How to Do Evals on a Bloated RAG PipelineTowards Data Science Comparing metrics across datasets and models
The post How to Do Evals on a Bloated RAG Pipeline appeared first on Towards Data Science.
Comparing metrics across datasets and models
The post How to Do Evals on a Bloated RAG Pipeline appeared first on Towards Data Science. Read More
As the internet becomes an essential part of daily life, its environmental footprint continues to grow. Data centers, constant connectivity, and resource-heavy browsing habits all contribute to energy consumption and digital waste. While individual users may not see this impact directly, the collective effect of everyday browsing is significant. Choosing a browser designed with Read More
A Ukrainian national pleaded guilty on Friday to conducting Nefilim ransomware attacks that targeted high-revenue businesses across the United States and other countries. […] Read More
(c) SANS Internet Storm Center. https://isc.sans.edu Creative Commons Attribution-Noncommercial 3.0 United States License. Read More
More than a 1,000 Docker Hardened Images (DHI) are now freely available and open source for software builders, under the Apache 2.0 license. […] Read More
Threat actors have been observed leveraging malicious dropper apps masquerading as legitimate applications to deliver an Android SMS stealer dubbed Wonderland in mobile attacks targeting users in Uzbekistan. “Previously, users received ‘pure’ Trojan APKs that acted as malware immediately upon installation,” Group-IB said in an analysis published last week. “Now, adversaries increasingly deploy Read More
An ASUS Live Update vulnerability tracked as CVE-2025-59374 has been making the rounds in infosec feeds, with some headlines implying recent or ongoing exploitation. A closer look, however, shows the CVE documents a historic supply-chain attack in an End-of-Life (EoL) software product, not a new attack. […] Read More
This AI finds simple rules where humans see only chaosArtificial Intelligence News — ScienceDaily A new AI developed at Duke University can uncover simple, readable rules behind extremely complex systems. It studies how systems evolve over time and reduces thousands of variables into compact equations that still capture real behavior. The method works across physics, engineering, climate science, and biology. Researchers say it could help scientists understand systems where traditional equations are missing or too complicated to write down.
A new AI developed at Duke University can uncover simple, readable rules behind extremely complex systems. It studies how systems evolve over time and reduces thousands of variables into compact equations that still capture real behavior. The method works across physics, engineering, climate science, and biology. Researchers say it could help scientists understand systems where traditional equations are missing or too complicated to write down. Read More
Adaptive Graph Pruning with Sudden-Events Evaluation for Traffic Prediction using Online Semi-Decentralized ST-GNNscs.AI updates on arXiv.org arXiv:2512.17352v1 Announce Type: cross
Abstract: Spatio-Temporal Graph Neural Networks (ST-GNNs) are well-suited for processing high-frequency data streams from geographically distributed sensors in smart mobility systems. However, their deployment at the edge across distributed compute nodes (cloudlets) createssubstantial communication overhead due to repeated transmission of overlapping node features between neighbouring cloudlets. To address this, we propose an adaptive pruning algorithm that dynamically filters redundant neighbour features while preserving the most informative spatial context for prediction. The algorithm adjusts pruning rates based on recent model performance, allowing each cloudlet to focus on regions experiencing traffic changes without compromising accuracy. Additionally, we introduce the Sudden Event Prediction Accuracy (SEPA), a novel event-focused metric designed to measure responsiveness to traffic slowdowns and recoveries, which are often missed by standard error metrics. We evaluate our approach in an online semi-decentralized setting with traditional FL, server-free FL, and Gossip Learning on two large-scale traffic datasets, PeMS-BAY and PeMSD7-M, across short-, mid-, and long-term prediction horizons. Experiments show that, in contrast to standard metrics, SEPA exposes the true value of spatial connectivity in predicting dynamic and irregular traffic. Our adaptive pruning algorithm maintains prediction accuracy while significantly lowering communication cost in all online semi-decentralized settings, demonstrating that communication can be reduced without compromising responsiveness to critical traffic events.
arXiv:2512.17352v1 Announce Type: cross
Abstract: Spatio-Temporal Graph Neural Networks (ST-GNNs) are well-suited for processing high-frequency data streams from geographically distributed sensors in smart mobility systems. However, their deployment at the edge across distributed compute nodes (cloudlets) createssubstantial communication overhead due to repeated transmission of overlapping node features between neighbouring cloudlets. To address this, we propose an adaptive pruning algorithm that dynamically filters redundant neighbour features while preserving the most informative spatial context for prediction. The algorithm adjusts pruning rates based on recent model performance, allowing each cloudlet to focus on regions experiencing traffic changes without compromising accuracy. Additionally, we introduce the Sudden Event Prediction Accuracy (SEPA), a novel event-focused metric designed to measure responsiveness to traffic slowdowns and recoveries, which are often missed by standard error metrics. We evaluate our approach in an online semi-decentralized setting with traditional FL, server-free FL, and Gossip Learning on two large-scale traffic datasets, PeMS-BAY and PeMSD7-M, across short-, mid-, and long-term prediction horizons. Experiments show that, in contrast to standard metrics, SEPA exposes the true value of spatial connectivity in predicting dynamic and irregular traffic. Our adaptive pruning algorithm maintains prediction accuracy while significantly lowering communication cost in all online semi-decentralized settings, demonstrating that communication can be reduced without compromising responsiveness to critical traffic events. Read More
Optimisation of Aircraft Maintenance Schedulescs.AI updates on arXiv.org arXiv:2512.17412v1 Announce Type: cross
Abstract: We present an aircraft maintenance scheduling problem, which requires suitably qualified staff to be assigned to maintenance tasks on each aircraft. The tasks on each aircraft must be completed within a given turn around window so that the aircraft may resume revenue earning service. This paper presents an initial study based on the application of an Evolutionary Algorithm to the problem. Evolutionary Algorithms evolve a solution to a problem by evaluating many possible solutions, focusing the search on those solutions that are of a higher quality, as defined by a fitness function. In this paper, we benchmark the algorithm on 60 generated problem instances to demonstrate the underlying representation and associated genetic operators.
arXiv:2512.17412v1 Announce Type: cross
Abstract: We present an aircraft maintenance scheduling problem, which requires suitably qualified staff to be assigned to maintenance tasks on each aircraft. The tasks on each aircraft must be completed within a given turn around window so that the aircraft may resume revenue earning service. This paper presents an initial study based on the application of an Evolutionary Algorithm to the problem. Evolutionary Algorithms evolve a solution to a problem by evaluating many possible solutions, focusing the search on those solutions that are of a higher quality, as defined by a fitness function. In this paper, we benchmark the algorithm on 60 generated problem instances to demonstrate the underlying representation and associated genetic operators. Read More
