arXiv subjects

Zhang

Publications and source records attributed to Zhang.

At least 91 records · Page 5Linked to original sources

arXiv2020

Undetectable Radios: Covert Communication under Spectral Mask Constraints

We consider the problem of covert communication over continuous-time additive white Gaussian noise (AWGN) channels under spectral mask constraints, wherein two legitimate parties attempt to communicate reliably in the presence of an eavesdropper that should be unable to estimate if communication takes place. The spectral mask constraint is imposed to restrict excessive radiation beyond the bandwidth of interest. We develop a communication scheme with theoretical reliability and covertness guarantees based on pulse amplitude modulation (PAM) with Binary Phase Shift Keying (BPSK) and root raised cosine (RRC) carrier pulses. Given a fixed transmission duration T and a spectral mask with bandwidth parameter W, we show that one can reliably and covertly transmit $O(\sqrt{WT})$ bits of information. We characterize the constant behind the $O$ and show that it is tight under some conditions.

cs.IT↗

arXiv2019

Double-Sparsity Learning Based Channel-and-Signal Estimation in Massive MIMO with Generalized Spatial Modulation

In this paper, we study joint antenna activity detection, channel estimation, and multiuser detection for massive multiple-input multiple-output (MIMO) systems with general spatial modulation (GSM). We first establish a double-sparsity massive MIMO model by considering the channel sparsity of the massive MIMO channel and the signal sparsity of GSM. Based on the double-sparsity model, we formulate a blind detection problem. To solve the blind detection problem, we develop message-passing based blind channel-and-signal estimation (BCSE) algorithm. The BCSE algorithm basically follows the affine sparse matrix factorization technique, but with critical modifications to handle the double-sparsity property of the model. We show that the BCSE algorithm significantly outperforms the existing blind and training-based algorithms, and is able to closely approach the genie bounds (with either known channel or known signal). In the BCSE algorithm, short pilots are employed to remove the phase and permutation ambiguities after sparse matrix factorization. To utilize the short pilots more efficiently, we further develop the semi-blind channel-and-signal estimation (SBCSE) algorithm to incorporate the estimation of the phase and permutation ambiguities into the iterative message-passing process. We show that the SBCSE algorithm substantially outperforms the counterpart algorithms including the BCSE algorithm in the short-pilot regime.

cs.IT↗

arXiv2019

Deep Reinforcement Learning for Personalized Search Story Recommendation

In recent years, \emph{search story}, a combined display with other organic channels, has become a major source of user traffic on platforms such as e-commerce search platforms, news feed platforms and web and image search platforms. The recommended search story guides a user to identify her own preference and personal intent, which subsequently influences the user's real-time and long-term search behavior. %With such an increased importance of search stories, As search stories become increasingly important, in this work, we study the problem of personalized search story recommendation within a search engine, which aims to suggest a search story relevant to both a search keyword and an individual user's interest. To address the challenge of modeling both immediate and future values of recommended search stories (i.e., cross-channel effect), for which conventional supervised learning framework is not applicable, we resort to a Markov decision process and propose a deep reinforcement learning architecture trained by both imitation learning and reinforcement learning. We empirically demonstrate the effectiveness of our proposed approach through extensive experiments on real-world data sets from JD.com.

cs.LG↗

arXiv2019

Artificial Intelligence-Enabled Cellular Networks: A Critical Path to Beyond-5G and 6G

Mobile Network Operators (MNOs) are in process of overlaying their conventional macro cellular networks with shorter range cells such as outdoor pico cells. The resultant increase in network complexity creates substantial overhead in terms of operating expenses, time, and labor for their planning and management. Artificial intelligence (AI) offers the potential for MNOs to operate their networks in a more organic and cost-efficient manner. We argue that deploying AI in 5G and Beyond will require surmounting significant technical barriers in terms of robustness, performance, and complexity. We outline future research directions, identify top 5 challenges, and present a possible roadmap to realize the vision of AI-enabled cellular networks for Beyond-5G and 6G.

cs.IT↗

arXiv2019

A closed-loop all-electronic pixel-wise adaptive imaging system for high dynamic range video

We demonstrated a CMOS imaging system that adapts each pixel's exposure and sampling rate to capture high dynamic range (HDR) videos. The system consist of a custom designed image sensor with pixel-wise exposure configurability and a real-time pixel exposure controller. These parts operate in a closed-loop to sample, detect and optimize each pixel's exposure and sampling rate to minimize local region's underexposure, overexposure and motion blurring. Exposure control is implemented using all-integrated electronics without external optical modulation. This reduces overall system size and power consumption. The image sensor is implemented using a standard 130nm CMOS process while the exposure controller is implemented on a computer. We performed experiments under complex lighting and motion condition to test performance of the system, and demonstrate the benefit of pixel-wise adaptive imaging on the performance of computer vision tasks such as segmentation, motion estimation and object recognition.

eess.IV↗

arXiv2019

Self-Tuning Sectorization: Deep Reinforcement Learning Meets Broadcast Beam Optimization

Beamforming in multiple input multiple output (MIMO) systems is one of the key technologies for modern wireless communication. Creating appropriate sector-specific broadcast beams are essential for enhancing the coverage of cellular network and for improving the broadcast operation for control signals. However, in order to maximize the coverage, patterns for broadcast beams need to be adapted based on the users' distribution and movement over time. In this work, we present self-tuning sectorization: a deep reinforcement learning framework to optimize MIMO broadcast beams autonomously and dynamically based on user' distribution in the network. Taking directly UE measurement results as input, deep reinforcement learning agent can track and predict the UE distribution pattern and come up with the best broadcast beams for each cell. Extensive simulation results show that the introduced framework can achieve the optimal coverage, and converge to the oracle solution for both single sector and multiple sectors environment, and for both periodic and Markov mobility patterns.

eess.SP↗

arXiv2019

An Efficient Strategy to Generate Atom Connecting Positions

Atom connecting positions(ACPs) are positions where an atom is connecting to another one or a few atoms, which is needed when constructing final state used in chain-of-state(CoS) methods for transition state(TS) locating and minimum energy path(MEP) searching, especially with bond formation. However, ACPs are generated with chemical insight and experience, which is not only low efficient and time wasting, but the manually generated structure may be far from the optimized one. A efficient method is presented here for generating ACPs which is based on spherical optimization and VSEPR theory without manual interfering. Several examples are testified to prove the efficiency and robustness of the method.

physics.chem-ph↗

arXiv2019

Efficient Chain-of-States Approach for Locating Transition State via Spherical Optimization

The Chain-of-states(CoS) methods like nudge elastic band(NEB) method can be used to determine the minimum energy path (MEP) and transition state (TS) between two end local minima. However, the CoS methods are inefficient for difficult cases where the two ends are far apart with chemically insignificant part(s) in the MEP. We present here a method based on spherical optimization (SOPT), in which the SOPT method generates model end structures for CoS methods under the constraint of constant root-mean-square distance (RMSD) between two ends that is chosen to cover only the chemically significant part. We demonstrate the robustness and efficiency of our method with two examples, the CHOH dissociation channel and the first step of Aldol reaction. In both cases, the SOPT-based NEB calculations always reach the convergence to the correct MEPs with much less computational cost, whereas the regular NEB calculations fail under certain setups.

physics.chem-ph↗

arXiv2019

Proceedings of the Fifth International Conference on Cloud and Robotics (ICCR2018)

The 5th edition of the International Conference on Cloud and Robotics (ICCR 2018 - http://cloudrobotics.info) will be held on November 12-14 2018 in Paris and Saint-Quentin, France. The conference is a co-event with GDR ALROB and the industry exposition Robonumerique (http://www.robonumerique.fr). The domain of cloud robotics aims to converge robots with computation, storage and communication resources provided by the cloud. The cloud may complement robotic resources in several ways, including crowd-sourcing knowledge databases, context information, computational offloading or data-intensive information processing for artificial intelligence. Today, the paradigms of cloud/fog/edge computing propose software architecture solutions for robots to share computations or offload them to ambiant and networked resources. Yet, combining distant computations with the real time constraints of robotics is very challenging. As the challenges in this domain are multi-disciplinary and similar in other research areas, Cloud Robotics aims at building bridges among experts from academia and industry working in different fields, such as robotics, cyber-physical systems, automotive, aerospace, machine learning, artificial intelligence, software architecture, big data analytics, Internet-of-Things, networked control and distributed cloud systems.

cs.RO↗

arXiv2019

Enable Super-resolution Parameter Estimation for Mm-wave Channel Sounding

This paper investigates the capability of millimeter-wave (mmWave) channel sounders with phased arrays to perform super-resolution parameter estimation, i.e., determine the parameters of multipath components (MPC), such as direction of arrival and delay, with resolution better than the Fourier resolution of the setup. We analyze the question both generally, and with respect to a particular novel multi-beam mmWave channel sounder that is capable of performing multiple-input-multiple-output (MIMO) measurements in dynamic environments. We firstly propose a novel two-step calibration procedure that provides higher-accuracy calibration data that are required for Rimax or SAGE. Secondly, we investigate the impact of center misalignment and residual phase noise on the performance of the parameter estimator. Finally we experimentally verify the calibration results and demonstrate the capability of our sounder to perform super-resolution parameter estimation.

cs.IT↗

arXiv2018

Zero-Shot Learning via Latent Space Encoding

Zero-Shot Learning (ZSL) is typically achieved by resorting to a class semantic embedding space to transfer the knowledge from the seen classes to unseen ones. Capturing the common semantic characteristics between the visual modality and the class semantic modality (e.g., attributes or word vector) is a key to the success of ZSL. In this paper, we propose a novel encoder-decoder approach, namely Latent Space Encoding (LSE), to connect the semantic relations of different modalities. Instead of requiring a projection function to transfer information across different modalities like most previous work, LSE per- forms the interactions of different modalities via a feature aware latent space, which is learned in an implicit way. Specifically, different modalities are modeled separately but optimized jointly. For each modality, an encoder-decoder framework is performed to learn a feature aware latent space via jointly maximizing the recoverability of the original space from the latent space and the predictability of the latent space from the original space. To relate different modalities together, their features referring to the same concept are enforced to share the same latent codings. In this way, the common semantic characteristics of different modalities are generalized with the latent representations. Another property of the proposed approach is that it is easily extended to more modalities. Extensive experimental results on four benchmark datasets (AwA, CUB, aPY, and ImageNet) clearly demonstrate the superiority of the proposed approach on several ZSL tasks, including traditional ZSL, generalized ZSL, and zero-shot retrieval (ZSR).

cs.CV↗

arXiv2018

Deep Air Learning: Interpolation, Prediction, and Feature Analysis of Fine-grained Air Quality

The interpolation, prediction, and feature analysis of fine-gained air quality are three important topics in the area of urban air computing. The solutions to these topics can provide extremely useful information to support air pollution control, and consequently generate great societal and technical impacts. Most of the existing work solves the three problems separately by different models. In this paper, we propose a general and effective approach to solve the three problems in one model called the Deep Air Learning (DAL). The main idea of DAL lies in embedding feature selection and semi-supervised learning in different layers of the deep learning network. The proposed approach utilizes the information pertaining to the unlabeled spatio-temporal data to improve the performance of the interpolation and the prediction, and performs feature selection and association analysis to reveal the main relevant features to the variation of the air quality. We evaluate our approach with extensive experiments based on real data sources obtained in Beijing, China. Experiments show that DAL is superior to the peer models from the recent literature when solving the topics of interpolation, prediction, and feature analysis of fine-gained air quality.

cs.LG↗

arXiv2018

Load Balancing for 5G Ultra-Dense Networks using Device-to-Device Communications

Load balancing is an effective approach to address the spatial-temporal fluctuation problem of mobile data traffic for cellular networks. The existing schemes that focus on channel borrowing from neighboring cells cannot be directly applied to future 5G wireless networks, because the neighboring cells will reuse the same spectrum band in 5G systems. In this paper, we consider an orthogonal frequency division multiple access~(OFDMA) ultra-dense small cell network, where Device-to-Device~(D2D) communication is advocated to facilitate load balancing without extra spectrum. Specifically, the data traffic can be effectively offloaded from a congested small cell to other underutilized small cells by D2D communications. The problem is naturally formulated as a joint resource allocation and D2D routing problem that maximizes the system sum-rate. To efficiently solve the problem, we decouple the problem into a resource allocation subproblem and a D2D routing subproblem. The two subproblems are solved iteratively as a monotonic optimization problem and a complementary geometric programming problem, respectively. Simulation results show that the data sum-rate in the neighboring small cells increases 20\% on average by offloading the data traffic in the congested small cell to the neighboring small cell base stations~(SBSs).

cs.IT↗

arXiv2017

Inequalities, Preferences and Rankings in US Sports Coach Hiring Networks

Hiring a head coach of a college sports team is vital which will definitely have a great influence on the later development of the team. However, a lot of attention has been focused on each coach's individual features. A systematic and quantitative analysis of the whole coach hiring market is lacking. In a coach hiring network, the coaches are actually voting with their feet. It is interesting to analyze what factors are affecting the "footprint" left by those head coaches. In this paper, we collect more than 12,000 head coach hiring records in two different popular sports from the NCAA. Using network-based methods, we build the coach hiring network in the NCAA men's basketball and football. We find that: (1).the coach hiring network is of great inequality in coach production with a Gini coefficient close to 0.60. (2).coaches prefer to work within the same geographical region and the same division to their alma maters'. (3).the coach production rankings we calculated using network-based methods are generally correlated to the authoritative rankings, but also show disaccord in specific time period. The results provide us a novel view and better understanding of the coach hiring market in the NCAA and shed new light on the coach hiring system.

cs.SI↗

arXiv2017

Machine Learning Based Detection of Clickbait Posts in Social Media

Clickbait (headlines) make use of misleading titles that hide critical information from or exaggerate the content on the landing target pages to entice clicks. As clickbaits often use eye-catching wording to attract viewers, target contents are often of low quality. Clickbaits are especially widespread on social media such as Twitter, adversely impacting user experience by causing immense dissatisfaction. Hence, it has become increasingly important to put forward a widely applicable approach to identify and detect clickbaits. In this paper, we make use of a dataset from the clickbait challenge 2017 (clickbait-challenge.com) comprising of over 21,000 headlines/titles, each of which is annotated by at least five judgments from crowdsourcing on how clickbait it is. We attempt to build an effective computational clickbait detection model on this dataset. We first considered a total of 331 features, filtered out many features to avoid overfitting and improve the running time of learning, and eventually selected the 60 most important features for our final model. Using these features, Random Forest Regression achieved the following results: MSE=0.035 MSE, Accuracy=0.82, and F1-sore=0.61 on the clickbait class.

cs.CL↗

arXiv2017

Electrical Vehicle Charging Station Profit Maximization: Admission, Pricing, and Online Scheduling

The rapid emergence of electric vehicles (EVs) demands an advanced infrastructure of publicly accessible charging stations that provide efficient charging services. In this paper, we propose a new charging station operation mechanism, the JoAP, which jointly optimizes the EV admission control, pricing, and charging scheduling to maximize the charging station's profit. More specifically, by introducing a tandem queueing network model, we analytically characterize the average charging station profit as a function of the admission control and pricing policies. Based on the analysis, we characterize the optimal JoAP algorithm. Through extensive simulations, we demonstrate that the proposed JoAP algorithm on average can achieve 330% and 531% higher profit than a widely adopted benchmark method under two representative waiting-time penalty rates.

eess.SY↗

arXiv2017

Blind Signal Detection in Massive MIMO: Exploiting the Channel Sparsity

In practical massive MIMO systems, a substantial portion of system resources are consumed to acquire channel state information (CSI), leading to a drastically lower system capacity compared with the ideal case where perfect CSI is available. In this paper, we show that the overhead for CSI acquisition can be largely compensated by the potential gain due to the sparsity of the massive MIMO channel in a certain transformed domain. To this end, we propose a novel blind detection scheme that simultaneously estimates the channel and data by factorizing the received signal matrix. We show that by exploiting the channel sparsity, our proposed scheme can achieve a DoF very close to the ideal case, provided that the channel is sufficiently sparse. Specifically, the achievable degree of freedom (DoF) has a fractional gap of only $1/T$ from the ideal DoF, where $T$ is the channel coherence time. This is a remarkable advance for understanding the performance limit of the massive MIMO system. We further show that the performance advantage of our proposed scheme in the asymptotic SNR regime carries over to the practical SNR regime. Numerical results demonstrate that our proposed scheme significantly outperforms its counterpart schemes in the practical SNR regime under various system configurations.

cs.IT↗

arXiv2017

Boosted Zero-Shot Learning with Semantic Correlation Regularization

We study zero-shot learning (ZSL) as a transfer learning problem, and focus on the two key aspects of ZSL, model effectiveness and model adaptation. For effective modeling, we adopt the boosting strategy to learn a zero-shot classifier from weak models to a strong model. For adaptable knowledge transfer, we devise a Semantic Correlation Regularization (SCR) approach to regularize the boosted model to be consistent with the inter-class semantic correlations. With SCR embedded in the boosting objective, and with a self-controlled sample selection for learning robustness, we propose a unified framework, Boosted Zero-shot classification with Semantic Correlation Regularization (BZ-SCR). By balancing the SCR-regularized boosted model selection and the self-controlled sample selection, BZ-SCR is capable of capturing both discriminative and adaptable feature-to-class semantic alignments, while ensuring the reliability and adaptability of the learned samples. The experiments on two ZSL datasets show the superiority of BZ-SCR over the state-of-the-arts.

cs.LG↗