Salzmann's legacy in mathematics documented.
arXiv research
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We consider the problem of efficient "on the fly" tuning of existing, or {\it legacy}, Artificial Intelligence (AI) systems. The legacy AI systems are allowed to be of arbitrary class, albeit the data they are using for computing interim or final decision responses should posses an underlying structure of a high-dimens…
The Midwest Geometry Conference 2007 was devoted to the substantial mathematical legacy of Thomas P. Branson who passed away unexpectedly the previous year. This contribution to the Proceedings briefly introduces this legacy. We also take the opportunity of recording his bibliography. Thomas Branson was on the Editoria…
Life and the mathematical legacy of the great mathematician A.V. Pogorelov.
One major challenge for the legacy measurements at the LHC is that the likelihood function is not tractable when the collected data is high-dimensional and the detector response has to be modeled. We review how different analysis strategies solve this issue, including the traditional histogram approach used in most par…
This note summarizes in an informal way some geometric properties of Anosov representations into the symplectic group, which were presented in a talk at the conference What is Next. The mathematical legacy of Bill Thurston, held in June 2014 in Cornell.
Survey of recent Kleinian representation convergence results.
This paper maps the insurability of AI risks across various insurance products.
This work proposes external correctors for quick AI error corrections without system modification.
We briefly review results on nonlinear kinetic equation of Boltzmann type which describe the evolution of wealth in a simple agents market. The mathematical structure of the underlying kinetic equations allows to use well-known techniques of wide use in kinetic theory of rarefied gases to obtain information on the proc…
Article explores Thurston's circle packing theorem in 3-manifold geometry.
We present powerful new analysis techniques to constrain effective field theories at the LHC. By leveraging the structure of particle physics processes, we extract extra information from Monte-Carlo simulations, which can be used to train neural network models that estimate the likelihood ratio. These methods scale wel…
In this paper, we introduce the first method that (1) can complete kernel matrices with completely missing rows and columns as opposed to individual missing kernel values, (2) does not require any of the kernels to be complete a priori, and (3) can tackle non-linear kernels. These aspects are necessary in practical app…
New approach optimizes sales process for B2B businesses.
Scout-Nd optimizes parameters of stochastic simulators efficiently.
In this paper, we study a stochastic optimal control problem with stochastic volatility. We prove the sufficient and necessary maximum principle for the proposed problem. Then we apply the results to solve an investment, consumption and life insurance problem with stochastic volatility, that is, we consider a wage earn…
Mandelbrot unified diverse fields with scaling concept.
Audited Conformal Prediction improves conditional coverage in pretrained models under distribution shift.
In his pioneering work from 1969, Jerry Levine introduced a complete set of invariants of algebraic concordance of knots. The evaluation of these invariants requires a factorization of the Alexander polynomial of the knot, and is therefore in practice often hard to realize. We thus propose the study of an alternative s…
Scaling relations, such as the IPAT equation and the Kaya identity, are useful for quickly gauging the scale of economic, technological, and demographic changes required to reduce environmental impacts and pressures; in the case of the Kaya identity, the environmental pressure is greenhouse gas emissions. However, when…
The problem of replicating the flexibility of human common-sense reasoning has captured the imagination of computer scientists since the early days of Alan Turing's foundational work on computation and the philosophy of artificial intelligence. In the intervening years, the idea of cognition as computation has emerged …
Cryptofinance generates yield through innovative decentralized finance.
Electronic power inverters are capable of quickly delivering reactive power to maintain customer voltages within operating tolerances and to reduce system losses in distribution grids. This paper proposes a systematic and data-driven approach to determine reactive power inverter output as a function of local measuremen…
The paper extends gradient flow and relaxation studies to non-flat Riemannian manifolds.
aweSOM accelerates SOM clustering for large datasets.
We develop, discuss, and compare several inference techniques to constrain theory parameters in collider experiments. By harnessing the latent-space structure of particle physics processes, we extract extra information from the simulator. This augmented data can be used to train neural networks that precisely estimate …
Deep learning predicts crop prices with improved accuracy.
Artificial Intelligence (AI) systems sometimes make errors and will make errors in the future, from time to time. These errors are usually unexpected, and can lead to dramatic consequences. Intensive development of AI and its practical applications makes the problem of errors more important. Total re-engineering of the…
Ethereum's Pectra upgrade introduces 0x02 compounding validators, offering higher stake and potential APR uplifts.
GenAI improves actuarial practices through case studies.
Paper uses GANs to simulate consumer transactions with SKU constraints.
Solves challenges of drone communication in cellular networks.
Jan Tinbergen, the first recipient of the Nobel Memorial Prize in Economics in 1969, obtained his PhD in physics at the University of Leiden under the supervision of Paul Ehrenfest in 1929. Among many achievements as an economist after his training as a physicist, Tinbergen proposed the so-called Gravity Model of inter…
ReQuestNet simplifies 5G channel estimation with a unified model.
Polylab is a MATLAB toolbox for multivariate polynomial modeling.
We take a closer look at the life and legacy of Micheal Milken. We discuss why Michael Milken, also know as the Junk Bond King, was not just any other King or run-of-the-mill Junk Dealer, but "The Junk Dealer". We find parallels between the three parts to any magic act and what Micheal Milken did, showing that his acco…
Rubin LSST DESC uses AI/ML for dark energy research.
A quantum framework optimizes collateral allocation for derivatives.
Medical ultrasound technology is widely used in routine clinical applications such as disease diagnosis and treatment as well as other applications like real-time monitoring of human tongue shapes and motions as visual feedback in second language training. Due to the low-contrast characteristic and noisy nature of ultr…
The work concerns the problem of reducing a pre-trained deep neuronal network to a smaller network, with just few layers, whilst retaining the network's functionality on a given task The proposed approach is motivated by the observation that the aim to deliver the highest accuracy possible in the broadest range of oper…
PICZL improves photometric redshifts for AGN in all-sky surveys.