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Kai Wu

Publications and source records attributed to Kai Wu.

At least 109 records · Page 6Linked to original sources
arXiv2024

Regularization in Space-Time Topology Optimization for Multi-Axis Additive Manufacturing

In additive manufacturing, the fabrication sequence has a large influence on the quality of manufactured components. While planning of the fabrication sequence is typically performed after the component has been designed, recent developments have demonstrated the possibility and benefits of simultaneous optimization of both the structural layout and the corresponding fabrication sequence. The simultaneous optimization approach, called space-time topology optimization, introduces a pseudo-time field to encode the manufacturing process order, alongside a pseudo-density field representing the structural layout. To comply with manufacturing principles, the pseudo-time field needs to be monotonic, i.e., free of local minima. However, explicitly formulated constraints are not always effective, particularly for complex structural layouts. In this paper, we introduce a novel method to regularize the pseudo-time field in space-time topology optimization. We conceptualize the monotonic additive manufacturing process as a virtual heat conduction process starting from the surface upon which a component is constructed layer by layer. The virtual temperature field, which shall not be confused with the actual temperature field during manufacturing, serves as an analogy for encoding the fabrication sequence. In this new formulation, we use local virtual heat conductivity coefficients as optimization variables to steer the temperature field and, consequently, the fabrication sequence. The virtual temperature field is inherently free of local minima due to the physics it resembles. We numerically validate the effectiveness of this regularization in space-time topology optimization under process-dependent loads, including gravity and thermomechanical loads.

math.OC↗
arXiv2024

HCPM: Hierarchical Candidates Pruning for Efficient Detector-Free Matching

Deep learning-based image matching methods play a crucial role in computer vision, yet they often suffer from substantial computational demands. To tackle this challenge, we present HCPM, an efficient and detector-free local feature-matching method that employs hierarchical pruning to optimize the matching pipeline. In contrast to recent detector-free methods that depend on an exhaustive set of coarse-level candidates for matching, HCPM selectively concentrates on a concise subset of informative candidates, resulting in fewer computational candidates and enhanced matching efficiency. The method comprises a self-pruning stage for selecting reliable candidates and an interactive-pruning stage that identifies correlated patches at the coarse level. Our results reveal that HCPM significantly surpasses existing methods in terms of speed while maintaining high accuracy. The source code will be made available upon publication.

cs.CV↗
arXiv2024

Tuning-Free Image Customization with Image and Text Guidance

Despite significant advancements in image customization with diffusion models, current methods still have several limitations: 1) unintended changes in non-target areas when regenerating the entire image; 2) guidance solely by a reference image or text descriptions; and 3) time-consuming fine-tuning, which limits their practical application. In response, we introduce a tuning-free framework for simultaneous text-image-guided image customization, enabling precise editing of specific image regions within seconds. Our approach preserves the semantic features of the reference image subject while allowing modification of detailed attributes based on text descriptions. To achieve this, we propose an innovative attention blending strategy that blends self-attention features in the UNet decoder during the denoising process. To our knowledge, this is the first tuning-free method that concurrently utilizes text and image guidance for image customization in specific regions. Our approach outperforms previous methods in both human and quantitative evaluations, providing an efficient solution for various practical applications, such as image synthesis, design, and creative photography.

cs.CV↗
arXiv2024

Performance Bounds and Optimization for CSI-Ratio based Bi-static Doppler Sensing in ISAC Systems

Bi-static sensing is crucial for exploring the potential of networked sensing capabilities in integrated sensing and communications (ISAC). However, it suffers from the challenging clock asynchronism issue. CSI ratio-based sensing is an effective means to address the issue. Its performance bounds, particular for Doppler sensing, have not been fully understood yet. This work endeavors to fill the research gap. Focusing on a single dynamic path in high-SNR scenarios, we derive the closed-form CRB. Then, through analyzing the mutual interference between dynamic and static paths, we simplify the CRB results by deriving close approximations, further unveiling new insights of the impact of numerous physical parameters on Doppler sensing. Moreover, utilizing the new CRB and analyses, we propose novel waveform optimization strategies for noise- and interference-limited sensing scenarios, which are also empowered by closed-form and efficient solutions. Extensive simulation results are provided to validate the preciseness of the derived CRB results and analyses, with the aid of the maximum-likelihood estimator. The results also demonstrate the substantial enhanced Doppler sensing accuracy and the sensing capabilities for low-speed target achieved by the proposed waveform design.

cs.IT↗
arXiv2024

Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt

Unsupervised Anomaly Detection (UAD) with incremental training is crucial in industrial manufacturing, as unpredictable defects make obtaining sufficient labeled data infeasible. However, continual learning methods primarily rely on supervised annotations, while the application in UAD is limited due to the absence of supervision. Current UAD methods train separate models for different classes sequentially, leading to catastrophic forgetting and a heavy computational burden. To address this issue, we introduce a novel Unsupervised Continual Anomaly Detection framework called UCAD, which equips the UAD with continual learning capability through contrastively-learned prompts. In the proposed UCAD, we design a Continual Prompting Module (CPM) by utilizing a concise key-prompt-knowledge memory bank to guide task-invariant `anomaly' model predictions using task-specific `normal' knowledge. Moreover, Structure-based Contrastive Learning (SCL) is designed with the Segment Anything Model (SAM) to improve prompt learning and anomaly segmentation results. Specifically, by treating SAM's masks as structure, we draw features within the same mask closer and push others apart for general feature representations. We conduct comprehensive experiments and set the benchmark on unsupervised continual anomaly detection and segmentation, demonstrating that our method is significantly better than anomaly detection methods, even with rehearsal training. The code will be available at https://github.com/shirowalker/UCAD.

cs.CV↗
arXiv2023

Signed Graph Neural Ordinary Differential Equation for Modeling Continuous-time Dynamics

Modeling continuous-time dynamics constitutes a foundational challenge, and uncovering inter-component correlations within complex systems holds promise for enhancing the efficacy of dynamic modeling. The prevailing approach of integrating graph neural networks with ordinary differential equations has demonstrated promising performance. However, they disregard the crucial signed information intrinsic to graphs, impeding their capacity to accurately capture real-world phenomena and leading to subpar outcomes. In response, we introduce a novel approach: a signed graph neural ordinary differential equation, adeptly addressing the limitations of miscapturing signed information. Our proposed solution boasts both flexibility and efficiency. To substantiate its effectiveness, we seamlessly integrate our devised strategies into three preeminent graph-based dynamic modeling frameworks: graph neural ordinary differential equations, graph neural controlled differential equations, and graph recurrent neural networks. Rigorous assessments encompass three intricate dynamic scenarios from physics and biology, as well as scrutiny across four authentic real-world traffic datasets. Remarkably outperforming the trio of baselines, empirical results underscore the substantial performance enhancements facilitated by our proposed approach.Our code can be found at https://github.com/beautyonce/SGODE.

cs.LG↗
arXiv2023

Node-downloadable frequency transfer system based on a mode-locked laser with over 100 km of fiber

To meet the requirements of time-frequency networks and enable frequency downloadability for nodes along the link, we demonstrated the extraction of stable frequency signals at nodes using a mode-locked laser under the condition of 100 km laboratory fiber. The node consists of a simple structure that utilizes widely used optoelectronic devices and enables plug-and-play applications. In addition, the node can recover frequency signals with multiple frequencies, which are useful for scenarios that require different frequencies. Here, we experimentally demonstrated a short-term frequency instability of $2.83\times {{10}^{-13}}$@1 s and a long-term frequency instability of $1.18\times {{10}^{-15}}$@10,000 s at the node, which is similar to that at the remote site of the frequency transfer system. At the same time, frequency signals with different frequencies also achieved stable extraction with the same performance at the node. Our results can support the distributed application under large-scale time-frequency networks.

physics.optics↗
arXiv2023

Detection of Network and Genuine Network Quantum Steering

The quantum network correlations play significant roles in long distance quantum communication,quantum cryptography and distributed quantum computing. Generally it is very difficult to characterize the multipartite quantum network correlations such as nonlocality, entanglement and steering. In this paper, we propose the network and the genuine network quantum steering models from the aspect of probabilities in the star network configurations. Linear and nonlinear inequalities are derived to detect the network and genuine network quantum steering when the central party performs one fixed measurement. We show that our criteria can detect more quantum network steering than that from the violation of the n-locality quantum networks. Moreover, it is shown that biseparable assemblages can demonstrate genuine network steering in the star network configurations.

quant-ph↗
arXiv2023

EMOFM: Ensemble MLP mOdel with Feature-based Mixers for Click-Through Rate Prediction

Track one of CTI competition is on click-through rate (CTR) prediction. The dataset contains millions of records and each field-wise feature in a record consists of hashed integers for privacy. For this task, the keys of network-based methods might be type-wise feature extraction and information fusion across different fields. Multi-layer perceptrons (MLPs) are able to extract field feature, but could not efficiently fuse features. Motivated by the natural fusion characteristic of cross attention and the efficiency of transformer-based structures, we propose simple plug-in mixers for field/type-wise feature fusion, and thus construct an field&type-wise ensemble model, namely EMOFM (Ensemble MLP mOdel with Feature-based Mixers). In the experiments, the proposed model is evaluated on the dataset, the optimization process is visualized and ablation studies are explored. It is shown that EMOFM outperforms compared baselines. In the end, we discuss on future work. WARNING: The comparison might not be fair enough since the proposed method is designed for this data in particular while compared methods are not. For example, EMOFM especially takes different types of interactions into consideration while others do not. Anyway, we do hope that the ideas inside our method could help other developers/learners/researchers/thinkers and so on.

cs.LG↗
arXiv2023

FEcMD: A multi-physics and multi-scale computational program for electron emission characteristics dynamically coupled with atomic structure in metal nano-emitters

Field emission coupled with molecular dynamics simulation (FEcMD) software package is a computational tool for studying the electron emission characteristics and the atomic structure evolution of micro- and nano-protrusions made of pure metals or multi-component alloys by means of multi-physics and multi-scale methodology. The implementations of molecular dynamics, the electrodynamics, and the heat conduction in FEcMD program are addressed. For molecular dynamics simulation, the Lennard-Jones potentials, embedded atomic method (EAM), and moment tensor potentials (MTP) are fully supported for both alloys and pure metals. In the electrodynamics, the FEcMD program incorporates the space charge fields (space charge potential and exchange-correlation effects) in the Wentzel-Kramers-Brillouin-Jeffreys (WKBJ) approximation to evaluate the field emission current density more reliably for nano-gaps between two metal electrodes. Additionally, the advanced two-temperature heat conduction model is implemented in FEcMD program, and which provides more reliable descriptions for the temperature evolutions of electron and phonon subsystems under the radiofrequency (RF) or pulse electric fields. Comprehensive benchmark tests are performed for each module in FEcMD software to validate the numerical results, and also to access the accuracy and efficiency of the implemented algorithms. Finally, some typical applications of FEcMD program are also demonstrated for understanding the evolution of temperature and electric field coupled with the dynamic changing of atomic structures for metal micro- and nano-protrusions.

physics.comp-ph↗
arXiv2023

SPELL: Semantic Prompt Evolution based on a LLM

Prompt engineering is a new paradigm for enhancing the performance of trained neural network models. For optimizing text-style prompts, existing methods usually individually operate small portions of a text step by step, which either breaks the fluency or could not globally adjust a prompt. Since large language models (LLMs) have powerful ability of generating coherent texts token by token, can we utilize LLMs for improving prompts? Based on this motivation, in this paper, considering a trained LLM as a text generator, we attempt to design a black-box evolution algorithm for automatically optimizing texts, namely SPELL (Semantic Prompt Evolution based on a LLM). The proposed method is evaluated with different LLMs and evolution parameters in different text tasks. Experimental results show that SPELL could rapidly improve the prompts indeed. We further explore the evolution process and discuss on the limitations, potential possibilities and future work.

cs.CL↗
arXiv2023

Learning to Generate Time Series Conditioned Graphs with Generative Adversarial Nets

Deep learning based approaches have been utilized to model and generate graphs subjected to different distributions recently. However, they are typically unsupervised learning based and unconditioned generative models or simply conditioned on the graph-level contexts, which are not associated with rich semantic node-level contexts. Differently, in this paper, we are interested in a novel problem named Time Series Conditioned Graph Generation: given an input multivariate time series, we aim to infer a target relation graph modeling the underlying interrelationships between time series with each node corresponding to each time series. For example, we can study the interrelationships between genes in a gene regulatory network of a certain disease conditioned on their gene expression data recorded as time series. To achieve this, we propose a novel Time Series conditioned Graph Generation-Generative Adversarial Networks (TSGG-GAN) to handle challenges of rich node-level context structures conditioning and measuring similarities directly between graphs and time series. Extensive experiments on synthetic and real-word gene regulatory networks datasets demonstrate the effectiveness and generalizability of the proposed TSGG-GAN.

cs.LG↗
arXiv2023

B2Opt: Learning to Optimize Black-box Optimization with Little Budget

The core challenge of high-dimensional and expensive black-box optimization (BBO) is how to obtain better performance faster with little function evaluation cost. The essence of the problem is how to design an efficient optimization strategy tailored to the target task. This paper designs a powerful optimization framework to automatically learn the optimization strategies from the target or cheap surrogate task without human intervention. However, current methods are weak for this due to poor representation of optimization strategy. To achieve this, 1) drawing on the mechanism of genetic algorithm, we propose a deep neural network framework called B2Opt, which has a stronger representation of optimization strategies based on survival of the fittest; 2) B2Opt can utilize the cheap surrogate functions of the target task to guide the design of the efficient optimization strategies. Compared to the state-of-the-art BBO baselines, B2Opt can achieve multiple orders of magnitude performance improvement with less function evaluation cost. We validate our proposal on high-dimensional synthetic functions and two real-world applications. We also find that deep B2Opt performs better than shallow ones.

cs.LG↗
arXiv2023

Network Collaborator: Knowledge Transfer Between Network Reconstruction and Community Detection

This paper focuses on jointly inferring network and community structures from the dynamics of complex systems. Although many approaches have been designed to solve these two problems solely, none of them consider explicit shareable knowledge across these two tasks. Community detection (CD) from dynamics and network reconstruction (NR) from dynamics are natural synergistic tasks that motivate the proposed evolutionary multitasking NR and CD framework, called network collaborator (NC). In the process of NC, the NR task explicitly transfers several better network structures for the CD task, and the CD task explicitly transfers a better community structure to assist the NR task. Moreover, to transfer knowledge from the NR task to the CD task, NC models the study of CD from dynamics to find communities in the dynamic network and then considers whether to transfer knowledge across tasks. A test suite for multitasking NR and CD problems (MTNRCDPs) is designed to verify the performance of NC. The experimental results conducted on the designed MTNRCDPs have demonstrated that joint NR with CD has a synergistic effect, where the network structure used to inform the existence of communities is also inherently employed to improve the reconstruction accuracy, which, in turn, can better demonstrate the discovering of the community structure. The code is available at: https://github.com/xiaofangxd/EMTNRCD.

cs.SI↗
arXiv2023

Discover governing differential equations from evolving systems

Discovering the governing equations of evolving systems from available observations is essential and challenging. In this paper, we consider a new scenario: discovering governing equations from streaming data. Current methods struggle to discover governing differential equations with considering measurements as a whole, leading to failure to handle this task. We propose an online modeling method capable of handling samples one by one sequentially by modeling streaming data instead of processing the entire dataset. The proposed method performs well in discovering ordinary differential equations (ODEs) and partial differential equations (PDEs) from streaming data. Evolving systems are changing over time, which invariably changes with system status. Thus, finding the exact change points is critical. The measurement generated from a changed system is distributed dissimilarly to before; hence, the difference can be identified by the proposed method. Our proposal is competitive in identifying the change points and discovering governing differential equations in three hybrid systems and two switching linear systems.

physics.comp-ph↗
arXiv2023

A Unified Framework for Online Data-Driven Predictive Control with Robust Safety Guarantees

Despite great successes, model predictive control (MPC) relies on an accurate dynamical model and requires high onboard computational power, impeding its wider adoption in engineering systems, especially for nonlinear real-time systems with limited computation power. These shortcomings of MPC motivate this work to make such a control framework more practically viable for real-world applications. Specifically, to remove the required accurate dynamical model and reduce the computational cost for nonlinear MPC (NMPC), this paper develops a unified online data-driven predictive control pipeline to efficiently control a system with guaranteed safety without incurring large computational complexity. The new aspect of this idea is learning not only the real system but also the control policy, which results in a reasonable computational cost for the data-driven predictive controllers. More specifically, we first develop a spatial temporal filter (STF)-based concurrent learning scheme to systematically identify system dynamics for general nonlinear systems. We then develop a robust control barrier function (RCBF) for safety guarantees in the presence of model uncertainties and learn the RCBF-based NMPC policy. Furthermore, to mitigate the performance degradation due to the existing model uncertainties, we propose an online policy correction scheme through perturbation analysis and design of an ancillary feedback controller. Finally, extensive simulations on two applications, cart-inverted pendulum and automotive powertrain control, are performed to demonstrate the efficacy of the proposed framework, which shows comparable performance with much lower computational cost in comparison with several benchmark algorithms.

eess.SY↗
arXiv2023

Hubbard Model on Triangular Lattice: Role of Charge Fluctuations

A chiral spin liquid (CSL) phase has been recently reported in the Hubbard model on a triangular lattice at half-filling. It emerges in an intermediate coupling regime, which is sandwiched between a $120^\circ$ antiferromagnetic (AFM) phase and a metallic phase as a function of on-site repulsion $U$. In this work, we examine how the charge fluctuation may cause the CSL and complex phase diagram. We first identify an exact sign structure of the model at arbitrary temperature, sample size, and doping, which is reduced from the original Fermi exchange signs of the electrons by finite $U$. In particular, the spin and charge degrees of freedom are generally entangled via the phase-string in the sign structure. By precisely switching off such a phase-string, the CSL phase is shown to disappear, with only the $120^\circ$ AFM order remaining down to weak $U$ by a density matrix renormalization group calculation. Here the charge fluctuation is effectively decoupled from the AFM background, which is a $120^\circ$ AFM order due to a geometric Berry phase in the sign structure of the triangular lattice. General implications for the Mott physics will also be discussed.

cond-mat.str-el↗
arXiv2023

Influence of planets on debris disks in star clusters I: the 50 AU Jupiter

Although debris disks may be common in exoplanet systems, only a few systems are known in which debris disks and planets coexist. Planets and the surrounding stellar population can have a significant impact on debris disk evolution. Here we study the dynamical evolution of debris structures around stars embedded in star clusters, aiming to determine how the presence of a planet affects the evolution of such structures. We combine NBODY6++GPU and REBOUND to carry out N-body simulations of planetary systems in star clusters (N=8000; Rh=0.78 pc) for a period of 100 Myr, in which 100 solar-type stars are assigned 200 test particles. Simulations are carried out with and without a Jupiter-mass planet at 50 au. We find that the planet destabilizes test particles and speeds up their evolution. The planet expels most particles in nearby and resonant orbits. Remaining test particles tend to retain small inclinations when the planet is present, and fewer test particles obtain retrograde orbits. Most escaping test particles with speeds smaller than the star cluster's escape speed originate from cold regions of the planetary system or from regions near the planet. We identify three regions within planetary systems in star clusters: (i) the private region of the planet, where few debris particles remain (40 - 60 au), (ii) the reach of the planet, in which particles are affected by the planet (0 - 400 au), and (iii) the territory of the planetary system, most particles outside which will eventually escape (0 - 700 au).

astro-ph.EP↗