From omelet lithography to state-of-the-art performance resists: resist screening with EUV Interference Lithography

Abstract Background With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing. Results A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. Conclusions Our experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.

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Contour Detection for Fibre of Preserved Szechuan Pickle Based on Dilated Convolution

Applied Sciences ◽

10.3390/app9132684 ◽

2019 ◽

Vol 9 (13) ◽

pp. 2684 ◽

Cited By ~ 1

Author(s):

Hongyang Li ◽

Lizhuang Liu ◽

Zhenqi Han ◽

Dan Zhao

Keyword(s):

Edge Detection ◽

State Of The Art ◽

Contour Detection ◽

Experimental Results ◽

Contour Method ◽

Class Differences ◽

Dilated Convolution ◽

The Mean ◽

Art Performance

Peeling fibre is an indispensable process in the production of preserved Szechuan pickle, the accuracy of which can significantly influence the quality of the products, and thus the contour method of fibre detection, as a core algorithm of the automatic peeling device, is studied. The fibre contour is a kind of non-salient contour, characterized by big intra-class differences and small inter-class differences, meaning that the feature of the contour is not discriminative. The method called dilated-holistically-nested edge detection (Dilated-HED) is proposed to detect the fibre contour, which is built based on the HED network and dilated convolution. The experimental results for our dataset show that the Pixel Accuracy (PA) is 99.52% and the Mean Intersection over Union (MIoU) is 49.99%, achieving state-of-the-art performance.

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Pruning by leveraging training dynamics

AI Communications ◽

10.3233/aic-210127 ◽

2021 ◽

pp. 1-21

Author(s):

Andrei C. Apostol ◽

Maarten C. Stol ◽

Patrick Forré

Keyword(s):

State Of The Art ◽

Object Classification ◽

Compression Technique ◽

Pruning Method ◽

Model Compression ◽

Art Performance

We propose a novel pruning method which uses the oscillations around 0, i.e. sign flips, that a weight has undergone during training in order to determine its saliency. Our method can perform pruning before the network has converged, requires little tuning effort due to having good default values for its hyperparameters, and can directly target the level of sparsity desired by the user. Our experiments, performed on a variety of object classification architectures, show that it is competitive with existing methods and achieves state-of-the-art performance for levels of sparsity of 99.6 % and above for 2 out of 3 of the architectures tested. Moreover, we demonstrate that our method is compatible with quantization, another model compression technique. For reproducibility, we release our code at https://github.com/AndreiXYZ/flipout.

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A Graph-based Evolutionary Algorithm for Automated Machine Learning

10.37686/ser.v1i2.77 ◽

2020 ◽

Author(s):

Fei Qi ◽

Zhaohui Xia ◽

Gaoyang Tang ◽

Hang Yang ◽

Yu Song ◽

...

Keyword(s):

Machine Learning ◽

Evolutionary Algorithm ◽

Parameter Optimization ◽

State Of The Art ◽

The State ◽

Complex Structures ◽

Architecture Evolution ◽

Automated Machine Learning ◽

Art Performance

As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate the workflow of machine learning. On the PMLB dataset, the proposed approach shows the state-of-the-art performance compared with TPOT, Autostacker, and auto-sklearn. Some of the optimized models are with complex structures which are difficult to obtain in manual design.

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Optimization Learning: Perspective, Method, and Applications

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/728 ◽

2020 ◽

Author(s):

Risheng Liu

Keyword(s):

Inverse Problems ◽

Theoretical Analysis ◽

Iterative Methods ◽

State Of The Art ◽

Learning Paradigm ◽

Rigorous Analysis ◽

The Core ◽

Globally Convergent ◽

Ill Posed ◽

Art Performance

Numerous tasks at the core of statistics, learning, and vision areas are specific cases of ill-posed inverse problems. Recently, learning-based (e.g., deep) iterative methods have been empirically shown to be useful for these problems. Nevertheless, integrating learnable structures into iterations is still a laborious process, which can only be guided by intuitions or empirical insights. Moreover, there is a lack of rigorous analysis of the convergence behaviors of these reimplemented iterations, and thus the significance of such methods is a little bit vague. We move beyond these limits and propose a theoretically guaranteed optimization learning paradigm, a generic and provable paradigm for nonconvex inverse problems, and develop a series of convergent deep models. Our theoretical analysis reveals that the proposed optimization learning paradigm allows us to generate globally convergent trajectories for learning-based iterative methods. Thanks to the superiority of our framework, we achieve state-of-the-art performance on different real applications.

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Specializing Word Embeddings (for Parsing) by Information Bottleneck (Extended Abstract)

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/658 ◽

2020 ◽

Author(s):

Xiang Lisa Li ◽

Jason Eisner

Keyword(s):

Dimensionality Reduction ◽

Semantic Information ◽

State Of The Art ◽

Word Embedding ◽

Discrete Version ◽

Word Embeddings ◽

Continuous Version ◽

Continuous Vector ◽

Information Bottleneck ◽

Art Performance

Pre-trained word embeddings like ELMo and BERT contain rich syntactic and semantic information, resulting in state-of-the-art performance on various tasks. We propose a very fast variational information bottleneck (VIB) method to nonlinearly compress these embeddings, keeping only the information that helps a discriminative parser. We compress each word embedding to either a discrete tag or a continuous vector. In the discrete version, our automatically compressed tags form an alternative tag set: we show experimentally that our tags capture most of the information in traditional POS tag annotations, but our tag sequences can be parsed more accurately at the same level of tag granularity. In the continuous version, we show experimentally that moderately compressing the word embeddings by our method yields a more accurate parser in 8 of 9 languages, unlike simple dimensionality reduction.

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Disentangled Feature Learning Network for Vehicle Re-Identification

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/66 ◽

2020 ◽

Author(s):

Yan Bai ◽

Yihang Lou ◽

Yongxing Dai ◽

Jun Liu ◽

Ziqian Chen ◽

...

Keyword(s):

State Of The Art ◽

Feature Learning ◽

Feature Representation ◽

Public Security ◽

The Public ◽

Common Features ◽

Learning Network ◽

Single Feature ◽

Art Performance

Vehicle Re-Identification (ReID) has attracted lots of research efforts due to its great significance to the public security. In vehicle ReID, we aim to learn features that are powerful in discriminating subtle differences between vehicles which are visually similar, and also robust against different orientations of the same vehicle. However, these two characteristics are hard to be encapsulated into a single feature representation simultaneously with unified supervision. Here we propose a Disentangled Feature Learning Network (DFLNet) to learn orientation specific and common features concurrently, which are discriminative at details and invariant to orientations, respectively. Moreover, to effectively use these two types of features for ReID, we further design a feature metric alignment scheme to ensure the consistency of the metric scales. The experiments show the effectiveness of our method that achieves state-of-the-art performance on three challenging datasets.

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Direct laser-patterned MXene–perovskite image sensor arrays for visible-near infrared photodetection

Materials Horizons ◽

10.1039/d0mh00537a ◽

2020 ◽

Vol 7 (7) ◽

pp. 1901-1911 ◽

Cited By ~ 3

Author(s):

Aobo Ren ◽

Jihua Zou ◽

Huagui Lai ◽

Yixuan Huang ◽

Liming Yuan ◽

...

Keyword(s):

Near Infrared ◽

Performance Metrics ◽

State Of The Art ◽

Sensor Arrays ◽

Image Sensor ◽

Top Down ◽

Solution Processed ◽

Direct Laser ◽

Infrared Photodetection ◽

Art Performance

Solution-processed MXene–perovskite image sensor arrays are realized by a top-down method, which combine desirable manufacturing advantages and state-of-the-art performance metrics.

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Named Entity Recognition with Bidirectional LSTM-CNNs

Transactions of the Association for Computational Linguistics ◽

10.1162/tacl_a_00104 ◽

2016 ◽

Vol 4 ◽

pp. 357-370 ◽

Cited By ~ 319

Author(s):

Jason P.C. Chiu ◽

Eric Nichols

Keyword(s):

Network Architecture ◽

High Performance ◽

State Of The Art ◽

Named Entity Recognition ◽

Entity Recognition ◽

Feature Engineering ◽

Named Entity ◽

Extensive Evaluation ◽

Bidirectional Lstm ◽

Art Performance

Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance. In this paper, we present a novel neural network architecture that automatically detects word- and character-level features using a hybrid bidirectional LSTM and CNN architecture, eliminating the need for most feature engineering. We also propose a novel method of encoding partial lexicon matches in neural networks and compare it to existing approaches. Extensive evaluation shows that, given only tokenized text and publicly available word embeddings, our system is competitive on the CoNLL-2003 dataset and surpasses the previously reported state of the art performance on the OntoNotes 5.0 dataset by 2.13 F1 points. By using two lexicons constructed from publicly-available sources, we establish new state of the art performance with an F1 score of 91.62 on CoNLL-2003 and 86.28 on OntoNotes, surpassing systems that employ heavy feature engineering, proprietary lexicons, and rich entity linking information.

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Highly efficient thermophones based on freestanding single-walled carbon nanotube films

Nanoscale Horizons ◽

10.1039/c9nh00164f ◽

2019 ◽

Vol 4 (5) ◽

pp. 1158-1163 ◽

Cited By ~ 8

Author(s):

Stepan A. Romanov ◽

Ali E. Aliev ◽

Boris V. Fine ◽

Anton S. Anisimov ◽

Albert G. Nasibulin

Keyword(s):

Carbon Nanotubes ◽

Carbon Nanotube ◽

State Of The Art ◽

Single Walled Carbon Nanotubes ◽

The State ◽

Single Walled Carbon Nanotube ◽

Single Walled Carbon ◽

Highly Efficient ◽

Art Performance ◽

Walled Carbon Nanotubes

We present the state-of-the-art performance of air-coupled thermophones made of thin, freestanding films of randomly oriented single-walled carbon nanotubes (SWCNTs).

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