Singly authored papers contribute the most to scientists’ impact

Utilizing citation data for 100,000 most-cited scientists in the Scopus database, this paper investigated how citations received by an author in different authorship affect his/her academic impact differently. Using a linear regression model as an estimation, it shows that the citations received as the single author of a paper elevates the academic impact the most, followed by that as the first (but not single) author, last author, and middle author. Differences also emerged when we probed into different research fields separately as in some fields citations in the four types of authorship do not differ a lot, and also in some fields, the last-authored citations could 'outweigh' the first-authored ones

Deep Joint Source Channel Coding With Attention Modules Over MIMO Channels

In this paper, we propose two deep joint source and channel coding (DJSCC) structures with attention modules for the multi-input multi-output (MIMO) channel, including a serial structure and a parallel structure. With singular value decomposition (SVD)-based precoding scheme, the MIMO channel can be decomposed into various sub-channels, and the feature outputs will experience sub-channels with different channel qualities. In the serial structure, one single network is used at both the transmitter and the receiver to jointly process data streams of all MIMO subchannels, while data steams of different MIMO subchannels are processed independently via multiple sub-networks in the parallel structure. The attention modules in both serial and parallel architectures enable the system to adapt to varying channel qualities and adjust the quantity of information outputs in accordance with the channel qualities. Experimental results demonstrate the proposed DJSCC structures have improved image transmission performance, and reveal the phenomenon via non-parameter entropy estimation that the learned DJSCC transceivers tend to transmit more information over better sub-channels

Clinical treatment on patients with infectious keratitis by chestnut thorn

AIM:To investigate the clinical treatment on patients with infectious keratitis by chestnut thorn. <p>METHODS: Retrospective analysis of 28 cases(28 eyes)with infectious keratitis due to chestnut thorn from June 2009 to October 2012. All patients had the clinical manifestations such as local infiltration, edema and ulcer formation. Chestnut thorn located deeply into corneal stroma, but did not penetrate into the anterior chamber. All patients underwent emergency surgery to remove chestnut thorn, of which 14 patients underwent corneal debridement joint multilayer amniotic membrane transplantation as the treatment group, and the other 14 patients refused amniotic membrane transplantation and had the chestnut thorn removed only as the control group. The corneal epithelial healing time, the degree of improvement of visual acuity and the incidence of complications were compared between the two groups after 3 months.<p>RESULTS: The corneal epithelial average healing time of the treatment group was significantly shortened compared with the control group(<i>t</i>=13.6, <i>P</i><0.05), the visual acuity of the treatment group was improved significantly higher than that in the control group, and the incidence of complications of the treatment group was significantly decreased compared with the control group. <p>CONCLUSION: For the patients with corneal ulcer due to deep chestnut thorn, emergency surgery of corneal debridement joint multilayer amniotic membrane transplantation can promote the repair of the cornea and prevent the occurrence of complications after injury

VisuaLizations As Intermediate Representations (VLAIR) : an approach for applying deep learning-based computer vision to non-image-based data

We thank the China Scholarship Council (CSC) for financially supporting my PhD study at University of St Andrews, UK, and NSERC Discovery Grant 2020-04401 (Miguel Nacenta).Deep learning algorithms increasingly support automated systems in areas such as human activity recognition and purchase recommendation. We identify a current trend in which data is transformed first into abstract visualizations and then processed by a computer vision deep learning pipeline. We call this VisuaLization As Intermediate Representation (VLAIR) and believe that it can be instrumental to support accurate recognition in a number of fields while also enhancing humans’ ability to interpret deep learning models for debugging purposes or in personal use. In this paper we describe the potential advantages of this approach and explore various visualization mappings and deep learning architectures. We evaluate several VLAIR alternatives for a specific problem (human activity recognition in an apartment) and show that VLAIR attains classification accuracy above classical machine learning algorithms and several other non-image-based deep learning algorithms with several data representations.Publisher PDFPeer reviewe

A La0.8Sr0.2MnO3/La0.6Sr0.4Co0.2Fe0.8O3−δ core–shell structured cathode by a rapid sintering process for solid oxide fuel cells

A La0.8Sr0.2MnO3 (LSM)/La0.6Sr0.4Co0.2Fe0.8O3−δ (LSCF) core–shell structured composite cathode of solid oxide fuel cells (SOFCs) has been fabricated by wet infiltration followed by a rapid sintering (RS) process. The RS is carried out by placing LSCF infiltrated LSM electrodes directly into a preheated furnace at 800 °C for 10 min and cooling down very quickly. The heating and cooling step takes about 20 s, substantially shorter than 10 h in the case of conventional sintering (CS) process. The results indicate the formation of a continuous and almost non-porous LSCF thin film on the LSM scaffold, forming a LSCF/LSM core–shell structure. Such RS-formed infiltrated LSCF–LSM cathodes show an electrode polarization resistance of 2.1 Ω cm2 at 700 °C, substantially smaller than 88.2 Ω cm2 of pristine LSM electrode. The core–shell structured LSCF–LSM electrodes also show good operating stability at 700 °C and 600 °C over 24–40 h

Multifocal micronodular pneumocyte hyperplasia with a novel mutation in TSC1: a case report

We report on a 34-year-old woman diagnosed with tuberous sclerosis complex. The patient was admitted for respiratory manifestations, while multi-organ involvement made the diagnostic process challenging. Genetic testing revealed a novel mutation TSC1 c.2094_2110del (p.His699Ter), which expands the disease-causing variant spectrum. Our results may facilitate the disease diagnostics and help to devise genetic counseling and targeted gene therapy

Visualization as Intermediate Representations (VLAIR) for human activity recognition

Ambient, binary, event-driven sensor data is useful for many human activity recognition applications such as smart homes and ambient-assisted living. These sensors are privacy-preserving, unobtrusive, inexpensive and easy to deploy in scenarios that require detection of simple activities such as going to sleep, and leaving the house. However, classification performance is still a challenge, especially when multiple people share the same space or when different activities take place in the same areas. To improve classification performance we develop what we call a Visualization as Intermediate Representations (VLAIR) approach. The main idea is to re-represent the data as visualizations (generated pixel images) in a similar way as how visualizations are created for humans to analyze and communicate data. Then we can feed these images to a convolutional neural network whose strength resides in extracting effective visual features. We have tested five variants (mappings) of the VLAIR approach and compared them to a collection of classifiers commonly used in classic human activity recognition. The best of the VLAIR approaches outperforms the best baseline, with strong advantage in recognising less frequent activities and distinguishing users and activities in common areas. We conclude the paper with a discussion on why and how VLAIR can be useful in human activity recognition scenarios and beyond.Postprin