Tootfinder

SourceSplice: Source Selection for Machine Learning Tasks
Ambarish Singh, Romila Pradhan
https://arxiv.org/abs/2507.22186 https://arxiv.org/pdf/2507.22186

SourceSplice: Source Selection for Machine Learning Tasks
Data quality plays a pivotal role in the predictive performance of machine learning (ML) tasks - a challenge amplified by the deluge of data sources available in modern organizations.Prior work in data discovery largely focus on metadata matching, semantic similarity or identifying tables that should be joined to answer a particular query, but do not consider source quality for high performance of the downstream ML task.This paper addresses the problem of determining the best subset of data sou…

A Clinically-Grounded Two-Stage Framework for Renal CT Report Generation
Renjie Liang, Zhengkang Fan, Jinqian Pan, Chenkun Sun, Russell Terry, Jie Xu
https://arxiv.org/abs/2506.23584

A Clinically-Grounded Two-Stage Framework for Renal CT Report Generation
Generating radiology reports from CT scans remains a complex task due to the nuanced nature of medical imaging and the variability in clinical documentation. In this study, we propose a two-stage framework for generating renal radiology reports from 2D CT slices. First, we extract structured abnormality features using a multi-task learning model trained to identify lesion attributes such as location, size, enhancement, and attenuation. These extracted features are subsequently combined with the…

Uniform asymptotics for a multidimensional renewal risk model with multivariate subexponential claims
Dimitrios G. Konstantinides, Jiajun Liu, Charalampos D. Passalidis
https://arxiv.org/abs/2506.11724

Uniform asymptotics for a multidimensional renewal risk model with multivariate subexponential claims
In this paper, we study a multidimensional risk model with a common renewal process and in the presence of a constant interest force. The claim sizes are independent and identically distributed random vectors, with the distribution of dependent components belonging to the class of multivariate subexponential distributions. We establish locally uniform asymptotic estimations for the entrance probability of the discounted aggregate claims into some rare sets, and further derive asymptotic estimat…

ContextBuddy: AI-Enhanced Contextual Insights for Security Alert Investigation (Applied to Intrusion Detection)
Ronal Singh, Mohan Baruwal Chhetri, Surya Nepal, Cecile Paris
https://arxiv.org/abs/2506.09365

ContextBuddy: AI-Enhanced Contextual Insights for Security Alert Investigation (Applied to Intrusion Detection)
Modern Security Operations Centres (SOCs) integrate diverse tools, such as SIEM, IDS, and XDR systems, offering rich contextual data, including alert enrichments, flow features, and similar case histories. Yet, analysts must still manually determine which of these contextual cues are most relevant when validating specific alerts. We introduce ContextBuddy, an AI assistant that learns from analysts' prior investigations to help them identify the most relevant context for new alerts. Rather than …

Chimera states on m-directed hypergraphs
Rommel Tchinda Djeudjo, Timoteo Carletti, Hiroya Nakao, Riccardo Muolo
https://arxiv.org/abs/2506.12511 https://…

Chimera states on m-directed hypergraphs
Chimera states are synchronization patterns in which coherent and incoherent regions coexist in systems of identical oscillators. This elusive phenomenon has attracted a lot of interest and has been widely studied, revealing several types of chimeras. Most cases involve reciprocal pairwise couplings, where each oscillator exerts and receives the same interaction, modeled via networks. However, real-world systems often have non-reciprocal, non-pairwise (many-body) interactions. From previous stu…