arXiv subjects

Hao Qin

Publications and source records attributed to Hao Qin.

At least 55 records · Page 3Linked to original sources

arXiv2024

Nonlinear chiral metasurfaces based on structured van der Waals materials

Nonlinear chiral photonics explores nonlinear response of chiral structures, and it offers a pathway to novel optical functionalities not accessible through linear or achiral systems. Here we present the first application of nanostructured van der Waals materials to nonlinear chiral photonics. We demonstrate the three orders of magnitude enhancement of the third-harmonic generation from hBN metasurfaces driven by quasi-bound states in the continuum and accompanied by strong nonlinear circular dichroism at the resonances. This novel platform for chiral metaphotonics can be employed for achieving large circular dichroism combined with high-efficiency harmonic generation in a broad frequency range.

physics.optics↗

arXiv2024

Enhancing Healthcare through Large Language Models: A Study on Medical Question Answering

In recent years, the application of Large Language Models (LLMs) in healthcare has shown significant promise in improving the accessibility and dissemination of medical knowledge. This paper presents a detailed study of various LLMs trained on the MedQuAD medical question-answering dataset, with a focus on identifying the most effective model for providing accurate medical information. Among the models tested, the Sentence-t5 combined with Mistral 7B demonstrated superior performance, achieving a precision score of 0.762. This model's enhanced capabilities are attributed to its advanced pretraining techniques, robust architecture, and effective prompt construction methodologies. By leveraging these strengths, the Sentence-t5 + Mistral 7B model excels in understanding and generating precise medical answers. Our findings highlight the potential of integrating sophisticated LLMs in medical contexts to facilitate efficient and accurate medical knowledge retrieval, thus significantly enhancing patient education and support.

cs.CL↗

arXiv2024

Adversarial Robustness of Open-source Text Classification Models and Fine-Tuning Chains

Context:With the advancement of artificial intelligence (AI) technology and applications, numerous AI models have been developed, leading to the emergence of open-source model hosting platforms like Hugging Face (HF). Thanks to these platforms, individuals can directly download and use models, as well as fine-tune them to construct more domain-specific models. However, just like traditional software supply chains face security risks, AI models and fine-tuning chains also encounter new security risks, such as adversarial attacks. Therefore, the adversarial robustness of these models has garnered attention, potentially influencing people's choices regarding open-source models. Objective:This paper aims to explore the adversarial robustness of open-source AI models and their chains formed by the upstream-downstream relationships via fine-tuning to provide insights into the potential adversarial risks. Method:We collect text classification models on HF and construct the fine-tuning chains.Then, we conduct an empirical analysis of model reuse and associated robustness risks under existing adversarial attacks from two aspects, i.e., models and their fine-tuning chains. Results:Despite the models' widespread downloading and reuse, they are generally susceptible to adversarial attack risks, with an average of 52.70% attack success rate. Moreover, fine-tuning typically exacerbates this risk, resulting in an average 12.60% increase in attack success rates. We also delve into the influence of factors such as attack techniques, datasets, and model architectures on the success rate, as well as the transitivity along the model chains.

cs.SE↗

arXiv2024

Probablistic Restoration with Adaptive Noise Sampling for 3D Human Pose Estimation

The accuracy and robustness of 3D human pose estimation (HPE) are limited by 2D pose detection errors and 2D to 3D ill-posed challenges, which have drawn great attention to Multi-Hypothesis HPE research. Most existing MH-HPE methods are based on generative models, which are computationally expensive and difficult to train. In this study, we propose a Probabilistic Restoration 3D Human Pose Estimation framework (PRPose) that can be integrated with any lightweight single-hypothesis model. Specifically, PRPose employs a weakly supervised approach to fit the hidden probability distribution of the 2D-to-3D lifting process in the Single-Hypothesis HPE model and then reverse-map the distribution to the 2D pose input through an adaptive noise sampling strategy to generate reasonable multi-hypothesis samples effectively. Extensive experiments on 3D HPE benchmarks (Human3.6M and MPI-INF-3DHP) highlight the effectiveness and efficiency of PRPose. Code is available at: https://github.com/xzhouzeng/PRPose.

cs.CV↗

arXiv2024

Collaborative Control Method of Transit Signal Priority Based on Cooperative Game and Reinforcement Learning

To address the low efficiency in priority signal control within intelligent transportation systems, this study introduces a novel eight-phase priority signal control method, CBQL-TSP, leveraging a hybrid decision-making framework that integrates cooperative game theory and reinforcement learning. This approach conceptualizes the allocation of bus signal priorities as a multi-objective decision-making problem across an eight-phase signal sequence, differentiating between priority and non-priority phases. It employs a cooperative game model to facilitate this differentiation. The developed hybrid decision-making algorithm, CBQL, effectively tackles the multi-objective decision-making challenges inherent in the eight-phase signal sequence. By computing the Shapley value function, it quantifies the marginal contributions of each participant, which in turn inform the construction of a state transition probability equation based on Shapley value ratios. Compared to conventional control methods, the CBQL-TSP method not only upholds the fairness principles of cooperative game theory but also harnesses the adaptive learning capabilities of Q-Learning. This enables dynamic adjustments to signal timing in response to real-time traffic conditions, significantly enhancing the flexibility and efficiency of priority signal control.

cs.GT↗

arXiv2024

Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded Rewards

We study $K$-armed bandit problems where the reward distributions of the arms are all supported on the $[0,1]$ interval. It has been a challenge to design regret-efficient randomized exploration algorithms in this setting. Maillard sampling \cite{maillard13apprentissage}, an attractive alternative to Thompson sampling, has recently been shown to achieve competitive regret guarantees in the sub-Gaussian reward setting \cite{bian2022maillard} while maintaining closed-form action probabilities, which is useful for offline policy evaluation. In this work, we propose the Kullback-Leibler Maillard Sampling (KL-MS) algorithm, a natural extension of Maillard sampling for achieving KL-style gap-dependent regret bound. We show that KL-MS enjoys the asymptotic optimality when the rewards are Bernoulli and has a worst-case regret bound of the form $O(\sqrt{μ^*(1-μ^*) K T \ln K} + K \ln T)$, where $μ^*$ is the expected reward of the optimal arm, and $T$ is the time horizon length.

cs.LG↗

arXiv2024

Large Language Model (LLM) AI text generation detection based on transformer deep learning algorithm

In this paper, a tool for detecting LLM AI text generation is developed based on the Transformer model, aiming to improve the accuracy of AI text generation detection and provide reference for subsequent research. Firstly the text is Unicode normalised, converted to lowercase form, characters other than non-alphabetic characters and punctuation marks are removed by regular expressions, spaces are added around punctuation marks, first and last spaces are removed, consecutive ellipses are replaced with single spaces and the text is connected using the specified delimiter. Next remove non-alphabetic characters and extra whitespace characters, replace multiple consecutive whitespace characters with a single space and again convert to lowercase form. The deep learning model combines layers such as LSTM, Transformer and CNN for text classification or sequence labelling tasks. The training and validation sets show that the model loss decreases from 0.127 to 0.005 and accuracy increases from 94.96 to 99.8, indicating that the model has good detection and classification ability for AI generated text. The test set confusion matrix and accuracy show that the model has 99% prediction accuracy for AI-generated text, with a precision of 0.99, a recall of 1, and an f1 score of 0.99, achieving a very high classification accuracy. Looking forward, it has the prospect of wide application in the field of AI text detection.

cs.CL↗

arXiv2024

Agent with Warm Start and Active Termination for Plane Localization in 3D Ultrasound

Standard plane localization is crucial for ultrasound (US) diagnosis. In prenatal US, dozens of standard planes are manually acquired with a 2D probe. It is time-consuming and operator-dependent. In comparison, 3D US containing multiple standard planes in one shot has the inherent advantages of less user-dependency and more efficiency. However, manual plane localization in US volume is challenging due to the huge search space and large fetal posture variation. In this study, we propose a novel reinforcement learning (RL) framework to automatically localize fetal brain standard planes in 3D US. Our contribution is two-fold. First, we equip the RL framework with a landmark-aware alignment module to provide warm start and strong spatial bounds for the agent actions, thus ensuring its effectiveness. Second, instead of passively and empirically terminating the agent inference, we propose a recurrent neural network based strategy for active termination of the agent's interaction procedure. This improves both the accuracy and efficiency of the localization system. Extensively validated on our in-house large dataset, our approach achieves the accuracy of 3.4mm/9.6° and 2.7mm/9.1° for the transcerebellar and transthalamic plane localization, respectively. Ourproposed RL framework is general and has the potential to improve the efficiency and standardization of US scanning.

eess.IV↗

arXiv2023

A Tunable Transition Metal Dichalcogenide Entangled Photon-Pair Source

Entangled photon-pair sources are at the core of quantum applications like quantum key distribution, sensing, and imaging. Operation in space-limited and adverse environments such as in satellite-based and mobile communication requires robust entanglement sources with minimal size and weight requirements. Here, we meet this challenge by realizing a cubic micrometer scale entangled photon-pair source in a 3R-stacked transition metal dichalcogenide crystal. Its crystal symmetry enables the generation of polarization-entangled Bell states without additional components and provides tunability by simple control of the pump polarization. Remarkably, generation rate and state tuning are decoupled, leading to equal generation efficiency and no loss of entanglement. Combining transition metal dichalcogenides with monolithic cavities and integrated photonic circuitry or using quasi-phasematching opens the gate towards ultrasmall and scalable quantum devices.

quant-ph↗

arXiv2023

Efficient multipartite entanglement purification with non-identical states

We present an efficient and general multipartite entanglement purification protocol (MEPP) for N-photon systems in Greenberger-Horne-Zeilinger (GHZ) states with non-identical input states. As a branch of entanglement purification, besides the cases of successful purification, the recurrence MEPP actually has the reusable discarded items which are usually regarded as a failure. Our protocol contains two parts for bit-flip error correction. The first one is the conventional MEPP, corresponding successful cases. The second one includes two efficient approaches, recycling purification with entanglement link and direct residual entanglement purification, that can utilize discarded items. We also make a comparison between two approaches. Which method to use depends on initial input states, and in most cases the approach of direct residual purification is optimal for it not only may obtain a higher fidelity entangled state but also it does not require additional sophisticated links. In addition, for phase-flip errors, the discarded items still have available residual entanglement in the case of different input states. With these approaches, this MEPP has a higher efficiency than all previous MEPPs and it may have potential applications in the future long-distance quantum communications and networks.

quant-ph↗

arXiv2023

Enhancement of Quantum Sensing in a Cavity Optomechanical System around Quantum Critical Point

The precision of quantum sensing could be improved by exploiting quantum phase transitions, where the physical quantity tends to diverge when the system approaches the quantum critical point. This critical enhancement phenomenon has been applied to the quantum Rabi model in a dynamic framework, showing a promising sensing enhancement without the need for complex initial state preparation. In this work, we present a quantum phase transition in the coupling cavity-mechanical oscillator system when the coupling strength crosses a critical point, determined by the effective detuning of cavity and frequency of mechanical mode. By utilizing this critical phenomenon, we obtain a prominent enhancement of quantum sensing, such as the position and momentum of the mechanical oscillator. This result provides an alternative method to enhance the quantum sensing of some physical quantities, such as mass, charge, and weak force, in a large mass system.

quant-ph↗

arXiv2023

Physics-Based Trajectory Design for Cellular-Connected UAV in Rainy Environments Based on Deep Reinforcement Learning

Cellular-connected unmanned aerial vehicles (UAVs) have gained increasing attention due to their potential to enhance conventional UAV capabilities by leveraging existing cellular infrastructure for reliable communications between UAVs and base stations. They have been used for various applications, including weather forecasting and search and rescue operations. However, under extreme weather conditions such as rainfall, it is challenging for the trajectory design of cellular UAVs, due to weak coverage regions in the sky, limitations of UAV flying time, and signal attenuation caused by raindrops. To this end, this paper proposes a physics-based trajectory design approach for cellular-connected UAVs in rainy environments. A physics-based electromagnetic simulator is utilized to take into account detailed environment information and the impact of rain on radio wave propagation. The trajectory optimization problem is formulated to jointly consider UAV flying time and signal-to-interference ratio, and is solved through a Markov decision process using deep reinforcement learning algorithms based on multi-step learning and double Q-learning. Optimal UAV trajectories are compared in examples with homogeneous atmosphere medium and rain medium. Additionally, a thorough study of varying weather conditions on trajectory design is provided, and the impact of weight coefficients in the problem formulation is discussed. The proposed approach has demonstrated great potential for UAV trajectory design under rainy weather conditions.

eess.SY↗

arXiv2021

Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning

Autonomous ultrasound (US) acquisition is an important yet challenging task, as it involves interpretation of the highly complex and variable images and their spatial relationships. In this work, we propose a deep reinforcement learning framework to autonomously control the 6-D pose of a virtual US probe based on real-time image feedback to navigate towards the standard scan planes under the restrictions in real-world US scans. Furthermore, we propose a confidence-based approach to encode the optimization of image quality in the learning process. We validate our method in a simulation environment built with real-world data collected in the US imaging of the spine. Experimental results demonstrate that our method can perform reproducible US probe navigation towards the standard scan plane with an accuracy of $4.91mm/4.65^\circ$ in the intra-patient setting, and accomplish the task in the intra- and inter-patient settings with a success rate of $92\%$ and $46\%$, respectively. The results also show that the introduction of image quality optimization in our method can effectively improve the navigation performance.

cs.RO↗

arXiv2021

An approach for security evaluation and certification of a complete quantum communication system

Although quantum communication systems are being deployed on a global scale, their realistic security certification is not yet available. Here we present a security evaluation and improvement protocol for complete quantum communication systems. The protocol subdivides a system by defining seven system implementation sub-layers based on a hierarchical order of information flow; then it categorises the known system implementation imperfections by hardness of protection and practical risk. Next, an initial analysis report lists all potential loopholes in its quantum-optical part. It is followed by interactions with the system manufacturer, testing and patching most loopholes, and re-assessing their status. Our protocol has been applied on multiple commercial quantum key distribution systems to improve their security. A detailed description of our methodology is presented with the example of a subcarrier-wave system. Our protocol is a step towards future security evaluation and security certification standards.

quant-ph↗

arXiv2021

Faking photon number on a transition-edge sensor

We study potential security vulnerabilities of a single-photon detector based on superconducting transition-edge sensor. In a simple experiment, we show that an adversary could fake a photon number result at a certain wavelength by sending a larger number of photons at a longer wavelength. In another experiment, we show that the detector can be blinded by bright continuous-wave light and then, a controlled response simulating single-photon detection can be produced by applying a bright light pulse. We model an intercept-and-resend attack on a quantum key distribution system that exploits the latter vulnerability and, under certain assumptions, succeeds to steal the key.

quant-ph↗

arXiv2020

Experimental vulnerability analysis of QKD based on attack ratings

Inspired by the methodology used for classical cryptographic hardware, we consider the use of attack ratings in the context of QKD security evaluation. To illustrate the relevance of this approach, we conduct an experimental vulnerability assessment of CV-QKD against saturation attacks, for two different attack strategies. The first strategy relies on inducing detector saturation by performing a large coherent displacement. This strategy is experimentally challenging and therefore translates into a high attack rating. We also propose and experimentally demonstrate a second attack strategy that simply consists in saturating the detector with an external laser. The low rating we obtain indicates that this attack constitutes a primary threat for practical CV-QKD systems. These results highlight the benefits of combining theoretical security considerations with vulnerability analysis based on attack ratings, in order to guide the design and engineering of practical QKD systems towards the highest possible security standards.

quant-ph↗

arXiv2018

Homodyne-detector-blinding attack in continuous-variable quantum key distribution

We propose an efficient strategy to attack a continuous-variable quantum key distribution (CV-QKD) system, that we call homodyne detector blinding. This attack strategy takes advantage of a generic vulnerability of homodyne receivers: a bright light pulse sent on the signal port can lead to a saturation of the detector electronics. While detector saturation has already been proposed to attack CV-QKD, the attack we study in this paper has the additional advantage of not requiring an eavesdropper to be phase locked with the homodyne receiver. We show that under certain conditions, an attacker can use a simple laser, incoherent with the homodyne receiver, to generate bright pulses and bias the excess noise to arbitrary small values, fully comprising CV-QKD security. These results highlight the feasibility and the impact of the detector blinding attack. We finally discuss how to design countermeasures in order to protect against this attack.

quant-ph↗

arXiv2016

Localized State in a Two-Dimensional Quantum Walk on a Disordered Lattice

We realize a pair of simultaneous ten-step one-dimensional quantum walks with two walkers sharing coins, which we prove is analogous to the ten-step two-dimensional quantum walk with a single walker holding a four-dimensional coin. Our experiment demonstrates a ten-step quantum walk over an 11x11 two-dimensional lattice with a line defect, thereby realizing a localized walker state.

quant-ph↗