An Improved Fata Morgana Algorithm for Global Optimization

Arabian Journal for Science and Engineering · 2025-07-05

Optimal acceptance of incompatible kidneys

Journal of the Operational Research Society · 2025-01-10 · 1 citations

Stochastic Derivative Estimation for Discontinuous Sample Performances: A Leibniz Integration Perspective

ArXiv.org · 2025-10-09

We develop a novel stochastic derivative estimation framework for sample performance functions that are discontinuous in the parameter of interest, based on the multidimensional Leibniz integral rule. When discontinuities arise from indicator functions, we embed the indicator functions into the sample space, yielding a continuous performance function over a parameter-dependent domain. Applying the Leibniz integral rule in this case produces a single-run, unbiased derivative estimator. For general discontinuous functions, we apply a change of variables to shift parameter dependence into the sample space and the underlying probability measure. Applying the Leibniz integral rule leads to two terms: a standard likelihood ratio (LR) term from differentiating the underlying probability measure and a surface integral from differentiating the boundary of the domain. Evaluating the surface integral may require simulating multiple sample paths. Our proposed Leibniz integration framework generalizes the generalized LR (GLR) method and provides intuition as to when the surface integral vanishes, thereby enabling single-run, easily implementable estimators. Numerical experiments demonstrate the effectiveness and robustness of our methods.

Glucagon-like peptide-1 receptor agonist use is not associated with reduced postoperative complications in obese patients after total shoulder arthroplasty

Seminars in Arthroplasty JSES · 2025-07-16 · 3 citations

Geographic travel distance does not influence preoperative patient expectations or postoperative clinical outcomes following total shoulder arthroplasty: a comparative analysis at an urban, tertiary care center

Seminars in Arthroplasty JSES · 2025-12-06

Promoting Workplace Comprehension Through Situated Interactive Simulation of Recruitment Mobile Game

Asian Conference on Education official conference proceedings/ACE · 2025-03-13

“Recruitment” is an important key and strategic tool for businesses to gain competitive capital and advantages. Businesses should disclose more accurate information about internal conditions and real job previews in recruitment activities to help job applicants understand the work environment and make the right decision to seek employment. This study used the thinglink digital platform

On Structural Properties of Risk-Averse Optimal Stopping Problems

ArXiv.org · 2025-11-02

We establish structural properties of optimal stopping problems under time-consistent dynamic (coherent) risk measures, focusing on value function monotonicity and the existence of control limit (threshold) optimal policies. While such results are well developed for risk-neutral (expected-value) models, they remain underexplored in risk-averse settings. Coherent risk measures typically lack the tower property and are subadditive rather than additive, complicating structural analysis. We show that value function monotonicity mirrors the risk-neutral case. Moreover, if the risk envelope associated with each coherent risk measure admits a minimal element, the risk-averse optimal stopping problem reduces to an equivalent risk-neutral formulation. We also develop a general procedure for identifying control limit optimal policies and use it to derive practical, verifiable conditions on the risk measures and MDP structure that guarantee their existence. We illustrate the theory and verify these conditions through optimal stopping problems arising in operations, marketing, and finance.

The Impact of ICU Occupancy on Deceased Donor Kidney Offer Acceptance Decisions

SSRN Electronic Journal · 2025-01-01

The Detection Algorithm for Cleaning and Selecting Stored Potatoes Based on an Improved YOLOv8

Learning and analytics in intelligent systems · 2025-01-01

Semantic Integrity Constraints: Declarative Guardrails for AI-Augmented Data Processing Systems

Proceedings of the VLDB Endowment · 2025-07-01 · 2 citations

AI-augmented data processing systems (DPSs) integrate large language models (LLMs) into query pipelines, allowing powerful semantic operations on structured and unstructured data. However, the reliability (a.k.a. trust) of these systems is fundamentally challenged by the potential for LLMs to produce errors, limiting their adoption in critical domains. To help address this reliability bottleneck, we introduce semantic integrity constraints (SICs) —a declarative abstraction for specifying and enforcing correctness conditions over LLM outputs in semantic queries. SICs generalize traditional database integrity constraints to semantic settings, supporting common types of constraints, such as grounding, soundness, and exclusion, with both reactive and proactive enforcement strategies. We argue that SICs provide a foundation for building reliable and auditable AI-augmented data systems. Specifically, we present a system design for integrating SICs into query planning and runtime execution and discuss its realization in AI-augmented DPSs. To guide and evaluate our vision, we outline several design goals—covering criteria around expressiveness, runtime semantics, integration, performance, and enterprise-scale applicability—and discuss how our framework addresses each, along with open research challenges.