arXiv subjects

Haoran Liu

Publications and source records attributed to Haoran Liu.

At least 73 records · Page 4Linked to original sources

arXiv2025

MKG-Rank: Enhancing Large Language Models with Knowledge Graph for Multilingual Medical Question Answering

Large Language Models (LLMs) have shown remarkable progress in medical question answering (QA), yet their effectiveness remains predominantly limited to English due to imbalanced multilingual training data and scarce medical resources for low-resource languages. To address this critical language gap in medical QA, we propose Multilingual Knowledge Graph-based Retrieval Ranking (MKG-Rank), a knowledge graph-enhanced framework that enables English-centric LLMs to perform multilingual medical QA. Through a word-level translation mechanism, our framework efficiently integrates comprehensive English-centric medical knowledge graphs into LLM reasoning at a low cost, mitigating cross-lingual semantic distortion and achieving precise medical QA across language barriers. To enhance efficiency, we introduce caching and multi-angle ranking strategies to optimize the retrieval process, significantly reducing response times and prioritizing relevant medical knowledge. Extensive evaluations on multilingual medical QA benchmarks across Chinese, Japanese, Korean, and Swahili demonstrate that MKG-Rank consistently outperforms zero-shot LLMs, achieving maximum 35.03% increase in accuracy, while maintaining an average retrieval time of only 0.0009 seconds.

cs.CL↗

arXiv2025

Towards Robust Probabilistic Modeling on SO(3) via Rotation Laplace Distribution

Estimating the 3DoF rotation from a single RGB image is an important yet challenging problem. As a popular approach, probabilistic rotation modeling additionally carries prediction uncertainty information, compared to single-prediction rotation regression. For modeling probabilistic distribution over SO(3), it is natural to use Gaussian-like Bingham distribution and matrix Fisher, however they are shown to be sensitive to outlier predictions, e.g. $180^\circ$ error and thus are unlikely to converge with optimal performance. In this paper, we draw inspiration from multivariate Laplace distribution and propose a novel rotation Laplace distribution on SO(3). Our rotation Laplace distribution is robust to the disturbance of outliers and enforces much gradient to the low-error region that it can improve. In addition, we show that our method also exhibits robustness to small noises and thus tolerates imperfect annotations. With this benefit, we demonstrate its advantages in semi-supervised rotation regression, where the pseudo labels are noisy. To further capture the multi-modal rotation solution space for symmetric objects, we extend our distribution to rotation Laplace mixture model and demonstrate its effectiveness. Our extensive experiments show that our proposed distribution and the mixture model achieve state-of-the-art performance in all the rotation regression experiments over both probabilistic and non-probabilistic baselines.

cs.CV↗

arXiv2025

Identification of metastable lattice distortion free charge density wave at photoinduced interface via TRARPES

The interplay between different degrees of freedom governs the emergence of correlated electronic states in quantum materials, with charge density waves (CDW) often coexisting with other exotic phases. Under thermal equilibrium, traditional CDW states are consequentially accompanied by structural phase transitions. In contrast, ultrafast photoexcitation allows access to exotic states where a single degree of freedom dominates in the time domain, enabling the study of underlying physics without interference. Here, we report the realization of a long-lived metastable CDW state without lattice distortion at the photoinduced interfaces in GdTe3 using time- and angle-resolved photoemission spectroscopy. After optical excitation above the CDW melting threshold, we identified emerged metastable interfaces through inverting the CDW-coupled lattice distortions, with lifetimes on the order of 10 picoseconds. These photoinduced interfaces represent a novel CDW state lacking the usual amplitude mode and lattice distortions, allowing quantification of the dominant role of electronic instabilities in CDW order. This work provides a new approach to disentangling electronic instabilities from electron-phonon coupling using a nonequilibrium method.

cond-mat.str-el↗

arXiv2025

Uni-NaVid: A Video-based Vision-Language-Action Model for Unifying Embodied Navigation Tasks

A practical navigation agent must be capable of handling a wide range of interaction demands, such as following instructions, searching objects, answering questions, tracking people, and more. Existing models for embodied navigation fall short of serving as practical generalists in the real world, as they are often constrained by specific task configurations or pre-defined maps with discretized waypoints. In this work, we present Uni-NaVid, the first video-based vision-language-action (VLA) model designed to unify diverse embodied navigation tasks and enable seamless navigation for mixed long-horizon tasks in unseen real-world environments. Uni-NaVid achieves this by harmonizing the input and output data configurations for all commonly used embodied navigation tasks and thereby integrating all tasks in one model. For training Uni-NaVid, we collect 3.6 million navigation data samples in total from four essential navigation sub-tasks and foster synergy in learning across them. Extensive experiments on comprehensive navigation benchmarks clearly demonstrate the advantages of unification modeling in Uni-NaVid and show it achieves state-of-the-art performance. Additionally, real-world experiments confirm the model's effectiveness and efficiency, shedding light on its strong generalizability.

cs.RO↗

arXiv2025

Coupling Dichroism in Strong-Coupled Chiral Molecule-Plasmon Nanoparticle System

The interaction between intense light-matter not only promotes emerging applications in quantum and nonlinear optics but also facilitates changes in material properties. Plasmons can significantly enhance not only molecular chirality but also the coupling strength. In this study, we investigate the coupling dichroism in a strongly coupled chiral molecule-plasmonic nanoparticle system using RT-TDDFT. By simulating the interaction between L/D- Phenylglycinol molecules and chiral aluminum clusters (Na-doped Al197Na4), we examine the effects of molecular chirality, cluster chirality, and the coupled effect in the system. Our results demonstrate that the achiral/chiral clusters induce significant spectral shifts and enhance molecular CD signals due to strong plasmon-molecule coupling. The electric-field distribution and transition contribution maps (TCMs) reveal the formation of bonding and antibonding polaritonic modes, modulated by molecular proximity to the cluster. Both of the coupling factor and decay rate of the coupled system will be modulated by the chirality of the molecules and the cluster. Furthermore, we find that increasing the number of coupled molecules leads to a substantial increase in the intensity of lower polaritonic modes, highlighting the collective behavior in multi-molecule systems due to the modal crosstalk or resonance between cluster chirality and molecular chirality. These findings provide valuable insights into the fundamental mechanisms governing plasmon-enhanced chirality at the atomic scale, which have implications for the design of highly sensitive chiral sensors and optoelectronic devices.

physics.optics↗

arXiv2025

Influence of conjugated structure for tunable molecular plasmons in peropyrene and its derivatives

Advances in research have sparked an increasing curiosity in understanding the plasmonic excitation properties of molecular-scale systems. Polycyclic aromatic hydrocarbons, as the fundamental building blocks of graphene, have been documented to possess plasmonic properties through experimental observations, making them prime candidates for investigation. By doping different elements, the conjugated structure of the molecule can be altered. In this study, the plasmonic excitation properties influenced by conjugated structures in peropyrene and its derivatives are investigated through first-principles calculations that combine the plasmonicity index, generalized plasmonicity index and transition contribution maps. For molecular plasmonic excitation, the conjugated structure can influence the oscillation modes of valence electrons, which is pivotal in yielding distinct field enhancement characteristics. Furthermore, charge doping can lead to a certain degree of alteration in the conjugated structures, and the doping of elements will result in varying degrees of such alteration, thereby initiating different trends in the evolution of plasmonic resonance. This further enhances the tunability of molecular plasmonic resonance. The results provide novel insights into the development and utilization of molecular plasmonic devices in practical applications.

cond-mat.mes-hall↗

arXiv2025

Issues with Neural Tangent Kernel Approach to Neural Networks

Neural tangent kernels (NTKs) have been proposed to study the behavior of trained neural networks from the perspective of Gaussian processes. An important result in this body of work is the theorem of equivalence between a trained neural network and kernel regression with the corresponding NTK. This theorem allows for an interpretation of neural networks as special cases of kernel regression. However, does this theorem of equivalence hold in practice? In this paper, we revisit the derivation of the NTK rigorously and conduct numerical experiments to evaluate this equivalence theorem. We observe that adding a layer to a neural network and the corresponding updated NTK do not yield matching changes in the predictor error. Furthermore, we observe that kernel regression with a Gaussian process kernel in the literature that does not account for neural network training produces prediction errors very close to that of kernel regression with NTKs. These observations suggest the equivalence theorem does not hold well in practice and puts into question whether neural tangent kernels adequately address the training process of neural networks.

stat.ML↗

arXiv2024

Learning Disentangled Equivariant Representation for Explicitly Controllable 3D Molecule Generation

We consider the conditional generation of 3D drug-like molecules with \textit{explicit control} over molecular properties such as drug-like properties (e.g., Quantitative Estimate of Druglikeness or Synthetic Accessibility score) and effectively binding to specific protein sites. To tackle this problem, we propose an E(3)-equivariant Wasserstein autoencoder and factorize the latent space of our generative model into two disentangled aspects: molecular properties and the remaining structural context of 3D molecules. Our model ensures explicit control over these molecular attributes while maintaining equivariance of coordinate representation and invariance of data likelihood. Furthermore, we introduce a novel alignment-based coordinate loss to adapt equivariant networks for auto-regressive de-novo 3D molecule generation from scratch. Extensive experiments validate our model's effectiveness on property-guided and context-guided molecule generation, both for de-novo 3D molecule design and structure-based drug discovery against protein targets.

cs.LG↗

arXiv2024

Vertical Emission of Blue Light from a Symmetry Breaking Plasmonic Nanocavity-Emitter System Supporting Bound States in the Continuum

The concept of photonic bound states in the continuum (BICs), introduced in structured metallic surface cavities, provides a crucial mechanism for designing plasmonic open-resonant cavities with high quality (high-Q) factors, making significant advances in plasmonic nanophotonics. However, the two major bottlenecks for plasmonic nanocavities: enhancing emission and big beam divergence for quantum emitters, due to the strong intrinsic Ohmic losses of metals. Here, we propose and realize a σh symmetry-breaking plasmonic honeycomb nanocavities (PHC) that support quasi-BIC resonance modes with high-Q factors. Our anodic oxidation-engineered strategy breaks out-of-plane symmetry while preserving in-plane symmetry, enabling the PHC to exhibit collective plasmonic lattice resonances (PLR) couplings and achieve Q-factors exceeding 106. Experimentally, we couple perovskite quantum dots (PQDs) to the PHC, demonstrating effective tuning of their emission properties and beam quality in the blue spectral region, achieving a 32-fold emission enhancement by suppress Ohmic loss and the life time of quantum emitters, simultaneously realize vertical emission in the 2.556 - 2.638 eV region, with a far-field hexagonal beam shape and a full width at half maximum of 12.6 degree under optimal coupling conditions. Furthermore, we demonstrate topological band inversion characterized by Zak phase transitions by continuously tuning the system parameters, confirming that the PHC supports topologically non-trivial q-BIC due to PLR coupling. The PHC presents itself as a promising next-generation, high-brightness nanoscale light source matrix, which can be directly scaled up to cover a wide wavelength range from UV to IR.

physics.optics↗

arXiv2024

Endoscopic Fourier-transform infrared spectroscopy through a fiber microprobe

Fourier-transform infrared spectroscopy (FTIR) is a powerful analytical method for not only the chemical identification of solid, liquid, and gas species, but also the quantification of their concentration. However, the chemical quantification capability of FTIR is significantly hindered when the analyte is surrounded by a strong IR absorbing medium, such as liquid solutions. To overcome this limit, here we develop an IR fiber microprobe that can be inserted into liquid medium, and obtain full FTIR spectra at points of interest. To benchmark this endoscopic FTIR method, we insert the microprobe into bulk water covering a ZnSe substrate and measure the IR transmittance of water as a function of the probe-substrate distance. The obtained vibrational modes, overall transmittance vs z profiles, quantitative absorption coefficients, and micro z-section IR transmittance spectra are all consistent with the standard IR absorption properties of water. The results pave the way for endoscopic chemical profiling inside bulk liquid solutions, promising for applications in many biological, chemical, and electrochemical systems.

physics.ins-det↗

arXiv2024

DexGraspNet 2.0: Learning Generative Dexterous Grasping in Large-scale Synthetic Cluttered Scenes

Grasping in cluttered scenes remains highly challenging for dexterous hands due to the scarcity of data. To address this problem, we present a large-scale synthetic benchmark, encompassing 1319 objects, 8270 scenes, and 427 million grasps. Beyond benchmarking, we also propose a novel two-stage grasping method that learns efficiently from data by using a diffusion model that conditions on local geometry. Our proposed generative method outperforms all baselines in simulation experiments. Furthermore, with the aid of test-time-depth restoration, our method demonstrates zero-shot sim-to-real transfer, attaining 90.7% real-world dexterous grasping success rate in cluttered scenes.

cs.RO↗

arXiv2024

Pulse shape discrimination based on the Tempotron: a powerful classifier on GPU

This study utilized the Tempotron, a robust classifier based on a third-generation neural network model, for pulse shape discrimination. By eliminating the need for manual feature extraction, the Tempotron model can process pulse signals directly, generating discrimination results based on prior knowledge. The study performed experiments using GPU acceleration, resulting in over 500 times faster compared to the CPU-based model, and investigated the impact of noise augmentation on the Tempotron performance. Experimental results substantiated that Tempotron serves as a formidable classifier, adept at accomplishing high discrimination accuracy on both AmBe and time-of-flight PuBe datasets. Furthermore, analyzing the neural activity of Tempotron during training shed light on its learning characteristics and aided in selecting its hyperparameters. Moreover, the study addressed the constraints and potential avenues for future development in utilizing the Tempotron for pulse shape discrimination. The dataset used in this study and the GPU-based Tempotron are publicly available on GitHub at https://github.com/HaoranLiu507/TempotronGPU.

eess.SP↗

arXiv2024

KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques

Large language models (LLMs) have demonstrated impressive generative capabilities with the potential to innovate in medicine. However, the application of LLMs in real clinical settings remains challenging due to the lack of factual consistency in the generated content. In this work, we develop an augmented LLM framework, KG-Rank, which leverages a medical knowledge graph (KG) along with ranking and re-ranking techniques, to improve the factuality of long-form question answering (QA) in the medical domain. Specifically, when receiving a question, KG-Rank automatically identifies medical entities within the question and retrieves the related triples from the medical KG to gather factual information. Subsequently, KG-Rank innovatively applies multiple ranking techniques to refine the ordering of these triples, providing more relevant and precise information for LLM inference. To the best of our knowledge, KG-Rank is the first application of KG combined with ranking models in medical QA specifically for generating long answers. Evaluation on four selected medical QA datasets demonstrates that KG-Rank achieves an improvement of over 18% in ROUGE-L score. Additionally, we extend KG-Rank to open domains, including law, business, music, and history, where it realizes a 14% improvement in ROUGE-L score, indicating the effectiveness and great potential of KG-Rank.

cs.CL↗

arXiv2024

Dataset for neutron and gamma-ray pulse shape discrimination

The publicly accessible dataset includes neutron and gamma-ray pulse signals for conducting pulse shape discrimination experiments. Several traditional and recently proposed pulse shape discrimination algorithms are utilized to evaluate the performance of pulse shape discrimination under raw pulse signals and noise-enhanced datasets. These algorithms comprise zero-crossing (ZC), charge comparison (CC), falling edge percentage slope (FEPS), frequency gradient analysis (FGA), pulse-coupled neural network (PCNN), ladder gradient (LG), and het-erogeneous quasi-continuous spiking cortical model (HQC-SCM). In addition to the pulse signals, this dataset includes the source code for all the aforementioned pulse shape discrimination methods. Moreover, the dataset provides the source code for schematic pulse shape discrimination performance evaluation and anti-noise performance evaluation. This feature enables researchers to evaluate the performance of these methods using standard procedures and assess their anti-noise ability under various noise conditions. In conclusion, this dataset offers a comprehensive set of resources for conducting pulse shape discrimination experiments and evaluating the performance of various pulse shape discrimination methods under different noise scenarios.

eess.SP↗

arXiv2024

Random-coupled Neural Network

Improving the efficiency of current neural networks and modeling them in biological neural systems have become popular research directions in recent years. Pulse-coupled neural network (PCNN) is a well applicated model for imitating the computation characteristics of the human brain in computer vision and neural network fields. However, differences between the PCNN and biological neural systems remain: limited neural connection, high computational cost, and lack of stochastic property. In this study, random-coupled neural network (RCNN) is proposed. It overcomes these difficulties in PCNN's neuromorphic computing via a random inactivation process. This process randomly closes some neural connections in the RCNN model, realized by the random inactivation weight matrix of link input. This releases the computational burden of PCNN, making it affordable to achieve vast neural connections. Furthermore, the image and video processing mechanisms of RCNN are researched. It encodes constant stimuli as periodic spike trains and periodic stimuli as chaotic spike trains, the same as biological neural information encoding characteristics. Finally, the RCNN is applicated to image segmentation, fusion, and pulse shape discrimination subtasks. It is demonstrated to be robust, efficient, and highly anti-noised, with outstanding performance in all applications mentioned above.

cs.CV↗

arXiv2024

Optical manipulation of the topological phase in ZrTe5 revealed by time- and angle-resolved photoemission

High-resolution time- and angle-resolved photoemission measurements were conducted on the topological insulator ZrTe5. With strong femtosecond photoexcitation, a possible ultrafast phase transition from a weak to a strong topological insulating phase was experimentally realized by recovering the energy gap inversion in a time scale that was shorter than 0.15 ps. This photoinduced transient strong topological phase can last longer than 2 ps at the highest excitation fluence studied, and it cannot be attributed to the photoinduced heating of electrons or modification of the conduction band filling. Additionally, the measured unoccupied electronic states are consistent with the first-principles calculation based on experimental crystal lattice constants, which favor a strong topological insulating phase. These findings provide new insights into the longstanding controversy about the strong and weak topological properties in ZrTe5, and they suggest that many-body effects including electron-electron interactions must be taken into account to understand the equilibrium weak topological insulating phase in ZrTe5.

cond-mat.mtrl-sci↗

arXiv2023

Everything Perturbed All at Once: Enabling Differentiable Graph Attacks

As powerful tools for representation learning on graphs, graph neural networks (GNNs) have played an important role in applications including social networks, recommendation systems, and online web services. However, GNNs have been shown to be vulnerable to adversarial attacks, which can significantly degrade their effectiveness. Recent state-of-the-art approaches in adversarial attacks rely on gradient-based meta-learning to selectively perturb a single edge with the highest attack score until they reach the budget constraint. While effective in identifying vulnerable links, these methods are plagued by high computational costs. By leveraging continuous relaxation and parameterization of the graph structure, we propose a novel attack method called Differentiable Graph Attack (DGA) to efficiently generate effective attacks and meanwhile eliminate the need for costly retraining. Compared to the state-of-the-art, DGA achieves nearly equivalent attack performance with 6 times less training time and 11 times smaller GPU memory footprint on different benchmark datasets. Additionally, we provide extensive experimental analyses of the transferability of the DGA among different graph models, as well as its robustness against widely-used defense mechanisms.

cs.LG↗

arXiv2023

Ultrafast Switching from the Charge Density Wave Phase to a Metastable Metallic State in 1T-TiSe$_2$

The ultrafast electronic structures of the charge density wave material 1T-TiSe$_2$ were investigated by high-resolution time- and angle-resolved photoemission spectroscopy. We found that the quasiparticle populations drove ultrafast electronic phase transitions in 1T-TiSe$_2$ within 100 fs after photoexcitation, and a metastable metallic state, which was significantly different from the equilibrium normal phase, was evidenced far below the charge density wave transition temperature. Detailed time- and pump-fluence-dependent experiments revealed that the photoinduced metastable metallic state was a result of the halted motion of the atoms through the coherent electron-phonon coupling process, and the lifetime of this state was prolonged to picoseconds with the highest pump fluence used in this study. Ultrafast electronic dynamics were well captured by the time-dependent Ginzburg-Landau model. Our work demonstrates a mechanism for realizing novel electronic states by photoinducing coherent motion of atoms in the lattice.

cond-mat.str-el↗