arXiv subjects

Jiangtao Wang

Publications and source records attributed to Jiangtao Wang.

At least 37 records · Page 2Linked to original sources

arXiv2025

Beyond Seamless: Unexpected Defective Merging in Single-Orientation Graphene

Single-orientation stitching of graphene has emerged as the predominant method for growth of large-area, high-quality graphene films. Particularly noteworthy is graphene grown on single-crystalline Cu(111)/sapphire substrates, which exhibits exceptionally planar oriented stitching due to the atomically smooth substrate, facilitating the formation of continuous, high-quality graphene monolayer. These single-orientation stitches have conventionally been regarded as seamless with negligible defect concentrations. In this report, we present experimental observations regarding graphene grown on single-crystalline Cu(111)/sapphire substrates. Among the graphene flakes with single-orientation, our findings reveal two major merging behaviors: one producing the expected seamless stitching, and another unexpectedly generating structural defects that create nanoscale pathways permitting water permeation. Notably, we identify a unique merging structure--overlapped junction, in which the edge of one graphene flake overlaps and lies atop the edge of another flake, rather than forming a continuous atomic stitch. This discovery challenges the conventional anticipation of single-orientation stitched graphene films as seamless single crystalline film, while offers unique perspective for graphene applications in molecular sieving, selective filtration membranes, and protective coatings.

cond-mat.mtrl-sci↗

arXiv2025

FFT-Free PAPR Reduction Methods for OFDM Signals

In this paper, we propose two low-complexity peak to average power ratio(PAPR) reduction algorithms for orthogonal frequency division multiplexing(OFDM) signals. The main content is as follows: First, a non-convex optimization model is established by minimizing the signal distortion power. Then, a customized alternating direction method of multipliers(ADMM) algorithm is proposed to solve the problem, named time domain ADMM(T-ADMM) along with an improved version called T-ADMM with constrain update(TCU-ADMM). In the algorithms, all subproblems can be solved analytically, and each iteration has linear computational complexity. These algorithms circumvents the challenges posed by repeated fast Fourier transform(FFT) and inverse FFT(IFFT) operations in traditional PAPR reduction algorithms. Additionally, we prove that the T-ADMM algorithm is theoretically guaranteed convergent if proper parameter is chosen. Finally, simulation results demonstrate the effectiveness of the proposed methods.

eess.SP↗

arXiv2025

Towards Non-Invasive Sediment Monitoring Using Muography: A Pilot Run at the Shanghai Outer Ring Tunnel

This study demonstrates the application of cosmic-ray muography as a non-invasive method for monitoring sediment accumulation and tidal influences in the Shanghai Outer Ring Tunnel, an immersed tube tunnel located beneath the Huangpu River in Shanghai, China. A portable, dual-layer plastic scintillator detector was deployed to conduct muon flux scans along the tunnel's length and to continuously monitor muon flux, allowing for the study of tidal effects. Geant4 simulations validated the correlation between muon attenuation and overburden thickness, incorporating sediment, water, and concrete layers. Key findings include a strong anti-correlation between the measured muon flux and the water levels observed at a nearby tide gauge. The results align with geotechnical data and simulations, especially in the region of interest, confirming muography's sensitivity to sediment dynamics. This work establishes muography as a robust tool for long-term, real-time monitoring of submerged infrastructure, offering significant advantages over conventional invasive techniques. The study underscores the potential for integrating muography into civil engineering practices to enhance safety and operational resilience in tidal environments.

physics.app-ph↗

arXiv2025

Phase II of the LAMOST-Kepler/K2 Survey. II. Time Domain of Medium-resolution Spectroscopic Observations from 2018 to 2023

The LAMOST-Kepler/K2 Medium-Resolution Spectroscopic Survey (LK-MRS) conducted time-domain medium-resolution spectroscopic observations of 20 LAMOST plates in the Kepler and K2 fields from 2018 to 2023, a phase designated as LK-MRS-I. A catalog of stellar parameters for a total of 36,588 stars, derived from the spectra collected during these five years, including the effective temperature, the surface gravity, the metallicity, the α-element abundance, the radial velocity, and v sin i of the target stars, is released, together with the weighted averages and uncertainties. At S/N = 10, the measurement uncertainties are 120 K, 0.18 dex, 0.13 dex, 0.08 dex, 1.9 km/s, and 4.0 km/s for the above parameters, respectively. Comparisons with the parameters provided by the APOGEE and GALAH surveys validate the effective temperature and surface gravity measurements, showing minor discrepancies in metallicity and α-element abundance values. We identified some peculiar star candidates, including 764 metal-poor stars, 174 very metal-poor stars, and 30 high-velocity stars. Moreover, we found 2,333 stars whose radial velocity seems to be variable. Using Kepler/K2 or TESS photometric data, we confirmed 371 periodic variable stars among the radial velocity variable candidates and classified their variability types. LK-MRS-I provides spectroscopic data being useful for studies of the Kepler and K2 fields. The LK-MRS project will continue collecting time-domain medium-resolution spectra for target stars during the third phase of LAMOST surveys, providing data to support further scientific research.

astro-ph.SR↗

arXiv2025

Simulation studies of a high-repetition-rate electron-driven surface muon beamline at SHINE

A high-repetition-rate pulsed muon source operating at approximately 50\,kHz holds the potential to improve the sensitivity of various particle physics and material science experiments involving muons. In this article, we propose utilizing the high-repetition-rate pulsed electron beam at the SHINE facility to generate a surface muon beam. Our simulation studies indicate that an 8\,GeV, 100\,pC charge pulsed electron beam impinging on a copper target can produce up to $2 \times 10^{3}$ muons per pulse. Beamline optimization results demonstrate that approximately 60 surface muons per electron bunch can be efficiently transported to the end of the beamline. This translates to a surface muon rate of $3 \times 10^{6}\,μ^{+}$/s when the pulsed electron beam is operated at 50\,kHz, which is comparable to existing muon facilities. This high-repetition-rate pulsed muon beam, with its ideal time structure, represents a unique and pioneering effort once constructed. It serves as a model for building cost-effective muon sources at existing electron machines with GeV electron energies. In addition to the typical challenges encountered in conventional muon beamlines, such as the installation and construction of the target station and beamline, the removal of substantial quantities of positrons is also a major challenge. A potential solution to this issue is also discussed.

physics.acc-ph↗

arXiv2025

Designing Unimodular Waveforms for MIMO Radar Based on Manifold Optimization Method

In this paper, we design unimodular waveforms with good correlation properties for multi-input multi-output (MIMO) radar systems. Specifically, first, we analyze the geometric properties of the unimodular constraint in the fourth-order polynomial minimization problem using Riemannian geometry theory. By embedding it into the search space, we transform the original non-convex optimization problem into an unconstrained problem on a Riemannian manifold. Then, we construct the manifold corresponding to the search space and the operators required for the customized optimization algorithm. Second, we develop a customized low-complexity unimodular manifold gradient descent (UM-GD) algorithm on the constructed manifold to solve the optimization problem in the normal-scale case, and propose its acceleration version unimodular manifold accelerated gradient descent (UM-AGD) algorithm, to speed up the convergence. In the large-scale case, we transform the objective function into the form of a summation of a large but finite number of loss functions and develop a customized unimodular manifold stochastic variance reduced gradient (UM-SVRG) algorithm to solve this problem. Compared to the existing bechmark method, which has a computational complexity of roughly $\mathcal{O}(M^4+3M^2N+3|\mathcal{D}| |\hatΘ|^2MN)$, UM-SVRG algorithm effectively reduces the computational complexity of each iteration to roughly $\mathcal{O}(|\mathcal{D}||\hatΘ|^2MN)$. Thirdly, we provide theoretical guarantees of convergence for both the UM-GD and UM-SVRG algorithms through appropriate parameter selection, and prove that the proposed algorithms can converge to a stationary point. Finally, numerical examples demonstrate the effectiveness of the proposed UM-GD and UM-SVRG algorithms.

eess.SP↗

arXiv2025

Unimodular Waveform Design for Integrated Sensing and Communication MIMO System via Manifold Optimization

Integrated sensing and communication (ISAC) has been widely recognized as one of the key technologies for 6G wireless networks. In this paper, we focus on the waveform design of ISAC system, which can realize radar sensing while also facilitate information transmission. The main content is as follows: first, we formulate the waveform design problem as a nonconvex and non-smooth model with a unimodulus constraint based on the measurement metric of the radar and communication system. Second, we transform the model into an unconstrained problem on the Riemannian manifold and construct the corresponding operators by analyzing the unimodulus constraint. Third, to achieve the solution efficiently, we propose a low-complexity non-smooth unimodulus manifold gradient descent (N-UMGD) algorithm with theoretical convergence guarantee. The simulation results show that the proposed algorithm can concentrate the energy of the sensing signal in the desired direction and realize information transmission with a low bit error rate.

eess.SP↗

arXiv2025

Data Pruning in Generative Diffusion Models

Data pruning is the problem of identifying a core subset that is most beneficial to training and discarding the remainder. While pruning strategies are well studied for discriminative models like those used in classification, little research has gone into their application to generative models. Generative models aim to estimate the underlying distribution of the data, so presumably they should benefit from larger datasets. In this work we aim to shed light on the accuracy of this statement, specifically answer the question of whether data pruning for generative diffusion models could have a positive impact. Contrary to intuition, we show that eliminating redundant or noisy data in large datasets is beneficial particularly when done strategically. We experiment with several pruning methods including recent-state-of-art methods, and evaluate over CelebA-HQ and ImageNet datasets. We demonstrate that a simple clustering method outperforms other sophisticated and computationally demanding methods. We further exhibit how we can leverage clustering to balance skewed datasets in an unsupervised manner to allow fair sampling for underrepresented populations in the data distribution, which is a crucial problem in generative models.

cs.LG↗

arXiv2025

Memory and Bandwidth are All You Need for Fully Sharded Data Parallel

Transformer models have revolutionized a wide spectrum of disciplines, especially in language processing. The recent success has proven that model size scalability is crucial for achieving superior performance metrics. However, training large transformer models is challenging even on modern hardware with powerful GPUs and high-speed interconnects. Existing studies primarily focus on optimizing model training distribution strategies to minimize memory footprint and enhance training speed, often overlooking the scalability challenges related to model size and hardware constraints. To address this oversight, we thoroughly investigate computational, memory, and network demands of training large transformers using the Fully Sharded Data Parallel (FSDP) distributed strategy across different hardware clusters. We explore the intricate relationships between model size and hardware setups to identify configurations that ensure maximum model and hardware efficiency, effective sequence length management, and optimal training throughput. A significant finding of our study is the critical interplay of the cluster's connection bandwidth and GPU memory size compared to the computational performance of GPUs. This interplay limits training efficiency, underscoring the role of both hardware characteristics as a possible bottleneck. By integrating theoretical analysis with simulations and empirical tests, we demonstrate how hardware limitations affect training efficacy, identifying key hardware thresholds and the impact of network connectivity. Our findings prompt a reassessment of training strategies guiding users on the way to finding hardware-optimal FSDP configurations, enhancing training efficiency for large-scale transformer models.

cs.DC↗

arXiv2025

Time Transfer: On Optimal Learning Rate and Batch Size In The Infinite Data Limit

One of the main challenges in optimal scaling of large language models (LLMs) is the prohibitive cost of hyperparameter tuning, particularly learning rate $η$ and batch size $B$. While techniques like $μ$P (Yang et al., 2022) provide scaling rules for optimal $η$ transfer in the infinite model size limit, the optimal scaling behavior in the infinite data size limit remains unknown. We fill in this gap by observing for the first time an intricate dependence of optimal $η$ scaling on the pretraining token budget $T$, $B$ and its relation to the critical batch size $B_\mathrm{crit}$, which we measure to evolve as $B_\mathrm{crit} \propto T$. Furthermore, we show that the optimal batch size is positively correlated with $B_\mathrm{crit}$: keeping it fixed becomes suboptimal over time even if learning rate is scaled optimally. Surprisingly, our results demonstrate that the observed optimal $η$ and $B$ dynamics are preserved with $μ$P model scaling, challenging the conventional view of $B_\mathrm{crit}$ dependence solely on loss value. Complementing optimality, we examine the sensitivity of loss to changes in learning rate, where we find the sensitivity to decrease with increase of $T$ and to remain constant with $μ$P model scaling. We hope our results make the first step towards a unified picture of the joint optimal data and model scaling.

cs.LG↗

arXiv2024

Scaling Image Tokenizers with Grouped Spherical Quantization

Vision tokenizers have gained a lot of attraction due to their scalability and compactness; previous works depend on old-school GAN-based hyperparameters, biased comparisons, and a lack of comprehensive analysis of the scaling behaviours. To tackle those issues, we introduce Grouped Spherical Quantization (GSQ), featuring spherical codebook initialization and lookup regularization to constrain codebook latent to a spherical surface. Our empirical analysis of image tokenizer training strategies demonstrates that GSQ-GAN achieves superior reconstruction quality over state-of-the-art methods with fewer training iterations, providing a solid foundation for scaling studies. Building on this, we systematically examine the scaling behaviours of GSQ, specifically in latent dimensionality, codebook size, and compression ratios, and their impact on model performance. Our findings reveal distinct behaviours at high and low spatial compression levels, underscoring challenges in representing high-dimensional latent spaces. We show that GSQ can restructure high-dimensional latent into compact, low-dimensional spaces, thus enabling efficient scaling with improved quality. As a result, GSQ-GAN achieves a 16x down-sampling with a reconstruction FID (rFID) of 0.50.

cs.CV↗

arXiv2024

TFDet: Target-Aware Fusion for RGB-T Pedestrian Detection

Pedestrian detection plays a critical role in computer vision as it contributes to ensuring traffic safety. Existing methods that rely solely on RGB images suffer from performance degradation under low-light conditions due to the lack of useful information. To address this issue, recent multispectral detection approaches have combined thermal images to provide complementary information and have obtained enhanced performances. Nevertheless, few approaches focus on the negative effects of false positives caused by noisy fused feature maps. Different from them, we comprehensively analyze the impacts of false positives on the detection performance and find that enhancing feature contrast can significantly reduce these false positives. In this paper, we propose a novel target-aware fusion strategy for multispectral pedestrian detection, named TFDet. TFDet achieves state-of-the-art performance on two multispectral pedestrian benchmarks, KAIST and LLVIP. TFDet can easily extend to multi-class object detection scenarios. It outperforms the previous best approaches on two multispectral object detection benchmarks, FLIR and M3FD. Importantly, TFDet has comparable inference efficiency to the previous approaches, and has remarkably good detection performance even under low-light conditions, which is a significant advancement for ensuring road safety.

cs.CV↗

arXiv2024

Stellar atmospheric parameters of $\sim$ 11,000 RR Lyrae stars from LAMOST Spectra

Accurate determination of the stellar atmospheric parameters of RR Lyrae stars (RRLs) requires short individual exposures of the spectra to mitigate pulsation effects. We present improved template matching methods to determine the stellar atmospheric parameters of RRLs from single-epoch spectra of LAMOST (Large Sky Area Multi-Object Fiber Spectroscopic Telescope, also known as the Guoshoujing telescope). We determine the radial velocities and stellar atmospheric parameters (effective temperature: $T_\mathrm{eff}$, surface gravity: $\log{g}$, and metallicity: [M/H]) of 10,486 and 1,027 RRLs from 42,729 low-resolution spectra (LRS) and 7,064 medium-resolution spectra (MRS) of LAMOST, respectively. Our results are in good agreement with the parameters of other databases, where the external uncertainties of $T_\mathrm{eff}$, $\log{g}$, and [M/H] for LRS/MRS are estimated to be 314/274 K, 0.42/0.29 dex, and 0.39/0.31 dex, respectively. We conclude with the variation characteristics of the radial velocities ($RV$) and stellar atmospheric parameters for RRLs during the pulsation phase. There is a significant difference of $28\pm21$ km/s between the peak-to-peak amplitude ($A_\mathrm{ptp}$) of $RV$ from H$α$ line ($RV_\mathrm{Hα}$) and from metal lines ($RV_\mathrm{metal}$) for RRab, whereas it is only $4\pm17$ km/s for RRc. The $A_\mathrm{ptp}$ of $T_\mathrm{eff}$ is $930\pm456$ and $409\pm375$ K for RRab and RRc, respectively. The $\log{g}$ of RRab show mild variation of approximately $0.23\pm0.42$ dex near the phase of $φ= 0.9$, while that of RRc almost remains constant. The [M/H] of RRab and RRc show a minor variation of about $0.25\pm0.50$ and $0.28\pm0.55$ dex, respectively, near the phase of $φ= 0.9$.

astro-ph.SR↗

arXiv2024

Remote-contact catalysis for target-diameter semiconducting carbon nanotube array

Electrostatic catalysis has been an exciting development in chemical synthesis (beyond enzymes catalysis) in recent years, boosting reaction rates and selectively producing certain reaction products. Most of the studies to date have been focused on using external electric field (EEF) to rearrange the charge distribution in small molecule reactions such as Diels-Alder addition, carbene reaction, etc. However, in order for these EEFs to be effective, a field on the order of 1 V/nm (10 MV/cm) is required, and the direction of the EEF has to be aligned with the reaction axis. Such a large and oriented EEF will be challenging for large-scale implementation, or materials growth with multiple reaction axis or steps. Here, we demonstrate that the energy band at the tip of an individual single-walled carbon nanotube (SWCNT) can be spontaneously shifted in a high-permittivity growth environment, with its other end in contact with a low-work function electrode (e.g., hafnium carbide or titanium carbide). By adjusting the Fermi level at a point where there is a substantial disparity in the density of states (DOS) between semiconducting (s-) and metallic (m-) SWCNTs, we achieve effective electrostatic catalysis for s-SWCNT growth assisted by a weak EEF perturbation (200V/cm). This approach enables the production of high-purity (99.92%) s-SWCNT horizontal arrays with narrow diameter distribution (0.95+-0.04 nm), targeting the requirement of advanced SWCNT-based electronics for future computing. These findings highlight the potential of electrostatic catalysis in precise materials growth, especially for s-SWCNTs, and pave the way for the development of advanced SWCNT-based electronics.

physics.app-ph↗

arXiv2023

Predict and Interpret Health Risk using EHR through Typical Patients

Predicting health risks from electronic health records (EHR) is a topic of recent interest. Deep learning models have achieved success by modeling temporal and feature interaction. However, these methods learn insufficient representations and lead to poor performance when it comes to patients with few visits or sparse records. Inspired by the fact that doctors may compare the patient with typical patients and make decisions from similar cases, we propose a Progressive Prototypical Network (PPN) to select typical patients as prototypes and utilize their information to enhance the representation of the given patient. In particular, a progressive prototype memory and two prototype separation losses are proposed to update prototypes. Besides, a novel integration is introduced for better fusing information from patients and prototypes. Experiments on three real-world datasets demonstrate that our model brings improvement on all metrics. To make our results better understood by physicians, we developed an application at http://ppn.ai-care.top. Our code is released at https://github.com/yzhHoward/PPN.

cs.LG↗

arXiv2023

OpenNet: Incremental Learning for Autonomous Driving Object Detection with Balanced Loss

Automated driving object detection has always been a challenging task in computer vision due to environmental uncertainties. These uncertainties include significant differences in object sizes and encountering the class unseen. It may result in poor performance when traditional object detection models are directly applied to automated driving detection. Because they usually presume fixed categories of common traffic participants, such as pedestrians and cars. Worsely, the huge class imbalance between common and novel classes further exacerbates performance degradation. To address the issues stated, we propose OpenNet to moderate the class imbalance with the Balanced Loss, which is based on Cross Entropy Loss. Besides, we adopt an inductive layer based on gradient reshaping to fast learn new classes with limited samples during incremental learning. To against catastrophic forgetting, we employ normalized feature distillation. By the way, we improve multi-scale detection robustness and unknown class recognition through FPN and energy-based detection, respectively. The Experimental results upon the CODA dataset show that the proposed method can obtain better performance than that of the existing methods.

cs.CV↗

arXiv2023

Electrical Tuning of Neutral and Charged Excitons with 1-nm Gate

Electrical control of individual spins and photons in solids is key for quantum technologies, but scaling down to small, static systems remains challenging. Here, we demonstrate nanoscale electrical tuning of neutral and charged excitons in monolayer WSe2 using 1-nm carbon nanotube gates. Electrostatic simulations reveal a confinement radius below 15 nm, reaching the exciton Bohr radius limit for few-layer dielectric spacing. In situ photoluminescence spectroscopy shows gate-controlled conversion between neutral excitons, negatively charged trions, and biexcitons at 4 K. Important for quantum information processing applications, our measurements indicate gating of a local 2D electron gas in the WSe2 layer, coupled to photons via trion transitions with binding energies exceeding 20 meV. The ability to deterministically tune and address quantum emitters using nanoscale gates provides a pathway towards large-scale quantum optoelectronic circuits and spin-photon interfaces for quantum networking.

cond-mat.mes-hall↗

arXiv2023

Cross-Edge Orchestration of Serverless Functions with Probabilistic Caching

Serverless edge computing adopts an event-based paradigm that provides back-end services on an as-used basis, resulting in efficient resource utilization. To improve the end-to-end latency and revenue, service providers need to optimize the number and placement of serverless containers while considering the system cost incurred by the provisioning. The particular reason for this circumstance is that frequently creating and destroying containers not only increases the system cost but also degrades the time responsiveness due to the cold-start process. Function caching is a common approach to mitigate the coldstart issue. However, function caching requires extra hardware resources and hence incurs extra system costs. Furthermore, the dynamic and bursty nature of serverless invocations remains an under-explored area. Hence, it is vitally important for service providers to conduct a context-aware request distribution and container caching policy for serverless edge computing. In this paper, we study the request distribution and container caching problem in serverless edge computing. We prove the proposed problem is NP-hard and hence difficult to find a global optimal solution. We jointly consider the distributed and resource constrained nature of edge computing and propose an optimized request distribution algorithm that adapts to the dynamics of serverless invocations with a theoretical performance guarantee. Also, we propose a context-aware probabilistic caching policy that incorporates a number of characteristics of serverless invocations. Via simulation and implementation results, we demonstrate the superiority of the proposed algorithm by outperforming existing caching policies in terms of the overall system cost and cold-start frequency by up to 62.1% and 69.1%, respectively.

cs.NI↗