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Probability of the share price going beyond the established corridor

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Probability of the share price going beyond the established ...

International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Sergei L. Semakov Faculty of Control and Applied Mathematics Moscow Institute of Physics and Technology Department of Mathematics Moscow Automobile and Road Construction Institute Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences the Russian Federation

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

A technology is proposed for estimating the probability that the share price will leave the established corridor by a given point in time. The exact expression is obtained for the required probability under the assumption that the behavior of the share price is described by the well-known model of Samuelson, according to which the relative change in price is the sum of the non-random trend and the Wiener process.

关键词： Share prices Market research Information technology

Identification of PK-PD Insulin Models using Experimental GIR Data

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arXiv 2024年

作者： Freil, Kirstine Sylvest Fritzen, Liv Olivia Boiroux, Dimitri Aradottir, Tinna B. Jorgensen, John Bagterp Department of Applied Mathematics and Computer Science Technical University of Denmark LyngbyDK-2800 Kgs. Denmark Novo Nordisk A/S SoborgDK-2860 Denmark

We present a method to estimate parameters in pharmacokinetic (PK) and pharmacodynamic (PD) models for glucose insulin dynamics in humans. The method combines 1) experimental glucose infusion rate (GIR) data from glucose clamp studies and 2) a PK-PD model to estimate parameters such that the model fits the data. Assuming that the glucose clamp is perfect, we do not need to know the details of the controller in the clamp, and the GIR can be computed directly from the PK-PD model. To illustrate the procedure, we use the glucoregulatory model developed by Hovorka and modify it to have a smooth non-negative endogeneous glucose production (EGP) term. We estimate PK-PD parameters for rapid-acting insulin analogs (Fiasp and NovoRapid). We use these PK-PD parameters to illustrate GIR for insulin analogs with 30% and 50% faster absorption time than currently available rapid-acting insulin analogs. We discuss the role of system identification using GIR data from glucose clamp studies and how such identified models can be used in automated insulin dosing (AID) systems with ultra rapid-acting insulin. Copyright © 2024, The Authors. All rights reserved.

关键词： Glucose

Clarifying Assumptions about Artificial Intelligence before Revolutionising Patent Law

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GRUR International 2022年第4期71卷 295-321页

作者： Kim, Daria Alber, Maximilian Kwok, Man Wai Mitrovic, Jelena Ramirez-Atencia, Cristian Perez, Jesus Alberto Rodriguez Zille, Heiner Max Planck Institute for Innovation and Competition Munich Germany Charite Institute of Pathology Berlin Germany Chinese University of Hong Kong Department of Physics (Formerly) Hong Kong BMBF Program for Women in AI Faculty of Computer Science and Mathematics Chair of Data Science University of Passau Germany Department of Computer Systems Universidad Politecnica de Madrid Spain School of Computing Science College of Science and Engineering University of Glasgow United Kingdom Faculty of Computer Science Otto von Guericke University Magdeburg Germany

This paper examines several widespread assumptions about artificial intelligence, particularly machine learning, that are often taken as factual premises in discussions on the future of patent law in the wake of 'artificial ingenuity'. The objective is to draw a more realistic and nuanced picture of the human-computer interaction in solving technical problems than where 'intelligent' systems autonomously yield inventions. A detailed technical perspective is presented for each assumption, followed by a discussion of pertinent uncertainties for patent law. Overall, it is argued that implications of machine learning for the patent system in its core tenets appear far less revolutionary than is often posited. © 2022 Published by OUP and CH Beck on behalf of GRUR e.V.

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Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?

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Self-supervised Benchmark Lottery on ImageNet: Do Marginal I...

International Joint Conference on Neural Networks (IJCNN)

作者： Utku Ozbulak Esla Timothy Anzaku Solha Kang Wesley De Neve Joris Vankerschaver Center for Biosystems and Biotech Data Science Ghent University Global Campus Republic of Korea Department of Electronics and Information Systems Ghent University Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Machine learning (ML) research strongly relies on benchmarks in order to determine the relative effectiveness of newly proposed models. Recently, a number of prominent research effort argued that a number of models that improve the state-of-the-art by a small margin tend to do so by winning what they call a "benchmark lottery". An important benchmark in the field of machine learning and computer vision is the ImageNet where newly proposed models are often showcased based on their performance on this dataset. Given the large number of self-supervised learning (SSL) frameworks that has been proposed in the past couple of years each coming with marginal improvements on the ImageNet dataset, in this work, we evaluate whether those marginal improvements on ImageNet translate to improvements on similar datasets or not. To do so, we investigate twelve popular SSL frameworks on five ImageNet variants and discover that models that seem to perform well on ImageNet may experience significant performance declines on similar datasets. Specifically, state-of-the-art frameworks such as DINO and Swav, which are praised for their performance, exhibit substantial drops in performance while MoCo and Barlow Twins displays comparatively good results. As a result, we argue that otherwise good and desirable properties of models remain hidden when benchmarking is only performed on the ImageNet validation set, making us call for more adequate benchmarking. To avoid the "benchmark lottery" on ImageNet and to ensure a fair benchmarking process, we investigate the usage of a unified metric that takes into account the performance of models on other ImageNet variant datasets.

关键词： Measurement computer vision Computational modeling Neural networks Machine learning Self-supervised learning Benchmark testing

Optimal Control of Interactions between Invasive Alien and Native Species in a Certain Time Period with the R-Pinn Approach

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SSRN

SSRN 2024年

作者： Adi, Yudi Ari Pratama, Danang A. Bakar, Maharani A. Surono, Sugiyarto Suparman Budiantoro, Agung Department of Mathematics Faculty of Applied Science and Technology Universitas Ahmad Dahlan Yogyakarta Indonesia Faculty of Computer Science and Mathematics Universiti Malaysia Terengganu Terengganu Kuala Nerus21030 Malaysia Faculty of Teacher Training and Education Universitas Ahmad Dahlan Yogyakarta Indonesia Department of Biology Faculty of Applied Science and Technology Universitas Ahmad Dahlan Yogyakarta Indonesia Department of Pharmacology and Clinical Pharmacy

Invasive introduced species are species that can spread to other areas and disrupt local ecosystems. Controlling the interactions between invasive species must be done in order to preserve the diversity in the ecosystem. Numerical simulations using mathematical models of reaction-diffusion systems were conducted to understand and predict how invasive species can affect river ecosystems and how optimal control can be achieved. A new approach method, r-PINN, which is a modification of the Physic-Informed Neural Network (PINN) with a proven reiteration strategy, can increase the speed of the training process. In this simulation, full control was applied in a certain time interval. When complete control was applied, the number of native species increased significantly and the number of invasive species decreased. However, the opposite was true when the control was not applied. The results of the r-PINN method used can also significantly reduce the computational time required compared to the standard PINN method. This shows that the reiteration strategy is effective in reducing computation time without losing the advantage of the loss function value. © 2024, The Authors. All rights reserved.

关键词： Abiotic

Consensus based optimization with memory effects: random selection and applications

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arXiv 2023年

作者： Borghi, Giacomo Grassi, Sara Pareschi, Lorenzo RWTH Aachen University Institute for Geometry and Applied Mathematics Aachen Germany University of Ferrara Department of Mathematics and Computer Science Center for Modelling Computing and Statistics Ferrara Italy

In this work we extend the class of Consensus-Based Optimization (CBO) metaheuristic methods by considering memory effects and a random selection strategy. The proposed algorithm iteratively updates a population of particles according to a consensus dynamics inspired by social interactions among individuals. The consensus point is computed taking into account the past positions of all particles. While sharing features with the popular Particle Swarm Optimization (PSO) method, the exploratory behavior is fundamentally different and allows better control over the convergence of the particle system. We discuss some implementation aspects which lead to an increased efficiency while preserving the success rate in the optimization process. In particular, we show how employing a random selection strategy to discard particles during the computation improves the overall performance. Several benchmark problems and applications to image segmentation and Neural Networks training are used to validate and test the proposed method. A theoretical analysis allows to recover convergence guarantees under mild assumptions on the objective function. This is done by first approximating the particles evolution with a continuous-in-time dynamics, and then by taking the mean-field limit of such dynamics. Convergence to a global minimizer is finally proved at the mean-field level. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

Rigorous justification of the hydrostatic approximation limit of viscous compressible flows

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arXiv 2023年

作者： Liu, Xin Titi, Edriss S. Department of Mathematics Texas A&M University College StationTX77843 United States Department of Applied Mathematics and Theoretical Physics University of Cambridge CambridgeCB3 0WA United Kingdom Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot76100 Israel

This paper considers the asymptotic limit of small aspect ratio between vertical and horizontal spatial scales for viscous isothermal compressible flows. In particular, it is observed that fast vertical acoustic waves arise and induce an averaging mechanism of the density in the vertical variable, which at the limit leads to the hydrostatic approximation of compressible flows, i.e., the compressible primitive equations of atmospheric dynamics. We justify the hydrostatic approximation for general as well as "well-prepared" initial data. The initial data is called well-prepared when it is close to the hydrostatic balance in a strong topology. Moreover, the convergence rate is calculated in the well-prepared initial data case in terms of the aspect ratio, as the latter goes to *** Codes 35B25, 35B40, 76N10, 76N30 © 2023, CC BY-NC-ND.

关键词： Compressible flow

Towards Fair and Robust Face Parsing for Generative AI: A Multi-Objective Approach

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arXiv 2025年

作者： Abraham, Sophia J. Hauenstein, Jonathan D. Scheirer, Walter J. Department of Computer Science and Engineering University of Notre Dame Notre Dame IN46556 United States Department of Applied and Computational Mathematics and Statistics University of Notre Dame Notre Dame IN46556 United States

Face parsing is a fundamental task in computer vision, enabling applications such as identity verification, facial editing, and controllable image synthesis. However, existing face parsing models often lack fairness and robustness, leading to biased segmentation across demographic groups and errors under occlusions, noise, and domain shifts. These limitations affect downstream face synthesis, where segmentation biases can degrade generative model outputs. We propose a multi-objective learning framework that optimizes accuracy, fairness, and robustness in face parsing. Our approach introduces a homotopy-based loss function that dynamically adjusts the importance of these objectives during training. To evaluate its impact, we compare multi-objective and single-objective U-Net models in a GAN-based face synthesis pipeline (Pix2PixHD). Our results show that fairness-aware and robust segmentation improves photorealism and consistency in face generation. Additionally, we conduct preliminary experiments using ControlNet, a structured conditioning model for diffusion-based synthesis, to explore how segmentation quality influences guided image generation. Our findings demonstrate that multi-objective face parsing improves demographic consistency and robustness, leading to higher-quality GAN-based synthesis.1 © 2025, CC BY-NC-ND.

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Variational Shapley Network: A Probabilistic Approach to Self-Explaining Shapley values with Uncertainty Quantification

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arXiv 2024年

作者： Ketenci, Mert Urteaga, Iñigo Rodriguez, Victor Alfonso Elhadad, Noémie Perotte, Adler Department of Computer Science Columbia University New York United States Basque Center for Applied Mathematics Basque Foundation for Science Basque Spain Department of Biomedical Informatics Columbia University New York United States

Shapley values have emerged as a foundational tool in machine learning (ML) for elucidating model decision-making processes. Despite their widespread adoption and unique ability to satisfy essential explainability axioms, computational challenges persist in their estimation when (i) evaluating a model over all possible subset of input feature combinations, (ii) estimating model marginals, and (iii) addressing variability in explanations. We introduce a novel, self-explaining method that simplifies the computation of Shapley values significantly, requiring only a single forward pass. Recognizing the deterministic treatment of Shapley values as a limitation, we explore incorporating a probabilistic framework to capture the inherent uncertainty in explanations. Unlike alternatives, our technique does not rely directly on the observed data space to estimate marginals;instead, it uses adaptable baseline values derived from a latent, feature-specific embedding space, generated by a novel masked neural network architecture. Evaluations on simulated and real datasets underscore our technique’s robust predictive and explanatory performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning