Stereodivergent Construction of Tertiary Fluorides in Vicinal Stereogenic Pairs by Allylic Substitution with Iridium and Copper Catalysts.

Although much effort has been spent on the enantioselective synthesis of tertiary alkyl fluorides, the synthesis of compounds containing such a stereogenic center within an array of stereocenters, particularly two vicinal ones, remains a synthetic challenge, and no method to control the configuration of each stereogenic center independently has been reported. We describe a strategy to achieve such a stereodivergent synthesis of vicinal stereogenic centers, one containing a fluorine atom, by forming the connecting carbon-carbon bond with a catalyst system comprising an iridium complex that controls the configuration at the electrophilic carbon and a copper catalyst that controls the configuration at the nucleophilic fluorine-containing carbon. These reactions occur with alkyl- and aryl-substituted allylic esters and the unstabilized enolates of azaaryl ketones, esters, and amides in high yield, diastereoselectivity, and enantioselectivity (generally >90% yield, >20:1 dr, 97-99% ee). Access to all four stereoisomers of products demonstrates the precise control of the two configurations independently. This methodology extends to the stereodivergent construction of vicinal quaternary and tertiary stereocenters in similarly high yield and selectivity. DFT calculations uncover the origin of stereoselectivity of copper enolate in allylic substitution

Lay intuitions about overall evaluations of experiences

Previous research has identified important determinants of overall evaluations for experiences lived across time. By means of a novel guessing task, I study what decision-makers themselves consider important. As Informants, some participants live and evaluate an experience. As Guessers, others have to infer its overall evaluation by asking Informants questions. I rewarded accurate inferences, and analyzed and classified the questions in four experiments involving auditory, gustatory and viewing experiences. Results show that Guessers thought of overall evaluations as reflecting average momentary impressions. Moreover and alternatively, they tended to consider the personality and attitudes of the experiencing person, experience-specific holistic judgments and behavioral intentions regarding the experience. Thus, according to lay intuitions, overall evaluations are more than a reflection of the experience’s momentary impressions

MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation

The goal of sequential recommendation (SR) is to predict a user's potential interested items based on her/his historical interaction sequences. Most existing sequential recommenders are developed based on ID features, which, despite their widespread use, often underperform with sparse IDs and struggle with the cold-start problem. Besides, inconsistent ID mappings hinder the model's transferability, isolating similar recommendation domains that could have been co-optimized. This paper aims to address these issues by exploring the potential of multi-modal information in learning robust and generalizable sequence representations. We propose MISSRec, a multi-modal pre-training and transfer learning framework for SR. On the user side, we design a Transformer-based encoder-decoder model, where the contextual encoder learns to capture the sequence-level multi-modal synergy while a novel interest-aware decoder is developed to grasp item-modality-interest relations for better sequence representation. On the candidate item side, we adopt a dynamic fusion module to produce user-adaptive item representation, providing more precise matching between users and items. We pre-train the model with contrastive learning objectives and fine-tune it in an efficient manner. Extensive experiments demonstrate the effectiveness and flexibility of MISSRec, promising an practical solution for real-world recommendation scenarios.Comment: Accepted to ACM MM 202

MoleculeQA: A Dataset to Evaluate Factual Accuracy in Molecular Comprehension

Large language models are playing an increasingly significant role in molecular research, yet existing models often generate erroneous information, posing challenges to accurate molecular comprehension. Traditional evaluation metrics for generated content fail to assess a model's accuracy in molecular understanding. To rectify the absence of factual evaluation, we present MoleculeQA, a novel question answering (QA) dataset which possesses 62K QA pairs over 23K molecules. Each QA pair, composed of a manual question, a positive option and three negative options, has consistent semantics with a molecular description from authoritative molecular corpus. MoleculeQA is not only the first benchmark for molecular factual bias evaluation but also the largest QA dataset for molecular research. A comprehensive evaluation on MoleculeQA for existing molecular LLMs exposes their deficiencies in specific areas and pinpoints several particularly crucial factors for molecular understanding.Comment: 19 pages, 8 figure