arXiv subjects

Chen Wang

Publications and source records attributed to Chen Wang.

At least 433 records · Page 24Linked to original sources

arXiv2024

Online robust estimation and bootstrap inference for function-on-scalar regression

We propose a novel and robust online function-on-scalar regression technique via geometric median to learn associations between functional responses and scalar covariates based on massive or streaming datasets. The online estimation procedure, developed using the average stochastic gradient descent algorithm, offers an efficient and cost-effective method for analyzing sequentially augmented datasets, eliminating the need to store large volumes of data in memory. We establish the almost sure consistency, $L_p$ convergence, and asymptotic normality of the online estimator. To enable efficient and fast inference of the parameters of interest, including the derivation of confidence intervals, we also develop an innovative two-step online bootstrap procedure to approximate the limiting error distribution of the robust online estimator. Numerical studies under a variety of scenarios demonstrate the effectiveness and efficiency of the proposed online learning method. A real application analyzing PM$_{2.5}$ air-quality data is also included to exemplify the proposed online approach.

stat.ME↗

arXiv2024

Zero-shot sketch-based remote sensing image retrieval based on multi-level and attention-guided tokenization

Effectively and efficiently retrieving images from remote sensing databases is a critical challenge in the realm of remote sensing big data. Utilizing hand-drawn sketches as retrieval inputs offers intuitive and user-friendly advantages, yet the potential of multi-level feature integration from sketches remains underexplored, leading to suboptimal retrieval performance. To address this gap, our study introduces a novel zero-shot, sketch-based retrieval method for remote sensing images, leveraging multi-level feature extraction, self-attention-guided tokenization and filtering, and cross-modality attention update. This approach employs only vision information and does not require semantic knowledge concerning the sketch and image. It starts by employing multi-level self-attention guided feature extraction to tokenize the query sketches, as well as self-attention feature extraction to tokenize the candidate images. It then employs cross-attention mechanisms to establish token correspondence between these two modalities, facilitating the computation of sketch-to-image similarity. Our method significantly outperforms existing sketch-based remote sensing image retrieval techniques, as evidenced by tests on multiple datasets. Notably, it also exhibits robust zero-shot learning capabilities and strong generalizability in handling unseen categories and novel remote sensing data. The method's scalability can be further enhanced by the pre-calculation of retrieval tokens for all candidate images in a database. This research underscores the significant potential of multi-level, attention-guided tokenization in cross-modal remote sensing image retrieval. For broader accessibility and research facilitation, we have made the code and dataset used in this study publicly available online. Code and dataset are available at https://github.com/Snowstormfly/Cross-modal-retrieval-MLAGT.

cs.CV↗

arXiv2024

AirIMU: Learning Uncertainty Propagation for Inertial Odometry

Inertial odometry (IO) using strap-down inertial measurement units (IMUs) is critical in many robotic applications where precise orientation and position tracking are essential. Prior kinematic motion model-based IO methods often use a simplified linearized IMU noise model and thus usually encounter difficulties in modeling non-deterministic errors arising from environmental disturbances and mechanical defects. In contrast, data-driven IO methods struggle to accurately model the sensor motions, often leading to generalizability and interoperability issues. To address these challenges, we present AirIMU, a hybrid approach to estimate the uncertainty, especially the non-deterministic errors, by data-driven methods and increase the generalization abilities using model-based methods. We demonstrate the adaptability of AirIMU using a full spectrum of IMUs, from low-cost automotive grades to high-end navigation grades. We also validate its effectiveness on various platforms, including hand-held devices, vehicles, and a helicopter that covers a trajectory of 262 kilometers. In the ablation study, we validate the effectiveness of our learned uncertainty in an IMU-GPS pose graph optimization experiment, achieving a 31.6\% improvement in accuracy. Experiments demonstrate that jointly training the IMU noise correction and uncertainty estimation synergistically benefits both tasks.

cs.RO↗

arXiv2024

On the Flux-Intensity Relation of Molecular Clouds

In this work, we report a study on the relationship between flux and intensity for molecular clouds. Our analysis is established on high-quality CO images from the Milky Way Imaging Scroll Painting (MWISP) project. The new flux-intensity relation characterizes the flux variation of molecular clouds above specific intensity levels. We found that the flux-intensity relation exhibits two prominent features. First, the flux-intensity relation generally follows exponential shapes; secondly, hierarchical structures of molecular clouds are imprinted on flux-intensity relations. Specifically, 12CO flux-intensity relations are composed of one or more exponential segments, and for molecular clouds with segmented flux-intensity relations, the edge and the flux of the high-temperature component are strikingly consistent with 13CO emission. Further analysis shows that a similar relationship also exists between 13CO flux-intensity relations and C18O emission. The mean brightness temperature of molecular clouds is tightly associated with the decay rate of flux, the break temperature of exponential segments, and, to a certain extent, the flux fraction of the high-temperature component. Broadly, the flux-intensity relation of a molecular tracer, either in optically thick or in optically thin cases, has the capability to outline the silhouette of internal structures of molecular clouds, proving to be a potent tool for probing structures of molecular clouds.

astro-ph.GA↗

arXiv2024

Probing the Gauge-boson Couplings of Axion-like Particle at the LHC and High-Luminosity LHC

In this work, we calculate the sensitivities on the gauge-boson couplings $g_{aZZ}$, $g_{aZγ}$, and $g_{aWW}$ of an axion-like particle (ALP) that one can achieve at the LHC with $\sqrt{s}=14$ TeV and integrated luminosities of 300 fb$^{-1}$ (current run) and 3000 fb$^{-1}$ (High-Luminosity LHC). We focus on the associated production processes $pp\to Za \to (l^+l^-)(γγ)$ and $pp\to W^\pm a \to (l^\pm ν)(γγ)$. We show that better sensitivities on these gauge couplings can be achieved at the LHC for $M_a = 1-100$ GeV, down to the level of $10^{-4}\,{\rm GeV}^{-1}$. In conclusion, this study emphasizes the significance of the investigated channels in constraining the ALP couplings at the LHC, offering valuable insights for future experiments dedicated to ALP detection.

hep-ph↗

arXiv2024

Efficient Computation for Invertibility Sequence of Banded Toeplitz Matrices

When solving systems of banded Toeplitz equations or calculating their inverses, it is necessary to determine the invertibility of the matrices beforehand. In this paper, we equate the invertibility of an $n$-order banded Toeplitz matrix with bandwidth $2k+1$ to that of a small $k*k$ matrix. By utilizing a specially designed algorithm, we compute the invertibility sequence of a class of banded Toeplitz matrices with a time complexity of $5k^2n/2+kn$ and a space complexity of $3k^2$ where $n$ is the size of the largest matrix. This enables efficient preprocessing when solving equation systems and inverses of banded Toeplitz matrices.

math.NA↗

arXiv2024

AI for Manufacturing and Healthcare: a chemistry and engineering perspective

Artificial Intelligence (AI) approaches are increasingly being applied to more and more domains of Science, Engineering, Chemistry, and Industries to not only improve efficiencies and enhance productivity, but also enable new capabilities. The new opportunities range from automated molecule design and screening, properties prediction, gaining insights of chemical reactions, to computer-aided design, predictive maintenance of systems, robotics, and autonomous vehicles. This review focuses on the new applications of AI in manufacturing and healthcare. For the Manufacturing Industries, we focus on AI and algorithms for (1) Battery, (2) Flow Chemistry, (3) Additive Manufacturing, (4) Sensors, and (5) Machine Vision. For Healthcare applications, we focus on: (1) Medical Vision (2) Diagnosis, (3) Protein Design, and (4) Drug Discovery. In the end, related topics are discussed, including physics integrated machine learning, model explainability, security, and governance during model deployment.

cond-mat.mtrl-sci↗

arXiv2024

Approximation Algorithm of Minimum All-Ones Problem for Arbitrary Graphs

Let $G=(V, E)$ be a graph and let each vertex of $G$ has a lamp and a button. Each button can be of $σ^+$-type or $σ$-type. Assume that initially some lamps are on and others are off. The button on vertex $x$ is of $σ^+$-type ($σ$-type, respectively) if pressing the button changes the lamp states on $x$ and on its neighbors in $G$ (the lamp states on the neighbors of $x$ only, respectively). Assume that there is a set $X\subseteq V$ such that pressing buttons on vertices of $X$ lights all lamps on vertices of $G$. In particular, it is known to hold when initially all lamps are off and all buttons are of $σ^+$-type. Finding such a set $X$ of the smallest size is NP-hard even if initially all lamps are off and all buttons are of $σ^+$-type. Using a linear algebraic approach we design a polynomial-time approximation algorithm for the problem such that for the set $X$ constructed by the algorithm, we have $|X|\le \min\{r,(|V|+{\rm opt})/2\},$ where $r$ is the rank of a (modified) adjacent matrix of $G$ and ${\rm opt}$ is the size of an optimal solution to the problem. To the best of our knowledge, this is the first polynomial-time approximation algorithm for the problem with a nontrivial approximation guarantee.

cs.DS↗

arXiv2024

Fine-tuning vision foundation model for crack segmentation in civil infrastructures

Large-scale foundation models have become the mainstream deep learning method, while in civil engineering, the scale of AI models is strictly limited. In this work, a vision foundation model is introduced for crack segmentation. Two parameter-efficient fine-tuning methods, adapter and low-rank adaptation, are adopted to fine-tune the foundation model in semantic segmentation: the Segment Anything Model (SAM). The fine-tuned CrackSAM shows excellent performance on different scenes and materials. To test the zero-shot performance of the proposed method, two unique datasets related to road and exterior wall cracks are collected, annotated and open-sourced, for a total of 810 images. Comparative experiments are conducted with twelve mature semantic segmentation models. On datasets with artificial noise and previously unseen datasets, the performance of CrackSAM far exceeds that of all state-of-the-art models. CrackSAM exhibits remarkable superiority, particularly under challenging conditions such as dim lighting, shadows, road markings, construction joints, and other interference factors. These cross-scenario results demonstrate the outstanding zero-shot capability of foundation models and provide new ideas for developing vision models in civil engineering.

cs.CV↗

arXiv2024

$\widetilde{O}(T^{-1})$ Convergence to (Coarse) Correlated Equilibria in Full-Information General-Sum Markov Games

No-regret learning has a long history of being closely connected to game theory. Recent works have devised uncoupled no-regret learning dynamics that, when adopted by all the players in normal-form games, converge to various equilibrium solutions at a near-optimal rate of $\widetilde{O}(T^{-1})$, a significant improvement over the $O(1/\sqrt{T})$ rate of classic no-regret learners. However, analogous convergence results are scarce in Markov games, a more generic setting that lays the foundation for multi-agent reinforcement learning. In this work, we close this gap by showing that the optimistic-follow-the-regularized-leader (OFTRL) algorithm, together with appropriate value update procedures, can find $\widetilde{O}(T^{-1})$-approximate (coarse) correlated equilibria in full-information general-sum Markov games within $T$ iterations. Numerical results are also included to corroborate our theoretical findings.

cs.GT↗

arXiv2024

Knowledge Graph Context-Enhanced Diversified Recommendation

The field of Recommender Systems (RecSys) has been extensively studied to enhance accuracy by leveraging users' historical interactions. Nonetheless, this persistent pursuit of accuracy frequently engenders diminished diversity, culminating in the well-recognized "echo chamber" phenomenon. Diversified RecSys has emerged as a countermeasure, placing diversity on par with accuracy and garnering noteworthy attention from academic circles and industry practitioners. This research explores the realm of diversified RecSys within the intricate context of knowledge graphs (KG). These KGs act as repositories of interconnected information concerning entities and items, offering a propitious avenue to amplify recommendation diversity through the incorporation of insightful contextual information. Our contributions include introducing an innovative metric, Entity Coverage, and Relation Coverage, which effectively quantifies diversity within the KG domain. Additionally, we introduce the Diversified Embedding Learning (DEL) module, meticulously designed to formulate user representations that possess an innate awareness of diversity. In tandem with this, we introduce a novel technique named Conditional Alignment and Uniformity (CAU). It adeptly encodes KG item embeddings while preserving contextual integrity. Collectively, our contributions signify a substantial stride towards augmenting the panorama of recommendation diversity within the realm of KG-informed RecSys paradigms.

cs.IR↗

arXiv2024

GeoReF: Geometric Alignment Across Shape Variation for Category-level Object Pose Refinement

Object pose refinement is essential for robust object pose estimation. Previous work has made significant progress towards instance-level object pose refinement. Yet, category-level pose refinement is a more challenging problem due to large shape variations within a category and the discrepancies between the target object and the shape prior. To address these challenges, we introduce a novel architecture for category-level object pose refinement. Our approach integrates an HS-layer and learnable affine transformations, which aims to enhance the extraction and alignment of geometric information. Additionally, we introduce a cross-cloud transformation mechanism that efficiently merges diverse data sources. Finally, we push the limits of our model by incorporating the shape prior information for translation and size error prediction. We conducted extensive experiments to demonstrate the effectiveness of the proposed framework. Through extensive quantitative experiments, we demonstrate significant improvement over the baseline method by a large margin across all metrics.

cs.CV↗

arXiv2024

Batch Array Codes

Batch codes are a type of codes specifically designed for coded distributed storage systems and private information retrieval protocols. These codes have got much attention in recent years due to their ability to enable efficient and secure storage in distributed systems. In this paper, we study an array code version of the batch codes, which is called the \emph{batch array code} (BAC). Under the setting of BAC, each node stores a bucket containing multiple code symbols and responds with a locally computed linear combination of the symbols in its bucket during the recovery of a requested symbol. We demonstrate that BACs can support the same type of requests as the original batch codes but with reduced redundancy. Specifically, we establish information theoretic lower bounds on the code lengths and provide several code constructions that confirm the tightness of the lower bounds for certain parameter regimes.

cs.IT↗

arXiv2024

Analytic approximations for massive close post-mass transfer binary systems

Massive binary evolution models are needed to predict massive star populations in star forming galaxies, the supernova diversity, and the number and properties of gravitational wave sources. Such models are often computed using so called rapid binary evolution codes, which approximate the evolution of the binary components based on detailed single star models. However, about one third of the interacting massive binary stars undergo mass transfer during core hydrogen burning (Case A mass transfer), whose outcome is difficult to derive from single star models. Here, we use a large grid of detailed binary evolution models for primaries in the initial mass range 10 to 40 Solar masses of LMC and SMC composition, to derive analytic fits for the key quantities needed in rapid binary evolution codes, i.e., the duration of core hydrogen burning, and the resulting donor star mass. Systems with shorter orbital periods produce up to 50% lighter stripped donors and have a up to 30% larger lifetime than wider systems. We find that both quantities depend strongly on the initial binary orbital period, but that the initial mass ratio and the mass transfer efficiency of the binary have little impact on the outcome. Our results are easily parameterisable and can be used to capture the effects of Case A mass transfer more accurately in rapid binary evolution codes.

astro-ph.SR↗

arXiv2024

Precoder Design for Massive MIMO Downlink with Matrix Manifold Optimization

We investigate the weighted sum-rate (WSR) maximization linear precoder design for massive multiple-input multiple-output (MIMO) downlink. We consider a single-cell system with multiple users and propose a unified matrix manifold optimization framework applicable to total power constraint (TPC), per-user power constraint (PUPC) and per-antenna power constraint (PAPC). We prove that the precoders under TPC, PUPC and PAPC are on distinct Riemannian submanifolds, and transform the constrained problems in Euclidean space to unconstrained ones on manifolds. In accordance with this, we derive Riemannian ingredients, including orthogonal projection, Riemannian gradient, Riemannian Hessian, retraction and vector transport, which are needed for precoder design in the matrix manifold framework. Then, Riemannian design methods using Riemannian steepest descent, Riemannian conjugate gradient and Riemannian trust region are provided to design the WSR-maximization precoders under TPC, PUPC or PAPC. Riemannian methods do not involve the inverses of the large dimensional matrices during the iterations, reducing the computational complexities of the algorithms. Complexity analyses and performance simulations demonstrate the advantages of the proposed precoder design.

cs.IT↗

arXiv2024

A Robust Power Model Training Framework for Cloud Native Runtime Energy Metric Exporter

Estimating power consumption in modern Cloud environments is essential for carbon quantification toward green computing. Specifically, it is important to properly account for the power consumed by each of the running applications, which are packaged as containers. This paper examines multiple challenges associated with this goal. The first challenge is that multiple customers are sharing the same hardware platform (multi-tenancy), where information on the physical servers is mostly obscured. The second challenge is the overhead in power consumption that the Cloud platform control plane induces. This paper addresses these challenges and introduces a novel pipeline framework for power model training. This allows versatile power consumption approximation of individual containers on the basis of available performance counters and other metrics. The proposed model utilizes machine learning techniques to predict the power consumed by the control plane and associated processes, and uses it for isolating the power consumed by the user containers, from the server power consumption. To determine how well the prediction results in an isolation, we introduce a metric termed isolation goodness. Applying the proposed power model does not require online power measurements, nor does it need information on the physical servers, configuration, or information on other tenants sharing the same machine. The results of cross-workload, cross-platform experiments demonstrated the higher accuracy of the proposed model when predicting power consumption of unseen containers on unknown platforms, including on virtual machines.

cs.DC↗

arXiv2024

Application of the Modular Bayesian Approach for Inverse Uncertainty Quantification in Nuclear Thermal-Hydraulics Systems

In the framework of BEPU (Best Estimate plus Uncertainty) methodology, the uncertainties involved in the simulations must be quantified to prove that the investigated design is acceptable. The output uncertainties are usually calculated by propagating input uncertainties through the simulation model, which requires knowledge of the model input uncertainties. However, in some best-estimate Thermal-Hydraulics (TH) codes such as TRACE, the physical model parameters used in empirical correlations may have large uncertainties, which are unknown to the code users. Therefore, obtaining uncertainty distributions of those parameters becomes crucial if we want to study the predictive uncertainty or output sensitivity. In this study, we present a Modular Bayesian approach that considers the presence model discrepancy during Bayesian calibration. Several TRACE physical model parameters are selected as calibration parameters in this work. Model discrepancy, also referred to as model inadequacy or model bias, accounts for the inaccuracy in computer simulation caused by underlying missing/insufficient physics, numerical approximation errors, and other errors of a computer code, even if all its parameters are fixed at their "true" values. Model discrepancy always exists in computer models because they are reduced representations of the reality. The consideration of model discrepancy is important because it can help avoid the "overfitting" problem in Bayesian calibration. This paper uses a set of steady-state experimental data from PSBT benchmark and it mainly aims at: (1) quantifying the uncertainties of TRACE physical model parameters based on experiment data; (2) quantifying the uncertainties in TRACE outputs based on inversely quantified physical model parameters uncertainties.

stat.AP↗

arXiv2024

AirShot: Efficient Few-Shot Detection for Autonomous Exploration

Few-shot object detection has drawn increasing attention in the field of robotic exploration, where robots are required to find unseen objects with a few online provided examples. Despite recent efforts have been made to yield online processing capabilities, slow inference speeds of low-powered robots fail to meet the demands of real-time detection-making them impractical for autonomous exploration. Existing methods still face performance and efficiency challenges, mainly due to unreliable features and exhaustive class loops. In this work, we propose a new paradigm AirShot, and discover that, by fully exploiting the valuable correlation map, AirShot can result in a more robust and faster few-shot object detection system, which is more applicable to robotics community. The core module Top Prediction Filter (TPF) can operate on multi-scale correlation maps in both the training and inference stages. During training, TPF supervises the generation of a more representative correlation map, while during inference, it reduces looping iterations by selecting top-ranked classes, thus cutting down on computational costs with better performance. Surprisingly, this dual functionality exhibits general effectiveness and efficiency on various off-the-shelf models. Exhaustive experiments on COCO2017, VOC2014, and SubT datasets demonstrate that TPF can significantly boost the efficacy and efficiency of most off-the-shelf models, achieving up to 36.4% precision improvements along with 56.3% faster inference speed. Code and Data are at: https://github.com/ImNotPrepared/AirShot.

cs.CV↗