RLAD: A Reliable Hippo-guided Multi-task Model for Alzheimer's Disease Diagnosis.

Early diagnosis of Alzheimer's disease (AD) is crucial for its prevention, and hippocampal atrophy is a significant lesion for early diagnosis. The current DL-based AD diagnosis methods only focus on either AD classification or hippocampus segmentation independently, neglecting the correlation between the two tasks and lacking pathological interpretability. To address this issue, we propose a Reliable Hippo-guided Learning model for Alzheimer's Disease diagnosis (RLAD), which employs multi-task learning for AD classification as a main task supplemented by hippocampus segmentation. More specifically, our model consists of 1) a hybrid shared features encoder that encodes local and global information in MRI to enhance the model's ability to learn discriminative features; 2) Task Specific Decoders to accomplish AD classification and hippocampus segmentation; and 3) Task Coordination module to correlate the two tasks and guide the classification task to focus on the hippocampus area. Our proposed RLAD model is evaluated on MRI scans of 1631 subjects from three independent datasets, including ADNI-1, ADNI-2, and HarP. Our extensive experimental results demonstrate that the proposed model significantly improves the performance of AD classification and hippocampus segmentation with strong generalization capabilities. Our implementation and model are available at https://github.com/LeoLjl/Explainable-Alzheimer-s-Disease-Diagnosis.

Research Progress on Router Devices for the OAM Optical Communication.

Vortex beams carrying orbital angular momentum (OAM) provide a new degree of freedom for light waves in addition to the traditional degrees of freedom, such as intensity, phase, frequency, time, and polarization. Due to the theoretically unlimited orthogonal states, the physical dimension of OAM is capable of addressing the problem of low information capacity. With the advancement of the OAM optical communication technology, OAM router devices (OAM-RDs) have played a key role in significantly improving the flexibility and practicability of communication systems. In this review, major breakthroughs in the OAM-RDs are summarized, and the latest technological standing is examined. Additionally, a detailed account of the recent works published on techniques related to the OAM-RDs has been categorized into five areas: channel multicasting, channel switching, channel filtering, channel hopping, and channel adding/extracting. Meanwhile, the principles, research methods, advantages, and disadvantages are discussed and summarized in depth while analyzing the future development trends and prospects of the OAM-RDs.

Vision transformers motivating superior OAM mode recognition in optical communications.

Orbital angular momentum (OAM) has recently obtained tremendous research interest in free-space optical communications (FSO). During signal transmission within the free-space link, atmospheric turbulence (AT) poses a significant challenge as it diminishes the signal strength and introduce intermodal crosstalk, significantly reducing OAM mode detection accuracy. This issue directly impacts the performance of OAM-based communication systems and leads to a reduction in received information. To address this critical bottleneck of low mode recognition accuracy in OAM-based FSO-communications, a deep learning method based on vision transformers (ViT) is proposed for what we believe is for the first time. Designed carefully by numerous experts, the advanced self-attention mechanism of ViT captures more global information from the input image. To train the model, pretraining on a large dataset, named IMAGENET is conducted. Subsequently, we performed fine-tuning on our specific dataset, consisting of OAM beams that have undergone varying AT strengths. The computer simulation shows that based on ViT method, the multiple OAM modes can be recognized with a high accuracy (nearly 100%) under weak-to-moderate turbulence and with almost 98% accuracy even under long transmission distance with strong turbulence (C N2=1×10-14). Our findings highlight that leveraging ViT enables robust detection of complex OAM beams, mitigating the adverse effects caused by atmospheric turbulence.

Application of MXenes in Perovskite Solar Cells: A Short Review.

Application of MXene materials in perovskite solar cells (PSCs) has attracted considerable attention owing to their supreme electrical conductivity, excellent carrier mobility, adjustable surface functional groups, excellent transparency and superior mechanical properties. This article reviews the progress made so far in using Ti3C2Tx MXene materials in the building blocks of perovskite solar cells such as electrodes, hole transport layer (HTL), electron transport layer (ETL) and perovskite photoactive layer. Moreover, we provide an outlook on the exciting opportunities this recently developed field offers, and the challenges faced in effectively incorporating MXene materials in the building blocks of PSCs for better operational stability and enhanced performance.

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms.

Technologies and services towards smart-vehicles and Intelligent-Transportation-Systems (ITS), continues to revolutionize many aspects of human life. This paper presents a detailed survey of current techniques and advancements in Automatic-Number-Plate-Recognition (ANPR) systems, with a comprehensive performance comparison of various real-time tested and simulated algorithms, including those involving computer vision (CV). ANPR technology has the ability to detect and recognize vehicles by their number-plates using recognition techniques. Even with the best algorithms, a successful ANPR system deployment may require additional hardware to maximize its accuracy. The number plate condition, non-standardized formats, complex scenes, camera quality, camera mount position, tolerance to distortion, motion-blur, contrast problems, reflections, processing and memory limitations, environmental conditions, indoor/outdoor or day/night shots, software-tools or other hardware-based constraint may undermine its performance. This inconsistency, challenging environments and other complexities make ANPR an interesting field for researchers. The Internet-of-Things is beginning to shape future of many industries and is paving new ways for ITS. ANPR can be well utilized by integrating with RFID-systems, GPS, Android platforms and other similar technologies. Deep-Learning techniques are widely utilized in CV field for better detection rates. This research aims to advance the state-of-knowledge in ITS (ANPR) built on CV algorithms; by citing relevant prior work, analyzing and presenting a survey of extraction, segmentation and recognition techniques whilst providing guidelines on future trends in this area.

Hysteresis Analysis of Hole-Transport-Material-Free Monolithic Perovskite Solar Cells with Carbon Counter Electrode by Current Density-Voltage and Impedance Spectra Measurements.

Due to the tremendous increase in power conversion efficiency (PCE) of organic-inorganic perovskite solar cells (PSCs), this technology has attracted much attention. Despite being the fastest-growing photovoltaic technology to date, bottlenecks such as current density-voltage (J-V) hysteresis have significantly limited further development. Current density measurements performed with different sweep scan speeds exhibit hysteresis and the photovoltaic parameters extracted from the current density-voltage measurements for both scan directions become questionable. A current density-voltage measurement protocol needs to be established which can be used to achieve reproducible results and to compare devices made in different laboratories. In this work, we report a hysteresis analysis of a hole-transport-material-free (HTM-free) carbon-counter-electrode-based PSC conducted by current density-voltage and impedance spectra measurements. The effect of sweep scan direction and time delay was examined on the J-V characteristics of the device. The hysteresis was observed to be strongly sweep scan direction and time delay dependent and decreased as the delay increased. The J-V analysis conducted in the reverse sweep scan direction at a lower sweep time delay of 0.2 s revealed very large increases in the short circuit current density and the power conversion efficiency of 57.7% and 56.1%, respectively, compared with the values obtained during the forward scan under the same conditions. Impedance spectroscopy (IS) investigations were carried out and the effects of sweep scan speed, time delay, and frequency were analyzed. The hysteresis was observed to be strongly sweep scan direction, sweep time delay, and frequency dependent. The correlation between J-V and IS data is provided. The wealth of photovoltaic and impendence spectroscopic data reported in this work on the hysteresis study of the HTM-free PSC may help in establishing a current density-voltage measurement protocol, identifying components and interfaces causing the hysteresis, and modeling of PSCs, eventually benefiting device performance and long-term stability.

A Dataset of Pulmonary Lesions With Multiple-Level Attributes and Fine Contours.

Lung cancer is a life-threatening disease and its diagnosis is of great significance. Data scarcity and unavailability of datasets is a major bottleneck in lung cancer research. In this paper, we introduce a dataset of pulmonary lesions for designing the computer-aided diagnosis (CAD) systems. The dataset has fine contour annotations and nine attribute annotations. We define the structure of the dataset in detail, and then discuss the relationship of the attributes and pathology, and the correlation between the nine attributes with the chi-square test. To demonstrate the contribution of our dataset to computer-aided system design, we define four tasks that can be developed using our dataset. Then, we use our dataset to model multi-attribute classification tasks. We discuss the performance in 2D, 2.5D, and 3D input modes of the classification model. To improve performance, we introduce two attention mechanisms and verify the principles of the attention mechanisms through visualization. Experimental results show the relationship between different models and different levels of attributes.

Redundancy and Attention in Convolutional LSTM for Gesture Recognition.

Convolutional long short-term memory (ConvLSTM) networks have been widely used for action/gesture recognition, and different attention mechanisms have also been embedded into ConvLSTM networks. This paper explores the redundancy of spatial convolutions and the effects of the attention mechanism in ConvLSTM, based on our previous gesture recognition architectures that combine the 3-D convolutional neural network (CNN) and ConvLSTM. Depthwise separable, group, and shuffle convolutions are used to replace the convolutional structures in ConvLSTM for the redundancy analysis. In addition, four ConvLSTM variants are derived for attention analysis: 1) by removing the convolutional structures of the three gates in ConvLSTM; 2) by applying the attention mechanism on the ConvLSTM input; and 3) by reconstructing the input and 4) output gates with the modified channelwise attention mechanism. Evaluation results demonstrate that the spatial convolutions in the three gates scarcely contribute to the spatiotemporal feature fusion and that the attention mechanisms embedded into the input and output gates cannot improve the feature fusion. In other words, ConvLSTM mainly contributes to the temporal fusion along with the recurrent steps to learn long-term spatiotemporal features when taking spatial or spatiotemporal features as input. A new LSTM variant is derived on this basis in which the convolutional structures are embedded only into the input-to-state transition of LSTM. The code of the LSTM variants is publicly available.\footnotehttps://github.com/GuangmingZhu/ConvLSTMForGR.