arXiv subjects

Na Liu

Publications and source records attributed to Na Liu.

At least 37 records · Page 2Linked to original sources

arXiv2024

Magnetic Switching in Monolayer 2D Diluted Magnetic Semiconductors via Spin-to- Spin Conversion

The integration of two-dimensional (2D) van der Waals (vdW) magnets with topological insulators or heavy metals holds great potential for realizing next-generation spintronic memory devices. However, achieving high-efficiency SOT switching of monolayer vdW magnets at room temperature poses a significant challenge, particularly without an external magnetic field. Here, we show field-free, deterministic, and nonvolatile SOT switching of perpendicular magnetization in the monolayer, diluted magnetic semiconductor (DMS), Fe-doped MoS2(Fe:MoS2) at up to 380 K with a current density of $7\times10^4 A cm^{-2}$. The in situ doping of Fe into monolayer MoS2 via chemical vapor deposition and the geometry-induced strain in the crystal break the rotational switching symmetry in Fe:MoS2, promoting field-free SOT switching by generating out-of-plane spins via spin-to-spin conversion. An apparent anomalous Hall effect (AHE) loop shift at a zero in-plane magnetic field verifies the existence of z spins in Fe:MoS2, inducing an antidamping-like torque that facilitates field-free SOT switching. A strong topological Hall effect (THE) was also observed, attributed to the interfacial Dzyaloshinskii-Moriya interaction (DMI), reducing the energy barrier for SOT switching. This field-free SOT application using a 2D ferromagnetic monolayer provides a new pathway for developing highly power-efficient spintronic memory devices.

cond-mat.mes-hall↗

arXiv2024

Metasurface-Integrated Polarization-Insensitive LCoS for Projection Displays

Liquid crystal on silicon (LCoS) panels, renowned for their high resolution and fill-factor, are integral to modern projection displays. However, their inherent polarization sensitivity constrains the upper limit of light utilization, increases system complexity and restricts broader applicability. Here, we demonstrate a dual-layer metasurface-integrated LCoS prototype that achieves polarization-insensitive, addressable amplitude modulation in the visible. Polarization sensitivity is eliminated in the reflective architecture through polarization conversion in the underlying metasurface and polarization-sensitive phase modulation of the liquid crystals (LC). This is further enhanced by the electrically tunable subwavelength grating formed by the upper metasurface and LC, resulting in a high-contrast, polarization-insensitive optical switch. We showcase a 64-pixel 2D addressable prototype capable of generating diverse projection patterns with high contrast. Compatible with existing LCoS processes, our metasurface device reduces system size and enhances energy efficiency, offering applications in projectors and AR/VR displays, with the potential to redefine projection chip technology.

physics.optics↗

arXiv2024

Two-Stage Radio Map Construction with Real Environments and Sparse Measurements

Radio map construction based on extensive measurements is accurate but expensive and time-consuming, while environment-aware radio map estimation reduces the costs at the expense of low accuracy. Considering accuracy and costs, a first-predict-then-correct (FPTC) method is proposed by leveraging generative adversarial networks (GANs). A primary radio map is first predicted by a radio map prediction GAN (RMP-GAN) taking environmental information as input. Then, the prediction result is corrected by a radio map correction GAN (RMC-GAN) with sparse measurements as guidelines. Specifically, the self-attention mechanism and residual-connection blocks are introduced to RMP-GAN and RMC-GAN to improve the accuracy, respectively. Experimental results validate that the proposed FPTC-GANs method achieves the best radio map construction performance, compared with the state-of-the-art methods.

eess.SP↗

arXiv2024

Dial-insight: Fine-tuning Large Language Models with High-Quality Domain-Specific Data Preventing Capability Collapse

The efficacy of large language models (LLMs) is heavily dependent on the quality of the underlying data, particularly within specialized domains. A common challenge when fine-tuning LLMs for domain-specific applications is the potential degradation of the model's generalization capabilities. To address these issues, we propose a two-stage approach for the construction of production prompts designed to yield high-quality data. This method involves the generation of a diverse array of prompts that encompass a broad spectrum of tasks and exhibit a rich variety of expressions. Furthermore, we introduce a cost-effective, multi-dimensional quality assessment framework to ensure the integrity of the generated labeling data. Utilizing a dataset comprised of service provider and customer interactions from the real estate sector, we demonstrate a positive correlation between data quality and model performance. Notably, our findings indicate that the domain-specific proficiency of general LLMs can be enhanced through fine-tuning with data produced via our proposed method, without compromising their overall generalization abilities, even when exclusively domain-specific data is employed for fine-tuning.

cs.CL↗

arXiv2024

A Physics-driven GraphSAGE Method for Physical Process Simulations Described by Partial Differential Equations

Physics-informed neural networks (PINNs) have successfully addressed various computational physics problems based on partial differential equations (PDEs). However, while tackling issues related to irregularities like singularities and oscillations, trained solutions usually suffer low accuracy. In addition, most current works only offer the trained solution for predetermined input parameters. If any change occurs in input parameters, transfer learning or retraining is required, and traditional numerical techniques also need an independent simulation. In this work, a physics-driven GraphSAGE approach (PD-GraphSAGE) based on the Galerkin method and piecewise polynomial nodal basis functions is presented to solve computational problems governed by irregular PDEs and to develop parametric PDE surrogate models. This approach employs graph representations of physical domains, thereby reducing the demands for evaluated points due to local refinement. A distance-related edge feature and a feature mapping strategy are devised to help training and convergence for singularity and oscillation situations, respectively. The merits of the proposed method are demonstrated through a couple of cases. Moreover, the robust PDE surrogate model for heat conduction problems parameterized by the Gaussian random field source is successfully established, which not only provides the solution accurately but is several times faster than the finite element method in our experiments.

cs.LG↗

arXiv2024

CasCast: Skillful High-resolution Precipitation Nowcasting via Cascaded Modelling

Precipitation nowcasting based on radar data plays a crucial role in extreme weather prediction and has broad implications for disaster management. Despite progresses have been made based on deep learning, two key challenges of precipitation nowcasting are not well-solved: (i) the modeling of complex precipitation system evolutions with different scales, and (ii) accurate forecasts for extreme precipitation. In this work, we propose CasCast, a cascaded framework composed of a deterministic and a probabilistic part to decouple the predictions for mesoscale precipitation distributions and small-scale patterns. Then, we explore training the cascaded framework at the high resolution and conducting the probabilistic modeling in a low dimensional latent space with a frame-wise-guided diffusion transformer for enhancing the optimization of extreme events while reducing computational costs. Extensive experiments on three benchmark radar precipitation datasets show that CasCast achieves competitive performance. Especially, CasCast significantly surpasses the baseline (up to +91.8%) for regional extreme-precipitation nowcasting.

cs.LG↗

arXiv2024

From LLM to Conversational Agent: A Memory Enhanced Architecture with Fine-Tuning of Large Language Models

This paper introduces RAISE (Reasoning and Acting through Scratchpad and Examples), an advanced architecture enhancing the integration of Large Language Models (LLMs) like GPT-4 into conversational agents. RAISE, an enhancement of the ReAct framework, incorporates a dual-component memory system, mirroring human short-term and long-term memory, to maintain context and continuity in conversations. It entails a comprehensive agent construction scenario, including phases like Conversation Selection, Scene Extraction, CoT Completion, and Scene Augmentation, leading to the LLMs Training phase. This approach appears to enhance agent controllability and adaptability in complex, multi-turn dialogues. Our preliminary evaluations in a real estate sales context suggest that RAISE has some advantages over traditional agents, indicating its potential for broader applications. This work contributes to the AI field by providing a robust framework for developing more context-aware and versatile conversational agents.

cs.CL↗

arXiv2023

DUMA: a Dual-Mind Conversational Agent with Fast and Slow Thinking

Inspired by the dual-process theory of human cognition, we introduce DUMA, a novel conversational agent framework that embodies a dual-mind mechanism through the utilization of two generative Large Language Models (LLMs) dedicated to fast and slow thinking respectively. The fast thinking model serves as the primary interface for external interactions and initial response generation, evaluating the necessity for engaging the slow thinking model based on the complexity of the complete response. When invoked, the slow thinking model takes over the conversation, engaging in meticulous planning, reasoning, and tool utilization to provide a well-analyzed response. This dual-mind configuration allows for a seamless transition between intuitive responses and deliberate problem-solving processes based on the situation. We have constructed a conversational agent to handle online inquiries in the real estate industry. The experiment proves that our method balances effectiveness and efficiency, and has a significant improvement compared to the baseline.

cs.CL↗

arXiv2023

Synthesized complex-frequency excitation for ultrasensitive molecular sensing

Detecting trace molecules remains a significant challenge. Surface-enhanced infrared absorption (SEIRA) based on plasmonic nanostructures, particularly graphene, has emerged as a promising approach to enhance sensing sensitivity. While graphene-based SEIRA offers advantages such as ultrahigh sensitivity and active tunability, intrinsic molecular damping weakens the interaction between vibrational modes and plasmons. Here, we demonstrate ultrahigh-sensitive molecular sensing based on synthesized complex-frequency waves (CFW). Our experiment shows that CFW can amplify the molecular signals (~1.2-nm-thick silk protein layer) detected by graphene-based sensor by at least an order of magnitude and can be universally applied to molecular sensing in different phases. Our approach is highly scalable and can facilitate the investigation of light-matter interactions, enabling diverse potential applications in fields such as optical spectroscopy, metasurfaces, optoelectronics, biomedicine and pharmaceutics.

physics.optics↗

arXiv2023

Real-Time Tracking of Coherent Oscillations of Electrons in a Nanodevice by Photo-assisted Tunnelling

Coherent collective oscillations of electrons excited in metallic nanostructures (localized surface plasmons) can confine incident light to atomic scales and enable strong light-matter interactions, which depend nonlinearly on the local field. Direct sampling of such collective electron oscillations in real-time is crucial to performing petahertz scale optical modulation, control, and readout in a quantum nanodevice. Here, we demonstrate real-time tracking of collective electron oscillations in an Au bowtie nanoantenna, by recording photo-assisted tunnelling currents generated by such oscillations in this quantum nanodevice. The collective electron oscillations show a noninstantaneous response to the driving laser fields with a decay time of nearly 10 femtoseconds. The temporal evolution of nonlinear electron oscillations resulting from the coherent nonlinear optical response of the nanodevice were also traced in real-time. The contributions of linear and nonlinear electron oscillations in the generated tunnelling currents in the nanodevice were precisely determined. A coherent control of electron oscillations in the nanodevice is illustrated directly in the time domain. Functioning in ambient conditions, the excitation, coherent control, and read-out of coherent electron oscillations pave the way toward on-chip light-wave electronics in quantum nanodevices.

physics.optics↗

arXiv2023

Layer-number-dependent spin Hall effects in transition metal monocarbides $M_{2}\rm{C}$ ($M=\rm{V}, \rm{Nb}, \rm{Ta}$)

The recent discovery of strong spin Hall effects (SHE) in 2D layered topological semimetals has attracted intensive attention due to its exotic electronic properties and potential applications in spintronic devices. In this paper, we systematically study the topological properties and intrinsic SHE of layered transition metal carbides $M_{2}\rm{C}$ ($M=\rm{V}, \rm{Nb}, \rm{Ta}$). The results show that both bulk and monolayer $M_{2}\rm{C}$ have symmetry-protected nodal points (NPs) and lines (NLs) originating from the $d$ band crossing near the Fermi level ($E_F$). The inclusion of SOC breaks the degeneracy of NLs and NPs, contributing to large spin Hall conductivity (SHC) up to $\sim$1100 and $\sim$200 $(\hbar / e)(Ω\mathrm{cm})^{-1}$ for bulk and monolayer Ta$_{2}$C, respectively. Remarkably, we find that magnitude of SHC exhibits a significant enhancement by increasing the layer number. For eight-layer Ta$_{2}$C, the maximum value of SHC can reach up to $\sim$600 $(\hbar / e)(Ω\mathrm{cm})^{-1}$, comparable to many reported 3D topological materials. Analysis of spin Berry curvature reveals that the large SHC originates from layer-number-dependent nodal line structure near the $E_F$, in which the repeated crossover between valence and conduction bands creates large amounts of NPs along the $Γ\rm{-K}$ route. Our findings not only provide a new platform for experimental research of low-dimensional SHE, but also suggest an effective way of realizing giant SHE by controlling layer thickness.

cond-mat.mtrl-sci↗

arXiv2022

Additive complementary dual codes over $\F_4$

A linear code is linear complementary dual (LCD) if it meets its dual trivially. LCD codes have been a hot topic recently due to Boolean masking application in the security of embarked electronics (Carlet and Guilley, 2014). Additive codes over $\F_4$ are $\F_4$-codes that are stable by codeword addition but not necessarily by scalar multiplication. An additive code over $\F_4$ is additive complementary dual (ACD) if it meets its dual trivially. The aim of this research is to study such codes which meet their dual trivially. All the techniques and problems used to study LCD codes are potentially relevant to ACD codes. Interesting constructions of ACD codes from binary codes are given with respect to the trace Hermitian and trace Euclidean inner product. The former product is relevant to quantum codes.

cs.IT↗

arXiv2022

A Survey on Hyperspectral Image Restoration: From the View of Low-Rank Tensor Approximation

The ability of capturing fine spectral discriminative information enables hyperspectral images (HSIs) to observe, detect and identify objects with subtle spectral discrepancy. However, the captured HSIs may not represent true distribution of ground objects and the received reflectance at imaging instruments may be degraded, owing to environmental disturbances, atmospheric effects and sensors' hardware limitations. These degradations include but are not limited to: complex noise (i.e., Gaussian noise, impulse noise, sparse stripes, and their mixtures), heavy stripes, deadlines, cloud and shadow occlusion, blurring and spatial-resolution degradation and spectral absorption, etc. These degradations dramatically reduce the quality and usefulness of HSIs. Low-rank tensor approximation (LRTA) is such an emerging technique, having gained much attention in HSI restoration community, with ever-growing theoretical foundation and pivotal technological innovation. Compared to low-rank matrix approximation (LRMA), LRTA is capable of characterizing more complex intrinsic structure of high-order data and owns more efficient learning abilities, being established to address convex and non-convex inverse optimization problems induced by HSI restoration. This survey mainly attempts to present a sophisticated, cutting-edge, and comprehensive technical survey of LRTA toward HSI restoration, specifically focusing on the following six topics: Denoising, Destriping, Inpainting, Deblurring, Super--resolution and Fusion. The theoretical development and variants of LRTA techniques are also elaborated. For each topic, the state-of-the-art restoration methods are compared by assessing their performance both quantitatively and visually. Open issues and challenges are also presented, including model formulation, algorithm design, prior exploration and application concerning the interpretation requirements.

eess.IV↗

arXiv2022

Detecting Textual Adversarial Examples Based on Distributional Characteristics of Data Representations

Although deep neural networks have achieved state-of-the-art performance in various machine learning tasks, adversarial examples, constructed by adding small non-random perturbations to correctly classified inputs, successfully fool highly expressive deep classifiers into incorrect predictions. Approaches to adversarial attacks in natural language tasks have boomed in the last five years using character-level, word-level, phrase-level, or sentence-level textual perturbations. While there is some work in NLP on defending against such attacks through proactive methods, like adversarial training, there is to our knowledge no effective general reactive approaches to defence via detection of textual adversarial examples such as is found in the image processing literature. In this paper, we propose two new reactive methods for NLP to fill this gap, which unlike the few limited application baselines from NLP are based entirely on distribution characteristics of learned representations: we adapt one from the image processing literature (Local Intrinsic Dimensionality (LID)), and propose a novel one (MultiDistance Representation Ensemble Method (MDRE)). Adapted LID and MDRE obtain state-of-the-art results on character-level, word-level, and phrase-level attacks on the IMDB dataset as well as on the later two with respect to the MultiNLI dataset. For future research, we publish our code.

cs.CL↗

arXiv2022

The $(n+1)$-centered operator on a Hilbert $C^*$-module

Let $T$ be an adjointable operator on a Hilbert $C^*$-module such that $T$ has the polar decomposition $T=UT|$. For each natural number $n$, $T$ is called an $(n+1)$-centered operator if $T^k=U^k|T^k|$ is the polar decomposition for $1\le k\le n+1$. This paper initiates the study of the $(n+1)$-centered operator via the generalized Aluthge transform and the generalized iterative Aluthge transform. Some new characterizations of the $(n+1)$-centered operator are provided.

math.OA↗

arXiv2021

Metasurface-Enabled On-Chip Multiplexed Diffractive Neural Networks in the Visible

Replacing electrons with photons is a compelling route towards light-speed, highly parallel, and low-power artificial intelligence computing. Recently, all-optical diffractive neural deep neural networks have been demonstrated. However, the existing architectures often comprise bulky components and, most critically, they cannot mimic the human brain for multitasking. Here, we demonstrate a multi-skilled diffractive neural network based on a metasurface device, which can perform on-chip multi-channel sensing and multitasking at the speed of light in the visible. The metasurface is integrated with a complementary metal oxide semiconductor imaging sensor. Polarization multiplexing scheme of the subwavelength nanostructures are applied to construct a multi-channel classifier framework for simultaneous recognition of digital and fashionable items. The areal density of the artificial neurons can reach up to 6.25x106/mm2 multiplied by the number of channels. Our platform provides an integrated solution with all-optical on-chip sensing and computing for applications in machine vision, autonomous driving, and precision medicine.

eess.SP↗

arXiv2021

Reconfigurable Plasmonic Chirality: Fundamentals and Applications

Molecular chirality is a geometric property that is of great importance in chemistry, biology, and medicine. Recently, plasmonic nanostructures that exhibit distinct chiroptical responses have attracted tremendous interest, given their ability to emulate the properties of chiral molecules with tailored and pronounced optical characteristics. However, the optical chirality of such human-made structures is in general static and cannot be manipulated postfabrication. Herein, different concepts to reconfigure the chiroptical responses of plasmonic nano- and micro-objects are outlined. Depending on the utilized strategies and stimuli, the chiroptical signature, the 3D structural conformation, or both can be reconfigured. Optical devices based on plasmonic nanostructures with reconfigurable chirality possess great potential in practical applications, ranging from polarization conversion elements to enantioselective analysis, chiral sensing, and catalysis.

physics.optics↗

arXiv2021

A light-driven three-dimensional plasmonic nanosystem that translates molecular motion into reversible chiroptical function

Nature has developed striking light-powered proteins such as bacteriorhodopsin, which can convert light energy into conformational changes for biological functions. Such natural machines are a great source of inspiration for creation of their synthetic analogues. However, synthetic molecular machines typically operate at the nanometre scale or below. Translating controlled operation of individual molecular machines to a larger dimension, for example, to 10-100 nm, which features many practical applications, is highly important but remains challenging. Here we demonstrate a light-driven plasmonic nanosystem that can amplify the molecular motion of azobenzene through the host nanostructure and consequently translate it into reversible chiroptical function with large amplitude modulation. Light is exploited as both energy source and information probe. Our plasmonic nanosystem bears unique features of optical addressability, reversibility and modulability, which are crucial for developing all-optical molecular devices with desired functionalities.

physics.optics↗