arXiv subjects

Haochuan Li

Publications and source records attributed to Haochuan Li.

At least 19 recordsLinked to original sources

arXiv2026

Measuring Research Difficulty of Academic Papers: A Case Study in Natural Language Processing

With the rapid growth of the number of academic papers, systematically evaluating the difficulty of research and its relationship to academic impact offers important significance for research topic selection and resource allocation. However, current studies lack quantitative assessments of research difficulty and its correlation with academic impact. This paper proposes a comprehensive evaluation system for research difficulty, incorporating factors such as academic collaboration, content, and references. Taking the field of Natural Language Processing (NLP) as a case study, we extract both internal and external features from academic papers, compute multiple research difficulty indicators. We assign their weights using the entropy weight method and perform a weighted sum to obtain the research difficulty score of academic papers. This paper uses the citation frequency of academic papers to measure academic impact. To validate our approach, NLP experts assessed the difficulty of a sample of papers, and correlation analyses confirmed the reliability of our measurement. Empirical results reveal that in NLP, factors such as the number of pages, reference count, and participation of high-level institutions are significantly associated with academic impact. Moreover, we identify an inverted U-shaped relationship between research difficulty and academic impact. It suggests that moderately difficult research tends to achieve greater academic impact.

cs.DL↗

arXiv2025

Generation of a CW anti-bunched photon field from a thin-film PPLN waveguide by two-photon interference with a weak coherent state

An anti-bunched photon field is produced from a thin-film ppln waveguide by mixing the on-chip two-photon state with a weak but matched coherent state. This is achieved by taking out the two-photon part of the coherent state via a destructive two-photon interference with the on-chip two-photon state. We achieve a photon rate of 100 kHz with a g2-value of 0.35. This anti-bunched light field will have applications in high-resolution quantum imaging such as long baseline quantum telescopy for enhancing the signal-to-noise ratio.

quant-ph↗

arXiv2025

Two-Stage Lithium Niobate Nonlinear Photonic Circuits for Low-Crosstalk and Broadband All Optical Wavelength Conversion

All optical wavelength converters (AOWCs) that can effectively and flexibly switch optical signals between different wavelength channels are essential elements in future optical fiber communications and quantum information systems. A promising strategy for achieving high-performance AOWCs is to leverage strong three-wave mixing processes in second-order nonlinear nanophotonic devices, specifically thin-film periodically poled lithium niobate (TF-PPLN) waveguides. By exploiting the advantages of sub-wavelength light confinement and dispersion engineering compared with their bulk counterparts, TF-PPLN waveguides provide a viable route for realizing highly efficient and broadband wavelength conversion. Nevertheless, most existing approaches rely on a single TF-PPLN device to perform both frequency doubling of the telecom pump and the wavelength conversion process, resulting in significant crosstalk between adjacent signal channels. Here, we address this challenge by demonstrating a two-stage TF-PPLN nonlinear photonic circuit that integrates a second-harmonic generation module, a signal wavelength conversion module, and multiple adiabatic directional coupler-based pump filters, on a single chip. By decoupling the two nonlinear processes and leveraging the high pump-filtering extinction ratio, we achieve low-crosstalk AOWC with a side-channel suppression ratio exceeding 25 dB, substantially surpassing the performance of single-stage devices. Furthermore, our device exhibits an ultra-broad conversion bandwidth of 110 nm and a relatively high conversion efficiency of -15.6 dB, making it an attractive solution for future photonic systems. The two-stage AOWC design shows promise for low-noise phase-sensitive amplification and quantum frequency conversion in future classical and quantum photonic systems.

physics.optics↗

arXiv2024

SILMM: Self-Improving Large Multimodal Models for Compositional Text-to-Image Generation

Large Multimodal Models (LMMs) have demonstrated impressive capabilities in multimodal understanding and generation, pushing forward advancements in text-to-image generation. However, achieving accurate text-image alignment for LMMs, particularly in compositional scenarios, remains challenging. Existing approaches, such as layout planning for multi-step generation and learning from human feedback or AI feedback, depend heavily on prompt engineering, costly human annotations, and continual upgrading, limiting flexibility and scalability. In this work, we introduce a model-agnostic iterative self-improvement framework (SILMM) that can enable LMMs to provide helpful and scalable self-feedback and optimize text-image alignment via Direct Preference Optimization (DPO). DPO can readily applied to LMMs that use discrete visual tokens as intermediate image representations; while it is less suitable for LMMs with continuous visual features, as obtaining generation probabilities is challenging. To adapt SILMM to LMMs with continuous features, we propose a diversity mechanism to obtain diverse representations and a kernel-based continuous DPO for alignment. Extensive experiments on three compositional text-to-image generation benchmarks validate the effectiveness and superiority of SILMM, showing improvements exceeding 30% on T2I-CompBench++ and around 20% on DPG-Bench.

cs.CV↗

arXiv2024

Efficient cryogenic nonlinear conversion processes in periodically-poled thin-film lithium niobate waveguides

Periodically poled thin-film lithium niobate (TFLN) waveguides, which enable efficient quadratic nonlinear processes, serve as crucial foundation for classical and quantum signal processing. To expand their application scope, we provide the first investigation of nonlinear conversion processes in periodically poled TFLN waveguides at cryogenic condition (7 K). Through systematic experimental characterization, we find that the periodically poled TFLN waveguide retains its high conversion efficiency at both cryogenic and room temperatures for both classical second-harmonic generation and quantum photon-pair generation processes. Particularly, the photon-pair source at cryogenic condition shows high brightness and broad bandwidth. These results demonstrate the significant potential of TFLN wavelength conversion devices for cryogenic applications and foster future scalable quantum photonic systems.

physics.optics↗

arXiv2024

TIGeR: Unifying Text-to-Image Generation and Retrieval with Large Multimodal Models

How humans can effectively and efficiently acquire images has always been a perennial question. A classic solution is text-to-image retrieval from an existing database; however, the limited database typically lacks creativity. By contrast, recent breakthroughs in text-to-image generation have made it possible to produce attractive and counterfactual visual content, but it faces challenges in synthesizing knowledge-intensive images. In this work, we rethink the relationship between text-to-image generation and retrieval, proposing a unified framework for both tasks with one single Large Multimodal Model (LMM). Specifically, we first explore the intrinsic discriminative abilities of LMMs and introduce an efficient generative retrieval method for text-to-image retrieval in a training-free manner. Subsequently, we unify generation and retrieval autoregressively and propose an autonomous decision mechanism to choose the best-matched one between generated and retrieved images as the response to the text prompt. To standardize the evaluation of unified text-to-image generation and retrieval, we construct TIGeR-Bench, a benchmark spanning both creative and knowledge-intensive domains. Extensive experiments on TIGeR-Bench and two retrieval benchmarks, i.e., Flickr30K and MS-COCO, demonstrate the superiority of our proposed framework.

cs.CV↗

arXiv2024

Advancing large-scale thin-film PPLN nonlinear photonics with segmented tunable micro-heaters

Thin-film periodically poled lithium niobate (TF-PPLN) devices have recently gained prominence for efficient wavelength conversion processes in both classical and quantum applications. However, the patterning and poling of TF-PPLN devices today are mostly performed at chip scales, presenting a significant bottleneck for future large-scale nonlinear photonic systems that require the integration of multiple nonlinear components with consistent performance and low cost. Here, we take a pivotal step towards this goal by developing a wafer-scale TF-PPLN nonlinear photonic platform, leveraging ultraviolet stepper lithography and an automated poling process. To address the inhomogeneous broadening of the quasi-phase matching (QPM) spectrum induced by film thickness variations across the wafer, we propose and demonstrate segmented thermal optic tuning modules that can precisely adjust and align the QPM peak wavelengths in each section. \hl{Using the segmented micro-heaters, we show the successful realignment of inhomogeneously broadened multi-peak QPM spectra with up to 57$\%$ enhancement of conversion efficiency. We achieve a high normalized conversion efficiency of 3802$\%$W$^{-1}$cm$^{-2}$ in a 6 mm long PPLN waveguide, recovering 84$\%$ of the theoretically predicted efficiency in this device.} The advanced fabrication techniques and segmented tuning architectures presented herein pave the way for wafer-scale integration of complex functional nonlinear photonic circuits with applications in quantum information processing, precision sensing and metrology, and low-noise-figure optical signal amplification.

physics.optics↗

arXiv2023

Convergence of Adam Under Relaxed Assumptions

In this paper, we provide a rigorous proof of convergence of the Adaptive Moment Estimate (Adam) algorithm for a wide class of optimization objectives. Despite the popularity and efficiency of the Adam algorithm in training deep neural networks, its theoretical properties are not yet fully understood, and existing convergence proofs require unrealistically strong assumptions, such as globally bounded gradients, to show the convergence to stationary points. In this paper, we show that Adam provably converges to $ε$-stationary points with ${O}(ε^{-4})$ gradient complexity under far more realistic conditions. The key to our analysis is a new proof of boundedness of gradients along the optimization trajectory of Adam, under a generalized smoothness assumption according to which the local smoothness (i.e., Hessian norm when it exists) is bounded by a sub-quadratic function of the gradient norm. Moreover, we propose a variance-reduced version of Adam with an accelerated gradient complexity of ${O}(ε^{-3})$.

math.OC↗

arXiv2023

Convex and Non-convex Optimization Under Generalized Smoothness

Classical analysis of convex and non-convex optimization methods often requires the Lipshitzness of the gradient, which limits the analysis to functions bounded by quadratics. Recent work relaxed this requirement to a non-uniform smoothness condition with the Hessian norm bounded by an affine function of the gradient norm, and proved convergence in the non-convex setting via gradient clipping, assuming bounded noise. In this paper, we further generalize this non-uniform smoothness condition and develop a simple, yet powerful analysis technique that bounds the gradients along the trajectory, thereby leading to stronger results for both convex and non-convex optimization problems. In particular, we obtain the classical convergence rates for (stochastic) gradient descent and Nesterov's accelerated gradient method in the convex and/or non-convex setting under this general smoothness condition. The new analysis approach does not require gradient clipping and allows heavy-tailed noise with bounded variance in the stochastic setting.

math.OC↗

arXiv2023

Unveiling the origins of quasi-phase matching spectral imperfections in thin-film lithium niobate frequency doublers

Thin-film lithium niobate (TFLN) based frequency doublers have been widely recognized as essential components for both classical and quantum optical communications. Nonetheless, the efficiency of these devices is hindered by imperfections present in the quasi-phase matching (QPM) spectrum. In this study, we present a thorough analysis of the spectral imperfections in TFLN frequency doublers with varying lengths, ranging from 5 mm to 15 mm. Employing a non-destructive diagnostic method based on scattered light imaging, we identify the sources and waveguide sections that contribute to the imperfections in the QPM spectrum. Furthermore, by mapping the TFLN film thickness across the entire waveguiding regions, we successfully reproduce the QPM spectra numerically, thus confirming the prominent influence of film thickness variations on the observed spectral imperfections. This comprehensive investigation provides valuable insights into the identification and mitigation of spectral imperfections in TFLN-based frequency doublers, paving the way toward the realization of nonlinear optical devices with enhanced efficiency and improved spectral fidelity.

physics.optics↗

arXiv2023

Variance-reduced Clipping for Non-convex Optimization

Gradient clipping is a standard training technique used in deep learning applications such as large-scale language modeling to mitigate exploding gradients. Recent experimental studies have demonstrated a fairly special behavior in the smoothness of the training objective along its trajectory when trained with gradient clipping. That is, the smoothness grows with the gradient norm. This is in clear contrast to the well-established assumption in folklore non-convex optimization, a.k.a. $L$--smoothness, where the smoothness is assumed to be bounded by a constant $L$ globally. The recently introduced $(L_0,L_1)$--smoothness is a more relaxed notion that captures such behavior in non-convex optimization. In particular, it has been shown that under this relaxed smoothness assumption, SGD with clipping requires $O(ε^{-4})$ stochastic gradient computations to find an $ε$--stationary solution. In this paper, we employ a variance reduction technique, namely SPIDER, and demonstrate that for a carefully designed learning rate, this complexity is improved to $O(ε^{-3})$ which is order-optimal. Our designed learning rate comprises the clipping technique to mitigate the growing smoothness. Moreover, when the objective function is the average of $n$ components, we improve the existing $O(nε^{-2})$ bound on the stochastic gradient complexity to $O(\sqrt{n} ε^{-2} + n)$, which is order-optimal as well. In addition to being theoretically optimal, SPIDER with our designed parameters demonstrates comparable empirical performance against variance-reduced methods such as SVRG and SARAH in several vision tasks.

cs.LG↗

arXiv2022

Tight Analysis of Extra-gradient and Optimistic Gradient Methods For Nonconvex Minimax Problems

Despite the established convergence theory of Optimistic Gradient Descent Ascent (OGDA) and Extragradient (EG) methods for the convex-concave minimax problems, little is known about the theoretical guarantees of these methods in nonconvex settings. To bridge this gap, for the first time, this paper establishes the convergence of OGDA and EG methods under the nonconvex-strongly-concave (NC-SC) and nonconvex-concave (NC-C) settings by providing a unified analysis through the lens of single-call extra-gradient methods. We further establish lower bounds on the convergence of GDA/OGDA/EG, shedding light on the tightness of our analysis. We also conduct experiments supporting our theoretical results. We believe our results will advance the theoretical understanding of OGDA and EG methods for solving complicated nonconvex minimax real-world problems, e.g., Generative Adversarial Networks (GANs) or robust neural networks training.

cs.LG↗

arXiv2022

Residuals of an Equilibrium Model for the Galaxy Reveal a State of Disequilibrium in the Solar Neighborhood

We simultaneously model the gravitational potential and phase space distribution function (DF) of giant stars near the Sun using the {\it Gaia} DR2 radial velocity catalog. We assume that the Galaxy is in equilibrium and is symmetric about both the spin axis of the disk and the Galactic midplane. The potential is taken as a sum of terms that nominally represent contributions from the gas disk, stellar disk, bulge, and dark matter halo. Our DF model for the giants comprise two components to account for a mix of thin and thick disk stars. The DF for each component is described by an analytic function of the energy, the spin angular momentum, and the vertical energy, in accord with Jeans theorem. We present model predictions for the radial and vertical forces within $\sim 2\,{\rm kpc}$ of the Sun, highlighting the rotation curve and vertical force profile in the Solar Neighbourhood. Finally, we show residuals for star counts in the $R-z$ and $z-v_z$ planes as well as maps of the mean radial and azimuthal velocities in the $z-v_z$ plane. Using our model for the potential, we also examine the star count residuals in action-frequency-angle coordinates. The {\it Gaia} phase spirals, velocity arches, some of the known moving groups and bending modes appear as well-defined features in these maps.

astro-ph.GA↗

arXiv2022

On Convergence of Gradient Descent Ascent: A Tight Local Analysis

Gradient Descent Ascent (GDA) methods are the mainstream algorithms for minimax optimization in generative adversarial networks (GANs). Convergence properties of GDA have drawn significant interest in the recent literature. Specifically, for $\min_{\mathbf{x}} \max_{\mathbf{y}} f(\mathbf{x};\mathbf{y})$ where $f$ is strongly-concave in $\mathbf{y}$ and possibly nonconvex in $\mathbf{x}$, (Lin et al., 2020) proved the convergence of GDA with a stepsize ratio $η_{\mathbf{y}}/η_{\mathbf{x}}=Θ(κ^2)$ where $η_{\mathbf{x}}$ and $η_{\mathbf{y}}$ are the stepsizes for $\mathbf{x}$ and $\mathbf{y}$ and $κ$ is the condition number for $\mathbf{y}$. While this stepsize ratio suggests a slow training of the min player, practical GAN algorithms typically adopt similar stepsizes for both variables, indicating a wide gap between theoretical and empirical results. In this paper, we aim to bridge this gap by analyzing the \emph{local convergence} of general \emph{nonconvex-nonconcave} minimax problems. We demonstrate that a stepsize ratio of $Θ(κ)$ is necessary and sufficient for local convergence of GDA to a Stackelberg Equilibrium, where $κ$ is the local condition number for $\mathbf{y}$. We prove a nearly tight convergence rate with a matching lower bound. We further extend the convergence guarantees to stochastic GDA and extra-gradient methods (EG). Finally, we conduct several numerical experiments to support our theoretical findings.

math.OC↗

arXiv2022

Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective

This work examines the deep disconnect between existing theoretical analyses of gradient-based algorithms and the practice of training deep neural networks. Specifically, we provide numerical evidence that in large-scale neural network training (e.g., ImageNet + ResNet101, and WT103 + TransformerXL models), the neural network's weights do not converge to stationary points where the gradient of the loss is zero. Remarkably, however, we observe that even though the weights do not converge to stationary points, the progress in minimizing the loss function halts and training loss stabilizes. Inspired by this observation, we propose a new perspective based on ergodic theory of dynamical systems to explain it. Rather than studying the evolution of weights, we study the evolution of the distribution of weights. We prove convergence of the distribution of weights to an approximate invariant measure, thereby explaining how the training loss can stabilize without weights necessarily converging to stationary points. We further discuss how this perspective can better align optimization theory with empirical observations in machine learning practice.

cs.LG↗

arXiv2022

Byzantine-Robust Federated Linear Bandits

In this paper, we study a linear bandit optimization problem in a federated setting where a large collection of distributed agents collaboratively learn a common linear bandit model. Standard federated learning algorithms applied to this setting are vulnerable to Byzantine attacks on even a small fraction of agents. We propose a novel algorithm with a robust aggregation oracle that utilizes the geometric median. We prove that our proposed algorithm is robust to Byzantine attacks on fewer than half of agents and achieves a sublinear $\tilde{\mathcal{O}}({T^{3/4}})$ regret with $\mathcal{O}(\sqrt{T})$ steps of communication in $T$ steps. Moreover, we make our algorithm differentially private via a tree-based mechanism. Finally, if the level of corruption is known to be small, we show that using the geometric median of mean oracle for robust aggregation further improves the regret bound.

cs.LG↗

arXiv2021

Lightcurve Evolution of the nearest Tidal Disruption Event: A late-time, radio-only flare

Tidal disruption events (TDEs) occur when a star passes close enough to a galaxy's supermassive black hole to be disrupted by tidal forces. We discuss new observations of IGRJ12580+0134, a TDE observed in NGC 4845 (d=17 Mpc) in November 2010, with the Karl G. Jansky Very Large Array (JVLA). We also discuss a reanalysis of 2010-2011 Swift and XMM-Newton observations, as well as new, late-time Swift observations. Our JVLA observations show a decay of the nuclear radio flux until 2015, when a plateau was seen, and then a significant (~factor 3) radio flare during 2016. The 2016 radio flare was also accompanied by radio spectral changes, but was not seen in the X-rays. We model the flare as resulting from the interaction of the nuclear jet with a cloud in the interstellar medium. This is distinct from late-time X-ray flares in a few other TDEs where changes in the accretion state and/or a fallback event were suggested, neither of which appears possible in this case. Our reanalysis of the Swift and XMM-Newton data from 2011 shows significant evidence for thermal emission from a disk, as well as a very soft power-law. This, in addition to the extreme X-ray flux increase seen in 2010 (a factor of >$100) bolsters the identification of IGRJ12580+0134 as a TDE, not an unusual AGN variability event.

astro-ph.HE↗

arXiv2021

Room-temperature continuous-wave Dirac-vortex topological lasers on silicon

Robust laser sources are a fundamental building block for contemporary information technologies. Originating from condensed-matter physics, the concept of topology has recently entered the realm of optics, offering fundamentally new design principles for lasers with enhanced robustness. In analogy to the well-known Majorana fermions in topological superconductors, Dirac-vortex states have recently been investigated in passive photonic systems and are now considered as a promising candidate for single-mode large-area lasers. Here, we experimentally realize the first Dirac-vortex topological lasers in InAs/InGaAs quantum-dot materials monolithically grown on a silicon substrate. We observe room-temperature continuous-wave single-mode linearly polarized vertical laser emission at a telecom wavelength. Most importantly, we confirm that the wavelength of the Dirac-vortex laser is topologically robust against variations in the cavity size, and its free spectral range defies the universal inverse scaling law with the cavity size. These lasers will play an important role in CMOS-compatible photonic and optoelectronic systems on a chip.

physics.optics↗