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Tong Zhou

Publications and source records attributed to Tong Zhou.

At least 73 records · Page 4Linked to original sources
arXiv2024

Deep Learning with HM-VGG: AI Strategies for Multi-modal Image Analysis

This study introduces the Hybrid Multi-modal VGG (HM-VGG) model, a cutting-edge deep learning approach for the early diagnosis of glaucoma. The HM-VGG model utilizes an attention mechanism to process Visual Field (VF) data, enabling the extraction of key features that are vital for identifying early signs of glaucoma. Despite the common reliance on large annotated datasets, the HM-VGG model excels in scenarios with limited data, achieving remarkable results with small sample sizes. The model's performance is underscored by its high metrics in Precision, Accuracy, and F1-Score, indicating its potential for real-world application in glaucoma detection. The paper also discusses the challenges associated with ophthalmic image analysis, particularly the difficulty of obtaining large volumes of annotated data. It highlights the importance of moving beyond single-modality data, such as VF or Optical Coherence Tomography (OCT) images alone, to a multimodal approach that can provide a richer, more comprehensive dataset. This integration of different data types is shown to significantly enhance diagnostic accuracy. The HM- VGG model offers a promising tool for doctors, streamlining the diagnostic process and improving patient outcomes. Furthermore, its applicability extends to telemedicine and mobile healthcare, making diagnostic services more accessible. The research presented in this paper is a significant step forward in the field of medical image processing and has profound implications for clinical ophthalmology.

eess.IV↗
arXiv2024

Bileve: Securing Text Provenance in Large Language Models Against Spoofing with Bi-level Signature

Text watermarks for large language models (LLMs) have been commonly used to identify the origins of machine-generated content, which is promising for assessing liability when combating deepfake or harmful content. While existing watermarking techniques typically prioritize robustness against removal attacks, unfortunately, they are vulnerable to spoofing attacks: malicious actors can subtly alter the meanings of LLM-generated responses or even forge harmful content, potentially misattributing blame to the LLM developer. To overcome this, we introduce a bi-level signature scheme, Bileve, which embeds fine-grained signature bits for integrity checks (mitigating spoofing attacks) as well as a coarse-grained signal to trace text sources when the signature is invalid (enhancing detectability) via a novel rank-based sampling strategy. Compared to conventional watermark detectors that only output binary results, Bileve can differentiate 5 scenarios during detection, reliably tracing text provenance and regulating LLMs. The experiments conducted on OPT-1.3B and LLaMA-7B demonstrate the effectiveness of Bileve in defeating spoofing attacks with enhanced detectability. Code is available at https://github.com/Tongzhou0101/Bileve-official.

cs.CR↗
arXiv2024

Robust Graph Neural Networks for Stability Analysis in Dynamic Networks

In the current context of accelerated globalization and digitalization, the complexity and uncertainty of financial markets are increasing, and the identification and prevention of economic risks have become a key link in maintaining the stability of the financial system. Traditional risk identification methods often have limitations because they are difficult to cope with the multi-level and dynamically changing complex relationships in financial networks. With the rapid development of financial technology, graph neural network (GNN) technology, as an emerging deep learning method, has gradually shown great potential in the field of financial risk management. GNN can map transaction behaviors, financial institutions, individuals, and their interactive relationships in financial networks into graph structures, and effectively capture potential patterns and abnormal signals in financial data through embedded representation learning. Using this technology, financial institutions can extract valuable information from complex transaction networks, identify hidden dangers or abnormal behaviors that may cause systemic risks in a timely manner, optimize decision-making processes, and improve the accuracy of risk warnings. This paper explores the economic risk identification algorithm based on the GNN algorithm, aiming to provide financial institutions and regulators with more intelligent technical tools to help maintain the security and stability of the financial market. Improving the efficiency of economic risk identification through innovative technical means is expected to further enhance the risk resistance of the financial system and lay the foundation for building a robust global financial system.

q-fin.ST↗
arXiv2024

Deep Learning-Based Channel Squeeze U-Structure for Lung Nodule Detection and Segmentation

This paper introduces a novel deep-learning method for the automatic detection and segmentation of lung nodules, aimed at advancing the accuracy of early-stage lung cancer diagnosis. The proposed approach leverages a unique "Channel Squeeze U-Structure" that optimizes feature extraction and information integration across multiple semantic levels of the network. This architecture includes three key modules: shallow information processing, channel residual structure, and channel squeeze integration. These modules enhance the model's ability to detect and segment small, imperceptible, or ground-glass nodules, which are critical for early diagnosis. The method demonstrates superior performance in terms of sensitivity, Dice similarity coefficient, precision, and mean Intersection over Union (IoU). Extensive experiments were conducted on the Lung Image Database Consortium (LIDC) dataset using five-fold cross-validation, showing excellent stability and robustness. The results indicate that this approach holds significant potential for improving computer-aided diagnosis systems, providing reliable support for radiologists in clinical practice and aiding in the early detection of lung cancer, especially in resource-limited settings

eess.IV↗
arXiv2024

Microwave Signatures of Topological Superconductivity in Planar Josephson Junctions

Planar Josephson junctions provide a platform to host topological superconductivity which, through manipulating Majorana bound states (MBS), could enable fault-tolerant quantum computing. However, what constitutes experimental signatures of topological superconductivity and how MBS can be detected remains strongly debated. In addition to spurious effects that mimic MBS, there is a challenge to discern the inherent topological signals in realistic systems with many topologically-trivial Andreev bound states, determining the transport properties of Josephson junctions. Guided by the advances in microwave spectroscopy, we theoretically study Al/InAs-based planar Josephson junction embedded into a radio-frequency superconducting quantum interference device to identify microwave signatures of topological superconductivity. Remarkably, by exploring the closing and reopening of a topological gap, we show that even in a wide planar Josephson junction with many Andreev bound states, such a topological signature is distinguishable in the resonance frequency shift of a microwave drive and the ``half-slope'' feature of the microwave absorption spectrum. Our findings provide an important step towards experimental detection of non-Abelian statistics and implementing scalable topological quantum computing.

cond-mat.mes-hall↗
arXiv2024

Flexible 3D Lane Detection by Hierarchical Shape MatchingFlexible 3D Lane Detection by Hierarchical Shape Matching

As one of the basic while vital technologies for HD map construction, 3D lane detection is still an open problem due to varying visual conditions, complex typologies, and strict demands for precision. In this paper, an end-to-end flexible and hierarchical lane detector is proposed to precisely predict 3D lane lines from point clouds. Specifically, we design a hierarchical network predicting flexible representations of lane shapes at different levels, simultaneously collecting global instance semantics and avoiding local errors. In the global scope, we propose to regress parametric curves w.r.t adaptive axes that help to make more robust predictions towards complex scenes, while in the local vision the structure of lane segment is detected in each of the dynamic anchor cells sampled along the global predicted curves. Moreover, corresponding global and local shape matching losses and anchor cell generation strategies are designed. Experiments on two datasets show that we overwhelm current top methods under high precision standards, and full ablation studies also verify each part of our method. Our codes will be released at https://github.com/Doo-do/FHLD.

cs.CV↗
arXiv2024

High order compact fully-discrete scheme for hyperbolic conversation laws

Based on the solution formula method, a series of one-step fully-discrete schemes, such as FWENO/Full-WENO has been proposed. Storing the by-products conservative variables at the half points (grid center) and using them as interpolation information to construct high-order schemes, we obtain a new class of one-step compact fully-discrete schemes. The new scheme can be associate with various non-oscillatory strategies. This paper takes state-of-the-art WENO-JS method as an example and proposes a family of compact fully-discrete WENO scheme. Detailed analysis is conducted on accuracy, errors, computational cost, efficiency and their connection with Hermite interpolation. Meanwhile, we design a new entropy flux linearization strategy for Euler equations to enhance its robustness, and also develop a multi-dimensional method for this compact fully-discrete framework. Due to the new scheme is one-step and utilizes stored by-products information for interpolation, it has a significant advantage in efficiency. For one-dimensional Euler equations, compared to the original FWENO, the computational cost only increases by 20-40%, while is approximately one-third of WENO+RK3. For two-dimensional case, a new special dimension-by-dimension strategy is applied. Although there is an additional computing cost, numerical experiments show that the new scheme only needs about 1/10 to 1/13 cost of that for WENO+RK3 when obtaining similar or even better resolution results, indicating that the new scheme is more efficient than semi-discrete schemes based on RK methods.

math.NA↗
arXiv2024

AdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computing

Private inference (PI) has emerged as a promising solution to execute computations on encrypted data, safeguarding user privacy and model parameters in edge computing. However, existing PI methods are predominantly developed considering constant resource constraints, overlooking the varied and dynamic resource constraints in diverse edge devices, like energy budgets. Consequently, model providers have to design specialized models for different devices, where all of them have to be stored on the edge server, resulting in inefficient deployment. To fill this gap, this work presents AdaPI, a novel approach that achieves adaptive PI by allowing a model to perform well across edge devices with diverse energy budgets. AdaPI employs a PI-aware training strategy that optimizes the model weights alongside weight-level and feature-level soft masks. These soft masks are subsequently transformed into multiple binary masks to enable adjustments in communication and computation workloads. Through sequentially training the model with increasingly dense binary masks, AdaPI attains optimal accuracy for each energy budget, which outperforms the state-of-the-art PI methods by 7.3\% in terms of test accuracy on CIFAR-100. The code of AdaPI can be accessed via https://github.com/jiahuiiiiii/AdaPI.

cs.LG↗
arXiv2024

Phase jumps in Josephson junctions with time-dependent spin-orbit coupling

Planar Josephson junctions (JJs), based on common superconductors and III-V semiconductors, are sought for Majorana states and fault-tolerant quantum computing. However, with gate-tunable spin-orbit coupling (SOC), we show that the range of potential applications of such JJs becomes much broader. The time-dependent SOC offers unexplored mechanisms for switching JJs, accompanied by the $2π$-phase jumps and the voltage pulses corresponding to the single-flux-quantum transitions, key to high-speed and low-power superconducting electronics. In a constant applied magnetic field, with Rashba and Dresselhaus SOC, anharmonic current-phase relations, calculated microscopically in these JJs, yield a nonreciprocal transport and superconducting diode effect. Together with the time-dependent SOC, this allows us to identify a switching mechanism at no applied current bias which supports fractional-flux-quantum superconducting circuits and neuromorphic computing.

cond-mat.supr-con↗
arXiv2024

CogMG: Collaborative Augmentation Between Large Language Model and Knowledge Graph

Large language models have become integral to question-answering applications despite their propensity for generating hallucinations and factually inaccurate content. Querying knowledge graphs to reduce hallucinations in LLM meets the challenge of incomplete knowledge coverage in knowledge graphs. On the other hand, updating knowledge graphs by information extraction and knowledge graph completion faces the knowledge update misalignment issue. In this work, we introduce a collaborative augmentation framework, CogMG, leveraging knowledge graphs to address the limitations of LLMs in QA scenarios, explicitly targeting the problems of incomplete knowledge coverage and knowledge update misalignment. The LLMs identify and decompose required knowledge triples that are not present in the KG, enriching them and aligning updates with real-world demands. We demonstrate the efficacy of this approach through a supervised fine-tuned LLM within an agent framework, showing significant improvements in reducing hallucinations and enhancing factual accuracy in QA responses. Our code and video are publicly available.

cs.CL↗
arXiv2024

Beyond the standard model of topological Josephson junctions: From crystalline anisotropy to finite-size and diode effects

A planar Josephson junction is a versatile platform to realize topological superconductivity over a large parameter space and host Majorana bound states. With a change in Zeeman field, this system undergoes a transition from trivial to topological superconductivity accompanied by a jump in the superconducting phase difference between the two superconductors. A standard model of these Josephson junctions, which can be fabricated to have a nearly perfect interfacial transparency, predicts a simple universal behavior. In that model, at the same value of Zeeman field for the topological transition, there is a $π$ phase jump and a minimum in the critical superconducting current, while applying a controllable phase difference yields a diamond-shaped topological region as a function of that phase difference and a Zeeman field. In contrast, even for a perfect interfacial transparency, we find a much richer and nonuniversal behavior as the width of the superconductor is varied or the Dresselhaus spin-orbit coupling is considered. The Zeeman field for the phase jump, not necessarily $π$, is different from the value for the minimum critical current, while there is a strong deviation from the diamond-like topological region. These Josephson junctions show a striking example of a nonreciprocal transport and superconducting diode effect, revealing the importance of our findings not only for topological superconductivity and fault-tolerant quantum computing, but also for superconducting spintronics.

cond-mat.mes-hall↗
arXiv2024

Character Sheaves on Reductive Lie Algebras in Positive Characteristic

We prove a microlocal characterisation of character sheaves on a reductive Lie algebra over an algebraically closed field of sufficiently large positive characteristic: a perverse irreducible G-equivariant sheaf is a character sheaf if and only if it has nilpotent singular support and is quasi-admissible. We also present geometric proofs, in positive characteristic, of the equivalence between being admissible and being a character sheaf, and various characterisations of cuspidal sheaves, following the work of Mirković.

math.RT↗
arXiv2024

TBNet: A Neural Architectural Defense Framework Facilitating DNN Model Protection in Trusted Execution Environments

Trusted Execution Environments (TEEs) have become a promising solution to secure DNN models on edge devices. However, the existing solutions either provide inadequate protection or introduce large performance overhead. Taking both security and performance into consideration, this paper presents TBNet, a TEE-based defense framework that protects DNN model from a neural architectural perspective. Specifically, TBNet generates a novel Two-Branch substitution model, to respectively exploit (1) the computational resources in the untrusted Rich Execution Environment (REE) for latency reduction and (2) the physically-isolated TEE for model protection. Experimental results on a Raspberry Pi across diverse DNN model architectures and datasets demonstrate that TBNet achieves efficient model protection at a low cost.

cs.CR↗
arXiv2024

Identification of Secondary Resonances of Nonlinear Systems using Phase-Locked Loop Testing

One unique feature of nonlinear dynamical systems is the existence of superharmonic and subharmonic resonances in addition to primary resonances. In this study, an effective vibration testing methodology is introduced for the experimental identification of these secondary resonances. The proposed method relies on phase-locked loop control combined with adaptive filters for online Fourier decomposition. To this end, the concept of a resonant phase lag is exploited to define the target phase lag to be followed during the experimental continuation process. The method is demonstrated using two systems featuring cubic nonlinearities, namely a numerical Duffing oscillator and a physical experiment comprising a clamped-clamped thin beam. The obtained results highlight that the control scheme can accurately characterize secondary resonances as well as track their backbone curves. A particularly salient feature of the developed algorithm is that, starting from the rest position, it facilitates an automatic and smooth dynamic state transfer toward one point of a subharmonic isolated branch, hence, inducing branch switching.

eess.SY↗
arXiv2023

Minimally-intrusive Navigation in Dense Crowds with Integrated Macro and Micro-level Dynamics

In mobile robot navigation, despite advancements, the generation of optimal paths often disrupts pedestrian areas. To tackle this, we propose three key contributions to improve human-robot coexistence in shared spaces. Firstly, we have established a comprehensive framework to understand disturbances at individual and flow levels. Our framework provides specialized computational strategies for in-depth studies of human-robot interactions from both micro and macro perspectives. By employing novel penalty terms, namely Flow Disturbance Penalty (FDP) and Individual Disturbance Penalty (IDP), our framework facilitates a more nuanced assessment and analysis of the robot navigation's impact on pedestrians. Secondly, we introduce an innovative sampling-based navigation system that adeptly integrates a suite of safety measures with the predictability of robotic movements. This system not only accounts for traditional factors such as trajectory length and travel time but also actively incorporates pedestrian awareness. Our navigation system aims to minimize disturbances and promote harmonious coexistence by considering safety protocols, trajectory clarity, and pedestrian engagement. Lastly, we validate our algorithm's effectiveness and real-time performance through simulations and real-world tests, demonstrating its ability to navigate with minimal pedestrian disturbance in various environments.

cs.RO↗
arXiv2023

Satellite Impact on Astronomical Observations Based on Elliptical Orbit Model

Space-based and ground-based telescopes have extensively documented the impact of satellites on astronomical observations. With the proliferation of satellite mega-constellation programs, their influence on astronomical observations has become undeniable. It is crucial to quantify the impact of satellites on telescopes. To address this need, we have enhanced the circular orbit model for satellites and introduced a methodology based on two-line element (TLE) orbit data. This involves constructing a satellite probability distribution model to evaluate the impact of satellites on telescopes. Using our method, we assessed the satellite impact on global observatories. The results indicate that the regions most severely affected by satellite interference currently are those near the equator, with latitudes around 50 and 80 degrees experiencing the most significant impact from low Earth orbit satellites. Furthermore, we validated the reliability of our method using imaging data obtained from the focal surface acquisition camera of the LAMOST telescope.

astro-ph.IM↗
arXiv2023

Towards the Unification of Generative and Discriminative Visual Foundation Model: A Survey

The advent of foundation models, which are pre-trained on vast datasets, has ushered in a new era of computer vision, characterized by their robustness and remarkable zero-shot generalization capabilities. Mirroring the transformative impact of foundation models like large language models (LLMs) in natural language processing, visual foundation models (VFMs) have become a catalyst for groundbreaking developments in computer vision. This review paper delineates the pivotal trajectories of VFMs, emphasizing their scalability and proficiency in generative tasks such as text-to-image synthesis, as well as their adeptness in discriminative tasks including image segmentation. While generative and discriminative models have historically charted distinct paths, we undertake a comprehensive examination of the recent strides made by VFMs in both domains, elucidating their origins, seminal breakthroughs, and pivotal methodologies. Additionally, we collate and discuss the extensive resources that facilitate the development of VFMs and address the challenges that pave the way for future research endeavors. A crucial direction for forthcoming innovation is the amalgamation of generative and discriminative paradigms. The nascent application of generative models within discriminative contexts signifies the early stages of this confluence. This survey aspires to be a contemporary compendium for scholars and practitioners alike, charting the course of VFMs and illuminating their multifaceted landscape.

cs.CV↗