20,222 research outputs found
Persistent supercurrents in a planar non-relativistic chiral fluid
We study the possible stationary persistent supercurrents flowing on a
cylindrical sample supporting a two-dimensional charged fluid. The internal
dynamics of the fluid is obtained by means of an effective theory in which the
fluid self-interacts through a gauge field. We find that the presence of
persistent supercurrents depends on what kind of gauge field it is. In
particular the current is zero if it is a Maxwell gauge field, and it is
maximal if it is a Chern-Simons gauge field. There is an intermediate behaviour
in presence of both Maxwell and Chern-Simons term. Therefore it appears that
persistent supercurrents are possible only if the fluid is chiral.Comment: 15 pages, plain tex, SISSA 133/94/E
Quaternion Convolutional Neural Networks for End-to-End Automatic Speech Recognition
Recently, the connectionist temporal classification (CTC) model coupled with
recurrent (RNN) or convolutional neural networks (CNN), made it easier to train
speech recognition systems in an end-to-end fashion. However in real-valued
models, time frame components such as mel-filter-bank energies and the cepstral
coefficients obtained from them, together with their first and second order
derivatives, are processed as individual elements, while a natural alternative
is to process such components as composed entities. We propose to group such
elements in the form of quaternions and to process these quaternions using the
established quaternion algebra. Quaternion numbers and quaternion neural
networks have shown their efficiency to process multidimensional inputs as
entities, to encode internal dependencies, and to solve many tasks with less
learning parameters than real-valued models. This paper proposes to integrate
multiple feature views in quaternion-valued convolutional neural network
(QCNN), to be used for sequence-to-sequence mapping with the CTC model.
Promising results are reported using simple QCNNs in phoneme recognition
experiments with the TIMIT corpus. More precisely, QCNNs obtain a lower phoneme
error rate (PER) with less learning parameters than a competing model based on
real-valued CNNs.Comment: Accepted at INTERSPEECH 201
Mutual Reinforcement Effects in Japanese Sentence Classification and Named Entity Recognition Tasks
Information extraction(IE) is a crucial subfield within natural language
processing. However, for the traditionally segmented approach to sentence
classification and Named Entity Recognition, the intricate interactions between
these individual subtasks remain largely uninvestigated. In this study, we
propose an integrative analysis, converging sentence classification with Named
Entity Recognition, with the objective to unveil and comprehend the mutual
reinforcement effect within these two information extraction subtasks. To
achieve this, we introduce a Sentence Classification and Named Entity
Recognition Multi-task (SCNM) approach that combines Sentence Classification
(SC) and Named Entity Recognition (NER). We develop a Sentence-to-Label
Generation (SLG) framework for SCNM and construct a Wikipedia dataset
containing both SC and NER. Using a format converter, we unify input formats
and employ a generative model to generate SC-labels, NER-labels, and associated
text segments. We propose a Constraint Mechanism (CM) to improve generated
format accuracy. Our results show SC accuracy increased by 1.13 points and NER
by 1.06 points in SCNM compared to standalone tasks, with CM raising format
accuracy from 63.61 to 100. The findings indicate mutual reinforcement effects
between SC and NER, and integration enhances both tasks' performance. We
additionally implemented the SLG framework on single SC task. It yielded
superior accuracies compared to the baseline on two distinct Japanese SC
datasets. Notably, in the experiment of few-shot learning, SLG framework shows
much better performance than fine-tune method. These empirical findings
contribute additional evidence to affirm the efficacy of the SLG framework.Comment: 25 pages, 12 figures, 19 tables. arXiv admin note: substantial text
overlap with arXiv:2306.1597
USA: Universal Sentiment Analysis Model & Construction of Japanese Sentiment Text Classification and Part of Speech Dataset
Sentiment analysis is a pivotal task in the domain of natural language
processing. It encompasses both text-level sentiment polarity classification
and word-level Part of Speech(POS) sentiment polarity determination. Such
analysis challenges models to understand text holistically while also
extracting nuanced information. With the rise of Large Language Models(LLMs),
new avenues for sentiment analysis have opened. This paper proposes enhancing
performance by leveraging the Mutual Reinforcement Effect(MRE) between
individual words and the overall text. It delves into how word polarity
influences the overarching sentiment of a passage. To support our research, we
annotated four novel Sentiment Text Classification and Part of Speech(SCPOS)
datasets, building upon existing sentiment classification datasets.
Furthermore, we developed a Universal Sentiment Analysis(USA) model, with a
7-billion parameter size. Experimental results revealed that our model
surpassed the performance of gpt-3.5-turbo across all four datasets,
underscoring the significance of MRE in sentiment analysis.Comment: Model already Open Sourced, Dataset will release soo
GIELLM: Japanese General Information Extraction Large Language Model Utilizing Mutual Reinforcement Effect
Information Extraction (IE) stands as a cornerstone in natural language
processing, traditionally segmented into distinct sub-tasks. The advent of
Large Language Models (LLMs) heralds a paradigm shift, suggesting the
feasibility of a singular model addressing multiple IE subtasks. In this vein,
we introduce the General Information Extraction Large Language Model (GIELLM),
which integrates text Classification, Sentiment Analysis, Named Entity
Recognition, Relation Extraction, and Event Extraction using a uniform
input-output schema. This innovation marks the first instance of a model
simultaneously handling such a diverse array of IE subtasks. Notably, the
GIELLM leverages the Mutual Reinforcement Effect (MRE), enhancing performance
in integrated tasks compared to their isolated counterparts. Our experiments
demonstrate State-of-the-Art (SOTA) results in five out of six Japanese mixed
datasets, significantly surpassing GPT-3.5-Turbo. Further, an independent
evaluation using the novel Text Classification Relation and Event
Extraction(TCREE) dataset corroborates the synergistic advantages of MRE in
text and word classification. This breakthrough paves the way for most IE
subtasks to be subsumed under a singular LLM framework. Specialized fine-tune
task-specific models are no longer needed.Comment: 10 pages, 6 figure
Sentence-to-Label Generation Framework for Multi-task Learning of Japanese Sentence Classification and Named Entity Recognition
Information extraction(IE) is a crucial subfield within natural language
processing. In this study, we introduce a Sentence Classification and Named
Entity Recognition Multi-task (SCNM) approach that combines Sentence
Classification (SC) and Named Entity Recognition (NER). We develop a
Sentence-to-Label Generation (SLG) framework for SCNM and construct a Wikipedia
dataset containing both SC and NER. Using a format converter, we unify input
formats and employ a generative model to generate SC-labels, NER-labels, and
associated text segments. We propose a Constraint Mechanism (CM) to improve
generated format accuracy. Our results show SC accuracy increased by 1.13
points and NER by 1.06 points in SCNM compared to standalone tasks, with CM
raising format accuracy from 63.61 to 100. The findings indicate mutual
reinforcement effects between SC and NER, and integration enhances both tasks'
performance.Comment: Accept in NLDB2023 as Long Pape
Empirical characteristics of different types of pedestrian streams
Reliable empirical data and proper understanding of pedestrian dynamics are
necessary for fire safety design. However, specifications and data in different
handbooks as well as experimental studies differ considerably. In this study,
series of experiments under laboratory conditions were carried out to study the
characteristics of uni- and bidirectional pedestrian streams in straight
corridor. The Voronoi method is used to resolve the fine structure of the
resulting velocity-density relations and spatial dependence of the
measurements. The result shows differences in the shape of the relation for
\rho > 1.0 m-2. The maximal specific flow of unidirectional streams is
significantly larger than that of all bidirectional streams examined
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