Xi Sigma Pi, Gamma Chapter

Xi Sigma Pi is a national honor society for students of forestry. Once a person has been initiated into the fraternity, they are a lifelong member of the national organization. The Iowa State University chapter of Xi Sigma Pi is Alpha Gamma, and includes faculty, staff, graduate students, and undergraduates within forestry and other departments

CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translation

Recent code translation techniques exploit neural machine translation models to translate source code from one programming language to another to satisfy production compatibility or to improve efficiency of codebase maintenance. Most existing code translation datasets only focus on a single pair of popular programming languages. To advance research on code translation and meet diverse requirements of real-world applications, we construct CodeTransOcean, a large-scale comprehensive benchmark that supports the largest variety of programming languages for code translation. CodeTransOcean consists of three novel multilingual datasets, namely, MultilingualTrans supporting translations between multiple popular programming languages, NicheTrans for translating between niche programming languages and popular ones, and LLMTrans for evaluating executability of translated code by large language models (LLMs). CodeTransOcean also includes a novel cross-framework dataset, DLTrans, for translating deep learning code across different frameworks. We develop multilingual modeling approaches for code translation and demonstrate their great potential in improving the translation quality of both low-resource and high-resource language pairs and boosting the training efficiency. We also propose a novel evaluation metric Debugging Success Rate@K for program-level code translation. Last but not least, we evaluate LLM ChatGPT on our datasets and investigate its potential for fuzzy execution predictions. We build baselines for CodeTransOcean and analyze challenges of code translation for guiding future research. The CodeTransOcean datasets and code are publicly available at https://github.com/WeixiangYAN/CodeTransOcean.Comment: Accepted by Findings of EMNLP 202

Model development for the estimation of urban air temperature based on surface temperature and NDVI - a case study in Szeged

Predictive models for urban air temperature (Tair) were developed by using urban land surface temperature (LST) retrieved from Landsat-8 and MODIS data, NDVI retrieved from Landsat-8 data and Tair measured by 24 climatological stations in Szeged. The investigation focused on summer period (June−September) during 2016−2019 in Szeged. The relationship between Tair and LST was analyzed by calculating Pearson correlation coefficient, root-mean-square error and mean-absolute error using the data of 2017−2019, then unary (LST) and binary (LST and NDVI) linear regression models were developed for estimating Tair. The data in 2016 were used to validate the accuracy of the models. Correlation analysis indicated that there were strong correlations during the nighttime and relatively weaker ones during the daytime. The errors between Tair and LSTMODIS-Night was the smallest, followed by LSTMODIS-Day and LSTLandsat-8 respectively. The validation results showed that all models could perform well, especially during nighttime with an error of less than 1.5℃. However, the addition of NDVI into the linear regression models did not significantly improve the accuracy of the models, and even had a negative effect. Finally, the influencing factors and temporal and spatial variability of the correlation between Tair and LST were analyzed. LSTLandsat-8 had a larger original error with Tair, but the regression model based on Landsat-8 had a stronger ability to reduce errors

Unsupervised Cross-Task Generalization via Retrieval Augmentation

Humans can perform unseen tasks by recalling relevant skills that are acquired previously and then generalizing them to the target tasks, even if there is no supervision at all. In this paper, we aim to improve such cross-task generalization ability of massive multi-task language models such as T0 (Sanh et al., 2021) in an unsupervised setting. We propose a retrieval-augmentation method named ReCross that takes a few unlabelled examples as queries to retrieve a small subset of upstream data and uses them to update the multi-task model for better generalization. Our empirical results show that the proposed ReCross consistently outperforms non-retrieval baselines by a significant margin.Comment: Project website: https://inklab.usc.edu/ReCross

SilverSight: A Multi-Task Chinese Financial Large Language Model Based on Adaptive Semantic Space Learning

Large language models (LLMs) are increasingly being applied across various specialized fields, leveraging their extensive knowledge to empower a multitude of scenarios within these domains. However, each field encompasses a variety of specific tasks that require learning, and the diverse, heterogeneous data across these domains can lead to conflicts during model task transfer. In response to this challenge, our study introduces an Adaptive Semantic Space Learning (ASSL) framework, which utilizes the adaptive reorganization of data distributions within the semantic space to enhance the performance and selection efficacy of multi-expert models. Utilizing this framework, we trained a financial multi-task LLM named "SilverSight". Our research findings demonstrate that our framework can achieve results close to those obtained with full data training using only 10% of the data, while also exhibiting strong generalization capabilities.Comment: 17 pages, 17 figure

In Situ X-ray Absorption Spectroscopy Studies of Kinetic Interaction between Platinum(II) Ions and UiO-66 Series Metal–Organic Frameworks

The interaction of guest Pt(II) ions with UiO-66–X (X = NH2, H, NO2, OMe, F) series metal–organic frameworks (MOFs) in aqueous solution was investigated using in situ X-ray absorption spectroscopy. All of these MOFs were found to be able to coordinate with Pt(II) ions. The Pt(II) ions in UiO-66–X MOFs generally coordinate with 1.6–2.4 Cl and 1.4–2.4 N or O atoms. We also studied the time evolution of the coordination structure and found that Pt(II) maintained a coordination number of 4 throughout the whole process. Furthermore, the kinetic parameters of the interaction of Pt(II) ions with UiO-66–X series MOFs (X = NH2, H, NO2, OMe, F) were determined by combinational linear fitting of extended X-ray absorption fine structure (EXAFS) spectra of the samples. The Pt(II) adsorption rate constants were found to be 0.063 h–1 for UiO-66–NH2 and 0.011–0.017 h–1 for other UiO-66–X (X = H, NO2, OMe, F) MOFs, which means that Pt(II) adsorption in UiO-66–NH2 is 4–6 times faster than that in other UiO-66 series MOFs. FTIR studies suggested that the carboxyl groups could be the major host ligands binding with Pt(II) ions in UiO-66 series MOFs, except for UiO-66–NH2, in which amino groups coordinate with Pt(II) ions

An inorganic capping strategy for the seeded growth of versatile bimetallic nanostructures

Metal nanostructures have attracted great attention in various fields due to their tunable properties through precisely tailored sizes, compositions and structures. Using mesoporous silica (mSiO2) as the inorganic capping agent and encapsulated Pt nanoparticles as the seeds, we developed a robust seeded growth method to prepare uniform bimetallic nanoparticles encapsulated in mesoporous silica shells (PtM@mSiO2, M = Pd, Rh, Ni and Cu). Unexpectedly, we found that the inorganic silica shell is able to accommodate an eight-fold volume increase in the metallic core by reducing its thickness. The bimetallic nanoparticles encapsulated in mesoporous silica shells showed enhanced catalytic properties and thermal stabilities compared with those prepared with organic capping agents. This inorganic capping strategy could find a broad application in the synthesis of versatile bimetallic nanostructures with exceptional structural control and enhanced catalytic properties

Research progress of plant exosome-like nanoparticles on characteristics, components and functions

Exosome-like nanoparticles （ELNs） are nano-sized extracellular vesicles enclosed by lipid bilayer membrane and secreted by cells; And carrying a variety of cargo, including lipids, proteins, miRNAs and secondary metabolites. Plant ELNs have received widespread attention due to their unique structure and excellent physiological activity. This review summarizes the isolation methods, characteristics, components and functions of plant ELNs, focusing on the application of multi-omics technologies in plant ELNs. In addition, some suggestions for further study of plant ELNs were put forward