NHP is a thoughtfully designed framework that has been demonstrated effective on temporal data, but our method can also be used for other models with parametric intensityfunctions. In this section, we prove the claim in section 2.2 that argmaxθJLL(θ) = Θ When we take the expectation under p, each summand gets weighted by the probability that x[0,t) and x[t,t+dt) would take on the values in that summand. Therefore,wehaveG θ( t, x[0, t)) < 0since the distributions in equation (9) are distinct for the given history x[0, t). This lemma says: if θ and θ are meaningfully different in that they predict different intensities at time t for some history, then they actually do so for a set of histories of non-zero measure, making this difference visible in the objective functions like JLL(θ) (see above) and JNC(θ) (see Appendix B). We use d to denote the maximal difference between the intensities over (t0,t00), i.e., d If x[0,t) doesn't have any event, then its probability p( x[0,t)) = exp( Suppose that t1 has been shifted by R. Recall that we need order-(1dt)I many such histories.

This article was originally written for the June 2024 issue of the Bulletin of European Association for Theoretical Computer Science, in the framework of the "Logic in Computer Science" column administered by Yuri Gurevich. In the following pages, the article is reproduced as is. The ongoing AI revolution raises many foundational problems. For quite a while, I felt that the issue needs to be addressed in this column. Not being an AI expert, I was looking for volunteers. This didn't work, and so one day I took a deep breath and started to write an article myself. Andreas Blass, my long-time collaborator, was reluctant to join me, but eventually he agreed. A hundred years ago, logic was almost synonymous with foundational studies. I tried to rekindle that tradition in [5]. The goal of the following dialog is to provoke young logicians with a taste for foundations to notice the foundational problems raised by the ongoing AI revolution. I think the most beautiful thing about deep learning is that it actually works. Q: I just learned that Daniel Kahneman, Nobel laureate in economics and the author of "Thinking, fast and slow" [7], passed away on March 27, 2024. I heard a lot about this book but have never read it.

We are happy to share that as of today, Epic Games has awarded more than $13 million dollars of financial support to more than 200 recipients as part of the company's $100 million Epic MegaGrants program. The initial series of funds disbursed marks a record for Epic's grant commitments, officially surpassing the four-year distributed total of the initiative's predecessor--Unreal Dev Grants--in eight months. "Through Epic MegaGrants we're supporting developers and tool creators at an unprecedented level, empowering talented teams and individuals to more easily realize their artistic and technical ambitions," said Marc Petit, General Manager, Unreal Engine, Epic Games. Epic MegaGrants typically range from $5,000 to $500,000 and cover a variety of endeavors to further ignite creativity and technological advancement within the 3D graphics community. Earlier this year Epic announced $1.2 million dollars in financial support over a three-year period to the free and open source 3D creation suite Blender, marking the largest singular grant in the initiative's history.

In order to more accurately situate and fit the neutrosophic logic into the framework of nonstandard analysis, we present the neutrosophic inequalities, neutrosophic equality, neutrosophic infimum and supremum, neutrosophic standard intervals, including the cases when the neutrosophic logic standard and nonstandard components T, I, F get values outside of the classical real unit interval [0, 1], and a brief evolution of neutrosophic operators. The paper intends to answer Imamura criticism that we found benefic in better understanding the nonstandard neutrosophic logic, although the nonstandard neutrosophic logic was never used in practical applications.

Systematic, or market, risk is one of the most studied risk models not only in financial engineering, but also in actuarial sciences, in business and corporate management, and in several other domains. It is associated to the beta (β) coefficient, which is familiar in the investment industry since Sharpe's capital asset pricing model (CAPM) [30]. The pitfalls and shortcomings of β have been detailed by a number of excellent authors.