arXiv subjects

Ye Liu

Publications and source records attributed to Ye Liu.

196 records · Page 11Linked to original sources

arXiv2019

Binary-Tree Encoding for Uniform Binary Sources in Index Modulation Systems

The problem of designing bit-to-pattern mappings and power allocation schemes for orthogonal frequency-division multiplexing (OFDM) systems that employ subcarrier index modulation (IM) is considered. We assume the binary source conveys a stream of independent, uniformly distributed bits to the pattern mapper, which introduces a constraint on the pattern transmission probability distribution that can be quantified using a binary tree formalism. Under this constraint, we undertake the task of maximizing the achievable rate subject to the availability of channel knowledge at the transmitter. The optimization variables are the pattern probability distribution (i.e., the bit-to-pattern mapping) and the transmit powers allocated to active subcarriers. To solve the problem, we first consider the relaxed problem where pattern probabilities are allowed to take any values in the interval [0,1] subject to a sum probability constraint. We develop (approximately) optimal solutions to the relaxed problem by using new bounds and asymptotic results, and then use a novel heuristic algorithm to project the relaxed solution onto a point in the feasible set of the constrained problem. Numerical analysis shows that this approach is capable of achieving the maximum mutual information for the relaxed problem in low and high-SNR regimes and offers noticeable benefits in terms of achievable rate relative to a conventional OFDM-IM benchmark.

cs.IT↗

arXiv2018

On environmental contours for marine and coastal design

Environmental contours are used in structural reliability analysis of marine and coastal structures as an approximate means to locate the boundary of the distribution of environmental variables, and hence sets of environmental conditions giving rise to extreme structural loads and responses. Outline guidance concerning the application of environmental contour methods is given in recent design guidelines from many organisations. However there is lack of clarity concerning the differences between approaches to environmental contour estimation reported in the literature, and regarding the relationship between the environmental contour, corresponding to some return period, and the extreme structural response for the same period. Hence there is uncertainty about precisely when environmental contours should be used, and how they should be used well. This article seeks to provide some assistance in understanding the fundamental issues regarding environmental contours and their use in structural reliability analysis. Approaches to estimating the joint distribution of environmental variables, and to estimating environmental contours based on that distribution, are described. Simple software for estimation of the joint distribution, and hence environmental contours, is illustrated (and is freely available from the authors). Extra assumptions required to relate the characteristics of environmental contour to structural failure are outlined. Alternative response-based methods not requiring environmental contours are summarised. The results of an informal survey of the metocean user community regarding environmental contours are presented. Finally, recommendations about when and how environmental contour methods should be used are made.

stat.AP↗

arXiv2018

Product Title Refinement via Multi-Modal Generative Adversarial Learning

Nowadays, an increasing number of customers are in favor of using E-commerce Apps to browse and purchase products. Since merchants are usually inclined to employ redundant and over-informative product titles to attract customers' attention, it is of great importance to concisely display short product titles on limited screen of cell phones. Previous researchers mainly consider textual information of long product titles and lack of human-like view during training and evaluation procedure. In this paper, we propose a Multi-Modal Generative Adversarial Network (MM-GAN) for short product title generation, which innovatively incorporates image information, attribute tags from the product and the textual information from original long titles. MM-GAN treats short titles generation as a reinforcement learning process, where the generated titles are evaluated by the discriminator in a human-like view.

cs.CL↗

arXiv2018

Data-driven Blockbuster Planning on Online Movie Knowledge Library

In the era of big data, logistic planning can be made data-driven to take advantage of accumulated knowledge in the past. While in the movie industry, movie planning can also exploit the existing online movie knowledge library to achieve better results. However, it is ineffective to solely rely on conventional heuristics for movie planning, due to a large number of existing movies and various real-world factors that contribute to the success of each movie, such as the movie genre, available budget, production team (involving actor, actress, director, and writer), etc. In this paper, we study a "Blockbuster Planning" (BP) problem to learn from previous movies and plan for low budget yet high return new movies in a totally data-driven fashion. After a thorough investigation of an online movie knowledge library, a novel movie planning framework "Blockbuster Planning with Maximized Movie Configuration Acquaintance" (BigMovie) is introduced in this paper. From the investment perspective, BigMovie maximizes the estimated gross of the planned movies with a given budget. It is able to accurately estimate the movie gross with a 0.26 mean absolute percentage error (and 0.16 for budget). Meanwhile, from the production team's perspective, BigMovie is able to formulate an optimized team with people/movie genres that team members are acquainted with. Historical collaboration records are utilized to estimate acquaintance scores of movie configuration factors via an acquaintance tensor. We formulate the BP problem as a non-linear binary programming problem and prove its NP-hardness. To solve it in polynomial time, BigMovie relaxes the hard binary constraints and addresses the BP problem as a cubic programming problem. Extensive experiments conducted on IMDB movie database demonstrate the capability of BigMovie for an effective data-driven blockbuster planning.

cs.AI↗

arXiv2018

ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection

Decentralized cryptocurrencies feature the use of blockchain to transfer values among peers on networks without central agency. Smart contracts are programs running on top of the blockchain consensus protocol to enable people make agreements while minimizing trusts. Millions of smart contracts have been deployed in various decentralized applications. The security vulnerabilities within those smart contracts pose significant threats to their applications. Indeed, many critical security vulnerabilities within smart contracts on Ethereum platform have caused huge financial losses to their users. In this work, we present ContractFuzzer, a novel fuzzer to test Ethereum smart contracts for security vulnerabilities. ContractFuzzer generates fuzzing inputs based on the ABI specifications of smart contracts, defines test oracles to detect security vulnerabilities, instruments the EVM to log smart contracts runtime behaviors, and analyzes these logs to report security vulnerabilities. Our fuzzing of 6991 smart contracts has flagged more than 459 vulnerabilities with high precision. In particular, our fuzzing tool successfully detects the vulnerability of the DAO contract that leads to USD 60 million loss and the vulnerabilities of Parity Wallet that have led to the loss of $30 million and the freezing of USD 150 million worth of Ether.

cs.SE↗

arXiv2018

Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis

Network analysis of human brain connectivity is critically important for understanding brain function and disease states. Embedding a brain network as a whole graph instance into a meaningful low-dimensional representation can be used to investigate disease mechanisms and inform therapeutic interventions. Moreover, by exploiting information from multiple neuroimaging modalities or views, we are able to obtain an embedding that is more useful than the embedding learned from an individual view. Therefore, multi-view multi-graph embedding becomes a crucial task. Currently, only a few studies have been devoted to this topic, and most of them focus on the vector-based strategy which will cause structural information contained in the original graphs lost. As a novel attempt to tackle this problem, we propose Multi-view Multi-graph Embedding (M2E) by stacking multi-graphs into multiple partially-symmetric tensors and using tensor techniques to simultaneously leverage the dependencies and correlations among multi-view and multi-graph brain networks. Extensive experiments on real HIV and bipolar disorder brain network datasets demonstrate the superior performance of M2E on clustering brain networks by leveraging the multi-view multi-graph interactions.

cs.LG↗

arXiv2018

A Quantitative Approach in Heuristic Evaluation of E-commerce Websites

This paper presents a pilot study on developing an instrument to predict the quality of e-commerce websites. The 8C model was adopted as the reference model of the heuristic evaluation. Each dimension of the 8C was mapped into a set of quantitative website elements, selected websites were scraped to get the quantitative website elements, and the score of each dimension was calculated. A software was developed in PHP for the experiments. In the training process, 10 experiments were conducted and quantitative analyses were regressively conducted between the experiments. The conversion rate was used to verify the heuristic evaluation of an e-commerce website after each experiment. The results showed that the mapping revisions between the experiments improved the performance of the evaluation instrument, therefore the experiment process and the quantitative mapping revision guideline proposed was on the right track. The software resulted from the experiment 10 can serve as the aimed e-commerce website evaluation instrument. The experiment results and the future work have been discussed.

cs.HC↗

arXiv2017

Second mod $2$ homology of Artin groups

In this paper, we compute the second mod $2$ homology of an arbitrary Artin group, without assuming the $K(π,1)$ conjecture. The key ingredients are (A) Hopf's formula for the second integral homology of a group and (B) Howlett's result on the second integral homology of Coxeter groups.

math.AT↗

arXiv2016

Action2Activity: Recognizing Complex Activities from Sensor Data

As compared to simple actions, activities are much more complex, but semantically consistent with a human's real life. Techniques for action recognition from sensor generated data are mature. However, there has been relatively little work on bridging the gap between actions and activities. To this end, this paper presents a novel approach for complex activity recognition comprising of two components. The first component is temporal pattern mining, which provides a mid-level feature representation for activities, encodes temporal relatedness among actions, and captures the intrinsic properties of activities. The second component is adaptive Multi-Task Learning, which captures relatedness among activities and selects discriminant features. Extensive experiments on a real-world dataset demonstrate the effectiveness of our work.

cs.CV↗

arXiv2016

Predicting Urban Water Quality with Ubiquitous Data

Urban water quality is of great importance to our daily lives. Prediction of urban water quality help control water pollution and protect human health. However, predicting the urban water quality is a challenging task since the water quality varies in urban spaces non-linearly and depends on multiple factors, such as meteorology, water usage patterns, and land uses. In this work, we forecast the water quality of a station over the next few hours from a data-driven perspective, using the water quality data and water hydraulic data reported by existing monitor stations and a variety of data sources we observed in the city, such as meteorology, pipe networks, structure of road networks, and point of interests (POIs). First, we identify the influential factors that affect the urban water quality via extensive experiments. Second, we present a multi-task multi-view learning method to fuse those multiple datasets from different domains into an unified learning model. We evaluate our method with real-world datasets, and the extensive experiments verify the advantages of our method over other baselines and demonstrate the effectiveness of our approach.

cs.CY↗

arXiv2016

On chromatic functors and stable partitions of graphs

The chromatic functor of a simple graph is a functorization of the chromatic polynomial. M. Yoshinaga showed in \cite{Yoshinaga2015} that two finite graphs have isomorphic chromatic functors if and only if they have the same chromatic polynomial. The key ingredient in the proof is the use of stable partitions of graphs. The latter is shown to be closely related to chromatic functors. In this note, we further investigate some interesting properties of chromatic functors associated to simple graphs using stable partitions. Our first result is the determination of the group of natural automorphisms of the chromatic functor, which is in general a larger group than the automorphism group of the graph. The second result is that the composition of the chromatic functor associated to a finite graph restricted to the category $\mathrm{FI}$ of finite sets and injections with the free functor into the category of complex vector spaces yields a consistent sequence of representations of symmetric groups which is representation stable in the sense of Church-Farb \cite{Church2013}.

math.CO↗

arXiv2016

Seasonal and Lunar month periods observed in natural neutron flux at high altitude

Air radon concentration measurement is useful for research on geophysical effects, but it is strongly sensitive to site geology and many geophysical and microclimatic processes such as wind, ventilation, air humidity and so on that induce very big fluctuations on the concentration of radon in air. On the contrary, monitoring the radon concentration in soil by measuring the thermal neutron flux reduces environmental effects. In this paper we report some experimental results on the natural thermal neutron flux as well as the concentration of air radon and its variations at 4300 m a.s.l. These results were obtained with unshielded thermal neutron scintillation detectors (en-detectors) and radon monitors located inside the ARGO-YBJ experimental hall. The correlation of these variations with the lunar month and 1-year period is undoubtedly confirmed. A method for earthquakes prediction provided by a global net of the en-detectors is currently under study.

physics.geo-ph↗

arXiv2016

Expectation on Observation of Supernova Remnants with the LHAASO Project

Supernova remnants (SNRs) are believed to be the most important acceleration sites for cosmic rays (CRs) below $\sim10^{15}$ eV in the Galaxy. High energy photons, either directly from the shocks of the SNRs or indirectly from the interaction between SNRs and the nearby clouds, are crucial probes for the CR acceleration. Big progresses on observations of SNRs have been achieved by space- and ground-based $γ$-ray facilities. However, whether $γ$-rays come from accelerated hadrons or not, as well as their connection with the CRs observed at Earth, remains in debate. Large High Altitude Air Shower Observatory (LHAASO), the next generation experiment, is designed to survey the northern part of the very high energy $γ$-ray sky from $\sim 0.3$ TeV to PeV with the sensitivity of $\lesssim1\%$ of the Crab nebula flux. In this paper, we indicate that LHAASO will be dedicated to enlarging the $γ$-ray SNR samples and improving the spectral and morphological measurements. These measurements, especially at energies above 30 TeV, will be important for us to finally understand the CR acceleration in SNRs.

astro-ph.HE↗

arXiv2016

Vanishing ranges for the mod $p$ cohomology of alternating subgroups of Coxeter groups

We obtain vanishing ranges for the mod $p$ cohomology of alternating subgroups of finite $p$-free Coxeter groups. Here a Coxeter group $W$ is $p$-free if the order of the product $st$ is prime to $p$ for every pair of Coxeter generators $s,t$ of $W$. Our result generalizes those for alternating groups formerly proved by Kleshchev-Nakano and Burichenko. As a byproduct, we obtain vanishing ranges for the twisted cohomology of finite $p$-free Coxeter groups with coefficients in the sign representations. In addition, a weak version of the main result is proved for a certain class of infinite Coxeter groups.

math.AT↗

arXiv2015

Test study on the RPS of TMSR-SF1 reactor

The reactor protection system (RPS), as a 1E-level safety system, should be designed and developed following a series of nuclear laws and technical disciplines.

physics.ins-det↗

arXiv2015

Simultaneous Feature Learning and Hash Coding with Deep Neural Networks

Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale image retrieval tasks. For most existing hashing methods, an image is first encoded as a vector of hand-engineering visual features, followed by another separate projection or quantization step that generates binary codes. However, such visual feature vectors may not be optimally compatible with the coding process, thus producing sub-optimal hashing codes. In this paper, we propose a deep architecture for supervised hashing, in which images are mapped into binary codes via carefully designed deep neural networks. The pipeline of the proposed deep architecture consists of three building blocks: 1) a sub-network with a stack of convolution layers to produce the effective intermediate image features; 2) a divide-and-encode module to divide the intermediate image features into multiple branches, each encoded into one hash bit; and 3) a triplet ranking loss designed to characterize that one image is more similar to the second image than to the third one. Extensive evaluations on several benchmark image datasets show that the proposed simultaneous feature learning and hash coding pipeline brings substantial improvements over other state-of-the-art supervised or unsupervised hashing methods.

cs.CV↗