arXiv subjects

Yu Wang

Publications and source records attributed to Yu Wang.

At least 937 records · Page 52Linked to original sources

arXiv2023

Super-resolution imaging through a multimode fiber: the physical upsampling of speckle-driven

Following recent advancements in multimode fiber (MMF), miniaturization of imaging endoscopes has proven crucial for minimally invasive surgery in vivo. Recent progress enabled by super-resolution imaging methods with a data-driven deep learning (DL) framework has balanced the relationship between the core size and resolution. However, most of the DL approaches lack attention to the physical properties of the speckle, which is crucial for reconciling the relationship between the magnification of super-resolution imaging and the quality of reconstruction quality. In the paper, we find that the interferometric process of speckle formation is an essential basis for creating DL models with super-resolution imaging. It physically realizes the upsampling of low-resolution (LR) images and enhances the perceptual capabilities of the models. The finding experimentally validates the role played by the physical upsampling of speckle-driven, effectively complementing the lack of information in data-driven. Experimentally, we break the restriction of the poor reconstruction quality at great magnification by inputting the same size of the speckle with the size of the high-resolution (HR) image to the model. The guidance of our research for endoscopic imaging may accelerate the further development of minimally invasive surgery.

physics.optics↗

arXiv2023

Fairness-Aware Graph Neural Networks: A Survey

Graph Neural Networks (GNNs) have become increasingly important due to their representational power and state-of-the-art predictive performance on many fundamental learning tasks. Despite this success, GNNs suffer from fairness issues that arise as a result of the underlying graph data and the fundamental aggregation mechanism that lies at the heart of the large class of GNN models. In this article, we examine and categorize fairness techniques for improving the fairness of GNNs. Previous work on fair GNN models and techniques are discussed in terms of whether they focus on improving fairness during a preprocessing step, during training, or in a post-processing phase. Furthermore, we discuss how such techniques can be used together whenever appropriate, and highlight the advantages and intuition as well. We also introduce an intuitive taxonomy for fairness evaluation metrics including graph-level fairness, neighborhood-level fairness, embedding-level fairness, and prediction-level fairness metrics. In addition, graph datasets that are useful for benchmarking the fairness of GNN models are summarized succinctly. Finally, we highlight key open problems and challenges that remain to be addressed.

cs.LG↗

arXiv2023

Attack-Resilient Supervisory Control of Discrete-Event Systems: A Finite-State Transducer Approach

Resilience to sensor and actuator attacks is a major concern in the supervisory control of discrete events in cyber-physical systems (CPS). In this work, we propose a new framework to design supervisors for CPS under attacks using finite-state transducers (FSTs) to model the effects of the discrete events. FSTs can capture a general class of regular-rewriting attacks in which an attacker can nondeterministically rewrite sensing/actuation events according to a given regular relation. These include common insertion, deletion, event-wise replacement, and finite-memory replay attacks. We propose new theorems and algorithms with polynomial complexity to design resilient supervisors against these attacks. We also develop an open-source tool in Python based on the results and illustrate its applicability through a case study

cs.FL↗

arXiv2023

Multiverse Transformer: 1st Place Solution for Waymo Open Sim Agents Challenge 2023

This technical report presents our 1st place solution for the Waymo Open Sim Agents Challenge (WOSAC) 2023. Our proposed MultiVerse Transformer for Agent simulation (MVTA) effectively leverages transformer-based motion prediction approaches, and is tailored for closed-loop simulation of agents. In order to produce simulations with a high degree of realism, we design novel training and sampling methods, and implement a receding horizon prediction mechanism. In addition, we introduce a variable-length history aggregation method to mitigate the compounding error that can arise during closed-loop autoregressive execution. On the WOSAC, our MVTA and its enhanced version MVTE reach a realism meta-metric of 0.5091 and 0.5168, respectively, outperforming all the other methods on the leaderboard.

cs.CV↗

arXiv2023

Electron transfer at molecule-metal interfaces under Floquet engineering: Rate constant and Floquet Marcus theory

Electron transfer (ET) at molecule-metal or molecule-semiconductor interfaces is a fundamental reaction that underlies all electro-chemical and molecular electronic processes as well as substrate-mediated surface photochemistry. In this study, we show that ET rates near a metal surface can be significantly manipulated by periodic modulations of an impurity level of the molecule near a metal surface. We employ the analytical Marcus theory and two numerical Floquet surface hopping algorithms that are developed previously, to calculate the ET rates near metal surface as a function of driving amplitudes and driving frequencies. We find that ET rates become faster with increasing the driving amplitude but no turnover effect, while have a turnover effect with increasing driving frequencies.

physics.chem-ph↗

arXiv2023

OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models

Diffusion probabilistic models (DPMs) are a new class of generative models that have achieved state-of-the-art generation quality in various domains. Despite the promise, one major drawback of DPMs is the slow generation speed due to the large number of neural network evaluations required in the generation process. In this paper, we reveal an overlooked dimension -- model schedule -- for optimizing the trade-off between generation quality and speed. More specifically, we observe that small models, though having worse generation quality when used alone, could outperform large models in certain generation steps. Therefore, unlike the traditional way of using a single model, using different models in different generation steps in a carefully designed \emph{model schedule} could potentially improve generation quality and speed \emph{simultaneously}. We design OMS-DPM, a predictor-based search algorithm, to optimize the model schedule given an arbitrary generation time budget and a set of pre-trained models. We demonstrate that OMS-DPM can find model schedules that improve generation quality and speed than prior state-of-the-art methods across CIFAR-10, CelebA, ImageNet, and LSUN datasets. When applied to the public checkpoints of the Stable Diffusion model, we are able to accelerate the sampling by 2$\times$ while maintaining the generation quality.

cs.LG↗

arXiv2023

Pushing the Limits of Unsupervised Unit Discovery for SSL Speech Representation

The excellent generalization ability of self-supervised learning (SSL) for speech foundation models has garnered significant attention. HuBERT is a successful example that utilizes offline clustering to convert speech features into discrete units for a masked language modeling pretext task. However, simply clustering features as targets by k-means does not fully inspire the model's performance. In this work, we present an unsupervised method to improve SSL targets. Two models are proposed, MonoBERT and PolyBERT, which leverage context-independent and context-dependent phoneme-based units for pre-training. Our models outperform other SSL models significantly on the LibriSpeech benchmark without the need for iterative re-clustering and re-training. Furthermore, our models equipped with context-dependent units even outperform target-improvement models that use labeled data during pre-training. How we progressively improve the unit discovery process is demonstrated through experiments.

cs.CL↗

arXiv2023

Enhanced Multimodal Representation Learning with Cross-modal KD

This paper explores the tasks of leveraging auxiliary modalities which are only available at training to enhance multimodal representation learning through cross-modal Knowledge Distillation (KD). The widely adopted mutual information maximization-based objective leads to a short-cut solution of the weak teacher, i.e., achieving the maximum mutual information by simply making the teacher model as weak as the student model. To prevent such a weak solution, we introduce an additional objective term, i.e., the mutual information between the teacher and the auxiliary modality model. Besides, to narrow down the information gap between the student and teacher, we further propose to minimize the conditional entropy of the teacher given the student. Novel training schemes based on contrastive learning and adversarial learning are designed to optimize the mutual information and the conditional entropy, respectively. Experimental results on three popular multimodal benchmark datasets have shown that the proposed method outperforms a range of state-of-the-art approaches for video recognition, video retrieval and emotion classification.

cs.CV↗

arXiv2023

Imaging magnetism evolution of magnetite to megabar pressure range with quantum sensors in diamond anvil cell

High-pressure diamond anvil cells have been widely used to create novel states of matter. Nevertheless, the lack of universal in-situ magnetic measurement techniques at megabar pressures makes it difficult to understand the underlying physics of materials' behavior at extreme conditions, such as high-temperature superconductivity of hydrides and the formation or destruction of the local magnetic moments in magnetic systems, etc. Here we break through the limitations of pressure on quantum sensors and develop the in-situ magnetic detection technique at megabar pressures with high sensitivity (~1μT/Hz^(1\2)) and sub-microscale spatial resolution. By directly imaging the magnetic field and the evolution of magnetic domains, we observe the macroscopic magnetic transition of Fe3O4 in the megabar pressure range from strong ferromagnetism (α-Fe3O4) to weak ferromagnetism (β-Fe3O4) and finally to non-magnetism (γ-Fe3O4). The scenarios for magnetic changes in Fe3O4 characterized here shed light on the direct magnetic microstructure observation in bulk materials at high pressure and contribute to understanding the mechanism of magnetic moment suppression related to spin crossover. The presented method can potentially investigate the spin-orbital coupling and magnetism-superconductivity competition in magnetic systems.

physics.app-ph↗

arXiv2023

MolCAP: Molecular Chemical reActivity pretraining and prompted-finetuning enhanced molecular representation learning

Molecular representation learning (MRL) is a fundamental task for drug discovery. However, previous deep-learning (DL) methods focus excessively on learning robust inner-molecular representations by mask-dominated pretraining framework, neglecting abundant chemical reactivity molecular relationships that have been demonstrated as the determining factor for various molecular property prediction tasks. Here, we present MolCAP to promote MRL, a graph pretraining Transformer based on chemical reactivity (IMR) knowledge with prompted finetuning. Results show that MolCAP outperforms comparative methods based on traditional molecular pretraining framework, in 13 publicly available molecular datasets across a diversity of biomedical tasks. Prompted by MolCAP, even basic graph neural networks are capable of achieving surprising performance that outperforms previous models, indicating the promising prospect of applying reactivity information for MRL. In addition, manual designed molecular templets are potential to uncover the dataset bias. All in all, we expect our MolCAP to gain more chemical meaningful insights for the entire process of drug discovery.

q-bio.BM↗

arXiv2023

Magnonic frequency comb in the magnomechanical resonator

An optical frequency comb is a spectrum of optical radiation which consists of evenly spaced and phase-coherent narrow spectral lines and is initially invented in laser for frequency metrology purposes. A direct analogue of frequency combs in the magnonic systems has not been demonstrated to date. In our experiment, we generate a new magnonic frequency comb in the resonator with giant mechanical oscillation through the magnomechanical interaction. We observe the magnonic frequency comb contains up to 20 comb lines, which are separated to the mechanical frequency of the 10.08 MHz. The thermal effect based on the strong pump power induces the cyclic oscillation of the magnon frequency shift, which leads to a periodic oscillation of the magnonic frequency comb. Moreover, we demonstrate the stabilization and control of the frequency spacing of the magnonic frequency comb via injection locking. Our work lays the groundwork of magnonic frequency combs for sensing and metrology.

physics.app-ph↗

arXiv2023

Atmospheric Recyling of Volatiles by Pebble-Accreting Planets

Planets, embedded in their natal discs, harbour hot envelopes. When pebbles are accreted by these planets, the contained volatile components may sublimate, enriching the envelope and potentially changing its thermodynamical properties. However, the envelopes of embedded planets actively exchange material with the disc, which would limit the buildup of a vapour-rich atmosphere. To properly investigate these processes, we have developed a new phase change module to treat the sublimation process with hydrodynamical simultions. Combined with the recently developed multi-dust fluid approach, we conduct 2D self-consistent hydrodynamic simulations to study how pebble sublimation influences the water content of super-Earths and sub-Neptunes. We find the extent and the amount of vapour that a planet is able to hold on to is determined by the relative size of the sublimation front and the atmosphere. When the sublimation front lies far inside the atmosphere, vapour tends to be locked deep in the atmosphere and keeps accumulating through a positive feedback mechanism. On the other hand, when the sublimation front exceeds the (bound) atmosphere, the ice component of incoming pebbles can be fully recycled and the vapour content reaches a low, steady value. Low disc temperature, small planet mass and high pebble flux (omitting accretion heating by pebbles) render the planet atmosphere vapour-rich while the reverse changes render it vapour-poor. The phase change module introduced here can in future studies also be employed to model the chemical composition of the gas in the vicinity of accreting planets and around snowlines.

astro-ph.EP↗

arXiv2023

Nonadiabatic dynamics near metal surfaces under Floquet engineering: Floquet electronic friction vs. Floquet surface hopping

In the previous study (arXiv:2303.00479), we have derived a Floquet classical master equation (FCME) to treat nonadiabatic dynamics near metal surfaces under Floquet engineering. We have also proposed trajectory surface hopping algorithm to solve the FCME. In this study, we map the FCME into a Floquet Fokker-Planck equation in the limit of fast Floquet driving and fast electron motion as compared to nuclear motion. The Fokker-Planck equation is then being solved using Langevin dynamics with explicit friction and random force from the nonadiabatic effects of hybridized electron and Floquet states. We benchmark the Floquet electronic friction (FEF) dynamics against Floquet quantum master equation (FQME) and Floquet surface hopping (FSH). We find that Floquet driving results in violation of the second fluctuation-dissipation theorem, which further gives rise to heating effects.

cond-mat.mes-hall↗

arXiv2023

Categorize Radio Interference using component and temporal analysis

Radio frequency interference (RFI) is a significant challenge faced by today's radio astronomers. While most past efforts were devoted to cleaning the RFI from the data, we develop a novel method for categorizing and cataloguing RFI for forensic purpose. We present a classifier that categorizes RFI into different types based on features extracted using Principal Component Analysis (PCA) and Fourier analysis. The classifier can identify narrowband non-periodic RFI above 2 sigma, narrowband periodic RFI above 3 sigma, and wideband impulsive RFI above 5 sigma with F1 scores between 0.87 and 0.91 in simulation. This classifier could be used to identify the sources of RFI as well as to clean RFI contamination (particularly in pulsar search). In the long-term analysis of the categorized RFI, we found a special type of drifting periodic RFI that is detrimental to pulsar search. We also found evidences of an increased rate of impulsive RFI when the telescope is pointing toward the cities. These results demonstrate this classifier's potential as a forensic tool for RFI environment monitoring of radio telescopes.

astro-ph.IM↗

arXiv2023

SelfEvolve: A Code Evolution Framework via Large Language Models

Large language models (LLMs) have already revolutionized code generation, after being pretrained on publicly available code data. However, while various methods have been proposed to augment LLMs with retrieved knowledge and enhance the quality of code generation, the performance of these retrieval-based methods is limited by the strength of the retrievers used. In addition, while LLMs show great emergent ability, they still struggle to produce the correct code in one turn. To address these challenges, we propose a novel two-step pipeline, called \autoknow, that leverages LLMs as both knowledge providers and self-reflective programmers. Unlike retrieval-based methods, \autoknow~obtains the knowledge from input prompts and generates intermediate code based on the generated knowledge. After that, \autoknow~asks LLM to act as an expert programmer to perform debugging for the generated code. This is achieved by receiving the error message from the interpreter, without requiring special test cases for correctness verification. We evaluate \autoknow~on three code generation datasets, including DS-1000 for data science code, HumanEval for software engineering code, and TransCoder for C++-to-Python translation. Our empirical experiments show that \autoknow~outperforms strong baselines by a significant margin on all datasets. We also conduct exhaustive analytical experiments to validate the effectiveness of the two stages of \autoknow, and find that both are superior to other prompting-based methods. Further scalability analysis demonstrates that \autoknow~can be adapted to other more advanced models, such as GPT-4, and bring consistent efficacy improvement.

cs.CL↗

arXiv2023

Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learning

Recent advances in robust semi-supervised learning (SSL) typically filter out-of-distribution (OOD) information at the sample level. We argue that an overlooked problem of robust SSL is its corrupted information on semantic level, practically limiting the development of the field. In this paper, we take an initial step to explore and propose a unified framework termed OOD Semantic Pruning (OSP), which aims at pruning OOD semantics out from in-distribution (ID) features. Specifically, (i) we propose an aliasing OOD matching module to pair each ID sample with an OOD sample with semantic overlap. (ii) We design a soft orthogonality regularization, which first transforms each ID feature by suppressing its semantic component that is collinear with paired OOD sample. It then forces the predictions before and after soft orthogonality decomposition to be consistent. Being practically simple, our method shows a strong performance in OOD detection and ID classification on challenging benchmarks. In particular, OSP surpasses the previous state-of-the-art by 13.7% on accuracy for ID classification and 5.9% on AUROC for OOD detection on TinyImageNet dataset. The source codes are publicly available at https://github.com/rain305f/OSP.

cs.CV↗

arXiv2023

Automatic Detection, Validation and Repair of Race Conditions in Interrupt-Driven Embedded Software

Interrupt-driven programs are widely deployed in safety-critical embedded systems to perform hardware and resource dependent data operation tasks. The frequent use of interrupts in these systems can cause race conditions to occur due to interactions between application tasks and interrupt handlers (or two interrupt handlers). Numerous program analysis and testing techniques have been proposed to detect races in multithreaded programs. Little work, however, has addressed race condition problems related to hardware interrupts. In this paper, we present SDRacer, an automated framework that can detect, validate and repair race conditions in interrupt-driven embedded software. It uses a combination of static analysis and symbolic execution to generate input data for exercising the potential races. It then employs virtual platforms to dynamically validate these races by forcing the interrupts to occur at the potential racing points. Finally, it provides repair candidates to eliminate the detected races. We evaluate SDRacer on nine real-world embedded programs written in C language. The results show that SDRacer can precisely detect and successfully fix race conditions.

cs.SE↗

arXiv2023

John-Nirenberg inequalities for noncommutative column BMO and Lipschitz martingales

In this paper, we continue the study of John-Nirenberg theorems for BMO/Lipschitz spaces in the noncommutative martingale setting. As conjectured from the classical case, a desired noncommutative ``stopping time" argument was discovered to obtain the distribution function inequality form of John-Nirenberg theorem. This not only provides another approach without using duality and interpolation to the results for spaces $\mathsf{bmo}^c(\mathcal M)$ and ${Λ^{c}_β}(\mathcal{M})$, but also allows us to find the desired version of John-Nirenberg inequalities for spaces $\mathcal{BMO}^c(\mathcal M)$ and ${\mathcal L^{c}_β}(\mathcal{M})$. And thus we solve two open questions after \cite{ref5, ref3}. As an application, we show that Lipschitz space is also the dual space of noncommutative Hardy space defined via symmetric atoms. Finally, our results for ${\mathcal L^{c}_β}(\mathcal{M})$ as well as the approach seem new even going back to the classical setting.

math.OA↗