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Lu Liu

Publications and source records attributed to Lu Liu.

At least 109 records · Page 6Linked to original sources
arXiv2024

Symbol Level Precoding for Systems with Improper Gaussian Interference

This paper focuses on precoding design in multi-antenna systems with improper Gaussian interference (IGI), characterized by correlated real and imaginary parts. We first study block level precoding (BLP) and symbol level precoding (SLP) assuming the receivers apply a pre-whitening filter to decorrelate and normalize the IGI. We then shift to the scenario where the base station (BS) incorporates the IGI statistics in the SLP design, which allows the receivers to employ a standard detection algorithm without pre-whitenting. Finally we address the case where the channel and statistics of the IGI are unknown, and we formulate robust BLP and SLP designs that minimize the worst case performance in such settings. Interestingly, we show that for BLP, the worst-case IGI is in fact proper, while for SLP the worst case occurs when the interference signal is maximally improper, with fully correlated real and imaginary parts. Numerical results reveal the superior performance of SLP in terms of symbol error rate (SER) and energy efficiency (EE), especially for the case where there is uncertainty in the non-circularity of the jammer.

eess.SP↗
arXiv2024

An Empirical Study on Information Extraction using Large Language Models

Human-like large language models (LLMs), especially the most powerful and popular ones in OpenAI's GPT family, have proven to be very helpful for many natural language processing (NLP) related tasks. Therefore, various attempts have been made to apply LLMs to information extraction (IE), which is a fundamental NLP task that involves extracting information from unstructured plain text. To demonstrate the latest representative progress in LLMs' information extraction ability, we assess the information extraction ability of GPT-4 (the latest version of GPT at the time of writing this paper) from four perspectives: Performance, Evaluation Criteria, Robustness, and Error Types. Our results suggest a visible performance gap between GPT-4 and state-of-the-art (SOTA) IE methods. To alleviate this problem, considering the LLMs' human-like characteristics, we propose and analyze the effects of a series of simple prompt-based methods, which can be generalized to other LLMs and NLP tasks. Rich experiments show our methods' effectiveness and some of their remaining issues in improving GPT-4's information extraction ability.

cs.CL↗
arXiv2024

An Empirical Study on Information Extraction using Large Language Models

Human-like large language models (LLMs), especially the most powerful and popular ones in OpenAI's GPT family, have proven to be very helpful for many natural language processing (NLP) related tasks. Therefore, various attempts have been made to apply LLMs to information extraction (IE), which is a fundamental NLP task that involves extracting information from unstructured plain text. To demonstrate the latest representative progress in LLMs' information extraction ability, we assess the information extraction ability of GPT-4 (the latest version of GPT at the time of writing this paper) from four perspectives: Performance, Evaluation Criteria, Robustness, and Error Types. Our results suggest a visible performance gap between GPT-4 and state-of-the-art (SOTA) IE methods. To alleviate this problem, considering the LLMs' human-like characteristics, we propose and analyze the effects of a series of simple prompt-based methods, which can be generalized to other LLMs and NLP tasks. Rich experiments show our methods' effectiveness and some of their remaining issues in improving GPT-4's information extraction ability.

cs.CL↗
arXiv2024

LegiLM: A Fine-Tuned Legal Language Model for Data Compliance

Ensuring compliance with international data protection standards for privacy and data security is a crucial but complex task, often requiring substantial legal expertise. This paper introduces LegiLM, a novel legal language model specifically tailored for consulting on data or information compliance. LegiLM leverages a pre-trained GDPR Fines dataset and has been fine-tuned to automatically assess whether particular actions or events breach data security and privacy regulations. By incorporating a specialized dataset that includes global data protection laws, meticulously annotated policy documents, and relevant privacy policies, LegiLM is optimized for addressing data compliance challenges. The model integrates advanced legal reasoning methods and information retrieval enhancements to enhance accuracy and reliability in practical legal consulting scenarios. Our evaluation using a custom benchmark dataset demonstrates that LegiLM excels in detecting data regulation breaches, offering sound legal justifications, and recommending necessary compliance modifications, setting a new benchmark for AI-driven legal compliance solutions. Our resources are publicly available at https://github.com/DAOLegalAI/LegiLM

cs.CL↗
arXiv2024

Reinforcement Learning from Human Feedback for Lane Changing of Autonomous Vehicles in Mixed Traffic

The burgeoning field of autonomous driving necessitates the seamless integration of autonomous vehicles (AVs) with human-driven vehicles, calling for more predictable AV behavior and enhanced interaction with human drivers. Human-like driving, particularly during lane-changing maneuvers on highways, is a critical area of research due to its significant impact on safety and traffic flow. Traditional rule-based decision-making approaches often fail to encapsulate the nuanced boundaries of human behavior in diverse driving scenarios, while crafting reward functions for learning-based methods introduces its own set of complexities. This study investigates the application of Reinforcement Learning from Human Feedback (RLHF) to emulate human-like lane-changing decisions in AVs. An initial RL policy is pre-trained to ensure safe lane changes. Subsequently, this policy is employed to gather data, which is then annotated by humans to train a reward model that discerns lane changes aligning with human preferences. This human-informed reward model supersedes the original, guiding the refinement of the policy to reflect human-like preferences. The effectiveness of RLHF in producing human-like lane changes is demonstrated through the development and evaluation of conservative and aggressive lane-changing models within obstacle-rich environments and mixed autonomy traffic scenarios. The experimental outcomes underscore the potential of RLHF to diversify lane-changing behaviors in AVs, suggesting its viability for enhancing the integration of AVs into the fabric of human-driven traffic.

cs.CE↗
arXiv2024

Local excitation of kagome spin ice magnetism in HoAgGe seen by scanning tunneling microscopy

The kagome spin ice can host frustrated magnetic excitations by flipping its local spin. Under an inelastic tunneling condition, the tip in a scanning tunneling microscope can flip the local spin, and we apply this technique to kagome metal HoAgGe with a long-range ordered spin ice ground state. Away from defects, we discover a pair of pronounced dips in the local tunneling spectrum at symmetrical bias voltages with negative intensity values, serving as a striking inelastic tunneling signal. This signal disappears above the spin ice formation temperature and has a dependence on the magnetic fields, demonstrating its intimate relation with the spin ice magnetism. We provide a two-level spin-flip model to explain the tunneling dips considering the spin ice magnetism under spin-orbit coupling. Our results uncover a local emergent excitation of spin ice magnetism in a kagome metal, suggesting that local electrical field induced spin flip climbs over a barrier caused by spin-orbital locking.

cond-mat.str-el↗
arXiv2024

Orbital doublet driven even-spin Chern insulators

Quantum spin Hall insulators hosting edge spin currents hold great potential for low-power spintronic devices. In this paper, we present a universal approach to achieve a high and near-quantized spin Hall conductance plateau within a sizable bulk gap. Using a nonmagnetic four-band model Hamiltonian, we demonstrate that an even-spin Chern (ESC) insulator can be accessed by tuning the sign of spin-orbit coupling (SOC) within a crystal symmetry-enforced orbital doublet. With the assistance of a high spin Chern number of $C_{S}=-2$ and spin $U$(1) quasisymmetry, this orbital-doublet-driven ESC phase is endowed with the near-double-quantized spin Hall conductance. We identify 12 crystallographic point groups supporting such a sign-tunable SOC. Furthermore, we apply our theory to realistic examples, and show the phase transition from a trivial insulator governed by positive SOC in the RuI$_{3}$ monolayer to an ESC insulator dominated by negative SOC in the RuBr$_{3}$ monolayer. This orbital-doublet-driven ESC insulator, RuBr$_{3}$, showcases nontrivial characteristics including helical edge states, near-double-quantized spin Hall conductance, and robust corner states. Our work provides different pathways in the pursuit of the long-sought quantum spin Hall insulators.

cond-mat.mtrl-sci↗
arXiv2024

Understanding the Ising zigzag antiferromagnetism of FePS3 and FePSe3 monolayers

This study investigates the spin-orbital states of FePS3 and FePSe3 monolayers and the origin of their Ising zigzag AFM, using DFT, crystal field level diagrams, superexchange analyses, and parallel tempering MC simulations. Our calculations show that under the trigonal elongation of the FeS6 (FeSe6) octahedra, the $e_g^π$ doublet of the Fe 3d crystal field levels lies lower than the $a_{1g}$ singlet by about 108 meV (123 meV), which is much larger than the strength of Fe 3d SOC. Then, the half-filled minority-spin $e_g^π$ doublet of the high-spin Fe$^{2+}$ ions ($d^{5\uparrow,1\downarrow}$) splits by the SOC into the lower $L_{z+}$ and higher $L_{z-}$ states. The spin-orbital ground state $d^{5\uparrow}$$L_{z+}^{1\downarrow}$ formally with $S_z$ = 2 and $L_z$ = 1 gives the large z-axis spin/orbital moments of 3.51/0.76 $μ_{B}$ (3.41/0.67 $μ_{B}$) for FePS$_3$ (FePSe$_3$) monolayer, and both the moments are reduced by the strong (stronger) Fe 3d hybridizations with S 3p (Se 4p) states. As a result, FePS3 (FePSe3) monolayer has a huge perpendicular single-ion anisotropy energy of 19.4 meV (14.9 meV), giving an Ising-type magnetism. Moreover, via the maximally localized Wannier functions, we find that the first nearest neighboring (1NN) Fe-Fe pair has large hopping parameters in between some specific orbitals, and so does the 3NN Fe-Fe pair. In contrast, the 2NN Fe-Fe pair has much smaller hopping parameters and the 4NN Fe-Fe pair has negligibly small ones. Then, a combination of those hopping parameters and the superexchange picture can readily explain the computed strong 1NN ferromagnetic coupling and the strong 3NN antiferromagnetic one but the relatively much smaller 2NN antiferromagnetic coupling. Furthermore, our PTMC simulations give TN of 119 K for FePS3 monolayer and also predict for FePSe3 monolayer the same magnetic structure with a close or even higher TN.

cond-mat.mtrl-sci↗
arXiv2024

Quantification of Entanglement and Coherence with Purity Detection

Entanglement and coherence are fundamental properties of quantum systems, promising to power near future quantum technologies, such as quantum computation, quantum communication and quantum metrology. Yet, their quantification, rather than mere detection, generally requires reconstructing the spectrum of quantum states, i.e., experimentally challenging measurement sets that increase exponentially with the system size. Here, we demonstrate quantitative bounds to operationally useful entanglement and coherence that are universally valid, analytically computable, and experimentally friendly. Specifically, our main theoretical results are lower and upper bounds to the coherent information and the relative entropy of coherence in terms of local and global purities of quantum states. To validate our proposal, we experimentally implement two purity detection methods in an optical system: shadow estimation with random measurements and collective measurements on pairs of state copies. The experiment shows that both the coherent information and the relative entropy of coherence of pure and mixed unknown quantum states can be bounded by purity functions. Our research offers an efficient means of verifying large-scale quantum information processing.

quant-ph↗
arXiv2024

Experimental virtual distillation of entanglement and coherence

Noise is in general inevitable and detrimental to practical and useful quantum communication and computation. Under the resource theory framework, resource distillation serves as a generic tool to overcome the effect of noise. Yet, conventional resource distillation protocols generally require operations on multi-copies of resource states, and strong limitations exist that restrict their practical utilities. Recently, by relaxing the setting of resource distillation to only approximating the measurement statistics instead of the quantum state, a resource-frugal protocol, virtual resource distillation, is proposed, which allows more effective distillation of noisy resources. Here, we report its experimental implementation on a photonic quantum system for the distillation of quantum coherence (up to dimension 4) and bipartite entanglement. We show the virtual distillation of the maximal superposed state of dimension four from the state of dimension two, an impossible task in conventional coherence distillation. Furthermore, we demonstrate the virtual distillation of entanglement with operations acting only on a single copy of the noisy EPR pair and showcase the quantum teleportation task using the virtually distilled EPR pair with a significantly improved fidelity of the teleported state. These results illustrate the feasibility of the virtual resource distillation method and pave the way for accurate manipulation of quantum resources with noisy quantum hardware.

quant-ph↗
arXiv2024

Infer Induced Sentiment of Comment Response to Video: A New Task, Dataset and Baseline

Existing video multi-modal sentiment analysis mainly focuses on the sentiment expression of people within the video, yet often neglects the induced sentiment of viewers while watching the videos. Induced sentiment of viewers is essential for inferring the public response to videos, has broad application in analyzing public societal sentiment, effectiveness of advertising and other areas. The micro videos and the related comments provide a rich application scenario for viewers induced sentiment analysis. In light of this, we introduces a novel research task, Multi-modal Sentiment Analysis for Comment Response of Video Induced(MSA-CRVI), aims to inferring opinions and emotions according to the comments response to micro video. Meanwhile, we manually annotate a dataset named Comment Sentiment toward to Micro Video (CSMV) to support this research. It is the largest video multi-modal sentiment dataset in terms of scale and video duration to our knowledge, containing 107,267 comments and 8,210 micro videos with a video duration of 68.83 hours. To infer the induced sentiment of comment should leverage the video content, so we propose the Video Content-aware Comment Sentiment Analysis (VC-CSA) method as baseline to address the challenges inherent in this new task. Extensive experiments demonstrate that our method is showing significant improvements over other established baselines.

cs.CV↗
arXiv2024

Experimental Hybrid Shadow Tomography and Distillation

Characterization of quantum states is a fundamental requirement in quantum science and technology. As a promising framework, shadow tomography shows significant efficiency in estimating linear functions, however, for the challenging nonlinear ones, it requires measurements at an exponential cost. Here, we implement an advanced shadow protocol, so-called hybrid shadow~(HS) tomography, to reduce the measurement cost in the estimation of nonlinear functions in an optical system. We design and realize a deterministic quantum Fredkin gate with single photon, achieving high process fidelity of $0.935\pm0.001$. Utilizing this novel Fredkin gate, we demonstrate HS in the estimations, like the higher-order moments up to 4, and reveal that the sample complexity of HS is significantly reduced compared with the original shadow protocol. Furthermore, we utilize these higher-degree functions to implement virtual distillation, which effectively extracts a high-purity quantum state from two noisy copies. The virtual distillation is also verified in a proof-of-principle demonstration of quantum metrology, further enhancing the accuracy of parameter estimation. Our results suggest that HS is efficient in state characterization and promising for quantum technologies.

quant-ph↗
arXiv2024

Event-Triggered Robust Cooperative Output Regulation for a Class of Linear Multi-Agent Systems with an Unknown Exosystem

This paper investigates the robust cooperative output regulation problem for a class of heterogeneous uncertain linear multi-agent systems with an unknown exosystem via event-triggered control (ETC). By utilizing the internal model approach and the adaptive control technique, a distributed adaptive internal model is constructed for each agent. Then, based on this internal model, a fully distributed ETC strategy composed of a distributed event-triggered adaptive output feedback control law and a distributed dynamic event-triggering mechanism is proposed, in which each agent updates its control input at its own triggering time instants. It is shown that under the proposed ETC strategy, the robust cooperative output regulation problem can be solved without requiring either the global information associated with the communication topology or the bounds of the uncertain or unknown parameters in each agent and the exosystem. A numerical example is provided to illustrate the effectiveness of the proposed control strategy.

eess.SY↗
arXiv2024

Bring Your Own Character: A Holistic Solution for Automatic Facial Animation Generation of Customized Characters

Animating virtual characters has always been a fundamental research problem in virtual reality (VR). Facial animations play a crucial role as they effectively convey emotions and attitudes of virtual humans. However, creating such facial animations can be challenging, as current methods often involve utilization of expensive motion capture devices or significant investments of time and effort from human animators in tuning animation parameters. In this paper, we propose a holistic solution to automatically animate virtual human faces. In our solution, a deep learning model was first trained to retarget the facial expression from input face images to virtual human faces by estimating the blendshape coefficients. This method offers the flexibility of generating animations with characters of different appearances and blendshape topologies. Second, a practical toolkit was developed using Unity 3D, making it compatible with the most popular VR applications. The toolkit accepts both image and video as input to animate the target virtual human faces and enables users to manipulate the animation results. Furthermore, inspired by the spirit of Human-in-the-loop (HITL), we leveraged user feedback to further improve the performance of the model and toolkit, thereby increasing the customization properties to suit user preferences. The whole solution, for which we will make the code public, has the potential to accelerate the generation of facial animations for use in VR applications.

cs.HC↗
arXiv2024

Superexchange interactions and magnetic anisotropy in MnPSe$_3$ monolayer

Two-dimensional van der Waals magnetic materials are of great current interest for their promising applications in spintronics. In this work, using density functional theory calculations in combination with the maximally localized Wannier functions method and the magnetic anisotropy analyses, we study the electronic and magnetic properties of MnPSe$_3$ monolayer. Our results show that it is a charge transfer antiferromagnetic (AF) insulator. For this Mn$^{2+}$ $3d^5$ system, although it seems straightforward to explain the AF ground state using the direct exchange, we find that the near 90$^\circ$ Mn-Se-Mn charge transfer type superexchange plays a dominant role in stabilizing the AF ground state. Moreover, our results indicate that although the shape anisotropy favors an out-of-plane spin orientation, the spin-orbit coupling (SOC) leads to the experimentally observed in-plane spin orientation. We prove that the actual dominant contribution to the magnetic anisotropy comes from the second-order perturbation of the SOC, by analyzing its distribution over the reciprocal space. Using the AF exchange and anisotropy parameters obtained from our calculations, our Monte Carlo simulations give the Néel temperature $T_{\rm N}=47$ K for MnPSe$_3$ monolayer, which agrees with the experimental 40 K. Furthermore, our calculations show that under a uniaxial tensile (compressive) strain, Néel vector would be parallel (perpendicular) to the strain direction, which well reproduces the recent experiments. We also predict that $T_{\rm N}$ would be increased by a compressive strain.

cond-mat.mtrl-sci↗
arXiv2024

Improvements to the Stochastic Series Expansion method for the $JQ_2$ model with a magnetic field

The Stochastic Series Expansion (SSE) quantum Monte Carlo method with directed loops is very efficient for spin and boson systems. The Heisenberg mode l and its generalizations, such as the $JQ_2$ model, are extensively simulated via this method. When introducing magnetic field in these models, the SSE method always combines the field with the diagonal part of the Heisenberg interactions (${S_i}^z{S_j}^z$) and take them as the new diagonal operators. In general, this treatment is reasonable. However, when studying Hamiltonians which have other interactions or even don't contain the Heisenberg interactio ns, this general treatment will not be efficient or even not work. We suggest that when doing directed-loop simulations, the magnetic field can be put in to other interactions. This new treatment, in some cases, improves the simulation efficiency. Using the $JQ_2$ model with magnetic field as an example, w e here demonstrate this new SSE method. Such new treatment significantly improves the efficiency when the $Q_2$ interactions are large. The autocorrelati ons are reduced a lot compared to the previous approach. In addition, we argue that we can divide the magnetic field into two parts and combine them with both the $J$ and $Q$ operators respectively. This treatment also improves the simulation efficiency. The underlying mechanism is that these two new SSE methods can utilize the main part or even all part of operators in operator products to do the directe d-loop updates. Such idea can also be applied to other models with magnetic field and it will speed up the simulations.

cond-mat.str-el↗
arXiv2024

Diagnosing $SO(5)$ Symmetry and First-Order Transition in the $J-Q_3$ Model via Entanglement Entropy

We study the scaling behavior of the Rényi entanglement entropy with smooth boundaries at the phase transition point of the two-dimensional $J-Q_3$ model. Using the recently developed scaling formula [Deng {\it et al.}, Phys. Rev. B {\textbf{108}, 125144 (2023)}], we find a subleading logarithmic term with a coefficient showing that the number of Goldstone modes is four, indicating the existence of the spontaneous symmetry breaking from an emergent $SO(5)$ to $O(4)$ in the thermodynamic limit, but restored in a finite size. This result shows that the believed deconfined quantum critical point of the $J-Q_{3}$ model is a weak first-order transition point. Our work provides a new way to distinguish a state with spontaneously broken continuous symmetry from a critical state. The method is particularly useful in identifying weak first-order phase transitions, which are hard to determine using conventional methods.

cond-mat.str-el↗
arXiv2024

FeS2 monolayer: a high valence and high-$T_{\rm C}$ Ising ferromagnet

Two-dimensional (2D) magnetic materials are of current great interest for their promising applications in spintronics. Strong magnetic coupling and anisotropy are both highly desirable for the achievement of a high temperature magnetic order. Here we propose the unusual high valent FeS$_2$ hexagonal monolayer as such a candidate for a strong Ising 2D ferromagnet (FM), by spin-orbital state analyses, first-principles calculations, and the renormalized spin-wave theory (RSWT). We find that very importantly, the high valent Fe$^{4+}$ ion is in the low-spin state ($t_{2g}^{4}$, $S$=1) with degenerate $t_{2g}$ orbitals rather than the high-spin state ($t_{2g}^{3}e_g^{1}$, $S$=2). It is the low-spin state that allows to carry a large perpendicular orbital moment and then produces a huge single ion anisotropy (SIA) of 25 meV/Fe. Moreover, the negative charge transfer character associated with the unusual high valence, strong Fe $3d$-S $3p$ hybridization, wide bands, and a small band gap all help to establish a strong superexchange. Indeed, our first-principles calculations confirm the strong FM superexchange and the huge perpendicular SIA, both of which are further enhanced by a compressive strain. Then, our RSWT calculations predict that the FM $T_{\rm C}$ is 261 K for the pristine FeS$_2$ monolayer and could be increased to 409 K under the compressive --5\% strain. The high $T_{\rm C}$ is also reproduced by our Monte Carlo (MC) simulations. Therefore, it is worth exploring the high-$T_{\rm C}$ Ising FMs in the high valent 2D magnetic materials with degenerate orbitals.

cond-mat.mtrl-sci↗