This paper introduces a new framework for clustering in a distributed network called Distributed Clustering based on Distributional Kernel (K) or KDC that produces the final clusters based on the similarity with respect to the distributions of initial clusters, as measured by K. It is the only framework that satisfies all three of the following properties. First, KDC guarantees that the combined clustering outcome from all sites is equivalent to the clustering outcome of its centralized counterpart from the combined dataset from all sites. Second, the maximum runtime cost of any site in distributed mode is smaller than the runtime cost in centralized mode. Third, it is designed to discover clusters of arbitrary shapes, sizes and densities. To the best of our knowledge, this is the first distributed clustering framework that employs a distributional kernel. The distribution-based clustering leads directly to significantly better clustering outcomes than existing methods of distributed clustering. In addition, we introduce a new clustering algorithm called Kernel Bounded Cluster Cores, which is the best clustering algorithm applied to KDC among existing clustering algorithms. We also show that KDC is a generic framework that enables a quadratic time clustering algorithm to deal with large datasets that would otherwise be impossible.

Comprehensive, precision-laser surveys, conducted via aircraft over the tiny Pacific island of Temwen, have revealed just how advanced its lost city Nan Madol once was. Sometimes called'the Venice of the Pacific,' this megalithic stone city has drawn comparisons to mythic Atlantis -- and even inspired horror writer H.P. Lovecraft, who drew upon news of the site's 1928 discovery as he wrote'The Call of Cthulhu.' But now scores of researchers are in a race to uncover the full extent of Nan Madol's ruins as they undertake plans to preserve the city as a UNESCO World Heritage Site. Their aerial surveys, conducted via LiDAR or'Light Detection and Ranging' laser-mapping, has uncovered'a sophisticated and extensive landscape of cultivation features hidden under Temwen Island's vegetation.' The discovery has promised to rewrite the history of many Pacific Island cultures, showing that societies once presumed to have relied on subsistence fishing and natural tropical bounty, were in fact engaged in sophisticated agricultural planning.

Teenager invents robot to solve Rubik's Cube BBCRuarcc the year 10 student who has programmed a robot that can solve a Rubik's Cube puzzle A 13-year-old schoolboy has invented a Lego robot that can solve a Rubik's cube. Ruarcc, from St Malachy's College in north Belfast, first took steps to create puzzle-solving robot prototypes in his second year at school, aged 12. This was made possible after the school launched its creative digital technology hub (CDTH) last year. Teacher Clare McGrath commented she "didn't believe" that Ruarcc's robot would work at first.'People are amazed my robot can solve Rubik's Cube' Ruarcc told BBC News NI it was "frustrating", but he worked on making it better. "People tend to be amazed that it can solve one," he said.

Generative Artificial Intelligence (AI) models may carry societal transformation to an extent demanding a delicate balance between opportunity and risk. This manuscript is the first of a series of White Papers informing the development of IEEE-SA's p3995: `Standard for the Implementation of Safeguards, Controls, and Preventive Techniques for Artificial Intelligence (AI) Models', Chair: Marina Cort\^{e}s (https://standards.ieee.org/ieee/3395/11378/). In this first horizon-scanning we identify key attention areas for standards activities in AI. We examine different principles for regulatory efforts, and review notions of accountability, privacy, data rights and mis-use. As a safeguards standard we devote significant attention to the stability of global infrastructures and consider a possible overdependence on cloud computing that may result from densely coupled AI components. We review the recent cascade-failure-like Crowdstrike event in July 2024, as an illustration of potential impacts on critical infrastructures from AI-induced incidents in the (near) future. It is the first of a set of articles intended as White Papers informing the audience on the standard development. Upcoming articles will focus on regulatory initiatives, technology evolution and the role of AI in specific domains.

As the most representative scenario of spatial-temporal forecasting tasks, the traffic forecasting task attracted numerous attention from machine learning community due to its intricate correlation both in space and time dimension. Existing methods often treat road networks over time as spatial-temporal graphs, addressing spatial and temporal representations independently. However, these approaches struggle to capture the dynamic topology of road networks, encounter issues with message passing mechanisms and over-smoothing, and face challenges in learning spatial and temporal relationships separately. To address these limitations, we propose treating nodes in road networks at different time steps as independent spatial-temporal tokens and feeding them into a vanilla Transformer to learn complex spatial-temporal patterns, design \textbf{STformer} achieving SOTA. Given its quadratic complexity, we introduce a variant \textbf{NSTformer} based on Nystr$\ddot{o}$m method to approximate self-attention with linear complexity but even slightly better than former in a few cases astonishingly. Extensive experimental results on traffic datasets demonstrate that the proposed method achieves state-of-the-art performance at an affordable computational cost. Our code is available at \href{https://github.com/XinyuNing/STformer-and-NSTformer}{https://github.com/XinyuNing/STformer-and-NSTformer}.

Enhancing visual odometry by exploiting sparse depth measurements from LiDAR is a promising solution for improving tracking accuracy of an odometry. Most existing works utilize a monocular pinhole camera, yet could suffer from poor robustness due to less available information from limited field-of-view (FOV). This paper proposes a panoramic direct LiDAR-assisted visual odometry, which fully associates the 360-degree FOV LiDAR points with the 360-degree FOV panoramic image datas. 360-degree FOV panoramic images can provide more available information, which can compensate inaccurate pose estimation caused by insufficient texture or motion blur from a single view. In addition to constraints between a specific view at different times, constraints can also be built between different views at the same moment. Experimental results on public datasets demonstrate the benefit of large FOV of our panoramic direct LiDAR-assisted visual odometry to state-of-the-art approaches.

During multimodal model training and reasoning, data samples may miss certain modalities and lead to compromised model performance due to sensor limitations, cost constraints, privacy concerns, data loss, and temporal and spatial factors. This survey provides an overview of recent progress in Multimodal Learning with Missing Modality (MLMM), focusing on deep learning techniques. It is the first comprehensive survey that covers the historical background and the distinction between MLMM and standard multimodal learning setups, followed by a detailed analysis of current MLMM methods, applications, and datasets, concluding with a discussion about challenges and potential future directions in the field.

While recent Zero-Shot Text-to-Speech (ZS-TTS) models have achieved high naturalness and speaker similarity, they fall short in accent fidelity and control. To address this issue, we propose zero-shot accent generation that unifies Foreign Accent Conversion (FAC), accented TTS, and ZS-TTS, with a novel two-stage pipeline. In the first stage, we achieve state-of-the-art (SOTA) on Accent Identification (AID) with 0.56 f1 score on unseen speakers. In the second stage, we condition ZS-TTS system on the pretrained speaker-agnostic accent embeddings extracted by the AID model. The proposed system achieves higher accent fidelity on inherent/cross accent generation, and enables unseen accent generation.

This study aims to improve knowledge-based question-answering (QA) systems by overcoming the limitations of existing Retrieval-Augmented Generation (RAG) models and implementing an advanced RAG system based on Graph technology to develop high-quality generative AI services. While existing RAG models demonstrate high accuracy and fluency by utilizing retrieved information, they may suffer from accuracy degradation as they generate responses using pre-loaded knowledge without reprocessing. Additionally, they cannot incorporate real-time data after the RAG configuration stage, leading to issues with contextual understanding and biased information. To address these limitations, this study implemented an enhanced RAG system utilizing Graph technology. This system is designed to efficiently search and utilize information. Specifically, it employs LangGraph to evaluate the reliability of retrieved information and synthesizes diverse data to generate more accurate and enhanced responses. Furthermore, the study provides a detailed explanation of the system's operation, key implementation steps, and examples through implementation code and validation results, thereby enhancing the understanding of advanced RAG technology. This approach offers practical guidelines for implementing advanced RAG systems in corporate services, making it a valuable resource for practical application.

As network data applications continue to expand, causal inference within networks has garnered increasing attention. However, hidden confounders complicate the estimation of causal effects. Most methods rely on the strong ignorability assumption, which presumes the absence of hidden confounders-an assumption that is both difficult to validate and often unrealistic in practice. To address this issue, we propose CgNN, a novel approach that leverages network structure as instrumental variables (IVs), combined with graph neural networks (GNNs) and attention mechanisms, to mitigate hidden confounder bias and improve causal effect estimation. By utilizing network structure as IVs, we reduce confounder bias while preserving the correlation with treatment. Our integration of attention mechanisms enhances robustness and improves the identification of important nodes. Validated on two real-world datasets, our results demonstrate that CgNN effectively mitigates hidden confounder bias and offers a robust GNN-driven IV framework for causal inference in complex network data.