arXiv subjects

Xuan Zhang

Publications and source records attributed to Xuan Zhang.

At least 109 records · Page 6Linked to original sources

arXiv2024

Consistent multiple-relaxation-time lattice Boltzmann method for the volume averaged Navier-Stokes equations

Recently, we notice that a pressure-based lattice Boltzmann (LB) method was established to recover the volume-averaged Navier-Stokes equations (VANSE), which serve as the cornerstone of various fluid-solid multiphase models. It decouples the pressure from density and exhibits excellent numerical performance, however, the widely adopted density-based LB scheme still suffers from significant spurious velocities and inconsistency with VANSE. To remedy this issue, a multiple-relaxation-time LB method is devised in this work, which incorporates a provisional equation of state in an adjusted density equilibrium distribution to decouple the void fraction from density. The Galilean invariance of the recovered VANSE is guaranteed by introducing a penalty source term in moment space, effectively eliminating unwanted numerical errors. Through the Chapman-Enskog analysis and detailed numerical validations, this novel method is proved to be capable of recovering VANSE with second-order accuracy consistently, and well-suited for handling void fraction fields with large gradients and spatiotemporal distributions.

physics.flu-dyn↗

arXiv2024

CONSULT: Contrastive Self-Supervised Learning for Few-shot Tumor Detection

Artificial intelligence aids in brain tumor detection via MRI scans, enhancing the accuracy and reducing the workload of medical professionals. However, in scenarios with extremely limited medical images, traditional deep learning approaches tend to fail due to the absence of anomalous images. Anomaly detection also suffers from ineffective feature extraction due to vague training process. Our work introduces a novel two-stage anomaly detection algorithm called CONSULT (CONtrastive Self-sUpervised Learning for few-shot Tumor detection). The first stage of CONSULT fine-tunes a pre-trained feature extractor specifically for MRI brain images, using a synthetic data generation pipeline to create tumor-like data. This process overcomes the lack of anomaly samples and enables the integration of attention mechanisms to focus on anomalous image segments. The first stage is to overcome the shortcomings of current anomaly detection in extracting features in high-variation data by incorporating Context-Aware Contrastive Learning and Self-supervised Feature Adversarial Learning. The second stage of CONSULT uses PatchCore for conventional feature extraction via the fine-tuned weights from the first stage. To summarize, we propose a self-supervised training scheme for anomaly detection, enhancing model performance and data reliability. Furthermore, our proposed contrastive loss, Tritanh Loss, stabilizes learning by offering a unique solution all while enhancing gradient flow. Finally, CONSULT achieves superior performance in few-shot brain tumor detection, demonstrating significant improvements over PatchCore by 9.4%, 12.9%, 10.2%, and 6.0% for 2, 4, 6, and 8 shots, respectively, while training exclusively on healthy images.

cs.CV↗

arXiv2024

RICASSO: Reinforced Imbalance Learning with Class-Aware Self-Supervised Outliers Exposure

In real-world scenarios, deep learning models often face challenges from both imbalanced (long-tailed) and out-of-distribution (OOD) data. However, existing joint methods rely on real OOD data, which leads to unnecessary trade-offs. In contrast, our research shows that data mixing, a potent augmentation technique for long-tailed recognition, can generate pseudo-OOD data that exhibit the features of both in-distribution (ID) data and OOD data. Therefore, by using mixed data instead of real OOD data, we can address long-tailed recognition and OOD detection holistically. We propose a unified framework called Reinforced Imbalance Learning with Class-Aware Self-Supervised Outliers Exposure (RICASSO), where "self-supervised" denotes that we only use ID data for outlier exposure. RICASSO includes three main strategies: Norm-Odd-Duality-Based Outlier Exposure: Uses mixed data as pseudo-OOD data, enabling simultaneous ID data rebalancing and outlier exposure through a single loss function. Ambiguity-Aware Logits Adjustment: Utilizes the ambiguity of ID data to adaptively recalibrate logits. Contrastive Boundary-Center Learning: Combines Virtual Boundary Learning and Dual-Entropy Center Learning to use mixed data for better feature separation and clustering, with Representation Consistency Learning for robustness. Extensive experiments demonstrate that RICASSO achieves state-of-the-art performance in long-tailed recognition and significantly improves OOD detection compared to our baseline (27% improvement in AUROC and 61% reduction in FPR on the iNaturalist2018 dataset). On iNaturalist2018, we even outperforms methods using real OOD data. The code will be made public soon.

cs.CV↗

arXiv2024

SAPD+: An Accelerated Stochastic Method for Nonconvex-Concave Minimax Problems

We propose a new stochastic method SAPD+ for solving nonconvex-concave minimax problems of the form $\min\max\mathcal{L}(x,y)=f(x)+Φ(x,y)-g(y)$, where $f,g$ are closed convex and $Φ(x,y)$ is a smooth function that is weakly convex in $x$, (strongly) concave in $y$. Let $δ^2$ denote the variance bound for the unbiased stochastic oracle used within SAPD+ to estimate $\nablaΦ$. When $δ>0$, for both strongly concave and merely concave settings, SAPD+ achieves the best known oracle complexities: $\mathcal{O}\Big(κ_y\max\Big\{1,\frac{δ^2}{ε^2}\Big\}\frac{L\mathcal{G}_0}{ε^{2}}\Big)$ for the strongly concave case without assuming compactness of the problem domain, and $\mathcal{O}\Big(\frac{L^3\mathcal{D}_y^2\mathcal{G}_0}{ε^{4}}\Big(1+\frac{δ^2}{ε^2}\Big)\Big)$ for the merely concave case, where $κ_y\geq 1$ is the condition number, $L$ is the Lipschitz constant of $\nabla Φ$, $\mathcal{G}_0$ is the primal-dual gap of the initial point, and $\mathcal{D}_y=\sup\{\|y\|:\ y\in\mathbf{dom} g\}$. We also propose SAPD+ with variance reduction, which enjoys $\mathcal{O}\Big(\max\Big\{κ_y,\sqrt{\fracδε}\Big\}\cdot (1+κ_y\fracδε)\frac{L\mathcal{G}_0}{ε^2}\Big)$ oracle complexity for weakly convex-strongly concave setting --this is the best known upper complexity bound in the literature for this setting and our paper establishes it for the first time. We demonstrate the efficiency of SAPD+ on a distributionally robust learning problem with a nonconvex regularizer and also on a multi-class classification problem in deep learning.

math.OC↗

arXiv2024

Ask-before-Plan: Proactive Language Agents for Real-World Planning

The evolution of large language models (LLMs) has enhanced the planning capabilities of language agents in diverse real-world scenarios. Despite these advancements, the potential of LLM-powered agents to comprehend ambiguous user instructions for reasoning and decision-making is still under exploration. In this work, we introduce a new task, Proactive Agent Planning, which requires language agents to predict clarification needs based on user-agent conversation and agent-environment interaction, invoke external tools to collect valid information, and generate a plan to fulfill the user's demands. To study this practical problem, we establish a new benchmark dataset, Ask-before-Plan. To tackle the deficiency of LLMs in proactive planning, we propose a novel multi-agent framework, Clarification-Execution-Planning (\texttt{CEP}), which consists of three agents specialized in clarification, execution, and planning. We introduce the trajectory tuning scheme for the clarification agent and static execution agent, as well as the memory recollection mechanism for the dynamic execution agent. Extensive evaluations and comprehensive analyses conducted on the Ask-before-Plan dataset validate the effectiveness of our proposed framework.

cs.CL↗

arXiv2024

Reliability-Based Planning of Cable Layout for Offshore Wind Farm Electrical Collector System Considering Post-Fault Network Reconfiguration

The electrical collector system (ECS) plays a crucial role in determining the performance of offshore wind farms (OWFs). Existing research has predominantly restricted ECS cable layouts to conventional radial or ring structures and employed graph theory heuristics for solutions. However, both economic efficiency and reliability of the OWFs heavily depend on their ECS structure, and the optimal ECS cable layout often deviates from typical configurations. In this context, this paper introduces a novel reliability-based ECS cable layout planning method for large-scale OWFs, employing a two-stage stochastic programming approach to address uncertainties of wind power and contingencies. To enhance reliability, the model incorporates optimal post-fault network reconfiguration strategies by adjusting wind turbine power supply paths through link cables. To tackle computation challenges arising from numerous contingency scenarios, a customized progressive contingency incorporation (CPCI) framework is developed to solve the model with higher efficiency by iteratively identifying non-trivial scenarios and solving the simplified problems. The convergence and optimality are theoretically proven. Numerical tests on several real-world OWFs validate the necessity of fully optimizing ECS structures and demonstrate the efficiency of the CPCI algorithm.

eess.SY↗

arXiv2024

Robust Accelerated Primal-Dual Methods for Computing Saddle Points

We consider strongly-convex-strongly-concave saddle point problems assuming we have access to unbiased stochastic estimates of the gradients. We propose a stochastic accelerated primal-dual (SAPD) algorithm and show that SAPD sequence, generated using constant primal-dual step sizes, linearly converges to a neighborhood of the unique saddle point. Interpreting the size of the neighborhood as a measure of robustness to gradient noise, we obtain explicit characterizations of robustness in terms of SAPD parameters and problem constants. Based on these characterizations, we develop computationally tractable techniques for optimizing the SAPD parameters, i.e., the primal and dual step sizes, and the momentum parameter, to achieve a desired trade-off between the convergence rate and robustness on the Pareto curve. This allows SAPD to enjoy fast convergence properties while being robust to noise as an accelerated method. SAPD admits convergence guarantees for the distance metric with a variance term optimal up to a logarithmic factor -which can be removed by employing a restarting strategy. We also discuss how convergence and robustness results extend to the convex-concave setting. Finally, we illustrate our framework on distributionally robust logistic regression problem.

math.OC↗

arXiv2024

Mitigating Pooling Bias in E-commerce Search via False Negative Estimation

Efficient and accurate product relevance assessment is critical for user experiences and business success. Training a proficient relevance assessment model requires high-quality query-product pairs, often obtained through negative sampling strategies. Unfortunately, current methods introduce pooling bias by mistakenly sampling false negatives, diminishing performance and business impact. To address this, we present Bias-mitigating Hard Negative Sampling (BHNS), a novel negative sampling strategy tailored to identify and adjust for false negatives, building upon our original False Negative Estimation algorithm. Our experiments in the Instacart search setting confirm BHNS as effective for practical e-commerce use. Furthermore, comparative analyses on public dataset showcase its domain-agnostic potential for diverse applications.

cs.IR↗

arXiv2024

A τ Matrix Based Approximate Inverse Preconditioning for Tempered Fractional Diffusion Equations

Tempered fractional diffusion equations are a crucial class of equations widely applied in many physical fields. In this paper, the Crank-Nicolson method and the tempered weighted and shifts Grünwald formula are firstly applied to discretize the tempered fractional diffusion equations. We then obtain that the coefficient matrix of the discretized system has the structure of the sum of the identity matrix and a diagonal matrix multiplied by a symmetric positive definite(SPD) Toeplitz matrix. Based on the properties of SPD Toeplitz matrices, we use $τ$ matrix approximate it and then propose a novel approximate inverse preconditioner to approximate the coefficient matrix. The $τ$ matrix based approximate inverse preconditioner can be efficiently computed using the discrete sine transform(DST). In spectral analysis, the eigenvalues of the preconditioned coefficient matrix are clustered around 1, ensuring fast convergence of Krylov subspace methods with the new preconditioner. Finally, numerical experiments demonstrate the effectiveness of the proposed preconditioner.

math.NA↗

arXiv2024

Permuted preconditioning for extended saddle point problem arising from Neumann boundary control

In this paper, a new block preconditioner is proposed for the saddle point problem arising from the Neumann boundary control problem. In order to deal with the singularity of the stiffness matrix, the saddle point problem is first extended to a new one by a regularization of the pure Neumann problem. Then after row permutations of the extended saddle point problem, a new block triangular preconditioner is constructed based on an approximation of the Schur complement. We analyze the eigenvalue properties of the preconditioned matrix and provide eigenvalue bounds. Numerical results illustrate the efficiency of the proposed preconditioning method.

math.NA↗

arXiv2024

RoSE-Opt: Robust and Efficient Analog Circuit Parameter Optimization with Knowledge-infused Reinforcement Learning

This paper proposes a learning framework, RoSE-Opt, to achieve robust and efficient analog circuit parameter optimization. RoSE-Opt has two important features. First, it incorporates key domain knowledge of analog circuit design, such as circuit topology, couplings between circuit specifications, and variations of process, supply voltage, and temperature, into the learning loop. This strategy facilitates the training of an artificial agent capable of achieving design goals by identifying device parameters that are optimal and robust. Second, it exploits a two-level optimization method, that is, integrating Bayesian optimization (BO) with reinforcement learning (RL) to improve sample efficiency. In particular, BO is used for a coarse yet quick search of an initial starting point for optimization. This sets a solid foundation to efficiently train the RL agent with fewer samples. Experimental evaluations on benchmarking circuits show promising sample efficiency, extraordinary figure-of-merit in terms of design efficiency and design success rate, and Pareto optimality in circuit performance of our framework, compared to previous methods. Furthermore, this work thoroughly studies the performance of different RL optimization algorithms, such as Deep Deterministic Policy Gradients (DDPG) with an off-policy learning mechanism and Proximal Policy Optimization (PPO) with an on-policy learning mechanism. This investigation provides users with guidance on choosing the appropriate RL algorithms to optimize the device parameters of analog circuits. Finally, our study also demonstrates RoSE-Opt's promise in parasitic-aware device optimization for analog circuits. In summary, our work reports a knowledge-infused BO-RL design automation framework for reliable and efficient optimization of analog circuits' device parameters.

cs.AR↗

arXiv2024

Hardware-Efficient Stabilization of Entanglement via Engineered Dissipation in Superconducting Circuits

Generation and preservation of quantum entanglement are among the primary tasks in quantum information processing. State stabilization via quantum bath engineering offers a resource-efficient approach to achieve this objective. However, current methods for engineering dissipative channels to stabilize target entangled states often require specialized hardware designs, complicating experimental realization and hindering their compatibility with scalable quantum computation architectures. In this work, we propose and experimentally demonstrate a stabilization protocol readily implementable in the mainstream integrated superconducting quantum circuits. The approach utilizes a Raman process involving a resonant (or nearly resonant) superconducting qubit array and their dedicated readout resonators to effectively emerge nonlocal dissipative channels. Leveraging individual controllability of the qubits and resonators, the protocol stabilizes two-qubit Bell states with a fidelity of $90.7\%$, marking the highest reported value in solid-state platforms to date. Furthermore, by extending this strategy to include three qubits, an entangled $W$ state is achieved with a fidelity of $86.2\%$, which has not been experimentally investigated before. Notably, the protocol is of practical interest since it only utilizes existing hardware common to standard operations in the underlying superconducting circuits, thereby facilitating the exploration of many-body quantum entanglement with dissipative resources.

quant-ph↗

arXiv2024

Dynamic Microgrid Formation Considering Time-dependent Contingency: A Distributionally Robust Approach

The increasing frequency of extreme weather events has posed significant risks to the operation of power grids. During long-duration extreme weather events, microgrid formation (MF) is an essential solution to enhance the resilience of the distribution systems by proactively partitioning the distribution system into several microgrids to mitigate the impact of contingencies. This paper proposes a distributionally robust dynamic microgrid formation (DR-DMF) approach to fully consider the temporal characteristics of line failure probability during long-duration extreme weather events like typhoons. The boundaries of each microgrid are dynamically adjusted to enhance the resilience of the system. Furthermore, the expected load shedding is minimized by a distributionally robust optimization model considering the uncertainty of line failure probability regarding the worst-case distribution of contingencies. The effectiveness of the proposed model is verified by numerical simulations on a modified IEEE 37-node system.

eess.SY↗

arXiv2024

Reinforcement Retrieval Leveraging Fine-grained Feedback for Fact Checking News Claims with Black-Box LLM

Retrieval-augmented language models have exhibited promising performance across various areas of natural language processing (NLP), including fact-critical tasks. However, due to the black-box nature of advanced large language models (LLMs) and the non-retrieval-oriented supervision signal of specific tasks, the training of retrieval model faces significant challenges under the setting of black-box LLM. We propose an approach leveraging Fine-grained Feedback with Reinforcement Retrieval (FFRR) to enhance fact-checking on news claims by using black-box LLM. FFRR adopts a two-level strategy to gather fine-grained feedback from the LLM, which serves as a reward for optimizing the retrieval policy, by rating the retrieved documents based on the non-retrieval ground truth of the task. We evaluate our model on two public datasets for real-world news claim verification, and the results demonstrate that FFRR achieves significant improvements over strong LLM-enabled and non-LLM baselines.

cs.CL↗

arXiv2024

BlissCam: Boosting Eye Tracking Efficiency with Learned In-Sensor Sparse Sampling

Eye tracking is becoming an increasingly important task domain in emerging computing platforms such as Augmented/Virtual Reality (AR/VR). Today's eye tracking system suffers from long end-to-end tracking latency and can easily eat up half of the power budget of a mobile VR device. Most existing optimization efforts exclusively focus on the computation pipeline by optimizing the algorithm and/or designing dedicated accelerators while largely ignoring the front-end of any eye tracking pipeline: the image sensor. This paper makes a case for co-designing the imaging system with the computing system. In particular, we propose the notion of "in-sensor sparse sampling", whereby the pixels are drastically downsampled (by 20x) within the sensor. Such in-sensor sampling enhances the overall tracking efficiency by significantly reducing 1) the power consumption of the sensor readout chain and sensor-host communication interfaces, two major power contributors, and 2) the work done on the host, which receives and operates on far fewer pixels. With careful reuse of existing pixel circuitry, our proposed BLISSCAM requires little hardware augmentation to support the in-sensor operations. Our synthesis results show up to 8.2x energy reduction and 1.4x latency reduction over existing eye tracking pipelines.

cs.AR↗

arXiv2024

Wall modes and the transition to bulk convection in rotating Rayleigh-Bénard convection

We investigate states of rapidly rotating Rayleigh-Bénard convection in a cylindrical cell over a range of Rayleigh number $3\times10^5\leq Ra \leq 5\times10^{9}$ and Ekman number $10^{-6} \leq Ek \leq 10^{-4}$ for Prandtl number $Pr = 0.8$ and aspect ratios $1/5 \leq Γ\leq 5$ using direct numerical simulations. We characterize, for perfectly insulating sidewall boundary conditions, the first transition to convection via wall mode instability and the nonlinear growth and instability of the resulting wall mode states including a secondary transition to time dependence. We show how the radial structure of the vertical velocity $u_z$ and the temperature $T$ is captured well by the linear eigenfunctions of the wall mode instability where the radial width of $u_z$ is $δ_{u_z} \sim Ek^{1/3} r/H$ whereas $δ_T \sim e^{-k r}$ ($k$ is the wavenumber of an laterally infinite wall mode state). The disparity in spatial scales for $Ek = 10^{-6}$ means that the heat transport is dominated by the radial structure of $u_z$ since $T$ varies slowly over the radial scale $δ_{u_z}$. We further describe how the transition to a state of bulk convection is influenced by the presence of the wall mode states. We use temporal and spatial scales as measures of the local state of convection and the Nusselt number $Nu$ as representative of global transport. Our results elucidate the evolution of the wall state of rotating convection and confirm that wall modes are strongly linked with the boundary zonal flow (BZF) being the robust remnant of nonlinear wall mode states. We also show how the heat transport ($Nu$) contributions of wall modes and bulk modes are related and discuss approaches to disentangling their relative contributions.

physics.flu-dyn↗

arXiv2024

SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations

We consider using deep neural networks to solve time-dependent partial differential equations (PDEs), where multi-scale processing is crucial for modeling complex, time-evolving dynamics. While the U-Net architecture with skip connections is commonly used by prior studies to enable multi-scale processing, our analysis shows that the need for features to evolve across layers results in temporally misaligned features in skip connections, which limits the model's performance. To address this limitation, we propose SineNet, consisting of multiple sequentially connected U-shaped network blocks, referred to as waves. In SineNet, high-resolution features are evolved progressively through multiple stages, thereby reducing the amount of misalignment within each stage. We furthermore analyze the role of skip connections in enabling both parallel and sequential processing of multi-scale information. Our method is rigorously tested on multiple PDE datasets, including the Navier-Stokes equations and shallow water equations, showcasing the advantages of our proposed approach over conventional U-Nets with a comparable parameter budget. We further demonstrate that increasing the number of waves in SineNet while maintaining the same number of parameters leads to a monotonically improved performance. The results highlight the effectiveness of SineNet and the potential of our approach in advancing the state-of-the-art in neural PDE solver design. Our code is available as part of AIRS (https://github.com/divelab/AIRS).

cs.LG↗

arXiv2024

Jointly Improving the Sample and Communication Complexities in Decentralized Stochastic Minimax Optimization

We propose a novel single-loop decentralized algorithm called DGDA-VR for solving the stochastic nonconvex strongly-concave minimax problem over a connected network of $M$ agents. By using stochastic first-order oracles to estimate the local gradients, we prove that our algorithm finds an $ε$-accurate solution with $\mathcal{O}(ε^{-3})$ sample complexity and $\mathcal{O}(ε^{-2})$ communication complexity, both of which are optimal and match the lower bounds for this class of problems. Unlike competitors, our algorithm does not require multiple communications for the convergence results to hold, making it applicable to a broader computational environment setting. To the best of our knowledge, this is the first such algorithm to jointly optimize the sample and communication complexities for the problem considered here.

math.OC↗