Results 231 to 240 of about 5,207,763 (284)

Survey on Visualization of Information Diffusion over Networks

Computer Graphics Forum, EarlyView.
Abstract Information Diffusion (ID) describes how a value (e.g., a pathogen, a rumor, a packet) spreads through an underlying “medium” network of elements (e.g., a social or computer network). Understanding the information diffusion process is essential to predicting trends, controlling misinformation, and enhancing decision‐making as well as ...
T. Baumgartl, M. Sondag, V. Filipov, M. Tuscher, S. Rajendran, S. Miksch, D. Archambault, A. Arleo, T. von Landesberger   +8 more
wiley   +1 more source

From Regression to Reasoning: Predicting M&A Announcement Returns With Large Language Models

European Financial Management, EarlyView.
ABSTRACT This study investigates whether large language models (LLMs) can predict short‐term market reactions to M&A announcements. We prompt OpenAI's latest reasoning models (o3, GPT‐5, and GPT‐5.1) to forecast whether the combined market value of acquirer and target will increase or decrease, drawing on deal‐, firm‐, and macroeconomic data for large ...
Maximilian Schreiter, Aldrik Struve, Maximilian Uhle   +2 more
wiley   +1 more source

Sensory contribution of emollient cascades in skin creams evaluated by CATA analysis

International Journal of Cosmetic Science, EarlyView.
This work includes emulsion composition and sensory analysis methodology, emollient cascade theory and study results. This study demonstrates how deliberate structuring of emollient cascades can be used as a formulation strategy to modulate both perceived skin feel and product appearance within emulsified systems.
Anna Gerken, Ellen Libby, Paolo Vigilante, Raihaanah Safee, Mihaly Szoboszlai, Gabriella Baki   +5 more
wiley   +1 more source

Regularization Approaches for High‐Dimensional Cox Models: A Case Study in Triple‐Negative Breast Cancer

International Statistical Review, EarlyView.
Summary In high‐dimensional survival analysis, effective variable selection is crucial both for model interpretation and predictive performance. This paper investigates Cox regression with lasso and adaptive lasso penalties in genomic datasets where covariates far outnumber observations.
Pilar González‐Barquero, Rosa E. Lillo, Álvaro Méndez‐Civieta   +2 more
wiley   +1 more source

The application of machine learning models to optimal TSP tour length estimation

International Transactions in Operational Research, EarlyView.
Abstract The traveling salesman problem (TSP) is a well‐known NP‐hard problem in combinatorial optimization, with numerous applications in logistics and elsewhere. This paper introduces a machine learning‐based approach to estimate the optimal tour length of the TSP, using linear regression, random forests (RF), and neural networks, including ...
Shuhan Kou, Bruce Golden, Luca Bertazzi
wiley   +1 more source

The Evolution of Quantitative Equity Investing: Half a Century of Alpha Research and Institutional Adaptation

Journal of Economic Surveys, EarlyView.
ABSTRACT This survey interprets the evolution of quantitative equity investing as a history of changing constraints rather than a succession of increasingly complicated forecasting models. Portfolio theory made diversification calculable; characteristic‐based investing converted value, momentum, profitability, and low‐risk effects into scalable ...
Xuan Feng, Julio P. Cardozo, Dehua Xia
wiley   +1 more source

Data-Driven Fusion Algorithms for Temperature-Drift Compensation of MEMS Gyroscopes: A Mini Review. [PDF]

Micromachines (Basel)
Lan H, Xu Y.
europepmc   +1 more source

Radar-Based Heart Rate Estimation Method Under Respiratory Harmonic Interference. [PDF]

Sensors (Basel)
Xu D, Li Y, Wu Z, Xie T, Gong P.
europepmc   +1 more source

Performance enhancement of ensemble empirical mode decomposition

Mechanical Systems and Signal Processing, 2010
Ensemble empirical mode decomposition (EEMD) is a newly developed method aimed at eliminating mode mixing present in the original empirical mode decomposition (EMD). To evaluate the performance of this new method, this paper investigates the effect of two parameters pertinent to EEMD: the amplitude of added white noise and the number of ensemble trials.
Ruqiang Yan, Robert Gao
exaly   +3 more sources

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Median ensemble empirical mode decomposition

Signal Processing, 2020
Abstract Ensemble empirical mode decomposition (EEMD) belongs to a class of noise-assisted EMD methods that are aimed at alleviating mode mixing caused by noise and signal intermittency. In this work, we propose a median ensembled version of EEMD (MEEMD) to help reduce the additional mode splitting problem of the original EEMD algorithm.
Xun Lang, Naveed Ur Rehman, Hongye Su
exaly   +2 more sources