arXiv subjects

Lu Zhou

Publications and source records attributed to Lu Zhou.

At least 55 records · Page 3Linked to original sources

arXiv2024

SSL-OTA: Unveiling Backdoor Threats in Self-Supervised Learning for Object Detection

The extensive adoption of Self-supervised learning(SSL) has led to an increased security threat from backdoor attacks. While existing research has mainly focused on backdoor attacks in image classification, there has been limited exploration of their implications for object detection. Object detection plays a critical role in security-sensitive applications, such as autonomous driving, where backdoor attacks seriously threaten human life and property. In this work, we propose the first backdoor attack designed for object detection tasks in SSL scenarios, called Object Transform Attack (SSL-OTA). SSL-OTA employs a trigger capable of altering predictions of the target object to the desired category, encompassing two attacks: Naive Attack(NA) and Dual-Source Blending Attack (DSBA). NA conducts data poisoning during downstream fine-tuning of the object detector, while DSBA additionally injects backdoors into the pre-trained encoder. We establish appropriate metrics and conduct extensive experiments on benchmark datasets, demonstrating the effectiveness of our proposed attack and its resistance to potential defenses. Notably, both NA and DSBA achieve high attack success rates (ASR) at extremely low poisoning rates (0.5%). The results underscore the importance of considering backdoor threats in SSL-based object detection and contribute a novel perspective to the field.

cs.CR↗

arXiv2024

Evaluation of the systematic error induced by quadratic Zeeman effect using hyperfine ground state exchange method in a long-baseline dual-species atom interferometer

The systematic error induced by the quadratic Zeeman effect is non-negligible in atom interferometers and must be precisely evaluated. We theoretically analyze the phase shift induced by the Zeeman effect, and use a hyperfine ground state exchange (HGSE) method to evaluate the systematic error in the long-baseline $^{85}$Rb-$^{87}$Rb dual-species atom interferometer due to the quadratic Zeeman effect. Compared to the two evaluation methods, mapping the absolute magnetic field in the interference region and performing phase measurements at different bias fields, the HGSE method could obtain the systematic error in real time in case of slow drifts of either the ambient magnetic field or other systematic effects irrelevant to the hyperfine ground states. To validate the effectiveness of the HGSE method, we also employ the mapping magnetic field method and modulating bias field method independently to cross-check and yield consistent results of three methods within an accuracy of $10^{-11}$ level. The HGSE method is helpful in evaluating and suppressing the quadratic Zeeman-effect-induced systematic error in long-baseline atom interferometer-based precision measurements, such as equivalence principle tests.

physics.atom-ph↗

arXiv2024

Study of the mechanism of electroacupuncture regulating ferroptosis, inhibiting bladder neck fibrosis, and improving bladder urination function after suprasacral spinal cord injury using proteomics

Purpose The aim of this study was to explore whether electroacupuncture regulates phenotypic transformation of smooth muscle cells by inhibiting ferroptosis and inhibiting fibrosis, thereby improving bladder urination function after suprasacral spinal cord injury (SSCI). Methods The experiment was divided into sham, model, and electroacupuncture group. After 10 days of electroacupuncture intervention, urodynamic examination was performed, and bladder neck was taken for HE staining, tandem mass tag (TMT)-based quantitative proteomics analysis, Western blot(WB) detection, ferrous ion concentration detection and Masson staining. Conclusion Electroacupuncture may prevent the phenotype of bladder neck smooth muscle cells from transforming from contraction type to synthesis type by inhibiting ferroptosis, inhibit bladder neck fibrosis, improve bladder neck compliance, and thus improve bladder urination function after SSCI.

q-bio.BM↗

arXiv2024

Yet another Improvement of Plantard Arithmetic for Faster Kyber on Low-end 32-bit IoT Devices

This paper presents another improved version of Plantard arithmetic that could speed up Kyber implementations on two low-end 32-bit IoT platforms (ARM Cortex-M3 and RISC-V) without SIMD extensions. Specifically, we further enlarge the input range of the Plantard arithmetic without modifying its computation steps. After tailoring the Plantard arithmetic for Kyber's modulus, we show that the input range of the Plantard multiplication by a constant is at least 2.14 times larger than the original design in TCHES2022. Then, two optimization techniques for efficient Plantard arithmetic on Cortex-M3 and RISC-V are presented. We show that the Plantard arithmetic supersedes both Montgomery and Barrett arithmetic on low-end 32-bit platforms. With the enlarged input range and the efficient implementation of the Plantard arithmetic on these platforms, we propose various optimization strategies for NTT/INTT. We minimize or entirely eliminate the modular reduction of coefficients in NTT/INTT by taking advantage of the larger input range of the proposed Plantard arithmetic on low-end 32-bit platforms. Furthermore, we propose two memory optimization strategies that reduce 23.50% to 28.31% stack usage for the speed-version Kyber implementation when compared to its counterpart on Cortex-M4. The proposed optimizations make the speed-version implementation more feasible on low-end IoT devices. Thanks to the aforementioned optimizations, our NTT/INTT implementation shows considerable speedups compared to the state-of-the-art work. Overall, we demonstrate the applicability of the speed-version Kyber implementation on memory-constrained IoT platforms and set new speed records for Kyber on these platforms.

cs.CY↗

arXiv2024

ESKNet-An enhanced adaptive selection kernel convolution for breast tumors segmentation

Breast cancer is one of the common cancers that endanger the health of women globally. Accurate target lesion segmentation is essential for early clinical intervention and postoperative follow-up. Recently, many convolutional neural networks (CNNs) have been proposed to segment breast tumors from ultrasound images. However, the complex ultrasound pattern and the variable tumor shape and size bring challenges to the accurate segmentation of the breast lesion. Motivated by the selective kernel convolution, we introduce an enhanced selective kernel convolution for breast tumor segmentation, which integrates multiple feature map region representations and adaptively recalibrates the weights of these feature map regions from the channel and spatial dimensions. This region recalibration strategy enables the network to focus more on high-contributing region features and mitigate the perturbation of less useful regions. Finally, the enhanced selective kernel convolution is integrated into U-net with deep supervision constraints to adaptively capture the robust representation of breast tumors. Extensive experiments with twelve state-of-the-art deep learning segmentation methods on three public breast ultrasound datasets demonstrate that our method has a more competitive segmentation performance in breast ultrasound images.

eess.IV↗

arXiv2023

Bright solitons in a spin-orbit-coupled dipolar Bose-Einstein condensate trapped within a double-lattice

By effectively controlling the dipole-dipole interaction, we investigate the characteristics of the ground state of bright solitons in a spin-orbit coupled dipolar Bose-Einstein condensate. The dipolar atoms are trapped within a double-lattice which consists of a linear and a nonlinear lattice. We derive the motion equations of the different spin components, taking the controlling mechanisms of the diolpe-dipole interaction into account. An analytical expression of dipole-dipole interaction is derived. By adjusting the dipole polarization angle, the dipole interaction can be adjusted from attraction to repulsion. On this basis, we study the generation and manipulation of the bright solitons using both the analytical variational method and numerical imaginary time evolution. The stability of the bright solitons is also analyzed and we map out the stability phase diagram. By adjusting the long-range dipole-dipole interaction, one can achieve manipulation of bright solitons in all aspects, including the existence, width, nodes, and stability. Considering the complexity of our system, our results will have enormous potential applications in quantum simulation of complex systems.

cond-mat.quant-gas↗

arXiv2023

Vortex-lattice structures in rotating Bose-Fermi superfluid mixtures

The system of Bose-Fermi superfluid mixture offers a playground to explore rich macroscopic quantum phenomena. In a recent experiment of Yao {\it et al.} [Phys. Rev. Lett. {\bf 117}, 145301 (2016)], $^{41}$K-$^{6}$Li superfluid mixture is implemented. Coupled quantized vortices are generated via rotating the superfluid mixture, and a few unconventional behaviors on the formations of vortex numbers are observed, which can be traced to boson-fermion interactions. Here we provide a theoretical insight into the unconventional behaviors observed in the experiment. To this end, the orbital-free density functional theory is hired, and its utility is validated by making comparison of the numerical results and a microscopic theory for vortex lattices in strongly interacting Fermi superfluids alone. We also predict interesting phenomena which can be readily explored experimentally, including the novel structures of bosonic-fermionic vortices, and attractive interactions between the bosonic and fermionic vortices.

cond-mat.quant-gas↗

arXiv2023

Sparse Federated Training of Object Detection in the Internet of Vehicles

As an essential component part of the Intelligent Transportation System (ITS), the Internet of Vehicles (IoV) plays a vital role in alleviating traffic issues. Object detection is one of the key technologies in the IoV, which has been widely used to provide traffic management services by analyzing timely and sensitive vehicle-related information. However, the current object detection methods are mostly based on centralized deep training, that is, the sensitive data obtained by edge devices need to be uploaded to the server, which raises privacy concerns. To mitigate such privacy leakage, we first propose a federated learning-based framework, where well-trained local models are shared in the central server. However, since edge devices usually have limited computing power, plus a strict requirement of low latency in IoVs, we further propose a sparse training process on edge devices, which can effectively lighten the model, and ensure its training efficiency on edge devices, thereby reducing communication overheads. In addition, due to the diverse computing capabilities and dynamic environment, different sparsity rates are applied to edge devices. To further guarantee the performance, we propose, FedWeg, an improved aggregation scheme based on FedAvg, which is designed by the inverse ratio of sparsity rates. Experiments on the real-life dataset using YOLO show that the proposed scheme can achieve the required object detection rate while saving considerable communication costs.

cs.LG↗

arXiv2023

Itinerant ferromagnetism entrenched by the anisotropy of spin-orbit coupling in a dipolar Fermi gas

We investigate the the itinerant ferromagnetism in a dipolar Fermi atomic system with the anisotropic spin-orbit coupling (SOC),which is traditionally explored with isotropic contact interaction.We first study the ferromagnetism transition boundaries and the properties of the ground states through the density and spin-flip distribution in momentum space, and we find that both the anisotropy and the magnitude of the SOC play an important role in this process. We propose a helpful scheme and a quantum control method which can be applied to conquering the difficulties of previous experimental observation of itinerant ferromagnetism. Our further study reveals that exotic Fermi surfaces and an abnormal phase region can exist in this system by controlling the anisotropy of SOC, which can provide constructive suggestions for the research and the application of a dipolar Fermi gas. Furthermore, we also calculate the ferromagnetism transition temperature and novel distributions in momentum space at finite temperature beyond the ground states from the perspective of experiment.

cond-mat.quant-gas↗

arXiv2023

Temporal-Channel Topology Enhanced Network for Skeleton-Based Action Recognition

Skeleton-based action recognition has become popular in recent years due to its efficiency and robustness. Most current methods adopt graph convolutional network (GCN) for topology modeling, but GCN-based methods are limited in long-distance correlation modeling and generalizability. In contrast, the potential of convolutional neural network (CNN) for topology modeling has not been fully explored. In this paper, we propose a novel CNN architecture, Temporal-Channel Topology Enhanced Network (TCTE-Net), to learn spatial and temporal topologies for skeleton-based action recognition. The TCTE-Net consists of two modules: the Temporal-Channel Focus module, which learns a temporal-channel focus matrix to identify the most critical feature representations, and the Dynamic Channel Topology Attention module, which dynamically learns spatial topological features, and fuses them with an attention mechanism to model long-distance channel-wise topology. We conduct experiments on NTU RGB+D, NTU RGB+D 120, and FineGym datasets. TCTE-Net shows state-of-the-art performance compared to CNN-based methods and achieves superior performance compared to GCN-based methods. The code is available at https://github.com/aikuniverse/TCTE-Net.

cs.CV↗

arXiv2023

Auto-weighted Multi-view Clustering for Large-scale Data

Multi-view clustering has gained broad attention owing to its capacity to exploit complementary information across multiple data views. Although existing methods demonstrate delightful clustering performance, most of them are of high time complexity and cannot handle large-scale data. Matrix factorization-based models are a representative of solving this problem. However, they assume that the views share a dimension-fixed consensus coefficient matrix and view-specific base matrices, limiting their representability. Moreover, a series of large-scale algorithms that bear one or more hyperparameters are impractical in real-world applications. To address the two issues, we propose an auto-weighted multi-view clustering (AWMVC) algorithm. Specifically, AWMVC first learns coefficient matrices from corresponding base matrices of different dimensions, then fuses them to obtain an optimal consensus matrix. By mapping original features into distinctive low-dimensional spaces, we can attain more comprehensive knowledge, thus obtaining better clustering results. Moreover, we design a six-step alternative optimization algorithm proven to be convergent theoretically. Also, AWMVC shows excellent performance on various benchmark datasets compared with existing ones. The code of AWMVC is publicly available at https://github.com/wanxinhang/AAAI-2023-AWMVC.

cs.LG↗

arXiv2022

PromptAttack: Prompt-based Attack for Language Models via Gradient Search

As the pre-trained language models (PLMs) continue to grow, so do the hardware and data requirements for fine-tuning PLMs. Therefore, the researchers have come up with a lighter method called \textit{Prompt Learning}. However, during the investigations, we observe that the prompt learning methods are vulnerable and can easily be attacked by some illegally constructed prompts, resulting in classification errors, and serious security problems for PLMs. Most of the current research ignores the security issue of prompt-based methods. Therefore, in this paper, we propose a malicious prompt template construction method (\textbf{PromptAttack}) to probe the security performance of PLMs. Several unfriendly template construction approaches are investigated to guide the model to misclassify the task. Extensive experiments on three datasets and three PLMs prove the effectiveness of our proposed approach PromptAttack. We also conduct experiments to verify that our method is applicable in few-shot scenarios.

cs.CL↗

arXiv2022

Combating Distribution Shift for Accurate Time Series Forecasting via Hypernetworks

Time series forecasting has widespread applications in urban life ranging from air quality monitoring to traffic analysis. However, accurate time series forecasting is challenging because real-world time series suffer from the distribution shift problem, where their statistical properties change over time. Despite extensive solutions to distribution shifts in domain adaptation or generalization, they fail to function effectively in unknown, constantly-changing distribution shifts, which are common in time series. In this paper, we propose Hyper Time- Series Forecasting (HTSF), a hypernetwork-based framework for accurate time series forecasting under distribution shift. HTSF jointly learns the time-varying distributions and the corresponding forecasting models in an end-to-end fashion. Specifically, HTSF exploits the hyper layers to learn the best characterization of the distribution shifts, generating the model parameters for the main layers to make accurate predictions. We implement HTSF as an extensible framework that can incorporate diverse time series forecasting models such as RNNs and Transformers. Extensive experiments on 9 benchmarks demonstrate that HTSF achieves state-of-the-art performances.

cs.LG↗

arXiv2022

Supersolid Gap Soliton in a Bose-Einstein Condensate and Optical Ring Cavity coupling system

The system of a transversely pumped Bose-Einstein condensate (BEC) coupled to a lossy ring cavity can favor a supersolid steady state. Here we find the existence of supersolid gap soliton in such a driven-dissipative system. By numerically solving the mean-field atom-cavity field coupling equations, gap solitons of a few different families have been identified. Their dynamical properties, including stability, propagation and soliton collision, are also studied. Due to the feedback atom-intracavity field interaction, these supersolid gap solitons show numerous new features compared with the usual BEC gap solitons in static optical lattices.

cond-mat.quant-gas↗

arXiv2022

Ontology Design Facilitating Wikibase Integration -- and a Worked Example for Historical Data

Wikibase -- which is the software underlying Wikidata -- is a powerful platform for knowledge graph creation and management. However, it has been developed with a crowd-sourced knowledge graph creation scenario in mind, which in particular means that it has not been designed for use case scenarios in which a tightly controlled high-quality schema, in the form of an ontology, is to be imposed, and indeed, independently developed ontologies do not necessarily map seamlessly to the Wikibase approach. In this paper, we provide the key ingredients needed in order to combine traditional ontology modeling with use of the Wikibase platform, namely a set of \emph{axiom} patterns that bridge the paradigm gap, together with usage instructions and a worked example for historical data.

cs.AI↗

arXiv2022

Imaginary spin-orbital coupling in parity-time symmetric systems with momentum-dependent gain and loss

Spin-orbital coupling (SOC) and parity-time ($\mathcal{PT}$) symmetry both have attracted paramount research interest in condensed matter physics, cold atom physics, optics and acoustics to develop spintronics, quantum computation, precise sensors and novel functionalities. Natural SOC is an intrinsic relativistic effect. However, there is an increasing interest in synthesized SOC nowadays. Here, we show that in a $\mathcal{PT}$-symmetric spin-1/2 system, the momentum-dependent balanced gain and loss can synthesize a new type of SOC, which we call imaginary SOC. The imaginary SOC can substantially change the energy spectrum of the system. Firstly, we show that it can generate a pure real energy spectrum with a double-valleys structure. Therefore, it has the ability to generate supersolid stripe states. Especially, the imaginary SOC stripe state can have a high contrast of one. Moreover, the imaginary SOC can also generate a spectrum with tunable complex energy band, in which the waves are either amplifying or decaying. Thus, the imaginary SOC would also find applications in the engineering of $\mathcal{PT}$-symmetry-based coherent wave amplifiers/absorbers. Potential experimental realizations of imaginary SOC are proposed in cold atomic gases and systems of coupled waveguides constituted of nonlocal gain and loss.

cond-mat.quant-gas↗

arXiv2021

Collision of two self-trapped atomic matter wave packets in an optical ring cavity

The interaction between atomic Bose-Einstein condensate (BEC) and light field in an optical ring cavity gives rise to many interesting phenomena such as supersolid and movable self-trapped matter wave packets. Here we examined the collision of two self-trapped atomic matter wave packets in an optical ring cavity, and abundant colliding phenomena have been found in the system. Depending on the magnitude of colliding velocity, the collision dynamics exhibit very different features compared with the cavity-free case. When the initial colliding velocities of the two wave packets are small, they correlatedly oscillate around their initial equilibrium positions with a small amplitude. Increasing the collision velocity leads to severe scattering of the BEC atoms; after the collision, the two self-trapped wave packets usually break into small pieces. Interestingly, we found that such a medium velocity collision is of great phase sensitivity, which may make the system useful in precision matter wave interferometry. When the colliding velocity is further increased, in the bad cavity limit, the two wave packets collide phenomenally similar to two classical particles -- they firstly approach each other, then separate with their shape virtually maintained. However, beyond the bad cavity limit, they experience severe spatial spreading.

cond-mat.quant-gas↗

arXiv2021

Neuro-Symbolic Artificial Intelligence: Current Trends

Neuro-Symbolic Artificial Intelligence -- the combination of symbolic methods with methods that are based on artificial neural networks -- has a long-standing history. In this article, we provide a structured overview of current trends, by means of categorizing recent publications from key conferences. The article is meant to serve as a convenient starting point for research on the general topic.

cs.AI↗