Nano Unmanned Aerial Vehicle (UAV) platforms are well-suited for tasks in confined spaces, such as indoor single-person tracking. However, they are constrained by payload, and milliwatt (mW) level computing budgets. To address these limitations, we present a fully on-board tracking system featuring Feather You Only Look Once (FeatherYOLO), an ultra-lightweight detector tailored to the embedded processor, and a custom image-based visual servoing controller deployed on a 29 gram Crazyflie 2.1 nano UAV. FeatherYOLO utilizes a depthwise-separable backbone with a decoupled, anchor-free head, requiring 20 thousand parameters, 1.94 million multiply-accumulate operations, and 224 kilobytes of memory. On our self-collected indoor human detection dataset (six participants across five sites) under a cross-subject and cross-environment held-out protocol, the model achieved 99.5% mean Average Precision (mAP) at an Intersection Over Union threshold of 0.50 and 75.0% mAP over thresholds from 0.50 to 0.95. On-board profiling reveals that pure inference consumes 46.5 mW at 150 Frames Per Second (FPS), accounting for less than 1% of the total flight power, and outperforms a recent nano UAV baseline consuming 225.7 mW at 43 FPS. The proposed visual servoing strategy was refined through flight trials and task-driven tuning, transforming detector outputs into stable, bounded velocity commands with hysteresis and filtering for closed-loop indoor tracking. Real-world flight tests validated the tracking performance with an average tracking success rate of 90.0%, succeeding in 36 out of 40 experimental runs. The primary flight challenges identified include collisions and target loss.

Despite the growing popularity of graph attention mechanisms, their theoretical understanding remains limited. This paper aims to explore the conditions under which these mechanisms are effective in node classification tasks through the lens of Contextual Stochastic Block Models (CSBMs). Our theoretical analysis reveals that incorporating graph attention mechanisms is *not universally beneficial*. Specifically, by appropriately defining *structure noise* and *feature noise* in graphs, we show that graph attention mechanisms can enhance classification performance when structure noise exceeds feature noise. Conversely, when feature noise predominates, simpler graph convolution operations are more effective. Furthermore, we examine the over-smoothing phenomenon and show that, in the high signal-to-noise ratio (SNR) regime, graph convolutional networks suffer from over-smoothing, whereas graph attention mechanisms can effectively resolve this issue. Building on these insights, we propose a novel multi-layer Graph Attention Network (GAT) architecture that significantly outperforms single-layer GATs in achieving *perfect node classification* in CSBMs, relaxing the SNR requirement from $\omega(\sqrt{\log n})$ to $\omega(\sqrt{\log n} / \sqrt[3]{n})$. To our knowledge, this is the first study to delineate the conditions for perfect node classification using multi-layer GATs. Our theoretical contributions are corroborated by extensive experiments on both synthetic and real-world datasets, highlighting the practical implications of our findings.

Computing Nash equilibria of zero-sum games in classical and quantum settings is extensively studied. For general-sum games, computing Nash equilibria is PPAD-hard and the computing of a more general concept called correlated equilibria has been widely explored in game theory. In this paper, we initiate the study of quantum algorithms for computing $\varepsilon$-approximate correlated equilibria (CE) and coarse correlated equilibria (CCE) in multi-player normal-form games. Our approach utilizes quantum improvements to the multi-scale Multiplicative Weight Update (MWU) method for CE calculations, achieving a query complexity of $\tilde{O}(m\sqrt{n})$ for fixed $\varepsilon$. For CCE, we extend techniques from quantum algorithms for zero-sum games to multi-player settings, achieving query complexity $\tilde{O}(m\sqrt{n}/\varepsilon^{2.5})$. Both algorithms demonstrate a near-optimal scaling in the number of players $m$ and actions $n$, as confirmed by our quantum query lower bounds.

Incorporating user-defined heuristic rules into neural text inference methods has the potential to align models with user intentions and domain knowledge, thereby improving interpretability. In this study, we introduce a novel rule pattern that includes both domain-specific keywords and the logical relationships between keywords, which can be defined by users. We propose an approach to integrate explicit rule-based reasoning with the semantic modeling capabilities of neural networks. Specifically, our method employs a parallel framework wherein a neural classifier is trained on labeled text data for prediction, while a Semantic-Logic Network (SLN) forms rule inference as a satisfiability problem. We use a Jensen-Shannon (JS) loss to ensure consistent predictions on both sides for mutual regularization. The experiment results show that our approach outperforms baseline methods. We also did ablation analysis on our method, it shows that the performance of both the SLN and the classifier contribute to the final results. Additionally, for the case that lacks explicit user rules, we propose a boosting method to automatically generate rules from labeled texts which is beneficial for text inference and improve the model performance.

Finite-sum optimization has wide applications in machine learning, covering important problems such as support vector machines, regression, etc. In this paper, we initiate the study of solving finite-sum optimization problems by quantum computing. Specifically, let $f_1,\ldots,f_n:\mathbb{R}^d\to\mathbb{R}$ be $\ell$-smooth convex functions and $\psi:\mathbb{R}^d\to\mathbb{R}$ be a $\mu$-strongly convex proximal function. The goal is to find an $\epsilon$-optimal point for $F(\mathbf{x})=\frac{1}{n}\sum_{i=1}^n f_i(\mathbf{x})+\psi(\mathbf{x})$. We give a quantum algorithm with complexity $\tilde{O}\big(n+\sqrt{d}+\sqrt{\ell/\mu}\big(n^{1/3}d^{1/3}+n^{-2/3}d^{5/6}\big)\big)$, improving the classical tight bound $\tilde{\Theta}\big(n+\sqrt{n\ell/\mu}\big)$. We also prove a quantum lower bound $\tilde{\Omega}(n+n^{3/4}(\ell/\mu)^{1/4})$ when $d$ is large enough. Both our quantum upper and lower bounds can extend to the cases where $\psi$ is not necessarily strongly convex, or each $f_i$ is Lipschitz but not necessarily smooth. In addition, when $F$ is nonconvex, our quantum algorithm can find an $\epsilon$-critial point using $\tilde{O}(n+\ell(d^{1/3}n^{1/3}+\sqrt{d})/\epsilon^2)$ queries.

Noncontact particle manipulation (NPM) shows great application potential than its conventional counterpart particularly in terms of non-invasiveness, and thus has significantly extended robotic manipulation capacity into bio- medical engineering, material science, etc. As NPM by means of electric, magnetic, and optical field has successfully demonstrated powerful strength in both academia and industry, NPM boosted by acoustic field, however, still faces staggering challenges. It is indeed in the very recent years that controllable dynamic airborne or waterborne acoustic field modulation technology emerged in academia. In this paper, we report our latest research regarding dexterous and dynamic noncontact micro-particle manipulation on water surface effected by acoustic field in terms of automated trapping, closed-loop positioning, and real-time motion planning, which can be applied to scenarios such as parallel 3D printing, cell assembly, etc. The main contribution of this work is we demonstrated the feasibility of objective-oriented and fully automated acoustic manipulation of micro-particle in precision scale based on robotic approach in 2D plane. Experiment results showed that the repetitive positioning accuracy can reach as high as 16 μm, which is essentially the pixel scale factor.

The recently developed event cameras can directly sense the motion in the scene by generating an asynchronous sequence of events, i.e., event streams, where each individual event (x, y, t) corresponds to the space-time location when a pixel sensor captures an intensity change. Compared with RGB cameras, event cameras are frameless but can capture much faster motion, therefore have great potential for recognizing gestures of fast motions. To deal with the unique output of event cameras, previous methods often treat event streams as time sequences, thus do not fully explore the space-time sparsity of the event stream data. In this work, we treat the event stream as a set of 3D points in space-time, i.e., space-time event clouds. To analyze event clouds and recognize gestures, we propose to leverage PointNet, a neural network architecture originally designed for matching and recognizing 3D point clouds. We further adapt PointNet to cater to event clouds for real-time gesture recognition. On the benchmark dataset of event camera based gesture recognition, i.e., IBM DVS128 Gesture dataset, our proposed method achieves a high accuracy of 97.08% and performs the best among existing methods.