Coverage Analysis of Relay Assisted Millimeter Wave Cellular Networks with Spatial Correlation

We propose a novel analytical framework for evaluating the coverage performance of a millimeter wave (mmWave) cellular network where idle user equipments (UEs) act as relays. In this network, the base station (BS) adopts either the direct mode to transmit to the destination UE, or the relay mode if the direct mode fails, where the BS transmits to the relay UE and then the relay UE transmits to the destination UE. To address the drastic rotational movements of destination UEs in practice, we propose to adopt selection combining at destination UEs. New expression is derived for the signal-to-interference-plus-noise ratio (SINR) coverage probability of the network. Using numerical results, we first demonstrate the accuracy of our new expression. Then we show that ignoring spatial correlation, which has been commonly adopted in the literature, leads to severe overestimation of the SINR coverage probability. Furthermore, we show that introducing relays into a mmWave cellular network vastly improves the coverage performance. In addition, we show that the optimal BS density maximizing the SINR coverage probability can be determined by using our analysis

Distribution channels of luxury fashion brands in Chinese market: A new strategy on digital business.

Lo sviluppo dei canali di distribuzione e una parte molto importante per il lusso al fine di poter vendere i propri prodotti in Cina. Il panorama del mercato di lusso ci aiuta a capire come funziona il Macro mercato in Cina, che potrebbe influenzare le decisioni dei principali brand.ope

DyCL: Dynamic Neural Network Compilation Via Program Rewriting and Graph Optimization

DL compiler's primary function is to translate DNN programs written in high-level DL frameworks such as PyTorch and TensorFlow into portable executables. These executables can then be flexibly executed by the deployed host programs. However, existing DL compilers rely on a tracing mechanism, which involves feeding a runtime input to a neural network program and tracing the program execution paths to generate the computational graph necessary for compilation. Unfortunately, this mechanism falls short when dealing with modern dynamic neural networks (DyNNs) that possess varying computational graphs depending on the inputs. Consequently, conventional DL compilers struggle to accurately compile DyNNs into executable code. To address this limitation, we propose \tool, a general approach that enables any existing DL compiler to successfully compile DyNNs. \tool tackles the dynamic nature of DyNNs by introducing a compilation mechanism that redistributes the control and data flow of the original DNN programs during the compilation process. Specifically, \tool develops program analysis and program transformation techniques to convert a dynamic neural network into multiple sub-neural networks. Each sub-neural network is devoid of conditional statements and is compiled independently. Furthermore, \tool synthesizes a host module that models the control flow of the DyNNs and facilitates the invocation of the sub-neural networks. Our evaluation demonstrates the effectiveness of \tool, achieving a 100\% success rate in compiling all dynamic neural networks. Moreover, the compiled executables generated by \tool exhibit significantly improved performance, running between $1.12\times$ and $20.21\times$ faster than the original DyNNs executed on general-purpose DL frameworks.Comment: This paper has been accepted to ISSTA 202

Experimental Variant Slope Soil Tank for Measurements of Runoff and Soil Erosion

Rainfall-runoff processes and the related soil erosion are pivotal research regions in hydrology, soil science, and environment science. Thus, physics model experiments in laboratory scale on the aspect of measuring runoff and soil are one of the best tools in this field. This chapter aims to specify the experimental variant slope soil tank at home and in the USA. The developing of experimental soil tank of variant slopes with artificial simulating rainfall system will assist to understand soil water motivation, runoff yield, and nonpoint source pollution

NMTSloth: Understanding and Testing Efficiency Degradation of Neural Machine Translation Systems

Neural Machine Translation (NMT) systems have received much recent attention due to their human-level accuracy. While existing works mostly focus on either improving accuracy or testing accuracy robustness, the computation efficiency of NMT systems, which is of paramount importance due to often vast translation demands and real-time requirements, has surprisingly received little attention. In this paper, we make the first attempt to understand and test potential computation efficiency robustness in state-of-the-art NMT systems. By analyzing the working mechanism and implementation of 1455 public-accessible NMT systems, we observe a fundamental property in NMT systems that could be manipulated in an adversarial manner to reduce computation efficiency significantly. Our key motivation is to generate test inputs that could sufficiently delay the generation of EOS such that NMT systems would have to go through enough iterations to satisfy the pre-configured threshold. We present NMTSloth, which develops a gradient-guided technique that searches for a minimal and unnoticeable perturbation at character-level, token-level, and structure-level, which sufficiently delays the appearance of EOS and forces these inputs to reach the naturally-unreachable threshold. To demonstrate the effectiveness of NMTSloth, we conduct a systematic evaluation on three public-available NMT systems: Google T5, AllenAI WMT14, and Helsinki-NLP translators. Experimental results show that NMTSloth can increase NMT systems' response latency and energy consumption by 85% to 3153% and 86% to 3052%, respectively, by perturbing just one character or token in the input sentence. Our case study shows that inputs generated by NMTSloth significantly affect the battery power in real-world mobile devices (i.e., drain more than 30 times battery power than normal inputs).Comment: This paper has been accepted to ESEC/FSE 202

catena-Poly[[[dibromidocadmium]-μ2-1,1′-(butane-1,4-di­yl)bis­(pyridinium-4-carboxyl­ate)] monohydrate]

In the title compound, {[CdBr2(C16H16N2O4)]·H2O}n, the CdII ion is six-coordinated by a Br2O4 donor set, with four O atoms from two bridging 1,1′-(butane-1,4-di­yl)bis­(pyridinium-4-carboxyl­ate) ligands. The ligands link the CdII ions into a zigzag chain extending along [01]. O—H⋯O and O—H⋯Br hydrogen bonds involving the uncoordinated water mol­ecules connect the chains