on reaction-diffusion models of Dpp gradients in Drosophila wing imaginal disc have showed that feedback (of the Hill function type) on (signaling) receptors and/or non-(signaling) receptors are of limited effectiveness in promoting robustness. Spatial nonuniformity of the feedback processes has also been shown theoretically to lead to serious shape distortion and a principal cause for ineffectiveness. Through mathematical modeling and analysis, the present article shows that spatially uniform nonlocal feedback mechanisms typically modify gradient shape through a shape parameter (that does not change with location). This in turn enables us to uncover new multi-feedback instrument for effective promotion of robust signaling gradients

Analysis of Cosequences of Faults in General Zero Transmission Lines of Power Supply Stations

Zero transmission line faults in power supply systems in large apartment houses, office blocks, offices and other structures cause voltage excursions,which are in the spotlight of this research. The phenomenon has been commented from the theoretical perspective, and emergency situations, which are likely to arise as a result ofmalfunction of single-phase power supply consumers, as well as the probable dangers of fire occurrences, have been revealed. We offer to install an appropriateprotective device to avoid such emergency situations

Investigating Grain Separation and Cleaning Efficiency Distribution of a Conventional Stationary Rasp-bar Sorghum Thresher

A stationary grain thresher was developed and used to study grain separation and cleaning efficiency distribution of the cleaning unit, fractionated by sieve and horizontal air stream, along the sieve length. The influence of feed rate, m, air speed, VA and sieve oscillation frequency, FS on cleaning efficiency of sorghum was explored. Grain separation along the sieve can be divided into three sections: increasing, peak and decreasing sections. Results showed that cleaning efficiency decreased with increasing sieve oscillations frequency and feed rate respectively. Cleaning loss increased with increasing sieve oscillation frequency, feed rate and air speed

Receptive Field Block Net for Accurate and Fast Object Detection

Current top-performing object detectors depend on deep CNN backbones, such as ResNet-101 and Inception, benefiting from their powerful feature representations but suffering from high computational costs. Conversely, some lightweight model based detectors fulfil real time processing, while their accuracies are often criticized. In this paper, we explore an alternative to build a fast and accurate detector by strengthening lightweight features using a hand-crafted mechanism. Inspired by the structure of Receptive Fields (RFs) in human visual systems, we propose a novel RF Block (RFB) module, which takes the relationship between the size and eccentricity of RFs into account, to enhance the feature discriminability and robustness. We further assemble RFB to the top of SSD, constructing the RFB Net detector. To evaluate its effectiveness, experiments are conducted on two major benchmarks and the results show that RFB Net is able to reach the performance of advanced very deep detectors while keeping the real-time speed. Code is available at https://github.com/ruinmessi/RFBNet.Comment: Accepted by ECCV 201

Determinants of physicians’ intention to collect data exhaustively in registries: an exploratory study in Bamako’s community health centres

Background: The incomplete collection of health datais a prevalent problem in healthcare systems around theworld, especially in developing countries. Missing datahinders progress in population health and perpetuatesinefficiencies in healthcare systems.Objective: This study aims to identify the factors that predict the intention of physicians practicing in community health centres of Bamako, Mali, to collect data exhaustively in medical registries.Design: A cross sectional studyMethod: In January and February 2011, we conducted a study with a random sample of thirty two physicians practicing in community health centres of Bamako, using a questionnaire. Data was analyzed by using descriptive statistics, correlations and linear regression.Main outcomes measures: Trained investigators administered a questionnaire measuring physicians’ sociodemographic and professional characteristics as well as constructs from the Theory of Planned Behaviour.Results: Our results showed that physicians’ intention to collect data exhaustively is influenced by subjective norms and by the physician’s number of years in practice.Conclusions: the results of this study could be used as a guide for health workers and decision makers to improve the quality of health information collected in community health centers.Keywords: Physicians’ intention, exhaustive data collection, Bamako, Community Health Centre, Missing dat

Single Shot Temporal Action Detection

Temporal action detection is a very important yet challenging problem, since videos in real applications are usually long, untrimmed and contain multiple action instances. This problem requires not only recognizing action categories but also detecting start time and end time of each action instance. Many state-of-the-art methods adopt the "detection by classification" framework: first do proposal, and then classify proposals. The main drawback of this framework is that the boundaries of action instance proposals have been fixed during the classification step. To address this issue, we propose a novel Single Shot Action Detector (SSAD) network based on 1D temporal convolutional layers to skip the proposal generation step via directly detecting action instances in untrimmed video. On pursuit of designing a particular SSAD network that can work effectively for temporal action detection, we empirically search for the best network architecture of SSAD due to lacking existing models that can be directly adopted. Moreover, we investigate into input feature types and fusion strategies to further improve detection accuracy. We conduct extensive experiments on two challenging datasets: THUMOS 2014 and MEXaction2. When setting Intersection-over-Union threshold to 0.5 during evaluation, SSAD significantly outperforms other state-of-the-art systems by increasing mAP from 19.0% to 24.6% on THUMOS 2014 and from 7.4% to 11.0% on MEXaction2.Comment: ACM Multimedia 201

Fermionic currents in topologically nontrivial braneworlds

We investigate the influence of a brane on the vacuum expectation value (VEV) of the current density for a charged fermionic field in background of locally AdS spacetime with an arbitrary number of toroidally compact dimensions and in the presence of a constant gauge field. Along compact dimensions the field operator obeys quasiperiodicity conditions with arbitrary phases and on the brane it is constrained by the bag boundary condition. The VEVs for the charge density and the components of the current density along uncompact dimensions vanish. The components along compact dimensions are decomposed into the brane-free and brane-induced contributions. The behavior of the latter in various asymptotic regions of the parameters is investigated. It particular, it is shown that the brane-induced contribution is mainly located near the brane and vanishes on the AdS boundary and on the horizon. An important feature is the finiteness of the current density on the brane. Applications are given to $Z_2$-symmetric braneworlds of the Randall-Sundrum type with compact dimensions for two classes of boundary conditions on the fermionic field. In the special case of three-dimensional spacetime, the corresponding results are applied for the investigation of the edge effects on the ground state current density induced in curved graphene tubes by an enclosed magnetic flux.Comment: 32 pages, 9 figures, PACS numbers: 04.62.+v, 03.70.+k, 98.80.-k, 61.46.F

The age of data-driven proteomics : how machine learning enables novel workflows

A lot of energy in the field of proteomics is dedicated to the application of challenging experimental workflows, which include metaproteomics, proteogenomics, data independent acquisition (DIA), non-specific proteolysis, immunopeptidomics, and open modification searches. These workflows are all challenging because of ambiguity in the identification stage; they either expand the search space and thus increase the ambiguity of identifications, or, in the case of DIA, they generate data that is inherently more ambiguous. In this context, machine learning-based predictive models are now generating considerable excitement in the field of proteomics because these predictive models hold great potential to drastically reduce the ambiguity in the identification process of the above-mentioned workflows. Indeed, the field has already produced classical machine learning and deep learning models to predict almost every aspect of a liquid chromatography-mass spectrometry (LC-MS) experiment. Yet despite all the excitement, thorough integration of predictive models in these challenging LC-MS workflows is still limited, and further improvements to the modeling and validation procedures can still be made. In this viewpoint we therefore point out highly promising recent machine learning developments in proteomics, alongside some of the remaining challenges

A framework for application of metabolic modeling in yeast to predict the effects of nsSNV in human orthologs

Background We have previously suggested a method for proteome wide analysis of variation at functional residues wherein we identified the set of all human genes with nonsynonymous single nucleotide variation (nsSNV) in the active site residue of the corresponding proteins. 34 of these proteins were shown to have a 1:1:1 enzyme:pathway:reaction relationship, making these proteins ideal candidates for laboratory validation through creation and observation of specific yeast active site knock-outs and downstream targeted metabolomics experiments. Here we present the next step in the workflow toward using yeast metabolic modeling to predict human metabolic behavior resulting from nsSNV. Results For the previously identified candidate proteins, we used the reciprocal best BLAST hits method followed by manual alignment and pathway comparison to identify 6 human proteins with yeast orthologs which were suitable for flux balance analysis (FBA). 5 of these proteins are known to be associated with diseases, including ribose 5-phosphate isomerase deficiency, myopathy with lactic acidosis and sideroblastic anaemia, anemia due to disorders of glutathione metabolism, and two porphyrias, and we suspect the sixth enzyme to have disease associations which are not yet classified or understood based on the work described herein. Conclusions Preliminary findings using the Yeast 7.0 FBA model show lack of growth for only one enzyme, but augmentation of the Yeast 7.0 biomass function to better simulate knockout of certain genes suggested physiological relevance of variations in three additional proteins. Thus, we suggest the following four proteins for laboratory validation: delta-aminolevulinic acid dehydratase, ferrochelatase, ribose-5 phosphate isomerase and mitochondrial tyrosyl-tRNA synthetase. This study indicates that the predictive ability of this method will improve as more advanced, comprehensive models are developed. Moreover, these findings will be useful in the development of simple downstream biochemical or mass-spectrometric assays to corroborate these predictions and detect presence of certain known nsSNVs with deleterious outcomes. Results may also be useful in predicting as yet unknown outcomes of active site nsSNVs for enzymes that are not yet well classified or annotated