The integration of AI agents into economic markets fundamentally alters the landscape of strategic interaction. We investigate the economic implications of expa...
Competitive sports require sophisticated tactical analysis, yet combat disciplines like boxing remain underdeveloped in AI-driven analytics due to the complexit...
Extractive summarization (ES) aims to generate a concise summary by selecting a subset of sentences from a document while maximizing relevance and minimizing re...
Ethiopia's Ministry of Health is upgrading health posts to improve access to essential services, particularly in rural areas. Limited resources, however, requir...
We present a novel probabilistic approach for optimal path experimental design. In this approach a discrete path optimization problem is defined on a static nav...
Transformer pretraining is increasingly constrained by memory and compute requirements, with the key-value (KV) cache emerging as a dominant bottleneck during t...
Predictive Process Monitoring is a branch of process mining that aims to predict the outcome of an ongoing process. Recently, it leveraged machine-and-deep lear...
As vision-language models (VLMs) tackle increasingly complex and multimodal tasks, the rapid growth of Key-Value (KV) cache imposes significant memory and compu...
Learning structured task representations from human demonstrations is essential for understanding long-horizon manipulation behaviors, particularly in bimanual ...
Information overload and misinformation create significant challenges in extracting meaningful narratives from large news collections. This paper defines the na...
Large-scale livestock operations pose significant risks to human health and the environment, while also being vulnerable to threats such as infectious diseases ...
Diffusion models now generate high-quality, diverse samples, with an increasing focus on more powerful models. Although ensembling is a well-known way to improv...
