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Yue Yu

Publications and source records attributed to Yue Yu.

At least 289 records · Page 16Linked to original sources
arXiv2023

Intelligence-Endogenous Management Platform for Computing and Network Convergence

Massive emerging applications are driving demand for the ubiquitous deployment of computing power today. This trend not only spurs the recent popularity of the \emph{Computing and Network Convergence} (CNC), but also introduces an urgent need for the intelligentization of a management platform to coordinate changing resources and tasks in the CNC. Therefore, in this article, we present the concept of an intelligence-endogenous management platform for CNCs called \emph{CNC brain} based on artificial intelligence technologies. It aims at efficiently and automatically matching the supply and demand with high heterogeneity in a CNC via four key building blocks, i.e., perception, scheduling, adaptation, and governance, throughout the CNC's life cycle. Their functionalities, goals, and challenges are presented. To examine the effectiveness of the proposed concept and framework, we also implement a prototype for the CNC brain based on a deep reinforcement learning technology. Also, it is evaluated on a CNC testbed that integrates two open-source and popular frameworks (OpenFaas and Kubernetes) and a real-world business dataset provided by Microsoft Azure. The evaluation results prove the proposed method's effectiveness in terms of resource utilization and performance. Finally, we highlight the future research directions of the CNC brain.

cs.AI↗
arXiv2023

Solving local constraint conditions in slave particle theory

With the Becchi-Rouet-Stora-Tyutin (BRST) quantization of gauge theory, we solve the long-standing difficult problem of the local constraint conditions, i.e., the single occupation of a slave particle per site, in the slave particle theory. This difficulty is actually caused by inconsistently dealing with the local Lagrange multiplier $λ_i$ which ensures the constraint: In the Hamiltonian formalism of the theory, $λ_i$ is time-independent and commutes with the Hamiltonian while in the Lagrangian formalism, $λ_i(t)$ becomes time-dependent and plays a role of gauge field. This implies that the redundant degrees of freedom of $λ_i(t)$ are introduced and must be removed by the additional constraint, the gauge fixing condition $\partial_t λ_i(t)=0$. In literature, this gauge fixing condition was missed. We add this gauge fixing condition and use the BRST quantization of gauge theory for Dirac's first-class constraints in the slave particle theory. This gauge fixing condition endows $λ_i(t)$ with dynamics and leads to important physical results. As an example, we study the Hubbard model at half-filling and find that the spinon is gapped in the weak $U$ and the system is indeed a conventional metal, which resolves the paradox that the weak coupling state is a superconductor in the previous slave boson mean field theory. For the $t$-$J$ model, we find that the dynamic effect of $λ_i(t)$ substantially suppresses the $d$-wave pairing gap and then the superconducting critical temperature may be lowered at least a factor of one-fifth of the mean field value which is of the order of 1000 K. The renormalized $T_c$ is then close to that in cuprates.

cond-mat.str-el↗
arXiv2023

Broadband Dispersive-Wave Emission Coupled with Two-Stage Soliton Self-Compression in Gas-Filled Anti-Resonant Hollow-Core Fibers

We studied the underlying mechanism of broadband dispersive-wave emission within a resonance band of gas-filled anti-resonant hollow-core fiber. Both theoretical and experimental results unveiled that the high-order soliton, launched into the hollow-core fiber, experienced two stages of pulse compression, resulting in a multi-peak structure of the dispersive-wave spectrum. Over the first-stage pulse compression, a sharp increase of the pulse peak power triggered the first time of dispersion-wave emission, and simultaneously caused ionization of the noble gas filled in the fiber core. Strong soliton-plasma interactions led to blue shifting of the pump pulse, and the blue-shifted pulse experienced a decreasing dispersion value in the fiber waveguide, resulting in an increase of its soliton order. Then, the second-stage pulse compression due to the high-order soliton effect triggered the second time of dispersive-wave emission at a phase-matched frequency slightly lower than that in the first stage. Multi-peak spectra of the output dispersive-waves and their formation dynamics were clearly observed in our experiments, which can be understood using a delicate coupling mechanism among three nonlinear effects including high-order-soliton compression, soliton-plasma interaction and phase-matched dispersive-wave emission. The output broadband dispersive-wave could be potentially compressed to sub-30 fs duration using precise chirp-compensation technique.

physics.optics↗
arXiv2023

FoodWise: Food Waste Reduction and Behavior Change on Campus with Data Visualization and Gamification

Food waste presents a substantial challenge with significant environmental and economic ramifications, and its severity on campus environments is of particular concern. In response to this, we introduce FoodWise, a dual-component system tailored to inspire and incentivize campus communities to reduce food waste. The system consists of a data storytelling dashboard that graphically displays food waste information from university canteens, coupled with a mobile web application that encourages users to log their food waste reduction actions and rewards active participants for their efforts. Deployed during a two-week food-saving campaign at The Hong Kong University of Science and Technology (HKUST) in March 2023, FoodWise engaged over 200 participants from the university community, resulting in the logging of over 800 daily food-saving actions. Feedback collected post-campaign underscores the system's efficacy in elevating user consciousness about food waste and prompting behavioral shifts towards a more sustainable campus. This paper also provides insights for enhancing our system, contributing to a broader discourse on sustainable campus initiatives.

cs.HC↗
arXiv2023

Fluctuation-driven, topology-stabilized order in a correlated nodal semimetal

The interplay between strong electron correlation and band topology is at the forefront of condensed matter research. As a direct consequence of correlation, magnetism enriches topological phases and also has promising functional applications. However, the influence of topology on magnetism remains unclear, and the main research effort has been limited to ground state magnetic orders. Here we report a novel order above the magnetic transition temperature in magnetic Weyl semimetal (WSM) CeAlGe. Such order shows a number of anomalies in electrical and thermal transport, and neutron scattering measurements. We attribute this order to the coupling of Weyl fermions and magnetic fluctuations originating from a three-dimensional Seiberg-Witten monopole, which qualitatively agrees well with the observations. Our work reveals a prominent role topology may play in tailoring electron correlation beyond ground state ordering, and offers a new avenue to investigate emergent electronic properties in magnetic topological materials.

cond-mat.str-el↗
arXiv2023

Personalized Federated Learning via Amortized Bayesian Meta-Learning

Federated learning is a decentralized and privacy-preserving technique that enables multiple clients to collaborate with a server to learn a global model without exposing their private data. However, the presence of statistical heterogeneity among clients poses a challenge, as the global model may struggle to perform well on each client's specific task. To address this issue, we introduce a new perspective on personalized federated learning through Amortized Bayesian Meta-Learning. Specifically, we propose a novel algorithm called \emph{FedABML}, which employs hierarchical variational inference across clients. The global prior aims to capture representations of common intrinsic structures from heterogeneous clients, which can then be transferred to their respective tasks and aid in the generation of accurate client-specific approximate posteriors through a few local updates. Our theoretical analysis provides an upper bound on the average generalization error and guarantees the generalization performance on unseen data. Finally, several empirical results are implemented to demonstrate that \emph{FedABML} outperforms several competitive baselines.

cs.LG↗
arXiv2023

Non-degenerate surface pair density wave in the Kagome superconductor CsV$_3$Sb$_5$ -- application to vestigial orders

On the Sb-layer of the Kagome superconductor CsV$_3$Sb$_5$, pair density wave states have been observed. When the high-temperature charge orderings are treated as static backgrounds, these PDW states exhibit the same wavevector in the effective 2D Brillouin zone. Interestingly, these PDW states break the same symmetry on the surface. Considering the presence of this non-degenerate PDW, we investigate the implications for the possible existence of a vestigial charge-4e phase with a non-zero center-of-mass momentum. To distinguish between different vestigial phases, we propose scanning tunneling microscopy experiments. We aim to provide insights into the nature of the vestigial phases and their distinct characteristics in CsV$_3$Sb$_5$. This research sheds light on the interplay between PDW states, charge orderings, and superconductivity of the Kagome superconductor.

cond-mat.supr-con↗
arXiv2023

Linearizations via Dirac delta function

This note is to show that the position-space embedding in \cite{ESP2021embedding} in the position and occupation bases can be obtained by considering the dynamics of Dirac delta function $$δ(\mathbf{x}- \mathbf{z}(t)) = δ(x_1-z_1(t))\cdots δ(x_d-z_d(t)),$$ where $\mathbf{z}(t)\in \mathbb{R}^d$ is the solution of a nonlinear dynamical system and $\mathbf{x}\in \mathbb{R}^d$ is a variable in the position space.

math.DS↗
arXiv2023

Practical Privacy-Preserving Gaussian Process Regression via Secret Sharing

Gaussian process regression (GPR) is a non-parametric model that has been used in many real-world applications that involve sensitive personal data (e.g., healthcare, finance, etc.) from multiple data owners. To fully and securely exploit the value of different data sources, this paper proposes a privacy-preserving GPR method based on secret sharing (SS), a secure multi-party computation (SMPC) technique. In contrast to existing studies that protect the data privacy of GPR via homomorphic encryption, differential privacy, or federated learning, our proposed method is more practical and can be used to preserve the data privacy of both the model inputs and outputs for various data-sharing scenarios (e.g., horizontally/vertically-partitioned data). However, it is non-trivial to directly apply SS on the conventional GPR algorithm, as it includes some operations whose accuracy and/or efficiency have not been well-enhanced in the current SMPC protocol. To address this issue, we derive a new SS-based exponentiation operation through the idea of 'confusion-correction' and construct an SS-based matrix inversion algorithm based on Cholesky decomposition. More importantly, we theoretically analyze the communication cost and the security of the proposed SS-based operations. Empirical results show that our proposed method can achieve reasonable accuracy and efficiency under the premise of preserving data privacy.

cs.CR↗
arXiv2023

ToolQA: A Dataset for LLM Question Answering with External Tools

Large Language Models (LLMs) have demonstrated impressive performance in various NLP tasks, but they still suffer from challenges such as hallucination and weak numerical reasoning. To overcome these challenges, external tools can be used to enhance LLMs' question-answering abilities. However, current evaluation methods do not distinguish between questions that can be answered using LLMs' internal knowledge and those that require external information through tool use. To address this issue, we introduce a new dataset called ToolQA, which is designed to faithfully evaluate LLMs' ability to use external tools for question answering. Our development of ToolQA involved a scalable, automated process for dataset curation, along with 13 specialized tools designed for interaction with external knowledge in order to answer questions. Importantly, we strive to minimize the overlap between our benchmark data and LLMs' pre-training data, enabling a more precise evaluation of LLMs' tool-use reasoning abilities. We conducted an in-depth diagnosis of existing tool-use LLMs to highlight their strengths, weaknesses, and potential improvements. Our findings set a new benchmark for evaluating LLMs and suggest new directions for future advancements. Our data and code are freely available to the broader scientific community on GitHub.

cs.CL↗
arXiv2023

The Sparse-Grid-Based Adaptive Spectral Koopman Method

The adaptive spectral Koopman (ASK) method was introduced to numerically solve autonomous dynamical systems that lay the foundation of numerous applications across different fields in science and engineering. Although ASK achieves high accuracy, it is computationally more expensive for multi-dimensional systems compared with conventional time integration schemes like Runge-Kutta. In this work, we combine the sparse grid and ASK to accelerate the computation for multi-dimensional systems. This sparse-grid-based ASK (SASK) method uses the Smolyak structure to construct multi-dimensional collocation points as well as associated polynomials that are used to approximate eigenfunctions of the Koopman operator of the system. In this way, the number of collocation points is reduced compared with using the tensor product rule. We demonstrate that SASK can be used to solve partial differential equations based-on their semi-discrete forms. Numerical experiments illustrate that SASK balances the accuracy with the computational cost, and hence accelerates ASK.

math.DS↗
arXiv2023

DyGen: Learning from Noisy Labels via Dynamics-Enhanced Generative Modeling

Learning from noisy labels is a challenge that arises in many real-world applications where training data can contain incorrect or corrupted labels. When fine-tuning language models with noisy labels, models can easily overfit the label noise, leading to decreased performance. Most existing methods for learning from noisy labels use static input features for denoising, but these methods are limited by the information they can provide on true label distributions and can result in biased or incorrect predictions. In this work, we propose the Dynamics-Enhanced Generative Model (DyGen), which uses dynamic patterns in the embedding space during the fine-tuning process of language models to improve noisy label predictions. DyGen uses the variational auto-encoding framework to infer the posterior distributions of true labels from noisy labels and training dynamics. Additionally, a co-regularization mechanism is used to minimize the impact of potentially noisy labels and priors. DyGen demonstrates an average accuracy improvement of 3.10% on two synthetic noise datasets and 1.48% on three real-world noise datasets compared to the previous state-of-the-art. Extensive experiments and analyses show the effectiveness of each component in DyGen. Our code is available for reproducibility on GitHub.

cs.CL↗
arXiv2023

Time complexity analysis of quantum algorithms via linear representations for nonlinear ordinary and partial differential equations

We construct quantum algorithms to compute the solution and/or physical observables of nonlinear ordinary differential equations (ODEs) and nonlinear Hamilton-Jacobi equations (HJE) via linear representations or exact mappings between nonlinear ODEs/HJE and linear partial differential equations (the Liouville equation and the Koopman-von Neumann equation). The connection between the linear representations and the original nonlinear system is established through the Dirac delta function or the level set mechanism. We compare the quantum linear systems algorithms based methods and the quantum simulation methods arising from different numerical approximations, including the finite difference discretisations and the Fourier spectral discretisations for the two different linear representations, with the result showing that the quantum simulation methods usually give the best performance in time complexity. We also propose the Schrödinger framework to solve the Liouville equation for the HJE with the Hamiltonian formulation of classical mechanics, since it can be recast as the semiclassical limit of the Wigner transform of the Schrödinger equation. Comparsion between the Schrödinger and the Liouville framework will also be made.

quant-ph↗
arXiv2023

Weakly-Supervised Scientific Document Classification via Retrieval-Augmented Multi-Stage Training

Scientific document classification is a critical task for a wide range of applications, but the cost of obtaining massive amounts of human-labeled data can be prohibitive. To address this challenge, we propose a weakly-supervised approach for scientific document classification using label names only. In scientific domains, label names often include domain-specific concepts that may not appear in the document corpus, making it difficult to match labels and documents precisely. To tackle this issue, we propose WANDER, which leverages dense retrieval to perform matching in the embedding space to capture the semantics of label names. We further design the label name expansion module to enrich the label name representations. Lastly, a self-training step is used to refine the predictions. The experiments on three datasets show that WANDER outperforms the best baseline by 11.9% on average. Our code will be published at https://github.com/ritaranx/wander.

cs.CL↗
arXiv2023

Local Boosting for Weakly-Supervised Learning

Boosting is a commonly used technique to enhance the performance of a set of base models by combining them into a strong ensemble model. Though widely adopted, boosting is typically used in supervised learning where the data is labeled accurately. However, in weakly supervised learning, where most of the data is labeled through weak and noisy sources, it remains nontrivial to design effective boosting approaches. In this work, we show that the standard implementation of the convex combination of base learners can hardly work due to the presence of noisy labels. Instead, we propose $\textit{LocalBoost}$, a novel framework for weakly-supervised boosting. LocalBoost iteratively boosts the ensemble model from two dimensions, i.e., intra-source and inter-source. The intra-source boosting introduces locality to the base learners and enables each base learner to focus on a particular feature regime by training new base learners on granularity-varying error regions. For the inter-source boosting, we leverage a conditional function to indicate the weak source where the sample is more likely to appear. To account for the weak labels, we further design an estimate-then-modify approach to compute the model weights. Experiments on seven datasets show that our method significantly outperforms vanilla boosting methods and other weakly-supervised methods.

cs.LG↗
arXiv2023

R-Mixup: Riemannian Mixup for Biological Networks

Biological networks are commonly used in biomedical and healthcare domains to effectively model the structure of complex biological systems with interactions linking biological entities. However, due to their characteristics of high dimensionality and low sample size, directly applying deep learning models on biological networks usually faces severe overfitting. In this work, we propose R-MIXUP, a Mixup-based data augmentation technique that suits the symmetric positive definite (SPD) property of adjacency matrices from biological networks with optimized training efficiency. The interpolation process in R-MIXUP leverages the log-Euclidean distance metrics from the Riemannian manifold, effectively addressing the swelling effect and arbitrarily incorrect label issues of vanilla Mixup. We demonstrate the effectiveness of R-MIXUP with five real-world biological network datasets on both regression and classification tasks. Besides, we derive a commonly ignored necessary condition for identifying the SPD matrices of biological networks and empirically study its influence on the model performance. The code implementation can be found in Appendix E.

cs.LG↗
arXiv2023

Superconductivity of anomalous pseudospin

Spin-orbit coupling driven by broken inversion symmetry ($I$) is known to lead to unusual magnetic response of superconductors, including extremely large critical fields for spin-singlet superconductors. This unusual response is also known to appear in materials that have $I$, provided there is local $I$-breaking: fermions participating in superconductivity reside on crystal sites that lack $I$. Here we show that this unusual response exists even when the crystal sites preserve $I$. Indeed, we argue that the symmetry of Kramers degenerate fermionic pseudospin is more relevant than the local crystal site symmetry. We examine and classify non-symmorphic materials with momentum space spin-textures that exhibit an anomalous pseudospin with different symmetry properties than usual spin-1/2. We find that this anomalous pseudospin does not depend on the existence of local $I$ breaking crystal sites and it optimizes the unusual magnetic response traditionally associated with locally noncentrosymmetric superconductors, dramatically extending the range of relevant materials. We further show this anomalous pseudospin leads to fully gapped `nodal' superconductors and provides additional insight into the breakdown of Blount's theorem for pseudospin triplet superconductors. We apply our results to UPt$_3$, BiS$_2$-based superconductors, Fe-based superconductors, and paramagnetic UCoGe.

cond-mat.supr-con↗
arXiv2023

Real-Time Sequential Conic Optimization for Multi-Phase Rocket Landing Guidance

We introduce a multi-phase rocket landing guidance framework that can handle nonlinear dynamics and does not mandate any additional mixed-integer or nonconvex constraints to handle discrete temporal events/switching. To achieve this, we first introduce sequential conic optimization (SeCO), a new paradigm for solving nonconvex optimal control problems that is entirely devoid of matrix factorizations and inversions. This framework combines sequential convex programming (SCP) and first-order conic optimization and can solve unified multi-phase trajectory optimization problems in real-time. The novel features of this framework are: (1) time-interval dilation, which enables multi-phase trajectory optimization with free-transition-time; (2) single-crossing compound state-triggered constraints, which are entirely convex if the trigger and constraint conditions are convex; (3) virtual state, which is a new approach to handling artificial infeasibility in SCP methods that preserves the shapes of the constraint sets; and, (4) the use of the proportional-integral projected gradient method (PIPG), a high-performance first-order conic optimization solver, in tandem with the penalized trust region (PTR) SCP algorithm. We demonstrate the efficacy and real-time capability of SeCO by solving a relevant multi-phase rocket landing guidance problem with nonlinear dynamics and convex constraints only, and observe that our solver is 2.7 times faster than a state-of-the-art convex optimization solver.

math.OC↗