arXiv subjects

Wenxin Mao

Publications and source records attributed to Wenxin Mao.

6 recordsLinked to original sources

arXiv2026

Reversible photo-switching optical functionality in two-dimensional mixed-halide hybrid perovskites

Ion migration in halide perovskites is often associated with defects, irreversible processes, and structural instability, making them largely impractical for photo-switching applications. Here, we demonstrate a physical mechanism for reversible, defect-free light-induced halide-ion swapping in two-dimensional mixed-halide perovskites. We find that the halide-ion swapping process arises from strong light-lattice coupling rather than defects. By combining nudged elastic band and photo-force calculations, we show that photo-induced forces perform non-equilibrium work that drives halide ions along the halide-exchange path without reaching the fully swapped configuration. Thus, the cumulative light-induced work can only partially overcome the ground-state activation energy barrier in the presence of light. Analysis of lattice dynamics identifies a few soft phonon modes, two of which are IR-active with oscillator strength $\approx -0.14$ e/(amu)$^{1/2}$, which may be considered the microscopic origin of light-induced halide-ion swapping. This microscopic origin is further supported by band-edge-selective electron-phonon coupling, which amplifies interactions among excited carriers under illumination and with halide-ion motion without inducing a uniform dynamical instability. Using GW (G-Green's function and W-screened Coulomb interaction) calculations, we accurately reproduce the experimentally observed optical absorption spectra in the absence of light, enabling us to describe the light-induced excited state reliably. We demonstrate a clear redshift in the optical spectra in the presence of light, which is due to light-induced bandgap renormalization. Overall, these findings not only establish an intrinsic, defect-free mechanism for photoswitchable optical functionality in 2D mixed-halide perovskites but also demonstrate an intrinsic self-resetting feature in the absence of light.

cond-mat.mtrl-sci↗

arXiv2025

Data Dependency-Aware Code Generation from Enhanced UML Sequence Diagrams

Large language models (LLMs) excel at generating code from natural language (NL) descriptions. However, the plain textual descriptions are inherently ambiguous and often fail to capture complex requirements like intricate system behaviors, conditional logic, and architectural constraints; implicit data dependencies in service-oriented architectures are difficult to infer and handle correctly. To bridge this gap, we propose a novel step-by-step code generation framework named UML2Dep by leveraging unambiguous formal specifications of complex requirements. First, we introduce an enhanced Unified Modeling Language (UML) sequence diagram tailored for service-oriented architectures. This diagram extends traditional visual syntax by integrating decision tables and API specifications, explicitly formalizing structural relationships and business logic flows in service interactions to rigorously eliminate linguistic ambiguity. Second, recognizing the critical role of data flow, we introduce a dedicated data dependency inference (DDI) task. DDI systematically constructs an explicit data dependency graph prior to actual code synthesis. To ensure reliability, we formalize DDI as a constrained mathematical reasoning task through novel prompting strategies, aligning with LLMs' excellent mathematical strengths. Additional static parsing and dependency pruning further reduce context complexity and cognitive load associated with intricate specifications, thereby enhancing reasoning accuracy and efficiency.

cs.AI↗

arXiv2025

Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities

Converting natural language (NL) questions into SQL queries, referred to as Text-to-SQL, has emerged as a pivotal technology for facilitating access to relational databases, especially for users without SQL knowledge. Recent progress in large language models (LLMs) has markedly propelled the field of natural language processing (NLP), opening new avenues to improve text-to-SQL systems. This study presents a systematic review of LLM-based text-to-SQL, focusing on four key aspects: (1) an analysis of the research trends in LLM-based text-to-SQL; (2) an in-depth analysis of existing LLM-based text-to-SQL techniques from diverse perspectives; (3) summarization of existing text-to-SQL datasets and evaluation metrics; and (4) discussion on potential obstacles and avenues for future exploration in this domain. This survey seeks to furnish researchers with an in-depth understanding of LLM-based text-to-SQL, sparking new innovations and advancements in this field.

cs.CL↗

arXiv2024

Learning in the Wild: Towards Leveraging Unlabeled Data for Effectively Tuning Pre-trained Code Models

Pre-trained code models have recently achieved substantial improvements in many code intelligence tasks. These models are first pre-trained on large-scale unlabeled datasets in a task-agnostic manner using self-supervised learning, and then fine-tuned on labeled datasets in downstream tasks. However, the labeled datasets are usually limited in size (i.e., human intensive efforts), which may hinder the performance of pre-trained code models in specific tasks. To mitigate this, one possible solution is to leverage the large-scale unlabeled data in the tuning stage by pseudo-labeling. However, directly employing the pseudo-labeled data can bring a large amount of noise, i.e., incorrect labels, leading to suboptimal performance. How to effectively leverage the noisy pseudo-labeled data is a challenging yet under-explored problem.In this paper, we propose a novel approach named HINT to improve pre-trained code models with large-scale unlabeled datasets by better utilizing the pseudo-labeled data. HINT includes two main modules: HybrId pseudo-labeled data selection and Noise-tolerant Training. In the hybrid pseudo-data selection module, considering the robustness issue, apart from directly measuring the quality of pseudo labels through training loss, we further propose to employ a retrieval-based method to filter low-quality pseudo-labeled data. The noise-tolerant training module aims to further mitigate the influence of errors in pseudo labels by training the model with a noise-tolerant loss function and by regularizing the consistency of model predictions.The experimental results show that HINT can better leverage those unlabeled data in a task-specific way and provide complementary benefits for pre-trained models, e.g., improving the best baseline model by 15.33%, 16.50%, and 8.98% on code summarization, defect detection, and assertion generation, respectively.

cs.SE↗

arXiv2023

JIANG: Chinese Open Foundation Language Model

With the advancements in large language model technology, it has showcased capabilities that come close to those of human beings across various tasks. This achievement has garnered significant interest from companies and scientific research institutions, leading to substantial investments in the research and development of these models. While numerous large models have emerged during this period, the majority of them have been trained primarily on English data. Although they exhibit decent performance in other languages, such as Chinese, their potential remains limited due to factors like vocabulary design and training corpus. Consequently, their ability to fully express their capabilities in Chinese falls short. To address this issue, we introduce the model named JIANG (Chinese pinyin of ginger) specifically designed for the Chinese language. We have gathered a substantial amount of Chinese corpus to train the model and have also optimized its structure. The extensive experimental results demonstrate the excellent performance of our model.

cs.CL↗

arXiv2022

Intensity Modulated Photocurrent Microspectrosopy for Next Generation Photovoltaics

In this report, we describe a large-area Laser Beam Induced Current (LBIC) microscope that has been adapted to perform Intensity Modulated Photocurrent Spectroscopy in an imaging mode combined with automated J-V electrical characterization. Henceforth we refer to the method as either Intensity Modulated Photocurrent Micro-spectroscopy or simply IMPS microscopy. Microscopy based IMPS aims to use the localised frequency response to correlate the optoelectrical response with a particular interface, bulk material, specific transport layer or transport parameter. The system developed here is specifically designed to track the decomposition of organic solar cells including hybrid-inorganic PSCs during long-term exposure to quantities such as light, oxygen, moisture, and heat

physics.ins-det↗