arXiv subjects

Yang Li

Publications and source records attributed to Yang Li.

At least 865 records · Page 48Linked to original sources

arXiv2024

Effect of light injection on the security of practical quantum key distribution

Quantum key distribution (QKD) based on the fundamental laws of quantum physics can allow the distribution of secure keys between distant users. However, the imperfections in realistic devices may lead to potential security risks, which must be accurately characterized and considered in practical security analysis. High-speed optical modulators, being as one of the core components of practical QKD systems, can be used to prepare the required quantum states. Here, we find that optical modulators based on LiNbO3, including phase modulators and intensity modulators, are vulnerable to photorefractive effect caused by external light injection. By changing the power of external light, eavesdroppers can control the intensities of the prepared states, posing a potential threat to the security of QKD. We have experimentally demonstrated the influence of light injection on LiNbO3-based optical modulators and analyzed the security risks caused by the potential green light injection attack, along with the corresponding countermeasures.

quant-ph↗

arXiv2023

Phoneme Hallucinator: One-shot Voice Conversion via Set Expansion

Voice conversion (VC) aims at altering a person's voice to make it sound similar to the voice of another person while preserving linguistic content. Existing methods suffer from a dilemma between content intelligibility and speaker similarity; i.e., methods with higher intelligibility usually have a lower speaker similarity, while methods with higher speaker similarity usually require plenty of target speaker voice data to achieve high intelligibility. In this work, we propose a novel method \textit{Phoneme Hallucinator} that achieves the best of both worlds. Phoneme Hallucinator is a one-shot VC model; it adopts a novel model to hallucinate diversified and high-fidelity target speaker phonemes based just on a short target speaker voice (e.g. 3 seconds). The hallucinated phonemes are then exploited to perform neighbor-based voice conversion. Our model is a text-free, any-to-any VC model that requires no text annotations and supports conversion to any unseen speaker. Objective and subjective evaluations show that \textit{Phoneme Hallucinator} outperforms existing VC methods for both intelligibility and speaker similarity.

cs.SD↗

arXiv2023

High precision atom interferometer-based dynamic gravimeter measurement by eliminating the cross-coupling effect

A dynamic gravimeter with an atomic interferometer (AI) can perform absolute gravity measurements with high precision. AI-based dynamic gravity measurement is a type of joint measurement that uses AI sensors and a classical accelerometer. The coupling of the two sensors may degrade the measurement precision. In this study, we analyzed the cross-coupling effect and introduced a recovery vector to suppress this effect. We improved the phase noise of the interference fringe by a factor of 1.9 by performing marine gravity measurements using an AI-based gravimeter and optimizing the recovery vector. Marine gravity measurements were performed, and high gravity measurement precision was achieved. The external and inner coincidence accuracies of the gravity measurement are 0.42 mGal and 0.46 mGal, which were improved by factors of 4.18 and 4.21 by optimizing the cross-coupling effect.

physics.app-ph↗

arXiv2023

Electro-optic frequency comb-enabled precise distance measurement with megahertz acquisition rate

Artificial intelligence empowered autonomous vehicles and robotics have to sense the fast-changing three-dimensional environment with high precision and speed. However, it is challenging for the state-of-the-art ambiguity-free light detection and ranging (LiDAR) techniques to achieve absolute distance measurement with simultaneous high precision and high acquisition rate. Here we demonstrate an electro-optic frequency comb-enabled precise absolute distance measurement method, repetition rate modulated frequency comb (RRMFC), with megahertz-level acquisition rate. To achieve RRMFC, we designed and fabricated an integrated lithium niobate phase modulator with a modulation length of 5 cm and a half-wave voltage of 1.52 V, leading to over 50 sidebands and a continuously tunable repetition rate. Leveraging these unique features, RRMFC can directly resolve distance in time domain, leading to an acquisition rate as high as 25 MHz and an Allan deviation down to 13.77 μm at an averaging time of 724 μs. Based on RRMFC, we achieved high-speed 3D imaging at millimeter-level precision with a single laser. RRMFC-based LiDAR allows the autonomous vehicles and robotics to sense the fine details of fast-changing environment with high precision.

physics.optics↗

arXiv2023

Astrocyte-Enabled Advancements in Spiking Neural Networks for Large Language Modeling

Within the complex neuroarchitecture of the brain, astrocytes play crucial roles in development, structure, and metabolism. These cells regulate neural activity through tripartite synapses, directly impacting cognitive processes such as learning and memory. Despite the growing recognition of astrocytes' significance, traditional Spiking Neural Network (SNN) models remain predominantly neuron-centric, overlooking the profound influence of astrocytes on neural dynamics. Inspired by these biological insights, we have developed an Astrocyte-Modulated Spiking Unit (AM-SU), an innovative framework that integrates neuron-astrocyte interactions into the computational paradigm, demonstrating wide applicability across various hardware platforms. Our Astrocyte-Modulated Spiking Neural Network (AstroSNN) exhibits exceptional performance in tasks involving memory retention and natural language generation, particularly in handling long-term dependencies and complex linguistic structures. The design of AstroSNN not only enhances its biological authenticity but also introduces novel computational dynamics, enabling more effective processing of complex temporal dependencies. Furthermore, AstroSNN shows low latency, high throughput, and reduced memory usage in practical applications, making it highly suitable for resource-constrained environments. By successfully integrating astrocytic dynamics into intelligent neural networks, our work narrows the gap between biological plausibility and neural modeling, laying the groundwork for future biologically-inspired neural computing research that includes both neurons and astrocytes.

cs.NE↗

arXiv2023

Semantic Draw Engineering for Text-to-Image Creation

Text-to-image generation is conducted through Generative Adversarial Networks (GANs) or transformer models. However, the current challenge lies in accurately generating images based on textual descriptions, especially in scenarios where the content and theme of the target image are ambiguous. In this paper, we propose a method that utilizes artificial intelligence models for thematic creativity, followed by a classification modeling of the actual painting process. The method involves converting all visual elements into quantifiable data structures before creating images. We evaluate the effectiveness of this approach in terms of semantic accuracy, image reproducibility, and computational efficiency, in comparison with existing image generation algorithms.

cs.HC↗

arXiv2023

PGN: A perturbation generation network against deep reinforcement learning

Deep reinforcement learning has advanced greatly and applied in many areas. In this paper, we explore the vulnerability of deep reinforcement learning by proposing a novel generative model for creating effective adversarial examples to attack the agent. Our proposed model can achieve both targeted attacks and untargeted attacks. Considering the specificity of deep reinforcement learning, we propose the action consistency ratio as a measure of stealthiness, and a new measurement index of effectiveness and stealthiness. Experiment results show that our method can ensure the effectiveness and stealthiness of attack compared with other algorithms. Moreover, our methods are considerably faster and thus can achieve rapid and efficient verification of the vulnerability of deep reinforcement learning.

cs.LG↗

arXiv2023

Relation-Aware Question Answering for Heterogeneous Knowledge Graphs

Multi-hop Knowledge Base Question Answering(KBQA) aims to find the answer entity in a knowledge graph (KG), which requires multiple steps of reasoning. Existing retrieval-based approaches solve this task by concentrating on the specific relation at different hops and predicting the intermediate entity within the reasoning path. During the reasoning process of these methods, the representation of relations are fixed but the initial relation representation may not be optimal. We claim they fail to utilize information from head-tail entities and the semantic connection between relations to enhance the current relation representation, which undermines the ability to capture information of relations in KGs. To address this issue, we construct a \textbf{dual relation graph} where each node denotes a relation in the original KG (\textbf{primal entity graph}) and edges are constructed between relations sharing same head or tail entities. Then we iteratively do primal entity graph reasoning, dual relation graph information propagation, and interaction between these two graphs. In this way, the interaction between entity and relation is enhanced, and we derive better entity and relation representations. Experiments on two public datasets, WebQSP and CWQ, show that our approach achieves a significant performance gain over the prior state-of-the-art. Our code is available on \url{https://github.com/yanmenxue/RAH-KBQA}.

cs.CL↗

arXiv2023

H-ensemble: An Information Theoretic Approach to Reliable Few-Shot Multi-Source-Free Transfer

Multi-source transfer learning is an effective solution to data scarcity by utilizing multiple source tasks for the learning of the target task. However, access to source data and model details is limited in the era of commercial models, giving rise to the setting of multi-source-free (MSF) transfer learning that aims to leverage source domain knowledge without such access. As a newly defined problem paradigm, MSF transfer learning remains largely underexplored and not clearly formulated. In this work, we adopt an information theoretic perspective on it and propose a framework named H-ensemble, which dynamically learns the optimal linear combination, or ensemble, of source models for the target task, using a generalization of maximal correlation regression. The ensemble weights are optimized by maximizing an information theoretic metric for transferability. Compared to previous works, H-ensemble is characterized by: 1) its adaptability to a novel and realistic MSF setting for few-shot target tasks, 2) theoretical reliability, 3) a lightweight structure easy to interpret and adapt. Our method is empirically validated by ablation studies, along with extensive comparative analysis with other task ensemble and transfer learning methods. We show that the H-ensemble can successfully learn the optimal task ensemble, as well as outperform prior arts.

cs.LG↗

arXiv2023

Assessing Estimation Uncertainty under Model Misspecification

Model misspecification is ubiquitous in data analysis because the data-generating process is often complex and mathematically intractable. Therefore, assessing estimation uncertainty and conducting statistical inference under a possibly misspecified working model is unavoidable. In such a case, classical methods such as bootstrap and asymptotic theory-based inference frequently fail since they rely heavily on the model assumptions. In this article, we provide a new bootstrap procedure, termed local residual bootstrap, to assess estimation uncertainty under model misspecification for generalized linear models. By resampling the residuals from the neighboring observations, we can approximate the sampling distribution of the statistic of interest accurately. Instead of relying on the score equations, the proposed method directly recreates the response variables so that we can easily conduct standard error estimation, confidence interval construction, hypothesis testing, and model evaluation and selection. It performs similarly to classical bootstrap when the model is correctly specified and provides a more accurate assessment of uncertainty under model misspecification, offering data analysts an easy way to guard against the impact of misspecified models. We establish desirable theoretical properties, such as the bootstrap validity, for the proposed method using the surrogate residuals. Numerical results and real data analysis further demonstrate the superiority of the proposed method.

stat.ME↗

arXiv2023

Infrared anomalies in ultrathin Ti3C2Tx MXene films

Visible transparent but infrared reflective materials are ideal candidates for both transparent conductive films and low-emissivity glass, which are highly desired in a broad variety of areas such as touchscreens and displays, photovoltaics, smart windows, and antistatic coatings. Ultrathin Ti3C2Tx MXene films are emerging as promising low-emissivity transparent candidates. However, the fundamental IR properties of Ti3C2Tx has not been revealed experimentally due to daunting challenges in the preparation of continuous, large-area, and ultrathin films of optical quality on flat substrates. Herein, we proposed a tape-free transfer method that can help prepare centimeter-size and ultrathin (down to 8 nm) Ti3C2Tx films on diverse optical substrates. Benefitting from this method, the refractive index and permittivity for Ti3C2Tx were successfully measured. Ti3C2Tx films exhibit large in-plane permittivity in the IR region, yielding maximum IR reflectance of 88% for bulk films. Interestingly, three anomalies were found in ultrathin Ti3C2Tx films: strong dispersion in the permittivity, interlayer space-dependent optical properties, and abnormally high IR absorption for a 15-nm-thick film. These anomalies are important guidelines in the design of Ti3C2Tx-based low-emissivity transparent films and other related devices, and may inspire other intriguing applications such as ultrathin IR absorption coatings and tunable IR optical devices.

cond-mat.mtrl-sci↗

arXiv2023

Learning a Low-Rank Feature Representation: Achieving Better Trade-Off between Stability and Plasticity in Continual Learning

In continual learning, networks confront a trade-off between stability and plasticity when trained on a sequence of tasks. To bolster plasticity without sacrificing stability, we propose a novel training algorithm called LRFR. This approach optimizes network parameters in the null space of the past tasks' feature representation matrix to guarantee the stability. Concurrently, we judiciously select only a subset of neurons in each layer of the network while training individual tasks to learn the past tasks' feature representation matrix in low-rank. This increases the null space dimension when designing network parameters for subsequent tasks, thereby enhancing the plasticity. Using CIFAR-100 and TinyImageNet as benchmark datasets for continual learning, the proposed approach consistently outperforms state-of-the-art methods.

cs.LG↗

arXiv2023

Joint Compression and Deadline Optimization for Wireless Federated Learning

Federated edge learning (FEEL) is a popular distributed learning framework for privacy-preserving at the edge, in which densely distributed edge devices periodically exchange model-updates with the server to complete the global model training. Due to limited bandwidth and uncertain wireless environment, FEEL may impose heavy burden to the current communication system. In addition, under the common FEEL framework, the server needs to wait for the slowest device to complete the update uploading before starting the aggregation process, leading to the straggler issue that causes prolonged communication time. In this paper, we propose to accelerate FEEL from two aspects: i.e., 1) performing data compression on the edge devices and 2) setting a deadline on the edge server to exclude the straggler devices. However, undesired gradient compression errors and transmission outage are introduced by the aforementioned operations respectively, affecting the convergence of FEEL as well. In view of these practical issues, we formulate a training time minimization problem, with the compression ratio and deadline to be optimized. To this end, an asymptotically unbiased aggregation scheme is first proposed to ensure zero optimality gap after convergence, and the impact of compression error and transmission outage on the overall training time are quantified through convergence analysis. Then, the formulated problem is solved in an alternating manner, based on which, the novel joint compression and deadline optimization (JCDO) algorithm is derived. Numerical experiments for different use cases in FEEL including image classification and autonomous driving show that the proposed method is nearly 30X faster than the vanilla FedAVG algorithm, and outperforms the state-of-the-art schemes.

cs.IT↗

arXiv2023

Explainable Trajectory Representation through Dictionary Learning

Trajectory representation learning on a network enhances our understanding of vehicular traffic patterns and benefits numerous downstream applications. Existing approaches using classic machine learning or deep learning embed trajectories as dense vectors, which lack interpretability and are inefficient to store and analyze in downstream tasks. In this paper, an explainable trajectory representation learning framework through dictionary learning is proposed. Given a collection of trajectories on a network, it extracts a compact dictionary of commonly used subpaths called "pathlets", which optimally reconstruct each trajectory by simple concatenations. The resulting representation is naturally sparse and encodes strong spatial semantics. Theoretical analysis of our proposed algorithm is conducted to provide a probabilistic bound on the estimation error of the optimal dictionary. A hierarchical dictionary learning scheme is also proposed to ensure the algorithm's scalability on large networks, leading to a multi-scale trajectory representation. Our framework is evaluated on two large-scale real-world taxi datasets. Compared to previous work, the dictionary learned by our method is more compact and has better reconstruction rate for new trajectories. We also demonstrate the promising performance of this method in downstream tasks including trip time prediction task and data compression.

cs.LG↗

arXiv2023

Sonogenetics is a novel antiarrhythmic treatment

Arrhythmia of the heart is a dangerous and potentially fatal condition. The current widely used treatment is the implantable cardioverter defibrillator (ICD), but it is invasive and affects the patient's quality of life. The sonogenetic treatment technique proposed here focuses transthoracic ultrasound on the heart, noninvasively stimulates endogenous stretch-activated Piezo1 ion channels on the focal region's cardiomyocyte plasma membrane, and restores normal heart rhythm. In contrast to anchoring the implanted ICD lead at a fixed position in the myocardium, the size and position of the ultrasound focal region can be selected dynamically by adjusting the signal phases of every piezoelectric chip on the wearable ultrasonic phased array, and it allows novel and efficient defibrillations. Based on the developed interdisciplinary electromechanical model of sonogenetic treatment, our analysis shows that the proposed ultrasound intensity and frequency will be safe and painless for humans and well below the limits established by the U.S. Food and Drug Administration.

physics.med-ph↗

arXiv2023

New criteria for boundedness and stability of nonlinear neutral delay differential equations by Krasnoselskii's fixed point theorem

In this paper, we study boundedness, uniform stability and asymptotic stability of a class of nonlinear neutral delay differential equations by using Krasnoselskii's fixed point theorem. The results obtained in this paper extend and improve the work of Jin and Luo(Nonlinear Anal 68:3307-3315,2008), and Benhadri, Mimia(Differ Equ Dyn Syst 29:3-19,2021). An example is given to illustrate the effectiveness of the proposed results.

math.DS↗

arXiv2023

Resource Allocation for Semantic Communication under Physical-layer Security

Semantic communication is deemed as a revolution of Shannon's paradigm in the six-generation (6G) wireless networks. It aims at transmitting the extracted information rather than the original data, which receivers will try to recover. Intuitively, the larger extracted information, the longer latency of semantic communication will be. Besides, larger extracted information will result in more accurate reconstructed information, thereby causing a higher utility of the semantic communication system. Shorter latency and higher utility are desirable objectives for the system, so there will be a trade-off between utility and latency. This paper proposes a joint optimization algorithm for total latency and utility. Moreover, security is essential for the semantic communication system. We incorporate the secrecy rate, a physical-layer security method, into the optimization problem. The secrecy rate is the communication rate at which no information is disclosed to an eavesdropper. Experimental results demonstrate that the proposed algorithm obtains the best joint optimization performance compared to the baselines.

eess.SP↗

arXiv2023

New ternary self-orthogonal codes and related LCD codes from weakly regular plateaued functions

A linear code is said to be self-orthogonal if it is contained in its dual. Self-orthogonal codes are of interest because of their important applications, such as for constructing linear complementary dual (LCD) codes and quantum codes. In this paper, we construct several new families of ternary self-orthogonal codes by employing weakly regular plateaued functions. Their parameters and weight distributions are completely determined. Then we apply these self-orthogonal codes to construct several new families of ternary LCD codes. As a consequence, we obtain many (almost) optimal ternary self-orthogonal codes and LCD codes.

cs.IT↗