arXiv subjects

Xiaodong Wang

Publications and source records attributed to Xiaodong Wang.

At least 91 records · Page 5Linked to original sources

arXiv2025

Heat Conduction and Thermal Switching Performance of Surface Plasmon Polaritons in Ag2Se Quantum Dot Composite Polymer Film

To stabilize the working temperature of an equipment, a solid-state thermal resistor is usually a requisite, which could adjust its heat conductance continuously according to the temperature. In this work, the thermal conductivity and the thermal switching performances of surface plasmon polaritons in the polymer films filled with Ag2Se quantum dots (QDs) were theoretically analyzed, and a theoretical model was also derived to reveal the dependence of the thermal conductivity on the temperature and the structure of a composite film, which is verified to be effective by numerical calculations. It shows that the thermal conductivity will decrease following ~t-3exp(ζ/Td) rule under the thin film limit, here t, d and T are film thickness, diameter of QDs and temperature, respectively, and ζ is a constant. A high thermal conductivity could be only realized at a device with a size lager than millimeter scale, due to the need of avoiding boundary scatterings of surface plasmon polaritons (SPPs). At the millimeter scale, the thermal conductivity could be reduced by 100 times by increasing temperature from 300 to 400 K, which suggests a very high thermal switching ratio almost in all kinds of solid-state thermal resistor. This study brings new insights in designing thermal resistor and understanding heat conduction in films by adjusting its structures.

cond-mat.mes-hall↗

arXiv2024

Learning Radiance Fields from a Single Snapshot Compressive Image

In this paper, we explore the potential of Snapshot Compressive Imaging (SCI) technique for recovering the underlying 3D scene structure from a single temporal compressed image. SCI is a cost-effective method that enables the recording of high-dimensional data, such as hyperspectral or temporal information, into a single image using low-cost 2D imaging sensors. To achieve this, a series of specially designed 2D masks are usually employed, reducing storage and transmission requirements and offering potential privacy protection. Inspired by this, we take one step further to recover the encoded 3D scene information leveraging powerful 3D scene representation capabilities of neural radiance fields (NeRF). Specifically, we propose SCINeRF, in which we formulate the physical imaging process of SCI as part of the training of NeRF, allowing us to exploit its impressive performance in capturing complex scene structures. In addition, we further integrate the popular 3D Gaussian Splatting (3DGS) framework and propose SCISplat to improve 3D scene reconstruction quality and training/rendering speed by explicitly optimizing point clouds into 3D Gaussian representations. To assess the effectiveness of our method, we conduct extensive evaluations using both synthetic data and real data captured by our SCI system. Experimental results demonstrate that our proposed approach surpasses the state-of-the-art methods in terms of image reconstruction and novel view synthesis. Moreover, our method also exhibits the ability to render high frame-rate multi-view consistent images in real time by leveraging SCI and the rendering capabilities of 3DGS. Codes will be available at: https://github.com/WU- CVGL/SCISplat.

cs.CV↗

arXiv2024

A hybrid framework for effective and efficient machine unlearning

Recently machine unlearning (MU) is proposed to remove the imprints of revoked samples from the already trained model parameters, to solve users' privacy concern. Different from the runtime expensive retraining from scratch, there exist two research lines, exact MU and approximate MU with different favorites in terms of accuracy and efficiency. In this paper, we present a novel hybrid strategy on top of them to achieve an overall success. It implements the unlearning operation with an acceptable computation cost, while simultaneously improving the accuracy as much as possible. Specifically, it runs reasonable unlearning techniques by estimating the retraining workloads caused by revocations. If the workload is lightweight, it performs retraining to derive the model parameters consistent with the accurate ones retrained from scratch. Otherwise, it outputs the unlearned model by directly modifying the current parameters, for better efficiency. In particular, to improve the accuracy in the latter case, we propose an optimized version to amend the output model with lightweight runtime penalty. We particularly study the boundary of two approaches in our frameworks to adaptively make the smart selection. Extensive experiments on real datasets validate that our proposals can improve the unlearning efficiency by 1.5$\times$ to 8$\times$ while achieving comparable accuracy.

cs.LG↗

arXiv2024

Liouville Theorems on pseudohermitian manifolds with nonnegative Tanaka-Webster curvature

In this paper we study positive solutions to the CR Yamabe equation in noncompact $(2n+1)$-dimensional Sasakian manifolds with nonnegative curvature. In particular, we show that the Heisenberg group $\mathbb{H}^1$ is the only (complete) Sasakian space with nonnegative Tanaka-Webster scalar curvature admitting a (nontrivial) positive solution. Moreover, under some natural assumptions, we prove this strong rigidity result in higher dimensions, extending the celebrated Jerison-Lee's result to curved manifolds.

math.DG↗

arXiv2024

HoloDrive: Holistic 2D-3D Multi-Modal Street Scene Generation for Autonomous Driving

Generative models have significantly improved the generation and prediction quality on either camera images or LiDAR point clouds for autonomous driving. However, a real-world autonomous driving system uses multiple kinds of input modality, usually cameras and LiDARs, where they contain complementary information for generation, while existing generation methods ignore this crucial feature, resulting in the generated results only covering separate 2D or 3D information. In order to fill the gap in 2D-3D multi-modal joint generation for autonomous driving, in this paper, we propose our framework, \emph{HoloDrive}, to jointly generate the camera images and LiDAR point clouds. We employ BEV-to-Camera and Camera-to-BEV transform modules between heterogeneous generative models, and introduce a depth prediction branch in the 2D generative model to disambiguate the un-projecting from image space to BEV space, then extend the method to predict the future by adding temporal structure and carefully designed progressive training. Further, we conduct experiments on single frame generation and world model benchmarks, and demonstrate our method leads to significant performance gains over SOTA methods in terms of generation metrics.

cs.CV↗

arXiv2024

Measures of intermediate pressures for geometric Lorenz attractors

Pressure measures the complexity of a dynamical system concerning a continuous observation function. A dynamical system is called to admit the intermediate pressure property if for any observation function, the measure theoretical pressures of all ergodic measures form an interval. We prove that the intermediate pressure property holds for $C^r (r\geq 2)$ generic geometric Lorenz attractors while it fails for $C^r (r\geq 2)$ dense geometric Lorenz attractors, which gives a sharp contrast. Similar results hold for $C^1$ singular hyperbolic attractors.

math.DS↗

arXiv2024

A $C^r$-connecting lemma for Lorenz attractors and its application on the space of ergodic measures

For every $r\in\mathbb{N}_{\geq 2}\cup\{\infty\}$, we prove a $C^r$-connecting lemma for Lorenz attractors. To be precise, for a Lorenz attractor of a $3$-dimensional $C^r$ ($r\geq 2$) vector field, a heteroclinic orbit associated to the singularity and a critical element can be created through arbitrarily small $C^r$-perturbations. As an application, we show that for $C^r$-dense geometric Lorenz attractors, the Dirac measure of the singularity is isolated inside the space of ergodic measures and thus the ergodic measure space is not connected; while for $C^r$-generic geometric Lorenz attractors, the space of ergodic measures is path connected with dense periodic measures. In particular, the generic part proves a conjecture proposed by C. Bonatti in $C^r$-topology for Lorenz attractors.

math.DS↗

arXiv2024

EffiQA: Efficient Question-Answering with Strategic Multi-Model Collaboration on Knowledge Graphs

While large language models (LLMs) have shown remarkable capabilities in natural language processing, they struggle with complex, multi-step reasoning tasks involving knowledge graphs (KGs). Existing approaches that integrate LLMs and KGs either underutilize the reasoning abilities of LLMs or suffer from prohibitive computational costs due to tight coupling. To address these limitations, we propose a novel collaborative framework named EffiQA that can strike a balance between performance and efficiency via an iterative paradigm. EffiQA consists of three stages: global planning, efficient KG exploration, and self-reflection. Specifically, EffiQA leverages the commonsense capability of LLMs to explore potential reasoning pathways through global planning. Then, it offloads semantic pruning to a small plug-in model for efficient KG exploration. Finally, the exploration results are fed to LLMs for self-reflection to further improve the global planning and efficient KG exploration. Empirical evidence on multiple KBQA benchmarks shows EffiQA's effectiveness, achieving an optimal balance between reasoning accuracy and computational costs. We hope the proposed new framework will pave the way for efficient, knowledge-intensive querying by redefining the integration of LLMs and KGs, fostering future research on knowledge-based question answering.

cs.CL↗

arXiv2024

Characterizing the current systems in the Martian ionosphere

When the solar wind interacts with the ionosphere of an unmagnetized planet, it induces currents that form an induced magnetosphere. These currents and their associated magnetic fields play a pivotal role in controlling the movement of charged particles, which is essential for understanding the escape of planetary ions. Unlike the well-documented magnetospheric current systems, the ionospheric current systems on unmagnetized planets remain less understood, which constrains the quantification of electrodynamic energy transfer from stars to these planets. Here, utilizing eight years of data from the Mars Atmosphere and Volatile EvolutioN (MAVEN) mission, we investigate the global distribution of ionospheric currents on Mars. We have identified two distinct current systems in the ionosphere: one aligns with the solar wind electric field yet exhibits hemispheric asymmetry perpendicular to the electric field direction; the other corresponds to the flow pattern of annually-averaged neutral winds. We propose that these two current systems are driven by the solar wind and atmospheric neutral winds, respectively. Our findings reveal that Martian ionospheric dynamics are influenced by the neutral winds from below and the solar wind from above, highlighting the complex and intriguing nature of current systems on unmagnetized planets.

astro-ph.EP↗

arXiv2024

Inflight Performance and Calibrations of the Lyman-alpha Solar Telescope on board the Advanced Space-based Solar Observatory

The Lyman-alpha Solar Telescope (LST) on board the Advanced Space-based Solar Observatory (ASO-S) is the first payload to image the full solar disk and the solar corona in both white-light (WL) and ultraviolet (UV) H I Lya, extending up to 2.5 solar radii (Rs). Since the launch of the ASO-S on 9 October 2022, LST has captured various significant solar activities including flares, prominences, coronal mass ejections (CMEs). LST covers different passbands of 121.6 nm, 360 nm and 700 nm. The Lya Solar Disk Imager (SDI) has a field of view (FOV) of 38.4 arcmin and a spatial resolution of around 9.5 arcsec, while the White-Light Solar Telescope (WST) has a FOV of 38.43 arcmin and a spatial resolution of around 3.0 arcsec. The FOV of the Lya Solar Corona Imager (SCI) reaches 81.1 arcmin and its spatial resolution is 4.3 arcsec. The stray-light level in the 700 nm waveband is about 7.8e-6 MSB (mean solar brightness) at 1.1 Rs and 7.6e-7 MSB at 2.5 Rs, and in the Lya waveband it is around 4.3e-3 MSB at 1.1 Rs and 4.1e-4 MSB at 2.5 Rs. This article will detail the results from on-orbit tests and calibrations.

astro-ph.SR↗

arXiv2024

Semi-Blind Multi-Tag Ambient Backscatter Communications Using Radar Signals

In this work, we consider a backscatter communication system wherein multiple asynchronous sources (tags) exploit the reverberation generated by a nearby radar transmitter as an ambient carrier to deliver a message to a common destination (reader) through a number of available subchannels. We propose a new encoding strategy wherein each tag transmits both pilot and data symbols on each subchannel and repeats some of the data symbols on multiple subchannels. We then exploit this signal structure to derive two semi-blind iterative algorithms for joint estimation of the data symbols and the subchannel responses that are also able to handle some missing measurements. The proposed encoding/decoding strategies are scalable with the number of tags and their payload and can achieve different tradeoffs in terms of transmission and error rates. Some numerical examples are provided to illustrate the merits of the proposed solutions.

eess.SP↗

arXiv2024

On physical measures of multi-singular hyperbolic vector fields

Bonatti and da Luz have introduced the class of \emph{multi-singular hyperbolic} vector fields to characterize systems whose periodic orbits and singularities do not bifurcate under perturbation (called star vector fields). In this paper, we study the Sina\"ı-Ruelle-Bowen measures for multi-singular hyperbolic vector fields: in a $C^1$ open and $C^1$ dense subset of multi-singular hyperbolic vector fields, each {$C^\infty$} one admits \emph{finitely} many physical measures whose basins cover a \emph{full} Lebesgue measure subset of the manifold. Similar results are also obtained for $C^1$ generic multi-singular hyperbolic vector fields.

math.DS↗

arXiv2024

Multi-level Shared Knowledge Guided Learning for Knowledge Graph Completion

In the task of Knowledge Graph Completion (KGC), the existing datasets and their inherent subtasks carry a wealth of shared knowledge that can be utilized to enhance the representation of knowledge triplets and overall performance. However, no current studies specifically address the shared knowledge within KGC. To bridge this gap, we introduce a multi-level Shared Knowledge Guided learning method (SKG) that operates at both the dataset and task levels. On the dataset level, SKG-KGC broadens the original dataset by identifying shared features within entity sets via text summarization. On the task level, for the three typical KGC subtasks - head entity prediction, relation prediction, and tail entity prediction - we present an innovative multi-task learning architecture with dynamically adjusted loss weights. This approach allows the model to focus on more challenging and underperforming tasks, effectively mitigating the imbalance of knowledge sharing among subtasks. Experimental results demonstrate that SKG-KGC outperforms existing text-based methods significantly on three well-known datasets, with the most notable improvement on WN18RR.

cs.CL↗

arXiv2024

SCINeRF: Neural Radiance Fields from a Snapshot Compressive Image

In this paper, we explore the potential of Snapshot Compressive Imaging (SCI) technique for recovering the underlying 3D scene representation from a single temporal compressed image. SCI is a cost-effective method that enables the recording of high-dimensional data, such as hyperspectral or temporal information, into a single image using low-cost 2D imaging sensors. To achieve this, a series of specially designed 2D masks are usually employed, which not only reduces storage requirements but also offers potential privacy protection. Inspired by this, to take one step further, our approach builds upon the powerful 3D scene representation capabilities of neural radiance fields (NeRF). Specifically, we formulate the physical imaging process of SCI as part of the training of NeRF, allowing us to exploit its impressive performance in capturing complex scene structures. To assess the effectiveness of our method, we conduct extensive evaluations using both synthetic data and real data captured by our SCI system. Extensive experimental results demonstrate that our proposed approach surpasses the state-of-the-art methods in terms of image reconstruction and novel view image synthesis. Moreover, our method also exhibits the ability to restore high frame-rate multi-view consistent images by leveraging SCI and the rendering capabilities of NeRF. The code is available at https://github.com/WU-CVGL/SCINeRF.

eess.IV↗

arXiv2024

SATBA: An Invisible Backdoor Attack Based On Spatial Attention

Backdoor attack has emerged as a novel and concerning threat to AI security. These attacks involve the training of Deep Neural Network (DNN) on datasets that contain hidden trigger patterns. Although the poisoned model behaves normally on benign samples, it exhibits abnormal behavior on samples containing the trigger pattern. However, most existing backdoor attacks suffer from two significant drawbacks: their trigger patterns are visible and easy to detect by backdoor defense or even human inspection, and their injection process results in the loss of natural sample features and trigger patterns, thereby reducing the attack success rate and model accuracy. In this paper, we propose a novel backdoor attack named SATBA that overcomes these limitations using spatial attention and an U-net based model. The attack process begins by using spatial attention to extract meaningful data features and generate trigger patterns associated with clean images. Then, an U-shaped model is used to embed these trigger patterns into the original data without causing noticeable feature loss. We evaluate our attack on three prominent image classification DNN across three standard datasets. The results demonstrate that SATBA achieves high attack success rate while maintaining robustness against backdoor defenses. Furthermore, we conduct extensive image similarity experiments to emphasize the stealthiness of our attack strategy. Overall, SATBA presents a promising approach to backdoor attack, addressing the shortcomings of previous methods and showcasing its effectiveness in evading detection and maintaining high attack success rate.

cs.CR↗

arXiv2024

Device Activity Detection and Channel Estimation for Millimeter-Wave Massive MIMO

Millimeter-Wave Massive MIMO is important for beyond 5G or 6G wireless communication networks. The goal of this paper is to establish successful communication between the cellular base stations and devices, focusing on the problem of joint user activity detection and channel estimation. Different from traditional compressed sensing (CS) methods that only use the sparsity of user activities, we develop several Approximate Message Passing (AMP) based CS algorithms by exploiting the sparsity of user activities and mmWave channels. First, a group soft-thresholding AMP is presented to utilize only the user activity sparsity. Second, a hard-thresholding AMP is proposed based on the on-grid CS approach. Third, a super-resolution AMP algorithm is proposed based on atomic norm, in which a greedy method is proposed as a super-resolution denoiser. And we smooth the denoiser based on Monte Carlo sampling to have Lipschitz continuity and present state evolution results. Extensive simulation results show that the proposed method outperforms the previous state-of-the-art methods.

eess.SP↗

arXiv2023

YAYI 2: Multilingual Open-Source Large Language Models

As the latest advancements in natural language processing, large language models (LLMs) have achieved human-level language understanding and generation abilities in many real-world tasks, and even have been regarded as a potential path to the artificial general intelligence. To better facilitate research on LLMs, many open-source LLMs, such as Llama 2 and Falcon, have recently been proposed and gained comparable performances to proprietary models. However, these models are primarily designed for English scenarios and exhibit poor performances in Chinese contexts. In this technical report, we propose YAYI 2, including both base and chat models, with 30 billion parameters. YAYI 2 is pre-trained from scratch on a multilingual corpus which contains 2.65 trillion tokens filtered by our pre-training data processing pipeline. The base model is aligned with human values through supervised fine-tuning with millions of instructions and reinforcement learning from human feedback. Extensive experiments on multiple benchmarks, such as MMLU and CMMLU, consistently demonstrate that the proposed YAYI 2 outperforms other similar sized open-source models.

cs.CL↗

arXiv2023

HybridChain: Fast, Accurate, and Secure Transaction Processing with Distributed Learning

In order to fully unlock the transformative power of distributed ledgers and blockchains, it is crucial to develop innovative consensus algorithms that can overcome the obstacles of security, scalability, and interoperability, which currently hinder their widespread adoption. This paper introduces HybridChain that combines the advantages of sharded blockchain and DAG distributed ledger, and a consensus algorithm that leverages decentralized learning. Our approach involves validators exchanging perceptions as votes to assess potential conflicts between transactions and the witness set, representing input transactions in the UTXO model. These perceptions collectively contribute to an intermediate belief regarding the validity of transactions. By integrating their beliefs with those of other validators, localized decisions are made to determine validity. Ultimately, a final consensus is achieved through a majority vote, ensuring precise and efficient validation of transactions. Our proposed approach is compared to the existing DAG-based scheme IOTA and the sharded blockchain Omniledger through extensive simulations. The results show that IOTA has high throughput and low latency but sacrifices accuracy and is vulnerable to orphanage attacks especially with low transaction rates. Omniledger achieves stable accuracy by increasing shards but has increased latency. In contrast, the proposed HybridChain exhibits fast, accurate, and secure transaction processing, and excellent scalability.

cs.DC↗