arXiv subjects

Yang Li

Publications and source records attributed to Yang Li.

At least 865 records · Page 48Linked to original sources

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↗

arXiv2023

Fine-Grained Extraction of Road Networks via Joint Learning of Connectivity and Segmentation

Road network extraction from satellite images is widely applicated in intelligent traffic management and autonomous driving fields. The high-resolution remote sensing images contain complex road areas and distracted background, which make it a challenge for road extraction. In this study, we present a stacked multitask network for end-to-end segmenting roads while preserving connectivity correctness. In the network, a global-aware module is introduced to enhance pixel-level road feature representation and eliminate background distraction from overhead images; a road-direction-related connectivity task is added to ensure that the network preserves the graph-level relationships of the road segments. We also develop a stacked multihead structure to jointly learn and effectively utilize the mutual information between connectivity learning and segmentation learning. We evaluate the performance of the proposed network on three public remote sensing datasets. The experimental results demonstrate that the network outperforms the state-of-the-art methods in terms of road segmentation accuracy and connectivity maintenance.

cs.CV↗

arXiv2023

Shedding light on charmonium

We investigate E1 radiative transitions within charmonium in a relativistic approach based on light-front QCD. In quantum field theory, two sets of processes are pure E1: $χ_{c0} \to J/ψγ$ ($ψ\to χ_{c0}γ$) and $h_c \to η_cγ$ ($η_c' \to h_cγ$), both involving the $P$-wave charmonia. We compute the E1 radiative decay widths as well as the corresponding transition form factors of various processes including those involving $2P$ states. These observables provide an access to the microscopic structures of the $P$-wave charmonium. We show that our parameter-free predictions are in excellent agreement with the experimental measurements as well as lattice simulations whenever available.

hep-ph↗

arXiv2023

Joint Task Partitioning and Parallel Scheduling in Device-Assisted Mobile Edge Networks

With the development of the Internet of Things (IoT), certain IoT devices have the capability to not only accomplish their own tasks but also simultaneously assist other resource-constrained devices. Therefore, this paper considers a device-assisted mobile edge computing system that leverages auxiliary IoT devices to alleviate the computational burden on the edge computing server and enhance the overall system performance. In this study, computationally intensive tasks are decomposed into multiple partitions, and each task partition can be processed in parallel on an IoT device or the edge server. The objective of this research is to develop an efficient online algorithm that addresses the joint optimization of task partitioning and parallel scheduling under time-varying system states, posing challenges to conventional numerical optimization methods. To address these challenges, a framework called online task partitioning action and parallel scheduling policy generation (OTPPS) is proposed, which is based on deep reinforcement learning (DRL). Specifically, the framework leverages a deep neural network (DNN) to learn the optimal partitioning action for each task by mapping input states. Furthermore, it is demonstrated that the remaining parallel scheduling problem exhibits NP-hard complexity when considering a specific task partitioning action. To address this subproblem, a fair and delay-minimized task scheduling (FDMTS) algorithm is designed. Extensive evaluation results demonstrate that OTPPS achieves near-optimal average delay performance and consistently high fairness levels in various environmental states compared to other baseline schemes.

cs.DC↗

arXiv2023

FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design

Recent advancements in Large Language Models (LLMs) have exhibited notable efficacy in question-answering (QA) tasks across diverse domains. Their prowess in integrating extensive web knowledge has fueled interest in developing LLM-based autonomous agents. While LLMs are efficient in decoding human instructions and deriving solutions by holistically processing historical inputs, transitioning to purpose-driven agents requires a supplementary rational architecture to process multi-source information, establish reasoning chains, and prioritize critical tasks. Addressing this, we introduce \textsc{FinMem}, a novel LLM-based agent framework devised for financial decision-making. It encompasses three core modules: Profiling, to customize the agent's characteristics; Memory, with layered message processing, to aid the agent in assimilating hierarchical financial data; and Decision-making, to convert insights gained from memories into investment decisions. Notably, \textsc{FinMem}'s memory module aligns closely with the cognitive structure of human traders, offering robust interpretability and real-time tuning. Its adjustable cognitive span allows for the retention of critical information beyond human perceptual limits, thereby enhancing trading outcomes. This framework enables the agent to self-evolve its professional knowledge, react agilely to new investment cues, and continuously refine trading decisions in the volatile financial environment. We first compare \textsc{FinMem} with various algorithmic agents on a scalable real-world financial dataset, underscoring its leading trading performance in stocks. We then fine-tuned the agent's perceptual span and character setting to achieve a significantly enhanced trading performance. Collectively, \textsc{FinMem} presents a cutting-edge LLM agent framework for automated trading, boosting cumulative investment returns.

q-fin.CP↗

arXiv2023

A Demand-Supply Cooperative Responding Strategy in Power System with High Renewable Energy Penetration

Industrial demand response (IDR) plays an important role in promoting the utilization of renewable energy (RE) in power systems. However, it will lead to power adjustments on the supply side, which is also a non-negligible factor in affecting RE utilization. To comprehensively analyze this impact while enhancing RE utilization, this paper proposes a power demand-supply cooperative response (PDSCR) strategy based on both day-ahead and intraday time scales. The day-ahead PDSCR determines a long-term scheme for responding to the predictable trends in RE supply. However, this long-term scheme may not be suitable when uncertain RE fluctuations occur on an intraday basis. Regarding intraday PDSCR, we formulate a profit-driven cooperation approach to address the issue of RE fluctuations. In this context, unreasonable profit distributions on the demand-supply side would lead to the conflict of interests and diminish the effectiveness of cooperative responses. To mitigate this issue, we derive multi-individual profit distribution marginal solutions (MIPDMSs) based on satisfactory profit distributions, which can also maximize cooperative profits. Case studies are conducted on an modified IEEE 24-bus system and an actual power system in China. The results verify the effectiveness of the proposed strategy for enhancing RE utilization, via optimizing the coordination of IDR flexibility with generation resources.

eess.SY↗

arXiv2023

Deep Reinforcement Learning Based Optimal Energy Management of Multi-energy Microgrids with Uncertainties

Multi-energy microgrid (MEMG) offers an effective approach to deal with energy demand diversification and new energy consumption on the consumer side. In MEMG, it is critical to deploy an energy management system (EMS) for efficient utilization of energy and reliable operation of the system. To help EMS formulate optimal dispatching schemes, a deep reinforcement learning (DRL)-based MEMG energy management scheme with renewable energy source (RES) uncertainty is proposed in this paper. To accurately describe the operating state of the MEMG, the off-design performance model of energy conversion devices is considered in scheduling. The nonlinear optimal dispatching model is expressed as a Markov decision process (MDP) and is then addressed by the twin delayed deep deterministic policy gradient (TD3) algorithm. In addition, to accurately describe the uncertainty of RES, the conditional-least squares generative adversarial networks (C-LSGANs) method based on RES forecast power is proposed to construct the scenarios set of RES power generation. The generated data of RES is used for scheduling to obtain caps and floors for the purchase of electricity and natural gas. Based on this, the superior energy supply sector can formulate solutions in advance to tackle the uncertainty of RES. Finally, the simulation analysis demonstrates the validity and superiority of the method.

eess.SY↗

arXiv2023

An Ultra-fast Quantum Random Number Generation Scheme Based on Laser Phase Noise

Based on the intrinsic random property of quantum mechanics, quantum random number generators allow for access of truly unpredictable random sequence and are now heading towards high performance and small miniaturization, among which a popular scheme is based on the laser phase noise. However, this scheme is generally limited in speed and implementation complexity, especially for chip integration. In this work, a general physical model based on wiener process for such schemes is introduced, which provides an approach to clearly explain the limitation on the generation rate and comprehensively optimize the system performance. We present an insight to exploit the potential bandwidth of the quantum entropy source that contains plentiful quantum randomness with a simple spectral filtering method and experimentally boost the bandwidth of the corresponding quantum entropy source to 20 GHz, based on which an ultra-fast generation rate of 218 Gbps is demonstrated, setting a new record for laser phase noise based schemes by one order of magnitude. Our proposal significantly enhances the ceiling speed of such schemes without requiring extra complex hardware, thus effectively benefits the corresponding chip integration with high performance and low implementation cost, which paves the way for its large-scale applications.

quant-ph↗

arXiv2023

110-GHz bandwidth integrated lithium niobate modulator without direct lithium niobate etching

Integrated thin film lithium niobate (TFLN) modulators are emerging as an appealing solution to high-speed data processing and transmission due to their high modulation speed and low driving voltage. The key step in fabricating integrated TFLN modulators is the high-quality etching of TFLN, which typically requires long-term optimization of fabrication recipe and specialized equipment. Here we present an integrated TFLN modulator by incorporating low-index rib loaded waveguides onto TFLN without direct etching of TFLN. Based on our systematic investigation into the theory and design methodology of the proposed design, we experimentally demonstrated a TFLN etching-free Mach-Zehnder modulator, featuring a flat electro-optic response up to 110 GHz and a voltage-length product of 2.53 V cm. By significantly simplifying the fabrication process, our design opens up new ways of mass production of high-speed integrated TFLN modulators at low cost.

physics.optics↗

arXiv2023

Correlations of Baryon and Charge Stopping in Heavy Ion Collisions

Baryon numbers are carried by valence quarks in the standard QCD picture of the baryon structure, while some theory proposed an alternative baryon number carrier, a non-perturbative Y-shaped configuration of the gluon field, called the baryon junction in the 1970s. However, neither of the theories has been verified experimentally. It was recently suggested to search for the baryon junction by investigating the correlation of net-charge and net-baryon yields at midrapidity in heavy-ion collisions. This paper presents studies of such correlations in collisions of various heavy ions from Oxygen to Uranium with the UrQMD Monte Carlo model. The UrQMD model implements valence quark transport as the primary means of charge and baryon stopping at midrapidity. Detailed study are also carried out for isobaric $_{40}^{96}\mathrm{Zr}$ + $_{40}^{96}\mathrm{Zr}$ and $_{44}^{96}\mathrm{Ru}$ + $_{44}^{96}\mathrm{Ru}$ collisions. We found a universal trend of the charge stopping with respect to the baryon stopping, and that the charge stopping is always more than the baryon stopping. This study provides a model baseline in valence quark transport for what is expected in net-charge and net-baryon yields at midrapidity of relativistic heavy-ion collisions.

nucl-th↗

arXiv2023

Improving Zero-shot Visual Question Answering via Large Language Models with Reasoning Question Prompts

Zero-shot Visual Question Answering (VQA) is a prominent vision-language task that examines both the visual and textual understanding capability of systems in the absence of training data. Recently, by converting the images into captions, information across multi-modalities is bridged and Large Language Models (LLMs) can apply their strong zero-shot generalization capability to unseen questions. To design ideal prompts for solving VQA via LLMs, several studies have explored different strategies to select or generate question-answer pairs as the exemplar prompts, which guide LLMs to answer the current questions effectively. However, they totally ignore the role of question prompts. The original questions in VQA tasks usually encounter ellipses and ambiguity which require intermediate reasoning. To this end, we present Reasoning Question Prompts for VQA tasks, which can further activate the potential of LLMs in zero-shot scenarios. Specifically, for each question, we first generate self-contained questions as reasoning question prompts via an unsupervised question edition module considering sentence fluency, semantic integrity and syntactic invariance. Each reasoning question prompt clearly indicates the intent of the original question. This results in a set of candidate answers. Then, the candidate answers associated with their confidence scores acting as answer heuristics are fed into LLMs and produce the final answer. We evaluate reasoning question prompts on three VQA challenges, experimental results demonstrate that they can significantly improve the results of LLMs on zero-shot setting and outperform existing state-of-the-art zero-shot methods on three out of four data sets. Our source code is publicly released at \url{https://github.com/ECNU-DASE-NLP/RQP}.

cs.CV↗