Miracle-dz/UniFAR-FTUs huggingface dataset:
Hierarchical Traffic Matrices: Axiomatic Foundations to Practical Traffic Matrix Synthesis

The traffic matrix of a network is useful in a variety of applications: network planning and forecasting, traffic engineering and anomaly detection. Much work has focused on estimating traffic matrices, but methods are often tested on limited data. There is then the possibility of unrepresentativeness of the datasets, and the lack of generalizability of the subsequent results. Synthesis can help alleviate this problem. In this paper, we examine a fundamental question: what constitutes a good class of statistical models for traffic matrix synthesis? The results of our study is the definition of a set of axioms specifying structure on traffic matrix models, including the incorporation of organizational structure (hierarchies) in network traffic. We introduce the Hierarchical Traffic Matrix (HTM) which satisfies these requirements. We then study the hierarchical structure of the GEANT network, a research network based in Europe, to validate our ideas. Finally, we illustrate how structure in traffic matrices can affect network topology design.
Analysis of the Melt Erosion Patterns at Rail-Armature Contact of Rail Launcher in Current Range of 10–20 kA/mm

Sliding electrical contact between rail and armature in rail launchers is characterized by high speed and large current. Melt erosion is caused by the current concentration on the contact surface of armature. This current melt erosion (CME) has been experimentally studied in the current range of 10–20 kA/mm with a 20-mm caliber lab-scale rail launcher, and payload separated method was used to keep the recovered armatures intact. Thus, the onset behavior and erosion patterns of CME can be successfully observed in such relatively low current densities. In this paper, we will further our study to analyze the patterns of CME with the increasing current densities under the effect of different initial mechanical contact force. Results show that CME all start at the side edge, and then spreading longitudinally and transversely with the increase of current. It is found that the starting position of CME appears near the trailing edge at small mechanical contact force and moves to the leading edge with larger mechanical contact force. At the same current magnitude, erosion amounts are much more under small mechanical contact force. Stationary mechanical contact pressures and current distribution of armature surface are simulated to investigate the effect of initial mechanical contact force on starting position of CME. Current distributes at the edges of actual contact zone and the maximum of current density appears at the side edges of armature. As the initial mechanical contact force increases, actual contact zone and current density move toward to the leading edge from trailing edge. It can be confirmed that the erosion starting position is at the side edge along actual contact zone and will move toward to the leading edge from trailing edge with the increase of initial mechanical contact force. Smaller initial mechanical contact force will cause more erosion at the same current magnitude because of larger current density. Furthermore, an average friction coefficient calculation method is proposed to evaluate the melt lubrication effect of CME. The result shows that the lubrication effect becomes better with larger current densities.
Robotic Fish Locomotion & Propulsion in Marine Environment: A Survey

Fish Robots, especially bio-inspired robots that mimic biological creatures are gaining popularity in research field. Mostly the mathematical modeling, controlling of their kinematics, dynamics, swimming mechanism are main areas of interest. An intelligent Aquatic Robot generally exhibits three major functions, viz. guidance, control, and navigation. This paper is a discussion about the gradual development of these Fish Robots and their swimming, propulsion, locomotion mechanisms etc. that involved. The main obj ective is to bring forward a conclusive, relevant and useful conclusion from existing literature so as to boost interest in the emerging area of these Fish Robots. The fish locomotion is one of the prime inspirations of Fish Robots, and most of the bio-inspired Fish Robots are designed by mimicking the fish swimming profiles. The fish swimming can broadly be classified as Body and/or Caudal Fin (BCF)movements or using Median and/or Paired fin (MPF)propulsion. The swimming profile and characteristics of mostly used propulsion mode of various Fish Robots are discussed here in this paper. It is known that robotic fish propulsion mechanism is very complex to realize in models, it involves several aspects such as shape of the robot, hydro-dynamics involved, pattern of movement, control system involved, mechanical properties, location of various mechanical components and properties of the material used etc. Various structures and materials used in existing Fish Robots and significance of selection are reviewed. In future we look forward to develop better alternative techniques for locomotion and maneuvering.
A herpesvirus entry mediator mutein with selective agonist action for the inhibitory receptor B and T lymphocyte attenuator

The human cytomegalovirus opening reading frame UL144 is an ortholog of the TNF receptor superfamily member, herpesvirus entry mediator (HVEM; TNFRSF14). HVEM binds the TNF ligands, LIGHT and LTa; the immunoglobulin inhibitory receptor, B and T lymphocyte attenuator (BTLA); and the natural killer cell–activating receptor CD160. However, UL144 selectively binds BTLA, avoiding activation of inflammatory signaling initiated by CD160 in natural killer cells. BTLA and CD160 cross-compete for binding HVEM, but the structural basis for the ligand selectivity by UL144 and how it acts as an anti-inflammatory agonist remains unclear. Here, we modeled the UL144 structure and characterized its binding with BTLA. The UL144 structure was predicted to closely mimic the surface of HVEM, and we also found that both HVEM and UL144 bind a common epitope of BTLA, whether engaged in trans or in cis, that is shared with a BTLA antibody agonist. On the basis of the UL144 selectivity, we engineered a BTLA-selective HVEM protein to understand the basis for ligand selectivity and BTLA agonism to develop novel anti-inflammatory agonists. This HVEM mutein did not bind CD160 or TNF ligands but did bind BTLA with 10-fold stronger affinity than wild-type HVEM and retained potent inhibitory activity that reduced T-cell receptor, B-cell receptor, and interferon signaling in B cells. In conclusion, using a viral immune evasion strategy that shows broad immune-ablating activity, we have identified a novel anti-inflammatory BTLA-selective agonist.
Video-Rate Video Inpainting

This paper considers the problem of video inpainting, i.e., to remove specified objects from an input video. Many methods have been developed for the problem so far, in which there is a trade-off between image quality and computational time. There was no method that can generate high-quality images in video rate. The key to video inpainting is how to establish correspondences from scene regions occluded in a frame to those observed in other frames. To break the trade-off, we propose to use CNNs as a solution to this key problem. We extend existing CNNs for the standard task of optical flow estimation to be able to estimate the flow of occluded background regions. The extension includes augmentation of their architecture and changes of their training method. We experimentally show that this approach works well despite its simplicity, and that a simple video inpainting method integrating this flow estimator runs in video rate (e.g., 32fps for 832 × 448 pixel videos on a standard PC with a GPU) while achieving image quality close to the state-of-the-art.
Optimisation of Gold Nanorod Array for Sensing Applications

Localised surface plasmon resonance (LSPR) based sensors are typically considered less sensitive than surface plasmon resonance (SPR) based devices. Here we present a device which utilizes a gold nanorod array to amplify the LSPR signal and sensitivity for biosensing applications. The device detects protein binding interactions and ensures selectivity to specific binding events by utilizing protein binding pairs. Antibody, anti-Lysozyme was immobilized on the surface to detect immune system enzyme, Lysozyme in solution. The current setup was capable of detecting Lysozyme at nanomolar concentrations (31.25nM). However, the current setup may not be optimized for maximum sensor signal. To determine the optimal setup required, the surface was modelled using finite element method software, COMSOL Multiphysics. In the model, the effect of surface parameters on the sensor signal was investigated. We found that the greatest magnitude of LSPR signal occurred for nanorod arrays with diameter of 25nm, height of 200nm and centre-to-centre spacing of 60nm. The electric field distribution was also calculated and the variation throughout the array was determined. It was found to be strongest in x-direction, at the top and middle of the nanorod at distances of 1nm and 4nm from the surface respectively. If immobilized proteins could be positioned at these distances and position on nanorod, binding interactions would experience greater fields and hence produce a stronger plasmonic response. In addition, protein binding occurring between nanorods at heights above the nanorod midpoint would also experience significantly stronger fields due to enhanced coupling. The results obtained show how the current setup is functional, but could be further modified for optimal sensitivity.
Block-Matching Convolutional Neural Network (BMCNN): Improving CNN-Based Denoising by Block-Matched Inputs

There are two main streams in up-to-date image denoising algorithms: non-local self similarity (NSS) prior based methods and convolutional neural network (CNN) based methods. The NSS based methods are favorable on images with regular and repetitive patterns while the CNN based methods perform better on irregular structures. In this paper, we propose a block-matching convolutional neural network (BMCNN) method that combines NSS prior and CNN. Initially, similar local patches in the input image are integrated into a 3D block. In order to prevent the noise from messing up the block matching, we first apply an existing denoising algorithm on the noisy image. The denoised image is employed as a pilot signal for the block matching, and then denoising function for the block is learned by a CNN structure. Experimental results show that the proposed BMCNN algorithm achieves state-of-the-art performance. In detail, BMCNN can restore both repetitive and irregular structures.
L4S: Low-Speed Software Synergetic Sampling and Detecting Long Flow for Data Center Network

Sampling and detecting long flow are important for traffic engineering in data center network. However, the result of accurate sampling based on hardware is usually associated with high sampling rate. In this paper, we proposed L4 $S$: a low-speed software synergetic sampling with a self-adaptive sampling rate adjustment algorithm for the problem that the sampling rate cannot match the network traffic and verified the feasibility of L4S in the long flow detection problem, The simulation results show that the L4S reduces the sampling rate from 1000:1 to 10000:1 when it achieves a comparable sampling effect with a relatively independent hardware sampling and a fixed sampling rate scheme.
Measuring of Node-node Distance Distribution on the Wide Area Network by Means of GNU/Linux Utilities

In this article, the research of the global network’s characteristics is discussed. The main measured characteristic is a distance (number of relays) between two network nodes. The method of building the set of IP-addresses in the global network for measurement is given. And the method of measurement with GNU/Linux utilities: traceroute, sed, awk, wc. The results are processed in GNU Octave. The distributions are presented in form of plots, and their characteristics are presented in tables. The characteristics have widely spaced distributions, but their skewness and kurtosis and always much larger than 0 and 3, and this is proven with probability 0.99 with directed tests of skewness and kurtosis. It is also has been found that measured distribution is changed over time with the relevance of 0.05.
Proactive Eavesdropping via Jamming in Full-duplex Cellular Networks with Antenna Selection

This paper studies the problem of antenna selection in a proactive monitoring via jamming scheme which consists of a suspicious single antenna pair, a multi-antenna full-duplex monitor and a cellular user. Specifically, based on the eavesdropping non-outage probability metric of the system, two antenna selection schemes to select single receive and single transmit antenna at the full-duplex monitor are proposed. We analytically derive the eavesdropping non-outage probability of the proposed antenna selection schemes. We further compare our results with the eavesdropping non-outage probability of the random antenna selection scheme. Our findings reveal that the performance of the proposed antenna selection schemes are dramatically improved by increasing the number of transmit and receive antennas at the full-duplex monitor.
A Comparative Study on Gaussian Process Regression-based Indoor Positioning Systems

Gaussian Process Regression (GPR) has been proved to be one of the most accurate ways of predicting online radio map for fingerprinting based localization, as it can better mimic the characteristics of wireless radio signals. However, the accuracy of the GPR model depends on the mean function used and most of the functions perform poorly while being used in localization. This paper presents a thorough comparative analysis on different Indoor Positioning Systems (IPS) exploiting GPR with different mean functions, among which zero mean and linear mean are the most commonly used ones. This paper also introduces two new mean functions-Single Hidden Layer Neural Network (NN) and Multiple Hidden Layer NN which outperforms traditional mean functions.
Measuring Infant's Length with an Image

It is important to measure an infant's length regularly to estimate the growth velocity to make sure that the infant is growing normally. Traditionally, measuring an infant's length is performed with an infantometer. However, the infant struggles and cries in the measuring process, and it often needs three persons to position the infant's head, legs, and the boards of the infantometer during the process. Thus, it is not practical for a parent to perform this measurement at home regularly. In this paper, we propose a new approach which allows the measurement of an infant's length using a cellphone picture without the need to position the infant. Our algorithm automatically calculates the 3D positions of the body parts and the total length of the infant with the help of round stickers. The round stickers can be put on the infant's body easily in a few seconds, before the picture is taken. This new technology would make frequent measurements of the infant's length and the tracking of the growth velocity possible.
On the Use of Parametric Identification Methods for Modeling a Small-Scale Pasteurization Plant

In this paper, system identification methods are used for the modeling of nonlinear systems within a small scale process plant, focusing the analysis on state-space descriptions. The experiments for the adjustment of parameters are discussed, taking in account the limitations of the plant. The experimental results reveal that the identification model schemes suggested are adequate and demonstrate their accuracy and efficiency.
Super-Node SLP: Optimized Vectorization for Code Sequences Containing Operators and Their Inverse Elements

SLP Auto-vectorization converts straight-line code into vector code. It scans input code for groups of instructions that can be combined into vectors and replaces them with their corresponding vector instructions. This work introduces Super-Node SLP (SN-SLP), a new SLP-style algorithm, optimized for expressions that include a commutative operator (such as addition) and its corresponding inverse element (subtraction). SN-SLP uses the algebraic properties of commutative operators and their inverse elements to enable additional transformations that extend auto-vectorization to cases difficult for state-of-the-art auto-vectorizing compilers. We implemented SN-SLP in LLVM. Our evaluation on a real system demonstrates considerable performance improvements of benchmark code with no significant change in compilation time.
Classification of Acoustic Events in a Kitchen Environment using Multiband Spectral Entropy

When the context of a scenario is studied with help of audio, distinct problems appear, which entail a challenge for all the acoustic event recognition systems, such as, noise, mixture of different types of sound sources, among others. The methods used for attending these problems are generally focused on two processes; the feature extraction process and the classification process. In this paper we propose to use Multiband Spectral Entropy Signatures (MSES) for extracting features from acoustic events with a background of mixture of sounds occurring in a kitchen environment. MSES takes into account the randomness of the signal, making it more robust to noise, loudness and spectral flatness. To test our proposal, we created a database of a mix-up of triples from a collection of sixteen real world kitchen sounds using 3dB of signal-to-noise rate. Our benchmark in this work is MFCC feature, since it is often used for this issue. With respect to the classification process, we use two similarity distances, cosine distance and Hamming distance. Experimental results indicate that MSES outper forms the MFCC feature in robustness and effectiveness, improving the performance of the classification process.
16.3 A −246dB Jitter-FoM 2.4GHz Calibration-Free Ring-Oscillator PLL Achieving 9% Jitter Variation Over PVT

Low-jitter phase-locked loops (PLLs) are critical building blocks in various systems, including wireless and wireline communications and ADCs. LC oscillators exhibit low phase noise (PN) but suffer from large area, magnetic coupling, and small tuning range. While ring voltage-controlled oscillators (RVCO) are free from the above issues, their inferior PN performance restricts their applicability. The injection-locked clock multiplier (ILCM) attains a low PN through phase realignment. However, both the PN and the reference spur deteriorate when the injection instant drifts over PVT. Although the frequency-tracking loop (FTL) can calibrate the center frequency, its power consumption, converging speed, and accuracy limit the performance of the ILCM [1, 2]. The Type-I PLL performs wideband filtering [3], but its efficiency is 3dB lower than that of the ILCM [4]. A PLL with fast phase-error correction (FPEC) [4] realizes a PN filtering close to the ILCM and keeps a small reference spur. Nevertheless, both Type-I and FPEC PLLs rely on the closed-loop wideband filtering to achieve a low PN. The drifted loop gain over PVT significantly degrades jitter performance and even causes instability, implying a trade-off between wideband filtering and stability. Consequently, a loop-gain calibration [4] is necessary to maintain the stability and low PN.
An Investigation of Speaker Clustering Algorithms in Adverse Acoustic Environments

Speaker clustering is an important problem of speech processing, such as speaker diarization, however, its behavior in adverse acoustic environments is lack of comprehensive study. To address this problem, we focus on investigating its components respectively. A speaker clustering system contains three components—a feature extraction front-end, a dimensionality reduction algorithm, and a clustering back-end. In this paper, we use the standard Gaussian mixture model based universal background model (GMM-UBM) as a front end to extract high-dimensional supervectors, and compare three dimensionality reduction algorithms as well as two clustering algorithms. The three dimensionality reduction algorithms are the principal component analysis (PCA), spectral clustering (SC), and multilayer bootstrap network (MBN). The two clustering algorithms are the k-means and agglomerative hierarchical clustering (AHC). We have conducted an extensive experiment with both in-domain and out-of-domain settings on the noisy versions of the NIST 2006 speaker recognition evaluation (SRE) and NIST 2008 SRE corpora. Experimental results in various noisy environments show that (i) the MBN based systems perform the best in most cases, while the SC based systems outperform the PCA based systems as well as the original supervector based systems; (ii) AHC is more robust than k-means.
Adaptive dysfunction of selenoproteins from the perspective of the triage theory: why modest selenium deficiency may increase risk of diseases of aging

The triage theory proposes that modest deficiency of any vitamin or mineral (V/M) could increase age‐related diseases. V/M‐dependent proteins required for short‐term survival and/or reproduction (i.e., “essential”) are predicted to be protected on V/M deficiency over other “nonessential” V/M‐dependent proteins needed only for long‐term health. The result is accumulation of insidious damage, increasing disease risk. We successfully tested the theory against published evidence on vitamin K. Here, we review about half of the 25 known mammalian selenoproteins; all of those with mouse knockout or human mutant pheno‐types that could be used as criteria for a classification of essential or nonessential. Five selenoproteins (Gpx4, Txnrd1, Txnrd2, Dio3, and Sepp1) were classified as essential and 7 (Gpx1, Gpx 2, Gpx 3, Dio1, Dio2, Msrb1, and SelN) nonessential. On modest selenium (Se) deficiency, nonessential selenoprotein activities and concentrations are preferentially lost, with one exception (Dio1 in the thyroid, which we predict is conditionally essential). Mechanisms include the requirement of a special form of tRNA sensitive to Se deficiency for translation of nonessential selenoprotein mRNAs except Dio1. The same set of age‐related diseases and conditions, including cancer, heart disease, and immune dysfunction, are prospectively associated with modest Se deficiency and also with genetic dysfunction of nonessential selenoproteins, suggesting that Se deficiency could be a causal factor, a possibility strengthened by mechanistic evidence. Modest Se deficiency is common in many parts of the world; optimal intake could prevent future disease. —McCann, J. C, Ames, B. N. Adaptive dysfunction of selenoproteins from the perspective of the triage theory: why modest selenium deficiency may increase risk of diseases of aging. FASEB J. 25, 1793‐1814 (2011). www.fasebj. org
Investigation of autonomous traction motor dynamic using method of mutual loading and computer simulation

Electric traction is one of the most promising technologies that can lead to significant improvements in vehicle performance, energy utilization efficiency, and pollution emissions. This paper presents various solutions for the power traction motors of hybrid electric vehicle (HEV), that is the most promising technology that has the advantages of high performance, high fuel efficiency, low emissions and long operating range [1]. The paper describes the development of the simulation model for a hybrid electric traction using MATLAB Simulink. Simulation results are presented below.