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Xue Ma

Publications and source records attributed to Xue Ma.

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Quantum Change Interval: Exact Asymptotics for Minimum Error Localization

We study a returning quantum change interval in which a source emits $\lvert\psi\rangle$ over one interval and $\lvert0\rangle$ elsewhere. A collective measurement on the full sequence identifies both endpoints with minimum error. We analyze the Gram matrix using Toeplitz comparison and F{\o}lner transfer, together with an exact decomposition by excitation number and interval hull. The resulting bounds establish asymptotic Bayes optimality of the square root measurement (SRM). Let $c=\lvert\langle0\vert\psi\rangle\rvert$ and $p_1(x)=4(1-x^2)K^2(x^2)/\pi^2$, where $K$ is the complete elliptic integral of the first kind. For a known interval length $i$, the SRM success probability and the Bayes optimum converge to the same Toeplitz symbol integral as the number $N$ of translations grows. For fixed $i$ and $0<c<1$, their gap is $P_{\mathrm{opt}}(G_{N,i})-P_{\mathrm{SRM}}(G_{N,i})=O_{i,c}(N^{-1/2})$. If $i$ and $N$ both diverge, their common limit is $p_1(c^2)$, with no constraint on their relative growth. For unknown length, the uniform prior over all $M_n=n(n+1)/2$ nonempty intervals gives the common limit $p_1(c)^2$ at fixed overlap. For a varying overlap $c_n$, set $\tau_n=n(1-c_n)(\log n)^2$. Uniformly for $0\leq\tau_n\leq T$, we obtain $M_nP_X=(1+2\sqrt{\tau_n}/\pi+\sqrt{2}\tau_n/\pi^2)^2+O_T(\log\log n/\log n)$, where $X\in\{\mathrm{tr},\mathrm{SRM},\mathrm{opt}\}$. More generally, if $c_n$ approaches one from below and $n p_1(c_n)\to\infty$, the same three quantities satisfy $P_X\sim p_1(c_n)^2$. These asymptotic laws also extend to joint detection and exact localization in the presence of a no change prior.

quant-ph

Robust Load Prediction of Power Network Clusters Based on Cloud-Model-Improved Transformer

Load data from power network clusters indicates economic development in each area, crucial for predicting regional trends and guiding power enterprise decisions. The Transformer model, a leading method for load prediction, faces challenges modeling historical data due to variables like weather, events, festivals, and data volatility. To tackle this, the cloud model's fuzzy feature is utilized to manage uncertainties effectively. Presenting an innovative approach, the Cloud Model Improved Transformer (CMIT) method integrates the Transformer model with the cloud model utilizing the particle swarm optimization algorithm, with the aim of achieving robust and precise power load predictions. Through comparative experiments conducted on 31 real datasets within a power network cluster, it is demonstrated that CMIT significantly surpasses the Transformer model in terms of prediction accuracy, thereby highlighting its effectiveness in enhancing forecasting capabilities within the power network cluster sector.

cs.LG

Beyond MOT: Semantic Multi-Object Tracking

Current multi-object tracking (MOT) aims to predict trajectories of targets (i.e., ''where'') in videos. Yet, knowing merely ''where'' is insufficient in many crucial applications. In comparison, semantic understanding such as fine-grained behaviors, interactions, and overall summarized captions (i.e., ''what'') from videos, associated with ''where'', is highly-desired for comprehensive video analysis. Thus motivated, we introduce Semantic Multi-Object Tracking (SMOT), that aims to estimate object trajectories and meanwhile understand semantic details of associated trajectories including instance captions, instance interactions, and overall video captions, integrating ''where'' and ''what'' for tracking. In order to foster the exploration of SMOT, we propose BenSMOT, a large-scale Benchmark for Semantic MOT. Specifically, BenSMOT comprises 3,292 videos with 151K frames, covering various scenarios for semantic tracking of humans. BenSMOT provides annotations for the trajectories of targets, along with associated instance captions in natural language, instance interactions, and overall caption for each video sequence. To our best knowledge, BenSMOT is the first publicly available benchmark for SMOT. Besides, to encourage future research, we present a novel tracker named SMOTer, which is specially designed and end-to-end trained for SMOT, showing promising performance. By releasing BenSMOT, we expect to go beyond conventional MOT by predicting ''where'' and ''what'' for SMOT, opening up a new direction in tracking for video understanding. We will release BenSMOT and SMOTer at https://github.com/Nathan-Li123/SMOTer.

cs.CV

TSViT: A Time Series Vision Transformer for Fault Diagnosis

Traditional fault diagnosis methods using Convolutional Neural Networks (CNNs) often struggle with capturing the temporal dynamics of vibration signals. To overcome this, the application of Transformer-based Vision Transformer (ViT) methods to fault diagnosis is gaining attraction. Nonetheless, these methods typically require extensive preprocessing, which increases computational complexity, potentially reducing the efficiency of the diagnosis process. Addressing this gap, this paper presents the Time Series Vision Transformer (TSViT), tailored for effective fault diagnosis. TSViT incorporates a convolutional layer to extract local features from vibration signals, alongside a transformer encoder to discern long-term temporal patterns. A thorough experimental comparison on three diverse datasets demonstrates TSViT's effectiveness and adaptability. Moreover, the paper delves into the influence of hyperparameter tuning on the model's performance, computational demand, and parameter count. Remarkably, TSViT achieves an unprecedented 100% average accuracy on two test sets and 99.99% on another, showcasing its exceptional diagnostic capabilities.

eess.SY

Fingering instability in Marangoni spreading on a deep layer of polymer solution

Spreading on the free surface of a complex fluid is ubiquitous in nature and industry, owing to the wide existence of complex fluids. Here we report on a fingering instability that develops during Marangoni spreading on a deep layer of polymer solution. In particular, the wavelength depends on molecular weight and concentration of the polymer solution. We use the Transmission Lattice Method to characterize the finger height at the micron scale. We model the evolution of spreading radius, involving viscoelastic and shear thinning effects, to suggest a more generalized law than the spreading of Newtonian fluids. We give physical explanation on the origin of the fingering instability as due to normal stresses at high shear rate generating high contact angle and deformation at the leading edge, and so selects the wavelength of the fingering instability. Understanding the spreading mechanism has particular implication in airway drug delivery, and surface coating with patterns.

physics.flu-dyn