scholarly journals Highly responsive UV-photodetectors based on single electrospun TiO2 nanofibres

2016 ◽  
Vol 4 (45) ◽  
pp. 10707-10714 ◽  
Author(s):  
Aday J. Molina-Mendoza ◽  
Alicia Moya ◽  
Riccardo Frisenda ◽  
Simon A. Svatek ◽  
Patricia Gant ◽  
...  
Keyword(s):  
Response Time ◽  
State Of The Art ◽  
High Sensitivity ◽  
Uv Photodetectors ◽  
Polarisation Sensitivity ◽  
Art Performance

Electrospun TiO2 single-nanofibres are employed as photodetectors with state-of-the-art performance: high sensitivity in the UV (responsivity of 90 A W−1 for 375 nm wavelength), response time of ∼5 s and light polarisation sensitivity.

scholarly journals PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Changyong Li ◽  
Yongxian Fan ◽  
Xiaodong Cai
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
State Of The Art ◽  
Experimental Results ◽  
Actual Situation ◽  
Controlled Experiments ◽  
Biomedical Image ◽  
Segmentation Methods ◽  
Art Performance

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.

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

2019 ◽  
Vol 9 (13) ◽  
pp. 2684 ◽  
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.

Genetically Engineered Pores as Metal Ion Biosensors

1993 ◽  
Vol 330 ◽  
Author(s):  
John Kasianowicz ◽  
Barbara Walker ◽  
Musti Krishnasastry ◽  
Hagan Bayley
Keyword(s):  
Response Time ◽  
Lipid Bilayers ◽  
Metal Ion ◽  
Dynamic Range ◽  
Divalent Cations ◽  
High Sensitivity ◽  
Genetically Engineered ◽  
Great Promise ◽  
Lipid Bilayer Membranes ◽  
Time Dynamic

ABSTRACTWe are adapting proteins that form pores in lipid bilayers for use as components of biosensors. Specifically, we have produced genetically engineered variants of the α hemolysin (αHL) fromStaphylococcusaureus with properties that are sensitive to low concentrations of divalent cations. For example, the pore-forming activity of one mutant (αHL-H5: residues 130–134 inclusive replaced with histidine) is inhibited by Zn2+at concentrations as low as 1 μM, as judged by the reduction in its ability to lyse rabbit red blood cells and to increase the conductance of planar lipid bilayer membranes. When αHL-H5 is added to the aqueous phase bathing one side of a planar membrane, the subsequent addition of 100 μM Zn2+to either side blocks the pores that form. This result suggests that at least part of the mutated region lines the channel lumen. Ca2+and Mg2+do not block the channel and therefore the H5 mutation confers a degree of analyte specificity to the αHL pore. The results suggest that genetically engineered pores have great promise for the rapid and sensitive detection of metal cations and we discuss the merits and potential limitations for their use in this application. Specifically, we examine the issues of selectivity, sensitivity, response time, dynamic range and longevity. Some of these properties are interdependent. For example, the goals of high sensitivity and rapid response time can be in conflict.

Pruning by leveraging training dynamics

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.

N and P on-line meters: requirements, maintenance and stability

1996 ◽  
Vol 33 (1) ◽  
pp. 147-157 ◽  
Author(s):  
Henrik A. Thomsen ◽  
Kenneth Kisbye
Keyword(s):  
Sample Preparation ◽  
Response Time ◽  
Ammonium Nitrate ◽  
State Of The Art ◽  
List Price ◽  
User Friendliness ◽  
On Line ◽  
The Cost ◽  
Preparation Design

State-of-the-art on-line meters for determination of ammonium, nitrate and phosphate are presented. The on-line meters employ different measuring principles and are available in many different designs differing with respect to size, calibration and cleaning principle, user-friendliness, response time, reagent and sample consumption. A study of Danish experiences on several plants has been conducted. The list price of an on-line meter is between USD 8000 and USD 35,000. To this should be added the cost of sample preparation, design, installation and running-in. The yearly operating for one meter are in the range of USD 200-2500 and the manpower consumption is in the range of 1-5 hours/month. The accuracy obtained is only slightly smaller than the accuracy on collaborative laboratory analyses, which is sufficient for most control purposes.

scholarly journals A Graph-based Evolutionary Algorithm for Automated Machine Learning

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.

scholarly journals Optimization Learning: Perspective, Method, and Applications

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.

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

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.

scholarly journals Disentangled Feature Learning Network for Vehicle Re-Identification

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.

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

2020 ◽  
Vol 7 (7) ◽  
pp. 1901-1911 ◽  
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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