arXiv subjects

Hao Jin

Publications and source records attributed to Hao Jin.

At least 37 records · Page 2Linked to original sources

arXiv2025

Towards Routing and Edge Computing in Satellite-Terrestrial Networks: A Column Generation Approach

Edge computing that enables satellites to process raw data locally is expected to bring further timeliness and flexibility to satellite-terrestrial networks (STNs). In this letter, we propose a three-layer edge computing protocol, where raw data collected by the satellites can be processed locally, or transmitted to other satellites or the ground station via multi-hop routing for further processing. The overall computing capacity of the proposed framework is maximized by determining the offloading strategy and routing formation, subject to channel capacity and hop constraints. Given that the problem scale grows exponentially with the number of satellites and maximum-allowed hops, the column generation approach is employed to obtain the global optimal solution by activating only a subset of variables. Numerical results reveal that the proposed three-layer computing protocol, when tolerating a 5-hop routing latency, achieves a 60% improvement in computation capacity compared to the single-layer local computing configuration.

eess.SY↗

arXiv2025

A novel Cercignani-Lampis boundary model for discrete velocity methods in predicting rarefied and multi-scale flows

To extend the discrete velocity method (DVM) and unified methods to more realistic boundary conditions, a Cercignani-Lampis (CL) boundary with different momentum and thermal energy accommodations is proposed and integrated into the DVM framework. By giving the macroscopic flux from the numerical quadrature of the incident molecular distribution, the reflected macroscopic flux can be obtained for the given accommodation coefficients. Then, an anisotropic Gaussian distribution can be found for the reflected molecules, whose parameters are determined by the calculated reflected macroscopic flux. These macroscopic flux and microscopic Gaussian distribution form a complete physical process for the reflected molecules. Furthermore, the CL boundary is integrated into the unified gas-kinetic scheme (UGKS), making it suitable for the simulation of both monatomic and diatomic gas flows, and it accommodates both the conventional Cartesian velocity space and the recently developed efficient unstructured velocity space. Moreover, this new GSI boundary is suitable for both explicit and implicit schemes, offering better performance for flow prediction. Finally, the performance of the new boundary is validated through a series of numerical tests covering a wide range of Knudsen and Mach numbers.

physics.flu-dyn↗

arXiv2025

Surface-enhanced Raman scattering and density functional theory study of selected-lanthanide-citrate complexes (lanthanide: La, Ce, Pr, Nd, Sm, Eu, and Gd)

In this study, we combined the surface-enhanced Raman scattering (SERS) with density functional theory (DFT) calculations to investigate the SERS spectra of lanthanide (Ln)-citrate complexes (Ln = La, Ce, Pr, Nd, Sm, Eu, and Gd) under 488, 532, and 660 nm laser excitations. Detailed vibrational analysis and peak assignments were performed based on SERS spectra simulated using an optimized DFT setting, in which small-core effective core potentials (ECPs) in the def2-tzvpd basis set were replaced by large-core ECPs. Characteristic SERS peaks appeared at 1065, 1315, and 1485 cm-1 were assigned to the γ(CH2)+v(C-O{\ldots}Ln), vsym(COO-)+γ(CH2), and vasym(COO-)+γ(CH2) vibrational bands, respectively. SERS intensity ratios were obtained by normalizing the peak intensity I near 1065 or 1485 cm-1 to that near 1315 cm-1. I1065/I1315 depended solely on the type of Ln3+ ion and was independent of the excitation wavelength. In contrast, I1485/I1315 increased with decreasing excitation wavelength, indicating additional enhancement by charge-transfer. Additionally, as the number of unpaired 4f electrons increased, Ln3+ in the coordination region attracted oxygen negative charges more strongly, reducing the electric dipole moment of the C-O bond and altering its symmetry.

physics.chem-ph↗

arXiv2025

EVA-MED: An Enhanced Valence-Arousal Multimodal Emotion Dataset for Emotion Recognition

We introduce a novel multimodal emotion recognition dataset that enhances the precision of Valence-Arousal Model while accounting for individual differences. This dataset includes electroencephalography (EEG), electrocardiography (ECG), and pulse interval (PI) from 64 participants. Data collection employed two emotion induction paradigms: video stimuli that targeted different valence levels (positive, neutral, and negative) and the Mannheim Multicomponent Stress Test (MMST), which induced high arousal through cognitive, emotional, and social stressors. To enrich the dataset, participants' personality traits, anxiety, depression, and emotional states were assessed using validated questionnaires. By capturing a broad spectrum of affective responses while accounting for individual differences, this dataset provides a robust resource for precise emotion modeling. The integration of multimodal physiological data with psychological assessments lays a strong foundation for personalized emotion recognition. We anticipate this resource will support the development of more accurate, adaptive, and individualized emotion recognition systems across diverse applications.

cs.HC↗

arXiv2025

From Architectural Sketch to Conceptual Representation: Using Structure-Aware Diffusion Model to Generate Renderings of School Buildings

Generative Artificial Intelligence (AI) has advanced rapidly, enabling the generation of renderings from architectural sketches. This progress has significantly improved the efficiency of communication and conceptual expression during the early stage of architectural design. However, generated images often lack the structural details from architects' sketches. While sketches typically emphasize the overall structure, crucial components such as windows and doors are often represented by simple lines or omitted entirely. For school buildings, it is essential to control architectural components, such as the shape and proportion of windows, as these factors directly influence the accuracy of the generated images in reflecting the architect's design intentions. To address this issue, we propose a structure-aware diffusion model for architectural image generation to refine expressing design intentions through retrieval augmentation. Our framework utilizes architectural components to enhance the generation process, addressing the details that may be lacking in the sketches. These components provide clear spatial and structural details, improving the model's ability to interpret and generate architectural details. The refined sketches, combined with text prompts, are fed into the proposed structure-aware diffusion model to generate detailed and realistic school building images. The experiment results demonstrate the effectiveness of our framework in generating architectural designs.

cs.GR↗

arXiv2025

Quantum Geometric Engineering of Dual Hall Effects in 2D Antiferromagnetic Bilayers via Interlayer Magnetic Coupling

The interplay between quantum geometry and magnetic order offers a novel strategy for designing next-generation nanodevices. Here, we demonstrate that interlayer magnetic coupling in two-dimensional (2D) CoPSe3 bilayers enables precise control over quantum geometric mechanisms, unlocking dual intrinsic Hall effects. Our first-principles calculations reveal that the altermagnetic (AM) phase exhibits a giant anisotropic anomalous Hall effect (AHE) ($σ_{xy}$ is approximately 46 S/cm) driven by Berry curvature localized at generic k-points, while the PT-symmetric antiferromagnetic (AFM) phase hosts an intrinsic second-order nonlinear anomalous Hall effect (NAHE) ($χ_{xyy}$ is approximately 160 $μ$S/V) originating from quantum metric accumulation at high-symmetry k-points. By tuning interlayer magnetic couplings, we achieve reversible switching between these phases, leveraging their distinct band structures and symmetry constraints. The Neel-vector-dependent AHE in the AM phase and the symmetry-protected NAHE in the AFM phase highlight quantum geometry as a versatile tool for manipulating transport properties. Our work establishes 2D antiferromagnets as a promising platform for multifunctional device architectures, bridging linear and nonlinear magnetoelectric responses through tailored quantum geometric engineering.

cond-mat.mes-hall↗

arXiv2025

RS-MoE: A Vision-Language Model with Mixture of Experts for Remote Sensing Image Captioning and Visual Question Answering

Remote Sensing Image Captioning (RSIC) presents unique challenges and plays a critical role in applications. Traditional RSIC methods often struggle to produce rich and diverse descriptions. Recently, with advancements in VLMs, efforts have emerged to integrate these models into the remote sensing domain and to introduce descriptive datasets specifically designed to enhance VLM training. This paper proposes RS-MoE, a first Mixture of Expert based VLM specifically customized for remote sensing domain. Unlike traditional MoE models, the core of RS-MoE is the MoE Block, which incorporates a novel Instruction Router and multiple lightweight Large Language Models (LLMs) as expert models. The Instruction Router is designed to generate specific prompts tailored for each corresponding LLM, guiding them to focus on distinct aspects of the RSIC task. This design not only allows each expert LLM to concentrate on a specific subset of the task, thereby enhancing the specificity and accuracy of the generated captions, but also improves the scalability of the model by facilitating parallel processing of sub-tasks. Additionally, we present a two-stage training strategy for tuning our RS-MoE model to prevent performance degradation due to sparsity. We fine-tuned our model on the RSICap dataset using our proposed training strategy. Experimental results on the RSICap dataset, along with evaluations on other traditional datasets where no additional fine-tuning was applied, demonstrate that our model achieves state-of-the-art performance in generating precise and contextually relevant captions. Notably, our RS-MoE-1B variant achieves performance comparable to 13B VLMs, demonstrating the efficiency of our model design. Moreover, our model demonstrates promising generalization capabilities by consistently achieving state-of-the-art performance on the Remote Sensing Visual Question Answering (RSVQA) task.

cs.CV↗

arXiv2024

Strain tuning of the nonlinear anomalous Hall effect in MoS2 monolayer

Due to the time reversal symmetry, the linear anomalous Hall effect (AHE) usually vanishes in MoS2 monolayer. In contrast, the nonlinear AHE plays an essential role in such system when the uniaxial strain breaks the C3v symmetry and eventually results in the nonzero Berry curvature dipole (BCD). We find that not only the magnitude of the AHE but also the nonlinear Hall angle can be tuned by the strain. Especially the nonlinear Hall angle exhibits a deep relationship which is analogy to the birefraction phenomenon in optics. It actually results from the pseudotensor nature of the BCD moment. Besides the ordinary positive and negative crystals in optics, there are two more birefraction-like cases corresponding to an imaginary refraction index ratio in monolayer MoS2. Our findings shed lights on the strain controlled electronic devices based on the two-dimensional (2D) materials with BCD.

cond-mat.mes-hall↗

arXiv2024

Non-quasiconvex dispersion of composite fermions and the fermionic Haffnian state in the first-excited Landau level

It has long been puzzling that fractional quantum Hall states in the first excited Landau level (1LL) often differ significantly from their counterparts in the lowest Landau level. We show that the dispersion of composite fermions (CFs) is a deterministic factor driving the distinction. We find that CFs with two quantized vortices in the 1LL have a non-quasiconvex dispersion. Consequently, in the filling fraction $7/3$, CFs occupy the second $Λ$-level instead of the first. The corresponding ground state wave function, based on the CF wave function ansatz, is identified to be the fermionic Haffnian wave function rather than the Laughlin wave function. The conclusion is supported by numerical evidence from exact diagonalizations in both disk and spherical geometries. Furthermore, we show that the dispersion becomes quasiconvex in wide quantum wells or for CFs with four quantized vortices in the filling fraction $11/5$, coinciding with observations that the distinction between the Landau levels disappears under these circumstances.

cond-mat.mes-hall↗

arXiv2024

Quantifying the photocurrent fluctuation in quantum materials by shot noise

The DC photocurrent can detect the topology and geometry of quantum materials without inversion symmetry. Herein, we propose that the DC shot noise (DSN), as the fluctuation of photocurrent operator, can also be a diagnostic of quantum materials. Particularly, we develop the quantum theory for DSNs in gapped systems and identify the shift and injection DSNs by dividing the second-order photocurrent operator into off-diagonal and diagonal contributions, respectively. Remarkably, we find that the DSNs can not be forbidden by inversion symmetry, while the constraint from time-reversal symmetry depends on the polarization of light. Furthermore, we show that the DSNs also encode the geometrical information of Bloch electrons, such as the Berry curvature and the quantum metric. Finally, guided by symmetry, we apply our theory to evaluate the DSNs in monolayer GeS and bilayer MoS$_2$ with and without inversion symmetry and find that the DSNs can be larger in centrosymmetric phase.

cond-mat.mtrl-sci↗

arXiv2024

Federated Control in Markov Decision Processes

We study problems of federated control in Markov Decision Processes. To solve an MDP with large state space, multiple learning agents are introduced to collaboratively learn its optimal policy without communication of locally collected experience. In our settings, these agents have limited capabilities, which means they are restricted within different regions of the overall state space during the training process. In face of the difference among restricted regions, we firstly introduce concepts of leakage probabilities to understand how such heterogeneity affects the learning process, and then propose a novel communication protocol that we call Federated-Q protocol (FedQ), which periodically aggregates agents' knowledge of their restricted regions and accordingly modifies their learning problems for further training. In terms of theoretical analysis, we justify the correctness of FedQ as a communication protocol, then give a general result on sample complexity of derived algorithms FedQ-X with the RL oracle , and finally conduct a thorough study on the sample complexity of FedQ-SynQ. Specifically, FedQ-X has been shown to enjoy linear speedup in terms of sample complexity when workload is uniformly distributed among agents. Moreover, we carry out experiments in various environments to justify the efficiency of our methods.

stat.ML↗

arXiv2024

Federated Reinforcement Learning with Constraint Heterogeneity

We study a Federated Reinforcement Learning (FedRL) problem with constraint heterogeneity. In our setting, we aim to solve a reinforcement learning problem with multiple constraints while $N$ training agents are located in $N$ different environments with limited access to the constraint signals and they are expected to collaboratively learn a policy satisfying all constraint signals. Such learning problems are prevalent in scenarios of Large Language Model (LLM) fine-tuning and healthcare applications. To solve the problem, we propose federated primal-dual policy optimization methods based on traditional policy gradient methods. Specifically, we introduce $N$ local Lagrange functions for agents to perform local policy updates, and these agents are then scheduled to periodically communicate on their local policies. Taking natural policy gradient (NPG) and proximal policy optimization (PPO) as policy optimization methods, we mainly focus on two instances of our algorithms, ie, {FedNPG} and {FedPPO}. We show that FedNPG achieves global convergence with an $\tilde{O}(1/\sqrt{T})$ rate, and FedPPO efficiently solves complicated learning tasks with the use of deep neural networks.

cs.LG↗

arXiv2023

Classification of La3+ and Gd3+ rare earth ions using surface-enhanced Raman scattering

In this study, surface-enhanced Raman scattering (SERS) spectra of different rare earth (RE) ion-citrate complexes were investigated for the first time for the qualitative classification of RE3+ ions. With the addition of RE3+ ions to citrate-capped silver nanoparticles in aqueous solutions, the Raman signals of RE-citrate complexes were enhanced, and characteristic peaks appeared near 1065 cm-1 and 1315 cm-1. The I1065/I1315 ratios of La-citrate and Gd-citrate were approximately 1 and 0.5, respectively. Thus, different RE3+ ions were classified based on the ratio of characteristic SERS peaks near 1065 cm-1 and 1315 cm-1. In addition, the effects of RE3+ ions in the RE-citrate complexes were analyzed based on density functional theory (DFT) calculations. Calculation results show that these characteristic peaks are attributed to the coordination of carboxyl and hydroxyl groups of citrates with the RE3+ ions, suggesting that these are spin-related bands of the RE-citrate complexes.

physics.chem-ph↗

arXiv2023

A Semi-automatic Oriental Ink Painting Framework for Robotic Drawing from 3D Models

Creating visually pleasing stylized ink paintings from 3D models is a challenge in robotic manipulation. We propose a semi-automatic framework that can extract expressive strokes from 3D models and draw them in oriental ink painting styles by using a robotic arm. The framework consists of a simulation stage and a robotic drawing stage. In the simulation stage, geometrical contours were automatically extracted from a certain viewpoint and a neural network was employed to create simplified contours. Then, expressive digital strokes were generated after interactive editing according to user's aesthetic understanding. In the robotic drawing stage, an optimization method was presented for drawing smooth and physically consistent strokes to the digital strokes, and two oriental ink painting styles termed as Noutan (shade) and Kasure (scratchiness) were applied to the strokes by robotic control of a brush's translation, dipping and scraping. Unlike existing methods that concentrate on generating paintings from 2D images, our framework has the advantage of rendering stylized ink paintings from 3D models by using a consumer-grade robotic arm. We evaluate the proposed framework by taking 3 standard models and a user-defined model as examples. The results show that our framework is able to draw visually pleasing oriental ink paintings with expressive strokes.

cs.RO↗

arXiv2023

Focusing on Relevant Responses for Multi-modal Rumor Detection

In the absence of an authoritative statement about a rumor, people may expose the truth behind such rumor through their responses on social media. Most rumor detection methods aggregate the information of all the responses and have made great progress. However, due to the different backgrounds of users, the responses have different relevance for discovering th suspicious points hidden in a rumor claim. The methods that focus on all the responding tweets would dilute the effect of the critical ones. Moreover, for a multi-modal rumor claim, the focus of a user may be on several words in the text or an object in the image, so the different modalities should be considered to select the relevant responses and verify the claim. In this paper, we propose a novel multi-modal rumor detection model, termed Focal Reasoning Model (FoRM), to filter out the irrelevant responses and further conduct fine-grained reasoning with the multi-modal claim and corresponding responses. Concretely, there are two main components in our FoRM: the coarse-grained selection and the fine-grained reasoning. The coarse-grained selection component leverages the post-level features of the responses to verify the claim and learns a relevant score of each response. Based on the relevant scores, the most relevant responses are reserved as the critical ones to the further reasoning. In the fine-grained reasoning component, we design a relation attention module to explore the fine-grained relations, i.e., token-to-token and token-to-object relations, between the reserved responses and the multi-modal claim for finding out the valuable clues. Extensive experiments have been conducted on two real-world datasets, and the results demonstrate that our proposed model outperforms all the baselines.

cs.SI↗

arXiv2022

Federated Reinforcement Learning with Environment Heterogeneity

We study a Federated Reinforcement Learning (FedRL) problem in which $n$ agents collaboratively learn a single policy without sharing the trajectories they collected during agent-environment interaction. We stress the constraint of environment heterogeneity, which means $n$ environments corresponding to these $n$ agents have different state transitions. To obtain a value function or a policy function which optimizes the overall performance in all environments, we propose two federated RL algorithms, \texttt{QAvg} and \texttt{PAvg}. We theoretically prove that these algorithms converge to suboptimal solutions, while such suboptimality depends on how heterogeneous these $n$ environments are. Moreover, we propose a heuristic that achieves personalization by embedding the $n$ environments into $n$ vectors. The personalization heuristic not only improves the training but also allows for better generalization to new environments.

cs.LG↗

arXiv2021

Giant dynamical electron-magnon coupling in metal-metal-ferromagnetic insulator heterostructure

Magnon-mediated spin transport across nonmagnetic metal (NM) and ferromagnetic insulator (FI) interface depends critically on electron-magnon coupling. We propose a novel route to enhance electron-magnon coupling dynamically from transport viewpoint. Using non-equilibrium Green's function a theoretical formalism for magnon-mediated spin current is developed. In the language of transport, the effective electron-magnon coupling at NM/FI interface is determined by self-energy of FI lead, which is proportional to density of states (DOS) at NM/FI interface due to nonlinear process of electron-magnon conversion. By modifying interfacial DOS, the spin conductance of 2D and 3D NM/FI systems can be increased by almost three orders of magnitude, setting up a new platform of manipulating dynamical electron-magnon coupling.

cond-mat.mes-hall↗

arXiv2021

Constrained Detecting Arrays: Mathematical Structures for Fault Identification in Combinatorial Interaction Testing

Context: Detecting arrays are mathematical structures aimed at fault identification in combinatorial interaction testing. However, they cannot be directly applied to systems that have constraints among test parameters. Such constraints are prevalent in real-world systems. Objectives: This paper proposes Constrained Detecting Arrays (CDAs), an extension of detecting arrays, which can be used for systems with constraints. Methods: The paper examines the properties and capabilities of CDAs with rigorous arguments. The paper also proposes two algorithms for constructing CDAs: One is aimed at generating minimum CDAs and the other is a heuristic algorithm aimed at fast generation of CDAs. The algorithms are evaluated through experiments using a benchmark dataset. Results: Experimental results show that the first algorithm can generate minimum CDAs if a sufficiently long generation time is allowed, and the second algorithm can generate minimum or near-minimum CDAs in a reasonable time. Conclusion: CDAs enhance detecting arrays to be applied to systems with constraints. The two proposed algorithms have different advantages with respect to the array size and generation time

cs.SE↗