arXiv subjects

Zhipeng Huang

Publications and source records attributed to Zhipeng Huang.

At least 37 records · Page 2Linked to original sources

arXiv2025

Generalized convergence of the deep BSDE method: a step towards fully-coupled FBSDEs and applications in stochastic control

We are concerned with high-dimensional coupled FBSDE systems approximated by the deep BSDE method of Han et al. (2018). It was shown by Han and Long (2020) that the errors induced by the deep BSDE method admit a posteriori estimate depending on the loss function, whenever the backward equation only couples into the forward diffusion through the Y process. We generalize this result to drift coefficients that may also depend on Z, and give sufficient conditions for convergence under standard assumptions. The resulting conditions are directly verifiable for any equation. Consequently, unlike in earlier theory, our convergence analysis enables the treatment of FBSDEs stemming from stochastic optimal control problems. In particular, we provide a theoretical justification for the non-convergence of the deep BSDE method observed in recent literature, and present direct guidelines for when convergence can be guaranteed in practice. Our theoretical findings are supported by several numerical experiments in high-dimensional settings.

math.NA↗

arXiv2024

Optimisation and Loss Analyses of Pulsed Field Magnetisation in a Superconducting Motor with Cryocooled Iron Cores

A 2D electromagnetic-thermal coupled numerical model has been developed using the finite element method and validated against experimental data to investigate a superconducting machine featuring high-temperature superconducting (HTS) tape stacks and cryocooled iron cores. The HTS stacks are transformed into trapped field stacks (TFSs) through pulsed field magnetisation (PFM), generating rotor fields. After PFM, the superconducting motor operates on the same principle as permanent magnet synchronous motors. This study explores the behaviour of HTS stacks by altering the stack's layer number from one to nine and adjusting the pulsed current amplitude from 250 A to 1000 A. The primary objective of this paper is to identify the optimal combination of pulsed current amplitudes and TFS layer numbers for achieving maximum magnetisation fields. The secondary objective is to evaluate the overall losses in both superconducting and non-superconducting parts of the machine during magnetisation, including heat generated in various layers of the TFS, and losses in the motor's active materials (copper windings and iron cores). Two motor configurations were proposed, and two calculation methods using linear interpolation of iron losses and steel grades were introduced to estimate the iron losses for the studied iron material, M270-35A. This pioneering study is expected to serve as a valuable reference for loss analyses and structural design considerations in developing superconducting machines.

cond-mat.supr-con↗

arXiv2024

Is the Gum Nebula an Important Interstellar Scattering Disk of Background Pulsars?

The Gum Nebula is a faint supernova remnant extending about 40 degrees across the southern sky, potentially affecting tens of background pulsars. Though the view that the Gum Nebula acts as a potential scattering screen for background pulsars has been recurrently mentioned over the past five decades, it has not been directly confirmed. We chose the strong background pulsar PSR~B0740$-$28 as a probe and monitored its diffractive interstellar scintillation (DISS) at 2.25~$\&$~8.60~GHz simultaneously for about two years using the Shanghai Tian Ma Radio Telescope (TMRT). DISS was detected at both frequencies and quantified by two-dimensional autocorrelation analysis. We calculated their scattering spectral index $α$ and found that 9/21 of the observations followed the theoretical predictions, while 4/21 of them clearly showed $α< 4$. This finding provides strong support for anomalous scattering along the pulsar line of sight, due to the large frequency lever arm and the simultaneous features of our dual-frequency observations. In comparison to the 2.25~GHz observations, scintillation arcs were observed in 10/21 of the secondary spectrum plots for 8.60~GHz observations. Consequently, the highest frequency record for pulsar scintillation arc detection was updated to 8.60~GHz. Our fitting results were the most direct evidence for the view that the Gum Nebula acts as the scattering screen for background pulsars, because both the distance ($245^{+69}_{-72}$~pc) and transverse speed ($22.4^{+4.1}_{-4.2}$~km/s) of the scintillation screen are comparable with related parameters of the Gum Nebula. Our findings indicated that anisotropic scattering provides a superior explanation for the annual modulation of scintillation arcs than isotropic scattering. Additionally, the orientation of its long axis was also fitted.

astro-ph.HE↗

arXiv2024

ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis

Despite recent advancements in neural 3D reconstruction, the dependence on dense multi-view captures restricts their broader applicability. In this work, we propose \textbf{ViewCrafter}, a novel method for synthesizing high-fidelity novel views of generic scenes from single or sparse images with the prior of video diffusion model. Our method takes advantage of the powerful generation capabilities of video diffusion model and the coarse 3D clues offered by point-based representation to generate high-quality video frames with precise camera pose control. To further enlarge the generation range of novel views, we tailored an iterative view synthesis strategy together with a camera trajectory planning algorithm to progressively extend the 3D clues and the areas covered by the novel views. With ViewCrafter, we can facilitate various applications, such as immersive experiences with real-time rendering by efficiently optimizing a 3D-GS representation using the reconstructed 3D points and the generated novel views, and scene-level text-to-3D generation for more imaginative content creation. Extensive experiments on diverse datasets demonstrate the strong generalization capability and superior performance of our method in synthesizing high-fidelity and consistent novel views.

cs.CV↗

arXiv2024

Ultrafast Charge Transfer Dynamics at the MoS$_2$/Au Interface Observed via Optical Spectroscopy under Ambient Conditions

To take advantage of the exceptional properties of atomically thin transition metal dichalcogenides (TMDC) for advanced devices and catalysts, integration with metallic surfaces is an efficacious approach for facilitating charge carrier injection and extraction from TMDC monolayers. Light-matter interactions predominantly occur at the K point in TMDC monolayers, making the charge carrier dynamics at this point essential for their optimal performance. However, direct access to and comprehensive understanding of the charge carrier dynamics at the K point of TMDC monolayer on a metal substrate remains challenging. In this study, we employed azimuth- and polarization-dependent final-state sum frequency generation (FS-SFG) spectroscopy to investigate the ultrafast dynamics of charge transfer at the K point of a MoS$_2$ monolayer interfaced with an Au substrate. We observed an ultrafast injection (sub-20 fs) of photoexcited hot electrons from the Au substrate to the conduction band minimum (CBM) of the MoS$_2$ monolayer. Subsequently, driven by an internal electric field induced by charge redistribution, injected hot electrons in MoS$_2$ experience a relaxation and fast return ($\sim2$ ps) from the CBM and a trap state mediated slow return ($\sim60$ ps) process. The direct optical observation of the full electron dynamics at the K point of MoS$_2$ monolayer in ambient conditions provides valuable insights into the mechanisms of charge carrier transfer across the TMDC-metal interface, informing the design of advanced TMDC-based devices with enhanced charge transfer rates.

cond-mat.mes-hall↗

arXiv2024

Convergence of the deep BSDE method for stochastic control problems formulated through the stochastic maximum principle

It is well-known that decision-making problems from stochastic control can be formulated by means of a forward-backward stochastic differential equation (FBSDE). Recently, the authors of Ji et al. 2022 proposed an efficient deep learning algorithm based on the stochastic maximum principle (SMP). In this paper, we provide a convergence result for this deep SMP-BSDE algorithm and compare its performance with other existing methods. In particular, by adopting a strategy as in Han and Long 2020, we derive a-posteriori estimate, and show that the total approximation error can be bounded by the value of the loss functional and the discretization error. We present numerical examples for high-dimensional stochastic control problems, both in case of drift- and diffusion control, which showcase superior performance compared to existing algorithms.

math.OC↗

arXiv2024

Converting High-Performance and Low-Latency SNNs through Explicit Modelling of Residual Error in ANNs

Spiking neural networks (SNNs) have garnered interest due to their energy efficiency and superior effectiveness on neuromorphic chips compared with traditional artificial neural networks (ANNs). One of the mainstream approaches to implementing deep SNNs is the ANN-SNN conversion, which integrates the efficient training strategy of ANNs with the energy-saving potential and fast inference capability of SNNs. However, under extreme low-latency conditions, the existing conversion theory suggests that the problem of misrepresentation of residual membrane potentials in SNNs, i.e., the inability of IF neurons with a reset-by-subtraction mechanism to respond to residual membrane potentials beyond the range from resting potential to threshold, leads to a performance gap in the converted SNNs compared to the original ANNs. This severely limits the possibility of practical application of SNNs on delay-sensitive edge devices. Existing conversion methods addressing this problem usually involve modifying the state of the conversion spiking neurons. However, these methods do not consider their adaptability and compatibility with neuromorphic chips. We propose a new approach based on explicit modeling of residual errors as additive noise. The noise is incorporated into the activation function of the source ANN, which effectively reduces the residual error. Our experiments on the CIFAR10/100 dataset verify that our approach exceeds the prevailing ANN-SNN conversion methods and directly trained SNNs concerning accuracy and the required time steps. Overall, our method provides new ideas for improving SNN performance under ultra-low-latency conditions and is expected to promote practical neuromorphic hardware applications for further development.

cs.NE↗

arXiv2024

RelationVLM: Making Large Vision-Language Models Understand Visual Relations

The development of Large Vision-Language Models (LVLMs) is striving to catch up with the success of Large Language Models (LLMs), yet it faces more challenges to be resolved. Very recent works enable LVLMs to localize object-level visual contents and ground text to them. Nonetheless, current LVLMs still struggle to precisely understand visual relations due to the lack of relevant data. In this work, we present RelationVLM, a large vision-language model capable of comprehending various levels and types of relations whether across multiple images or within a video. Specifically, we devise a multi-stage relation-aware training scheme and a series of corresponding data configuration strategies to bestow RelationVLM with the capabilities of understanding semantic relations, temporal associations and geometric transforms. Extensive case studies and quantitative evaluations show RelationVLM has strong capability in understanding such relations and emerges impressive in-context capability of reasoning from few-shot examples by comparison. This work fosters the advancements of LVLMs by enabling them to support a wider range of downstream applications toward artificial general intelligence.

cs.CV↗

arXiv2024

VisualCritic: Making LMMs Perceive Visual Quality Like Humans

At present, large multimodal models (LMMs) have exhibited impressive generalization capabilities in understanding and generating visual signals. However, they currently still lack sufficient capability to perceive low-level visual quality akin to human perception. Can LMMs achieve this and show the same degree of generalization in this regard? If so, not only could the versatility of LMMs be further enhanced, but also the challenge of poor cross-dataset performance in the field of visual quality assessment could be addressed. In this paper, we explore this question and provide the answer "Yes!". As the result of this initial exploration, we present VisualCritic, the first LMM for broad-spectrum image subjective quality assessment. VisualCritic can be used across diverse data right out of box, without any requirements of dataset-specific adaptation operations like conventional specialist models. As an instruction-following LMM, VisualCritic enables new capabilities of (1) quantitatively measuring the perceptual quality of given images in terms of their Mean Opinion Score (MOS), noisiness, colorfulness, sharpness, and other numerical indicators, (2) qualitatively evaluating visual quality and providing explainable descriptions, (3) discerning whether a given image is AI-generated or photographic. Extensive experiments demonstrate the efficacy of VisualCritic by comparing it with other open-source LMMs and conventional specialist models over both AI-generated and photographic images.

cs.CV↗

arXiv2024

Integration of Conventional Surface Science Techniques with Surface-Sensitive Azimuthal and Polarization Dependent Femtosecond-Resolved Sum Frequency Generation Spectroscopy

Experimental insight into the elementary processes underlying charge transfer across interfaces has blossomed with the wide-spread availability of ultra-high vacuum set-ups that allow the preparation and characterization of solid surfaces with well-defined molecular adsorbates over a wide ranges of temperatures. Thick layers of molecular adsorbates or heterostructures of 2D materials generally preclude the use of electrons or atoms as probes in such characterization. However with linear photon-in/photon-out techniques it is often challenging to assign the observed optical response to a particular portion of the interface. We and prior workers have demonstrated in work under ambient conditions that by full characterization of the symmetry of the second order nonlinear optical susceptibility, i.e. the $χ^{(2)}$, in sum frequency generation (SFG) spectroscopy, this problem can be overcome. Here we describe an ultra-high vacuum system built to allow conventional UHV sample preparation and characterization, femtosecond and polarization resolved SFG spectroscopy, the azimuthal sample rotation necessary to fully describe $χ^{(2)}$ symmetry and with sufficient stability to allow scanning SFG microscopy. We demonstrate these capabilities in proof-of-principle measurements on CO adsorbed on Pt(111) and of the clean Ag(111) surface. Because this set-up allows both full characterization of the nonlinear susceptibility and the temperature control and sample preparation/characterization of conventional UHV set-ups we expect it to be of great utility in investigation of both the basic physics and applications of solid, 2D material heterostructures.

physics.chem-ph↗

arXiv2023

iEDA: An Open-Source Intelligent Physical Implementation Toolkit and Library

Open-source EDA shows promising potential in unleashing EDA innovation and lowering the cost of chip design. This paper presents an open-source EDA project, iEDA, aiming for building a basic infrastructure for EDA technology evolution and closing the industrial-academic gap in the EDA area. iEDA now covers the whole flow of physical design (including Floorplan, Placement, CTS, Routing, Timing Optimization etc.), and part of the analysis tools (Static Timing Analysis and Power Analysis). To demonstrate the effectiveness of iEDA, we implement and tape out three chips of different scales (from 700k to 1.5M gates) on different process nodes (110nm and 28nm) with iEDA. iEDA is publicly available from the project home page http://ieda.oscc.cc.

cs.AR↗

arXiv2023

Adaptive Frequency Filters As Efficient Global Token Mixers

Recent vision transformers, large-kernel CNNs and MLPs have attained remarkable successes in broad vision tasks thanks to their effective information fusion in the global scope. However, their efficient deployments, especially on mobile devices, still suffer from noteworthy challenges due to the heavy computational costs of self-attention mechanisms, large kernels, or fully connected layers. In this work, we apply conventional convolution theorem to deep learning for addressing this and reveal that adaptive frequency filters can serve as efficient global token mixers. With this insight, we propose Adaptive Frequency Filtering (AFF) token mixer. This neural operator transfers a latent representation to the frequency domain via a Fourier transform and performs semantic-adaptive frequency filtering via an elementwise multiplication, which mathematically equals to a token mixing operation in the original latent space with a dynamic convolution kernel as large as the spatial resolution of this latent representation. We take AFF token mixers as primary neural operators to build a lightweight neural network, dubbed AFFNet. Extensive experiments demonstrate the effectiveness of our proposed AFF token mixer and show that AFFNet achieve superior accuracy and efficiency trade-offs compared to other lightweight network designs on broad visual tasks, including visual recognition and dense prediction tasks.

cs.CV↗

arXiv2023

Analytical Solution of Poisson's Equation with Application to VLSI Global Placement

Poisson's equation has been used in VLSI global placement for describing the potential field caused by a given charge density distribution. Unlike previous global placement methods that solve Poisson's equation numerically, in this paper, we provide an analytical solution of the equation to calculate the potential energy of an electrostatic system. The analytical solution is derived based on the separation of variables method and an exact density function to model the block distribution in the placement region, which is an infinite series and converges absolutely. Using the analytical solution, we give a fast computation scheme of Poisson's equation and develop an effective and efficient global placement algorithm called Pplace. Experimental results show that our Pplace achieves smaller placement wirelength than ePlace and NTUplace3. With the pervasive applications of Poisson's equation in scientific fields, in particular, our effective, efficient, and robust computation scheme for its analytical solution can provide substantial impacts on these fields.

cs.OH↗

arXiv2023

A femtosecond time resolved view of vibrationally assisted electron transfer across the metal/aqueous interface

Understanding heterogeneous charge transfer is crucial if we are to build the best electrolyzers, fuel cells and photoelectrochemical water splitting devices that chemistry allows. Because the elementary processes involved have timescales ranging from femto- to milliseconds, direct simulation is not generally possible. Model Hamiltonian approaches thus have a crucial role in gaining mechanistic insight. Current generations of such theories describe a reactant(s) or product(s) that interacts with electrolyte via a single effective interaction. Such approaches thus obscure the extent to which particular solvent fluctuations influence charge transfer. Here we demonstrate experimentally that for a prototypical system, a ferrocene terminated alkane thiol self-assembled monolayer (SAM) on gold in contact with aqueous electrolyte, charge transfer from the Au to the ferrocene can be induced by vibrational excitation of the ferrocene aromatic CH. Intriguingly the energy of the aromatic CH vibration, 0.38 eV, is a large fraction of the effective solvent interaction strength inferred for the ferrocene/ferrocenium system in prior electrochemical studies: 0.85 eV. Our results thus demonstrate the coupling of charge transfer to a specific solvent motion and more generally imply that solvent may affect reduction/oxidation rates in electrocatalysis by coupling to a few distinct solvent motions. Identifying these motions is crucial in rationalizing trends in reactivity with change in electrolyte and thus in pursuing electrolyte engineering from first principles.

physics.chem-ph↗

arXiv2023

Deep Frequency Filtering for Domain Generalization

Improving the generalization ability of Deep Neural Networks (DNNs) is critical for their practical uses, which has been a longstanding challenge. Some theoretical studies have uncovered that DNNs have preferences for some frequency components in the learning process and indicated that this may affect the robustness of learned features. In this paper, we propose Deep Frequency Filtering (DFF) for learning domain-generalizable features, which is the first endeavour to explicitly modulate the frequency components of different transfer difficulties across domains in the latent space during training. To achieve this, we perform Fast Fourier Transform (FFT) for the feature maps at different layers, then adopt a light-weight module to learn attention masks from the frequency representations after FFT to enhance transferable components while suppressing the components not conducive to generalization. Further, we empirically compare the effectiveness of adopting different types of attention designs for implementing DFF. Extensive experiments demonstrate the effectiveness of our proposed DFF and show that applying our DFF on a plain baseline outperforms the state-of-the-art methods on different domain generalization tasks, including close-set classification and open-set retrieval.

cs.CV↗

arXiv2022

A Latent Space Model for HLA Compatibility Networks in Kidney Transplantation

Kidney transplantation is the preferred treatment for people suffering from end-stage renal disease. Successful kidney transplants still fail over time, known as graft failure; however, the time to graft failure, or graft survival time, can vary significantly between different recipients. A significant biological factor affecting graft survival times is the compatibility between the human leukocyte antigens (HLAs) of the donor and recipient. We propose to model HLA compatibility using a network, where the nodes denote different HLAs of the donor and recipient, and edge weights denote compatibilities of the HLAs, which can be positive or negative. The network is indirectly observed, as the edge weights are estimated from transplant outcomes rather than directly observed. We propose a latent space model for such indirectly-observed weighted and signed networks. We demonstrate that our latent space model can not only result in more accurate estimates of HLA compatibilities, but can also be incorporated into survival analysis models to improve accuracy for the downstream task of predicting graft survival times.

cs.LG↗

arXiv2022

The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks

The stochastic block model (SBM) is one of the most widely used generative models for network data. Many continuous-time dynamic network models are built upon the same assumption as the SBM: edges or events between all pairs of nodes are conditionally independent given the block or community memberships, which prevents them from reproducing higher-order motifs such as triangles that are commonly observed in real networks. We propose the multivariate community Hawkes (MULCH) model, an extremely flexible community-based model for continuous-time networks that introduces dependence between node pairs using structured multivariate Hawkes processes. We fit the model using a spectral clustering and likelihood-based local refinement procedure. We find that our proposed MULCH model is far more accurate than existing models both for predictive and generative tasks.

stat.ME↗

arXiv2022

A Mutually Exciting Latent Space Hawkes Process Model for Continuous-time Networks

Networks and temporal point processes serve as fundamental building blocks for modeling complex dynamic relational data in various domains. We propose the latent space Hawkes (LSH) model, a novel generative model for continuous-time networks of relational events, using a latent space representation for nodes. We model relational events between nodes using mutually exciting Hawkes processes with baseline intensities dependent upon the distances between the nodes in the latent space and sender and receiver specific effects. We demonstrate that our proposed LSH model can replicate many features observed in real temporal networks including reciprocity and transitivity, while also achieving superior prediction accuracy and providing more interpretable fits than existing models.

cs.LG↗