An Example of E-Commerce Platform in Anhui Tobacco Corporation

Anhui Tobacco Corporation (ATC) is a large state-owned group corporation. After a long period of planned economy system, ATC is not suit for marketing economy. With rigid management method, bad service and high trade cost, ATC is weak in competition. Shortly after 1-year’s construction, ATC built up an E-commerce platform which has the largest bargain and perfectly combines the traditional industry with modern information technology. The information system is based on a network connecting all the subordinate factories, branches and wholesale center. It is centered by e-business, including e-commerce platform, centralized fund management system, and ERP for manufacturing factories and MIS for sales firms will be applied later. Since the platform went into operation in Oct.,2001,an average of thousand boxes of cigarette has been dealt. Up to March,2002, the trading fund amounts to 400 million yuan. This figure is forecasted to be one billion in 2002. At the same time, income grows rapidly. The allot income increases 24% compared with the same period last year; wholesale increases 14%, profit increases 21%. Information system must service the strategy target, and be driven by management innovation. The success is due to idea renewing, leadership, organization and Training from beginning to end

Cooperative Advertising in a Supply Chain with Horizontal Competition

Cooperative advertising programs are usually provided by manufacturers to stimulate retailers investing more in local advertising to increase the sales of their products or services. While previous literature on cooperative advertising mainly focuses on a “single-manufacturer single-retailer” framework, the decision-making framework with “multiple-manufacturer single-retailer” becomes more realistic because of the increasing power of retailers as well as the increased competition among the manufacturers. In view of this, in this paper we investigate the cooperative advertising program in a “two-manufacturer single-retailer” supply chain in three different scenarios; that is, (i) each channel member makes decisions independently; (ii) the retailer is vertically integrated with one manufacturer; (iii) two manufacturers are horizontally integrated. Utilizing differential game theory, the open-loop equilibrium-advertising strategies of each channel member are obtained and compared. Also, we investigate the effects of competitive intensity on the firm’s profit in three different scenarios by using the numerical analysis

Cooperative Advertising in a Supply Chain with Horizontal Competition

Cooperative advertising programs are usually provided by manufacturers to stimulate retailers investing more in local advertising to increase the sales of their products or services. While previous literature on cooperative advertising mainly focuses on a "singlemanufacturer single-retailer" framework, the decision-making framework with "multiple-manufacturer single-retailer" becomes more realistic because of the increasing power of retailers as well as the increased competition among the manufacturers. In view of this, in this paper we investigate the cooperative advertising program in a "two-manufacturer single-retailer" supply chain in three different scenarios; that is, (i) each channel member makes decisions independently; (ii) the retailer is vertically integrated with one manufacturer; (iii) two manufacturers are horizontally integrated. Utilizing differential game theory, the open-loop equilibriumadvertising strategies of each channel member are obtained and compared. Also, we investigate the effects of competitive intensity on the firm's profit in three different scenarios by using the numerical analysis

Seismic Waveform Inversion Using the Finite-Difference Contrast Source Inversion Method

This paper extends the finite-difference contrast source inversion method to reconstruct the mass density for two-dimensional elastic wave inversion in the framework of the full-waveform inversion. The contrast source inversion method is a nonlinear iterative method that alternatively reconstructs contrast sources and contrast function. One of the most outstanding advantages of this inversion method is the highly computational efficiency, since it does not need to simulate a full forward problem for each inversion iteration. Another attractive feature of the inversion method is that it is of strong capability in dealing with nonlinear inverse problems in an inhomogeneous background medium, because a finite-difference operator is used to represent the differential operator governing the two-dimensional elastic wave propagation. Additionally, the techniques of a multiplicative regularization and a sequential multifrequency inversion are employed to enhance the quality of reconstructions for this inversion method. Numerical reconstruction results show that the inversion method has an excellent performance for reconstructing the objects embedded inside a homogeneous or an inhomogeneous background medium

Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems

We propose Februus; a new idea to neutralize highly potent and insidious Trojan attacks on Deep Neural Network (DNN) systems at run-time. In Trojan attacks, an adversary activates a backdoor crafted in a deep neural network model using a secret trigger, a Trojan, applied to any input to alter the model's decision to a target prediction---a target determined by and only known to the attacker. Februus sanitizes the incoming input by surgically removing the potential trigger artifacts and restoring the input for the classification task. Februus enables effective Trojan mitigation by sanitizing inputs with no loss of performance for sanitized inputs, Trojaned or benign. Our extensive evaluations on multiple infected models based on four popular datasets across three contrasting vision applications and trigger types demonstrate the high efficacy of Februus. We dramatically reduced attack success rates from 100% to near 0% for all cases (achieving 0% on multiple cases) and evaluated the generalizability of Februus to defend against complex adaptive attacks; notably, we realized the first defense against the advanced partial Trojan attack. To the best of our knowledge, Februus is the first backdoor defense method for operation at run-time capable of sanitizing Trojaned inputs without requiring anomaly detection methods, model retraining or costly labeled data.Comment: 16 pages, to appear in the 36th Annual Computer Security Applications Conference (ACSAC 2020

MHC class I loci of the Bar-Headed goose (Anser indicus)

MHC class I proteins mediate functions in anti-pathogen defense. MHC diversity has already been investigated by many studies in model avian species, but here we chose the bar-headed goose, a worldwide migrant bird, as a non-model avian species. Sequences from exons encoding the peptide-binding region (PBR) of MHC class I molecules were isolated from liver genomic DNA, to investigate variation in these genes. These are the first MHC class I partial sequences of the bar-headed goose to be reported. A preliminary analysis suggests the presence of at least four MHC class I genes, which share great similarity with those of the goose and duck. A phylogenetic analysis of bar-headed goose, goose and duck MHC class I sequences using the NJ method supports the idea that they all cluster within the anseriforms clade

Access Point Selection and Clustering Methods with Minimal Switching for Green Cell-Free Massive MIMO Networks

As a novel beyond fifth-generation (5G) concept, cell-free massive MIMO (multiple-input multiple-output) recently has become a promising physical-layer technology where an enormous number of distributed access points (APs), coordinated by a central processing unit (CPU), cooperate to coherently serve a large number of user equipments (UEs) in the same time/frequency resource. However, denser AP deployment in cell-free networks as well as an exponentially growing number of mobile UEs lead to higher power consumption. What is more, similar to conventional cellular networks, cell-free massive MIMO networks are dimensioned to provide the required quality of service (QoS) to the UEs under heavy traffic load conditions, and thus they might be underutilized during low traffic load periods, leading to inefficient use of both spectral and energy resources. Aiming at the implementation of energy-efficient cell-free networks, several approaches have been proposed in the literature, which consider different AP switch ON/OFF (ASO) strategies for power minimization. Different from prior works, this thesis focuses on additional factors other than ASO that have an adverse effect not only on total power consumption but also on implementation complexity and operation cost. For instance, too frequent ON/OFF switching in an AP can lead to tapering off the potential power saving of ASO by incurring extra power consumption due to excessive switching. Indeed, frequent switching of APs might also result in thermal fatigue and serious lifetime degeneration. Moreover, time variations in the AP-UE association in favor of energy saving in a dynamic network bring additional signaling and implementation complexity. Thus, in the first part of the thesis, we propose a multi-objective optimization problem that aims to minimize the total power consumption together with AP switching and AP-UE association variations in comparison to the state of the network in the previous state. The proposed problem is cast in mixed integer quadratic programming form and solved optimally. Our simulation results show that by limiting AP switching (node switching) and AP-UE association reformation switching (link switching), the total power consumption from APs only slightly increases but the number of average switching drops significantly regardless of node switching or link switching. It achieves a good balance on the trade-off between radio power consumption and the side effects excessive switching will bring. In the second part of the thesis, we consider a larger cell-free massive MIMO network by dividing the total area into disjoint network-centric clusters, where the APs in each cluster are connected to a separate CPU. In each cluster, cell-free joint transmission is locally implemented to achieve a scalable network implementation. Motivated by the outcomes of the first part, we reshape our dynamic network simulator to keep the active APs for a given spatial traffic pattern the same as long as the mean arrival rates of the UEs are constant. Moreover, the initially formed AP-UE association for a particular UE is not allowed to change. In that way, we make the number of node and link switching zero throughout the considered time interval. For this dynamic network, we propose a deep reinforcement learning (DRL) framework that learns the policy of maximizing long-term energy efficiency (EE) for a given spatially-varying traffic density. The active AP density of each network-centric cluster and the boundaries of the clusters are learned by the trained agent to maximize the EE. The DRL algorithm is shown to learn a non-trivial joint cluster geometry and AP density with at least 7% improvement in terms of EE compared to the heuristically-developed benchmarks.Som ett nytt koncept bortom den femte generationen (5G) har cellfri massiv MIMO (multiple input multiple output) nyligen blivit en lovande teknik för det fysiska lagret där ett enormt antal distribuerade åtkomstpunkter (AP), som samordnas av en central processorenhet (CPU), samarbetar för att på ett sammanhängande sätt betjäna ett stort antal användarutrustningar (UE) i samma tids- och frekvensresurs. En tätare utplacering av AP:er i cellfria nät samt ett exponentiellt växande antal mobila användare leder dock till högre energiförbrukning. Dessutom är cellfria massiva MIMO-nät, i likhet med konventionella cellulära nät, dimensionerade för att ge den erforderliga tjänstekvaliteten (QoS) till enheterna under förhållanden med hög trafikbelastning, och därför kan de vara underutnyttjade under perioder med låg trafikbelastning, vilket leder till ineffektiv användning av både spektral- och energiresurser. För att genomföra energieffektiva cellfria nät har flera metoder föreslagits i litteraturen, där olika ASO-strategier (AP switch ON/OFF) beaktas för att minimera energiförbrukningen. Till skillnad från tidigare arbeten fokuserar den här avhandlingen på andra faktorer än ASO som har en negativ effekt inte bara på den totala energiförbrukningen utan också på komplexiteten i genomförandet och driftskostnaden. Till exempel kan alltför frekventa ON/OFF-omkopplingar i en AP leda till att ASO:s potentiella energibesparingar avtar genom extra energiförbrukning på grund av överdriven omkoppling. Frekventa omkopplingar av AP:er kan också leda till termisk trötthet och allvarlig försämring av livslängden. Dessutom medför tidsvariationer i AP-UE-associationen till förmån för energibesparingar i ett dynamiskt nät ytterligare signalering och komplexitet i genomförandet. I den första delen av avhandlingen föreslår vi därför ett optimeringsproblem med flera mål som syftar till att minimera den totala energiförbrukningen tillsammans med växling av AP och variationer i AP-UE-associationen i jämförelse med nätets tillstånd i det föregående läget. Det föreslagna problemet är en blandad helhetsmässig kvadratisk programmering och löses optimalt. Våra simuleringsresultat visar att genom att begränsa växling av AP (node switching) och växling av AP-UE-association (link switching) ökar den totala energiförbrukningen från AP:erna endast något, men antalet genomsnittliga växlingar ökar, oavsett om det rör sig om node switching eller link switching. Det ger en bra balans mellan radiokraftförbrukning och de bieffekter som överdriven växling medför. I den andra delen av avhandlingen tar vi hänsyn till ett större cellfritt massivt MIMO-nätverk genom att dela upp det totala området i disjunkta nätverkscentrerade kluster, där AP:erna i varje kluster är anslutna till en separat CPU. I varje kluster genomförs cellfri gemensam överföring lokalt för att uppnå en skalbar nätverksimplementering. Motiverat av resultaten i den första delen omformar vi vår dynamiska nätverkssimulator så att de aktiva AP:erna för ett givet rumsligt trafikmönster är desamma så länge som den genomsnittliga ankomsthastigheten för de enskilda enheterna är konstant. Dessutom tillåts inte den ursprungligen bildade AP-UE-associationen för en viss användare att förändras. På så sätt gör vi antalet nod- och länkbyten till noll under hela det aktuella tidsintervallet. För detta dynamiska nätverk föreslår vi ett ramverk för djup förstärkningsinlärning (DRL) som lär sig en strategi för att maximera energieffektiviteten på lång sikt för en given rumsligt varierande trafiktäthet. Den aktiva AP-tätheten i varje nätverkscentrerat kluster och klustrens gränser lärs av den utbildade agenten för att maximera EE. Det visas att DRL-algoritmen lär sig en icke-trivial gemensam klustergeometri och AP-täthet med minst 7% förbättring av EE jämfört med de heuristiskt utvecklade riktmärkena