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

Generating Network Trajectories Using Gradient Descent in State Space

A local and simple learning algorithm is introduced that gradually minimizes an error function for neural states of a general network. Unlike standard backpropagation algorithms, it is based on linearizing the neurodynamics, which are interpreted as constraints for the di#erent network variables. From the resulting equations, the weight update is deduced which has a minimal norm and produces state changes directed precisely towards target values. As an application, it is shown how to generate desired neural state space curves on recurrent Hopfield-type networks. 1. Introduction Neural networks are flexible architectures that allow for universal computations. One of the fundamental problems is supervised learning: how to build internal representations of desired neural outputs. Very popular algorithms are gradient descent algorithms as originally proposed for learning on feedforward architectures [9]. To adjust the synaptic weights, the error in the output is propagated backwards layer..

Songbirds are excellent auditory discriminators, irrespective of age and experience

Human infants but not adults possess the ability to perceive differences between non-native language phoneme categories. The predominant explanation for this age-related decline in discriminative ability is the effect of statistical learning driven by sensory exposure: phoneme categories of the native language take precedence, have a higher frequency of occurrence and may encompass category distinctions in non-native languages. Alternatively, one could explain the decline through a reduction in discriminative abilities attributable to ageing. Thus, to what extent is auditory perception influenced either by experience or by age-related processes? Here, we attempted to answer this question, which cannot easily be disentangled in humans, in songbirds, which share many properties with humans: both learn the statistical distribution of sounds in their environment, both possess neural circuits to process vocalizations of their own species and plasticity in these circuits is subject to critical periods. To study the effects of experience and ageing, we trained zebra finches, Taeniopygia guttata, to discriminate short from long versions of a single zebra finch song syllable type. Birds in four groups distinguished by their age (old versus young) and level of auditory experience (with song experience versus completely isolated from song) could learn to discriminate arbitrarily fine differences between song syllables, although we found a trend that upholds the statistical learning hypothesis: birds with song experience performed better than birds with no experience. Furthermore, birds in all groups were able to generalize their learning to new stimuli of the same type, and they were able to rapidly adapt their learned discrimination boundaries. Finally, we found that songbirds could accurately discriminate randomly selected renditions of a stereotyped adult song syllable, revealing a flexible ability to discriminate conspecific vocalizations

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

Nearest neighbours reveal fast and slow components of motor learning

ISSN:0028-0836ISSN:1476-468

MemSum: Extractive Summarization of Long Documents Using Multi-Step Episodic Markov Decision Processes

We introduce MemSum (Multi-step Episodic Markov decision process extractive SUMmarizer), a reinforcement-learning-based extractive summarizer enriched at each step with information on the current extraction history. When MemSum iteratively selects sentences into the summary, it considers a broad information set that would intuitively also be used by humans in this task: 1) the text content of the sentence, 2) the global text context of the rest of the document, and 3) the extraction history consisting of the set of sentences that have already been extracted. With a lightweight architecture, MemSum obtains state-of-the-art test-set performance (ROUGE) in summarizing long documents taken from PubMed, arXiv, and GovReport. Ablation studies demonstrate the importance of local, global, and history information. A human evaluation confirms the high quality and low redundancy of the generated summaries, stemming from MemSum's awareness of extraction history

Lesions of a higher auditory brain area during a sensorimotor period do not impair birdsong learning

ISSN:2297-824