arXiv subjects

Shun Zhang

Publications and source records attributed to Shun Zhang.

At least 73 records · Page 4Linked to original sources

arXiv2023

Two-Bit RIS-Aided Communications at 3.5GHz: Some Insights from the Measurement Results Under Multiple Practical Scenes

In this paper, we propose a two-bit reconfigurable intelligent surface (RIS)-aided communication system, which mainly consists of a two-bit RIS, a transmitter and a receiver. A corresponding prototype verification system is designed to perform experimental tests in practical environments. The carrier frequency is set as 3.5GHz, and the RIS array possesses 256 units, each of which adopts two-bit phase quantization. In particular, we adopt a self-developed broadband intelligent communication system 40MHz-Net (BICT-40N) terminal in order to fully acquire the channel information. The terminal mainly includes a baseband board and a radio frequency (RF) front-end board, where the latter can achieve 26 dB transmitting link gain and 33 dB receiving link gain. The orthogonal frequency division multiplexing (OFDM) signal is used for the terminal, where the bandwidth is 40MHz and the subcarrier spacing is 625KHz. Also, the terminal supports a series of modulation modes, including QPSK, QAM, etc.Through experimental tests, we validate a few functions and properties of the RIS as follows. First, we validate a novel RIS power consumption model, which considers both the static and the dynamic power consumption. Besides, we demonstrate the existence of the imaging interference and find that two-bit RIS can lower the imaging interference about 10 dBm. Moreover, we verify that the RIS can outperform the metal plate in terms of the beam focusing performance. In addition, we find that the RIS has the ability to improve the channel stationarity. Then, we realize the multi-beam reflection of the RIS utilizing the pattern addition (PA) algorithm. Lastly, we validate the existence of the mutual coupling between different RIS units.

eess.SP↗

arXiv2023

Hyper-Decision Transformer for Efficient Online Policy Adaptation

Decision Transformers (DT) have demonstrated strong performances in offline reinforcement learning settings, but quickly adapting to unseen novel tasks remains challenging. To address this challenge, we propose a new framework, called Hyper-Decision Transformer (HDT), that can generalize to novel tasks from a handful of demonstrations in a data- and parameter-efficient manner. To achieve such a goal, we propose to augment the base DT with an adaptation module, whose parameters are initialized by a hyper-network. When encountering unseen tasks, the hyper-network takes a handful of demonstrations as inputs and initializes the adaptation module accordingly. This initialization enables HDT to efficiently adapt to novel tasks by only fine-tuning the adaptation module. We validate HDT's generalization capability on object manipulation tasks. We find that with a single expert demonstration and fine-tuning only 0.5% of DT parameters, HDT adapts faster to unseen tasks than fine-tuning the whole DT model. Finally, we explore a more challenging setting where expert actions are not available, and we show that HDT outperforms state-of-the-art baselines in terms of task success rates by a large margin.

cs.LG↗

arXiv2023

Planning with Large Language Models for Code Generation

Existing large language model-based code generation pipelines typically use beam search or sampling algorithms during the decoding process. Although the programs they generate achieve high token-matching-based scores, they often fail to compile or generate incorrect outputs. The main reason is that conventional Transformer decoding algorithms may not be the best choice for code generation. In this work, we propose a novel Transformer decoding algorithm, Planning-Guided Transformer Decoding (PG-TD), that uses a planning algorithm to do lookahead search and guide the Transformer to generate better programs. Specifically, instead of simply optimizing the likelihood of the generated sequences, the Transformer makes use of a planner to generate candidate programs and test them on public test cases. The Transformer can therefore make more informed decisions and generate tokens that will eventually lead to higher-quality programs. We also design a mechanism that shares information between the Transformer and the planner to make our algorithm computationally efficient. We empirically evaluate our framework with several large language models as backbones on public coding challenge benchmarks, showing that 1) it can generate programs that consistently achieve higher performance compared with competing baseline methods; 2) it enables controllable code generation, such as concise codes and highly-commented codes by optimizing modified objective.

cs.LG↗

arXiv2022

Geo-MOEA: A Multi-Objective Evolutionary Algorithm with Geo-obfuscation for Mobile Crowdsourcing Workers

The rapid development of mobile Internet and sharing economy brings the prosperity of Spatial Crowdsourcing (SC). SC applications assign various tasks according to reported location information of task's requesters and outsourced workers (such as DiDi, MeiTuan and Uber). However, SC-servers are often untrustworthy and the exposure of users' locations raises privacy concerns. In this paper, we design a framework called Geo-MOEA (Multi-Objective Evolutionary Algorithm with Geo-obfuscation) to protect location privacy of workers involved on SC platform in mobile networks environment. We propose an adaptive regionalized obfuscation approach with inference error bounds based on geo-indistinguishability (a strong notion of differential privacy), which is suitable for the context of large-scale location data and task allocations. This enables each worker to report a pseudo-location that is adaptively generated with a personalized inference error threshold. Moreover, as a popular computational intelligence method, MOEA is introduced to optimize the trade-off between SC service availability and privacy protection while ensuring theoretically the most general condition on protection location sets for larger search space. Finally, the experimental results on two public datasets show that our Geo-MOEA approach achieves up to 20% reduction in service quality loss while guaranteeing differential and geo-distortion location privacy.

cs.CR↗

arXiv2022

Generalized ideal convergence on quasi-continuous domains

In this paper,the concepts of generalized ideal inf-limit and generalized ideal final lower bound limit are introduced in the directed complete poset,and their relations with Scott topology and Lawson topology are studied. The main results are as follows: (1) On directed complete posets,generalized ideal inf-limit topology is consistent with Scott topology; (2) Generalized ideal inf-limiti convergence is topological if and only if directed complete posets are quasi-continuous domains; (3) In quasi-continuous domain,generalized ideal final lower bound limit topology is consistent with Lawson topology;(4) In meet continuous directed complete posets,the generalized ideal final lower bound limit convergence is topological if and only if the directed complete poset is continuous.

math.GN↗

arXiv2022

Benchmarking Adversarial Patch Against Aerial Detection

DNNs are vulnerable to adversarial examples, which poses great security concerns for security-critical systems. In this paper, a novel adaptive-patch-based physical attack (AP-PA) framework is proposed, which aims to generate adversarial patches that are adaptive in both physical dynamics and varying scales, and by which the particular targets can be hidden from being detected. Furthermore, the adversarial patch is also gifted with attack effectiveness against all targets of the same class with a patch outside the target (No need to smear targeted objects) and robust enough in the physical world. In addition, a new loss is devised to consider more available information of detected objects to optimize the adversarial patch, which can significantly improve the patch's attack efficacy (Average precision drop up to 87.86% and 85.48% in white-box and black-box settings, respectively) and optimizing efficiency. We also establish one of the first comprehensive, coherent, and rigorous benchmarks to evaluate the attack efficacy of adversarial patches on aerial detection tasks. Finally, several proportionally scaled experiments are performed physically to demonstrate that the elaborated adversarial patches can successfully deceive aerial detection algorithms in dynamic physical circumstances. The code is available at https://github.com/JiaweiLian/AP-PA.

cs.CV↗

arXiv2022

KG-MTT-BERT: Knowledge Graph Enhanced BERT for Multi-Type Medical Text Classification

Medical text learning has recently emerged as a promising area to improve healthcare due to the wide adoption of electronic health record (EHR) systems. The complexity of the medical text such as diverse length, mixed text types, and full of medical jargon, poses a great challenge for developing effective deep learning models. BERT has presented state-of-the-art results in many NLP tasks, such as text classification and question answering. However, the standalone BERT model cannot deal with the complexity of the medical text, especially the lengthy clinical notes. Herein, we develop a new model called KG-MTT-BERT (Knowledge Graph Enhanced Multi-Type Text BERT) by extending the BERT model for long and multi-type text with the integration of the medical knowledge graph. Our model can outperform all baselines and other state-of-the-art models in diagnosis-related group (DRG) classification, which requires comprehensive medical text for accurate classification. We also demonstrated that our model can effectively handle multi-type text and the integration of medical knowledge graph can significantly improve the performance.

cs.CL↗

arXiv2022

DPIVE: A Regionalized Location Obfuscation Scheme with Personalized Privacy Levels

The popularity of cyber-physical systems is fueling the rapid growth of location-based services. This poses the risk of location privacy disclosure. Effective privacy preservation is foremost for various mobile applications. Recently, geo-indistinguishability and expected inference error are proposed for limiting location leakages. In this paper, we argue that personalization means regionalization for geo-indistinguishability, and we propose a regionalized location obfuscation mechanism called DPIVE with personalized utility sensitivities. This substantially corrects the differential and distortion privacy problem of PIVE framework proposed by Yu et al. on NDSS 2017. We develop DPIVE with two phases. In Phase I, we determine disjoint sets by partitioning all possible positions such that different locations in the same set share the Protection Location Set (PLS). In Phase II, we construct a probability distribution matrix in which the rows corresponding to the same PLS have their own sensitivity of utility (PLS diameter). Moreover, by designing QK-means algorithm for more search space in 2-D space, we improve DPIVE with refined location partition and present fine-grained personalization, enabling each location to have its own privacy level endowed with a customized privacy budget. Experiments with two public datasets demonstrate that our mechanisms have the superior performance, typically on skewed locations.

cs.CR↗

arXiv2022

Reconfigurable Intelligent Surface for Near Field Communications: Beamforming and Sensing

Reconfigurable intelligent surface (RIS) can improve the communications between a source and a destination. Recently, continuous aperture RIS is proved to have better communication performance than discrete aperture RIS and has received much attention. However, the conventional continuous aperture RIS is designed to convert the incoming planar waves into the outgoing planar waves, which is not the optimal reflecting scheme when the receiver is not a planar array and is located in the near field of the RIS. In this paper, we consider two types of receivers in the radiating near field of the RIS: (1) when the receiver is equipped with a uniform linear array (ULA), we design RIS coefficient to convert planar waves into cylindrical waves; (2) when the receiver is equipped with a single antenna, we design RIS coefficient to convert planar waves into spherical waves. We then propose the maximum likelihood (ML) method and the focal scanning (FS) method to sense the location of the receiver based on the analytic expression of the reflection coefficient, and derive the corresponding position error bound (PEB). Simulation results demonstrate that the proposed scheme can reduce energy leakage and thus enlarge the channel capacity compared to the conventional scheme. Moreover, the location of the receiver could be accurately sensed by the ML method with large computation complexity or be roughly sensed by the FS method with small computation complexity.

eess.SP↗

arXiv2022

Joint Channel Estimation and Data Detection for Hybrid RIS aided Millimeter Wave OTFS Systems

For high mobility communication scenario, the recently emerged orthogonal time frequency space (OTFS) modulation introduces a new delay-Doppler domain signal space, and can provide better communication performance than traditional orthogonal frequency division multiplexing system. This article focuses on the joint channel estimation and data detection (JCEDD) for hybrid reconfigurable intelligent surface (HRIS) aided millimeter wave (mmWave) OTFS systems. Firstly, a new transmission structure is designed. Within the pilot durations of the designed structure, partial HRIS elements are alternatively activated. The time domain channel model is then exhibited. Secondly, the received signal model for both the HRIS over time domain and the base station over delay-Doppler domain are studied. Thirdly, by utilizing channel parameters acquired at the HRIS, an HRIS beamforming design strategy is proposed. For the OTFS transmission, we propose a JCEDD scheme over delay-Doppler domain. In this scheme, message passing (MP) algorithm is designed to simultaneously obtain the equivalent channel gain and the data symbols. On the other hand, the channel parameters, i.e., the Doppler shift, the channel sparsity, and the channel variance, are updated through expectation-maximization (EM) algorithm. By iteratively executing the MP and EM algorithm, both the channel and the unknown data symbols can be accurately acquired. Finally, simulation results are provided to validate the effectiveness of our proposed JCEDD scheme.

eess.SP↗

arXiv2022

Battling Gibbs Phenomenon: On Finite Element Approximations of Discontinuous Solutions of PDEs

In this paper, we want to clarify the Gibbs phenomenon when continuous and discontinuous finite elements are used to approximate discontinuous or nearly discontinuous PDE solutions from the approximation point of view. For a simple step function, we explicitly compute its continuous and discontinuous piecewise constant or linear projections on discontinuity matched or non-matched meshes. For the simple discontinuity-aligned mesh case, piecewise discontinuous approximations are always good. For the general non-matched case, we explain that the piecewise discontinuous constant approximation combined with adaptive mesh refinements is a good choice to achieve accuracy without overshoots. For discontinuous piecewise linear approximations, non-trivial overshoots will be observed unless the mesh is matched with discontinuity. For continuous piecewise linear approximations, the computation is based on a "far-away assumption", and non-trivial overshoots will always be observed under regular meshes. We calculate the explicit overshoot values for several typical cases. Numerical tests are conducted for a singularly-perturbed reaction-diffusion equation and linear hyperbolic equations to verify our findings in the paper. Also, we discuss the $L^1$-minimization-based methods and do not recommend such methods due to their similar behavior to $L^2$-based methods and more complicated implementations.

math.NA↗

arXiv2022

Computer Vision-Aided Reconfigurable Intelligent Surface-Based Beam Tracking: Prototyping and Experimental Results

In this paper, we propose a novel computer vision-based approach to aid Reconfigurable Intelligent Surface (RIS) for dynamic beam tracking and then implement the corresponding prototype verification system. A camera is attached at the RIS to obtain the visual information about the surrounding environment, with which RIS identifies the desired reflected beam direction and then adjusts the reflection coefficients according to the pre-designed codebook. Compared to the conventional approaches that utilize channel estimation or beam sweeping to obtain the reflection coefficients, the proposed one not only saves beam training overhead but also eliminates the requirement for extra feedback links. We build a 20-by-20 RIS running at 5.4 GHz and develop a high-speed control board to ensure the real-time refresh of the reflection coefficients. Meanwhile we implement an independent peer-to-peer communication system to simulate the communication between the base station and the user equipment. The vision-aided RIS prototype system is tested in two mobile scenarios: RIS works in near-field conditions as a passive array antenna of the base station; RIS works in far-field conditions to assist the communication between the base station and the user equipment. The experimental results show that RIS can quickly adjust the reflection coefficients for dynamic beam tracking with the help of visual information.

eess.SP↗

arXiv2022

Prompting Decision Transformer for Few-Shot Policy Generalization

Humans can leverage prior experience and learn novel tasks from a handful of demonstrations. In contrast to offline meta-reinforcement learning, which aims to achieve quick adaptation through better algorithm design, we investigate the effect of architecture inductive bias on the few-shot learning capability. We propose a Prompt-based Decision Transformer (Prompt-DT), which leverages the sequential modeling ability of the Transformer architecture and the prompt framework to achieve few-shot adaptation in offline RL. We design the trajectory prompt, which contains segments of the few-shot demonstrations, and encodes task-specific information to guide policy generation. Our experiments in five MuJoCo control benchmarks show that Prompt-DT is a strong few-shot learner without any extra finetuning on unseen target tasks. Prompt-DT outperforms its variants and strong meta offline RL baselines by a large margin with a trajectory prompt containing only a few timesteps. Prompt-DT is also robust to prompt length changes and can generalize to out-of-distribution (OOD) environments.

cs.LG↗

arXiv2022

Efficient reversible data hiding via two layers of double-peak embedding

Reversible data hiding continues to attract significant attention in recent years. In particular, an increasing number of authors focus on the higher significant bit (HSB) plane of an image which can yield more redundant space. On the other hand, the lower significant bit planes are often ignored for embedding in existing schemes due to their harm to the embedding rate. This paper proposes an efficient reversible data hiding scheme via a double-peak two-layer embedding (DTLE) strategy with prediction error expansion. The higher six-bit planes of the image are assigned as the HSB plane, and double prediction error peaks are applied in either embedding layer. This makes fuller use of the redundancy space of images compared with the one error peak strategy. Moreover, we carry out the median-edge detector pre-processing for complex images to reduce the size of the auxiliary information. A series of experimental results show that our DTLE approach achieves up to 83% higher embedding rate on real-world datasets while guaranteeing better image quality.

cs.CR↗

arXiv2022

CVR-LSE: Compact Vectorization Representation of Local Static Environments for Unmanned Ground Vehicles

According to the requirement of general static obstacle detection, this paper proposes a compact vectorization representation approach of local static environments for unmanned ground vehicles. At first, by fusing the data of LiDAR and IMU, high-frequency pose information is obtained. Then, through the two-dimensional (2D) obstacle points generation, the process of grid map maintenance with a fixed size is proposed. Finally, the local static environment is described via multiple convex polygons, which is realized throungh the double threshold-based boundary simplification and the convex polygon segmentation. Our proposed approach has been applied in a practical driverless project in the park, and the qualitative experimental results on typical scenes verify the effectiveness and robustness. In addition, the quantitative evaluation shows the superior performance on making use of fewer number of points information (decreased by about 60%) to represent the local static environment compared with the traditional grid map-based methods. Furthermore, the performance of running time (15ms) shows that the proposed approach can be used for real-time local static environment perception. The corresponding code can be accessed at https://github.com/ghm0819/cvr_lse.

cs.RO↗

arXiv2022

Least-Squares Methods with Nonconforming Finite Elements for General Second-Order Elliptic Equations

In this paper, we study least-squares finite element methods (LSFEM) for general second-order elliptic equations with nonconforming finite element approximations. The equation may be indefinite. For the two-field potential-flux div LSFEM with Crouzeix-Raviart (CR) element approximation, we present three proofs of the discrete solvability under the condition that mesh size is small enough. One of the proof is based on the coerciveness of the original bilinear form. The other two are based on the minimal assumption of the uniqueness of the solution of the second-order elliptic equation. A counterexample shows that div least-squares functional does not have norm equivalence in the sum space of $H^1$ and CR finite element spaces. Thus it cannot be used as an a posteriori error estimator. Several versions of reliable and efficient error estimators are proposed for the method. We also propose a three-filed potential-flux-intensity div-curl least-squares method with general nonconforming finite element approximations. The norm equivalence in the abstract nonconforming piecewise $H^1$-space is established for the three-filed formulation on the minimal assumption of the uniqueness of the solution of the second-order elliptic equation. The three-filed div-curl nonconforming formulation thus has no restriction on the mesh size, and the least-squares functional can be used as the built-in a posteriori error estimator. Under some restrictive conditions, we also discuss a potential-flux div-curl least-squares method.

math.NA↗

arXiv2022

Several Proofs of Coerciveness of First-Order System Least-Squares Methods for General Second-Order Elliptic PDEs

In this paper, we present proofs of the coerciveness of first-order system least-squares methods for general (possibly indefinite) second-order linear elliptic PDEs under a minimal uniqueness assumption. For general linear second-order elliptic PDEs, the uniqueness, existence, and well-posedness are equivalent due to the compactness of the operator and Fredholm alternative. Thus only a minimal uniqueness assumption is assumed: the homogeneous equation has a unique zero solution. The coerciveness of the standard variational problem is not required. The paper's main contribution is our first proof, which is a straightforward and short proof using the inf-sup stability of the standard variational formulation. The proof can potentially be applied to other equations or settings once having the standard formulation's stability. We also present two other proofs for the least-squares methods of general second-order linear elliptic PDEs. The second proof is based on a lemma introduced in the discontinuous Petrov-Galerkin method, and the third proof is based on various stability analyses of the decomposed problems. As an application, we also discuss least-squares finite element methods for problems with a nonsingular $H^{-1}$ right-hand side.

math.NA↗

arXiv2021

Deep Learning-based Time-varying Channel Estimation for RIS Assisted Communication

Reconfigurable intelligent surface (RIS) is considered as a revolutionary technology for future wireless communication networks. In this letter, we consider the acquisition of the time-varying cascaded channels, which is a challenging task due to the massive number of passive RIS elements and the small channel coherence time. To reduce the pilot overhead, a deep learning-based channel extrapolation is implemented over both antenna and time domains. We divide the neural network into two parts, i.e., the time-domain and the antenna-domain extrapolation networks, where the neural ordinary differential equations (ODE) are utilized. In the former, ODE accurately describes the dynamics of the RIS channels and improves the recurrent neural network's performance of time series reconstruction. In the latter, ODE is resorted to modify the relations among different data layers in a feedforward neural network. We cascade the two networks and jointly train them. Simulation results show that the proposed scheme can effectively extrapolate the cascaded RIS channels in high mobility scenario.

eess.SP↗