arXiv subjects

Pengfei Li

Publications and source records attributed to Pengfei Li.

At least 127 records · Page 7Linked to original sources

arXiv2025

The coherence peak of unconventional superconductors in the charge channel

In this work, we carry out a systematic investigation of the coherence peak in unconventional superconductors as they transition into the superconducting phase at $T_c$. Using $d$-wave cuprates as an example, we reveal the presence of a coherence peak below $T_c$ in the charge channel. The nuclear quadrupole relaxation rate is shown to be an effective method for detecting this unconventional coherence peak, with the superconducting coherence factor playing a pivotal role in its emergence. Additionally, we explore the influence of correlation effects, which further enhance this phenomenon. Extending our analysis, we demonstrate the existence of a similar coherence peak in ultrasonic attenuation and iron-based superconductors. Our findings offer a fresh perspective on probing superconducting gap symmetry in unconventional superconductors.

cond-mat.supr-con↗

arXiv2025

Unconventional Coherence Peak in Cuprate Superconductors

The Hebel-Slichter coherence peak, observed in the spin-lattice relaxation rate $1/T_1$ just below the critical temperature $T_{\rm c}$, serves as a crucial experimental validation of the Bardeen-Cooper-Schrieffer pairing symmetry in conventional superconductors. However, no coherence peak in $1/T_1$ has been observed in unconventional superconductors like cuprates. In this study, an unconventional coherence peak is identified for the first time using nuclear quadrupole resonance on YBa$_2$Cu$_4$O$_8$, pointing to a distinctive pairing symmetry. The spin-lattice relaxation rate in nuclear quadrupole resonance and nuclear magnetic resonance with nuclear spin $I>1/2$ comprises the magnetic relaxation rate $1/T_{1}^{\rm mag}$, which probes magnetic fluctuations, and the quadrupole relaxation rate $1/T_{1}^{\rm quad}$, which probes charge fluctuations. By utilizing $^{63}$Cu and $^{65}$Cu isotopes, we successfully distinguish $1/T_{1}^{\rm mag}$ and $1/T_{1 }^{\rm quad}$ of YBa$_2$Cu$_4$O$_8$ and reveal the presence of the coherence peak in $1/T_{1 }^{\rm quad}$ but not in $1/T_{1}^{\rm mag}$, in contrast to conventional superconductors. Our finding demonstrates that unconventional superconductors do not exhibit a coherence peak in $1/T_{1}$ when the relaxation is due to fluctuations of the hyperfine field. Conversely, a coherence peak is expected when the relaxation is caused by electric field gradient fluctuations, due to the different coherence factors between charge and magnetic fluctuations. Our successful measurements of $1/T_{1}$ for the chains of YBa$_2$Cu$_4$O$_8$ suggest that, should the conditions for predominant quadrupole relaxation be satisfied, this phenomenon could provide a novel approach to exploring the unconventional nature of the pairing mechanism in other superconductors.

cond-mat.supr-con↗

arXiv2024

LiON: Learning Point-wise Abstaining Penalty for LiDAR Outlier DetectioN Using Diverse Synthetic Data

LiDAR-based semantic scene understanding is an important module in the modern autonomous driving perception stack. However, identifying outlier points in a LiDAR point cloud is challenging as LiDAR point clouds lack semantically-rich information. While former SOTA methods adopt heuristic architectures, we revisit this problem from the perspective of Selective Classification, which introduces a selective function into the standard closed-set classification setup. Our solution is built upon the basic idea of abstaining from choosing any inlier categories but learns a point-wise abstaining penalty with a margin-based loss. Apart from learning paradigms, synthesizing outliers to approximate unlimited real outliers is also critical, so we propose a strong synthesis pipeline that generates outliers originated from various factors: object categories, sampling patterns and sizes. We demonstrate that learning different abstaining penalties, apart from point-wise penalty, for different types of (synthesized) outliers can further improve the performance. We benchmark our method on SemanticKITTI and nuScenes and achieve SOTA results. Codes are available at https://github.com/Daniellli/LiON/.

cs.CV↗

arXiv2024

Fast and Slow Gradient Approximation for Binary Neural Network Optimization

Binary Neural Networks (BNNs) have garnered significant attention due to their immense potential for deployment on edge devices. However, the non-differentiability of the quantization function poses a challenge for the optimization of BNNs, as its derivative cannot be backpropagated. To address this issue, hypernetwork based methods, which utilize neural networks to learn the gradients of non-differentiable quantization functions, have emerged as a promising approach due to their adaptive learning capabilities to reduce estimation errors. However, existing hypernetwork based methods typically rely solely on current gradient information, neglecting the influence of historical gradients. This oversight can lead to accumulated gradient errors when calculating gradient momentum during optimization. To incorporate historical gradient information, we design a Historical Gradient Storage (HGS) module, which models the historical gradient sequence to generate the first-order momentum required for optimization. To further enhance gradient generation in hypernetworks, we propose a Fast and Slow Gradient Generation (FSG) method. Additionally, to produce more precise gradients, we introduce Layer Recognition Embeddings (LRE) into the hypernetwork, facilitating the generation of layer-specific fine gradients. Extensive comparative experiments on the CIFAR-10 and CIFAR-100 datasets demonstrate that our method achieves faster convergence and lower loss values, outperforming existing baselines.Code is available at http://github.com/two-tiger/FSG .

cs.LG↗

arXiv2024

Receiver operating characteristic curve analysis with non-ignorable missing disease status

This article considers the receiver operating characteristic (ROC) curve analysis for medical data with non-ignorable missingness in the disease status. In the framework of the logistic regression models for both the disease status and the verification status, we first establish the identifiability of model parameters, and then propose a likelihood method to estimate the model parameters, the ROC curve, and the area under the ROC curve (AUC) for the biomarker. The asymptotic distributions of these estimators are established. Via extensive simulation studies, we compare our method with competing methods in the point estimation and assess the accuracy of confidence interval estimation under various scenarios. To illustrate the application of the proposed method in practical data, we apply our method to the National Alzheimer's Coordinating Center data set.

stat.ME↗

arXiv2024

Online Budgeted Matching with General Bids

Online Budgeted Matching (OBM) is a classic problem with important applications in online advertising, online service matching, revenue management, and beyond. Traditional online algorithms typically assume a small bid setting, where the maximum bid-to-budget ratio (κ) is infinitesimally small. While recent algorithms have tried to address scenarios with non-small or general bids, they often rely on the Fractional Last Matching (FLM) assumption, which allows for accepting partial bids when the remaining budget is insufficient. This assumption, however, does not hold for many applications with indivisible bids. In this paper, we remove the FLM assumption and tackle the open problem of OBM with general bids. We first establish an upper bound of 1-κon the competitive ratio for any deterministic online algorithm. We then propose a novel meta algorithm, called MetaAd, which reduces to different algorithms with first known provable competitive ratios parameterized by the maximum bid-to-budget ratio κ\in [0, 1]. As a by-product, we extend MetaAd to the FLM setting and get provable competitive algorithms. Finally, we apply our competitive analysis to the design learning-augmented algorithms.

cs.GT↗

arXiv2024

Gas thermodynamics meets galaxy kinematics: Joint mass measurements for eROSITA galaxy clusters

The mass of galaxy clusters is a critical quantity for probing cluster cosmology and testing theories of gravity, but its measurement could be biased given assumptions are inevitable. In this paper, we employ and compare two mass proxies for galaxy clusters: thermodynamics of the intracluster medium and kinematics of member galaxies. We select 22 galaxy clusters from the cluster catalog in the first SRG/eROSITA All-Sky Survey (eRASS1) that have sufficient optical and near-infrared observations. We generate multi-band images in the energy range of (0.3, 7) keV for each cluster, and derive their temperature profiles, gas mass profiles and hydrostatic mass profiles using a parametric approach that does not assume dark matter halo models. With spectroscopically confirmed member galaxies collected from multiple surveys, we numerically solve the spherical Jeans equation for their dynamical mass profiles. Our results quantify the correlation between dynamical mass and line-of-sight velocity dispersion with an rms scatter of 0.14 dex. We find the two mass proxies lead to roughly the same total mass, with no observed systematic bias. As such, the $σ_8$ tension is not specific to hydrostatic mass or weak lensing shears, but also appears with galaxy kinematics. We also compare our hydrostatic masses with the latest weak lensing masses inferred with scaling relations. The comparison shows the weak lensing mass is significantly higher than our hydrostatic mass by $\sim$110%. This might explain the significantly larger value of $σ_8$ from the latest measurement using eRASS1 clusters than almost all previous estimates in the literature. Finally, we test the radial acceleration relation (RAR) established in disk galaxies. We confirm the missing baryon problem in the inner region of galaxy clusters using three independent mass proxies for the first time.

astro-ph.CO↗

arXiv2024

The empirical laws of galaxy dynamics: from gas kinematics to weak lensing

Galaxies obey a set of strict dynamical laws, which imply a close coupling between the visible matter (stars and gas) and the observed dynamics (set by dark matter in the standard cosmological context). Here we review recent results from weak gravitational lensing, which allows studying the empirical laws of galaxy dynamics out to exceedingly large radii in both late-type galaxies (LTGs) and early-type galaxies (ETGs). We focus on three laws: (1) the circular velocity curves of both LTGs and ETGs remain indefinitely flat out to several hundreds of kpc; (2) the same baryonic Tully-Fisher relation is followed by LTGs and ETGs; (3) the same radial acceleration relation (RAR) is followed by LTGs and ETGs. Combining galaxy data with Solar System data, the RAR covers about 16 orders of magnitude in the Newtonian baryonic acceleration. Remarkably, these empirical facts were predicted a priori by MOND.

astro-ph.GA↗

arXiv2024

Parameter-Efficient Fine-Tuning Medical Multimodal Large Language Models for Medical Visual Grounding

Multimodal Large Language Models (MLLMs) inherit the superior text understanding capabilities of LLMs and extend these capabilities to multimodal scenarios. These models achieve excellent results in the general domain of multimodal tasks. However, in the medical domain, the substantial training costs and the requirement for extensive medical data pose challenges to the development of medical MLLMs. Furthermore, due to the free-text form of answers, tasks such as visual grounding that need to produce output in a prescribed form become difficult for MLLMs. So far, there have been no medical MLLMs works in medical visual grounding area. For the medical vision grounding task, which involves identifying locations in medical images based on short text descriptions, we propose Parameter-efficient Fine-tuning medical multimodal large language models for Medcial Visual Grounding (PFMVG). To validate the performance of the model, we evaluate it on a public benchmark dataset for medical visual grounding, where it achieves competitive results, and significantly outperforming GPT-4v. Our code will be open sourced after peer review.

cs.CV↗

arXiv2024

Learning-Augmented Decentralized Online Convex Optimization in Networks

This paper studies decentralized online convex optimization in a networked multi-agent system and proposes a novel algorithm, Learning-Augmented Decentralized Online optimization (LADO), for individual agents to select actions only based on local online information. LADO leverages a baseline policy to safeguard online actions for worst-case robustness guarantees, while staying close to the machine learning (ML) policy for average performance improvement. In stark contrast with the existing learning-augmented online algorithms that focus on centralized settings, LADO achieves strong robustness guarantees in a decentralized setting. We also prove the average cost bound for LADO, revealing the tradeoff between average performance and worst-case robustness and demonstrating the advantage of training the ML policy by explicitly considering the robustness requirement.

cs.LG↗

arXiv2024

Dual-AEB: Synergizing Rule-Based and Multimodal Large Language Models for Effective Emergency Braking

Automatic Emergency Braking (AEB) systems are a crucial component in ensuring the safety of passengers in autonomous vehicles. Conventional AEB systems primarily rely on closed-set perception modules to recognize traffic conditions and assess collision risks. To enhance the adaptability of AEB systems in open scenarios, we propose Dual-AEB, a system combines an advanced multimodal large language model (MLLM) for comprehensive scene understanding and a conventional rule-based rapid AEB to ensure quick response times. To the best of our knowledge, Dual-AEB is the first method to incorporate MLLMs within AEB systems. Through extensive experimentation, we have validated the effectiveness of our method. The source code will be available at https://github.com/ChipsICU/Dual-AEB.

cs.RO↗

arXiv2024

Exploring Adversarial Robustness of Deep State Space Models

Deep State Space Models (SSMs) have proven effective in numerous task scenarios but face significant security challenges due to Adversarial Perturbations (APs) in real-world deployments. Adversarial Training (AT) is a mainstream approach to enhancing Adversarial Robustness (AR) and has been validated on various traditional DNN architectures. However, its effectiveness in improving the AR of SSMs remains unclear. While many enhancements in SSM components, such as integrating Attention mechanisms and expanding to data-dependent SSM parameterizations, have brought significant gains in Standard Training (ST) settings, their potential benefits in AT remain unexplored. To investigate this, we evaluate existing structural variants of SSMs with AT to assess their AR performance. We observe that pure SSM structures struggle to benefit from AT, whereas incorporating Attention yields a markedly better trade-off between robustness and generalization for SSMs in AT compared to other components. Nonetheless, the integration of Attention also leads to Robust Overfitting (RO) issues. To understand these phenomena, we empirically and theoretically analyze the output error of SSMs under AP. We find that fixed-parameterized SSMs have output error bounds strictly related to their parameters, limiting their AT benefits, while input-dependent SSMs may face the problem of error explosion. Furthermore, we show that the Attention component effectively scales the output error of SSMs during training, enabling them to benefit more from AT, but at the cost of introducing RO due to its high model complexity. Inspired by this, we propose a simple and effective Adaptive Scaling (AdS) mechanism that brings AT performance close to Attention-integrated SSMs without introducing the issue of RO. Our code is available at https://github.com/Biqing-Qi/Exploring-Adversarial-Robustness-of-Deep-State-Space-Models.git.

cs.LG↗

arXiv2024

Statistical Inference with Nonignorable Non-Probability Survey Samples

Statistical inference with non-probability survey samples is an emerging topic in survey sampling and official statistics and has gained increased attention from researchers and practitioners in the field. Much of the existing literature, however, assumes that the participation mechanism for non-probability samples is ignorable. In this paper, we develop a pseudo-likelihood approach to estimate participation probabilities for nonignorable non-probability samples when auxiliary information is available from an existing reference probability sample. We further construct three estimators for the finite population mean using regression-based prediction, inverse probability weighting (IPW), and augmented IPW estimators, and study their asymptotic properties. Variance estimation for the proposed methods is considered within the same framework. The efficiency of our proposed methods is demonstrated through simulation studies and a real data analysis using the ESPACOV survey on the effects of the COVID-19 pandemic in Spain.

stat.ME↗

arXiv2024

Paramagnetic contribution in superconductors with different-mass Cooper pairs

Cooper pairs formed by two electrons with different effective masses are common in multiband superconductors, pair density wave states, and other superconducting systems with multiple degrees of freedom. In this paper, we show that there are paramagnetic contributions to the superfluid stiffness in superconductors with different-mass Cooper pairs. This paramagnetic response is owing to the relative motion between two electrons with different mass. Additionally, we identify a particle-hole dichotomy effect related to this mass difference. We investigate the paramagnetic contributions based on the linear-response theory in two-band superconductors with interband pairings and in pair density wave states, respectively. Our results offer a different perspective of the electromagnetic superfluid stiffness in unconventional superconductors beyond the traditional BCS response.

cond-mat.supr-con↗

arXiv2024

Symmetry-breaking bifurcations of pure-quartic solitons in dual-core couplers

We investigate spontaneous symmetry- and antisymmetry-breaking bifurcations of solitons in a nonlinear dual-core waveguide with the pure-quartic dispersion and Kerr nonlinearity. Symmetric, antisymmetric, and asymmetric pure-quartic solitons (PQSs) are found, and their stability domains are identified. The bifurcations for both the symmetric and antisymmetric PQSs are of the supercritical type (alias phase transitions of the second kind). Direct simulations of the perturbed evolution of PQSs corroborate their stability boundaries predicted by the analysis of small perturbations.

nlin.PS↗

arXiv2024

OccLLaMA: An Occupancy-Language-Action Generative World Model for Autonomous Driving

The rise of multi-modal large language models(MLLMs) has spurred their applications in autonomous driving. Recent MLLM-based methods perform action by learning a direct mapping from perception to action, neglecting the dynamics of the world and the relations between action and world dynamics. In contrast, human beings possess world model that enables them to simulate the future states based on 3D internal visual representation and plan actions accordingly. To this end, we propose OccLLaMA, an occupancy-language-action generative world model, which uses semantic occupancy as a general visual representation and unifies vision-language-action(VLA) modalities through an autoregressive model. Specifically, we introduce a novel VQVAE-like scene tokenizer to efficiently discretize and reconstruct semantic occupancy scenes, considering its sparsity and classes imbalance. Then, we build a unified multi-modal vocabulary for vision, language and action. Furthermore, we enhance LLM, specifically LLaMA, to perform the next token/scene prediction on the unified vocabulary to complete multiple tasks in autonomous driving. Extensive experiments demonstrate that OccLLaMA achieves competitive performance across multiple tasks, including 4D occupancy forecasting, motion planning, and visual question answering, showcasing its potential as a foundation model in autonomous driving.

cs.CV↗

arXiv2024

Charge 4$e$ superconductor: a wavefunction approach

We apply a wavefunction approach to study the charge 4$e$ superconductor. The wavefunction takes a similar structure to the BCS wavefunction for charge 2$e$ superconductors. The mean-field Hamiltonian and the Green's functions of charge 4$e$ are constructed. The physical properties including the density of states, gauge invariance, flux quantization, superfluid density and Josephson effects are systematically studied. All these results form a microscopic description of the charge 4$e$ SC and can be served as the hallmarks for experimental verifications.

cond-mat.supr-con↗

arXiv2024

Shape-restricted transfer learning analysis for generalized linear regression model

Transfer learning has emerged as a highly sought-after and actively pursued research area within the statistical community. The core concept of transfer learning involves leveraging insights and information from auxiliary datasets to enhance the analysis of the primary dataset of interest. In this paper, our focus is on datasets originating from distinct yet interconnected distributions. We assume that the training data conforms to a standard generalized linear model, while the testing data exhibit a connection to the training data based on a prior probability shift assumption. Ultimately, we discover that the two-sample conditional means are interrelated through an unknown, nondecreasing function. We integrate the power of generalized estimating equations with the shape-restricted score function, creating a robust framework for improved inference regarding the underlying parameters. We theoretically establish the asymptotic properties of our estimator and demonstrate, through simulation studies, that our method yields more accurate parameter estimates compared to those based solely on the testing or training data. Finally, we apply our method to a real-world example.

stat.ME↗