MFCNet: Mining Features Context Network for RGB–IR Person Re-Identification

RGB–IR cross modality person re-identification (RGB–IR Re-ID) is an important task for video surveillance in poorly illuminated or dark environments. In addition to the common challenge of Re-ID, the large cross-modality variations between RGB and IR images must be considered. The existing RGB–IR Re-ID methods use different network structures to learn the global shared features associated with multi-modalities. However, most global shared feature learning methods are sensitive to background clutter, and contextual feature relationships are not considered among the mined features. To solve these problems, this paper proposes a dual-path attention network architecture MFCNet. SGA (Spatial-Global Attention) module embedded in MFCNet includes spatial attention and global attention branches to mine discriminative features. First, the SGA module proposed in this paper focuses on the key parts of the input image to obtain robust features. Next, the module mines the contextual relationships among features to obtain discriminative features and improve network performance. Finally, extensive experiments demonstrate that the performance of the network architecture proposed in this paper is better than that of state-of-the-art methods under various settings. In the all-search mode of the SYSU and RegDB data sets, the rank-1 accuracy reaches 51.64% and 69.76%, respectively.

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MH-MetroNet—A Multi-Head CNN for Passenger-Crowd Attendance Estimation

Journal of Imaging ◽

10.3390/jimaging6070062 ◽

2020 ◽

Vol 6 (7) ◽

pp. 62

Author(s):

Pier Luigi Mazzeo ◽

Riccardo Contino ◽

Paolo Spagnolo ◽

Cosimo Distante ◽

Ettore Stella ◽

...

Keyword(s):

Network Architecture ◽

Network Performance ◽

State Of The Art ◽

Mean Absolute Error ◽

Absolute Error ◽

Input Image ◽

Mean Square ◽

Metro Station ◽

Crowd Counting ◽

Density Map

Knowing an accurate passengers attendance estimation on each metro car contributes to the safely coordination and sorting the crowd-passenger in each metro station. In this work we propose a multi-head Convolutional Neural Network (CNN) architecture trained to infer an estimation of passenger attendance in a metro car. The proposed network architecture consists of two main parts: a convolutional backbone, which extracts features over the whole input image, and a multi-head layers able to estimate a density map, needed to predict the number of people within the crowd image. The network performance is first evaluated on publicly available crowd counting datasets, including the ShanghaiTech part_A, ShanghaiTech part_B and UCF_CC_50, and then trained and tested on our dataset acquired in subway cars in Italy. In both cases a comparison is made against the most relevant and latest state of the art crowd counting architectures, showing that our proposed MH-MetroNet architecture outperforms in terms of Mean Absolute Error (MAE) and Mean Square Error (MSE) and passenger-crowd people number prediction.

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Modified Hybrid Discriminant Analysis Methods and Their Applications in Machine Learning

Discrete Dynamics in Nature and Society ◽

10.1155/2020/1512391 ◽

2020 ◽

Vol 2020 ◽

pp. 1-5

Author(s):

Liwen Huang

Keyword(s):

Discriminant Analysis ◽

Real Data ◽

Classification Algorithms ◽

Data Sets ◽

Analysis Method ◽

Linear Discriminant ◽

Discriminant Model ◽

Discriminant Models ◽

The Common ◽

Better Than

This paper presents a new hybrid discriminant analysis method, and this method combines the ideas of linearity and nonlinearity to establish a two-layer discriminant model. The first layer is a linear discriminant model, which is mainly used to determine the distinguishable samples and subsample; the second layer is a nonlinear discriminant model, which is used to determine the subsample type. Numerical experiments on real data sets show that this method performs well compared to other classification algorithms, and its stability is better than the common discriminant models.

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Scanning Secondary Electron Microscopy of Myofibrils Using Transmission Techniques

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100116401 ◽

1975 ◽

Vol 33 ◽

pp. 556-557

Author(s):

M. K. Lamvik

Keyword(s):

Electron Microscopy ◽

Electron Microscope ◽

Carbon Films ◽

Photographic Recording ◽

Transmission Electron ◽

Thin Carbon ◽

The Common ◽

Scanning Electron ◽

Better Than ◽

Cellular Organelles

When observing small objects such as cellular organelles by scanning electron microscopy, it is often valuable to use the techniques of transmission electron microscopy. The common practice of mounting and coating for SEM may not always be necessary. These possibilities are illustrated using vertebrate skeletal muscle myofibrils.Micrographs for this study were made using a Hitachi HFS-2 scanning electron microscope, with photographic recording usually done at 60 seconds per frame. The instrument was operated at 25 kV, with a specimen chamber vacuum usually better than 10-7 torr. Myofibrils were obtained from rabbit back muscle using the method of Zak et al. To show the component filaments of this contractile organelle, the myofibrils were partially disrupted by agitation in a relaxing medium. A brief centrifugation was done to clear the solution of most of the undisrupted myofibrils before a drop was placed on the grid. Standard 3 mm transmission electron microscope grids covered with thin carbon films were used in this study.

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FOSquare: A Novel Optical HPC Interconnect Network Architecture Based on Fast Optical Switches with Distributed Optical Flow Control

Photonics ◽

10.3390/photonics8010011 ◽

2021 ◽

Vol 8 (1) ◽

pp. 11

Author(s):

Fulong Yan ◽

Changshun Yuan ◽

Chao Li ◽

Xiong Deng

Keyword(s):

Flow Control ◽

Network Architecture ◽

Network Performance ◽

Optical Switches ◽

Low Power Consumption ◽

Low Latency ◽

High Bandwidth ◽

Fast Flow ◽

Real Traffic ◽

Interconnect Network

Interconnecting networks adopting Fast Optical Switches (FOS) can achieve high bandwidth, low latency, and low power consumption. We propose and demonstrate a novel interconnecting topology based on FOS (FOSquare) with distributed fast flow control which is suitable for HPC infrastructures. We also present an Optimized Mapping (OPM) algorithm that maps the most communication-related processes inside a rack. We numerically investigate and compare the network performance of FOSquare with Leaf-Spine under real traffic traces collected by running multiple applications (CG, MG, MILC, and MINI_MD) in an HPC infrastructure. The numerical results show that the FOSquare can reduce >10% latency with respect to Leaf-Spine under the scenario of 16 available cores.

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Fighting Together against the Pandemic: Learning Multiple Models on Tomography Images for COVID-19 Diagnosis

AI ◽

10.3390/ai2020016 ◽

2021 ◽

Vol 2 (2) ◽

pp. 261-273

Author(s):

Mario Manzo ◽

Simone Pellino

Keyword(s):

Network Architecture ◽

State Of The Art ◽

Ensemble Classification ◽

Effective Vaccine ◽

Rt Pcr ◽

Neural Network Architecture ◽

Experimental Phase ◽

Different Types ◽

Polymerase Chain ◽

Better Than

COVID-19 has been a great challenge for humanity since the year 2020. The whole world has made a huge effort to find an effective vaccine in order to save those not yet infected. The alternative solution is early diagnosis, carried out through real-time polymerase chain reaction (RT-PCR) tests or thorax Computer Tomography (CT) scan images. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for image analysis. They optimize the classification design task, which is essential for an automatic approach with different types of images, including medical. In this paper, we adopt a pretrained deep convolutional neural network architecture in order to diagnose COVID-19 disease from CT images. Our idea is inspired by what the whole of humanity is achieving, as the set of multiple contributions is better than any single one for the fight against the pandemic. First, we adapt, and subsequently retrain for our assumption, some neural architectures that have been adopted in other application domains. Secondly, we combine the knowledge extracted from images by the neural architectures in an ensemble classification context. Our experimental phase is performed on a CT image dataset, and the results obtained show the effectiveness of the proposed approach with respect to the state-of-the-art competitors.

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A Novel LSTM Model with Interaction Dual Attention for Radar Echo Extrapolation

Remote Sensing ◽

10.3390/rs13020164 ◽

2021 ◽

Vol 13 (2) ◽

pp. 164

Author(s):

Chuyao Luo ◽

Xutao Li ◽

Yongliang Wen ◽

Yunming Ye ◽

Xiaofeng Zhang

Keyword(s):

Short Term Memory ◽

Weather Forecast ◽

Vital Role ◽

Data Sets ◽

Short Term ◽

Learning Techniques ◽

Radar Echo ◽

Hidden States ◽

Better Than

The task of precipitation nowcasting is significant in the operational weather forecast. The radar echo map extrapolation plays a vital role in this task. Recently, deep learning techniques such as Convolutional Recurrent Neural Network (ConvRNN) models have been designed to solve the task. These models, albeit performing much better than conventional optical flow based approaches, suffer from a common problem of underestimating the high echo value parts. The drawback is fatal to precipitation nowcasting, as the parts often lead to heavy rains that may cause natural disasters. In this paper, we propose a novel interaction dual attention long short-term memory (IDA-LSTM) model to address the drawback. In the method, an interaction framework is developed for the ConvRNN unit to fully exploit the short-term context information by constructing a serial of coupled convolutions on the input and hidden states. Moreover, a dual attention mechanism on channels and positions is developed to recall the forgotten information in the long term. Comprehensive experiments have been conducted on CIKM AnalytiCup 2017 data sets, and the results show the effectiveness of the IDA-LSTM in addressing the underestimation drawback. The extrapolation performance of IDA-LSTM is superior to that of the state-of-the-art methods.

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Critical Aspects of Person Counting and Density Estimation

Journal of Imaging ◽

10.3390/jimaging7020021 ◽

2021 ◽

Vol 7 (2) ◽

pp. 21

Author(s):

Roland Perko ◽

Manfred Klopschitz ◽

Alexander Almer ◽

Peter M. Roth

Keyword(s):

Density Estimation ◽

Network Architecture ◽

Reference Data ◽

State Of The Art ◽

Limit State ◽

Ground Truth ◽

Data Sets ◽

Ground Truth Generation ◽

Baseline Approach ◽

Critical Aspects

Many scientific studies deal with person counting and density estimation from single images. Recently, convolutional neural networks (CNNs) have been applied for these tasks. Even though often better results are reported, it is often not clear where the improvements are resulting from, and if the proposed approaches would generalize. Thus, the main goal of this paper was to identify the critical aspects of these tasks and to show how these limit state-of-the-art approaches. Based on these findings, we show how to mitigate these limitations. To this end, we implemented a CNN-based baseline approach, which we extended to deal with identified problems. These include the discovery of bias in the reference data sets, ambiguity in ground truth generation, and mismatching of evaluation metrics w.r.t. the training loss function. The experimental results show that our modifications allow for significantly outperforming the baseline in terms of the accuracy of person counts and density estimation. In this way, we get a deeper understanding of CNN-based person density estimation beyond the network architecture. Furthermore, our insights would allow to advance the field of person density estimation in general by highlighting current limitations in the evaluation protocols.

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Evaluation of Gas-Liquid Chromatography in Assays for Blood Volatiles

Clinical Chemistry ◽

10.1093/clinchem/11.11.1023 ◽

1965 ◽

Vol 11 (11) ◽

pp. 1023-1035 ◽

Cited By ~ 8

Author(s):

Alan Mather ◽

Angel Assimos

Keyword(s):

Liquid Chromatography ◽

Alcohol Concentration ◽

Quantitative Assay ◽

Gas Liquid Chromatography ◽

Internal Reference ◽

Detection And Identification ◽

Large Populations ◽

The Common ◽

Low Levels ◽

Better Than

Abstract A simple screening by gas-liquid chromatography (GLC) can provide definitive answers in the detection and identification of a number of volatile substances, including acetone and the common alcohols. After identification, quantitative assay by an internal-reference technic yields highly specific values for ethyl alcohol concentration with a precision at least equal to (and for low levels, better than) that of conventional assays. The unique advantage of GLC is in its simultaneous quantitative assay of mixtures, some of which cannot be satisfactorily assayed or even recognized in any other way. The combination of speed and negligible sample volumes render the technic valuable for sequential studies on capillary blood samples and, potentially, for mass screening of large populations.

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Capsule-LPI: a LncRNA–protein interaction predicting tool based on a capsule network

BMC Bioinformatics ◽

10.1186/s12859-021-04171-y ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Ying Li ◽

Hang Sun ◽

Shiyao Feng ◽

Qi Zhang ◽

Siyu Han ◽

...

Keyword(s):

Protein Interactions ◽

State Of The Art ◽

Recognition Performance ◽

Feature Learning ◽

Biological Processes ◽

Multimodal Features ◽

Learning Architectures ◽

Motif Information ◽

Experimental Comparisons ◽

Better Than

Abstract Background Long noncoding RNAs (lncRNAs) play important roles in multiple biological processes. Identifying LncRNA–protein interactions (LPIs) is key to understanding lncRNA functions. Although some LPIs computational methods have been developed, the LPIs prediction problem remains challenging. How to integrate multimodal features from more perspectives and build deep learning architectures with better recognition performance have always been the focus of research on LPIs. Results We present a novel multichannel capsule network framework to integrate multimodal features for LPI prediction, Capsule-LPI. Capsule-LPI integrates four groups of multimodal features, including sequence features, motif information, physicochemical properties and secondary structure features. Capsule-LPI is composed of four feature-learning subnetworks and one capsule subnetwork. Through comprehensive experimental comparisons and evaluations, we demonstrate that both multimodal features and the architecture of the multichannel capsule network can significantly improve the performance of LPI prediction. The experimental results show that Capsule-LPI performs better than the existing state-of-the-art tools. The precision of Capsule-LPI is 87.3%, which represents a 1.7% improvement. The F-value of Capsule-LPI is 92.2%, which represents a 1.4% improvement. Conclusions This study provides a novel and feasible LPI prediction tool based on the integration of multimodal features and a capsule network. A webserver (http://csbg-jlu.site/lpc/predict) is developed to be convenient for users.

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A data-driven neural network architecture for sentiment analysis

Data Technologies and Applications ◽

10.1108/dta-03-2018-0017 ◽

2019 ◽

Vol 53 (1) ◽

pp. 2-19 ◽

Cited By ~ 1

Author(s):

Erion Çano ◽

Maurizio Morisio

Keyword(s):

Neural Network ◽

Sentiment Analysis ◽

Network Architecture ◽

Network Models ◽

Data Sets ◽

Feature Maps ◽

Neural Network Architecture ◽

Neural Network Models ◽

Content Type ◽

Max Pooling

Purpose The fabulous results of convolution neural networks in image-related tasks attracted attention of text mining, sentiment analysis and other text analysis researchers. It is, however, difficult to find enough data for feeding such networks, optimize their parameters, and make the right design choices when constructing network architectures. The purpose of this paper is to present the creation steps of two big data sets of song emotions. The authors also explore usage of convolution and max-pooling neural layers on song lyrics, product and movie review text data sets. Three variants of a simple and flexible neural network architecture are also compared. Design/methodology/approach The intention was to spot any important patterns that can serve as guidelines for parameter optimization of similar models. The authors also wanted to identify architecture design choices which lead to high performing sentiment analysis models. To this end, the authors conducted a series of experiments with neural architectures of various configurations. Findings The results indicate that parallel convolutions of filter lengths up to 3 are usually enough for capturing relevant text features. Also, max-pooling region size should be adapted to the length of text documents for producing the best feature maps. Originality/value Top results the authors got are obtained with feature maps of lengths 6–18. An improvement on future neural network models for sentiment analysis could be generating sentiment polarity prediction of documents using aggregation of predictions on smaller excerpt of the entire text.

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