arXiv:2604.02447v1 Announce Type: new Abstract: Multi-agent trajectory generation in team sports requires models that capture both the diversity of possible plays and realistic spatial coordination between players on plays. Standard generative approaches such as Conditional Variational Autoencoders (CVAE) and diffusion models struggle with this task, exhibiting posterior collapse or convergence to the dataset mean. Moreover, most trajectory prediction methods operate in a forecasting regime that requires multiple frames of observed history, limiting their use for play design where […]

Ver mais

arXiv:2602.00974v1 Announce Type: new Abstract: Label-supervised manifold alignment bridges the gap between unsupervised and correspondence-based paradigms by leveraging shared label information to align multimodal datasets. Still, most existing methods rely on Euclidean geometry to model intra-domain relationships. This approach can fail when features are only weakly related to the task of interest, leading to noisy, semantically misleading structure and degraded alignment quality. To address this limitation, we introduce FoSTA (Forest-guided Semantic Transport Alignment), a scalable alignment framework that […]

Ver mais

Non Pneumatic tires offer a promising alternative to pneumatic tires. However, their discontinuous spoke structures present challenges in stiffness tuning, durability, and high speed vibration. This study introduces an integrated generative design and machine learning driven framework to optimize UPTIS type spoke geometries for passenger vehicles. Upper and lower spoke profiles were parameterized using high order polynomial representations, enabling the creation of approximately 250 generative designs through PCHIP based geometric variation. Machine learning models like KRR for stiffness […]

Ver mais

The modern platform engineering mandate is clear: adopt Kubernetes, embrace microservices, and accelerate velocity. In theory, this leads to efficiency; in practice, if you operate within highly regulated sectors — Finance, Utilities, Defense, Healthcare, etc. — the journey often slows down due to significant networking and compliance requirements. While the wider developer community utilizes fully managed queues and streaming services (like AWS SQS or Confluent Cloud), enterprise architects in regulated spaces are confronted with a fundamental modernization challenge: How do you leverage […]

Ver mais

arXiv:2603.05556v1 Announce Type: new Abstract: Integer sequences in the OEIS span values from single-digit constants to astronomical factorials and exponentials, making prediction challenging for standard tokenised models that cannot handle out-of-vocabulary values or exploit periodic arithmetic structure. We present IntSeqBERT, a dual-stream Transformer encoder for masked integer-sequence modelling on OEIS. Each sequence element is encoded along two complementary axes: a continuous log-scale magnitude embedding and sin/cos modulo embeddings for 100 residues (moduli $2$–$101$), fused via FiLM. Three prediction […]

Ver mais

arXiv:2601.00208v1 Announce Type: new Abstract: A significant impediment to high performance in key-value stores is the high cost of thread switching or stalls. While there are many sources for this, a major one is the contention for resources. And this cost increases with load as conflicting operations more frequently try to access data concurrently. Traditional latch-based approaches usually handle these situations by blocking one or more contending threads. Latch-free techniques can avoid this behavior. But the payoff may […]

Ver mais