Results 101 to 110 of about 286,084 (291)

Forecasting Count Data With Varying Dispersion: A Latent‐Variable Approach

Journal of Forecasting, EarlyView.
ABSTRACT Count data, such as product sales and disease case counts, are common in business forecasting and many areas of science. Although the Poisson distribution is the best known model for such data, its use is severely limited by its assumption that the dispersion is a fixed function of the mean, which rarely holds in real‐world scenarios.
Easton Huch, Candace Berrett, Mason Ferlic, Kimberly F. Sellers   +3 more
wiley   +1 more source

A multimodal approach for depression detection using semi-automatic data annotation and deterministic machine learning methods

Научно-технический вестник информационных технологий, механики и оптики
A trending task of automatic psycho-emotional human state detection was studied in this work. A scientific interest to researches devoted to the automatic multimodal depression detection can arise out of the widespread of anxiety-depressive disorders and
A. N. Velichko, A. A. Karpov
doaj   +1 more source

Penerapan Non-Deterministic Finite Automata (NFA) dan Decision Making Menggunakan Algoritma Monte Carlo Tree Search (MCTS) Menentukan Perilaku Non-Player Character (NPC) Pada Game The Last Hope

Jurnal CoSciTech (Computer Science and Information Technology), 2023
Perkembangan game juga berkembang pesat di Indonesia, banyak game baru yang tersebar di industri game. Para desainer game berlomba-lomba membuat game-game terbaru karena melihat peluang yang dihadirkan oleh banyaknya pengguna ponsel yang terbiasa bermain game. Pembuatan game ini dilakukan menggunakan software Unity 2D, menggunakan bahasa C# (C ...
openaire   +1 more source

Minimizing finite automata is computationally hard [PDF]

, 2002
It is known that deterministic finite automata (DFAs) can be algorithmically minimized, i.e., a DFA M can be converted to an equivalent DFA M' which has a minimal number of states. The minimization can be done efficiently [6].
Malcher, Andreas
core  

Intraday Functional PCA Forecasting of Cryptocurrency Returns

Journal of Forecasting, EarlyView.
ABSTRACT We study the functional PCA (FPCA) forecasting method in application to functions of intraday returns on Bitcoin. We show that improved interval forecasts of future return functions are obtained when the conditional heteroscedasticity of return functions is taken into account.
Joann Jasiak, Cheng Zhong
wiley   +1 more source

Quadratic Hedging of American Options Under GARCH Models

Journal of Futures Markets, EarlyView.
ABSTRACT American options are widely traded in financial markets, yet there is a scarcity of literature on hedging in incomplete markets. In this paper, we derive optimal hedging ratios and option values using Local Risk Minimization (LRM) and Global Risk Minimization (GRM) hedging strategies through dynamic programming.
Junmei Ma, Chen Wang, Wei Xu
wiley   +1 more source

Functionally complementary bacterial inoculant coordinates arbuscular mycorrhizal fungi to improve Angelica sinensis root yield and quality

iMetaOmics, EarlyView.
Comprehensive understanding of how diverse PGPR strains enhance the rhizosphere microenvironment remains a considerable challenge. Here, we provide experimental evidence that a functionally synergistic composite microbial formulation can markedly enhance growth performance and improve the quality attributes in Angelica sinensis.
Zongyu Zhang, Yun Wang, Jiecai Zhao, Gaofeng Chen, Andéole Niyongabo Turatsinze, Liang Yue, Yuan Tian, Xiaofan Xie, Ailing Ye, Meilan Zhang, Lingling Wu, Zengxiang Guo, Yubao Zhang, Ruoyu Wang   +13 more
wiley   +1 more source

Challenges and Opportunities in Machine Learning for Light‐Emitting Polymers

Macromolecular Rapid Communications, EarlyView.
The performance of light‐emitting polymers emerges from coupled effects of chemical diversity, morphology, and exciton dynamics across multiple length scales. This Perspective reviews recent design strategies and experimental challenges, and discusses how machine learning can unify descriptors, data, and modeling approaches to efficiently navigate ...
Tian Tian, Yinyin Bao
wiley   +1 more source

New Opportunities For the Integration of Artificial Intelligence With Materials Science: From Large Language Models to Embodied Large Models

Materials Genome Engineering Advances, EarlyView.
This review first introduces the diversified applications of large language models in materials discovery. Subsequently, the evolution of autonomous experimentation platforms empowered by large language models is analyzed. Finally, four key future research interests are proposed to develop embodied large models for driving autonomous experimentation ...
Zhen Song, Yishuai Cai, Yadong Yu, Pan Xiang, Minglong Li, Zhe Liu, Mengyan Dai   +6 more
wiley   +1 more source

Potential for use of Al/machine learning for pharmacovigilance: Is there a role for regulators?

British Journal of Clinical Pharmacology, EarlyView.
Christina Gao, Ashley M. Hopkins, Andrew Rowland, Stephen Bacchi   +3 more
wiley   +1 more source