arXiv subjects

David Delgado

Publications and source records attributed to David Delgado.

5 recordsLinked to original sources

arXiv2026

A framework for assessing the capabilities of code generation of constraint domain-specific languages with large language models

Large language models (LLMs) can be used to support software development tasks, e.g., through code completion or code generation. However, their effectiveness drops significantly when considering less popular programming languages such as domain-specific languages (DSLs). In this paper, we propose a generic framework for evaluating the capabilities of LLMs generating DSL code from textual specifications. The generated code is assessed from the perspectives of well-formedness and correctness. This framework is applied to a particular type of DSL, constraint languages, focusing our experiments on OCL and Alloy and comparing their results to those achieved for Python, a popular general-purpose programming language. Experimental results show that, in general, LLMs have better performance for Python than for OCL and Alloy. LLMs with smaller context windows such as open-source LLMs may be unable to generate constraint-related code, as this requires managing both the constraint and the domain model where it is defined. Moreover, some improvements to the code generation process such as code repair (asking an LLM to fix incorrect code) or multiple attempts (generating several candidates for each coding task) can improve the quality of the generated code. Meanwhile, other decisions like the choice of a prompt template have less impact. All these dimensions can be systematically analyzed using our evaluation framework, making it possible to decide the most effective way to set up code generation for a particular type of task.

cs.SE↗

arXiv2024

Search for reactor-produced millicharged particles with Skipper-CCDs at the CONNIE and Atucha-II experiments

Millicharged particles, proposed by various extensions of the standard model, can be created in pairs by high-energy photons within nuclear reactors and can interact electromagnetically with electrons in matter. Recently, the existence of a plasmon peak in the interaction cross-section with silicon in the eV range was highlighted as a promising approach to enhance low-energy sensitivities. The CONNIE and Atucha-II reactor neutrino experiments utilize Skipper-CCD sensors, which enable the detection of interactions in the eV range. We present world-leading limits on the charge of millicharged particles within a mass range spanning six orders of magnitude, derived through a comprehensive analysis and the combination of data from both experiments.

hep-ex↗

arXiv2024

The RoSiD Tool: Empowering Users to Design Multimodal Signals for Human-Robot Collaboration

Robots that cooperate with humans must be effective at communicating with them. However, people have varied preferences for communication based on many contextual factors, such as culture, environment, and past experience. To communicate effectively, robots must take those factors into consideration. In this work, we present the Robot Signal Design (RoSiD) tool to empower people to easily self-specify communicative preferences for collaborative robots. We show through a participatory design study that the RoSiD tool enables users to create signals that align with their communicative preferences, and we illuminate how this tool can be further improved.

cs.RO↗

arXiv2023

Neural lasso: a unifying approach of lasso and neural networks

In recent years, there is a growing interest in combining techniques attributed to the areas of Statistics and Machine Learning in order to obtain the benefits of both approaches. In this article, the statistical technique lasso for variable selection is represented through a neural network. It is observed that, although both the statistical approach and its neural version have the same objective function, they differ due to their optimization. In particular, the neural version is usually optimized in one-step using a single validation set, while the statistical counterpart uses a two-step optimization based on cross-validation. The more elaborated optimization of the statistical method results in more accurate parameter estimation, especially when the training set is small. For this reason, a modification of the standard approach for training neural networks, that mimics the statistical framework, is proposed. During the development of the above modification, a new optimization algorithm for identifying the significant variables emerged. Experimental results, using synthetic and real data sets, show that this new optimization algorithm achieves better performance than any of the three previous optimization approaches.

stat.ML↗

arXiv2020

Marginal likelihood computation for model selection and hypothesis testing: an extensive review

This is an up-to-date introduction to, and overview of, marginal likelihood computation for model selection and hypothesis testing. Computing normalizing constants of probability models (or ratio of constants) is a fundamental issue in many applications in statistics, applied mathematics, signal processing and machine learning. This article provides a comprehensive study of the state-of-the-art of the topic. We highlight limitations, benefits, connections and differences among the different techniques. Problems and possible solutions with the use of improper priors are also described. Some of the most relevant methodologies are compared through theoretical comparisons and numerical experiments.

stat.CO↗