Results 91 to 100 of about 62,688 (226)

Enhancing Demand Forecasting in Retail: A Comprehensive Analysis of Sales Promotional Effects on the Entire Demand Life Cycle

open access: yesJournal of Forecasting, Volume 45, Issue 1, Page 293-315, January 2026.
ABSTRACT Sales promotions pose challenges to retail operations by causing sudden fluctuations in demand, not only during the promotional period but also across the entire sales promotional life cycle. Previous research has predominantly focused on promotional and nonpromotional periods, often overlooking the postpromotional phase, where demand ...
Harsha Chamara Hewage   +2 more
wiley   +1 more source

To Cache or Not to Cache

open access: yesAlgorithms
Unlike conventional CPU caches, non-datapath caches, such as host-side flash caches which are extensively used as storage caches, have distinct requirements.
Steven Lyons, Raju Rangaswami
doaj   +1 more source

U‐KAN for Multi‐Nuclei Segmentation Using an Adaptive Sliding Window Approach

open access: yesInternational Journal of Imaging Systems and Technology, Volume 36, Issue 1, January 2026.
ABSTRACT Accurate segmentation of nuclei in histopathological images is critical for improving diagnostic precision and advancing computational pathology. Deep learning models employed for this task must effectively handle structural variability while offering transparent and interpretable predictions to ensure clinical reliability.
Usman Ali   +3 more
wiley   +1 more source

Fast Differentially Private Matrix Factorization

open access: yes, 2015
Differentially private collaborative filtering is a challenging task, both in terms of accuracy and speed. We present a simple algorithm that is provably differentially private, while offering good performance, using a novel connection of differential ...
Ahn S.   +17 more
core   +1 more source

Automating Algorithm Experiments With ALGator: From Problem Modeling to Reproducible Results

open access: yesSoftware: Practice and Experience, Volume 56, Issue 1, Page 26-41, January 2026.
ABSTRACT Background Theoretical algorithm analysis provides fundamental insights into algorithm complexity but relies on simplified and often outdated computational models. Experimental algorithmics complements this approach by evaluating the empirical performance of algorithm implementations on real data and modern computing platforms.
Tomaž Dobravec
wiley   +1 more source

Implementation and Evaluation of Parallel Computing Approaches for Large‐Domain, Process‐Based Hydrologic Simulations

open access: yesJournal of Advances in Modeling Earth Systems, Volume 18, Issue 1, January 2026.
Abstract Process‐based hydrologic simulations in large domains generally require intensive computing resources. In this study, we implement various parallelization approaches within a process‐based hydrologic solver, SUMMA, including the Message Passing Interface (MPI), Open Multi‐Processing (OMP), and the Actor Model, to enable high‐performance ...
Junwei Guo   +7 more
wiley   +1 more source

DR.SGX: Hardening SGX Enclaves against Cache Attacks with Data Location Randomization

open access: yes, 2019
Recent research has demonstrated that Intel's SGX is vulnerable to software-based side-channel attacks. In a common attack, the adversary monitors CPU caches to infer secret-dependent data accesses patterns. Known defenses have major limitations, as they
Brasser, Ferdinand   +5 more
core   +1 more source

Proximu$: Efficiently Scaling DNN Inference in Multi-core CPUs through Near-Cache Compute [PDF]

open access: green, 2020
Anant Nori   +7 more
openalex   +1 more source

On-Chip Cache Procedure and Device for Efficient CPU and GPU Access Request Arbitration [PDF]

open access: bronze, 2023
M Sampoornam   +37 more
openalex   +1 more source

Dynamic Intercity Ride‐Sharing Optimisation Based on Two‐Stage Information Feedback

open access: yesIET Intelligent Transport Systems, Volume 20, Issue 1, January/December 2026.
The article introduces a novel two‐stage scheduling approach for intercity dynamic ridesharing that separates the process into a coarse scheduling phase (with online and offline vehicle matching) and a fine scheduling phase that refines the solution using a large neighbourhood search algorithm. By integrating deep Q‐learning to dynamically trigger fine
Cheng Wang, Shangyu Gao, Jin Jiang
wiley   +1 more source

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