Barts Health NHS Trust has announced that Clop ransomware actors have stolen files from a database by exploiting a vulnerability in its Oracle E-business Suite software. […] Read More
A new agentic browser attack targeting Perplexity’s Comet browser that’s capable of turning a seemingly innocuous email into a destructive action that wipes a user’s entire Google Drive contents, findings from Straiker STAR Labs show. The zero-click Google Drive Wiper technique hinges on connecting the browser to services like Gmail and Google Drive to automate […]
The European Commission has fined X €120 million ($140 million) for violating transparency obligations under the Digital Services Act (DSA). […] Read More
The FBI warns of criminals altering images shared on social media and using them as fake proof of life photos in virtual kidnapping ransom scams. […] Read More
Manufacturers are the top target for cyberattacks in 2025 because of their still-plentiful cybersecurity gaps and a lack of expertise. Read More
A critical security flaw has been disclosed in Apache Tika that could result in an XML external entity (XXE) injection attack. The vulnerability, tracked as CVE-2025-66516, is rated 10.0 on the CVSS scoring scale, indicating maximum severity. “Critical XXE in Apache Tika tika-core (1.13-3.2.1), tika-pdf-module (2.0.0-3.2.1) and tika-parsers (1.13-1.28.5) modules on all platforms allows an Read […]
On the Challenge of Converting TensorFlow Models to PyTorchTowards Data Science How to upgrade and optimize legacy AI/ML models
The post On the Challenge of Converting TensorFlow Models to PyTorch appeared first on Towards Data Science.
How to upgrade and optimize legacy AI/ML models
The post On the Challenge of Converting TensorFlow Models to PyTorch appeared first on Towards Data Science. Read More
A maximum-severity vulnerability affecting the React JavaScript library is under attack by Chinese-nexus actors, further stressing the need to patch now. Read More
The Machine Learning “Advent Calendar” Day 5: GMM in ExcelTowards Data Science This article introduces the Gaussian Mixture Model as a natural extension of k-Means, by improving how distance is measured through variances and the Mahalanobis distance. Instead of assigning points to clusters with hard boundaries, GMM uses probabilities learned through the Expectation–Maximization algorithm – the general form of Lloyd’s method.
Using simple Excel formulas, we implement EM step by step in 1D and 2D, and we visualise how the Gaussian curves or ellipses move during training. The means shift, the variances adjust, and the shapes gradually settle around the true structure of the data.
GMM provides a richer, more flexible way to model clusters, and becomes intuitive once the process is made visible in a spreadsheet.
The post The Machine Learning “Advent Calendar” Day 5: GMM in Excel appeared first on Towards Data Science.
This article introduces the Gaussian Mixture Model as a natural extension of k-Means, by improving how distance is measured through variances and the Mahalanobis distance. Instead of assigning points to clusters with hard boundaries, GMM uses probabilities learned through the Expectation–Maximization algorithm – the general form of Lloyd’s method.
Using simple Excel formulas, we implement EM step by step in 1D and 2D, and we visualise how the Gaussian curves or ellipses move during training. The means shift, the variances adjust, and the shapes gradually settle around the true structure of the data.
GMM provides a richer, more flexible way to model clusters, and becomes intuitive once the process is made visible in a spreadsheet.
The post The Machine Learning “Advent Calendar” Day 5: GMM in Excel appeared first on Towards Data Science. Read More
Single-Round Scalable Analytic Federated Learningcs.AI updates on arXiv.org arXiv:2512.03336v1 Announce Type: cross
Abstract: Federated Learning (FL) is plagued by two key challenges: high communication overhead and performance collapse on heterogeneous (non-IID) data. Analytic FL (AFL) provides a single-round, data distribution invariant solution, but is limited to linear models. Subsequent non-linear approaches, like DeepAFL, regain accuracy but sacrifice the single-round benefit. In this work, we break this trade-off. We propose SAFLe, a framework that achieves scalable non-linear expressivity by introducing a structured head of bucketed features and sparse, grouped embeddings. We prove this non-linear architecture is mathematically equivalent to a high-dimensional linear regression. This key equivalence allows SAFLe to be solved with AFL’s single-shot, invariant aggregation law. Empirically, SAFLe establishes a new state-of-the-art for analytic FL, significantly outperforming both linear AFL and multi-round DeepAFL in accuracy across all benchmarks, demonstrating a highly efficient and scalable solution for federated vision.
arXiv:2512.03336v1 Announce Type: cross
Abstract: Federated Learning (FL) is plagued by two key challenges: high communication overhead and performance collapse on heterogeneous (non-IID) data. Analytic FL (AFL) provides a single-round, data distribution invariant solution, but is limited to linear models. Subsequent non-linear approaches, like DeepAFL, regain accuracy but sacrifice the single-round benefit. In this work, we break this trade-off. We propose SAFLe, a framework that achieves scalable non-linear expressivity by introducing a structured head of bucketed features and sparse, grouped embeddings. We prove this non-linear architecture is mathematically equivalent to a high-dimensional linear regression. This key equivalence allows SAFLe to be solved with AFL’s single-shot, invariant aggregation law. Empirically, SAFLe establishes a new state-of-the-art for analytic FL, significantly outperforming both linear AFL and multi-round DeepAFL in accuracy across all benchmarks, demonstrating a highly efficient and scalable solution for federated vision. Read More
