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Li Yang

Publications and source records attributed to Li Yang.

At least 145 records · Page 8Linked to original sources
arXiv2023

Ray computational ghost imaging based on rotational modulation method

The CGI (CGI) has the potential of low cost, low dose, and high resolution, which is very attractive for the development of radiation imaging field. However, many sub-coding plates must be used in the modulation process, which greatly affects the development of CGI technology. In order to reduce the coding plates, we refer to the rotation method of computed tomography (CT), then propose a novel CGI method based on rotational modulation method of a single-column striped coding plate. This method utilizes the spatial variation of a single sub-coding plate (rotation) to realize multiple modulation of the ray field and improves the utilization rate of a single sub-coding plate. However, for this rotation scheme of CGI, the traditional binary modulation matrix is no longer applicable. To obtain the system matrix of the rotated striped coding plate, an area model based on beam boundaries is established. Subsequently, numerical and Monte Carlo simulations were conducted. The results reveal that our scheme enables high-quality imaging of N*N resolution objects using only N sub-coding plates, under both full-sampling and under-sampling scenarios. Moreover, our scheme demonstrates superiority over the Hadamard scheme in both imaging quality and the number of required sub-coding plates, whether in scenarios of full-sampling or under-sampling. Finally, an α ray imaging platform was established to further demonstrate the feasibility of the rotational modulation method. By employing our scheme, a mere 8 sub-coding plates were employed to achieve CGI of the radiation source intensity distribution, achieving a resolution of 8*8. Therefore, the novel ray CGI based on rotational modulation method can achieve high-quality imaging effect with fewer sub-coding plates, which has important practical value and research significance for promoting single-pixel radiation imaging technology.

physics.med-ph↗
arXiv2023

ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data

Radiology report generation, as a key step in medical image analysis, is critical to the quantitative analysis of clinically informed decision-making levels. However, complex and diverse radiology reports with cross-source heterogeneity pose a huge generalizability challenge to the current methods under massive data volume, mainly because the style and normativity of radiology reports are obviously distinctive among institutions, body regions inspected and radiologists. Recently, the advent of large language models (LLM) offers great potential for recognizing signs of health conditions. To resolve the above problem, we collaborate with the Second Xiangya Hospital in China and propose ChatRadio-Valuer based on the LLM, a tailored model for automatic radiology report generation that learns generalizable representations and provides a basis pattern for model adaptation in sophisticated analysts' cases. Specifically, ChatRadio-Valuer is trained based on the radiology reports from a single institution by means of supervised fine-tuning, and then adapted to disease diagnosis tasks for human multi-system evaluation (i.e., chest, abdomen, muscle-skeleton, head, and maxillofacial $\&$ neck) from six different institutions in clinical-level events. The clinical dataset utilized in this study encompasses a remarkable total of \textbf{332,673} observations. From the comprehensive results on engineering indicators, clinical efficacy and deployment cost metrics, it can be shown that ChatRadio-Valuer consistently outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and GPT-4 et al., in terms of the diseases diagnosis from radiology reports. ChatRadio-Valuer provides an effective avenue to boost model generalization performance and alleviate the annotation workload of experts to enable the promotion of clinical AI applications in radiology reports.

cs.CL↗
arXiv2023

Photostrictive Effect and Structure Phase Transition via Nonlinear Photocurrent

The phenomena of crystal size changes and structural phase transitions induced by light irradiation have garnered significant interest due to their potential for tuning and controlling a wide range of material properties through highly cooperative interactions. However, a microscopic theory that can comprehensively explain these phenomena in correlation with photon frequency and polarization has remained highly desirable. In this work, we propose that nonlinear photocurrent may correspond to driving these effects, which arise from a competition between light-injected energy and structural variations. By conducting first-principles simulations and comparing them with two established experiments, we show that shift current, a second-order photocurrent, can induce photostriction and nonreciprocal structure phase transitions. The quantitative comparisons across key parameters such as light frequency, irradiation time, polarization, and intensity provide further support for the nonlinear photocurrent mechanism. Beyond shift current, this microscopic understanding proposes to utilize more types of nonlinear photocurrent to enhance light-structure interactions and control material properties.

cond-mat.mtrl-sci↗
arXiv2023

Giant 2D Skyrmion Topological Hall Effect with Ultrawide Temperature Window and Low-Current Manipulation in 2D Room-Temperature Ferromagnetic Crystals

The discovery and manipulation of topological Hall effect (THE), an abnormal magnetoelectric response mostly related to the Dzyaloshinskii-Moriya interaction (DMI), are promising for next-generation spintronic devices based on topological spin textures such as magnetic skyrmions. However, most skyrmions and THE are stabilized in a narrow temperature window either below or over room temperature with high critical current manipulation. It is still elusive and challenging to achieve large THE with both wide temperature window till room temperature and low critical current manipulation. Here, by using controllable, naturally-oxidized, sub-20 and sub-10 nm 2D van der Waals room-temperature ferromagnetic Fe3GaTe2-x crystals, robust 2D THE with ultrawide temperature window ranging in three orders of magnitude from 2 to 300 K is reported, combining with giant THE of ~5.4 micro-ohm cm at 10 K and ~0.15 micro-ohm cm at 300 K which is 1-3 orders of magnitude larger than that of all known room-temperature 2D skyrmion systems. Moreover, room-temperature current-controlled THE is also realized with a low critical current density of ~6.2*10^5 A cm^-2. First-principles calculations unveil natural oxidation-induced highly-enhanced 2D interfacial DMI reasonable for robust giant THE. This work paves the way to room-temperature, electrically-controlled 2D THE-based practical spintronic devices.

cond-mat.mtrl-sci↗
arXiv2023

Accelerating GW calculations of point defects with the defect-patched screening approximation

The GW approximation has been widely accepted as an ab initio tool for calculating defect levels with many-electron effect included. However, the GW simulation cost increases dramatically with the system size, and, unfortunately, large supercells are often required to model low-density defects that are experimentally relevant. In this work, we propose to accelerate GW calculations of point defects by reducing the simulation cost of the many-electron screening, which is the primary computational bottleneck. The random-phase approximation of many-electron screening is divided into two parts: one is the intrinsic screening, calculated using a unit cell of pristine structures, and the other is the defect-induced screening, calculated using the supercell within a small energy window. Depending on specific defects, one may only need to consider the intrinsic screening or include the defect contribution. This approach avoids the summation of many conductions states of supercells and significantly reduces the simulation time. We have applied it to calculating various point defects, including neutral and charged defects in two-dimensional and bulk systems with small or large bandgaps. The results consist with those from the direct GW simulations, and the agreements are further improved at the dilute-defect limit, which is experimentally relevant but extremely challenging for direct GW simulations. This defect-patched screening approach not only clarifies the roles of defects in many-electron screening but also paves the way to fast screen defect structures/materials for novel applications, including single-photon sources, quantum qubits, and quantum sensors.

cond-mat.mtrl-sci↗
arXiv2023

MF-NeRF: Memory Efficient NeRF with Mixed-Feature Hash Table

Neural radiance field (NeRF) has shown remarkable performance in generating photo-realistic novel views. Among recent NeRF related research, the approaches that involve the utilization of explicit structures like grids to manage features achieve exceptionally fast training by reducing the complexity of multilayer perceptron (MLP) networks. However, storing features in dense grids demands a substantial amount of memory space, resulting in a notable memory bottleneck within computer system. Consequently, it leads to a significant increase in training times without prior hyper-parameter tuning. To address this issue, in this work, we are the first to propose MF-NeRF, a memory-efficient NeRF framework that employs a Mixed-Feature hash table to improve memory efficiency and reduce training time while maintaining reconstruction quality. Specifically, we first design a mixed-feature hash encoding to adaptively mix part of multi-level feature grids and map it to a single hash table. Following that, in order to obtain the correct index of a grid point, we further develop an index transformation method that transforms indices of an arbitrary level grid to those of a canonical grid. Extensive experiments benchmarking with state-of-the-art Instant-NGP, TensoRF, and DVGO, indicate our MF-NeRF could achieve the fastest training time on the same GPU hardware with similar or even higher reconstruction quality.

cs.CV↗
arXiv2023

LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning

The automation of code review activities, a long-standing pursuit in software engineering, has been primarily addressed by numerous domain-specific pre-trained models. Despite their success, these models frequently demand extensive resources for pre-training from scratch. In contrast, Large Language Models (LLMs) provide an intriguing alternative, given their remarkable capabilities when supplemented with domain-specific knowledge. However, their potential for automating code review tasks remains largely unexplored. In response to this research gap, we present LLaMA-Reviewer, an innovative framework that leverages the capabilities of LLaMA, a popular LLM, in the realm of code review. Mindful of resource constraints, this framework employs parameter-efficient fine-tuning (PEFT) methods, delivering high performance while using less than 1% of trainable parameters. An extensive evaluation of LLaMA-Reviewer is conducted on two diverse, publicly available datasets. Notably, even with the smallest LLaMA base model consisting of 6.7B parameters and a limited number of tuning epochs, LLaMA-Reviewer equals the performance of existing code-review-focused models. The ablation experiments provide insights into the influence of various fine-tuning process components, including input representation, instruction tuning, and different PEFT methods. To foster continuous progress in this field, the code and all PEFT-weight plugins have been made open-source.

cs.SE↗
arXiv2023

Room-Temperature Highly-Tunable Coercivity and Highly-Efficient Nonvolatile Multi-States Magnetization Switching by Small Current in Single 2D Ferromagnet Fe$_3$GaTe$_2$

Room-temperature electrically-tuned coercivity and nonvolatile multi-states magnetization switching is crucial for next-generation low-power 2D spintronics. However, most methods have limited ability to adjust the coercivity of ferromagnetic systems, and room-temperature electrically-driven magnetization switching shows high critical current density and high power dissipation. Here, highly-tunable coercivity and highly-efficient nonvolatile multi-states magnetization switching are achieved at room temperature in single-material based devices by 2D van der Waals itinerant ferromagnet Fe$_3$GaTe$_2$. The coercivity can be readily tuned up to ~98.06% at 300 K by a tiny in-plane electric field that is 2-5 orders of magnitude smaller than that of other ferromagnetic systems. Moreover, the critical current density and power dissipation for room-temperature magnetization switching in 2D Fe$_3$GaTe$_2$ are down to ~1.7E5 A cm$^{-2}$ and ~4E12 W m$^{-3}$, respectively. Such switching power dissipation is 2-6 orders of magnitude lower than that of other 2D ferromagnetic systems. Meanwhile, multi-states magnetization switching are presented by continuously controlling the current, which can dramatically enhance the information storage capacity and develop new computing methodology. This work opens the avenue for room-temperature electrical control of ferromagnetism and potential applications for vdW-integrated 2D spintronics.

cond-mat.mtrl-sci↗
arXiv2023

Forensic Data Analytics for Anomaly Detection in Evolving Networks

In the prevailing convergence of traditional infrastructure-based deployment (i.e., Telco and industry operational networks) towards evolving deployments enabled by 5G and virtualization, there is a keen interest in elaborating effective security controls to protect these deployments in-depth. By considering key enabling technologies like 5G and virtualization, evolving networks are democratized, facilitating the establishment of point presences integrating different business models ranging from media, dynamic web content, gaming, and a plethora of IoT use cases. Despite the increasing services provided by evolving networks, many cybercrimes and attacks have been launched in evolving networks to perform malicious activities. Due to the limitations of traditional security artifacts (e.g., firewalls and intrusion detection systems), the research on digital forensic data analytics has attracted more attention. Digital forensic analytics enables people to derive detailed information and comprehensive conclusions from different perspectives of cybercrimes to assist in convicting criminals and preventing future crimes. This chapter presents a digital analytics framework for network anomaly detection, including multi-perspective feature engineering, unsupervised anomaly detection, and comprehensive result correction procedures. Experiments on real-world evolving network data show the effectiveness of the proposed forensic data analytics solution.

cs.CR↗
arXiv2023

Interface-tuning of ferroelectricity and quadruple-well state in CuInP$_2$S$_6$ via ferroelectric oxide

Ferroelectric van der Waals CuInP$_2$S$_6$ possesses intriguing quadruple-well states and negative piezoelectricity. Its technological implementation has been impeded by the relatively low Curie temperature (bulk $T_C$ ~42 °C) and the lack of precise domain control. Here we show that CuInP$_2$S$_6$ can be immune to the finite size effect and exhibits enhanced ferroelectricity, piezoelectricity, and polar alignment in the ultrathin limit when interfaced with ferroelectric oxide PbZr$_{0.2}$Ti$_{0.8}$O$_3$ films. Piezoresponse force microscopy studies reveal that the polar domains in thin CuInP$_2$S$_6$ fully conform to those of underlying PbZr$_{0.2}$Ti$_{0.8}$O$_3$, where the piezoelectric coefficient changes sign and increases sharply with reducing thickness. High temperature $in$ $situ$ domain imaging points to a significantly enhanced $T_C$ exceeding 200 °C for 13 nm CuInP$_2$S$_6$ on PbZr$_{0.2}$Ti$_{0.8}$O$_3$. Density functional theory modeling and Monte Carlo simulations show that the enhanced polar alignment and $T_C$ can be attributed to interface-mediated structure distortion in CuInP$_2$S$_6$. Our study provides an effective material strategy to engineer the polar properties of CuInP$_2$S$_6$ for flexible nanoelectronic, optoelectronic, and mechanical applications.

cond-mat.mtrl-sci↗
arXiv2023

Observation of abnormal resistance-temperature behavior along with diamagnetic transition in Pb$_{10-x}$Cu$_x$(PO$_4$)$_6$O-based composite

Recently, Sukbae Lee et al.reported that material Pb$_{10-x}$Cu$_x$(PO$_4$)$_6$O (LK-99) has a series of characteristics of room temperature superconductors, including diamagnetic transition, resistance jump, nearly zero-resistance, magnetic field-dependent IV characteristics and so on (10.6111/JKCGCT.2023.33.2.061, arXiv:2307.12008, arXiv:2307.12037). However, whether LK-99 is really a room temperature superconductor is still controversial. On the one hand, some people think that the relatively weak diamagnetism of LK-99 reported by Sukbae Lee et al. is not the Meissner effect. On the other hand, there are doubts about the authenticity of its zero-resistance test results. Global replication studies have shown that LK-99 does have a large diamagnetic (arXiv:2308.01516), and also found a zero-resistance behavior at a low temperature of 110 $^\circ$K (arXiv:2308.01192). However, up to now, there is still no direct reproducible evidence to support Sukbae Lee et al.'s conclusion that LK-99 is a room temperature superconductor. Here, a distinct resistance jump was observed at about 387 $^\circ$K under ambient pressure in our experiment for unclear reason including possible impurity's contribution. The overall resistance of the test LK-99 sample still shows semiconductivity, and the resistance cannot really drop to zero. Our findings indicate that to identify the true potential of LK-99, high quality crystals without impurity are very important.

cond-mat.supr-con↗
arXiv2023

Successful growth and room temperature ambient-pressure magnetic levitation of LK-99

Recently, Sukbae Lee et al. reported inspiring experimental findings on the atmospheric superconductivity of a modified lead apatite crystal (LK-99) at room temperature (10.6111/JKCGCT.2023.33.2.061, arXiv: 2307.12008, arXiv: 2307.12037). They claimed that the synthesized LK-99 materials exhibit the Meissner levitation phenomenon of superconductors and have a superconducting transition temperature (Tc) higher than 400 K. Here, for the first time, we successfully verify and synthesize the LK-99 crystals which can be magnetically levitated with larger levitated angle than Sukbae Lee's sample at room temperature. It is expected to realize the true potential of room temperature, non-contact superconducting magnetic levitation in near future.

cond-mat.supr-con↗
arXiv2023

SMC-UDA: Structure-Modal Constraint for Unsupervised Cross-Domain Renal Segmentation

Medical image segmentation based on deep learning often fails when deployed on images from a different domain. The domain adaptation methods aim to solve domain-shift challenges, but still face some problems. The transfer learning methods require annotation on the target domain, and the generative unsupervised domain adaptation (UDA) models ignore domain-specific representations, whose generated quality highly restricts segmentation performance. In this study, we propose a novel Structure-Modal Constrained (SMC) UDA framework based on a discriminative paradigm and introduce edge structure as a bridge between domains. The proposed multi-modal learning backbone distills structure information from image texture to distinguish domain-invariant edge structure. With the structure-constrained self-learning and progressive ROI, our methods segment the kidney by locating the 3D spatial structure of the edge. We evaluated SMC-UDA on public renal segmentation datasets, adapting from the labeled source domain (CT) to the unlabeled target domain (CT/MRI). The experiments show that our proposed SMC-UDA has a strong generalization and outperforms generative UDA methods.

cs.CV↗
arXiv2023

Electrostatic moiré potential from twisted-hBN layers

Moiré superlattices formed by vertically stacking van der Waals layers host a rich variety of correlated electronic phases and function as novel photonic materials. The moiré potential of the superlattice, however, is fixed by the interlayer coupling of the stacked functional layers (e.g. graphene) and dependent on carrier types (e.g. electrons or holes) and valleys (e.g. Γ vs. K). In contrast, twisted hexagonal boron nitride (hBN) layers are predicted to impose a periodic electrostatic potential that may be used to engineer the properties of an adjacent functional thin layer. Here, we show that this potential is described by a simple theory of electric polarization originating from the interfacial charge redistribution, validated by its dependence on supercell sizes and distance from the twisted interfaces. We demonstrate that the potential depth and profile can be further controlled by assembling a double moiré structure. When the twist angles are similar at the two interfaces, the potential is deepened by adding the potential from the two twisted interfaces, reaching ~ 400 meV. When the twist angles are dissimilar at the two interfaces, multi-level polarization states are observed. As an example of controlling a functional layer, we demonstrate how the electrostatic potential from a twisted hBN substrate impedes exciton diffusion in a semiconductor monolayer. These findings suggest exciting opportunities for engineering properties of an adjacent functional layer using the surface potential of a twisted hBN substrate.

cond-mat.mtrl-sci↗
arXiv2023

Point Defects in Two-Dimensional RuCl3

Defects are crucial in determining a variety of material properties especially in low dimensions. In this work, we study point defects in monolayer alpha-phase Ruthenium (III) chloride (alpha-RuCl3), a promising candidate to realize quantum spin liquid with nearly degenerate magnetic states. Our first-principles simulations reveal that Cl vacancies, Ru vacancies, and oxygen substitutional defects are the most energetically stable point defects. Besides, these point defects break the magnetic degeneracy: Cl vacancies and oxygen substitutional defects energetically favor the zigzag-antiferromagnetic configuration while Ru vacancies favor the ferromagnetic configuration, shedding light on understanding the observed magnetic structures and further defect engineering of magnetism in monolayer α-RuCl3. We further calculated their electronic structures and optical absorption spectra. The polarization symmetry of optical responses provides a convenient signature to identify the point defect types and long-range magnetic orders.

cond-mat.mtrl-sci↗
arXiv2023

Minimizing CNOT-count in quantum circuit of the extended Shor's algorithm for ECDLP

Since the elliptic curve discrete logarithms problem (ECDLP) was proposed, it has been widely used in cryptosystem because of its strong security. Although the proposal of the extended Shor's algorithm offers hope for cracking ECDLP, it is debatable whether the algorithm can actually pose a threat in practice. From the perspective of the quantum circuit of the algorithm, we analyze the feasibility of cracking ECDLP with improved quantum circuits using an ion trap quantum computer. We give precise quantum circuits for extended Shor's algorithm to calculate discrete logarithms on elliptic curves over prime fields, including modulus subtraction, three different modulus multiplication, modulus inverse, and windowed arithmetic. Whereas previous studies mostly focused on minimizing the number of qubits or the depth of the circuit, we minimize the number of CNOTs, which greatly affects the time to run the algorithm on an ion trap quantum computer. First, we give the implementation of the basic arithmetic with the lowest known number of CNOTs and the construction of an improved modular inverse, point addition, and the windowing technique. Then, we precisely estimate the number of improved quantum circuits needed to perform the extended Shor's algorithm for factoring an n-bit integer. We analyze the running time and feasibility of the extended Shor's algorithm on an ion trap quantum computer according to the number of CNOTs. Finally, we discussed the lower bound of the number of CNOTs needed to implement the extended Shor's algorithm.

quant-ph↗
arXiv2023

ChatABL: Abductive Learning via Natural Language Interaction with ChatGPT

Large language models (LLMs) such as ChatGPT have recently demonstrated significant potential in mathematical abilities, providing valuable reasoning paradigm consistent with human natural language. However, LLMs currently have difficulty in bridging perception, language understanding and reasoning capabilities due to incompatibility of the underlying information flow among them, making it challenging to accomplish tasks autonomously. On the other hand, abductive learning (ABL) frameworks for integrating the two abilities of perception and reasoning has seen significant success in inverse decipherment of incomplete facts, but it is limited by the lack of semantic understanding of logical reasoning rules and the dependence on complicated domain knowledge representation. This paper presents a novel method (ChatABL) for integrating LLMs into the ABL framework, aiming at unifying the three abilities in a more user-friendly and understandable manner. The proposed method uses the strengths of LLMs' understanding and logical reasoning to correct the incomplete logical facts for optimizing the performance of perceptual module, by summarizing and reorganizing reasoning rules represented in natural language format. Similarly, perceptual module provides necessary reasoning examples for LLMs in natural language format. The variable-length handwritten equation deciphering task, an abstract expression of the Mayan calendar decoding, is used as a testbed to demonstrate that ChatABL has reasoning ability beyond most existing state-of-the-art methods, which has been well supported by comparative studies. To our best knowledge, the proposed ChatABL is the first attempt to explore a new pattern for further approaching human-level cognitive ability via natural language interaction with ChatGPT.

cs.CL↗
arXiv2023

Topological phase transition from periodic edge states in moiré superlattices

Topological mosaic pattern (TMP) can be formed in two-dimensional (2D) moiré superlattices, a set of periodic and spatially separated domains with distinct topologies give rise to periodic edge states on the domain walls. In this study, we demonstrate that these periodic edge states play a crucial role in determining global topological properties. By developing a continuum model for periodic edge states with C6z and C3z rotational symmetry, we predict that a global topological phase transition at the charge neutrality point (CNP) can be driven by the size of domain walls and moiré periodicity. The Wannier representation analysis reveals that these periodic edge states are fundamentally chiral px +- ipy orbitals. The interplay between on-site chiral orbital rotation and neighboring hopping among chiral orbitals leads to band inversion and a topological phase transition. Our work establishes a general model for tuning local and global topological phases, paving the way for future research on strongly correlated topological flat minibands within topological mosaic patterns

cond-mat.mtrl-sci↗