Authored by Derrick Jackson & Co-Author Lisa Yu | Last updated 09/22/2025 Pressed For TimeReview or Download our 2-3 min Quick Slides or the 5-7 min Article Insights to gain knowledge with the time you have! AWS Cloud Practitioner Certification: Complete 2025 Overview to Study Hours, Salary & Career Value The AWS Certified Cloud Practitioner costs $100 and takes […]
Creating and Deploying an MCP Server from ScratchTowards Data Scienceon September 22, 2025 at 5:55 pm A step-by-step guide for putting an MCP server online in minutes
The post Creating and Deploying an MCP Server from Scratch appeared first on Towards Data Science.
A step-by-step guide for putting an MCP server online in minutes
The post Creating and Deploying an MCP Server from Scratch appeared first on Towards Data Science. Read More
Integrating DataHub into Jira: A Practical Guide Using DataHub ActionsTowards Data Scienceon September 22, 2025 at 5:39 pm A walkthrough of how to integrate metadata changes in DataHub into Jira workflows using the DataHub Actions Framework
The post Integrating DataHub into Jira: A Practical Guide Using DataHub Actions appeared first on Towards Data Science.
A walkthrough of how to integrate metadata changes in DataHub into Jira workflows using the DataHub Actions Framework
The post Integrating DataHub into Jira: A Practical Guide Using DataHub Actions appeared first on Towards Data Science. Read More
Self-Supervised Cross-Modal Learning for Image-to-Point Cloud Registrationcs.AI updates on arXiv.org
Self-Supervised Cross-Modal Learning for Image-to-Point Cloud Registrationcs.AI updates on arXiv.orgon September 22, 2025 at 4:00 am arXiv:2509.15882v1 Announce Type: cross
Abstract: Bridging 2D and 3D sensor modalities is critical for robust perception in autonomous systems. However, image-to-point cloud (I2P) registration remains challenging due to the semantic-geometric gap between texture-rich but depth-ambiguous images and sparse yet metrically precise point clouds, as well as the tendency of existing methods to converge to local optima. To overcome these limitations, we introduce CrossI2P, a self-supervised framework that unifies cross-modal learning and two-stage registration in a single end-to-end pipeline. First, we learn a geometric-semantic fused embedding space via dual-path contrastive learning, enabling annotation-free, bidirectional alignment of 2D textures and 3D structures. Second, we adopt a coarse-to-fine registration paradigm: a global stage establishes superpoint-superpixel correspondences through joint intra-modal context and cross-modal interaction modeling, followed by a geometry-constrained point-level refinement for precise registration. Third, we employ a dynamic training mechanism with gradient normalization to balance losses for feature alignment, correspondence refinement, and pose estimation. Extensive experiments demonstrate that CrossI2P outperforms state-of-the-art methods by 23.7% on the KITTI Odometry benchmark and by 37.9% on nuScenes, significantly improving both accuracy and robustness.
arXiv:2509.15882v1 Announce Type: cross
Abstract: Bridging 2D and 3D sensor modalities is critical for robust perception in autonomous systems. However, image-to-point cloud (I2P) registration remains challenging due to the semantic-geometric gap between texture-rich but depth-ambiguous images and sparse yet metrically precise point clouds, as well as the tendency of existing methods to converge to local optima. To overcome these limitations, we introduce CrossI2P, a self-supervised framework that unifies cross-modal learning and two-stage registration in a single end-to-end pipeline. First, we learn a geometric-semantic fused embedding space via dual-path contrastive learning, enabling annotation-free, bidirectional alignment of 2D textures and 3D structures. Second, we adopt a coarse-to-fine registration paradigm: a global stage establishes superpoint-superpixel correspondences through joint intra-modal context and cross-modal interaction modeling, followed by a geometry-constrained point-level refinement for precise registration. Third, we employ a dynamic training mechanism with gradient normalization to balance losses for feature alignment, correspondence refinement, and pose estimation. Extensive experiments demonstrate that CrossI2P outperforms state-of-the-art methods by 23.7% on the KITTI Odometry benchmark and by 37.9% on nuScenes, significantly improving both accuracy and robustness. Read More
Data Visualization Explained: What It Is and Why It MattersTowards Data Scienceon September 21, 2025 at 4:00 pm A brief introduction to data visualization and its importance in today’s technological landscape.
The post Data Visualization Explained: What It Is and Why It Matters appeared first on Towards Data Science.
A brief introduction to data visualization and its importance in today’s technological landscape.
The post Data Visualization Explained: What It Is and Why It Matters appeared first on Towards Data Science. Read More
Python Can Now Call MojoTowards Data Scienceon September 21, 2025 at 2:00 pm Boost your runtimes with lightning-fast Mojo code
The post Python Can Now Call Mojo appeared first on Towards Data Science.
Boost your runtimes with lightning-fast Mojo code
The post Python Can Now Call Mojo appeared first on Towards Data Science. Read More
Building LLM Apps That Can See, Think, and Integrate: Using o3 with Multimodal Input and Structured OutputTowards Data Scienceon September 20, 2025 at 4:00 pm A hands-on example of building a time-series anomaly detection system entirely through visualization and prompting
The post Building LLM Apps That Can See, Think, and Integrate: Using o3 with Multimodal Input and Structured Output appeared first on Towards Data Science.
A hands-on example of building a time-series anomaly detection system entirely through visualization and prompting
The post Building LLM Apps That Can See, Think, and Integrate: Using o3 with Multimodal Input and Structured Output appeared first on Towards Data Science. Read More
The SyncNet Research Paper, Clearly ExplainedTowards Data Scienceon September 20, 2025 at 2:00 pm A Deep Dive into “Out of Time: Automated Lip Sync in the Wild”
The post The SyncNet Research Paper, Clearly Explained appeared first on Towards Data Science.
A Deep Dive into “Out of Time: Automated Lip Sync in the Wild”
The post The SyncNet Research Paper, Clearly Explained appeared first on Towards Data Science. Read More
How to Select the 5 Most Relevant Documents for AI SearchTowards Data Scienceon September 19, 2025 at 12:30 pm Improve the document retrieval step of your RAG pipeline
The post How to Select the 5 Most Relevant Documents for AI Search appeared first on Towards Data Science.
Improve the document retrieval step of your RAG pipeline
The post How to Select the 5 Most Relevant Documents for AI Search appeared first on Towards Data Science. Read More
Leveraging Artificial Intelligence as a Strategic Growth Catalyst for Small and Medium-sized Enterprisescs.AI updates on arXiv.orgon September 19, 2025 at 4:00 am arXiv:2509.14532v1 Announce Type: cross
Abstract: Artificial Intelligence (AI) has transitioned from a futuristic concept reserved for large corporations to a present-day, accessible, and essential growth lever for Small and Medium-sized Enterprises (SMEs). For entrepreneurs and business leaders, strategic AI adoption is no longer an option but an imperative for competitiveness, operational efficiency, and long-term survival. This report provides a comprehensive framework for SME leaders to navigate this technological shift, offering the foundational knowledge, business case, practical applications, and strategic guidance necessary to harness the power of AI. The quantitative evidence supporting AI adoption is compelling; 91% of SMEs using AI report that it directly boosts their revenue. Beyond top-line growth, AI drives profound operational efficiencies, with studies showing it can reduce operational costs by up to 30% and save businesses more than 20 hours of valuable time each month. This transformation is occurring within the context of a seismic economic shift; the global AI market is projected to surge from $233.46 Billion in 2024 to an astonishing $1.77 Trillion by 2032. This paper demystifies the core concepts of AI, presents a business case based on market data, details practical applications, and lays out a phased, actionable adoption strategy.
arXiv:2509.14532v1 Announce Type: cross
Abstract: Artificial Intelligence (AI) has transitioned from a futuristic concept reserved for large corporations to a present-day, accessible, and essential growth lever for Small and Medium-sized Enterprises (SMEs). For entrepreneurs and business leaders, strategic AI adoption is no longer an option but an imperative for competitiveness, operational efficiency, and long-term survival. This report provides a comprehensive framework for SME leaders to navigate this technological shift, offering the foundational knowledge, business case, practical applications, and strategic guidance necessary to harness the power of AI. The quantitative evidence supporting AI adoption is compelling; 91% of SMEs using AI report that it directly boosts their revenue. Beyond top-line growth, AI drives profound operational efficiencies, with studies showing it can reduce operational costs by up to 30% and save businesses more than 20 hours of valuable time each month. This transformation is occurring within the context of a seismic economic shift; the global AI market is projected to surge from $233.46 Billion in 2024 to an astonishing $1.77 Trillion by 2032. This paper demystifies the core concepts of AI, presents a business case based on market data, details practical applications, and lays out a phased, actionable adoption strategy. Read More