The efficient discovery of academic literature is critical for research progress, yet many researchers have difficulties in finding literature. This work proposes InstInfo: a novel just-in-time literature recommendation system for presentations. InstInfo transcribes audio in real-time and recommends literature according to the ideas being discussed, thereby helping researchers ground presentations in academic literature while saving them the time of having to manually search. Informal usability studies show that InstInfo is easy to use and that researchers find value in the recommendations. InstInfo can be accessed at https://instinfo.com.

The ability for a student to discover and learn from online material outside of a course's core curriculum is critical for generalized understanding. However, such a process is often limited by various factors, including efficiency, redundancy, feasibility, and a lack of domain knowledge. We propose to optimize this discovery process and facilitate student collaboration in seeking online information relevant to the course by leveraging the Community Digital Library (CDL) as a tool for enabling students and instructors to enrich course content by collaboratively indexing, searching, and discovering course-related webpages. As a collaborative learning tool, the CDL also enables instructors to identify topics in the lectures that should be improved over time. We report promising preliminary results using CDL for a graduate course.

In this demonstration, we present TextData, a novel online system that enables users to both "save what they know" and "find what they don't". TextData was developed based on the Community Digital Library (CDL) system. Although the CDL allowed users to bookmark webpages with plain text and provided search and recommendation, it fell short in key features. To better help users save what they know, TextData offers the addition of markdown to submissions for providing a richer method of note-taking. To better help users find what they don't, TextData provides methods for visualizing the relationships among submissions and provides in-context interactive search intent prediction with question-answering via a generative large language model. TextData is free-to-use, can be accessed online, and the source code is publicly available.

We present MolT5 -a self-supervised learning framework for pretraining models on a vast amount of unlabeled natural language text and molecule strings.MolT5 allows for new, useful, and challenging analogs of traditional vision-language tasks, such as molecule captioning and text-based de novo molecule generation (altogether: translation between molecules and language), which we explore for the first time.Since MolT5 pretrains models on single-modal data, it helps overcome the chemistry domain shortcoming of data scarcity.Furthermore, we consider several metrics, including a new cross-modal embedding-based metric, to evaluate the tasks of molecule captioning and text-based molecule generation.Our results show that MolT5-based models are able to generate outputs, both molecules and captions, which in many cases are high quality 1 .

The ability of an agent to comprehend a sentence is tightly connected to the agent's prior experiences and background knowledge. The paper suggests to interpret comprehension as a modality and proposes a complete bimodal logical system that describes an interplay between comprehension and knowledge modalities.

Abstract The article compares two different approaches of incorporating probability into coalition logics. One is based on the semantics of games with stochastic transitions and the other on games with the stochastic failures. The work gives an example of a non-trivial property of coalition power for the first approach and a complete axiomatization for the second approach. It turns out that the logical properties of the coalition power modality under the second approach depend on whether the modal language allows the empty coalition. The main technical results for the games with stochastic failures are a strong completeness theorem for the logical system without the empty coalition and an incompleteness theorem which shows that there is no strongly complete logical system in the language with the empty coalition.