arXiv subjects

Xiang Li

Publications and source records attributed to Xiang Li.

At least 991 records · Page 55Linked to original sources

arXiv2023

Microwave conductivity due to impurity scattering in cuprate superconductors

The microwave surface impedance measurements on cuprate superconductors provide the crucial information of the effect of the impurity scattering on the quasiparticle transport, however, the full understanding of the effect of the impurity scattering on the quasiparticle transport is still a challenging issue. Here based on the microscopic octet scattering model, the effect of the impurity scattering on the low-temperature microwave conductivity in cuprate superconductors is investigated in the self-consistent $T$-matrix approach. The impurity-dressed electron propagator obtained in the Fermi-arc-tip approximation of the quasiparticle excitations and scattering processes is employed to derive the electron current-current correlation function by taking into account the impurity-induced vertex correction. It is shown that the microwave conductivity spectrum is a non-Drude-like, with a sharp cusp-like peak extending to zero-energy and a high-energy tail falling slowly with energy. Moreover, the microwave conductivity decreases with the increase of the impurity concentration or with the increase of the strength of the impurity scattering potential. In a striking contrast to the dome-like shape of the doping dependence of the superconducting transition temperature, the microwave conductivity exhibits a reverse dome-like shape of the doping dependence. The theory also show that the highly unconventional features of the microwave conductivity are generated by both the strong electron correlation and impurity-scattering effects.

cond-mat.supr-con↗

arXiv2023

Characterizations for multi-sublinear operators and their commutators on three kinds of generalized weighted Morrey spaces and applications

The main questions raised in this paper are to find the sufficient conditions that make multi-sublinear operators $T$ and their commutators ${T_{\prod \vec b }}$, ${T_{\sum {\vec b} }}$ to be bounded on three kinds of generalized weighted Morrey spaces. We give the main theorems of this paper to solve the above related questions. As corollaries of the main theorems, we give sufficient and necessary conditions for a class of multi-sublinear operators which are bounded on three kinds of generalized weighted Morrey spaces. As some inportant applications, we apply the main results to the multilinear vector-valued Calderón-Zygmund operators, multilinear Littlewood-Paley square operators, multilinear pseudo-differential operators and multilinear paraproducts.

math.FA↗

arXiv2023

High-speed 4 ${\times}$ 4 silicon photonic electro-optic switch, operating at the 2 μm waveband

The escalating need for expansive data bandwidth, and the resulting capacity constraints of the single mode fiber (SMF) have positioned the 2-$μ$m waveband as a prospective window for emerging applications in optical communication. This has initiated an ecosystem of silicon photonic components in the region driven by CMOS compatibility, low cost, high efficiency and potential for large-scale integration. In this study, we demonstrate a plasma dispersive, 4 ${\times}$ 4 electro-optic switch operating at the 2-$μ$m waveband with the shortest switching times. The demonstrated switch operates across a 45-nm bandwidth, with 10-90% rise and 90-10% fall time of 1.78 ns and 3.02 ns respectively. In a 4 ${\times}$ 4 implementation, crosstalk below -15 dB and power consumption below 19.15 mW across all 16 ports are indicated. The result brings high-speed optical switching to the portfolio of devices at the promising waveband.

physics.optics↗

arXiv2023

Mixed radial-angular bounds for a class of integral operators on Heisenberg groups

In this paper, we will prove the sharp bounds of various operators in mixed radial angular spaces on Heisenberg groups. It mainly includes the boundedness of linear transformation eigenvalue operator in mixed radial angular space; Sharp Bounds of Hilbert Operator and Hardy-Littlewood-P$\acute{o}$lya Operator in mixed radial-angular space

math.CA↗

arXiv2023

Exploring Multimodal Approaches for Alzheimer's Disease Detection Using Patient Speech Transcript and Audio Data

Alzheimer's disease (AD) is a common form of dementia that severely impacts patient health. As AD impairs the patient's language understanding and expression ability, the speech of AD patients can serve as an indicator of this disease. This study investigates various methods for detecting AD using patients' speech and transcripts data from the DementiaBank Pitt database. The proposed approach involves pre-trained language models and Graph Neural Network (GNN) that constructs a graph from the speech transcript, and extracts features using GNN for AD detection. Data augmentation techniques, including synonym replacement, GPT-based augmenter, and so on, were used to address the small dataset size. Audio data was also introduced, and WavLM model was used to extract audio features. These features were then fused with text features using various methods. Finally, a contrastive learning approach was attempted by converting speech transcripts back to audio and using it for contrastive learning with the original audio. We conducted intensive experiments and analysis on the above methods. Our findings shed light on the challenges and potential solutions in AD detection using speech and audio data.

eess.AS↗

arXiv2023

Segment Anything Model (SAM) for Radiation Oncology

In this study, we evaluate the performance of the Segment Anything Model (SAM) in clinical radiotherapy. Our results indicate that SAM's 'segment anything' mode can achieve clinically acceptable segmentation results in most organs-at-risk (OARs) with Dice scores higher than 0.7. SAM's 'box prompt' mode further improves the Dice scores by 0.1 to 0.5. Considering the size of the organ and the clarity of its boundary, SAM displays better performance for large organs with clear boundaries but performs worse for smaller organs with unclear boundaries. Given that SAM, a model pre-trained purely on natural images, can handle the delineation of OARs from medical images with clinically acceptable accuracy, these results highlight SAM's robust generalization capabilities with consistent accuracy in automatic segmentation for radiotherapy. In other words, SAM can achieve delineation of different OARs at different sites using a generic automatic segmentation model. SAM's generalization capabilities across different disease sites suggest that it is technically feasible to develop a generic model for automatic segmentation in radiotherapy.

eess.IV↗

arXiv2023

Review of Large Vision Models and Visual Prompt Engineering

Visual prompt engineering is a fundamental technology in the field of visual and image Artificial General Intelligence, serving as a key component for achieving zero-shot capabilities. As the development of large vision models progresses, the importance of prompt engineering becomes increasingly evident. Designing suitable prompts for specific visual tasks has emerged as a meaningful research direction. This review aims to summarize the methods employed in the computer vision domain for large vision models and visual prompt engineering, exploring the latest advancements in visual prompt engineering. We present influential large models in the visual domain and a range of prompt engineering methods employed on these models. It is our hope that this review provides a comprehensive and systematic description of prompt engineering methods based on large visual models, offering valuable insights for future researchers in their exploration of this field.

cs.CV↗

arXiv2023

Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference

Alzheimer's disease (AD) is a neurodegenerative disorder that is beginning with amyloidosis, followed by neuronal loss and deterioration in structure, function, and cognition. The accumulation of amyloid-beta in the brain, measured through 18F-florbetapir (AV45) positron emission tomography (PET) imaging, has been widely used for early diagnosis of AD. However, the relationship between amyloid-beta accumulation and AD pathophysiology remains unclear, and causal inference approaches are needed to uncover how amyloid-beta levels can impact AD development. In this paper, we propose a graph varying coefficient neural network (GVCNet) for estimating the individual treatment effect with continuous treatment levels using a graph convolutional neural network. We highlight the potential of causal inference approaches, including GVCNet, for measuring the regional causal connections between amyloid-beta accumulation and AD pathophysiology, which may serve as a robust tool for early diagnosis and tailored care.

cs.LG↗

arXiv2023

Exploring the linear space of Feynman integrals via generating functions

Deriving a comprehensive set of reduction rules for Feynman integrals has been a longstanding challenge. In this paper, we present a proposed solution to this problem utilizing generating functions of Feynman integrals. By establishing and solving differential equations of these generating functions, we are able to derive a system of reduction rules that effectively reduce any associated Feynman integrals to their bases. We illustrate this method through various examples and observe its potential value in numerous scenarios.

hep-ph↗

arXiv2023

FedPower: Privacy-Preserving Distributed Eigenspace Estimation

Eigenspace estimation is fundamental in machine learning and statistics, which has found applications in PCA, dimension reduction, and clustering, among others. The modern machine learning community usually assumes that data come from and belong to different organizations. The low communication power and the possible privacy breaches of data make the computation of eigenspace challenging. To address these challenges, we propose a class of algorithms called \textsf{FedPower} within the federated learning (FL) framework. \textsf{FedPower} leverages the well-known power method by alternating multiple local power iterations and a global aggregation step, thus improving communication efficiency. In the aggregation, we propose to weight each local eigenvector matrix with {\it Orthogonal Procrustes Transformation} (OPT) for better alignment. To ensure strong privacy protection, we add Gaussian noise in each iteration by adopting the notion of \emph{differential privacy} (DP). We provide convergence bounds for \textsf{FedPower} that are composed of different interpretable terms corresponding to the effects of Gaussian noise, parallelization, and random sampling of local machines. Additionally, we conduct experiments to demonstrate the effectiveness of our proposed algorithms.

stat.ML↗

arXiv2023

Higher-order Graph Attention Network for Stock Selection with Joint Analysis

Stock selection is important for investors to construct profitable portfolios. Graph neural networks (GNNs) are increasingly attracting researchers for stock prediction due to their strong ability of relation modelling and generalisation. However, the existing GNN methods only focus on simple pairwise stock relation and do not capture complex higher-order structures modelling relations more than two nodes. In addition, they only consider factors of technical analysis and overlook factors of fundamental analysis that can affect the stock trend significantly. Motivated by them, we propose higher-order graph attention network with joint analysis (H-GAT). H-GAT is able to capture higher-order structures and jointly incorporate factors of fundamental analysis with factors of technical analysis. Specifically, the sequential layer of H-GAT take both types of factors as the input of a long-short term memory model. The relation embedding layer of H-GAT constructs a higher-order graph and learn node embedding with GAT. We then predict the ranks of stock return. Extensive experiments demonstrate the superiority of our H-GAT method on the profitability test and Sharp ratio over both NSDAQ and NYSE datasets

q-fin.ST↗

arXiv2023

Characterizations for multilinear fractional maximal and integral operators and their commutators on generalized weighted Morrey spaces and applications

This paper is devoted to studying the boundedness of multilinear operartors and their commutators on generalized weighted Morrey spaces, which includes multilinear fractional maximal operator and multilinear fractional integral operator. Moreover, we show that two different characterizations for the boundedness of multilinear fractional maximal operators and their commutators on generalized weighted Morrey spaces under different conditions. As some inportant applications, we give the boundedness of multilinear fractional integral operator on generalized weighted Besov-Morrey spaces and also obtain two embedding theorems as well as apriori estimates for the sub-Laplacian $\mathcal L$.

math.CA↗

arXiv2023

Boundedness of the multilinear integral operators on Heisenberg group

In this paper, we will obtain the sharp constant for multilinear integral operator on Heisenberg group Lebesgue space which is based on the Stein-Weiss lemma, the boundedness for multilinear integral operator on Heisenberg group $A_p$ weighted Morrey space and the sharp constant for multilinear integral operator on Heisenberg group two power weight Morrey space.

math.CA↗

arXiv2023

Peekaboo: Text to Image Diffusion Models are Zero-Shot Segmentors

Recently, text-to-image diffusion models have shown remarkable capabilities in creating realistic images from natural language prompts. However, few works have explored using these models for semantic localization or grounding. In this work, we explore how an off-the-shelf text-to-image diffusion model, trained without exposure to localization information, can ground various semantic phrases without segmentation-specific re-training. We introduce an inference time optimization process capable of generating segmentation masks conditioned on natural language prompts. Our proposal, Peekaboo, is a first-of-its-kind zero-shot, open-vocabulary, unsupervised semantic grounding technique leveraging diffusion models without any training. We evaluate Peekaboo on the Pascal VOC dataset for unsupervised semantic segmentation and the RefCOCO dataset for referring segmentation, showing results competitive with promising results. We also demonstrate how Peekaboo can be used to generate images with transparency, even though the underlying diffusion model was only trained on RGB images - which to our knowledge we are the first to attempt. Please see our project page, including our code: https://ryanndagreat.github.io/peekaboo

cs.CV↗

arXiv2023

AD-AutoGPT: An Autonomous GPT for Alzheimer's Disease Infodemiology

In this pioneering study, inspired by AutoGPT, the state-of-the-art open-source application based on the GPT-4 large language model, we develop a novel tool called AD-AutoGPT which can conduct data collection, processing, and analysis about complex health narratives of Alzheimer's Disease in an autonomous manner via users' textual prompts. We collated comprehensive data from a variety of news sources, including the Alzheimer's Association, BBC, Mayo Clinic, and the National Institute on Aging since June 2022, leading to the autonomous execution of robust trend analyses, intertopic distance maps visualization, and identification of salient terms pertinent to Alzheimer's Disease. This approach has yielded not only a quantifiable metric of relevant discourse but also valuable insights into public focus on Alzheimer's Disease. This application of AD-AutoGPT in public health signifies the transformative potential of AI in facilitating a data-rich understanding of complex health narratives like Alzheimer's Disease in an autonomous manner, setting the groundwork for future AI-driven investigations in global health landscapes.

cs.CL↗

arXiv2023

Modeling Dual Period-Varying Preferences for Takeaway Recommendation

Takeaway recommender systems, which aim to accurately provide stores that offer foods meeting users' interests, have served billions of users in our daily life. Different from traditional recommendation, takeaway recommendation faces two main challenges: (1) Dual Interaction-Aware Preference Modeling. Traditional recommendation commonly focuses on users' single preferences for items while takeaway recommendation needs to comprehensively consider users' dual preferences for stores and foods. (2) Period-Varying Preference Modeling. Conventional recommendation generally models continuous changes in users' preferences from a session-level or day-level perspective. However, in practical takeaway systems, users' preferences vary significantly during the morning, noon, night, and late night periods of the day. To address these challenges, we propose a Dual Period-Varying Preference modeling (DPVP) for takeaway recommendation. Specifically, we design a dual interaction-aware module, aiming to capture users' dual preferences based on their interactions with stores and foods. Moreover, to model various preferences in different time periods of the day, we propose a time-based decomposition module as well as a time-aware gating mechanism. Extensive offline and online experiments demonstrate that our model outperforms state-of-the-art methods on real-world datasets and it is capable of modeling the dual period-varying preferences. Moreover, our model has been deployed online on Meituan Takeaway platform, leading to an average improvement in GMV (Gross Merchandise Value) of 0.70%.

cs.IR↗

arXiv2023

Lexical Speaker Error Correction: Leveraging Language Models for Speaker Diarization Error Correction

Speaker diarization (SD) is typically used with an automatic speech recognition (ASR) system to ascribe speaker labels to recognized words. The conventional approach reconciles outputs from independently optimized ASR and SD systems, where the SD system typically uses only acoustic information to identify the speakers in the audio stream. This approach can lead to speaker errors especially around speaker turns and regions of speaker overlap. In this paper, we propose a novel second-pass speaker error correction system using lexical information, leveraging the power of modern language models (LMs). Our experiments across multiple telephony datasets show that our approach is both effective and robust. Training and tuning only on the Fisher dataset, this error correction approach leads to relative word-level diarization error rate (WDER) reductions of 15-30% on three telephony datasets: RT03-CTS, Callhome American English and held-out portions of Fisher.

eess.AS↗

arXiv2023

Crossed Andreev reflection and elastic co-tunneling in a three-site Kitaev chain nanowire device

The formation of a topological superconducting phase in a quantum-dot-based Kitaev chain requires nearest neighbor crossed Andreev reflection and elastic co-tunneling. Here we report on a hybrid InSb nanowire in a three-site Kitaev chain geometry - the smallest system with well-defined bulk and edge - where two superconductor-semiconductor hybrids separate three quantum dots. We demonstrate pairwise crossed Andreev reflection and elastic co-tunneling between both pairs of neighboring dots and show sequential tunneling processes involving all three quantum dots. These results are the next step towards the realization of topological superconductivity in long Kitaev chain devices with many coupled quantum dots.

cond-mat.mes-hall↗