arXiv subjects

Jie Gao

Publications and source records attributed to Jie Gao.

At least 199 records · Page 11Linked to original sources

arXiv2019

A Price-Based Iterative Double Auction for Charger Sharing Markets

The unprecedented growth of demand for charging electric vehicles (EVs) calls for novel expansion solutions to today's charging networks. Riding on the wave of the proliferation of sharing economy, Airbnb-like charger sharing markets opens the opportunity to expand the existing charging networks without requiring costly and time-consuming infrastructure investments, yet the successful design of such markets relies on innovations at the interface between game theory, mechanism design, and large scale optimization. In this paper, we propose a price-based iterative double auction for charger sharing markets where charger owners rent out their under-utilized chargers to the charge-needing EV drivers. Charger owners and EV drivers form a two-sided market which is cleared by a price-based double auction. Chargers' locations, availability, and time unit costs as well as the EV drivers' time, distance constraints, and preferences are considered in the allocation and scheduling process. The goal is to compute social welfare maximizing allocations which benefits both charger owners and EV drivers and, in turn, ensure the continuous growth of the market. We prove that the proposed double auction is budget balanced, individually rational, and that it is a weakly dominant strategy for EV drivers and charger owners to truthfully report their charging time constraints. In addition, results from our computation study show that the double auction achieves on average 94% efficiency compared with the optimal solutions and scales well to larger problem instances.

cs.GT↗

arXiv2019

Performing Co-Membership Attacks Against Deep Generative Models

In this paper we propose a new membership attack method called co-membership attacks against deep generative models including Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). Specifically, membership attack aims to check whether a given instance x was used in the training data or not. A co-membership attack checks whether the given bundle of n instances were in the training, with the prior knowledge that the bundle was either entirely used in the training or none at all. Successful membership attacks can compromise the privacy of training data when the generative model is published. Our main idea is to cast membership inference of target data x as the optimization of another neural network (called the attacker network) to search for the latent encoding to reproduce x. The final reconstruction error is used directly to conclude whether x was in the training data or not. We conduct extensive experiments on a variety of datasets and generative models showing that: our attacker network outperforms prior membership attacks; co-membership attacks can be substantially more powerful than single attacks; and VAEs are more susceptible to membership attacks compared to GANs.

cs.LG↗

arXiv2019

Network Utility Maximization based on Incentive Mechanism for Truthful Reporting of Local Information

Classic network utility maximization problems are usually solved assuming all information is available, implying that information not locally available is always truthfully reported. This may not be practical in all scenarios, especially in distributed/semi-distributed networks. In this paper, incentive for truthful reporting in network optimizations with local information is studied. A novel general model for extending network utility maximization (NUM) problems to incorporate local information is proposed, which allows each user to choose its own objective locally and/or privately. Two specific problems, i.e., a user-centric problem (UCP) and a network-centric problem (NCP), are studied. In the UCP, a network center aims to maximize the collective benefit of all users, and truthful reporting from the users regarding their local information is necessary for finding the solution. We show that the widely-adopted dual pricing cannot guarantee truthful information reporting from a user unless the resource is over-supplied or the price is too high for this user to afford. In the NCP, the network center has its own objective and preferred solution, and incentive needs to be provided for the users to adopt the solution it prefers. Truthful reporting from users is necessary for the center to determine the incentives and achieve its solution. For two-user and multiuser cases, we propose two mechanisms to motivate truthful reporting from users while guaranteeing nonnegative utility gains for both the users and the center. A case study on underlay D2D communication illustrates the application of the UCP and NCP. Simulations are conducted for the D2D application to validate the analytical results and demonstrate the proposed mechanisms.

cs.GT↗

arXiv2019

Contention Intensity based Distributed Coordination for V2V Safety Message Broadcast

In this paper, we propose a contention intensity based distributed coordination (CIDC) scheme for safety message broadcast. By exploiting the high-frequency and periodical features of the safety message broadcast, the application-layer design of the CIDC enables each vehicle to estimate the instantaneous channel contention intensity in a fully distributed manner. With the contention intensity information, the MAC layer design of CIDC allows vehicles to adopt a better channel access strategy compared to the 802.11p. This is because CIDC selects the initial back-off counter for each new packet deterministically, i.e., based on the contention intensity, instead of randomly. The proposed CIDC is modeled, and key performance indicators in terms of the packet collision probability and average contention delay, are derived. It is shown that the proposed change in the initial counter selection leads to a system model completely different from the classic Markov chain based model. Moreover, the proposed CIDC, fully distributed and compatible with the 802.11p, can achieve both a much lower collision probability and a smaller contention delay compared with 802.11p at the cost of a small communication and computation overhead. Extensive simulation results demonstrate the effectiveness of the CIDC in both of the accurate and the erroneous contention intensity estimation scenarios.

eess.SY↗

arXiv2019

The Study of Dynamic Caching via State Transition Field -- the Case of Time-Invariant Popularity

This two-part paper investigates cache replacement schemes with the objective of developing a general model to unify the analysis of various replacement schemes and illustrate their features. To achieve this goal, we study the dynamic process of caching in the vector space and introduce the concept of state transition field (STF) to model and characterize replacement schemes. In the first part of this work, we consider the case of time-invariant content popularity based on the independent reference model (IRM). In such case, we demonstrate that the resulting STFs are static, and each replacement scheme leads to a unique STF. The STF determines the expected trace of the dynamic change in the cache state distribution, as a result of content requests and replacements, from any initial point. Moreover, given the replacement scheme, the STF is only determined by the content popularity. Using four example schemes including random replacement (RR) and least recently used (LRU), we show that the STF can be used to analyze replacement schemes such as finding their steady states, highlighting their differences, and revealing insights regarding the impact of knowledge of content popularity. Based on the above results, STF is shown to be useful for characterizing and illustrating replacement schemes. Extensive numeric results are presented to demonstrate analytical STFs and STFs from simulations for the considered example replacement schemes.

cs.OS↗

arXiv2019

The Study of Dynamic Caching via State Transition Field -- the Case of Time-Varying Popularity

In the second part of this two-part paper, we extend the study of dynamic caching via state transition field (STF) to the case of time-varying content popularity. The objective of this part is to investigate the impact of time-varying content popularity on the STF and how such impact accumulates to affect the performance of a replacement scheme. Unlike the case in the first part, the STF is no longer static over time, and we introduce instantaneous STF to model it. Moreover, we demonstrate that many metrics, such as instantaneous state caching probability and average cache hit probability over an arbitrary sequence of requests, can be found using the instantaneous STF. As a steady state may not exist under time-varying content popularity, we characterize the performance of replacement schemes based on how the instantaneous STF of a replacement scheme after a content request impacts on its cache hit probability at the next request. From this characterization, insights regarding the relations between the pattern of change in the content popularity, the knowledge of content popularity exploited by the replacement schemes, and the effectiveness of these schemes under time-varying popularity are revealed. In the simulations, different patterns of time-varying popularity, including the shot noise model, are experimented. The effectiveness of example replacement schemes under time-varying popularity is demonstrated, and the numerical results support the observations from the analytic results.

cs.OS↗

arXiv2019

On Privacy of Socially Contagious Attributes

A commonly used method to protect user privacy in data collection is to perform randomized perturbation on user's real data before collection so that aggregated statistics can still be inferred without endangering secrets held by individuals. In this paper, we take a closer look at the validity of Differential Privacy guarantees, when the sensitive attributes are subject to social influence and contagions. We first show that in the absence of any knowledge about the contagion network, an adversary that tries to predict the real values from perturbed ones, cannot achieve an area under the ROC curve (AUC) above $1-(1-δ)/(1+e^\varepsilon)$, if the dataset is perturbed using an $(\varepsilon,δ)$-differentially private mechanism. Then, we show that with the knowledge of the contagion network and model, one can do significantly better. We demonstrate that our method passes the performance limit imposed by differential privacy. Our experiments also reveal that nodes with high influence on others are at more risk of revealing their secrets than others. The performance is shown through extensive experiments on synthetic and real-world networks.

cs.SI↗

arXiv2019

Neural Language Model Based Training Data Augmentation for Weakly Supervised Early Rumor Detection

The scarcity and class imbalance of training data are known issues in current rumor detection tasks. We propose a straight-forward and general-purpose data augmentation technique which is beneficial to early rumor detection relying on event propagation patterns. The key idea is to exploit massive unlabeled event data sets on social media to augment limited labeled rumor source tweets. This work is based on rumor spreading patterns revealed by recent rumor studies and semantic relatedness between labeled and unlabeled data. A state-of-the-art neural language model (NLM) and large credibility-focused Twitter corpora are employed to learn context-sensitive representations of rumor tweets. Six different real-world events based on three publicly available rumor datasets are employed in our experiments to provide a comparative evaluation of the effectiveness of the method. The results show that our method can expand the size of an existing rumor data set nearly by 200% and corresponding social context (i.e., conversational threads) by 100% with reasonable quality. Preliminary experiments with a state-of-the-art deep learning-based rumor detection model show that augmented data can alleviate over-fitting and class imbalance caused by limited train data and can help to train complex neural networks (NNs). With augmented data, the performance of rumor detection can be improved by 12.1% in terms of F-score. Our experiments also indicate that augmented training data can help to generalize rumor detection models on unseen rumors.

cs.CL↗

arXiv2019

Topology Based Scalable Graph Kernels

We propose a new graph kernel for graph classification and comparison using Ollivier Ricci curvature. The Ricci curvature of an edge in a graph describes the connectivity in the local neighborhood. An edge in a densely connected neighborhood has positive curvature and an edge serving as a local bridge has negative curvature. We use the edge curvature distribution to form a graph kernel which is then used to compare and cluster graphs. The curvature kernel uses purely the graph topology and thereby works for settings when node attributes are not available.

cs.LG↗

arXiv2019

Community Detection on Networks with Ricci Flow

Many complex networks in the real world have community structures -- groups of well-connected nodes with important functional roles. It has been well recognized that the identification of communities bears numerous practical applications. While existing approaches mainly apply statistical or graph theoretical/combinatorial methods for community detection, in this paper, we present a novel geometric approach which enables us to borrow powerful classical geometric methods and properties. By considering networks as geometric objects and communities in a network as a geometric decomposition, we apply curvature and discrete Ricci flow, which have been used to decompose smooth manifolds with astonishing successes in mathematics, to break down communities in networks. We tested our method on networks with ground-truth community structures, and experimentally confirmed the effectiveness of this geometric approach.

cs.SI↗

arXiv2019

The International Linear Collider. A Global Project

A large, world-wide community of physicists is working to realise an exceptional physics program of energy-frontier, electron-positron collisions with the International Linear Collider (ILC). This program will begin with a central focus on high-precision and model-independent measurements of the Higgs boson couplings. This method of searching for new physics beyond the Standard Model is orthogonal to and complements the LHC physics program. The ILC at 250 GeV will also search for direct new physics in exotic Higgs decays and in pair-production of weakly interacting particles. Polarised electron and positron beams add unique opportunities to the physics reach. The ILC can be upgraded to higher energy, enabling precision studies of the top quark and measurement of the top Yukawa coupling and the Higgs self-coupling. The key accelerator technology, superconducting radio-frequency cavities, has matured. Optimised collider and detector designs, and associated physics analyses, were presented in the ILC Technical Design Report, signed by 2400 scientists. There is a strong interest in Japan to host this international effort. A detailed review of the many aspects of the project is nearing a conclusion in Japan. Now the Japanese government is preparing for a decision on the next phase of international negotiations, that could lead to a project start within a few years. The potential timeline of the ILC project includes an initial phase of about 4 years to obtain international agreements, complete engineering design and prepare construction, and form the requisite international collaboration, followed by a construction phase of 9 years.

hep-ex↗

arXiv2018

Topology optimization of micro-structured materials featured with the specific mechanical properties

Micro-structured materials consisting of an array of microstructures are engineered to provide the specific material properties. This present work investigates the design of cellular materials under the framework of level set, so as to optimize the topologies and shapes of these porous material microstructures. Firstly, the energy-based homogenization method (EBHM) is applied to evaluate the material effective properties based on the topology of the material cell, where the effective elasticity property is evaluated by the average stress and strain theorems. Secondly, a parametric level set method (PLSM) is employed to optimize the microstructural topology until the specific mechanical properties can be achieved, including the maximum bulk modulus, the maximum shear modulus and their combinations, as well as the negative Poisson's ratio (NPR). The complicated topological shape optimization of the material microstructure has been equivalent to evolve the sizes of the expansion coefficients in the interpolation of the level set function. Finally, several numerical examples are fully discussed to demonstrate the effectiveness of the developed method. A series of new and interesting material cells with the specific mechanical properties can be found.

cs.CE↗

arXiv2018

Improved Bounds on Information Dissemination by Manhattan Random Waypoint Model

With the popularity of portable wireless devices it is important to model and predict how information or contagions spread by natural human mobility -- for understanding the spreading of deadly infectious diseases and for improving delay tolerant communication schemes. Formally, we model this problem by considering $M$ moving agents, where each agent initially carries a \emph{distinct} bit of information. When two agents are at the same location or in close proximity to one another, they share all their information with each other. We would like to know the time it takes until all bits of information reach all agents, called the \textit{flood time}, and how it depends on the way agents move, the size and shape of the network and the number of agents moving in the network. We provide rigorous analysis for the \MRWP model (which takes paths with minimum number of turns), a convenient model used previously to analyze mobile agents, and find that with high probability the flood time is bounded by $O\big(N\log M\lceil(N/M) \log(NM)\rceil\big)$, where $M$ agents move on an $N\times N$ grid. In addition to extensive simulations, we use a data set of taxi trajectories to show that our method can successfully predict flood times in both experimental settings and the real world.

cs.MA↗

arXiv2018

Network Alignment by Discrete Ollivier-Ricci Flow

In this paper, we consider the problem of approximately aligning/matching two graphs. Given two graphs $G_{1}=(V_{1},E_{1})$ and $G_{2}=(V_{2},E_{2})$, the objective is to map nodes $u, v \in G_1$ to nodes $u',v'\in G_2$ such that when $u, v$ have an edge in $G_1$, very likely their corresponding nodes $u', v'$ in $G_2$ are connected as well. This problem with subgraph isomorphism as a special case has extra challenges when we consider matching complex networks exhibiting the small world phenomena. In this work, we propose to use `Ricci flow metric', to define the distance between two nodes in a network. This is then used to define similarity of a pair of nodes in two networks respectively, which is the crucial step of network alignment. %computed by discrete graph curvatures and graph Ricci flows. Specifically, the Ricci curvature of an edge describes intuitively how well the local neighborhood is connected. The graph Ricci flow uniformizes discrete Ricci curvature and induces a Ricci flow metric that is insensitive to node/edge insertions and deletions. With the new metric, we can map a node in $G_1$ to a node in $G_2$ whose distance vector to only a few preselected landmarks is the most similar. The robustness of the graph metric makes it outperform other methods when tested on various complex graph models and real world network data sets (Emails, Internet, and protein interaction networks)\footnote{The source code of computing Ricci curvature and Ricci flow metric are available: https://github.com/saibalmars/GraphRicciCurvature}.

cs.SI↗

arXiv2018

A Driver ASIC for Scientific CCD Detectors Using 180nm Technology

In order to achieve the driver function for several types of scientific CCD detector from E2V Co Ltd, and decreasing the size of electronics of CCD detector system, an Application-specified Integrated Circuit (ASIC) was designed. It provides multi-channel clocks and bias voltage for CCD driver. In the ASIC, the clock drivers are made of a clock switch circuit and high voltage amplifier. Two 8-bit current-steering DACs are used to adjust the driver capability and high-level voltage of clocks. The bias drivers are generated by 8-bit current-steering DACs and off-chip operation amplifiers. The Global Foundry 180 nm BCDlite technology is selected to implement this design. The first version of design has been finished and the tests have been done.

physics.ins-det↗

arXiv2018

SemRe-Rank: Improving Automatic Term Extraction By Incorporating Semantic Relatedness With Personalised PageRank

Automatic Term Extraction deals with the extraction of terminology from a domain specific corpus, and has long been an established research area in data and knowledge acquisition. ATE remains a challenging task as it is known that there is no existing ATE methods that can consistently outperform others in any domain. This work adopts a refreshed perspective to this problem: instead of searching for such a 'one-size-fit-all' solution that may never exist, we propose to develop generic methods to 'enhance' existing ATE methods. We introduce SemRe-Rank, the first method based on this principle, to incorporate semantic relatedness - an often overlooked venue - into an existing ATE method to further improve its performance. SemRe-Rank incorporates word embeddings into a personalised PageRank process to compute 'semantic importance' scores for candidate terms from a graph of semantically related words (nodes), which are then used to revise the scores of candidate terms computed by a base ATE algorithm. Extensively evaluated with 13 state-of-the-art base ATE methods on four datasets of diverse nature, it is shown to have achieved widespread improvement over all base methods and across all datasets, with up to 15 percentage points when measured by the Precision in the top ranked K candidate terms (the average for a set of K's), or up to 28 percentage points in F1 measured at a K that equals to the expected real terms in the candidates (F1 in short). Compared to an alternative approach built on the well-known TextRank algorithm, SemRe-Rank can potentially outperform by up to 8 points in Precision at top K, or up to 17 points in F1.

cs.IR↗

arXiv2018

SMDP-based Downlink Packet Scheduling Scheme for Solar Energy Assisted Heterogeneous Networks

Renewable energy assisted heterogeneous networks can improve system capacity and reduce conventional energy consumption. In this paper, we propose a semi-Markov decision process (SMDP)-based downlink packet scheduling scheme for solar energy assisted heterogeneous networks (HetNets), where solar radiation is modeled as a continuous-time Markov chain (CTMC) and the arrivals of multi-class downlink packets are modeled as Poisson processes. The proposed downlink packet scheduling scheme can be compatible with the mainstream wireless packet networks such as long-term evolution (LTE) networks and the fifth-generation (5G) networks because the SMDP is a real-time admission control model. To obtain an asymptotically optimal downlink packet scheduling policy, we solve the semi-Markov decision problem using the relative value iteration algorithm under average criterion and the value iteration algorithm under discounted criterion, respectively. The simulation results show that the average cost of the SMDP-based packet scheduling scheme is less than that of the greedy packet scheduling scheme.

cs.NI↗

arXiv2017

Study of $J/ψ$ ${\to}$ $D_{s,d}V$ decays with perturbative QCD approach

Inspired by the recent measurements on two-body nonleptonic $J/ψ$ weak decay at BESIII, the charm-changing $J/ψ$ ${\to}$ $D_{s,d}V$ weak decays are studied with perturbative QCD approach, where $V$ denotes $ρ$ and $K^{\ast}$ vector mesons. It is found that branching ratio for $J/ψ$ ${\to}$ $D_{s}ρ$ decay can reach up to ${\cal O}(10^{-9})$, which is within the potential measurement capability of the future high-luminosity experiments.

hep-ph↗