arXiv subjects

Ying Li

Publications and source records attributed to Ying Li.

At least 235 records · Page 13Linked to original sources

arXiv2025

Investigation of $Λ_c \to (Λ,n)\ell^+ ν_\ell $ Decays in Standard Model and Beyond

In this work, we study the decays $Λ_c \to (Λ, n) \ell^+ ν_\ell$ ($\ell = μ, e$) within a model-independent framework. We calculate the helicity amplitudes for all possible four-fermion operators, including the interactions between different new physics (NP) operators. The form factors for the $Λ_c \to (Λ, n)$ transitions are taken from lattice QCD calculations. We present detailed results for the branching fractions and other key observables. Although our results are generally consistent with previous studies, we find that the predicted central values for the branching fractions are approximately $10\%$ larger than the experimental measurements. Additionally, we explore the potential impacts of NP, focusing particularly on the scenario in which NP particles couple exclusively to the muon. Using Wilson coefficients fitted from $D$ and $D_s$ meson decays, we examine NP effects in the $Λ_c \to (Λ, n) μ^+ ν_μ$ decay channels. It is found that although most potential contributions of NP are obscured by the uncertainties inherent in both theory and experiment, the right-handed scalar operator could suppress the branching fraction of $Λ_c \to (Λ, n) μ^+ ν_μ$ by up to $10\%$. We also highlight that the ratio of the forward-backward asymmetry in the $Λ_c \to (Λ, n) μ^+ ν_μ$ decay to that in the $Λ_c \to (Λ, n) e^+ ν_e$ decay provides a novel probe for NP, as it is less sensitive to hadronic uncertainties and is largely unaffected by current NP operators. All of our results can be tested in ongoing experiments such as BESIII, Belle-II, and LHCb, as well as in future high-energy facilities like the Super Tau-Charm Factory (STCF) and the Circular Electron Positron Collider (CEPC).

hep-ph↗

arXiv2025

Quantum-enhanced neural networks for quantum many-body simulations

Neural quantum states (NQS) have gained prominence in variational quantum Monte Carlo methods in approximating ground-state wavefunctions. Despite their success, they face limitations in optimization, scalability, and expressivity in addressing certain problems. In this work, we propose a quantum-neural hybrid framework that combines parameterized quantum circuits with neural networks to model quantum many-body wavefunctions. This approach combines the efficient sampling and optimization capabilities of autoregressive neural networks with the enhanced expressivity provided by quantum circuits. Numerical simulations demonstrate the scalability and accuracy of the hybrid ansatz in spin systems and quantum chemistry problems. Our results reveal that the hybrid method achieves notably lower relative energy compared to standalone NQS. These findings underscore the potential of quantum-neural hybrid methods for tackling challenging problems in quantum many-body simulations.

quant-ph↗

arXiv2025

Demonstrating quantum error mitigation on logical qubits

A long-standing challenge in quantum computing is developing technologies to overcome the inevitable noise in qubits. To enable meaningful applications in the early stages of fault-tolerant quantum computing, devising methods to suppress post-correction logical failures is becoming increasingly crucial. In this work, we propose and experimentally demonstrate the application of zero-noise extrapolation, a practical quantum error mitigation technique, to error correction circuits on state-of-the-art superconducting processors. By amplifying the noise on physical qubits, the circuits yield outcomes that exhibit a predictable dependence on noise strength, following a polynomial function determined by the code distance. This property enables the effective application of polynomial extrapolation to mitigate logical errors. Our experiments demonstrate a universal reduction in logical errors across various quantum circuits, including fault-tolerant circuits of repetition and surface codes. We observe a favorable performance in multi-round error correction circuits, indicating that this method remains effective when the circuit depth increases. These results advance the frontier of quantum error suppression technologies, opening a practical way to achieve reliable quantum computing in the early fault-tolerant era.

quant-ph↗

arXiv2025

Harnessing the Power of Vibration Motors to Develop Miniature Untethered Robotic Fishes

Miniature underwater robots play a crucial role in the exploration and development of marine resources, particularly in confined spaces and high-pressure deep-sea environments. This study presents the design, optimization, and performance of a miniature robotic fish, powered by the oscillation of bio-inspired fins. These fins feature a rigid-flexible hybrid structure and use an eccentric rotating mass (ERM) vibration motor as the excitation source to generate high-frequency unidirectional oscillations that induce acoustic streaming for propulsion. The drive mechanism, powered by miniature ERM vibration motors, eliminates the need for complex mechanical drive systems, enabling complete isolation of the entire drive system from the external environment and facilitating the miniaturization of the robotic fish. A compact, untethered robotic fish, measuring 85*60*45 mm^3, is equipped with three bio-inspired fins located at the pectoral and caudal positions. Experimental results demonstrate that the robotic fish achieves a maximum forward swimming speed of 1.36 body lengths (BL) per second powered by all fins and minimum turning radius of 0.6 BL when powered by a single fin. These results underscore the significance of employing the ERM vibration motor in advancing the development of highly maneuverable, miniature untethered underwater robots for various marine exploration tasks.

cs.RO↗

arXiv2025

Resilient Distributed Control for Uncertain Nonlinear Interconnected Systems under Network Anomaly

We address a distributed adaptive control methodology for nonlinear interconnected systems possibly affected by network anomalies. In the framework of adaptive approximation, the distributed controller and parameter estimator are designed by exploiting a backstepping approach. The stability of the distributed control system under anomalies is analyzed, where both local and neighboring anomaly effects are considered. To quantify the resilience of the interconnected system under the action of network anomalies, we derive bounds on the duration of each anomaly and the resting time between two consecutive anomalies. Specifically, when each anomaly duration is smaller than our designed upper bound, the interconnected system controlled by the distributed approximation-based controller remains asymptotically stable. Moreover, if the resting time between two consecutive anomalies is larger than the proposed bound, then all signals of the control system are guaranteed to be bounded. In the paper, we show that under the action of the proposed distributed adaptive controller, the interconnected system remains stable in the presence of network anomalies, with both the qualitative and quantitative resilient conditions. Extensive simulation results show the effectiveness of our theoretical results.

eess.SY↗

arXiv2025

DeCon: Detecting Incorrect Assertions via Postconditions Generated by a Large Language Model

Recently, given the docstring for the target problem and the target function signature, large language models (LLMs) have been used not only to generate source code, but also to generate test cases, consisting of test inputs and assertions (e.g., in the form of checking an actual output against the expected output). However, as shown by our empirical study on assertions generated by four LLMs for the HumanEval benchmark, over 62% of the generated assertions are incorrect (i.e., failed on the ground-truth problem solution). To detect incorrect assertions (given the docstring and the target function signature along with a sample of example inputs and outputs), in this paper, we propose a new approach named DeCon to effectively detect incorrect assertions via LLM-generated postconditions for the target problem (a postcondition is a predicate that must always be true just after the execution of the ground-truth problem solution). Our approach requires a small set of I/O examples (i.e., a sample of example inputs and outputs) for the target problem (e.g., the I/O examples included in the docstring for a target problem in HumanEval). We use the given I/O examples to filter out those LLM-generated postconditions that are violated by at least one given I/O example. We then use the remaining postconditions to detect incorrect assertions as those assertions that violate at least one remaining postcondition. Experimental results show that DeCon can detect averagely more than 64% (63% and 65.5% detected by GPT-3.5 and GPT-4, respectively) incorrect assertions generated by four state-of-the-art LLMs, and DeCon can also improve the effectiveness of these LLMs in code generation by 4% in terms of Pass@1. In addition, although DeCon might filter out correct assertions, the fault-finding ability of the remaining correct assertions decreases only slightly.

cs.SE↗

arXiv2024

Improving image quality of the Solar Disk Imager (SDI) of the Lyman-alpha Solar Telescope (LST) onboard the ASO-S mission

The in-flight calibration and performance of the Solar Disk Imager (SDI), which is a pivotal instrument of the Lyman-alpha Solar Telescope (LST) onboard the Advanced Space-based Solar Observatory (ASO-S) mission, suggested a much lower spatial resolution than expected. In this paper, we developed the SDI point-spread function (PSF) and Image Bivariate Optimization Algorithm (SPIBOA) to improve the quality of SDI images. The bivariate optimization method smartly combines deep learning with optical system modeling. Despite the lack of information about the real image taken by SDI and the optical system function, this algorithm effectively estimates the PSF of the SDI imaging system directly from a large sample of observational data. We use the estimated PSF to conduct deconvolution correction to observed SDI images, and the resulting images show that the spatial resolution after correction has increased by a factor of more than three with respect to the observed ones. Meanwhile, our method also significantly reduces the inherent noise in the observed SDI images. The SPIBOA has now been successfully integrated into the routine SDI data processing, providing important support for the scientific studies based on the data. The development and application of SPIBOA also pave new ways to identify astronomical telescope systems and enhance observational image quality. Some essential factors and precautions in applying the SPIBOA method are also discussed.

astro-ph.SR↗

arXiv2024

Higher twist corrections to doubly-charmed baryonic $B$ decays

Baryonic two-body $B$ mesons decays have been measured experimentally for some time, which provides an excellent ground for studying the QCD of baryonic $B$ decays. In this work, we investigate the two-body doubly-charmed baryonic $B$ decays in the perturbative QCD (PQCD) approach including higher twist contributions to the light-cone distribution amplitudes (LCDAs). The charmed baryon LCDAs are included up to the twist four and the effect of the subleading component of the $B$ meson LCDAs is studied for details. With the inclusion of these higher-power contributions, the PQCD results on rates can explain the current experimental data well. Moreover, we note that the SU(3) symmetry breaking is important in the concerned processes and play an essential role in understanding the measurements of $\mathcal{B}(\overline{B}^0\to Λ_c^+\overline Λ_c^-)$ and $ \mathcal{B}({B}^-\toΞ_c^0\overline Λ_c^-)$. We also evaluate the angular asymmetries for the first time, which have neither been measured experimentally nor calculated theoretically. All these predictions can be tested in future.

hep-ph↗

arXiv2024

The impact of China's economic growth on poverty alleviation: From absolute to relative poverty

This paper investigates the extent to which China's economic growth and development influence poverty levels, focusing on the dichotomy between absolute and relative poverty. Leveraging data from sources like the World Bank, Statista, and Macrotrends, and employing economic frameworks such as the Lewis Model, Poverty Headcount Ratio, and Gini Coefficient, the study examines China's transformation from combating absolute poverty to addressing relative poverty. The findings highlight that robust economic growth from 2011 to 2022, driven by urban development and rural infrastructure investments, successfully eradicated absolute poverty and elevated rural incomes. However, this progress also exacerbated income inequality, as evidenced by a rising Gini Coefficient, complicating efforts to alleviate relative poverty. Through multidimensional analyses encompassing regional disparities, migration patterns, educational access, and societal factors, the paper underscores the dual impact of economic development on poverty alleviation. It concludes by advocating for policies that balance economic growth with equitable resource distribution to tackle persistent relative poverty and foster sustainable development.

econ.GN↗

arXiv2024

Performance Analysis and Code Design for Resistive Random-Access Memory Using Channel Decomposition Approach

A novel framework for performance analysis and code design is proposed to address the sneak path (SP) problem in resistive random-access memory (ReRAM) arrays. The main idea is to decompose the ReRAM channel, which is both non-ergodic and data-dependent, into multiple stationary memoryless channels. A finite-length performance bound is derived by analyzing the capacity and dispersion of these stationary memoryless channels. Furthermore, leveraging this channel decomposition, a practical sparse-graph code design is proposed using density evolution. The obtained channel codes are not only asymptotic capacity approaching but also close to the derived finite-length performance bound.

cs.IT↗

arXiv2024

Statistical Estimations for Non-Ergodic Vasicek Model Driven by Two Types of Gaussian Processes

We study the joint asymptotic distribution of the least squares estimator of the parameter $(θ,\,μ)$ for the non-ergodic Vasicek models driven by seven specific Gaussian processes. %The similar result concerning to the non-ergodic Ornstein-Uhlenbeck process is a by-product. To facilitate the proofs, we extract two common hypotheses from the covariance functions of the seven Gaussian processes and develop two types of new inner product formulas for functions of bounded variation in the reproducing kernel Hilbert space of the Gaussian processes. The integration by parts for normalized bounded variation functions is essential to the inner product formulas. We apply the inner product formulas of the seven Gaussian processes to check the set of conditions of Es-Sebaiy, Es.Sebaiy (2021).

math.PR↗

arXiv2024

A Perspective for Adapting Generalist AI to Specialized Medical AI Applications and Their Challenges

The integration of Large Language Models (LLMs) into medical applications has sparked widespread interest across the healthcare industry, from drug discovery and development to clinical decision support, assisting telemedicine, medical devices, and healthcare insurance applications. This perspective paper aims to discuss the inner workings of building LLM-powered medical AI applications and introduces a comprehensive framework for their development. We review existing literature and outline the unique challenges of applying LLMs in specialized medical contexts. Additionally, we introduce a three-step framework to organize medical LLM research activities: 1) Modeling: breaking down complex medical workflows into manageable steps for developing medical-specific models; 2) Optimization: optimizing the model performance with crafted prompts and integrating external knowledge and tools, and 3) System engineering: decomposing complex tasks into subtasks and leveraging human expertise for building medical AI applications. Furthermore, we offer a detailed use case playbook that describes various LLM-powered medical AI applications, such as optimizing clinical trial design, enhancing clinical decision support, and advancing medical imaging analysis. Finally, we discuss various challenges and considerations for building medical AI applications with LLMs, such as handling hallucination issues, data ownership and compliance, privacy, intellectual property considerations, compute cost, sustainability issues, and responsible AI requirements.

cs.CL↗

arXiv2024

Origin of the insulating state in the Kitaev candidate Cu$_2$IrO$_3$

Through a combination of crystal symmetry analysis and density functional theory calculations we unveil a possible microscopic origin of the unexpected insulating behavior reported in the honeycomb Kitaev material Cu$_2$IrO$_3$. Our study suggests that this material hosts an instability towards charge ordering of the Ir ions, with alternating magnetic Ir$^{4+}$ and non-magnetic Ir$^{3+}$ ions arranged on the honeycomb lattice. In this case, the next-nearest-neighbor interactions that couple magnetic Ir$^{4+}$ ions form an enlarged triangular lattice, instead of the expected honeycomb lattice. The magnetic Cu$^{2+}$ ions located at the centre of the iridium honeycomb voids also form a triangular lattice, and additionally contribute to the magnetization of the system. Together, the interpenetrated Ir$^{4+}$ and Cu$^{2+}$ triangular lattices present a novel type of honeycomb Kitaev lattice composed of two types of magnetic ions.

cond-mat.str-el↗

arXiv2024

MONOPOLY: Learning to Price Public Facilities for Revaluing Private Properties with Large-Scale Urban Data

The value assessment of private properties is an attractive but challenging task which is widely concerned by a majority of people around the world. A prolonged topic among us is ``\textit{how much is my house worth?}''. To answer this question, most experienced agencies would like to price a property given the factors of its attributes as well as the demographics and the public facilities around it. However, no one knows the exact prices of these factors, especially the values of public facilities which may help assess private properties. In this paper, we introduce our newly launched project ``Monopoly'' (named after a classic board game) in which we propose a distributed approach for revaluing private properties by learning to price public facilities (such as hospitals etc.) with the large-scale urban data we have accumulated via Baidu Maps. To be specific, our method organizes many points of interest (POIs) into an undirected weighted graph and formulates multiple factors including the virtual prices of surrounding public facilities as adaptive variables to parallelly estimate the housing prices we know. Then the prices of both public facilities and private properties can be iteratively updated according to the loss of prediction until convergence. We have conducted extensive experiments with the large-scale urban data of several metropolises in China. Results show that our approach outperforms several mainstream methods with significant margins. Further insights from more in-depth discussions demonstrate that the ``Monopoly'' is an innovative application in the interdisciplinary field of business intelligence and urban computing, and it will be beneficial to tens of millions of our users for investments and to the governments for urban planning as well as taxation.

cs.AI↗

arXiv2024

A Review of Electromagnetic Elimination Methods for low-field portable MRI scanner

This paper analyzes conventional and deep learning methods for eliminating electromagnetic interference (EMI) in MRI systems. We compare traditional analytical and adaptive techniques with advanced deep learning approaches. Key strengths and limitations of each method are highlighted. Recent advancements in active EMI elimination, such as external EMI receiver coils, are discussed alongside deep learning methods, which show superior EMI suppression by leveraging neural networks trained on MRI data. While deep learning improves EMI elimination and diagnostic capabilities, it introduces security and safety concerns, particularly in commercial applications. A balanced approach, integrating conventional reliability with deep learning's advanced capabilities, is proposed for more effective EMI suppression in MRI systems.

physics.med-ph↗

arXiv2024

Renormalized and entropy solutions to the general nonlinear parabolic equations in Musielak-Orlicz spaces

We study the well-posedness of solutions to the general nonlinear parabolic equations with merely integrable data in time-dependent Musielak-Orlicz spaces. With the help of a density argument, we establish the existence and uniqueness of both renormalized and entropy solutions. Moreover, we conclude that the entropy and renormalized solutions for this equation are equivalent. Our results cover a variety of problems, including those with Orlicz growth, variable exponents, and double-phase growth.

math.AP↗

arXiv2024

Energy-Aware Multi-Agent Reinforcement Learning for Collaborative Execution in Mission-Oriented Drone Networks

Mission-oriented drone networks have been widely used for structural inspection, disaster monitoring, border surveillance, etc. Due to the limited battery capacity of drones, mission execution strategy impacts network performance and mission completion. However, collaborative execution is a challenging problem for drones in such a dynamic environment as it also involves efficient trajectory design. We leverage multi-agent reinforcement learning (MARL) to manage the challenge in this study, letting each drone learn to collaboratively execute tasks and plan trajectories based on its current status and environment. Simulation results show that the proposed collaborative execution model can successfully complete the mission at least 80% of the time, regardless of task locations and lengths, and can even achieve a 100% success rate when the task density is not way too sparse. To the best of our knowledge, our work is one of the pioneer studies on leveraging MARL on collaborative execution for mission-oriented drone networks; the unique value of this work lies in drone battery level driving our model design.

cs.NI↗

arXiv2024

Reconfigurable, zero-energy, and wide-temperature loss-assisted thermal nonreciprocal metamaterials

Thermal nonreciprocity plays a vital role in chip heat dissipation, energy-saving design, and high-temperature hyperthermia, typically realized through the use of advanced metamaterials with nonlinear, advective, spatiotemporal, or gradient properties. However, challenges such as fixed structural designs with limited adjustability, high energy consumption, and a narrow operational temperature range remain prevalent. Here, a systematic framework is introduced to achieve reconfigurable, zero-energy, and wide-temperature thermal nonreciprocity by transforming wasteful heat loss into a valuable regulatory tool. Vertical slabs composed of natural bulk materials enable asymmetric heat loss through natural convection, disrupting the inversion symmetry of thermal conduction. The reconfigurability of this system stems from the ability to modify heat loss by adjusting thermal conductivity, size, placement, and quantity of the slabs. Moreover, this structure allows for precise control of zero-energy thermal nonreciprocity across a broad temperature spectrum, utilizing solely environmental temperature gradients without additional energy consumption. This research presents a different approach to achieving nonreciprocity, broadening the potential for nonreciprocal devices such as thermal diodes and topological edge states, and inspiring further exploration of nonreciprocity in other loss-based systems.

physics.app-ph↗