scholarly journals Neural Simile Recognition with Cyclic Multitask Learning and Local Attention

2020 ◽  
Vol 34 (05) ◽  
pp. 9515-9522 ◽  
Author(s):  
Jiali Zeng ◽  
Linfeng Song ◽  
Jinsong Su ◽  
Jun Xie ◽  
Wei Song ◽  
...  
Keyword(s):  
Recent Work ◽  
State Of The Art ◽  
Source Code ◽  
Multitask Learning ◽  
Learning Framework ◽  
Current State ◽  
Sentence Classification ◽  
Component Extraction ◽  
Simple Parameter ◽  
Parameter Sharing

Simile recognition is to detect simile sentences and to extract simile components, i.e., tenors and vehicles. It involves two subtasks: simile sentence classification and simile component extraction. Recent work has shown that standard multitask learning is effective for Chinese simile recognition, but it is still uncertain whether the mutual effects between the subtasks have been well captured by simple parameter sharing. We propose a novel cyclic multitask learning framework for neural simile recognition, which stacks the subtasks and makes them into a loop by connecting the last to the first. It iteratively performs each subtask, taking the outputs of the previous subtask as additional inputs to the current one, so that the interdependence between the subtasks can be better explored. Extensive experiments show that our framework significantly outperforms the current state-of-the-art model and our carefully designed baselines, and the gains are still remarkable using BERT. Source Code of this paper are available on https://github.com/DeepLearnXMU/Cyclic.

scholarly journals Unsupervised Learning of Monocular Depth and Ego-Motion using Conditional PatchGANs

2019 ◽  
Author(s):  
Madhu Vankadari ◽  
Swagat Kumar ◽  
Anima Majumder ◽  
Kaushik Das
Keyword(s):  
High Frequency ◽  
State Of The Art ◽  
Image Sequence ◽  
Input Image ◽  
Performance Comparison ◽  
Structural Defects ◽  
Learning Framework ◽  
Current State ◽  
Input And Output ◽  
Monocular Depth

This paper presents a new GAN-based deep learning framework for estimating absolute scale awaredepth and ego motion from monocular images using a completely unsupervised mode of learning.The proposed architecture uses two separate generators to learn the distribution of depth and posedata for a given input image sequence. The depth and pose data, thus generated, are then evaluated bya patch-based discriminator using the reconstructed image and its corresponding actual image. Thepatch-based GAN (or PatchGAN) is shown to detect high frequency local structural defects in thereconstructed image, thereby improving the accuracy of overall depth and pose estimation. Unlikeconventional GANs, the proposed architecture uses a conditioned version of input and output of thegenerator for training the whole network. The resulting framework is shown to outperform all existing deep networks in this field and beating the current state-of-the-art method by 8.7% in absoluteerror and 5.2% in RMSE metric. To the best of our knowledge, this is first deep network based modelto estimate both depth and pose simultaneously using a conditional patch-based GAN paradigm.The efficacy of the proposed approach is demonstrated through rigorous ablation studies and exhaustive performance comparison on the popular KITTI outdoor driving dataset.

scholarly journals Generating Adversarial Examples for Holding Robustness of Source Code Processing Models

2020 ◽  
Vol 34 (01) ◽  
pp. 1169-1176
Author(s):  
Huangzhao Zhang ◽  
Zhuo Li ◽  
Ge Li ◽  
Lei Ma ◽  
Yang Liu ◽  
...  
Keyword(s):  
Programming Languages ◽  
State Of The Art ◽  
Source Code ◽  
Natural Languages ◽  
Current State ◽  
Automated Processing ◽  
Adversarial Examples ◽  
Adversarial Training ◽  
New Challenges ◽  
And Performance

Automated processing, analysis, and generation of source code are among the key activities in software and system lifecycle. To this end, while deep learning (DL) exhibits a certain level of capability in handling these tasks, the current state-of-the-art DL models still suffer from non-robust issues and can be easily fooled by adversarial attacks.Different from adversarial attacks for image, audio, and natural languages, the structured nature of programming languages brings new challenges. In this paper, we propose a Metropolis-Hastings sampling-based identifier renaming technique, named \fullmethod (\method), which generates adversarial examples for DL models specialized for source code processing. Our in-depth evaluation on a functionality classification benchmark demonstrates the effectiveness of \method in generating adversarial examples of source code. The higher robustness and performance enhanced through our adversarial training with \method further confirms the usefulness of DL models-based method for future fully automated source code processing.

scholarly journals Learning from Easy to Complex: Adaptive Multi-Curricula Learning for Neural Dialogue Generation

2020 ◽  
Vol 34 (05) ◽  
pp. 7472-7479
Author(s):  
Hengyi Cai ◽  
Hongshen Chen ◽  
Cheng Zhang ◽  
Yonghao Song ◽  
Xiaofang Zhao ◽  
...  
Keyword(s):  
State Of The Art ◽  
Data Driven ◽  
Generation Model ◽  
Dialogue Systems ◽  
Multiple Perspectives ◽  
Learning Efficiency ◽  
Learning Framework ◽  
Current State ◽  
Efficiency And Effectiveness ◽  
Complex Adaptive

Current state-of-the-art neural dialogue systems are mainly data-driven and are trained on human-generated responses. However, due to the subjectivity and open-ended nature of human conversations, the complexity of training dialogues varies greatly. The noise and uneven complexity of query-response pairs impede the learning efficiency and effects of the neural dialogue generation models. What is more, so far, there are no unified dialogue complexity measurements, and the dialogue complexity embodies multiple aspects of attributes—specificity, repetitiveness, relevance, etc. Inspired by human behaviors of learning to converse, where children learn from easy dialogues to complex ones and dynamically adjust their learning progress, in this paper, we first analyze five dialogue attributes to measure the dialogue complexity in multiple perspectives on three publicly available corpora. Then, we propose an adaptive multi-curricula learning framework to schedule a committee of the organized curricula. The framework is established upon the reinforcement learning paradigm, which automatically chooses different curricula at the evolving learning process according to the learning status of the neural dialogue generation model. Extensive experiments conducted on five state-of-the-art models demonstrate its learning efficiency and effectiveness with respect to 13 automatic evaluation metrics and human judgments.

scholarly journals Robotic manipulation and sensing of deformable objects in domestic and industrial applications: a survey

2018 ◽  
Vol 37 (7) ◽  
pp. 688-716 ◽  
Author(s):  
Jose Sanchez ◽  
Juan-Antonio Corrales ◽  
Belhassen-Chedli Bouzgarrou ◽  
Youcef Mezouar
Keyword(s):  
Recent Work ◽  
State Of The Art ◽  
Industrial Applications ◽  
Robotic Manipulation ◽  
Deformable Objects ◽  
Food Handling ◽  
Future Directions ◽  
Deformable Object ◽  
Surgical Assistance ◽  
Current State

We present a survey of recent work on robot manipulation and sensing of deformable objects, a field with relevant applications in diverse industries such as medicine (e.g. surgical assistance), food handling, manufacturing, and domestic chores (e.g. folding clothes). We classify the reviewed approaches into four categories based on the type of object they manipulate. Furthermore, within this object classification, we divide the approaches based on the particular task they perform on the deformable object. Finally, we conclude this survey with a discussion of the current state-of-the-art approaches and propose future directions within the proposed classification.

scholarly journals WebAL Comes of Age: A Review of the First 21 Years of Artificial Life on the Web

2016 ◽  
Vol 22 (3) ◽  
pp. 364-407 ◽  
Author(s):  
Tim Taylor ◽  
Joshua E. Auerbach ◽  
Josh Bongard ◽  
Jeff Clune ◽  
Simon Hickinbotham ◽  
...  
Keyword(s):  
Recent Work ◽  
Artificial Life ◽  
State Of The Art ◽  
Web Based ◽  
Web Technologies ◽  
Current State ◽  
The Many ◽  
Exciting Area ◽  
Future Work ◽  
The Web

We present a survey of the first 21 years of web-based artificial life (WebAL) research and applications, broadly construed to include the many different ways in which artificial life and web technologies might intersect. Our survey covers the period from 1994—when the first WebAL work appeared—up to the present day, together with a brief discussion of relevant precursors. We examine recent projects, from 2010–2015, in greater detail in order to highlight the current state of the art. We follow the survey with a discussion of common themes and methodologies that can be observed in recent work and identify a number of likely directions for future work in this exciting area.

scholarly journals CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning

2020 ◽  
Vol 34 (05) ◽  
pp. 9507-9514 ◽  
Author(s):  
Daojian Zeng ◽  
Haoran Zhang ◽  
Qianying Liu
Keyword(s):  
Detailed Analysis ◽  
State Of The Art ◽  
Effective Model ◽  
Model Structure ◽  
Entity Extraction ◽  
Learning Framework ◽  
Task Learning ◽  
Current State ◽  
Significant Attention

Joint extraction of entities and relations has received significant attention due to its potential of providing higher performance for both tasks. Among existing methods, CopyRE is effective and novel, which uses a sequence-to-sequence framework and copy mechanism to directly generate the relation triplets. However, it suffers from two fatal problems. The model is extremely weak at differing the head and tail entity, resulting in inaccurate entity extraction. It also cannot predict multi-token entities (e.g. Steven Jobs). To address these problems, we give a detailed analysis of the reasons behind the inaccurate entity extraction problem, and then propose a simple but extremely effective model structure to solve this problem. In addition, we propose a multi-task learning framework equipped with copy mechanism, called CopyMTL, to allow the model to predict multi-token entities. Experiments reveal the problems of CopyRE and show that our model achieves significant improvement over the current state-of-the-art method by 9% in NYT and 16% in WebNLG (F1 score). Our code is available at https://github.com/WindChimeRan/CopyMTL

scholarly journals Data-Oriented Language Implementation of the Lattice–Boltzmann Method for Dense and Sparse Geometries

2021 ◽  
Vol 11 (20) ◽  
pp. 9495
Author(s):  
Tadeusz Tomczak
Keyword(s):  
Lattice Boltzmann ◽  
High Performance ◽  
State Of The Art ◽  
Source Code ◽  
Memory Access ◽  
Memory Bandwidth ◽  
New Approach ◽  
Current State ◽  
Access Patterns ◽  
Boltzmann Method

The performance of lattice–Boltzmann solver implementations usually depends mainly on memory access patterns. Achieving high performance requires then complex code which handles careful data placement and ordering of memory transactions. In this work, we analyse the performance of an implementation based on a new approach called the data-oriented language, which allows the combination of complex memory access patterns with simple source code. As a use case, we present and provide the source code of a solver for D2Q9 lattice and show its performance on GTX Titan Xp GPU for dense and sparse geometries up to 40962 nodes. The obtained results are promising, around 1000 lines of code allowed us to achieve performance in the range of 0.6 to 0.7 of maximum theoretical memory bandwidth (over 2.5 and 5.0 GLUPS for double and single precision, respectively) for meshes of sizes above 10242 nodes, which is close to the current state-of-the-art. However, we also observed relatively high and sometimes difficult to predict overheads, especially for sparse data structures. The additional issue was also a rather long compilation, which extended the time of short simulations, and a lack of access to low-level optimisation mechanisms.

Behavioral Genetics: Concepts for Research and Practice in Language Development and Disorders

1995 ◽  
Vol 38 (5) ◽  
pp. 1126-1142 ◽  
Author(s):  
Jeffrey W. Gilger
Keyword(s):  
Language Development ◽  
Behavioral Genetics ◽  
State Of The Art ◽  
Genetic Research ◽  
Great Promise ◽  
Behavioral Genetic ◽  
Fine Grained ◽  
Future Goals ◽  
Current State ◽  
Research Designs

This paper is an introduction to behavioral genetics for researchers and practioners in language development and disorders. The specific aims are to illustrate some essential concepts and to show how behavioral genetic research can be applied to the language sciences. Past genetic research on language-related traits has tended to focus on simple etiology (i.e., the heritability or familiality of language skills). The current state of the art, however, suggests that great promise lies in addressing more complex questions through behavioral genetic paradigms. In terms of future goals it is suggested that: (a) more behavioral genetic work of all types should be done—including replications and expansions of preliminary studies already in print; (b) work should focus on fine-grained, theory-based phenotypes with research designs that can address complex questions in language development; and (c) work in this area should utilize a variety of samples and methods (e.g., twin and family samples, heritability and segregation analyses, linkage and association tests, etc.).

Schizophrenia Research: Current State of the Art

1976 ◽  
Vol 21 (7) ◽  
pp. 497-498
Author(s):  
STANLEY GRAND
Keyword(s):  
State Of The Art ◽  
Current State

Umbral Calculus

10.37236/24 ◽  
2002 ◽  
Vol 1000 ◽  
Author(s):  
A. Di Bucchianico ◽  
D. Loeb
Keyword(s):  
19Th Century ◽  
Finite Differences ◽  
State Of The Art ◽  
Umbral Calculus ◽  
Current State ◽  
Logical Foundation ◽  
Complete Bibliography ◽  
The 19Th Century ◽  
Mathematical Literature

We survey the mathematical literature on umbral calculus (otherwise known as the calculus of finite differences) from its roots in the 19th century (and earlier) as a set of “magic rules” for lowering and raising indices, through its rebirth in the 1970’s as Rota’s school set it on a firm logical foundation using operator methods, to the current state of the art with numerous generalizations and applications. The survey itself is complemented by a fairly complete bibliography (over 500 references) which we expect to update regularly.

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