Orbital angular momentum mode-demultiplexing scheme with partial angular receiving aperture

For long distance orbital angular momentum (OAM) based transmission, the conventional whole beam receiving scheme encounters the difficulty of large aperture due to the divergence of OAM beams. We propose a novel partial receiving scheme, using a restricted angular aperture to receive and demultiplex multi-OAM-mode beams. The scheme is theoretically analyzed to show that a regularly spaced OAM mode set remain orthogonal and therefore can be de-multiplexed. Experiments have been carried out to verify the feasibility. This partial receiving scheme can serve as an effective method with both space and cost savings for the OAM communications. It is applicable to both free space OAM optical communications and radio frequency (RF) OAM communications

Experimental Study of Oblique Pedestrian Streams

The intersecting of pedestrian streams is a common phenomenon which would lead to the pedestrian deceleration, stopping, and even threat to the safety of walking. The organization of pedestrian flow is a critical factor which influences the intersection traffic. The aim of this paper is to study the characteristics of oblique pedestrian streams by a set of pedestrian experiments. Two groups of experiment participants, three volume levels and five intersecting angles were tested. The qualitative analysis and quantitative analysis methods were applied to find out the relationship between the pedestrian streams angle and pedestrian characteristics. The results indicated that the mean and median speed, exit traffic efficiency decreased initially and increased afterwards with the increase of intersecting angles when the volume was 1,000 p/h/m and 3,000 p/h/m, while the speed standard deviation changing inversely. However, these four factors show the opposite variation tendency in volume 5,000 p/h/m. Meanwhile, the quadratic function was selected to fit them. It is found that the worst speeds of pedestrian streams were 131° and 122° in volume 1,000 p/h/m and 3,000 p/h/m, respectively, and the greatest influence on pedestrian streams was 125° in volume 5,000 p/h/m. The results of this research can help establish the foundation for the organization and optimization of intersecting pedestrian streams.</p

IOB: Integrating Optimization Transfer and Behavior Transfer for Multi-Policy Reuse

Humans have the ability to reuse previously learned policies to solve new tasks quickly, and reinforcement learning (RL) agents can do the same by transferring knowledge from source policies to a related target task. Transfer RL methods can reshape the policy optimization objective (optimization transfer) or influence the behavior policy (behavior transfer) using source policies. However, selecting the appropriate source policy with limited samples to guide target policy learning has been a challenge. Previous methods introduce additional components, such as hierarchical policies or estimations of source policies' value functions, which can lead to non-stationary policy optimization or heavy sampling costs, diminishing transfer effectiveness. To address this challenge, we propose a novel transfer RL method that selects the source policy without training extra components. Our method utilizes the Q function in the actor-critic framework to guide policy selection, choosing the source policy with the largest one-step improvement over the current target policy. We integrate optimization transfer and behavior transfer (IOB) by regularizing the learned policy to mimic the guidance policy and combining them as the behavior policy. This integration significantly enhances transfer effectiveness, surpasses state-of-the-art transfer RL baselines in benchmark tasks, and improves final performance and knowledge transferability in continual learning scenarios. Additionally, we show that our optimization transfer technique is guaranteed to improve target policy learning.Comment: 26 pages, 9 figure

Sketch Input Method Editor: A Comprehensive Dataset and Methodology for Systematic Input Recognition

With the recent surge in the use of touchscreen devices, free-hand sketching has emerged as a promising modality for human-computer interaction. While previous research has focused on tasks such as recognition, retrieval, and generation of familiar everyday objects, this study aims to create a Sketch Input Method Editor (SketchIME) specifically designed for a professional C4I system. Within this system, sketches are utilized as low-fidelity prototypes for recommending standardized symbols in the creation of comprehensive situation maps. This paper also presents a systematic dataset comprising 374 specialized sketch types, and proposes a simultaneous recognition and segmentation architecture with multilevel supervision between recognition and segmentation to improve performance and enhance interpretability. By incorporating few-shot domain adaptation and class-incremental learning, the network's ability to adapt to new users and extend to new task-specific classes is significantly enhanced. Results from experiments conducted on both the proposed dataset and the SPG dataset illustrate the superior performance of the proposed architecture. Our dataset and code are publicly available at https://github.com/GuangmingZhu/SketchIME.Comment: The paper has been accepted by ACM Multimedia 202

Excitation of extraordinary modes inside the source of Saturn's kilometric radiation

The electron cyclotron maser instability (ECMI) of extraordinary mode waves was investigated with the parameters observed in Saturn's kilometric radiation (SKR) sources. Previous studies employed simplified dispersion relations, and did not consider the excitation of the relativistic (R) mode. This mode is introduced by considering the relativistic effect in plasmas consisting of both cold and hot electrons. Using particle-in-cell simulations, we investigated the excitation of R and X modes based on the measured data. Using the reported value of the density ratio of energetic to total electrons $n_e/n_0=24\%$, the most unstable mode is the R mode. The escaping X-mode emissions are amplified only if the energetic electrons are dominant with $n_e/n_0 \ge 90\%$. For these cases, only the X mode is excited and the R mode disappears due to its strong coupling. The results are well in line with the linear kinetic theory of ECMI. The properties of both the R and X modes are consistent with the observed SKR emissions. This raises questions about the nature of the measured electric field fluctuations within ``presumed'' SKR sources. The study provides new insights into the ECMI process relevant to SKR emission mechanisms

Repurposing Niclosamide as a Novel Anti-SARS-CoV-2 Drug by Restricting Entry Protein CD147

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the global coronavirus disease 2019 (COVID-19) pandemic, and the search for effective treatments has been limited. Furthermore, the rapid mutations of SARS-CoV-2 have posed challenges to existing vaccines and neutralizing antibodies, as they struggle to keep up with the increased viral transmissibility and immune evasion. However, there is hope in targeting the CD147-spike protein, which serves as an alternative point for the entry of SARS-CoV-2 into host cells. This protein has emerged as a promising therapeutic target for the development of drugs against COVID-19. Here, we demonstrate that the RNA-binding protein Human-antigen R (HuR) plays a crucial role in the post-transcriptional regulation of CD147 by directly binding to its 3′-untranslated region (UTR). We observed a decrease in CD147 levels across multiple cell lines upon HuR depletion. Furthermore, we identified that niclosamide can reduce CD147 by lowering the cytoplasmic translocation of HuR and reducing CD147 glycosylation. Moreover, our investigation revealed that SARS-CoV-2 infection induces an upregulation of CD147 in ACE2-expressing A549 cells, which can be effectively neutralized by niclosamide in a dose-dependent manner. Overall, our study unveils a novel regulatory mechanism of regulating CD147 through HuR and suggests niclosamide as a promising therapeutic option against COVID-19

Deep Learning Based Instance Segmentation in 3D Biomedical Images Using Weak Annotation

Instance segmentation in 3D images is a fundamental task in biomedical image analysis. While deep learning models often work well for 2D instance segmentation, 3D instance segmentation still faces critical challenges, such as insufficient training data due to various annotation difficulties in 3D biomedical images. Common 3D annotation methods (e.g., full voxel annotation) incur high workloads and costs for labeling enough instances for training deep learning 3D instance segmentation models. In this paper, we propose a new weak annotation approach for training a fast deep learning 3D instance segmentation model without using full voxel mask annotation. Our approach needs only 3D bounding boxes for all instances and full voxel annotation for a small fraction of the instances, and uses a novel two-stage 3D instance segmentation model utilizing these two kinds of annotation, respectively. We evaluate our approach on several biomedical image datasets, and the experimental results show that (1) with full annotated boxes and a small amount of masks, our approach can achieve similar performance as the best known methods using full annotation, and (2) with similar annotation time, our approach outperforms the best known methods that use full annotation.Comment: Accepted by MICCAI 201