Learning and Prediction Theory of Distributed Least Squares

With the fast development of the sensor and network technology, distributed estimation has attracted more and more attention, due to its capability in securing communication, in sustaining scalability, and in enhancing safety and privacy. In this paper, we consider a least-squares (LS)-based distributed algorithm build on a sensor network to estimate an unknown parameter vector of a dynamical system, where each sensor in the network has partial information only but is allowed to communicate with its neighbors. Our main task is to generalize the well-known theoretical results on the traditional LS to the current distributed case by establishing both the upper bound of the accumulated regrets of the adaptive predictor and the convergence of the distributed LS estimator, with the following key features compared with the existing literature on distributed estimation: Firstly, our theory does not need the previously imposed independence, stationarity or Gaussian property on the system signals, and hence is applicable to stochastic systems with feedback control. Secondly, the cooperative excitation condition introduced and used in this paper for the convergence of the distributed LS estimate is the weakest possible one, which shows that even if any individual sensor cannot estimate the unknown parameter by the traditional LS, the whole network can still fulfill the estimation task by the distributed LS. Moreover, our theoretical analysis is also different from the existing ones for distributed LS, because it is an integration of several powerful techniques including stochastic Lyapunov functions, martingale convergence theorems, and some inequalities on convex combination of nonnegative definite matrices.Comment: 14 pages, submitted to IEEE Transactions on Automatic Contro

Generative Adversarial Mapping Networks

Generative Adversarial Networks (GANs) have shown impressive performance in generating photo-realistic images. They fit generative models by minimizing certain distance measure between the real image distribution and the generated data distribution. Several distance measures have been used, such as Jensen-Shannon divergence, $f$-divergence, and Wasserstein distance, and choosing an appropriate distance measure is very important for training the generative network. In this paper, we choose to use the maximum mean discrepancy (MMD) as the distance metric, which has several nice theoretical guarantees. In fact, generative moment matching network (GMMN) (Li, Swersky, and Zemel 2015) is such a generative model which contains only one generator network $G$ trained by directly minimizing MMD between the real and generated distributions. However, it fails to generate meaningful samples on challenging benchmark datasets, such as CIFAR-10 and LSUN. To improve on GMMN, we propose to add an extra network $F$, called mapper. $F$ maps both real data distribution and generated data distribution from the original data space to a feature representation space $\mathcal{R}$, and it is trained to maximize MMD between the two mapped distributions in $\mathcal{R}$, while the generator $G$ tries to minimize the MMD. We call the new model generative adversarial mapping networks (GAMNs). We demonstrate that the adversarial mapper $F$ can help $G$ to better capture the underlying data distribution. We also show that GAMN significantly outperforms GMMN, and is also superior to or comparable with other state-of-the-art GAN based methods on MNIST, CIFAR-10 and LSUN-Bedrooms datasets.Comment: 9 pages, 7 figure

MUGC: Machine Generated versus User Generated Content Detection

As advanced modern systems like deep neural networks (DNNs) and generative AI continue to enhance their capabilities in producing convincing and realistic content, the need to distinguish between user-generated and machine generated content is becoming increasingly evident. In this research, we undertake a comparative evaluation of eight traditional machine-learning algorithms to distinguish between machine-generated and human-generated data across three diverse datasets: Poems, Abstracts, and Essays. Our results indicate that traditional methods demonstrate a high level of accuracy in identifying machine-generated data, reflecting the documented effectiveness of popular pre-trained models like RoBERT. We note that machine-generated texts tend to be shorter and exhibit less word variety compared to human-generated content. While specific domain-related keywords commonly utilized by humans, albeit disregarded by current LLMs (Large Language Models), may contribute to this high detection accuracy, we show that deeper word representations like word2vec can capture subtle semantic variances. Furthermore, readability, bias, moral, and affect comparisons reveal a discernible contrast between machine-generated and human generated content. There are variations in expression styles and potentially underlying biases in the data sources (human and machine-generated). This study provides valuable insights into the advancing capacities and challenges associated with machine-generated content across various domains.Comment: 11 pages, 16 figure

ShapeGrasp: Zero-Shot Task-Oriented Grasping with Large Language Models through Geometric Decomposition

Task-oriented grasping of unfamiliar objects is a necessary skill for robots in dynamic in-home environments. Inspired by the human capability to grasp such objects through intuition about their shape and structure, we present a novel zero-shot task-oriented grasping method leveraging a geometric decomposition of the target object into simple, convex shapes that we represent in a graph structure, including geometric attributes and spatial relationships. Our approach employs minimal essential information - the object's name and the intended task - to facilitate zero-shot task-oriented grasping. We utilize the commonsense reasoning capabilities of large language models to dynamically assign semantic meaning to each decomposed part and subsequently reason over the utility of each part for the intended task. Through extensive experiments on a real-world robotics platform, we demonstrate that our grasping approach's decomposition and reasoning pipeline is capable of selecting the correct part in 92% of the cases and successfully grasping the object in 82% of the tasks we evaluate. Additional videos, experiments, code, and data are available on our project website: https://shapegrasp.github.io/.Comment: 8 page

Modulation of the Meridional Structures of the Indo-Pacific Warm Pool on the Response of the Hadley Circulation to Tropical SST

By decomposing the variations of the Hadley circulation (HC) and tropical zonal-mean sea surface temperature (SST) into the equatorially asymmetric (HEA for HC, SEA for SST) and symmetric (HES for HC, SES for SST) components, the varying response of the HC to different SST meridional structures under warm and cold conditions of the Indo-Pacific warm pool (IPWP) is investigated over the period 1979–2016. The response of the HC to SST evidences an asymmetric variation between warm and cold IPWP conditions; that is, the response ratio of HEA to SEA relative to that of HES to SES is ~5 under warm conditions and ~2 under cold conditions. This asymmetry is primarily due to a decrease in the HEA-to-SEA ratio under cold IPWP conditions, and is driven by changes in the meridional distribution of SST anomalies. Equatorial asymmetric (symmetric) SST anomalies are dominated by warm (cold) IPWP conditions. Thus, variations of SEA are suppressed under cold IPWP conditions, contributing to the observed weakening of the HEA-to-SEA ratio. The results presented here indicate that the HC is more sensitive to the underlying SST when the IPWP is warmer, during which the variation of SEA is enhanced, suggesting a recent strengthening of the response of the HC to SST, as the IPWP has warmed over the past several decades, and highlighting the importance of the IPWP meridional structures rather than the overall warming of the HC

Large-scale Point Cloud Registration Based on Graph Matching Optimization

Point Clouds Registration is a fundamental and challenging problem in 3D computer vision. It has been shown that the isometric transformation is an essential property in rigid point cloud registration, but the existing methods only utilize it in the outlier rejection stage. In this paper, we emphasize that the isometric transformation is also important in the feature learning stage for improving registration quality. We propose a \underline{G}raph \underline{M}atching \underline{O}ptimization based \underline{Net}work (denoted as GMONet for short), which utilizes the graph matching method to explicitly exert the isometry preserving constraints in the point feature learning stage to improve %refine the point representation. Specifically, we %use exploit the partial graph matching constraint to enhance the overlap region detection abilities of super points ($i.e.,$ down-sampled key points) and full graph matching to refine the registration accuracy at the fine-level overlap region. Meanwhile, we leverage the mini-batch sampling to improve the efficiency of the full graph matching optimization. Given high discriminative point features in the evaluation stage, we utilize the RANSAC approach to estimate the transformation between the scanned pairs. The proposed method has been evaluated on the 3DMatch/3DLoMatch benchmarks and the KITTI benchmark. The experimental results show that our method achieves competitive performance compared with the existing state-of-the-art baselines