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The annual Data Team Awards celebrate the critical contributions of data teams to various sectors, spotlighting their role in driving progress and positive change within their organizations.This year, we've seen an exceptional number of more than 200 nominations from around the world, emphasizing the widespread impact of innovation in both data science and artificial intelligence. With the Data + AI Summit around the corner, we are excited to feature the innovators in our six award categories and highlight their remarkable journeys to data-led breakthroughs.The Data for Good Award honors teams that have harnessed the power of data, analytics, and AI…

[Submitted on 24 May 2024] View a PDF of the paper titled CausalConceptTS: Causal Attributions for Time Series Classification using High Fidelity Diffusion Models, by Juan Miguel Lopez Alcaraz and 1 other authors View PDF HTML (experimental) Abstract:Despite the excelling performance of machine learning models, understanding the decisions of machine learning models remains a long-standing goal. While commonly used attribution methods in explainable AI attempt to address this issue, they typically rely on associational rather than causal relationships. In this study, within the context of time series classification, we introduce a novel framework to assess the causal effect of concepts,…

arXiv:2405.15826v1 Announce Type: new Abstract: 3D Transformers have achieved great success in point cloud understanding and representation. However, there is still considerable scope for further development in effective and efficient Transformers for large-scale LiDAR point cloud scene segmentation. This paper proposes a novel 3D Transformer framework, named 3D Learnable Supertoken Transformer (3DLST). The key contributions are summarized as follows. Firstly, we introduce the first Dynamic Supertoken Optimization (DSO) block for efficient token clustering and aggregating, where the learnable supertoken definition avoids the time-consuming pre-processing of traditional superpoint generation. Since the learnable supertokens can be dynamically optimized by multi-level deep features…

arXiv:2405.15936v1 Announce Type: new Abstract: This paper investigates the application of pre-trained large language models (LLMs) for spam email classification using zero-shot prompting. We evaluate the performance of both open-source (Flan-T5) and proprietary LLMs (ChatGPT, GPT-4) on the well-known SpamAssassin dataset. Two classification approaches are explored: (1) truncated raw content from email subject and body, and (2) classification based on summaries generated by ChatGPT. Our empirical analysis, leveraging the entire dataset for evaluation without further training, reveals promising results. Flan-T5 achieves a 90% F1-score on the truncated content approach, while GPT-4 reaches a 95% F1-score using summaries. While these initial…

(PopTika/Shutterstock) From the industrial revolution to the first computer, tech innovation has consistently revolutionized and enhanced the way we live and work. Most recently, AI burst onto the scene creating a cascade of hype, promise, and a variety of new AI-focused jobs and use cases – but with this shift have come concerns about job displacement and the long-term risks of such a novel technology. While everyone has an opinion on AI, people might not realize they’ve been using it for years. Most of us have daily interactions with friendly bots and virtual assistants, like Alexa, Siri, or even the…

The below is a summary of my latest episode of the Synthetic Minds Podcast. Would you trust your consciousness to live forever in a digital afterlife? The future of humanity might depend on it. In the latest episode of the Synthetic Minds podcast, Dr. Mark van Rijmenam sits down with Richard K. Morgan, the visionary author behind “Altered Carbon.” Morgan's novel, which has been adapted into a popular Netflix series, explores a future where human consciousness can be digitized and transferred between bodies, or “sleeves.” This concept not only redefines personal identity and mortality but also raises profound ethical questions…

arXiv:2405.15861v1 Announce Type: new Abstract: Federated Learning (FL) offers a promising framework for collaborative and privacy-preserving machine learning across distributed data sources. However, the substantial communication costs associated with FL pose a significant challenge to its efficiency. Specifically, in each communication round, the communication costs scale linearly with the model's dimension, which presents a formidable obstacle, especially in large model scenarios. Despite various communication efficient strategies, the intrinsic dimension-dependent communication cost remains a major bottleneck for current FL implementations. In this paper, we introduce a novel dimension-free communication strategy for FL, leveraging zero-order optimization techniques. We propose a new algorithm,…

arXiv:2405.15817v1 Announce Type: new Abstract: This paper addresses the limitations of physical models in the current field of image dehazing by proposing an innovative dehazing network (CL2S). Building on the DM2F model, it identifies issues in its ablation experiments and replaces the original logarithmic function model with a trigonometric (sine) model. This substitution aims to better fit the complex and variable distribution of haze. The approach also integrates the atmospheric scattering model and other elementary functions to enhance dehazing performance. Experimental results demonstrate that CL2S achieves outstanding performance on multiple dehazing datasets, particularly in maintaining image details and color authenticity.…

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol