Probabilistic models, such as hidden Markov models or Bayesian networks, are commonly used to model biological data. Much of their popularity can be attributed to the existence of efficient and robust ...

Abstract: This paper focuses on the state estimator design problem for a switched neural network (SNN) with probabilistic quantized outputs, where the switching process is governed by a sojourn ...

Abstract: In this article, the stabilization problem of discrete-time power systems subject to random abrupt changes is studied via asynchronous control. In this regard, the transient faults in the ...

Expression-based systems that use only native alignments tend to produce exon-intron structures that are quite accurate. Their primary limitation is that there are many genomes for which little or no ...

If human sensorimotor intelligence can be recovered as a learned model, systems can be trained that map perception into ...

Trump administration stops Anthropic’s newest models, how AI tokens are like the Y2K problem, revisit your bot-blocking ...

GPT-5.6 was already running in Codex for some users before OpenAI’s government-approved preview opened to partners. A ...

Moving forward requires coordinated technical, policy, and educational responses. An outright ban on AI in peer review, as is ...

Chinese AI models are rapidly closing the gap with U.S. frontier systems. This analysis examines what their growing ...

You may have even been tempted yourself to put down cash on your favorite pop-culture hunch. But the recent Google case ...

These short anomaly-detection puzzles are designed to illustrate how reasoning often depends on identifying inconsistencies ...