A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Automated dialogue quality evaluation using user satisfaction estimates across multiple domains.
problem Lack of automated and domain-independent dialogue quality evaluation metrics.
method Created a new Response Quality annotation scheme, introduced five domain-independent feature sets, and experimented with six machine learning models.
result Gradient Boosting Regression model achieved best prediction performance, with a 16% relative improvement in binary satisfaction class prediction accuracy.
New metric estimates user satisfaction for dialogue quality evaluation.
problem Sparse and intrusive explicit user feedback for dialogue quality evaluation.
method Created a new Response Quality annotation scheme and developed a turn-level User Satisfaction metric using five domain-independent feature sets and six machine learning models.
result Gradient boosting regression achieved best correlation of ~0.79 between predicted and annotated user satisfaction labels.
In this paper we consider an interval portfolio selection problem with uncertain returns and introduce an inclusive concept of satisfaction index for interval inequality relation. Based on the satisfaction index, we propose an approach to reduce the interval programming problem with uncertain objective and constraints …
Enhanced neural network framework improves constraint satisfaction with topological conditioning.
problem Maintaining semantic coherence while satisfying physical and logical constraints in neuro-symbolic reasoning.
method Integrates topological conditioning with gradient stabilization mechanisms using Forman-Ricci curvature, Deep Delta Learning, and Covariance Matrix Adaptation Evolution Strategy.
result Achieves mean energy reduction to 1.15 compared to baseline values of 11.68, with 95 percent success rate.
We extend the classical risk minimization model with scalar risk measures to the general case of set-valued risk measures. The problem we obtain is a set-valued optimization model and we propose a goal programming-based approach with satisfaction function to obtain a solution which represents the best compromise betwee…
Despite the growing importance of multilingual aspect of web search, no appropriate offline metrics to evaluate its quality are proposed so far. At the same time, personal language preferences can be regarded as intents of a query. This approach translates the multilingual search problem into a particular task of searc…
Combines deep learning and logic constraints for visual generation.
problem Generating images with specific properties using deep learning and logic constraints.
method Formulates visual generation as a constrained satisfaction problem, using deep architectures to model variables and t-norm theory for logic variables.
result Promising results in generating handwritten characters and face transformations.
This paper presents a new approach for training artificial neural networks using techniques for solving the constraint satisfaction problem (CSP). The quotient gradient system (QGS) is a trajectory-based method for solving the CSP. This study converts the training set of a neural network into a CSP and uses the QGS to …
We determine how an individual can use life insurance to meet a bequest goal. We assume that the individual's consumption is met by an income, such as a pension, life annuity, or Social Security. Then, we consider the wealth that the individual wants to devote towards heirs (separate from any wealth related to the afor…
The study examines how different interpolation methods affect the decomposition of life insurance surplus.
problem The impact of different interpolation methods on the decomposition of life insurance surplus.
method The study uses the IASU decomposition method to analyze the effects of different interpolation methods (Lee-Carter and linear) on the surplus decomposition.
result Lee-Carter and linear interpolation yield almost identical decompositions, while constant approximations result in different decompositions.
Dynamical systems with large state-spaces are often expensive to thoroughly explore experimentally. Coarse-graining methods aim to define simpler systems which are more amenable to analysis and exploration; most current methods, however, focus on a priori state aggregation based on similarities in transition rates, whi…