Grounding Gene Mentions with Respect to Gene Database Identifiers

We describe our submission for task 1B of the BioCreAtIvE competition which is concerned with grounding gene mentions with respect to databases of organism gene identifiers. Several approaches to gene identification, lookup, and disambiguation are presented. Results are presented with two possible baseline systems and a discussion of the source of precision and recall errors as well as an estimate of precision and recall for an organism-specific tagger bootstrapped from gene synonym lists and the task 1B training data. 1

A Joint Model for Definition Extraction with Syntactic Connection and Semantic Consistency

Definition Extraction (DE) is one of the well-known topics in Information Extraction that aims to identify terms and their corresponding definitions in unstructured texts. This task can be formalized either as a sentence classification task (i.e., containing term-definition pairs or not) or a sequential labeling task (i.e., identifying the boundaries of the terms and definitions). The previous works for DE have only focused on one of the two approaches, failing to model the inter-dependencies between the two tasks. In this work, we propose a novel model for DE that simultaneously performs the two tasks in a single framework to benefit from their inter-dependencies. Our model features deep learning architectures to exploit the global structures of the input sentences as well as the semantic consistencies between the terms and the definitions, thereby improving the quality of the representation vectors for DE. Besides the joint inference between sentence classification and sequential labeling, the proposed model is fundamentally different from the prior work for DE in that the prior work has only employed the local structures of the input sentences (i.e., word-to-word relations), and not yet considered the semantic consistencies between terms and definitions. In order to implement these novel ideas, our model presents a multi-task learning framework that employs graph convolutional neural networks and predicts the dependency paths between the terms and the definitions. We also seek to enforce the consistency between the representations of the terms and definitions both globally (i.e., increasing semantic consistency between the representations of the entire sentences and the terms/definitions) and locally (i.e., promoting the similarity between the representations of the terms and the definitions)

Two-dimensional gauge anomalies and p -adic numbers

We show how methods of number theory can be used to study anomalies in gauge quantum field theories in spacetime dimension two. To wit, the anomaly cancellation conditions for the abelian part of the local anomaly admit solutions if and only if they admit solutions in the reals and in the p-adics for every prime p and we use this to build an algorithm to find all solutions

Query Resolution for Conversational Search with Limited Supervision

In this work we focus on multi-turn passage retrieval as a crucial component of conversational search. One of the key challenges in multi-turn passage retrieval comes from the fact that the current turn query is often underspecified due to zero anaphora, topic change, or topic return. Context from the conversational history can be used to arrive at a better expression of the current turn query, defined as the task of query resolution. In this paper, we model the query resolution task as a binary term classification problem: for each term appearing in the previous turns of the conversation decide whether to add it to the current turn query or not. We propose QuReTeC (Query Resolution by Term Classification), a neural query resolution model based on bidirectional transformers. We propose a distant supervision method to automatically generate training data by using query-passage relevance labels. Such labels are often readily available in a collection either as human annotations or inferred from user interactions. We show that QuReTeC outperforms state-of-the-art models, and furthermore, that our distant supervision method can be used to substantially reduce the amount of human-curated data required to train QuReTeC. We incorporate QuReTeC in a multi-turn, multi-stage passage retrieval architecture and demonstrate its effectiveness on the TREC CAsT dataset.Comment: SIGIR 2020 full conference pape

Insights into Hydration Dynamics and Cooperative Interactions in Glycerol-Water Mixtures by Terahertz Dielectric Spectroscopy.

We report relaxation dynamics of glycerol-water mixtures as probed by megahertz-to-terahertz dielectric spectroscopy in a frequency range from 50 MHz to 0.5 THz at room temperature. The dielectric relaxation spectra reveal several polarization processes at the molecular level with different time constants and dielectric strengths, providing an understanding of the hydrogen-bonding network in glycerol-water mixtures. We have determined the structure of hydration shells around glycerol molecules and the dynamics of bound water as a function of glycerol concentration in solutions using the Debye relaxation model. The experimental results show the existence of a critical glycerol concentration of ∼7.5 mol %, which is related to the number of water molecules in the hydration layer around a glycerol molecule. At higher glycerol concentrations, water molecules dispersed in a glycerol network become abundant and eventually dominate, and four distinct relaxation processes emerge in the mixtures. The relaxation dynamics and hydration structure in glycerol-water mixtures are further probed with molecular dynamics simulations, which confirm the physical picture revealed by the dielectric spectroscopy