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

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

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

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

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

E-Beam Induced Micropattern Generation and Amorphization of L-Cysteine-Functionalized Graphene Oxide Nano-composites

The evolution of dynamic processes in graphene-family materials are of great interest for both scientific purposes and technical applications. Scanning electron microscopy and transmission electron microscopy outstand among the techniques that allow both observing and controlling such dynamic processes in real time. On the other hand, functionalized graphene oxide emerges as a favorable candidate from graphene-family materials for such an investigation due to its distinctive properties, that encompass a large surface area, robust thermal stability, and noteworthy electrical and mechanical properties after its reduction. Here, we report on studies of surface structure and adsorption dynamics of L-Cysteine on electrochemically exfoliated graphene oxides basal plane. We show that electron beam irradiation prompts an amorphization of functionalized graphene oxide along with the formation of micropatterns of controlled geometry composed of L-Cysteine-Graphene oxide nanostructures. The controlled growth and predetermined arrangement of micropatterns as well as controlled structure disorder induced by e beam amorphization, in its turn potentially offering tailored properties and functionalities paving the way for potential applications in nanotechnology, sensor development, and surface engineering. Our findings demonstrate that graphene oxide can cover L-Cysteine in such a way to provide a control on the positioning of emerging microstructures about 10-20 um in diameter. Besides, Raman and SAED measurement analyses yield above 50% amorphization in a material. The results of our studies demonstrate that such a technique enables the direct creation of micropatterns of L-Cysteine-Graphene oxide eliminating the need for complicated mask patterning procedures

Bose-Einstein Condensation of Helium and Hydrogen inside Bundles of Carbon Nanotubes

Helium atoms or hydrogen molecules are believed to be strongly bound within the interstitial channels (between three carbon nanotubes) within a bundle of many nanotubes. The effects on adsorption of a nonuniform distribution of tubes are evaluated. The energy of a single particle state is the sum of a discrete transverse energy Et (that depends on the radii of neighboring tubes) and a quasicontinuous energy Ez of relatively free motion parallel to the axis of the tubes. At low temperature, the particles occupy the lowest energy states, the focus of this study. The transverse energy attains a global minimum value (Et=Emin) for radii near Rmin=9.95 Ang. for H2 and 8.48 Ang.for He-4. The density of states N(E) near the lowest energy is found to vary linearly above this threshold value, i.e. N(E) is proportional to (E-Emin). As a result, there occurs a Bose-Einstein condensation of the molecules into the channel with the lowest transverse energy. The transition is characterized approximately as that of a four dimensional gas, neglecting the interactions between the adsorbed particles. The phenomenon is observable, in principle, from a singular heat capacity. The existence of this transition depends on the sample having a relatively broad distribution of radii values that include some near Rmin.Comment: 21 pages, 9 figure

Improved protein structure prediction using potentials from deep learning

Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence1. This problem is of fundamental importance as the structure of a protein largely determines its function2; however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It is possible to infer which amino acid residues are in contact by analysing covariation in homologous sequences, which aids in the prediction of protein structures3. Here we show that we can train a neural network to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions. Using this information, we construct a potential of mean force4 that can accurately describe the shape of a protein. We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures. The resulting system, named AlphaFold, achieves high accuracy, even for sequences with fewer homologous sequences. In the recent Critical Assessment of Protein Structure Prediction5 (CASP13)—a blind assessment of the state of the field—AlphaFold created high-accuracy structures (with template modelling (TM) scores6 of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a considerable advance in protein-structure prediction. We expect this increased accuracy to enable insights into the function and malfunction of proteins, especially in cases for which no structures for homologous proteins have been experimentally determined7

Mastering the game of Go without human knowledge

A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo

Automatic Classification of Roof Shapes for Multicopter Emergency Landing Site Selection

Geographic information systems (GIS) now provide accurate maps of terrain, roads, waterways, and building footprints and heights. Aircraft, particularly small unmanned aircraft systems, can exploit additional information such as building roof structure to improve navigation accuracy and safety particularly in urban regions. This paper proposes a method to automatically label building roof shape types. Satellite imagery and LIDAR data from Witten, Germany are fed to convolutional neural networks (CNN) to extract salient feature vectors. Supervised training sets are automatically generated from pre-labeled buildings contained in the OpenStreetMap database. Multiple CNN architectures are trained and tested, with the best performing networks providing a condensed feature set for support vector machine and decision tree classifiers. Satellite and LIDAR data fusion is shown to provide greater classification accuracy than through use of either data type individually