Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital Twin Networks: Learning Dynamic Network Behaviors from Network Flows

The Digital Twin Network (DTN) is a key enabling technology for efficient and intelligent network management in modern communication networks. Learning dynamic net-work behaviors at the flow granularity is a core element for realizing DTN with accurate network modelling. However, it is challenging due to the complexity of network architectures and the proliferation of emerging network applications. In this paper, we devise a Packet-Action Sequence Model to represent all possible packets behaviors in a unified way. Besides, we propose a novel and effective algorithm to assess whether the behavior pattern is time dependent or independent by using the temporal characteristics of packets in a network flow, so as to learn the key factors of packets that contribute to network behaviors. Based on two typical scenarios, i.e., packet caching and routing, the experimental results verify that the proposed algorithm can identify network behavior patterns and learn key factors affecting the behaviors with over 99 % accuracy.
The Development and Application of Artificial Intelligence Chips

For artificial intelligence industry, the computational power of the chip and the ability to accelerate the artificial intelligence networks and algorithms restrict the development of artificial intelligence. But nowadays, there is no paper about reviewing each kind of artificial intelligence chips, which make it difficult to know about them. Therefore, this paper will give a comprehensive description of artificial intelligence chip, and analyze the development and the application of various artificial intelligence chips as well as the development trend of them. At the same time, we will show their shortcomings. In this paper, we analyze the development and the principle of artificial intelligence chips which were classified into three categories: Graphics Processor, Field Programmable Gate Array, Application Specific Integrated Circuit and Brain Inspired chip. Besides, this paper introduces the application of various chips in different scenarios. In the last, this paper presents the development trend of the artificial intelligence chips. The research of low-power general-purpose chip for artificial intelligence analyzed in this paper will become one of the development trends in the future, which will affect the development of artificial intelligence.
Chiral squaring and KLT relations

We demonstrate that amplitudes based on matter supermultiplets can be combined to provide amplitudes of vector supermultiplets by means of KLT relations. In practice we do this by developing a procedure for removing supersymmetry supercharges from super Yang-Mills theory and supergravity supermultiplets, reducing them to vector and chiral supermultiplets respectively. This way, we reduce the super KLT relations to chiral KLT relations making chiral squaring of amplitudes manifest. We study these chiral KLT relations, discussing permutation symmetry and vanishing relations. Finally some explicit calculations are done to show how the relations work in detail.
Multiparametric ultrasound and machine learning for prostate cancer localization

A cost-effective, widely available, and practical di-agnostic imaging tool for prostate cancer (PCa) localization is still lacking. Recently, the contrast-ultrasound dispersion imaging (CUDI) technique has been developed for PCa localization by quantifying dynamic contrast-enhanced ultrasound (DCE-US) acquisitions. Tissue stiffness is an additional PCa biomarker that can be quantified by ultrasound shear-wave elastography (SWE). In this work, a dedicated preprocessing of 3D DCE-US acquisitions was investigated by using multilinear singular value decomposition (MLSVD), aiming at improving the CUDI performance. Moreover, the diagnostic potential of a multiparametric ultrasound imaging approach combining 3D CUDI features with SWE tissue elasticity for clinically significant (cs)PCa localization was evaluated by comparison with the histopathological outcome of systematic biopsies. In this multiparametric approach, the performance of five classifiers was evaluated and compared for biopsy-region csPCa classification. The classification performance was assessed by the area under the Receiver Operating Characteristics curve (AUC) in a k-fold cross validation fashion comprising sequential floating forward selection of the features. The combination of CUDI features with MLSVD preprocessing and SWE elasticity yielded the best AUC=0.87 for csPCa localization. Our results suggest 3D multiparametric ultrasound imaging approach combing a dedicated preprocessing step to be a useful tool for PCa diagnostics.
A Bilinear Framework For Adaptive Speech Dereverberation Combining Beamforming And Linear Prediction

Speech dereverberation algorithms based on multichannel linear prediction (MCLP) are effective under various acoustic conditions. This paper proposes a bilinear form for the MCLP based dereverberation, where the MCLP filter is expressed as a Kronecker product of a spatial filter and a temporal filter. Then, a recursive least-squares (RLS)-based algorithm is derived for adaptive speech dereverberation. Compared with the original MCLP-based adaptive algorithm, the advantages of the proposed method are twofold: (1) the computational complexity is significantly reduced and is more suitable for dynamic scenarios, since fewer parameters have to be estimated per signal-block observation; and (2) it is more robust to noise by optimizing the spatial filter as a weighted minimum power distortionless response (wMPDR) beamformer. Simulation results validate the advantages of the proposed algorithm.
Robust PCA for Through-the-Wall Radar Imaging

Through-the-wall radar imaging (TWRI) is an on-going field of research which aims at investigating the inside of a building from its outside. In the most common setting, it seeks to detect or monitor stationary targets. Departing from usual delay-and-sum techniques, sparse recovery problems have been proposed to solve this detection problem. These methods rely on a preprocessing step where an appropriate separation of wall and target subspaces is first performed to remove the front wall response. In this work, we explore one-step methods using joint low-rank and sparse decomposition methods through the Robust PCA (RPCA) framework. The novelty is a one-step recovery from a structured inversion problem for which we tailor an Alternating Direction Method of Multipliers (ADMM) algorithm. We validate and compare our method on simulations.
Dynamics of braided coronal loops - II. Cascade to multiple small-scale reconnection events

Aims. Our aim is to investigate the resistive relaxation of a magnetic loop that contains braided magnetic flux but no net current or helicity. The loop is subject to line-tied boundary conditions. We investigate the dynamical processes that occur during this relaxation, in particular the magnetic reconnection that occurs, and discuss the nature of the final equilibrium. Methods. The three-dimensional evolution of a braided magnetic field is followed in a series of resistive MHD simulations. Results. It is found that, following an instability within the loop, a myriad of thin current layers forms, via a cascade-like process. This cascade becomes more developed and continues for a longer period of time for higher magnetic Reynolds number. During the cascade, magnetic flux is reconnected multiple times, with the level of this "multiple reconnection" positively correlated with the magnetic Reynolds number. Eventually the system evolves into a state with no more small-scale current layers. This final state is found to approximate a non-linear force-free field consisting of two flux tubes of oppositely-signed twist embedded in a uniform background field.
Whale Optimization Algorithm Based Optimal Operation of Power Distribution Systems

This paper solves the optimal operation problem of distribution systems using renewable energy sources and voltage regulators. The developed model makes use of Whale Optimization Algorithm based optimization. We apply the proposed optimization model to a modified low voltage distribution system of Almaty, Kazakhstan. Three different cases were simulated. The cases considered are medium load level with medium PV power output, high load level with medium PV output, and high load level and low PV power outputs. The simulation results show that the proposed algorithm is able lower the active power losses.
Painless Transposition of Reproducible Distributed Environments with NixOS Compose

Development of environments for distributed systems is a tedious and time-consuming iterative process. The reproducibility of such environments is a crucial factor for rigorous scientific contributions. We think that being able to smoothly test environments both locally and on a target dis-tributed platform makes development cycles faster and reduces the friction to adopt better experimental practices. To address this issue, this paper introduces the notion of environment transposition and implements it in NixOS Compose, a tool that generates reproducible distributed environments. It enables users to deploy their environments on virtualized (docker, QEMU) or physical (Grid'5000) platforms with the same unique description of the environment. We show that NixOS Compose enables to build reproducible environments without overhead by comparing it to state-of-the-art solutions for the generation of distributed environments (EnOSlib and Kameleon). NixOS Compose actually enables substantial performance improvements on image building time over Kameleon (up to 11x faster for initial builds and up to 19x faster when building a variation of an existing environment).
Voltage Control of Unbalanced Distribution Systems with Penetration of Renewable Sources: A Gradient-Based Optimization Approach

The penetration of distributed energy resources (DERs), including renewable energy sources (RES), into electric power systems has led to several challenges for the system operators. Despite various economic and environmental benefits offered by RES, the issue of voltage rise due to active power injection from RES is still an open problem. On the other hand, voltage decrease due to high load in distribution systems is another challenge faced by operators. In this study, we investigated the problem of over-voltage and under-voltage in the operation of unbalanced 3-phase distribution systems with penetration of RES. Moreover, We utilize derivative-based Exterior Penalty Function (EPF) optimization to solve the voltage deviation problem. The results of the tests conducted on a modified IEEE 13 Bus Test System have confirmed that the use of the tap changer voltage regulators and reactive power from PVs connected close to inverters can effectively contribute to the voltage control problem.
Robust tensor regression with applications in imaging

Tensor regression models have gained popularity in problems where covariates are tensors (multidimensional arrays) such as images. Tensor regression models are able to efficiently exploit the temporal and/or spatial structure of tensor covariates (e.g., in hyperspectral or fMRI images) by imposing a low-rank assumption on the parameter tensor. In this paper, we propose a robust tensor regression estimation method within the framework of Kruskal tensor regression model. We consider Huber's concomitant criterion for regression and scale as it offers a good tradeoff between robustness and computational feasibility. An efficient alternating minimization algorithm is proposed for estimating the unknown regression parameters. Our simulation studies with synthetic image signals illustrate that the proposed estimator performs similarly compared to benchmark method when errors are Gaussians but offers superior performance in heavy-tailed noise, while having similar computational complexity.
Energy-Efficient High-Performance Photonic Backplane Network for Rack-Scale Computing Systems

With the mainstream paradigm in high-performance computer architecture being shifted considerably from single-core systems toward multi-core computing systems, system performance and energy efficiency are extensively challenged by the communication capacity among processing units and memories/storage. The emerging silicon photonics, on the other hand, promises exceedingly high bandwidth, low latency, and low energy consumption by enabling optical interconnects between entities. However, the inherent distinction between optical interconnects and electrical ones requires fundamentally different methods to compose a system-level communication network. This paper proposes a systematic approach to optimizing the overall energy-delay product of the photonic backplane network for rack-scale computing systems. The experimental results show the system with proposed 2-layer photonic backplane network with the floorplan optimization improves system performance by 3x and improve system performance per energy consumption by around 46%, which can also maintain good scalability.
Classification of Hand Gestures using sEMG Signals and Hilbert-Huang Transform

Artificial intelligence is effectively utilized for hand gesture classification in myoelectric systems. In this study, hand movement classification is performed with ML algorithms using electromyography (EMG) signals of 7 hand gestures. The Hilbert-Huang Transform (HHT) was applied to the preprocessed EMG signals to obtain the Hilbert-Huang spectrum (HHS). Six different Gray Level Co-occurrence Matrix (GLCM)-based features were extracted from HHS images. In order to validate the proposed method, the same features were extracted from the snapshots of EMG signals and intrinsic mode functions (IMF) extracted by empirical mode decomposition (EMD), separately. These features are classified with 29 different Machine learning (ML) approaches in the MATLAB® environment. Among these three approaches, the HHS-based novel method yielded the best performance, with an accuracy of 90.87% from the Cubic Support Vector Machine (SVM). The novel HHS and GLCM-based approach may be used in EMG-based biomedical systems as a promising alternative.
Sector-Beam Antenna Array for 77 GHz Automotive RADAR Systems

This paper presents a reconfigurable sector-beam antenna that operates at 77 GHz for automotive RADAR systems. The proposed antenna array is composed of three dipoles with three open circuits used as switches. Therefore, the proposed antenna can achieve three reconfigurable beams by controlling the switches states. The design processes are presented, and the structure is optimized and simulated. According to the simulated results, seven modes with a global matching bandwidth of [75–81 GHz] (S11< 10 dB) can be selected with peak gain of 6.18 dBi and good total efficiency of 78%. beam-steered directional patterns (covering 180 °) can be electronically tuned depending on the working states. The comparison to other state-of-the-art antennas shows that the proposed reconfigurable array has a compact size, high gain and easy to design. This proposed antenna is well suitable for 77 GHz automotive RADAR systems.
Simple and Low-Computational Losses Modeling for Efficiency Enhancement of Differential Inverters with High Accuracy at Different Modulation Schemes

The modeling of losses at differential inverters is becoming crucial, especially for enhancing efficiency and reliability of their DC-DC modules. Losses modeling based on sinusoidal duty cycle, at line frequency $F_{L}$, without considering differential inverters characteristics and switching frequency ($F_{s}$) of their DC-DC modules is not accurate, needs high computational demand, and is not applicable for different PWM modulation schemes. In this paper, a simple and accurate losses modeling for differential inverters is proposed based on two-stage calculation process. In first stage, the losses is calculated based on the switching frequencies for DC-DC modules $F_{s}$. Then, in the second stage, the losses is averaged according to operating frequency of differential inverters $F_{L}$. The decoupling between both frequencies facilitates the easy insertion of differential inverters characteristics such as static linearization approach and low order even harmonics. Also, it easily obtains the RMS currents in terms of module parameters which reduce the required computational calculations. The proposed modeling is applicable for most modulation schemes such as SVMS, CMS and DMS, thanks to the decoupling property of proposed losses modeling. It is generic for single-phase, three-phase, and multi-phase differential inverters and thoroughly supported efficiency improvement even at modular differential inverters. The flow chart of the presented methodology is explained in detail and effectively applied for many DC-DC modules. For verification, a differential inverter based on SEPIC modules is introduced to validate the accuracy of the proposed losses modeling.
Public-Private Policy Partnerships

Partnerships between the public and private sectors to fulfill public functions are on the increase at every level of government. In the United States and Canada they currently operate in most policy areas, and in the U.S. trial programs are planned by the Internal Revenue Service, the Census Bureau, and the Social Security Administration.Partnerships represent the second generation of efforts to bring competitive market discipline to bear on government operations. Unlike the first generation of privatizing efforts, partnering involves sharing both responsibility and financial risk. In the best situations, the strengths of each sector maximize overall performance. In these cases, partnering institutionalizes collaborative arrangements in which the differences between the sectors become blurred.This is the first book to evaluate public-private partnerships in a broad range of policy areas. The chapters focus on education, health care and health policy, welfare, prisons, the criminal justice system, environmental policy, energy policy, technology research and development, and transportation. The contributors come from a number of fields, including political science, education, law, economics, and public health. They merge experiential and social-scientific findings to examine how partnerships perform, to identify the conditions in which they work best, and to determine when they might be expected to fail.Contributors : Ronald J. Daniels, James A. Dunn, Jr., Sheldon Kamieniecki, Harry M. Levin, Stephen H. Linder, Nicholas P. Lovrich, Jr., Mark Carl Rom, Pauline Vaillancourt Rosenau, Walter A. Rosenbaum, Anne Larason Schneider, David Shafie, Julie Silvers, Michael S. Sparer, Joseph E. Stiglitz, Michael J. Trebilcock, Scott J. Wallsten.
Socio-Ecological Vulnerability in Aba Prefecture, Western Sichuan Plateau: Evaluation, Driving Forces and Scenario Simulation

With the social and economic development in recent years, human activities have been more extensive and intensified. As a result, ecosystems are damaged to varying degrees, and regional ecological environments tend to be weaker. The socio-ecological system in Aba Prefecture, Western Sichuan Plateau, China, the researched area, also faces increasingly serious problems. To advance ecological civilization development in a coordinated way across the country, the national government and the competent authorities have launched a series of new strategies. Research on socio-ecological vulnerability, a major part of the ecosystem protection and restoration program, is provided with powerful spatial data observation and analysis tools thanks to the invention and development of remote sensing and geographic information system technologies. This study was based on the vulnerability scoping diagram (VSD) framework. Multi-source data such as digital elevation model (DEM), geographical data such as land use types, soil and geological disasters, remote sensing image data, meteorological data and social statistics data from 2005 to 2019 were used to construct the temporal social-ecosystem vulnerability evaluation index database of Aba Prefecture, Western Sichuan Plateau. The spatial principal component analysis (SPCA) is applied to evaluating the socio-ecological vulnerability and analyzing its spatial-temporal variation in Aba Prefecture, Western Sichuan Plateau. To probe into the driving effects of various impact factors on the socio-ecological vulnerability, the Geodetector is used to analyze the driving factors. The ordered weighted average (OWA) method is applied to the multi-scenario analysis of socio-ecological vulnerability in the researched area. The conclusions of this study are as follows: (1) from 2005 to 2019, the spatial distribution characteristics of exposure and sensitivity in Aba Prefecture were higher in the southeast and lower in the northwest, and the overall spatial distribution characteristics of socio-ecological system vulnerability showed that the degree of vulnerability increased from the north to the southeast. (2) Extreme natural climate conditions play a leading role in the driving of socio-ecosystem vulnerability, followed by human production activities and geological hazards. (3) The degree of social-ecosystem vulnerability in Aba Prefecture will increase with the increase of decision risk coefficient. The results of social-ecosystem vulnerability under the status quo scenario are similar to those in 2010 and 2019, indicating that the selected evaluation factors can reflect the actual social-ecosystem vulnerability. In the sustainable guided scenario and the unsustainable guided scenario, the proportion of the area of the social-ecosystem severe vulnerability level was at the minimum value and the maximum value, respectively.