Processing Large Datasets with Dask and Scikit-learnKDnuggets This article uncovers how to harness Dask for scalable data processing, even under limited hardware constraints.This article uncovers how to harness Dask for scalable data processing, even under limited hardware constraints.
This article uncovers how to harness Dask for scalable data processing, even under limited hardware constraints.This article uncovers how to harness Dask for scalable data processing, even under limited hardware constraints. Read More
Robotics with Python: Q-Learning vs Actor-Critic vs Evolutionary AlgorithmsTowards Data Science Build a Custom 3D Environment for your RL Robot
The post Robotics with Python: Q-Learning vs Actor-Critic vs Evolutionary Algorithms appeared first on Towards Data Science.
Build a Custom 3D Environment for your RL Robot
The post Robotics with Python: Q-Learning vs Actor-Critic vs Evolutionary Algorithms appeared first on Towards Data Science. Read More
LLMs Are Randomized AlgorithmsTowards Data Science A surprising connection between the newest AI models and a 50-year old academic field
The post LLMs Are Randomized Algorithms appeared first on Towards Data Science.
A surprising connection between the newest AI models and a 50-year old academic field
The post LLMs Are Randomized Algorithms appeared first on Towards Data Science. Read More
A Russian-speaking threat behind an ongoing, mass phishing campaign has registered more than 4,300 domain names since the start of the year. The activity, per Netcraft security researcher Andrew Brandt, is designed to target customers of the hospitality industry, specifically hotel guests who may have travel reservations with spam emails. The campaign is said to […]
Cybersecurity researchers have uncovered a malicious Chrome extension that poses as a legitimate Ethereum wallet but harbors functionality to exfiltrate users’ seed phrases. The name of the extension is “Safery: Ethereum Wallet,” with the threat actor describing it as a “secure wallet for managing Ethereum cryptocurrency with flexible settings.” It was uploaded to the Chrome […]
FractalCloud: A Fractal-Inspired Architecture for Efficient Large-Scale Point Cloud Processingcs.AI updates on arXiv.org arXiv:2511.07665v1 Announce Type: cross
Abstract: Three-dimensional (3D) point clouds are increasingly used in applications such as autonomous driving, robotics, and virtual reality (VR). Point-based neural networks (PNNs) have demonstrated strong performance in point cloud analysis, originally targeting small-scale inputs. However, as PNNs evolve to process large-scale point clouds with hundreds of thousands of points, all-to-all computation and global memory access in point cloud processing introduce substantial overhead, causing $O(n^2)$ computational complexity and memory traffic where n is the number of points}. Existing accelerators, primarily optimized for small-scale workloads, overlook this challenge and scale poorly due to inefficient partitioning and non-parallel architectures. To address these issues, we propose FractalCloud, a fractal-inspired hardware architecture for efficient large-scale 3D point cloud processing. FractalCloud introduces two key optimizations: (1) a co-designed Fractal method for shape-aware and hardware-friendly partitioning, and (2) block-parallel point operations that decompose and parallelize all point operations. A dedicated hardware design with on-chip fractal and flexible parallelism further enables fully parallel processing within limited memory resources. Implemented in 28 nm technology as a chip layout with a core area of 1.5 $mm^2$, FractalCloud achieves 21.7x speedup and 27x energy reduction over state-of-the-art accelerators while maintaining network accuracy, demonstrating its scalability and efficiency for PNN inference.
arXiv:2511.07665v1 Announce Type: cross
Abstract: Three-dimensional (3D) point clouds are increasingly used in applications such as autonomous driving, robotics, and virtual reality (VR). Point-based neural networks (PNNs) have demonstrated strong performance in point cloud analysis, originally targeting small-scale inputs. However, as PNNs evolve to process large-scale point clouds with hundreds of thousands of points, all-to-all computation and global memory access in point cloud processing introduce substantial overhead, causing $O(n^2)$ computational complexity and memory traffic where n is the number of points}. Existing accelerators, primarily optimized for small-scale workloads, overlook this challenge and scale poorly due to inefficient partitioning and non-parallel architectures. To address these issues, we propose FractalCloud, a fractal-inspired hardware architecture for efficient large-scale 3D point cloud processing. FractalCloud introduces two key optimizations: (1) a co-designed Fractal method for shape-aware and hardware-friendly partitioning, and (2) block-parallel point operations that decompose and parallelize all point operations. A dedicated hardware design with on-chip fractal and flexible parallelism further enables fully parallel processing within limited memory resources. Implemented in 28 nm technology as a chip layout with a core area of 1.5 $mm^2$, FractalCloud achieves 21.7x speedup and 27x energy reduction over state-of-the-art accelerators while maintaining network accuracy, demonstrating its scalability and efficiency for PNN inference. Read More
Uhale Android-based digital picture frames come with multiple critical security vulnerabilities and some of them download and execute malware at boot time. […] Read MoreUhale Android-based digital picture frames come with multiple critical security vulnerabilities and some of them download and execute malware at boot time. […]
AIA Forecaster: Technical Reportcs.AI updates on arXiv.org arXiv:2511.07678v1 Announce Type: new
Abstract: This technical report describes the AIA Forecaster, a Large Language Model (LLM)-based system for judgmental forecasting using unstructured data. The AIA Forecaster approach combines three core elements: agentic search over high-quality news sources, a supervisor agent that reconciles disparate forecasts for the same event, and a set of statistical calibration techniques to counter behavioral biases in large language models. On the ForecastBench benchmark (Karger et al., 2024), the AIA Forecaster achieves performance equal to human superforecasters, surpassing prior LLM baselines. In addition to reporting on ForecastBench, we also introduce a more challenging forecasting benchmark sourced from liquid prediction markets. While the AIA Forecaster underperforms market consensus on this benchmark, an ensemble combining AIA Forecaster with market consensus outperforms consensus alone, demonstrating that our forecaster provides additive information. Our work establishes a new state of the art in AI forecasting and provides practical, transferable recommendations for future research. To the best of our knowledge, this is the first work that verifiably achieves expert-level forecasting at scale.
arXiv:2511.07678v1 Announce Type: new
Abstract: This technical report describes the AIA Forecaster, a Large Language Model (LLM)-based system for judgmental forecasting using unstructured data. The AIA Forecaster approach combines three core elements: agentic search over high-quality news sources, a supervisor agent that reconciles disparate forecasts for the same event, and a set of statistical calibration techniques to counter behavioral biases in large language models. On the ForecastBench benchmark (Karger et al., 2024), the AIA Forecaster achieves performance equal to human superforecasters, surpassing prior LLM baselines. In addition to reporting on ForecastBench, we also introduce a more challenging forecasting benchmark sourced from liquid prediction markets. While the AIA Forecaster underperforms market consensus on this benchmark, an ensemble combining AIA Forecaster with market consensus outperforms consensus alone, demonstrating that our forecaster provides additive information. Our work establishes a new state of the art in AI forecasting and provides practical, transferable recommendations for future research. To the best of our knowledge, this is the first work that verifiably achieves expert-level forecasting at scale. Read More
CISA warned federal agencies to fully patch two actively exploited vulnerabilities in Cisco Adaptive Security Appliances (ASA) and Firepower devices. […] Read MoreCISA warned federal agencies to fully patch two actively exploited vulnerabilities in Cisco Adaptive Security Appliances (ASA) and Firepower devices. […]
The Race for Every New CVE Based on multiple 2025 industry reports: roughly 50 to 61 percent of newly disclosed vulnerabilities saw exploit code weaponized within 48 hours. Using the CISA Known Exploited Vulnerabilities Catalog as a reference, hundreds of software flaws are now confirmed as actively targeted within days of public disclosure. Each new […]
