A Superdirective Beamforming Approach with Impedance Coupling and Field Coupling for Compact Antenna Arrays

In most multiple-input multiple-output (MIMO) communication systems, the antenna spacing is generally no less than half a wavelength. It helps to reduce the mutual coupling and therefore facilitate the system design. The maximum array gain equals the number of antennas in this settings. However, when the antenna spacing is made very small, the array gain of a compact array can be proportional to the square of the number of antennas - a value much larger than the traditional array. To achieve this so-called ``superdirectivity" however, the calculation of the excitation coefficients (beamforming vector) is known to be a challenging problem. In this paper, we address this problem with a novel double coupling-based superdirective beamforming method. In particular, we categorize the antenna coupling effects to impedance coupling and field coupling. By characterizing these two coupling in model, we derive the beamforming vector for superdirective arrays. In order to obtain the field coupling matrix, we propose a spherical wave expansion approach, which is effective in both simulations and realistic scenarios. Moreover, a prototype of the independently controlled superdirective antenna array is developed. Full-wave electromagnetic simulations and real-world experiments validate the effectiveness of our proposed approaches, and superdirectivity is achieved in reality by a compact array with 4 and 5 dipole antennas.Comment: arXiv admin note: text overlap with arXiv:2204.1154

Real-time Animation Generation and Control on Rigged Models via Large Language Models

We introduce a novel method for real-time animation control and generation on rigged models using natural language input. First, we embed a large language model (LLM) in Unity to output structured texts that can be parsed into diverse and realistic animations. Second, we illustrate LLM's potential to enable flexible state transition between existing animations. We showcase the robustness of our approach through qualitative results on various rigged models and motions.Comment: Accepted to NeurIPS Workshop on ML for Creativity and Design 202

Towards Efficient 3D Object Detection in Bird's-Eye-View Space for Autonomous Driving: A Convolutional-Only Approach

3D object detection in Bird's-Eye-View (BEV) space has recently emerged as a prevalent approach in the field of autonomous driving. Despite the demonstrated improvements in accuracy and velocity estimation compared to perspective view methods, the deployment of BEV-based techniques in real-world autonomous vehicles remains challenging. This is primarily due to their reliance on vision-transformer (ViT) based architectures, which introduce quadratic complexity with respect to the input resolution. To address this issue, we propose an efficient BEV-based 3D detection framework called BEVENet, which leverages a convolutional-only architectural design to circumvent the limitations of ViT models while maintaining the effectiveness of BEV-based methods. Our experiments show that BEVENet is 3$\times$ faster than contemporary state-of-the-art (SOTA) approaches on the NuScenes challenge, achieving a mean average precision (mAP) of 0.456 and a nuScenes detection score (NDS) of 0.555 on the NuScenes validation dataset, with an inference speed of 47.6 frames per second. To the best of our knowledge, this study stands as the first to achieve such significant efficiency improvements for BEV-based methods, highlighting their enhanced feasibility for real-world autonomous driving applications

A new chromosome-scale duck genome shows a major histocompatibility complex with several expanded multigene families

BACKGROUND: The duck (Anas platyrhynchos) is one of the principal natural hosts of influenza A virus (IAV), harbors almost all subtypes of IAVs and resists to many IAVs which cause extreme virulence in chicken and human. However, the response of duck's adaptive immune system to IAV infection is poorly characterized due to lack of a detailed gene map of the major histocompatibility complex (MHC).RESULTS: We herein reported a chromosome-scale Beijing duck assembly by integrating Nanopore, Bionano, and Hi-C data. This new reference genome SKLA1.0 covers 40 chromosomes, improves the contig N50 of the previous duck assembly with highest contiguity (ZJU1.0) of more than a 5.79-fold, surpasses the chicken and zebra finch references in sequence contiguity and contains a complete genomic map of the MHC. Our 3D MHC genomic map demonstrated that gene family arrangement in this region was primordial; however, families such as AnplMHCI, AnplMHCIIβ, AnplDMB, NKRL (NK cell receptor-like genes) and BTN underwent gene expansion events making this area complex. These gene families are distributed in two TADs and genes sharing the same TAD may work in a co-regulated model.CONCLUSIONS: These observations supported the hypothesis that duck's adaptive immunity had been optimized with expanded and diversified key immune genes which might help duck to combat influenza virus. This work provided a high-quality Beijing duck genome for biological research and shed light on new strategies for AIV control.</p

Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale

Neural Architecture Search (NAS) has demonstrated its efficacy in computer vision and potential for ranking systems. However, prior work focused on academic problems, which are evaluated at small scale under well-controlled fixed baselines. In industry system, such as ranking system in Meta, it is unclear whether NAS algorithms from the literature can outperform production baselines because of: (1) scale - Meta ranking systems serve billions of users, (2) strong baselines - the baselines are production models optimized by hundreds to thousands of world-class engineers for years since the rise of deep learning, (3) dynamic baselines - engineers may have established new and stronger baselines during NAS search, and (4) efficiency - the search pipeline must yield results quickly in alignment with the productionization life cycle. In this paper, we present Rankitect, a NAS software framework for ranking systems at Meta. Rankitect seeks to build brand new architectures by composing low level building blocks from scratch. Rankitect implements and improves state-of-the-art (SOTA) NAS methods for comprehensive and fair comparison under the same search space, including sampling-based NAS, one-shot NAS, and Differentiable NAS (DNAS). We evaluate Rankitect by comparing to multiple production ranking models at Meta. We find that Rankitect can discover new models from scratch achieving competitive tradeoff between Normalized Entropy loss and FLOPs. When utilizing search space designed by engineers, Rankitect can generate better models than engineers, achieving positive offline evaluation and online A/B test at Meta scale.Comment: Wei Wen and Kuang-Hung Liu contribute equall

Genomic monitoring of SARS-CoV-2 uncovers an Nsp1 deletion variant that modulates type I interferon response

The SARS-CoV-2 virus, the causative agent of COVID-19, is undergoing constant mutation. Here, we utilized an integrative approach combining epidemiology, virus genome sequencing, clinical phenotyping, and experimental validation to locate mutations of clinical importance. We identified 35 recurrent variants, some of which are associated with clinical phenotypes related to severity. One variant, containing a deletion in the Nsp1-coding region (D500-532), was found in more than 20% of our sequenced samples and associates with higher RT-PCR cycle thresholds and lower serum IFN-beta levels of infected patients. Deletion variants in this locus were found in 37 countries worldwide, and viruses isolated from clinical samples or engineered by reverse genetics with related deletions in Nsp1 also induce lower IFN-beta responses in infected Calu-3 cells. Taken together, our virologic surveillance characterizes recurrent genetic diversity and identified mutations in Nsp1 of biological and clinical importance, which collectively may aid molecular diagnostics and drug design.Peer reviewe

Multi-Label Fundus Image Classification Using Attention Mechanisms and Feature Fusion

Fundus diseases can cause irreversible vision loss in both eyes if not diagnosed and treated immediately. Due to the complexity of fundus diseases, the probability of fundus images containing two or more diseases is extremely high, while existing deep learning-based fundus image classification algorithms have low diagnostic accuracy in multi-labeled fundus images. In this paper, a multi-label classification of fundus disease with binocular fundus images is presented, using a neural network algorithm model based on attention mechanisms and feature fusion. The algorithm highlights detailed features in binocular fundus images, and then feeds them into a ResNet50 network with attention mechanisms to extract fundus image lesion features. The model obtains global features of binocular images through feature fusion and uses Softmax to classify multi-label fundus images. The ODIR binocular fundus image dataset was used to evaluate the network classification performance and conduct ablation experiments. The model’s backend is the Tensorflow framework. Through experiments on the test images, this method achieved accuracy, precision, recall, and F1 values of 94.23%, 99.09%, 99.23%, and 99.16%, respectively

Knowledge Mapping Analysis of Transnational Agricultural Land Investment Research

With the expansion of the global transnational agricultural planting scale, research on transnational agricultural land investment is growing. In order to analyze the development context and basic characteristics of this research, and to discover the research hotspots and frontiers, this study used documentation and bibliometric methods to examine the achievements of it. The results show the following: (1) Transnational agricultural land investment research is mainly focused on the social sciences, development studies, economics, environmental sciences and geography. (2) The concentration of researchers in this research field is not high, and there is still a lack of authoritative researchers with high influence. The cooperation network has been initially formed between research institutions. Among them, universities and research institutes are the main institutions of transnational agricultural land investment research, but the degree of integration among the research teams is not high. (3) The evolution of the research theme of the field has experienced three stages—an embryonic stage, growth stage and stable stage—and the research content shows a trend of continuous divergence and deepening. (4) From 2005 to 2019, the research hotspots of the research focused on “Land Grabbing, Global Land, Africa, Investment”. At present, the emerging frontier research topics are “Indonesia, Livelihood, Trajectory and Sustainability”. With many years of development, the research has become an obvious "land" attribute, independent from traditional agricultural economic research, and the research topics are becoming more and more mature, refined and diversified. Transnational agricultural land investment research is attracting continuous attention from scholars in multiple disciplines and fields

JNK Signaling Pathway Suppresses LPS-Mediated Apoptosis of HK-2 Cells by Upregulating NGAL

Objective. To explore the role of the c-Jun N-terminal kinase (JNK) signaling pathway in upregulated NGAL expression and its antiapoptotic mechanism in lipopolysaccharide (LPS)-mediated renal tubular epithelial cell injury. Methods. In vitro, HK-2 cells were divided into five groups (Con, LPS 1 h, LPS 3 h, LPS 6 h, and LPS 12 h groups) based on the time of LPS (10 μM) treatment. NGAL and caspase-3 gene expression levels were detected by RT-PCR to assess dynamic changes. HK-2 cells were pretreated with SP600125 (20 μM) for 2 hours, followed by LPS (10 μM) stimulation for 3 hours. NGAL and caspase-3 gene expression levels were then determined. Results. NGAL mRNA was increased significantly within 6 hours, and caspase-3 mRNA was increased within 3 hours after treatment (P<0.05). Correlation analysis showed a high correlation between their expression (r = 0.448, P<0.05). After pretreatment with SP600125, mRNA expression of NGAL in the LPS group was inhibited, while that of caspase-3 was increased significantly. The NGAL mRNA expression level in the SB + LPS group was decreased significantly compared with that in the LPS group, but it was slightly higher than that in the SP group (∼1.5 times of that in the Con group). However, caspase-3 mRNA expression was increased significantly in the SB + LPS group (P<0.001) (3.5 times of that in the Con group). It also showed a significant increase compared with SP and LPS groups (P<0.001 vs. SB group; P<0.05 vs. LPS group). We also found that NGAL and caspase 3 proteins were increased significantly in LPS and SP + LPS groups, but SP600125 decreased the NGAL level by almost 35% and increased the caspase 3 level by 50% in the SP + LPS group compared with the LPS group (P<0.05). Conclusions. The JNK signaling pathway inhibits LPS-mediated apoptosis of renal tubular epithelial cells by upregulating NGAL