1,100 research outputs found
Embedding-based Scientific Literature Discovery in a Text Editor Application
Each claim in a research paper requires all relevant prior knowledge to be
discovered, assimilated, and appropriately cited. However, despite the
availability of powerful search engines and sophisticated text editing
software, discovering relevant papers and integrating the knowledge into a
manuscript remain complex tasks associated with high cognitive load. To define
comprehensive search queries requires strong motivation from authors,
irrespective of their familiarity with the research field. Moreover, switching
between independent applications for literature discovery, bibliography
management, reading papers, and writing text burdens authors further and
interrupts their creative process. Here, we present a web application that
combines text editing and literature discovery in an interactive user
interface. The application is equipped with a search engine that couples
Boolean keyword filtering with nearest neighbor search over text embeddings,
providing a discovery experience tuned to an author's manuscript and his
interests. Our application aims to take a step towards more enjoyable and
effortless academic writing.
The demo of the application (https://SciEditorDemo2020.herokuapp.com/) and a
short video tutorial (https://youtu.be/pkdVU60IcRc) are available online
Large-scale Hierarchical Alignment for Data-driven Text Rewriting
We propose a simple unsupervised method for extracting pseudo-parallel
monolingual sentence pairs from comparable corpora representative of two
different text styles, such as news articles and scientific papers. Our
approach does not require a seed parallel corpus, but instead relies solely on
hierarchical search over pre-trained embeddings of documents and sentences. We
demonstrate the effectiveness of our method through automatic and extrinsic
evaluation on text simplification from the normal to the Simple Wikipedia. We
show that pseudo-parallel sentences extracted with our method not only
supplement existing parallel data, but can even lead to competitive performance
on their own.Comment: RANLP 201
Cross-intensity functions and the estimate of spike-time jitter
Correlation measures are important tools for the analysis of simultaneously recorded spike trains. A well-known measure with probabilistic interpretation is the cross-intensity function (CIF), which is an estimate of the conditional probability that a neuron spikes as a function of the time lag to spikes in another neuron. The non-commutative nature of the CIF is particularly useful when different neuron classes are studied that can be distinguished based on their anatomy or physiology. Here we explore the utility of the CIF for estimating spike-time jitter in synaptic interactions between neuron pairs of connected classes. When applied to spike train pairs from sleeping songbirds, we are able to distinguish fast synaptic interactions mediated primarily by AMPA receptors from slower interactions mediated by NMDA receptors. We also find that spike jitter increases with the time lag between spikes, reflecting the accumulation of noise in neural activity sequences, such as in synfire chains. In conclusion, we demonstrate some new utility of the CIF as a spike-train measur
Nearly extensive sequential memory lifetime achieved by coupled nonlinear neurons
Many cognitive processes rely on the ability of the brain to hold sequences
of events in short-term memory. Recent studies have revealed that such memory
can be read out from the transient dynamics of a network of neurons. However,
the memory performance of such a network in buffering past information has only
been rigorously estimated in networks of linear neurons. When signal gain is
kept low, so that neurons operate primarily in the linear part of their
response nonlinearity, the memory lifetime is bounded by the square root of the
network size. In this work, I demonstrate that it is possible to achieve a
memory lifetime almost proportional to the network size, "an extensive memory
lifetime", when the nonlinearity of neurons is appropriately utilized. The
analysis of neural activity revealed that nonlinear dynamics prevented the
accumulation of noise by partially removing noise in each time step. With this
error-correcting mechanism, I demonstrate that a memory lifetime of order
can be achieved.Comment: 21 pages, 5 figures, the manuscript has been accepted for publication
in Neural Computatio
Reply to: Venous aneurysms of saphena magna: Is this really a rare disease?
The known methods of acoustical calculation in buildings disregard the phenomenon of structural sound transmission, whereas its effect can reach from 2 to 12 dB. The purpose of this paper is to develop the calculation method for sound transmission and vibrations in connected vibroacoustic systems. Theoretical research methods were used based on the theory of statistical energy analysis (SEA) and the theory of self-consistent sound fields with regard to dual nature of sound formation - resonance and inertia. Based on M. Sedov's method of sound fields consistency, a calculation method for sound insulation was developed with integration in SEA methodology. Use of the developed method allows predicting sound transmission through a double-panel partition with the account of adjacent structures
Character-level Chinese-English Translation through ASCII Encoding
Character-level Neural Machine Translation (NMT) models have recently
achieved impressive results on many language pairs. They mainly do well for
Indo-European language pairs, where the languages share the same writing
system. However, for translating between Chinese and English, the gap between
the two different writing systems poses a major challenge because of a lack of
systematic correspondence between the individual linguistic units. In this
paper, we enable character-level NMT for Chinese, by breaking down Chinese
characters into linguistic units similar to that of Indo-European languages. We
use the Wubi encoding scheme, which preserves the original shape and semantic
information of the characters, while also being reversible. We show promising
results from training Wubi-based models on the character- and subword-level
with recurrent as well as convolutional models.Comment: 7 pages, 3 figures, 3rd Conference on Machine Translation (WMT18),
201
SciLit: A Platform for Joint Scientific Literature Discovery, Summarization and Citation Generation
Scientific writing involves retrieving, summarizing, and citing relevant
papers, which can be time-consuming processes in large and rapidly evolving
fields. By making these processes inter-operable, natural language processing
(NLP) provides opportunities for creating end-to-end assistive writing tools.
We propose SciLit, a pipeline that automatically recommends relevant papers,
extracts highlights, and suggests a reference sentence as a citation of a
paper, taking into consideration the user-provided context and keywords. SciLit
efficiently recommends papers from large databases of hundreds of millions of
papers using a two-stage pre-fetching and re-ranking literature search system
that flexibly deals with addition and removal of a paper database. We provide a
convenient user interface that displays the recommended papers as extractive
summaries and that offers abstractively-generated citing sentences which are
aligned with the provided context and which mention the chosen keyword(s). Our
assistive tool for literature discovery and scientific writing is available at
https://scilit.vercel.appComment: Accepted at ACL 2023 System Demonstratio
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