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Xiao Li

Publications and source records attributed to Xiao Li.

At least 325 records · Page 18Linked to original sources
arXiv2023

Fooling thermal infrared pedestrian detectors in real world using small bulbs

Thermal infrared detection systems play an important role in many areas such as night security, autonomous driving, and body temperature detection. They have the unique advantages of passive imaging, temperature sensitivity and penetration. But the security of these systems themselves has not been fully explored, which poses risks in applying these systems. We propose a physical attack method with small bulbs on a board against the state of-the-art pedestrian detectors. Our goal is to make infrared pedestrian detectors unable to detect real-world pedestrians. Towards this goal, we first showed that it is possible to use two kinds of patches to attack the infrared pedestrian detector based on YOLOv3. The average precision (AP) dropped by 64.12% in the digital world, while a blank board with the same size caused the AP to drop by 29.69% only. After that, we designed and manufactured a physical board and successfully attacked YOLOv3 in the real world. In recorded videos, the physical board caused AP of the target detector to drop by 34.48%, while a blank board with the same size caused the AP to drop by 14.91% only. With the ensemble attack techniques, the designed physical board had good transferability to unseen detectors. We also proposed the first physical multispectral (infrared and visible) attack. By using a combination method, we successfully hide from the visible light and infrared object detection systems at the same time.

cs.CV↗
arXiv2023

A Solvable Model for Discrete Time Crystal Enforced by Nonsymmorphic Dynamical Symmetry

Discrete time crystal is a class of nonequilibrium quantum systems exhibiting subharmonic responses to external periodic driving. Here we propose a class of discrete time crystals enforced by nonsymmorphic dynamical symmetry. We start with a system with nonsymmorphic dynamical symmetry, in which the instantaneous eigenstates become Möbius twisted, hence doubling the period of the instantaneous state. The exact solution of the time-dependent Schrödinger equation shows that the system spontaneously exhibits a period extension without undergoing quantum superposition states for a series of specifc evolution frequencies or in the limit of long evolution period. Moreover, in such case the system gains a π Berry phase after two periods' evolution. Finally, we show that the subharmonic response is stable even when many-body interactions are introduced, indicating a DTC phase in the thermodynamic limit.

quant-ph↗
arXiv2023

Multi-Abstractive Neural Controller: An Efficient Hierarchical Control Architecture for Interactive Driving

As learning-based methods make their way from perception systems to planning/control stacks, robot control systems have started to enjoy the benefits that data-driven methods provide. Because control systems directly affect the motion of the robot, data-driven methods, especially black box approaches, need to be used with caution considering aspects such as stability and interpretability. In this paper, we describe a differentiable and hierarchical control architecture. The proposed representation, called \textit{multi-abstractive neural controller}, uses the input image to control the transitions within a novel discrete behavior planner (referred to as the visual automaton generative network, or \textit{vAGN}). The output of a vAGN controls the parameters of a set of dynamic movement primitives which provides the system controls. We train this neural controller with real-world driving data via behavior cloning and show improved explainability, sample efficiency, and similarity to human driving.

cs.RO↗
arXiv2023

Engineering subharmonic responses beyond prethermalization via Floquet scar states

In this work we propose a new scheme to engineer subharmonic responses via scar states in a generalized PXP model. We first show that the generalized PXP model also possesses a band of scar states like the pristine PXP model does. In addition, we reveal that a generalized forward scattering approximation (FSA) still works for these scar states. We further argue that the FSA subspace exhibits an $SO(3)$ symmetry, which enables an $SO(3)$-FSA approach for the scar states. When such a model is placed under periodic driving, nontrivial Floquet scar states emerge in the quasienergy spectrum. One appealing feature of such Floquet scar states is that they can support subharmonic responses akin to the discrete time crystalline phase. In particular, such subharmonic responses can exist beyond the conventional prethermalization regime, where either large frequencies or large driving amplitudes are required.

cond-mat.stat-mech↗
arXiv2023

Multi-Scenario Broadband Channel Measurement and Modeling for Sub-6 GHz RIS-Assisted Wireless Communication Systems

Reconfigurable intelligent surface (RIS)-empowered communication, has been considered widely as one of the revolutionary technologies for next generation networks. However, due to the novel propagation characteristics of RISs, underlying RIS channel modeling and measurement research is still in its infancy and not fully investigated. In this paper, we conduct multi-scenario broadband channel measurements and modeling for RIS-assisted communications at the sub-6 GHz band. The measurements are carried out in three scenarios covering outdoor, indoor, and outdoor-to-indoor (O2I) environments, which suffer from non-line-of-sight (NLOS) propagation inherently. Three propagation modes including intelligent reflection with RIS, specular reflection with RIS and the mode without RIS, are taken into account in each scenario. In addition, considering the cascaded characteristics of RIS-assisted channel by nature, two modified empirical models including floating-intercept (FI) and close-in (CI) are proposed, which cover distance and angle domains. The measurement results rooted in 2096 channel acquisitions verify the prediction accuracy of these proposed models. Moreover, the propagation characteristics for RIS-assisted channels, including path loss (PL) gain, PL exponent, spatial consistency, time dispersion, frequency stationarity, etc., are compared and analyzed comprehensively. These channel measurement and modeling results may lay the groundwork for future applications of RIS-assisted communication systems in practice.

cs.IT↗
arXiv2023

Case Weighted Adaptive Power Priors for Hybrid Control Analyses with Time-to-Event Data

We develop a method for hybrid analyses that uses external controls to augment internal control arms in randomized controlled trials (RCT) where the degree of borrowing is determined based on similarity between RCT and external control patients to account for systematic differences (e.g. unmeasured confounders). The method represents a novel extension of the power prior where discounting weights are computed separately for each external control based on compatibility with the randomized control data. The discounting weights are determined using the predictive distribution for the external controls derived via the posterior distribution for time-to-event parameters estimated from the RCT. This method is applied using a proportional hazards regression model with piecewise constant baseline hazard. A simulation study and a real-data example are presented based on a completed trial in non-small cell lung cancer. It is shown that the case weighted adaptive power prior provides robust inference under various forms of incompatibility between the external controls and RCT population.

stat.ME↗
arXiv2023

Multi-timescale Channel Customization for Transmission Design in RIS-assisted MIMO Systems

The performance of transmission schemes is heavily influenced by the wireless channel, which is typically considered an uncontrollable factor. However, the introduction of reconfigurable intelligent surfaces (RISs) to wireless communications enables the customization of a preferred channel for adopted transmissions by reshaping electromagnetic waves. In this study, we propose multi-timescale channel customization for RIS-assisted multiple-input multiple-output systems to facilitate transmission design. Specifically, we customize a high-rank channel for spatial multiplexing (SM) transmission and a highly correlated rank-1 channel for beamforming (BF) transmission by designing the phase shifters of the RIS with statistical channel state information in the angle-coherent time to improve spectral efficiency (SE). We derive closed-form expressions for the approximation and upper bound of the ergodic SE and compare them to investigate the relative SE performance of SM and BF transmissions. In terms of reliability enhancement, we customize a fast-changing channel in the symbol timescale to achieve more diversity gain for SM and BF transmissions. Extensive numerical results demonstrate that flexible customization of channel characteristics for a specific transmission scheme can achieve a tradeoff between SE and bit error ratio performance.

cs.IT↗
arXiv2023

Channel Customization for Limited Feedback in RIS-assisted FDD Systems

Reconfigurable intelligent surfaces (RISs) represent a pioneering technology to realize smart electromagnetic environments by reshaping the wireless channel. \textcolor[rgb]{0,0,0}{Jointly designing the transceiver and RIS relies on the channel state information (CSI), whose feedback has not been investigated in multi-RIS-assisted frequency division duplexing systems.} In this study, the limited feedback of the RIS-assisted wireless channel is examined by capitalizing on the ability of the RIS in channel customization. \textcolor[rgb]{0,0,0}{By configuring the phase shifters of the surfaces using statistical CSI, we customize a sparse channel in rich-scattering environments, which significantly reduces the feedback overhead in designing the transceiver and RISs. Since the channel is customized in terms of singular value decomposition (SVD) with full-rank, the optimal SVD transceiver can be approached without a matrix decomposition and feeding back the complete channel parameters. The theoretical spectral efficiency (SE) loss of the proposed transceiver and RIS design is derived by considering the limited CSI quantization. To minimize the SE loss, a bit partitioning algorithm that splits the limited number of bits to quantize the CSI is developed.} Extensive numerical results show that the channel customization-based transceiver with reduced CSI can achieve satisfactory performance compared with the optimal transceiver with full CSI. Given the limited number of feedback bits, the bit partitioning algorithm can minimize the SE loss by adaptively allocating bits to quantize the channel parameters.

cs.IT↗
arXiv2023

Channel Customization for Joint Tx-RISs-Rx Design in Hybrid mmWave Systems

In strong line-of-sight millimeter-wave (mmWave) wireless systems, the rank-deficient channel severely hampers spatial multiplexing. To address this inherent deficiency, multiple reconfigurable-intelligent-surfaces (RISs) are introduced in this study to customize the wireless channel. Utilizing the RIS to reshape electromagnetic waves, we theoretically show that a favorable channel with an arbitrary tunable rank and a minimized truncated condition number can be established by elaborately designing the placement and reflection matrix of RISs. Different from existing works on multi-RISs, the number of elements needed for each RIS to combat the path loss and the limited phase control is also considered. On the basis of the proposed channel customization, a joint transmitter-RISs-receiver (Tx-RISs-Rx) design under a hybrid mmWave system is investigated to maximize the spectral efficiency. Using the proposed scheme, the optimal singular value decomposition-based hybrid beamforming at the Tx and Rx can be obtained without matrix decomposition for the digital and analog beamforming. The bottoms of the sub-channel mode in the water-filling algorithm, which are conventionally uncontrollable, are proven to be independently adjustable by RISs. Moreover, the transmit power required for realizing multi-stream transmission is derived. Numerical results are presented to verify our theoretical analysis and exhibit substantial gains over systems without RISs.

cs.IT↗
arXiv2023

SCoDA: Domain Adaptive Shape Completion for Real Scans

3D shape completion from point clouds is a challenging task, especially from scans of real-world objects. Considering the paucity of 3D shape ground truths for real scans, existing works mainly focus on benchmarking this task on synthetic data, e.g. 3D computer-aided design models. However, the domain gap between synthetic and real data limits the generalizability of these methods. Thus, we propose a new task, SCoDA, for the domain adaptation of real scan shape completion from synthetic data. A new dataset, ScanSalon, is contributed with a bunch of elaborate 3D models created by skillful artists according to scans. To address this new task, we propose a novel cross-domain feature fusion method for knowledge transfer and a novel volume-consistent self-training framework for robust learning from real data. Extensive experiments prove our method is effective to bring an improvement of 6%~7% mIoU.

cs.CV↗
arXiv2023

Distributed Random Reshuffling over Networks

In this paper, we consider distributed optimization problems where $n$ agents, each possessing a local cost function, collaboratively minimize the average of the local cost functions over a connected network. To solve the problem, we propose a distributed random reshuffling (D-RR) algorithm that invokes the random reshuffling (RR) update in each agent. We show that D-RR inherits favorable characteristics of RR for both smooth strongly convex and smooth nonconvex objective functions. In particular, for smooth strongly convex objective functions, D-RR achieves $\mathcal{O}(1/T^2)$ rate of convergence (where $T$ counts epoch number) in terms of the squared distance between the iterate and the global minimizer. When the objective function is assumed to be smooth nonconvex, we show that D-RR drives the squared norm of gradient to $0$ at a rate of $\mathcal{O}(1/T^{2/3})$. These convergence results match those of centralized RR (up to constant factors) and outperform the distributed stochastic gradient descent (DSGD) algorithm if we run a relatively large number of epochs. Finally, we conduct a set of numerical experiments to illustrate the efficiency of the proposed D-RR method on both strongly convex and nonconvex distributed optimization problems.

math.OC↗
arXiv2023

Quantized Phase Alignment by Discrete Phase Shifts for Reconfigurable Intelligent Surface-Assisted Communication Systems

Reconfigurable intelligent surface (RIS) has aroused a surge of interest in recent years. In this paper, we investigate the joint phase alignment and phase quantization on discrete phase shift designs for RIS-assisted single-input single-output (SISO) system. Firstly, the phenomena of phase distribution in far field and near field are respectively unveiled, paving the way for discretization of phase shift for RIS. Then, aiming at aligning phases, the phase distribution law and its underlying degree-of-freedom (DoF) are characterized, serving as the guideline of phase quantization strategies. Subsequently, two phase quantization methods, dynamic threshold phase quantization (DTPQ) and equal interval phase quantization (EIPQ), are proposed to strengthen the beamforming effect of RIS. DTPQ is capable of calculating the optimal discrete phase shifts with linear complexity in the number of unit cells on RIS, whilst EIPQ is a simplified method with a constant complexity yielding sub-optimal solution. Simulation results demonstrate that both methods achieve substantial improvements on power gain, stability, and robustness over traditional quantization methods. The path loss (PL) scaling law under discrete phase shift of RIS is unveiled for the first time, with the phase shifts designed by DTPQ due to its optimality. Additionally, the field trials conducted at 2.6 GHz and 35 GHz validate the favourable performance of the proposed methods in practical communication environment.

cs.IT↗
arXiv2023

Two-shot Video Object Segmentation

Previous works on video object segmentation (VOS) are trained on densely annotated videos. Nevertheless, acquiring annotations in pixel level is expensive and time-consuming. In this work, we demonstrate the feasibility of training a satisfactory VOS model on sparsely annotated videos-we merely require two labeled frames per training video while the performance is sustained. We term this novel training paradigm as two-shot video object segmentation, or two-shot VOS for short. The underlying idea is to generate pseudo labels for unlabeled frames during training and to optimize the model on the combination of labeled and pseudo-labeled data. Our approach is extremely simple and can be applied to a majority of existing frameworks. We first pre-train a VOS model on sparsely annotated videos in a semi-supervised manner, with the first frame always being a labeled one. Then, we adopt the pre-trained VOS model to generate pseudo labels for all unlabeled frames, which are subsequently stored in a pseudo-label bank. Finally, we retrain a VOS model on both labeled and pseudo-labeled data without any restrictions on the first frame. For the first time, we present a general way to train VOS models on two-shot VOS datasets. By using 7.3% and 2.9% labeled data of YouTube-VOS and DAVIS benchmarks, our approach achieves comparable results in contrast to the counterparts trained on fully labeled set. Code and models are available at https://github.com/yk-pku/Two-shot-Video-Object-Segmentation.

cs.CV↗
arXiv2023

Structural Multiplane Image: Bridging Neural View Synthesis and 3D Reconstruction

The Multiplane Image (MPI), containing a set of fronto-parallel RGBA layers, is an effective and efficient representation for view synthesis from sparse inputs. Yet, its fixed structure limits the performance, especially for surfaces imaged at oblique angles. We introduce the Structural MPI (S-MPI), where the plane structure approximates 3D scenes concisely. Conveying RGBA contexts with geometrically-faithful structures, the S-MPI directly bridges view synthesis and 3D reconstruction. It can not only overcome the critical limitations of MPI, i.e., discretization artifacts from sloped surfaces and abuse of redundant layers, and can also acquire planar 3D reconstruction. Despite the intuition and demand of applying S-MPI, great challenges are introduced, e.g., high-fidelity approximation for both RGBA layers and plane poses, multi-view consistency, non-planar regions modeling, and efficient rendering with intersected planes. Accordingly, we propose a transformer-based network based on a segmentation model. It predicts compact and expressive S-MPI layers with their corresponding masks, poses, and RGBA contexts. Non-planar regions are inclusively handled as a special case in our unified framework. Multi-view consistency is ensured by sharing global proxy embeddings, which encode plane-level features covering the complete 3D scenes with aligned coordinates. Intensive experiments show that our method outperforms both previous state-of-the-art MPI-based view synthesis methods and planar reconstruction methods.

cs.CV↗
arXiv2023

Static Power Consumption Modeling and Measurement of Reconfigurable Intelligent Surfaces

Reconfigurable intelligent surfaces (RISs) are anticipated to transform wireless communication in a way that is both economical and energy efficient. Revealing the practical power consumption characteristics of RISs can provide an essential toolkit for the optimal design of RIS-assisted wireless communication systems and energy efficiency performance evaluation. Based on our previous work that modeled the dynamic power consumption of RISs, we henceforth concentrate more on static power consumption. We first divide the RIS hardware into three basic parts: the FPGA control board, the drive circuits, and the RIS unit cells. The first two parts are mainly to be investigated and the last part has been modeled as the dynamic power consumption in the previous work. In this work, the power consumption of the FPGA control board is regarded as a constant value, however, that of the drive circuit is a variant that is affected by the number of control signals and its self-power consumption characteristics. Therefore, we model the power consumption of the drive circuits of various kinds of RISs, i.e., PIN diode-/Varactor diode-/RF switch-based RIS. Finally, the measurement results and typical value of static power consumption are illustrated and discussed.

cs.IT↗
arXiv2023

Data Imputation for Sparse Radio Maps in Indoor Positioning (Extended Version)

Indoor location-based services rely on the availability of sufficiently accurate positioning in indoor spaces. A popular approach to positioning relies on so-called radio maps that contain pairs of a vector of Wi-Fi signal strength indicator values (RSSIs), called a fingerprint, and a location label, called a reference point (RP), in which the fingerprint was observed. The positioning accuracy depends on the quality of the radio maps and their fingerprints. Radio maps are often sparse, with many pairs containing vectors missing many RSSIs as well as RPs. Aiming to improve positioning accuracy, we present a complete set of techniques to impute such missing values in radio maps. We differentiate two types of missing RSSIs: missing not at random (MNAR) and missing at random (MAR). Specifically, we design a framework encompassing a missing RSSI differentiator followed by a data imputer for missing values. The differentiator identifies MARs and MNARs via clustering-based fingerprint analysis. Missing RSSIs and RPs are then imputed jointly by means of a novel encoder-decoder architecture that leverages temporal dependencies in data collection as well as correlations among fingerprints and RPs. A time-lag mechanism is used to consider the aging of data, and a sparsity-friendly attention mechanism is used to focus attention score calculation on observed data. Extensive experiments with real data from two buildings show that our proposal outperforms the alternatives with significant advantages in terms of imputation accuracy and indoor positioning accuracy.

cs.DB↗
arXiv2023

Finite-Time Analysis of Fully Decentralized Single-Timescale Actor-Critic

Decentralized Actor-Critic (AC) algorithms have been widely utilized for multi-agent reinforcement learning (MARL) and have achieved remarkable success. Apart from its empirical success, the theoretical convergence property of decentralized AC algorithms is largely unexplored. Most of the existing finite-time convergence results are derived based on either double-loop update or two-timescale step sizes rule, and this is the case even for centralized AC algorithm under a single-agent setting. In practice, the \emph{single-timescale} update is widely utilized, where actor and critic are updated in an alternating manner with step sizes being of the same order. In this work, we study a decentralized \emph{single-timescale} AC algorithm.Theoretically, using linear approximation for value and reward estimation, we show that the algorithm has sample complexity of $\tilde{\mathcal{O}}(\varepsilon^{-2})$ under Markovian sampling, which matches the optimal complexity with a double-loop implementation (here, $\tilde{\mathcal{O}}$ hides a logarithmic term). When we reduce to the single-agent setting, our result yields new sample complexity for centralized AC using a single-timescale update scheme. The central to establishing our complexity results is \emph{the hidden smoothness of the optimal critic variable} we revealed. We also provide a local action privacy-preserving version of our algorithm and its analysis. Finally, we conduct experiments to show the superiority of our algorithm over the existing decentralized AC algorithms.

cs.LG↗
arXiv2023

Convergence of Random Reshuffling Under The Kurdyka-Łojasiewicz Inequality

We study the random reshuffling (RR) method for smooth nonconvex optimization problems with a finite-sum structure. Though this method is widely utilized in practice such as the training of neural networks, its convergence behavior is only understood in several limited settings. In this paper, under the well-known Kurdyka-Lojasiewicz (KL) inequality, we establish strong limit-point convergence results for RR with appropriate diminishing step sizes, namely, the whole sequence of iterates generated by RR is convergent and converges to a single stationary point in an almost sure sense. In addition, we derive the corresponding rate of convergence, depending on the KL exponent and the suitably selected diminishing step sizes. When the KL exponent lies in $[0,\frac12]$, the convergence is at a rate of $\mathcal{O}(t^{-1})$ with $t$ counting the iteration number. When the KL exponent belongs to $(\frac12,1)$, our derived convergence rate is of the form $\mathcal{O}(t^{-q})$ with $q\in (0,1)$ depending on the KL exponent. The standard KL inequality-based convergence analysis framework only applies to algorithms with a certain descent property. We conduct a novel convergence analysis for the non-descent RR method with diminishing step sizes based on the KL inequality, which generalizes the standard KL framework. We summarize our main steps and core ideas in an informal analysis framework, which is of independent interest. As a direct application of this framework, we also establish similar strong limit-point convergence results for the reshuffled proximal point method.

math.OC↗