arXiv subjects

Jin Zhao

Publications and source records attributed to Jin Zhao.

At least 55 records · Page 3Linked to original sources

arXiv2024

Neural Network-based High-index Saddle Dynamics Method for Searching Saddle Points and Solution Landscape

The high-index saddle dynamics (HiSD) method is a powerful approach for computing saddle points and solution landscape. However, its practical applicability is constrained by the need for the explicit energy function expression. To overcome this challenge, we propose a neural network-based high-index saddle dynamics (NN-HiSD) method. It utilizes neural network-based surrogate model to approximates the energy function, allowing the use of the HiSD method in the cases where the energy function is either unavailable or computationally expensive. We further enhance the efficiency of the NN-HiSD method by incorporating momentum acceleration techniques, specifically Nesterov's acceleration and the heavy-ball method. We also provide a rigorous convergence analysis of the NN-HiSD method. We conduct numerical experiments on systems with and without explicit energy functions, specifically including the alanine dipeptide model and bacterial ribosomal assembly intermediates for the latter, demonstrating the effectiveness and reliability of the proposed method.

cs.LG↗

arXiv2024

SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework

Traditional federated learning (FL) frameworks rely heavily on terrestrial networks, where coverage limitations and increasing bandwidth congestion significantly hinder model convergence. Fortunately, the advancement of low-Earth orbit (LEO) satellite networks offers promising new communication avenues to augment traditional terrestrial FL. Despite this potential, the limited satellite-ground communication bandwidth and the heterogeneous operating environments of ground devices-including variations in data, bandwidth, and computing power-pose substantial challenges for effective and robust satellite-assisted FL. To address these challenges, we propose SatFed, a resource-efficient satellite-assisted heterogeneous FL framework. SatFed implements freshness-based model prioritization queues to optimize the use of highly constrained satellite-ground bandwidth, ensuring the transmission of the most critical models. Additionally, a multigraph is constructed to capture real-time heterogeneous relationships between devices, including data distribution, terrestrial bandwidth, and computing capability. This multigraph enables SatFed to aggregate satellite-transmitted models into peer guidance, enhancing local training in heterogeneous environments. Extensive experiments with real-world LEO satellite networks demonstrate that SatFed achieves superior performance and robustness compared to state-of-the-art benchmarks.

cs.DC↗

arXiv2024

Resilience-Oriented DG Siting and Sizing Considering Energy Equity Constraint

Extreme weather events can cause widespread power outages and huge economic losses. Low-income customers are more vulnerable to power outages because they live in areas with poorly equipped distribution systems. However, existing approaches to improve grid resilience focus on the overall condition of the system and ignore the outage experiences of low-income customers, which leads to significant energy inequities in resilience. Therefore, this paper explores a new resilience-oriented planning method for distributed generator (DG) siting and sizing, by embedding an additional energy equity constraint (EEC). First, the expected load shedding index (ELSI) is defined as the ratio of the load shedding to the original load, which quantifies the resilience-oriented energy equity. Then, the DG siting and sizing problem is formulated as a two-stage stochastic programming with the EEC. The first stage determines the optimal sites and sizes of DG units under investment constraints and EECs, while the second stage optimizes expected costs of unserved load. A subsidiary variable is introduced to ensure the model's solvability. Finally, numerical studies are performed on the IEEE 33-bus and 123-bus systems to verify the effectiveness of the proposed DG planning model in achieving energy equity. Three observations are presented as future guidelines for resilience-oriented DG planning.

eess.SY↗

arXiv2024

PSNE: Efficient Spectral Sparsification Algorithms for Scaling Network Embedding

Network embedding has numerous practical applications and has received extensive attention in graph learning, which aims at mapping vertices into a low-dimensional and continuous dense vector space by preserving the underlying structural properties of the graph. Many network embedding methods have been proposed, among which factorization of the Personalized PageRank (PPR for short) matrix has been empirically and theoretically well supported recently. However, several fundamental issues cannot be addressed. (1) Existing methods invoke a seminal Local Push subroutine to approximate \textit{a single} row or column of the PPR matrix. Thus, they have to execute $n$ ($n$ is the number of nodes) Local Push subroutines to obtain a provable PPR matrix, resulting in prohibitively high computational costs for large $n$. (2) The PPR matrix has limited power in capturing the structural similarity between vertices, leading to performance degradation. To overcome these dilemmas, we propose PSNE, an efficient spectral s\textbf{P}arsification method for \textbf{S}caling \textbf{N}etwork \textbf{E}mbedding, which can fast obtain the embedding vectors that retain strong structural similarities. Specifically, PSNE first designs a matrix polynomial sparser to accelerate the calculation of the PPR matrix, which has a theoretical guarantee in terms of the Frobenius norm. Subsequently, PSNE proposes a simple but effective multiple-perspective strategy to enhance further the representation power of the obtained approximate PPR matrix. Finally, PSNE applies a randomized singular value decomposition algorithm on the sparse and multiple-perspective PPR matrix to get the target embedding vectors. Experimental evaluation of real-world and synthetic datasets shows that our solutions are indeed more efficient, effective, and scalable compared with ten competitors.

cs.LG↗

arXiv2024

Modeling Heat Conduction with Dual-Dissipative Variables: A Mechanism-Data Fusion Method

Many macroscopic non-Fourier heat conduction models have been developed in the past decades based on Chapman-Enskog, Hermite or other small perturbation expansion methods. These macroscopic models have made great success on capturing non-Fourier thermal behaviors in solid materials, but most of them are limited by small Knudsen numbers and incapable of capturing highly non-equilibrium or ballistic thermal transport. In this paper, we provide a new strategy for constructing macroscopic non-Fourier heat conduction modeling, that is, using data-driven deep learning methods combined with non-equilibrium thermodynamics instead of small perturbation expansion. We present the mechanism-data fusion method, an approach that seamlessly integrates the rigorous framework of Conservation-Dissipation Formalism (CDF) with the flexibility of machine learning to model non-Fourier heat conduction. Leveraging the conservation-dissipation principle with dual-dissipative variables, we derive an interpretable series of partial differential equations, fine-tuned through a training strategy informed by data from the phonon Boltzmann transport equation. Moreover, we also present the inner-step operation to narrow the gap from the discrete form to the continuous system. Through numerical tests, our model demonstrates excellent predictive capabilities across various heat conduction regimes, including diffusive, hydrodynamic and ballistic regimes, and displays its robustness and precision even with discontinuous initial conditions.

physics.comp-ph↗

arXiv2024

PSMC: Provable and Scalable Algorithms for Motif Conductance Based Graph Clustering

Higher-order graph clustering aims to partition the graph using frequently occurring subgraphs. Motif conductance is one of the most promising higher-order graph clustering models due to its strong interpretability. However, existing motif conductance based graph clustering algorithms are mainly limited by a seminal two-stage reweighting computing framework, needing to enumerate all motif instances to obtain an edge-weighted graph for partitioning. However, such a framework has two-fold vital defects: (1) It can only provide a quadratic bound for the motif with three vertices, and whether there is provable clustering quality for other motifs is still an open question. (2) The enumeration procedure of motif instances incurs prohibitively high costs against large motifs or large dense graphs due to combinatorial explosions. Besides, expensive spectral clustering or local graph diffusion on the edge-weighted graph also makes existing methods unable to handle massive graphs with millions of nodes. To overcome these dilemmas, we propose a Provable and Scalable Motif Conductance algorithm PSMC, which has a fixed and motif-independent approximation ratio for any motif. Specifically, PSMC first defines a new vertex metric Motif Resident based on the given motif, which can be computed locally. Then, it iteratively deletes the vertex with the smallest motif resident value very efficiently using novel dynamic update technologies. Finally, it outputs the locally optimal result during the above iterative process. To further boost efficiency, we propose several effective bounds to estimate the motif resident value of each vertex, which can greatly reduce computational costs. Empirical results show that our proposed algorithms achieve 3.2-32 times speedup and improve the quality by at least 12 times than the baselines.

cs.CC↗

arXiv2024

A Survey on Large Language Models from Concept to Implementation

Recent advancements in Large Language Models (LLMs), particularly those built on Transformer architectures, have significantly broadened the scope of natural language processing (NLP) applications, transcending their initial use in chatbot technology. This paper investigates the multifaceted applications of these models, with an emphasis on the GPT series. This exploration focuses on the transformative impact of artificial intelligence (AI) driven tools in revolutionizing traditional tasks like coding and problem-solving, while also paving new paths in research and development across diverse industries. From code interpretation and image captioning to facilitating the construction of interactive systems and advancing computational domains, Transformer models exemplify a synergy of deep learning, data analysis, and neural network design. This survey provides an in-depth look at the latest research in Transformer models, highlighting their versatility and the potential they hold for transforming diverse application sectors, thereby offering readers a comprehensive understanding of the current and future landscape of Transformer-based LLMs in practical applications.

cs.CL↗

arXiv2024

Mechanistic Insights into Non-Adiabatic Interband Transitions on a Semiconductor Surface Induced by Hydrogen Atom Collisions

To understand the recently observed mysterious non-adiabatic energy transfer for hyperthermal H atom scattering from a semiconductor surface, Ge(111)c(2*8), we present a mixed quantum-classical non-adiabatic molecular dynamics model based on time-dependent evolution of Kohn-Sham orbitals and a classical path approximation. Our results suggest that facile non-adiabatic transitions occur selectively at the rest atom site, featuring excitation of valance band electrons to the conduction band, but not at the adatom site. This drastic site specificity can be attributed to the changes of the local band structure upon energetic H collisions at different surface sites, leading to transient near-degeneracies and significant couplings between occupied and unoccupied orbitals at the rest atom, but not at the adatom. These insights shed valuable light on the collisional induced non-adiabatic dynamics at semiconductor surfaces.

physics.chem-ph↗

arXiv2024

Exploring Lightweight Federated Learning for Distributed Load Forecasting

Federated Learning (FL) is a distributed learning scheme that enables deep learning to be applied to sensitive data streams and applications in a privacy-preserving manner. This paper focuses on the use of FL for analyzing smart energy meter data with the aim to achieve comparable accuracy to state-of-the-art methods for load forecasting while ensuring the privacy of individual meter data. We show that with a lightweight fully connected deep neural network, we are able to achieve forecasting accuracy comparable to existing schemes, both at each meter source and at the aggregator, by utilising the FL framework. The use of lightweight models further reduces the energy and resource consumption caused by complex deep-learning models, making this approach ideally suited for deployment across resource-constrained smart meter systems. With our proposed lightweight model, we are able to achieve an overall average load forecasting RMSE of 0.17, with the model having a negligible energy overhead of 50 mWh when performing training and inference on an Arduino Uno platform.

cs.LG↗

arXiv2024

GIDN: A Lightweight Graph Inception Diffusion Network for High-efficient Link Prediction

In this paper, we propose a Graph Inception Diffusion Networks(GIDN) model. This model generalizes graph diffusion in different feature spaces, and uses the inception module to avoid the large amount of computations caused by complex network structures. We evaluate GIDN model on Open Graph Benchmark(OGB) datasets, reached an 11% higher performance than AGDN on ogbl-collab dataset.

cs.LG↗

arXiv2024

Graph for Science: From API based Programming to Graph Engine based Programming for HPC

Modern scientific applications predominantly run on large-scale computing platforms, necessitating collaboration between scientific domain experts and high-performance computing (HPC) experts. While domain experts are often skilled in customizing domain-specific scientific computing routines, which often involves various matrix computations, HPC experts are essential for achieving efficient execution of these computations on large-scale platforms. This process often involves utilizing complex parallel computing libraries tailored to specific matrix computation scenarios. However, the intricate programming procedure and the need for deep understanding in both application domains and HPC poses significant challenges to the widespread adoption of scientific computing. In this research, we observe that matrix computations can be transformed into equivalent graph representations, and that by utilizing graph processing engines, HPC experts can be freed from the burden of implementing efficient scientific computations. Based on this observation, we introduce a graph engine-based scientific computing (Graph for Science) paradigm, which provides a unified graph programming interface, enabling domain experts to promptly implement various types of matrix computations. The proposed paradigm leverages the underlying graph processing engine to achieve efficient execution, eliminating the needs for HPC expertise in programming large-scale scientific applications. Our results show that the graph engine-based scientific computing paradigm achieves performance comparable to the best-performing implementations based on existing parallel computing libraries and bespoke implementations. Importantly, the paradigm greatly simplifies the development of scientific computations on large-scale platforms, reducing the programming difficulty for scientists and facilitating broader adoption of scientific computing.

cs.DC↗

arXiv2024

FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data

Clustered federated learning (CFL) is proposed to mitigate the performance deterioration stemming from data heterogeneity in federated learning (FL) by grouping similar clients for cluster-wise model training. However, current CFL methods struggle due to inadequate integration of global and intra-cluster knowledge and the absence of an efficient online model similarity metric, while treating the cluster count as a fixed hyperparameter limits flexibility and robustness. In this paper, we propose an adaptive CFL framework, named FedAC, which (1) efficiently integrates global knowledge into intra-cluster learning by decoupling neural networks and utilizing distinct aggregation methods for each submodule, significantly enhancing performance; (2) includes a costeffective online model similarity metric based on dimensionality reduction; (3) incorporates a cluster number fine-tuning module for improved adaptability and scalability in complex, heterogeneous environments. Extensive experiments show that FedAC achieves superior empirical performance, increasing the test accuracy by around 1.82% and 12.67% on CIFAR-10 and CIFAR-100 datasets, respectively, under different non-IID settings compared to SOTA methods.

cs.LG↗

arXiv2023

Ab-initio Simulations of Coherent Phonon-Induced Pumping of Carriers in Zirconium Pentatelluride

Laser-driven coherent phonons can act as modulated strain fields and modify the adiabatic ground state topology of quantum materials. Here we use time-dependent first-principles and effective model calculations to simulate the effect of the coherent phonon induced by strong terahertz electric field on electronic carriers in the topological insulator ZrTe$_5$. We show that a coherent $A_\text{1g}$ Raman mode modulation can effectively pump carriers across the band gap, even though the phonon energy is about an order of magnitude smaller than the equilibrium band gap. We reveal the microscopic mechanism of this effect which occurs via Landau-Zener-Stückelberg tunneling of Bloch electrons in a narrow region in the Brillouin zone center where the transient energy gap closes when the system switches from strong to weak topological insulator. The quantum dynamics simulation results are in excellent agreement with recent pump-probe experiments in ZrTe$_5$ at low temperature.

cond-mat.mtrl-sci↗

arXiv2023

Deep Learning-Assisted Simultaneous Targets Sensing and Super-Resolution Imaging

Recently, metasurfaces have experienced revolutionary growth in the sensing and superresolution imaging field, due to their enabling of subwavelength manipulation of electromagnetic waves. However, the addition of metasurfaces multiplies the complexity of retrieving target information from the detected fields. Besides, although the deep learning method affords a compelling platform for a series of electromagnetic problems, many studies mainly concentrate on resolving one single function and limit the research's versatility. In this study, a multifunctional deep neural network is demonstrated to reconstruct target information in a metasurface targets interactive system. Firstly, the interactive scenario is confirmed to tolerate the system noises in a primary verification experiment. Then, fed with the electric field distributions, the multitask deep neural network can not only sense the quantity and permittivity of targets but also generate superresolution images with high precision. The deep learning method provides another way to recover targets' diverse information in metasurface based target detection, accelerating the progression of target reconstruction areas. This methodology may also hold promise for inverse reconstruction or forward prediction problems in other electromagnetic scenarios.

eess.SP↗

arXiv2023

Fe$_{1+y}$Te$_{x}$Se$_{1-x}$: a delicate and tunable Majorana material

We report the observation for the p$_{z}$ electron band and the band inversion in Fe$_{1+y}$Te$_{x}$Se$_{1-x}$ with angle-resolved photoemission spectroscopy. Furthermore, we found that excess Fe (y>0) inhibits the topological band inversion in Fe$_{1+y}$Te$_{x}$Se$_{1-x}$, which explains the absence of Majorana zero modes in previous reports for Fe$_{1+y}$Te$_{x}$Se$_{1-x}$ with excess Fe. Based on our analysis of different amounts of Te doping and excess Fe, we propose a delicate topological phase in this material. Thanks to this delicate phase, one may be able to tune the topological transition via applying lattice strain or carrier doping.

cond-mat.supr-con↗

arXiv2022

Acceleration strategy of source iteration method for the stationary phonon Boltzmann transport equation

Mesoscopic numerical simulation has become an important tool in thermal management and energy harvesting at the micro/nano scale, where the Fourier's law failed. However, it is not easy to efficiently solve the phonon Boltzmann transport equation (BTE) from ballistic to diffusive limit. In order to accelerate convergence, an implicit synthetic iterative scheme is developed for the stationary phonon BTE, in which a macroscopic moment equation is invoked and solved iteratively coupled with the typical source iteration of the kinetic equation. Different from previous numerical interpolation, the phonon BTE is solved again at the cell interface along the group velocity direction within a certain length when reconstructing the interfacial phonon distribution function. Fourier stability analysis shows that the present method could converge faster than the source iteration method in the (near) diffusive regime. Numerical results prove that the present scheme can capture the ballistic-diffusive effects correctly and efficiently. The present acceleration framework could be a powerful tool for simulating practical thermal engineering problems in the future.

physics.comp-ph↗

arXiv2022

Ab initio Real-Time Quantum Dynamics of Charge Carriers in Momentum Space

Application of the nonadiabatic molecular dynamics (NAMD) approach is severely limited to studying carrier dynamics in the momentum space, since a supercell is required to sample the phonon excitation and electron-phonon (e-ph) interaction at different momenta in a molecular dynamics simulation. Here, we develop an ab initio approach for the real-time quantum dynamics for charge carriers in the momentum space (NAMD_k) by directly introducing the e-ph coupling into the Hamiltonian based on the harmonic approximation. The NAMD_k approach maintains the quantum zero-point energy and proper phonon dispersion, and includes memory effects of phonon excitation. The application of NAMD_k to the hot carrier dynamics in graphene reveals the phonon-specific relaxation mechanism. An energy threshold of 0.2eV, defined by two optical phonon modes strongly coupled to the electrons, separates the hot electron relaxation into fast and slow regions with the lifetimes of pico- and nano-seconds, respectively. The NAMD_k approach provides a powerful tool to understand real-time carrier dynamics in the momentum space for different materials.

physics.app-ph↗

arXiv2022

Evolution of the strange-metal scattering in momentum space of electron-doped ${\rm La}_{2-x}{\rm Ce}_x{\rm CuO}_4$

The linear-in-temperature resistivity is one of the important mysteries in the strange metal state of high-temperature cuprate superconductors. To uncover this anomalous property, the energy-momentum-dependent imaginary part of the self-energy Im ${\rm Σ}(k, ω)$ holds the key information. Here we perform systematic doping, momentum, and temperature-dependent angle-resolved photoemission spectroscopy measurements of electron-doped cuprate ${\rm La}_{2-x}{\rm Ce}_x{\rm CuO}_4$ and extract the evolution of the strange metal scattering in momentum space. At low doping levels and low temperatures, Im ${\rmΣ} \propto ω$ dependence dominates the whole momentum space. For high doping levels and high temperatures, Im ${\rmΣ} \propto ω^2$ shows up, starting from the antinodal region. By comparing with the hole-doped cuprates ${\rm La}_{2-x}{\rm Sr}_x{\rm CuO}_4$ and ${\rm Bi}_2{\rm Sr}_2{\rm CaCu}_2{\rm O}_8$, we find a dichotomy of the scattering rate exists along the nodal and antinodal direction, which is ubiquitous in the cuprate family. Our work provides new insight into the strange metal state in cuprates.

cond-mat.supr-con↗