arXiv subjects

Jiacheng Sun

Publications and source records attributed to Jiacheng Sun.

At least 55 records · Page 3Linked to original sources

arXiv2025

Photocurrent Nanoscopy of Quantum Hall Bulk

Understanding nanoscale electronic and thermal transport of two-dimensional (2D) electron systems in the quantum Hall regime, particularly in the bulk insulating state, poses considerable challenges. One of the primary difficulties arises from the presence of chiral edge channels, whose transport behavior obscures the investigation of the insulating bulk. Using near-field (NF) optical and photocurrent (PC) nanoscopy, we probe real-space variations of the optical and thermal dynamics of graphene in the quantum Hall regime without relying on complex sample or electrode geometries. Near the charge neutrality point (CNP), we detect strong optical and photothermal signals from resonant inter-Landau level (LL) magnetoexciton excitations between the 0th and +-1st LLs, which gradually weaken with increasing doping due to Pauli blocking. Interestingly, at higher doping levels and full integer LL fillings, photothermal signals reappear across the entire sample over a ~10-micrometer scale, indicating unexpectedly long cooling lengths and nonlocal photothermal heating through the insulating bulk. This observation suggests thermal conductivity persists for the localized states even as electronic transport is suppressed - a clear violation of the Wiedemann-Franz (WF) law. Our experiments provide novel insights into nanoscale thermal and electronic transport in incompressible 2D gases, highlighting the roles of magnetoexcitons and chiral edge states in the thermo-optoelectric dynamics of Dirac quantum Hall state.

cond-mat.mes-hall↗

arXiv2025

Towards Transformer-Based Aligned Generation with Self-Coherence Guidance

We introduce a novel, training-free approach for enhancing alignment in Transformer-based Text-Guided Diffusion Models (TGDMs). Existing TGDMs often struggle to generate semantically aligned images, particularly when dealing with complex text prompts or multi-concept attribute binding challenges. Previous U-Net-based methods primarily optimized the latent space, but their direct application to Transformer-based architectures has shown limited effectiveness. Our method addresses these challenges by directly optimizing cross-attention maps during the generation process. Specifically, we introduce Self-Coherence Guidance, a method that dynamically refines attention maps using masks derived from previous denoising steps, ensuring precise alignment without additional training. To validate our approach, we constructed more challenging benchmarks for evaluating coarse-grained attribute binding, fine-grained attribute binding, and style binding. Experimental results demonstrate the superior performance of our method, significantly surpassing other state-of-the-art methods across all evaluated tasks. Our code is available at https://scg-diffusion.github.io/scg-diffusion.

cs.CV↗

arXiv2025

Adding Additional Control to One-Step Diffusion with Joint Distribution Matching

While diffusion distillation has enabled one-step generation through methods like Variational Score Distillation, adapting distilled models to emerging new controls -- such as novel structural constraints or latest user preferences -- remains challenging. Conventional approaches typically requires modifying the base diffusion model and redistilling it -- a process that is both computationally intensive and time-consuming. To address these challenges, we introduce Joint Distribution Matching (JDM), a novel approach that minimizes the reverse KL divergence between image-condition joint distributions. By deriving a tractable upper bound, JDM decouples fidelity learning from condition learning. This asymmetric distillation scheme enables our one-step student to handle controls unknown to the teacher model and facilitates improved classifier-free guidance (CFG) usage and seamless integration of human feedback learning (HFL). Experimental results demonstrate that JDM surpasses baseline methods such as multi-step ControlNet by mere one-step in most cases, while achieving state-of-the-art performance in one-step text-to-image synthesis through improved usage of CFG or HFL integration.

cs.CV↗

arXiv2025

Learning Few-Step Diffusion Models by Trajectory Distribution Matching

Accelerating diffusion model sampling is crucial for efficient AIGC deployment. While diffusion distillation methods -- based on distribution matching and trajectory matching -- reduce sampling to as few as one step, they fall short on complex tasks like text-to-image generation. Few-step generation offers a better balance between speed and quality, but existing approaches face a persistent trade-off: distribution matching lacks flexibility for multi-step sampling, while trajectory matching often yields suboptimal image quality. To bridge this gap, we propose learning few-step diffusion models by Trajectory Distribution Matching (TDM), a unified distillation paradigm that combines the strengths of distribution and trajectory matching. Our method introduces a data-free score distillation objective, aligning the student's trajectory with the teacher's at the distribution level. Further, we develop a sampling-steps-aware objective that decouples learning targets across different steps, enabling more adjustable sampling. This approach supports both deterministic sampling for superior image quality and flexible multi-step adaptation, achieving state-of-the-art performance with remarkable efficiency. Our model, TDM, outperforms existing methods on various backbones, such as SDXL and PixArt-$α$, delivering superior quality and significantly reduced training costs. In particular, our method distills PixArt-$α$ into a 4-step generator that outperforms its teacher on real user preference at 1024 resolution. This is accomplished with 500 iterations and 2 A800 hours -- a mere 0.01% of the teacher's training cost. In addition, our proposed TDM can be extended to accelerate text-to-video diffusion. Notably, TDM can outperform its teacher model (CogVideoX-2B) by using only 4 NFE on VBench, improving the total score from 80.91 to 81.65. Project page: https://tdm-t2x.github.io/

cs.CV↗

arXiv2025

Overlay-aware Variation Study of Flip FET and Benchmark with CFET

In this work, we carried out an overlay-aware variation study on Flip FET (FFET) considering the impact on RC parasitics induced by the lithography misalignment in backside processes, and benchmarked it with CFET in terms of the power-performance (PP) and variation sources. The iso-leakage frequency degrades up to 2.20% with layout misalignment of 4 nm. It's found that the Drain Merge resistance degrades significantly with misalignment increasing and is identified as the major variation source. Through careful DTCO with design rule optimization, the variation can be greatly suppressed, while the resistance fluctuation of the DM also drops substantially. Monte Carlo random experiments were also conducted, validating the variation reduction. Comparing with the CFET featuring self-aligned gate and much less overlay induced misalignment, fortunately, FFET's PP is still better except when misalignment reaches 8 nm, which is out of spec and nearly impossible. Considering the variabilities induced by the high aspect ratio processes, CFET still faces big challenges compared with FFET.

cond-mat.mes-hall↗

arXiv2024

Loss-driven miniaturized bound state in continuum biosensing system

Optical metasurface has brought a revolution in label-free molecular sensing, attracting extensive attention. Currently, such sensing approaches are being designed to respond to peak wavelengths with a higher Q factor in the visible and near-infrared regions.Nevertheless, a higher Q factor that enhances light confinement will inevitably deteriorate the wavelength sensitivity and complicate the sensing system. We propose a Q-switched sensing mechanism, which enables the real part of the refractive index to effectively perturbate the damping loss of the oscillator, resulting in a boost of peak intensity.Consequently, a higher Q factor in Q-switched sensor can further enhance the peak sensitivity while remaining compatible with broadband light sources, simultaneously meeting the requirements of high performance and a compact system.This is achieved in a unique 3D bound-state-in-continuum (BIC) metasurface which can be mass-produced by wafer-scale aluminum-nanoimprinting technology and provides a peak intensity sensitivity up to 928 %/RIU.Therefore, a miniaturized BIC biosensing system is realized, with a limit of detection to 10E-5 refractive index units and 129 aM extracellular vesicles in clinical lung cancer diagnosis, both of which are magnitudes lower than those of current state-of-the-art biosensors. It further demonstrates significant potential for home cancer self-testing equipment for post-operative follow-up. This Q-switched sensing mechanism offers a new perspective for the commercialization of advanced and practical BIC optical biosensing systems in real-setting scenarios.

physics.optics↗

arXiv2024

GTA: Global Tracklet Association for Multi-Object Tracking in Sports

Multi-object tracking in sports scenarios has become one of the focal points in computer vision, experiencing significant advancements through the integration of deep learning techniques. Despite these breakthroughs, challenges remain, such as accurately re-identifying players upon re-entry into the scene and minimizing ID switches. In this paper, we propose an appearance-based global tracklet association algorithm designed to enhance tracking performance by splitting tracklets containing multiple identities and connecting tracklets seemingly from the same identity. This method can serve as a plug-and-play refinement tool for any multi-object tracker to further boost their performance. The proposed method achieved a new state-of-the-art performance on the SportsMOT dataset with HOTA score of 81.04%. Similarly, on the SoccerNet dataset, our method enhanced multiple trackers' performance, consistently increasing the HOTA score from 79.41% to 83.11%. These significant and consistent improvements across different trackers and datasets underscore our proposed method's potential impact on the application of sports player tracking. We open-source our project codebase at https://github.com/sjc042/gta-link.git.

cs.CV↗

arXiv2024

From Flip FET to Flip 3D Integration (F3D): Maximizing the Scaling Potential of Wafer Both Sides Beyond Conventional 3D Integration

In this work, we proposed a new 3D integration technology: the Flip 3D integration (F3D), consisting of the 3D transistor stacking, the 3D dual-sided interconnects, the 3D die-to-die stacking and the dual-sided Monolithic 3D (M3D). Based on a 32-bit FFET RISCV core, besides the scaling benefits of the Flip FET (FFET), the dual-sided signal routing shows even more routing flexibility with 6.8% area reduction and 5.9% EDP improvement. Novel concepts of Multi-Flipping processes (Double Flips and Triple Flips) were proposed to relax the thermal budget constraints in the F3D and thus support the dual-sided M3D in the F3D. The core's EDP and frequency are improved by up to 3.2% and 2.3% respectively, after BEOL optimizations based on the Triple Flips compared with unoptimized ones.

cond-mat.mes-hall↗

arXiv2024

Shortcuts to adiabatic non-Abelian braiding on silicon photonic chips

The non-Abelian braiding describes the exchange behavior of anyons, which can be leveraged to encode qubits for quantum computing. Recently, this concept has been realized in classical photonic and acoustic systems. However, these implementations are constrained by adiabatic conditions, necessitating long operation distances and impeding practical applications. Here, we conceive and demonstrate a shortcut to adiabatic (STA) braiding of telecommunication light in three-dimensional silicon photonic chips. Our device comprises tri-layer silicon waveguides stacked and embedded in the SU-8 polymer, employing an STA strategy to expedite the braiding operations and give rise to compact devices that function as photonic quantum X, Y, and Z gates. We further experimentally observed non-Abelian braiding behaviors based on this STA-braiding scheme. Remarkably, this achievement represents the most compact braiding apparatus ever reported, with a size reduction of nearly three orders of magnitude compared to previous works. This work presents a feasible approach to accelerating adiabatic braiding evolutions, paving the way for compact, CMOS-compatible non-Abelian photonic devices.

physics.optics↗

arXiv2024

ToddlerAct: A Toddler Action Recognition Dataset for Gross Motor Development Assessment

Assessing gross motor development in toddlers is crucial for understanding their physical development and identifying potential developmental delays or disorders. However, existing datasets for action recognition primarily focus on adults, lacking the diversity and specificity required for accurate assessment in toddlers. In this paper, we present ToddlerAct, a toddler gross motor action recognition dataset, aiming to facilitate research in early childhood development. The dataset consists of video recordings capturing a variety of gross motor activities commonly observed in toddlers aged under three years old. We describe the data collection process, annotation methodology, and dataset characteristics. Furthermore, we benchmarked multiple state-of-the-art methods including image-based and skeleton-based action recognition methods on our datasets. Our findings highlight the importance of domain-specific datasets for accurate assessment of gross motor development in toddlers and lay the foundation for future research in this critical area. Our dataset will be available at https://github.com/ipl-uw/ToddlerAct.

cs.CV↗

arXiv2024

Bound-extended mode transition in type-II synthetic photonic Weyl heterostructures

Photonic structures with Weyl points (WPs), including type-I and type-II, promise nontrivial surface modes and intriguing light manipulations for their three-dimensional topological bands. While previous studies mainly focus on exploring WPs in a uniform Weyl structure, here we establish Weyl heterostructures (i.e., a nonuniform Weyl lattice) with different rotational orientations in the synthetic dimension by nanostructured photonic waveguides. In this work, we unveil a transition between bound and extended modes on the interface of type-II Weyl heterostructures by tuning their rotational phases, despite the reversed topological order across the interface. This mode transition is also manifested from the total transmission to total reflection at the interface. All of these unconventional effects are attributed to the tilted dispersion of type-II Weyl band structure that can lead to mismatched bands and gaps across the interface. As a comparison, the type-I Weyl heterostructures lack the phase transition due to the untilted band structure. This work establishes a flexible scheme of artificial Weyl heterostructures that opens a new avenue towards high-dimensional topological effects and significantly enhances our capabilities in on-chip light manipulations.

physics.optics↗

arXiv2024

Observation of topology transition in Floquet non-Hermitian skin effects in silicon photonics

Non-Hermitian physics has greatly enriched our understanding of nonequilibrium phenomena and uncovered novel effects such as the non-Hermitian skin effect (NHSE) that has profoundly revolutionized the field. NHSE is typically predicted in systems with nonreciprocal couplings which, however, are difficult to realize in experiments. Without nonreciprocal couplings, the NHSE can also emerge in systems with coexisting gauge fields and loss or gain (e.g., in Floquet non-Hermitian systems). However, such Floquet NHSE remains largely unexplored in experiments. Here, we realize the Floquet NHSEs in periodically modulated optical waveguides integrated on a silicon photonics platform. By engineering the artificial gauge fields induced by the periodical modulation, we observe various Floquet NHSEs and unveil their rich topological transitions. Remarkably, we discover the transitions between the normal unipolar NHSEs and an unconventional bipolar NHSE which is accompanied by the directional reversal of the NHSEs. The underlying physics is revealed by the band winding in complex quasienergy space which undergoes a topology change from isolated loops with the same winding to linked loops with opposite windings. Our work unfolds a new route toward Floquet NHSEs originating from the interplay between gauge fields and dissipation effects and offers fundamentally new ways for steering light and other waves.

physics.optics↗

arXiv2024

On an optimal problem of bilinear forms

We study an optimization problem originated from the Grothendieck constant. A generalized normal equation is proposed and analyzed. We establish a correspondence between solutions of the general normal equation and its dual equation. Explicit solutions are described for the two-dimensional case.

math.FA↗

arXiv2024

Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models

Due to the ease of training, ability to scale, and high sample quality, diffusion models (DMs) have become the preferred option for generative modeling, with numerous pre-trained models available for a wide variety of datasets. Containing intricate information about data distributions, pre-trained DMs are valuable assets for downstream applications. In this work, we consider learning from pre-trained DMs and transferring their knowledge to other generative models in a data-free fashion. Specifically, we propose a general framework called Diff-Instruct to instruct the training of arbitrary generative models as long as the generated samples are differentiable with respect to the model parameters. Our proposed Diff-Instruct is built on a rigorous mathematical foundation where the instruction process directly corresponds to minimizing a novel divergence we call Integral Kullback-Leibler (IKL) divergence. IKL is tailored for DMs by calculating the integral of the KL divergence along a diffusion process, which we show to be more robust in comparing distributions with misaligned supports. We also reveal non-trivial connections of our method to existing works such as DreamFusion, and generative adversarial training. To demonstrate the effectiveness and universality of Diff-Instruct, we consider two scenarios: distilling pre-trained diffusion models and refining existing GAN models. The experiments on distilling pre-trained diffusion models show that Diff-Instruct results in state-of-the-art single-step diffusion-based models. The experiments on refining GAN models show that the Diff-Instruct can consistently improve the pre-trained generators of GAN models across various settings.

cs.LG↗

arXiv2023

Iterative Scale-Up ExpansionIoU and Deep Features Association for Multi-Object Tracking in Sports

Deep learning-based object detectors have driven notable progress in multi-object tracking algorithms. Yet, current tracking methods mainly focus on simple, regular motion patterns in pedestrians or vehicles. This leaves a gap in tracking algorithms for targets with nonlinear, irregular motion, like athletes. Additionally, relying on the Kalman filter in recent tracking algorithms falls short when object motion defies its linear assumption. To overcome these issues, we propose a novel online and robust multi-object tracking approach named deep ExpansionIoU (Deep-EIoU), which focuses on multi-object tracking for sports scenarios. Unlike conventional methods, we abandon the use of the Kalman filter and leverage the iterative scale-up ExpansionIoU and deep features for robust tracking in sports scenarios. This approach achieves superior tracking performance without adopting a more robust detector, all while keeping the tracking process in an online fashion. Our proposed method demonstrates remarkable effectiveness in tracking irregular motion objects, achieving a score of 77.2% HOTA on the SportsMOT dataset and 85.4% HOTA on the SoccerNet-Tracking dataset. It outperforms all previous state-of-the-art trackers on various large-scale multi-object tracking benchmarks, covering various kinds of sports scenarios. The code and models are available at https://github.com/hsiangwei0903/Deep-EIoU.

cs.CV↗

arXiv2023

Gigahertz-rate-switchable wavefront shaping through integration of metasurfaces with photonic integrated circuit

Achieving spatiotemporal control of light at high-speeds presents immense possibilities for various applications in communication, computation, metrology, and sensing. The integration of subwavelength metasurfaces and optical waveguides offers a promising approach to manipulate light across multiple degrees of freedom at high-speed in compact photonic integrated circuit (PICs) devices. Here, we demonstrate a gigahertz-rate-switchable wavefront shaping by integrating metasurface, lithium niobite on insulator (LNOI) photonic waveguide and electrodes within a PIC device. As proofs of concept, we showcase the generation of a focus beam with reconfigurable arbitrary polarizations, switchable focusing with lateral focal positions and focal length, orbital angular momentum light beams (OAMs) as well as Bessel beams. Our measurements indicate modulation speeds of up to gigahertz rate. This integrated platform offers a versatile and efficient means of controlling light field at high-speed within a compact system, paving the way for potential applications in optical communication, computation, sensing, and imaging.

physics.optics↗

arXiv2023

Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels

In an effort to further advance semi-supervised generative and classification tasks, we propose a simple yet effective training strategy called dual pseudo training (DPT), built upon strong semi-supervised learners and diffusion models. DPT operates in three stages: training a classifier on partially labeled data to predict pseudo-labels; training a conditional generative model using these pseudo-labels to generate pseudo images; and retraining the classifier with a mix of real and pseudo images. Empirically, DPT consistently achieves SOTA performance of semi-supervised generation and classification across various settings. In particular, with one or two labels per class, DPT achieves a Fréchet Inception Distance (FID) score of 3.08 or 2.52 on ImageNet 256x256. Besides, DPT outperforms competitive semi-supervised baselines substantially on ImageNet classification tasks, achieving top-1 accuracies of 59.0 (+2.8), 69.5 (+3.0), and 74.4 (+2.0) with one, two, or five labels per class, respectively. Notably, our results demonstrate that diffusion can generate realistic images with only a few labels (e.g., <0.1%) and generative augmentation remains viable for semi-supervised classification. Our code is available at https://github.com/ML-GSAI/DPT.

cs.CV↗

arXiv2023

Complexity Matters: Rethinking the Latent Space for Generative Modeling

In generative modeling, numerous successful approaches leverage a low-dimensional latent space, e.g., Stable Diffusion models the latent space induced by an encoder and generates images through a paired decoder. Although the selection of the latent space is empirically pivotal, determining the optimal choice and the process of identifying it remain unclear. In this study, we aim to shed light on this under-explored topic by rethinking the latent space from the perspective of model complexity. Our investigation starts with the classic generative adversarial networks (GANs). Inspired by the GAN training objective, we propose a novel "distance" between the latent and data distributions, whose minimization coincides with that of the generator complexity. The minimizer of this distance is characterized as the optimal data-dependent latent that most effectively capitalizes on the generator's capacity. Then, we consider parameterizing such a latent distribution by an encoder network and propose a two-stage training strategy called Decoupled Autoencoder (DAE), where the encoder is only updated in the first stage with an auxiliary decoder and then frozen in the second stage while the actual decoder is being trained. DAE can improve the latent distribution and as a result, improve the generative performance. Our theoretical analyses are corroborated by comprehensive experiments on various models such as VQGAN and Diffusion Transformer, where our modifications yield significant improvements in sample quality with decreased model complexity.

cs.LG↗