“How do you even know what ideophones mean?”

Gesture ◽  
2020 ◽  
Vol 19 (2-3) ◽  
pp. 161-195
Author(s):  
Janis B. Nuckolls
Keyword(s):  
Semantic Features ◽  
Semantic Map ◽  
Form Class ◽  
Fine Grained ◽  
Amazonian Ecuador ◽  
Using Data

Abstract Using data from the Northern Pastaza (qvc) and Upper Napo Quichua (quw) dialects of Amazonian Ecuador, this paper argues that the semantics of ideophones, a highly marked form class of expressive words, is principled and describable with a combination of sensori-semantic features and a fine-grained typology of gestures, based on insights from Streeck (2008) and others. Specifically, ideophones’ sensori-semantics are broken down into a semantic map consisting of 3 super- and 7 subcategorical distinctions. The greater the number of categories encoded by an ideophone’s semantics, the greater are the range of gestures used. Finally, gesture types identified by Streeck (2008) and others, were found among a very different group of people who are not western, educated, industrialized, rich, or democratic. Further research into ideophones and their gestures may find broader significance for ideophone semantics, and more generally, for the interrelations between language and gesture.

Pipelining Localized Semantic Features for Fine-Grained Action Recognition

2014 ◽  
pp. 481-496 ◽  
Author(s):  
Yang Zhou ◽  
Bingbing Ni ◽  
Shuicheng Yan ◽  
Pierre Moulin ◽  
Qi Tian
Keyword(s):  
Action Recognition ◽  
Semantic Features ◽  
Fine Grained

Distractor-Aware Tracking with Multi-Task and Dynamic Feature Learning

2020 ◽  
pp. 2150031
Author(s):  
Weichun Liu ◽  
Xiaoan Tang ◽  
Chenglin Zhao
Keyword(s):  
Correlation Filter ◽  
Coarse Grained ◽  
Dynamic Feature ◽  
Semantic Features ◽  
Low Level ◽  
Fine Grained ◽  
Semantic Embedding ◽  
Training Stage ◽  
Online Tracking ◽  
High Level

Recently, deep trackers based on the siamese networking are enjoying increasing popularity in the tracking community. Generally, those trackers learn a high-level semantic embedding space for feature representation but lose low-level fine-grained details. Meanwhile, the learned high-level semantic features are not updated during online tracking, which results in tracking drift in presence of target appearance variation and similar distractors. In this paper, we present a novel end-to-end trainable Convolutional Neural Network (CNN) based on the siamese network for distractor-aware tracking. It enhances target appearance representation in both the offline training stage and online tracking stage. In the offline training stage, this network learns both the low-level fine-grained details and high-level coarse-grained semantics simultaneously in a multi-task learning framework. The low-level features with better resolution are complementary to semantic features and able to distinguish the foreground target from background distractors. In the online stage, the learned low-level features are fed into a correlation filter layer and updated in an interpolated manner to encode target appearance variation adaptively. The learned high-level features are fed into a cross-correlation layer without online update. Therefore, the proposed tracker benefits from both the adaptability of the fine-grained correlation filter and the generalization capability of the semantic embedding. Extensive experiments are conducted on the public OTB100 and UAV123 benchmark datasets. Our tracker achieves state-of-the-art performance while running with a real-time frame-rate.

Improving the Bag-of-Words model with Spatial Pyramid matching using data augmentation for fine-grained arbitrary-oriented ship classification

2019 ◽  
Vol 10 (9) ◽  
pp. 826-834 ◽  
Author(s):  
Viet Hung Luu ◽  
Van Kiet Dinh ◽  
Nguyen Hoang Hoa Luong ◽  
Quang Hung Bui ◽  
Thi Nhat Thanh Nguyen
Keyword(s):  
Data Augmentation ◽  
Bag Of Words ◽  
Spatial Pyramid Matching ◽  
Fine Grained ◽  
Using Data ◽  
Ship Classification ◽  
Pyramid Matching ◽  
Spatial Pyramid

scholarly journals Hybrid Attention Network for Language-Based Person Search

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5279
Author(s):  
Yang Li ◽  
Huahu Xu ◽  
Junsheng Xiao
Keyword(s):  
Image Features ◽  
Attention Mechanism ◽  
Feature Representation ◽  
Semantic Features ◽  
Retrieval Task ◽  
Attention Network ◽  
Fine Grained ◽  
Person Search ◽  
High Level ◽  
Language Description

Language-based person search retrieves images of a target person using natural language description and is a challenging fine-grained cross-modal retrieval task. A novel hybrid attention network is proposed for the task. The network includes the following three aspects: First, a cubic attention mechanism for person image, which combines cross-layer spatial attention and channel attention. It can fully excavate both important midlevel details and key high-level semantics to obtain better discriminative fine-grained feature representation of a person image. Second, a text attention network for language description, which is based on bidirectional LSTM (BiLSTM) and self-attention mechanism. It can better learn the bidirectional semantic dependency and capture the key words of sentences, so as to extract the context information and key semantic features of the language description more effectively and accurately. Third, a cross-modal attention mechanism and a joint loss function for cross-modal learning, which can pay more attention to the relevant parts between text and image features. It can better exploit both the cross-modal and intra-modal correlation and can better solve the problem of cross-modal heterogeneity. Extensive experiments have been conducted on the CUHK-PEDES dataset. Our approach obtains higher performance than state-of-the-art approaches, demonstrating the advantage of the approach we propose.

Recruitment of tall arborescent palms in the Yasuní National Park, Amazonian Ecuador: are large treefall gaps important?

1999 ◽  
Vol 15 (3) ◽  
pp. 355-366 ◽  
Author(s):  
J.-C. Svenning
Keyword(s):  
Common Species ◽  
Height Growth ◽  
Phase 3 ◽  
Growth And Survival ◽  
Treefall Gaps ◽  
Yasuni National Park ◽  
Light Requirements ◽  
Palm Species ◽  
Amazonian Ecuador ◽  
Using Data

An evaluation of whether large arborescent palms depend on large treefall gaps for recruitment to the adult stage is reported. Specifically three hypotheses were tested: (1) The light requirement of juveniles of tall arborescent palms increases as they grow in size. (2) Tall arborescent palms depend on gaps over 0.10 ha for growth and survival to maturity, due to high light requirements during the stem height growth phase. (3) Stilt-rooted palms are not dependent on gaps for juvenile height growth. The hypotheses were evaluated using data on size and crown position of individuals of tall arborescent palm species as well as data on the forest-phase in which each individual grew. The study site was a 50-ha plot in old-growth rain forest in Amazonian Ecuador. The first hypothesis was accepted for the two most common species, Iriartea deltoidea and Oenocarpus bataua, but tentatively rejected for the arborescent palm community as a whole. The second hypothesis was rejected for Iriartea and the community as a whole. Only Oenocarpus had strongly gap-dependent recruitment. The results for Iriartea and Oenocarpus were consistent with the third hypothesis.

On the use of translation corpora in contrastive linguistics

2015 ◽  
Vol 15 (1) ◽  
pp. 4-33 ◽  
Author(s):  
Volker Gast
Keyword(s):  
Quantitative Methods ◽  
Theoretical Work ◽  
Coarse Grained ◽  
Testing Hypotheses ◽  
Fine Grained ◽  
Contrastive Linguistics ◽  
Using Data ◽  
The One ◽  
Impersonal Pronoun ◽  
Impersonal Pronouns

This article argues for a type of corpus-based contrastive research that is item-specific, predictive and hypothesis-driven. It reports on a programmatic study of the ways in which impersonalization is expressed in English and German. Impersonalization is taken to be epitomized by human impersonal pronouns like German man (e.g. Man lebt nur einmal ‘You/one only live(s) once’). English does not have a specialized impersonal pronoun like Germ. man and uses a variety of strategies instead. The question arises what determines the choice of a given impersonalization strategy in English. Drawing on relevant theoretical work and using data from a translation corpus (Europarl), variables potentially affecting the distribution of impersonalization strategies in English are identified, and their influence on the choice of a strategy is determined. By testing hypotheses derived from theoretical work and using multivariate quantitative methods of analysis, the study is intended to illustrate how bridges can be built between fine-grained semantic analyses, on the one hand, and more coarse-grained, but empirically valid, corpus research, on the other.

scholarly journals VulSniper: Focus Your Attention to Shoot Fine-Grained Vulnerabilities

2019 ◽  
Author(s):  
Xu Duan ◽  
Jingzheng Wu ◽  
Shouling Ji ◽  
Zhiqing Rui ◽  
Tianyue Luo ◽  
...  
Keyword(s):  
Computer Security ◽  
Detection Accuracy ◽  
Semantic Features ◽  
Vulnerability Detection ◽  
Accurate Identification ◽  
Fine Grained ◽  
Graph Design ◽  
Benchmark Datasets ◽  
Code Property ◽  
Similar Code

With the explosive development of information technology, vulnerabilities have become one of the major threats to computer security. Most vulnerabilities with similar patterns can be detected effectively by static analysis methods. However, some vulnerable and non-vulnerable code is hardly distinguishable, resulting in low detection accuracy. In this paper, we define the accurate identification of vulnerabilities in similar code as a fine-grained vulnerability detection problem. We propose VulSniper which is designed to detect fine-grained vulnerabilities more effectively. In VulSniper, attention mechanism is used to capture the critical features of the vulnerabilities. Especially, we use bottom-up and top-down structures to learn the attention weights of different areas of the program. Moreover, in order to fully extract the semantic features of the program, we generate the code property graph, design a 144-dimensional vector to describe the relation between the nodes, and finally encode the program as a feature tensor. VulSniper achieves F1-scores of 80.6% and 73.3% on the two benchmark datasets, the SARD Buffer Error dataset and the SARD Resource Management Error dataset respectively, which are significantly higher than those of the state-of-the-art methods.

scholarly journals Vocal development in a large-scale crosslinguistic corpus

2019 ◽  
Author(s):  
Meg Cychosz ◽  
Alejandrina Cristia ◽  
Elika Bergelson ◽  
Marisa Casillas ◽  
Gladys Baudet ◽  
...  
Keyword(s):  
Large Scale ◽  
Linguistically Diverse ◽  
Natural Environments ◽  
Cultural Backgrounds ◽  
Fine Grained ◽  
Audio Recordings ◽  
Using Data ◽  
Future Work ◽  
Multiple Languages ◽  
Analyze Data

This study evaluates whether early vocalizations develop in similar ways in children across diverse cultural contexts. We analyze data from daylong audio-recordings of 49 children (1-36 months) from five different language/cultural backgrounds. Citizen scientists annotated these recordings to determine if child vocalizations contained canonical transitions or not (e.g., "ba'' versus "ee''). Results revealed that the proportion of clips reported to contain canonical transitions increased with age. Further, this proportion exceeded 0.15 by around 7 months, replicating and extending previous findings on canonical vocalization development but using data from the natural environments of a culturally and linguistically diverse sample. This work explores how crowdsourcing can be used to annotate corpora, helping establish developmental milestones relevant to multiple languages and cultures. Lower inter-annotator reliability on the crowdsourcing platform, relative to more traditional in-lab expert annotators, means that a larger number of unique annotators and/or annotations are required and that crowdsourcing may not be a suitable method for more fine-grained annotation decisions. Audio clips used for this project are compiled into a large-scale infant vocal corpus that is available for other researchers to use in future work.

scholarly journals Form Class and Its Use in Estimating Sawtimber Volume of Sugar Maple Trees

2010 ◽  
Vol 27 (2) ◽  
pp. 75-77
Author(s):  
Ralph D. Nyland ◽  
Diane Kiernan
Keyword(s):  
New York ◽  
Sugar Maple ◽  
Prediction Equation ◽  
Diameter Class ◽  
Form Class ◽  
Standing Trees ◽  
Class Average ◽  
Using Data ◽  
Estimate Form

Abstract The Mesavage-Girard form class for a composite of 242 sugar maple sawtimber trees in New York averaged 82 (±5.3), differing significantly from the regional average form class of 79 originally recommended in the Mesavage-Girard form class volume tables. Also, for 8 of 16 stands, the measured form class differed significantly from the recommended regional average. Findings suggest a need to estimate form class for each tree when making an inventory of sawtimber volume. A regression analysis using data from the 242 sample trees provided a prediction equation for estimating diameter inside bark at 17 ft height on the basis of dbh. That allows determination of a unique Mesavage-Girard form class for each diameter class of sugar maple trees and use of that diameter class average when computing the board-foot volume of standing trees.

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