Chirality Recognition/transfer/amplification: Rotational Spectroscopic And Chiroptical Spectroscopic Studies

A molecule is chiral if its mirror image cannot be superimposed onto itself. Chirality serves an essential function in life. Our research program centres on understanding mechanisms of chirality recognition/transfer/amplification at the molecular level. To achieve this goal, we apply and develop new spectroscopic tools to characterize structural and dynamical properties of chiral molecules and non-covalent interactions among them in the gas phase, solution, cold rare gas matrices and at liquid-liquid interfaces. We emphasize the connection between the gas phase and condense phase results obtained using high resolution spectroscopy and several chiroptical spectroscopies, respectively. For example, we examined the conformational landscape and chirality recognition in the binary adducts of tetrahydro-2-furoic acid using chirped pulse Fourier transform microwave spectroscopy. The unusual conformational distributions and chirality controlled conformational preferences will be discussed. Using vibrational circular dichroism, we followed the first few steps of self-aggregation of this acid in cold rare gas matrices and compared the results to those obtained in the gas phase and in solution previously. In the second example, I will discuss the unusually strong solvent ‘Raman optical activity’ features observed in a solution of a chiral nickel complex even though the solvent is achiral and the search for its origin. The interplay between experiment and theory is essential for all the work described

Improving Relevance Feedback with Unbiased Estimate of User\u27s Information Need

Relevance feedback is an effective and widely accepted method in information retrieval to improve performance. Relevance feedback generally uses an adaptive learning method to estimate the userís information need. In this research, we propose an alternative two-stage sampling method to obtain an unbiased estimate of the userís information need. Our estimate shows not only improved retrieval performance, but also better prevention of query drift, which troubles traditional relevance feedback. We also give theoretical justification and empirical support for this method

Using Computational Tools To Enhance Learning In An Undergraduate Molecular Spectroscopy Course

In molecular spectroscopy, our models of the molecular world are built on rigorous spectroscopic experimentation and rich interplay between theory and experiment. To instill such appreciation to undergraduate students, who have little experience in either spectroscopic experiments and theory, is challenging. We have developed a new computational laboratory component to complement the material covered in a senior undergraduate course on molecular spectroscopy. Specifically, we focus on illustrating molecular spectroscopic concepts (some of which can be quite abstract and complicated) taught in class with electronic structure calculations. This talk will describe our implementation and the learning outcome. Two particular examples will be discussed. One is related to the misconception that electron density is the main factor responsible for NMR chemical shifts and how we utilize both experimental data and calculations to help students overcome this common misconception. The other deals with differences in geometries, for example, those obtained using rotational constants directly, isotopic substitution procedures, and electronic structure calculations. This talk will also discuss how the above activities worked in practice and the improvements we plan to implement next time

Chinese Open Instruction Generalist: A Preliminary Release

Instruction tuning is widely recognized as a key technique for building generalist language models, which has attracted the attention of researchers and the public with the release of InstructGPT~\citep{ouyang2022training} and ChatGPT\footnote{\url{https://chat.openai.com/}}. Despite impressive progress in English-oriented large-scale language models (LLMs), it is still under-explored whether English-based foundation LLMs can perform similarly on multilingual tasks compared to English tasks with well-designed instruction tuning and how we can construct the corpora needed for the tuning. To remedy this gap, we propose the project as an attempt to create a Chinese instruction dataset by various methods adapted to the intrinsic characteristics of 4 sub-tasks. We collect around 200k Chinese instruction tuning samples, which have been manually checked to guarantee high quality. We also summarize the existing English and Chinese instruction corpora and briefly describe some potential applications of the newly constructed Chinese instruction corpora. The resulting \textbf{C}hinese \textbf{O}pen \textbf{I}nstruction \textbf{G}eneralist (\textbf{COIG}) corpora are available in Huggingface\footnote{\url{https://huggingface.co/datasets/BAAI/COIG}} and Github\footnote{\url{https://github.com/FlagOpen/FlagInstruct}}, and will be continuously updated

Initial Online Trust Building: A Social Learning Theory Perspective

With the rapid expansion of e-commerce, trust has become a central research topic in online environment for its key role in affecting e-commerce success. Our study focuses on the initial online trust building for brick-and-click companies. Building upon social learning theory, we propose a framework to examine the learning processes and important antecedents to online trust building. To demonstrate the utility of the framework, we apply it to the initial online trust building for brick-and-click firms. Our results suggest that the social learning theory is a viable tool to understand customer’s trust building process. Based on the effective learning processes identified for trust building, firms can allocate their resource accordingly