Unified Joint Matrix-Monotonic Optimization of MIMO Training Sequences and Transceivers

Channel estimation and transmission constitute the most fundamental functional modules of multiple-input multiple-output (MIMO) communication systems. The underlying key tasks corresponding to these modules are training sequence optimization and transceiver optimization. Hence, we jointly optimize the linear transmit precoder and the training sequence of MIMO systems using the metrics of their effective mutual information (MI), effective mean squared error (MSE), effective weighted MI, effective weighted MSE, as well as their effective generic Schur-convex and Schur-concave functions. Both statistical channel state information (CSI) and estimated CSI are considered at the transmitter in the joint optimization. A unified framework termed as joint matrix-monotonic optimization is proposed. Based on this, the optimal precoder matrix and training matrix structures can be derived for both CSI scenarios. Then, based on the optimal matrix structures, our linear transceivers and their training sequences can be jointly optimized. Compared to state-of-the-art benchmark algorithms, the proposed algorithms visualize the bold explicit relationships between the attainable system performance of our linear transceivers conceived and their training sequences, leading to implementation ready recipes. Finally, several numerical results are provided, which corroborate our theoretical results and demonstrate the compelling benefits of our proposed pilot-aided MIMO solutions.Comment: 29 pages, 7 figure

3DSAM-adapter: Holistic Adaptation of SAM from 2D to 3D for Promptable Medical Image Segmentation

Despite that the segment anything model (SAM) achieved impressive results on general-purpose semantic segmentation with strong generalization ability on daily images, its demonstrated performance on medical image segmentation is less precise and not stable, especially when dealing with tumor segmentation tasks that involve objects of small sizes, irregular shapes, and low contrast. Notably, the original SAM architecture is designed for 2D natural images, therefore would not be able to extract the 3D spatial information from volumetric medical data effectively. In this paper, we propose a novel adaptation method for transferring SAM from 2D to 3D for promptable medical image segmentation. Through a holistically designed scheme for architecture modification, we transfer the SAM to support volumetric inputs while retaining the majority of its pre-trained parameters for reuse. The fine-tuning process is conducted in a parameter-efficient manner, wherein most of the pre-trained parameters remain frozen, and only a few lightweight spatial adapters are introduced and tuned. Regardless of the domain gap between natural and medical data and the disparity in the spatial arrangement between 2D and 3D, the transformer trained on natural images can effectively capture the spatial patterns present in volumetric medical images with only lightweight adaptations. We conduct experiments on four open-source tumor segmentation datasets, and with a single click prompt, our model can outperform domain state-of-the-art medical image segmentation models on 3 out of 4 tasks, specifically by 8.25%, 29.87%, and 10.11% for kidney tumor, pancreas tumor, colon cancer segmentation, and achieve similar performance for liver tumor segmentation. We also compare our adaptation method with existing popular adapters, and observed significant performance improvement on most datasets.Comment: 14 pages, 6 figures, 5 table

Mechanistic basis for mitigating drought tolerance by selenium application in tobacco (Nicotiana tabacum L.): a multi-omics approach

The lack of irrigation water in agricultural soils poses a significant constraint on global crop production. In-depth investigation into microRNAs (miRNAs) has been widely used to achieve a comprehensive understanding of plant defense mechanisms. However, there is limited knowledge on the association of miRNAs with drought tolerance in cigar tobacco. In this study, a hydroponic experiment was carried out to identify changes in plant physiological characteristics, miRNA expression and metabolite profile under drought stress, and examine the mitigating effects of selenium (Se) application. The shoot dry weight of drought-stressed plants was approximately half (50.3%) of that in non-stressed (control) conditions. However, plants supplied with Se attained 38.8% greater shoot dry weight as compared to plants with no Se supply under drought stress. Thirteen miRNAs were identified to be associated with drought tolerance. These included 7 known (such as nta-miR156b and nta-miR166a) and 6 novel miRNAs (such as novel-nta-miR156-5p and novel-nta-miR209-5p) with the target genes of squamosa promoter-binding-like protein 4 (SPL4), serine/threonine protein phosphatase 2A (PPP2A), cation/calcium exchanger 4-like (CCX4), extensin-1-like (EXT1) and reduced wall acetylation 2 (RWA2). Further investigation revealed that the expression levels of Ext1 and RWA2 were significantly decreased under drought stress but increased with Se addition. Moreover, key metabolites such as catechin and N-acetylneuraminic acid were identified, which may play a role in the regulation of drought tolerance. The integrated analysis of miRNA sequencing and metabolome highlighted the significance of the novel-nta-miR97-5p- LRR-RLK- catechin pathway in regulating drought tolerance. Our findings provide valuable insights into the molecular mechanisms underlying drought tolerance and Se-induced stress alleviation in cigar tobacco

Adaptive Collision Avoidance for Multiple UAVs in Urban Environments

The increasing number of unmanned aerial vehicles (UAVs) in low-altitude airspace is seriously threatening the safety of the urban environment. This paper proposes an adaptive collision avoidance method for multiple UAVs (mUAVs), aiming to provide a safe guidance for UAVs at risk of collision. The proposed method is formulated as a two−layer resolution framework with the considerations of speed adjustment and rerouting strategies. The first layer is established as a deep reinforcement learning (DRL) model with a continuous state space and action space that adaptively selects the most suitable resolution strategy for UAV pairs. The second layer is developed as a collaborative mUAV collision avoidance model, which combines a three-dimensional conflict detection and conflict resolution pool to perform resolution. To train the DRL model, in this paper, a deep deterministic policy gradient (DDPG) algorithm is introduced and improved upon. The results demonstrate that the average time required to calculate a strategy is 0.096 s, the success rate reaches 95.03%, and the extra flight distance is 26.8 m, which meets the real-time requirements and provides a reliable reference for human intervention. The proposed method can adapt to various scenarios, e.g., different numbers and positions of UAVs, with interference from random factors. The improved DDPG algorithm can also significantly improve convergence speed and save training time

Research on Adaptive Friction Compensation of Digital Hydraulic Cylinder Based on LuGre Friction Model

This paper aims to eliminate nonlinear friction from the performance of the digital hydraulic cylinder to enable it to have good adaptive ability. First, a mathematical model of a digital hydraulic cylinder based on the LuGre friction model was established, and then a dual-observer structure was designed to estimate the unobservable state variables in the friction model. The Lyapunov method is used to prove the global asymptotic stability of the closed-loop system using the adaptive friction compensation method. Finally, Simulink is used to simulate the system performance. The simulation results indicate that the addition of adaptive friction compensation control can effectively reduce system static error, suppress system limit loop oscillation, “position decapitation,” “speed dead zone,” and low-speed creep phenomena, and improve the overall performance of the digital hydraulic cylinder. The control method has practical application value for improving the performance index of the digital hydraulic cylinder

Collaborative Attention Memory Network for Video Object Segmentation

Semi-supervised video object segmentation is a fundamental yet Challenging task in computer vision. Embedding matching based CFBI series networks have achieved promising results by foreground-background integration approach. Despite its superior performance, these works exhibit distinct shortcomings, especially the false predictions caused by little appearance instances in first frame, even they could easily be recognized by previous frame. Moreover, they suffer from object's occlusion and error drifts. In order to overcome the shortcomings , we propose Collaborative Attention Memory Network with an enhanced segmentation head. We introduce a object context scheme that explicitly enhances the object information, which aims at only gathering the pixels that belong to the same category as a given pixel as its context. Additionally, a segmentation head with Feature Pyramid Attention(FPA) module is adopted to perform spatial pyramid attention structure on high-level output. Furthermore, we propose an ensemble network to combine STM network with all these new refined CFBI network. Finally, we evaluated our approach on the 2021 Youtube-VOS challenge where we obtain 6th place with an overall score of 83.5\%.Comment: Technical Report. Proposed systems attain 6th in YouTube-VOS challenge 202

Significance of CAVI, hs-CRP and Homocysteine in subclinical arteriosclerosis among a healthy population in China

Purpose: The purpose of this study is to evaluate the ability of cardio-ankle vascular index (CAVI), high-sensitivity C-reactive protein (hs-CRP) levels and homocysteine (Hcy) levels to screen for subclinical arteriosclerosis (subAs) in an apparently healthy population, with the view to obtaining an optimal diagnostic marker or profile for subAs. Methods: Subjects (152) undergoing routine health examinations were recruited and divided into two groups: carotid arteriosclerosis (CA) and non-carotid arteriosclerosis (NCA), according to carotid intima-media thickness (CMIT). CAVI was calculated based on blood pressure and pulse wave velocity. Serum hs-CRP and Hcy levels were also measured. A Receiver Operating Characteristic (ROC) curve was plotted to evaluate the efficacy of each in carotid arteriosclerosis screening. Ten parameter combinations, designated W1 to W10, were compared in terms of sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Results: The levels of all three parameters were significantly higher in the CA group, compared with the NCA group. ROC curves showed that the area under the curve (AUC) for CAVI was 0.708 (95%CI: 0.615-0.800), which is significantly larger than that of either hs-CRP (0.622) or Hcy (0.630), respectively (P < 0.001). Maximum sensitivity (100%) and NPV (100%) were attained with W10, while maximum specificity (86.2%) and PPV (46.7%) were obtained with W7. With W9, the maximum Youden index (0.416) was obtained, with a sensitivity of 77.8% and specificity of 63.8%. Conclusions: CAVI is more effective than hs-CRP or Hcy for subAs screening. The optimal profile was obtained with a combination of CAVI and other parameters

Phylogenetic study of Ameiurus melas based on complete mitochondrial DNA sequence

In our research, 16 sets of primers were used to amplify contiguous, overlapping segments of the complete mitochondrial DNA (mtDNA) of Ameiurus melas. The total length of the mitochondrial genome is 16 512 bp and deposited in the GenBank with accession no. KT804702. The gene arrangement and transcriptional direction were similar to other bony fishes which contained 37 genes (13 protein-coding genes, 2 ribosomal RNA, and 22 transfer RNAs) and a major non-coding control region. The G contents were lowest (17.36%) and the nucleotide skewness for the coding strands of A. melas (GC-skew = -0.24) is biased toward G and the negative GC-skew ranges from -0.44 (ND6) to -0.13 (CO1). The phylogenetic studies indicate that A. melas and the Ictalurus punctatus are cluster together, they are sister group. However, the phylogenetic relationship of other Cyprininae has some differences, such as Pangasianodon gigas and Silurus asotus, which need further research

A unified joint optimization of training sequences and transceivers based on matrix-monotonic optimization

Channel estimation and data transmission constitute the most fundamental functional modules of multiple-input multiple-output (MIMO) communication systems. The underlying key tasks corresponding to these modules are training sequence optimization and transceiver optimization. Hence, we jointly optimize the linear transmit precoder and the training sequence of MIMO systems using the metrics of their effective mutual information (MI), effective mean squared error (MSE), effective weighted MI, effective weighted MSE, as well as their effective generic Schur-convex and Schur-concave functions. Both statistical channel state information (CSI) and estimated CSI are considered at the transmitter in the joint optimization. A unified framework termed as joint matrix-monotonic optimization is proposed. Based on this, the optimal precoder matrix and training matrix structures can be derived for both CSIscenarios. Then, based on the optimal matrix structures, our linear transceivers and their training sequences can be jointly optimized. Compared to state-of-the-art benchmark algorithms, the proposed algorithms visualize the bold explicit relationships between the attainable system performance of our linear transceivers conceived and their training sequences, leading to implementation ready recipes. Finally, several numerical results are provided, which corroborate our theoretical results and demonstrate the compelling benefits of our proposed pilot-aided MIMO solutions