Integrated Communication, Sensing, and Computation Framework for 6G Networks.

In the sixth generation (6G) era, intelligent machine network (IMN) applications, such as intelligent transportation, require collaborative machines with communication, sensing, and computation (CSC) capabilities. This article proposes an integrated communication, sensing, and computation (ICSAC) framework for 6G to achieve the reciprocity among CSC functions to enhance the reliability and latency of communication, accuracy and timeliness of sensing information acquisition, and privacy and security of computing to realize the IMN applications. Specifically, the sensing and communication functions can merge into unified platforms using the same transmit signals, and the acquired real-time sensing information can be exploited as prior information for intelligent algorithms to enhance the performance of communication networks. This is called the computing-empowered integrated sensing and communications (ISAC) reciprocity. Such reciprocity can further improve the performance of distributed computation with the assistance of networked sensing capability, which is named the sensing-empowered integrated communications and computation (ICAC) reciprocity. The above ISAC and ICAC reciprocities can enhance each other iteratively and finally lead to the ICSAC reciprocity. To achieve these reciprocities, we explore the potential enabling technologies for the ICSAC framework. Finally, we present the evaluation results of crucial enabling technologies to show the feasibility of the ICSAC framework.

AMSCN: A Novel Dual-Task Model for Automatic Modulation Classification and Specific Emitter Identification.

Specific emitter identification (SEI) and automatic modulation classification (AMC) are generally two separate tasks in the field of radio monitoring. Both tasks have similarities in terms of their application scenarios, signal modeling, feature engineering, and classifier design. It is feasible and promising to integrate these two tasks, with the benefit of reducing the overall computational complexity and improving the classification accuracy of each task. In this paper, we propose a dual-task neural network named AMSCN that simultaneously classifies the modulation and the transmitter of the received signal. In the AMSCN, we first use a combination of DenseNet and Transformer as the backbone network to extract the distinguishable features; then, we design a mask-based dual-head classifier (MDHC) to reinforce the joint learning of the two tasks. To train the AMSCN, a multitask cross-entropy loss is proposed, which is the sum of the cross-entropy loss of the AMC and the cross-entropy loss of the SEI. Experimental results show that our method achieves performance gains for the SEI task with the aid of additional information from the AMC task. Compared with the traditional single-task model, our classification accuracy of the AMC is generally consistent with the state-of-the-art performance, while the classification accuracy of the SEI is improved from 52.2% to 54.7%, which demonstrates the effectiveness of the AMSCN.

MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review.

With autonomous driving developing in a booming stage, accurate object detection in complex scenarios attract wide attention to ensure the safety of autonomous driving. Millimeter wave (mmWave) radar and vision fusion is a mainstream solution for accurate obstacle detection. This article presents a detailed survey on mmWave radar and vision fusion based obstacle detection methods. First, we introduce the tasks, evaluation criteria, and datasets of object detection for autonomous driving. The process of mmWave radar and vision fusion is then divided into three parts: sensor deployment, sensor calibration, and sensor fusion, which are reviewed comprehensively. Specifically, we classify the fusion methods into data level, decision level, and feature level fusion methods. In addition, we introduce three-dimensional(3D) object detection, the fusion of lidar and vision in autonomous driving and multimodal information fusion, which are promising for the future. Finally, we summarize this article.

Genetic and functional analysis of the Zn(II)2Cys6 transcription factor HADA-1 in Hypsizygus marmoreus.

Zn(II)2Cys6 transcription factors are critical for the reproductive growth and sexual development of fungi, but their roles in Basidiomycota remain unclear. In this study, the Hypsizygus marmoreus gene hada-1 was shown to encode a Zn(II)2Cys6 transcription factor, the growth rate of mycelia was decreased, hyphae were angulated, and fruiting body development was hindered in the hada-1-silenced strains. In addition, mitochondrial stability was lost, and the mitochondria morphologies changed from oval shaped to dumbbell or linear shaped in the silenced strains. Regarding mitochondrial instability, the mitochondrial complex II, III, and V activities and adenosine triphosphate content were significantly decreased. At the same time, the activities of the carbohydrate metabolism-related enzymes glucose-6-plosphatase, glucose dehydrogenase, and laccase were significantly decreased, which might have resulted in the reduction of carbon metabolism. Furthermore, hada-1 was shown to regulate the reactive oxygen species (ROS) level; compared with the wild-type (WT) strain, the silenced mycelia exhibited higher ROS contents and were more sensitive to oxidative stress. Taken together, these results indicate that, as a global regulator, hada-1 plays crucial roles in mycelial growth, fruiting body development, carbon metabolism, mitochondrial stability, and oxidative stress in the basidiomycete H. marmoreus. KEY POINTS: • Zn(II)2Cys6 transcription factor, mitochondrial stability, fruiting body development.

Interference Analysis for mmWave Automotive Radar Considering Blockage Effect.

Due to the increasing number of vehicles equipped with millimeter wave (mmWave) radars, interference among automotive radars is becoming a major issue. This paper explores the automotive radar interference in both two-lane and multi-lane scenarios using stochastic geometry. We derive closed-form expressions for mean and variance of interference power considering directional antenna with constant and Gaussian decaying gains. In view of the sensitivity of mmWave radar signals to the blockages, we propose a blockage model including partially and completely blocking, and then calculate the effective number of the interferers. By means of modeling randomness for interferers and blockages as Poisson point process, we characterize the statistics of radar interference under different conditions. We further utilize the interference characterization to estimate the successful ranging probability of automotive radars. These theoretical analyses are verified by using Monte Carlo simulations. The results show that the increasing interfering density and ranging distance largely degrade the radar detection performance, whereas the interference levels decrease as blockage intensity increases.

The functions of glutathione peroxidase in ROS homeostasis and fruiting body development in Hypsizygus marmoreus.

Glutathione peroxidase (GPX) is one of the most important antioxidant enzymes for maintaining reactive oxygen species (ROS) homeostasis. Although studies on fungi have suggested many important physiological functions of GPX, few studies have examined the role of this enzyme in Basidiomycetes, particularly its functions in fruiting body developmental processes. In the present study, GPX-silenced (GPxi) strains were obtained by using RNA interference. The GPxi strains of Hypsizygus marmoreus showed defects in mycelial growth and fruiting body development. In addition, the results indicated essential roles of GPX in controlling ROS homeostasis by regulating intracellular H2O2 levels, maintaining GSH/GSSG balance, and promoting antioxidant enzyme activity. Furthermore, lignocellulose enzyme activity levels were reduced and the mitochondrial phenotype and mitochondrial complex activity levels were changed in the H. marmoreus GPxi strains, possibly in response to impediments to mycelial growth and fruiting body development. These findings indicate that ROS homeostasis has a complex influence on growth, fruiting body development, GSH/GSSG balance, and carbon metabolism in H. marmoreus.Key points• ROS balance, energy metabolism, fruiting development.

Spatial Attention Fusion for Obstacle Detection Using MmWave Radar and Vision Sensor.

For autonomous driving, it is important to detect obstacles in all scales accurately for safety consideration. In this paper, we propose a new spatial attention fusion (SAF) method for obstacle detection using mmWave radar and vision sensor, where the sparsity of radar points are considered in the proposed SAF. The proposed fusion method can be embedded in the feature-extraction stage, which leverages the features of mmWave radar and vision sensor effectively. Based on the SAF, an attention weight matrix is generated to fuse the vision features, which is different from the concatenation fusion and element-wise add fusion. Moreover, the proposed SAF can be trained by an end-to-end manner incorporated with the recent deep learning object detection framework. In addition, we build a generation model, which converts radar points to radar images for neural network training. Numerical results suggest that the newly developed fusion method achieves superior performance in public benchmarking. In addition, the source code will be released in the GitHub.

Online Siamese Network for Visual Object Tracking.

Offline-trained Siamese networks are not robust to the environmental complication in visual object tracking. Without online learning, the Siamese network cannot learn from instance domain knowledge and adapt to appearance changes of targets. In this paper, a new lightweight Siamese network is proposed for feature extraction. To cope with the dynamics of targets and backgrounds, the weight in the proposed Siamese network is updated in an online manner during the tracking process. In order to enhance the discrimination capability, the cross-entropy loss is integrated into the contrastive loss. Inspired by the face verification algorithm DeepID2, the Bayesian verification model is applied for candidate selection. In general, visual object tracking can benefit from face verification algorithms. Numerical results suggest that the newly developed algorithm achieves comparable performance in public benchmarks.

Adaptive Object Tracking via Multi-Angle Analysis Collaboration.

Although tracking research has achieved excellent performance in mathematical angles, it is still meaningful to analyze tracking problems from multiple perspectives. This motivation not only promotes the independence of tracking research but also increases the flexibility of practical applications. This paper presents a significant tracking framework based on the multi-dimensional state⁻action space reinforcement learning, termed as multi-angle analysis collaboration tracking (MACT). MACT is comprised of a basic tracking framework and a strategic framework which assists the former. Especially, the strategic framework is extensible and currently includes feature selection strategy (FSS) and movement trend strategy (MTS). These strategies are abstracted from the multi-angle analysis of tracking problems (observer's attention and object's motion). The content of the analysis corresponds to the specific actions in the multidimensional action space. Concretely, the tracker, regarded as an agent, is trained with Q-learning algorithm and ϵ -greedy exploration strategy, where we adopt a customized rewarding function to encourage robust object tracking. Numerous contrast experimental evaluations on the OTB50 benchmark demonstrate the effectiveness of the strategies and improvement in speed and accuracy of MACT tracker.

Riemannian Spatio-Temporal Features of Locomotion for Individual Recognition.

Individual recognition based on skeletal sequence is a challenging computer vision task with multiple important applications, such as public security, human⁻computer interaction, and surveillance. However, much of the existing work usually fails to provide any explicit quantitative differences between different individuals. In this paper, we propose a novel 3D spatio-temporal geometric feature representation of locomotion on Riemannian manifold, which explicitly reveals the intrinsic differences between individuals. To this end, we construct mean sequence by aligning related motion sequences on the Riemannian manifold. The differences in respect to this mean sequence are modeled as spatial state descriptors. Subsequently, a temporal hierarchy of covariance are imposed on the state descriptors, making it a higher-order statistical spatio-temporal feature representation, showing unique biometric characteristics for individuals. Finally, we introduce a kernel metric learning method to improve the classification accuracy. We evaluated our method on two public databases: the CMU Mocap database and the UPCV Gait database. Furthermore, we also constructed a new database for evaluating running and analyzing two major influence factors of walking. As a result, the proposed approach achieves promising results in all experiments.

Optimal Base Station Density of Dense Network: From the Viewpoint of Interference and Load.

Network densification is attracting increasing attention recently due to its ability to improve network capacity by spatial reuse and relieve congestion by offloading. However, excessive densification and aggressive offloading can also cause the degradation of network performance due to problems of interference and load. In this paper, with consideration of load issues, we study the optimal base station density that maximizes the throughput of the network. The expected link rate and the utilization ratio of the contention-based channel are derived as the functions of base station density using the Poisson Point Process (PPP) and Markov Chain. They reveal the rules of deployment. Based on these results, we obtain the throughput of the network and indicate the optimal deployment density under different network conditions. Extensive simulations are conducted to validate our analysis and show the substantial performance gain obtained by the proposed deployment scheme. These results can provide guidance for the network densification.

Construction and application of a gene silencing system using a dual promoter silencing vector in Hypsizygus marmoreus.

As efficient reverse genetic tools are lacking, molecular genetics research has been limited in Hypsizygus marmoreus. In this study, we firstly constructed a gene-silencing method using a dual promoter vector (DPV) which was driven by gpd and 35 S promoters. The DPV was introduced into H. marmoreus via a simple electroporation procedure and the highest silenced rate of ura3 gene was 76.6%, indicating that the DPV might be suitable for gene silencing in basidiomycete. In this silencing system, the endogenous orotidine 5'-monophosphate decarboxylase gene (ura3) was used as a selectable marker. Besides, we also constructed another silencing system which could silence the ura3 and other genes (lcc1 encoded laccase1) together in H. marmoreus, and named it as co-silencing system. In the co-silenced transformants, we found that the mycelia were thinner and the growth was slower than in the wild-type and control2 strains, which was accordant with the previous study of lcc1 gene, indicating that the selective efficiency of the RNAi-mediated silencing of several genes might be increased by co-silencing ura3. The development of this molecular tool might improve functional studies of multiple genes in the basidiomycete H. marmoreus and also provide a reference for studies of other basidiomycetes.

BgCut: automatic ship detection from UAV images.

Ship detection in static UAV aerial images is a fundamental challenge in sea target detection and precise positioning. In this paper, an improved universal background model based on Grabcut algorithm is proposed to segment foreground objects from sea automatically. First, a sea template library including images in different natural conditions is built to provide an initial template to the model. Then the background trimap is obtained by combing some templates matching with region growing algorithm. The output trimap initializes Grabcut background instead of manual intervention and the process of segmentation without iteration. The effectiveness of our proposed model is demonstrated by extensive experiments on a certain area of real UAV aerial images by an airborne Canon 5D Mark. The proposed algorithm is not only adaptive but also with good segmentation. Furthermore, the model in this paper can be well applied in the automated processing of industrial images for related researches.

Predicting current and future potential distributions of the greater bandicoot rat (Bandicota indica) under climate change conditions.

BACKGROUND: Rodent infestation is a global problem. Rodents cause huge harm to agriculture, forestry, and animal husbandry around the world and spread various zoonoses. In this study, we simulated the potentially suitable habitats of Bandicota indica and predicted the impact of future climate change on its distribution under different socio-economic pathway scenarios of CMIP6 using a parameter-optimized maximum entropy (MaxEnt) model. RESULTS: The average area under the receiver operating characteristic curve (AUC) value (0.958 ± 0.006) after ten repetitions proved the high accuracy of the MaxEnt model. Model results show that the annual mean temperature (≥ 15.93 °C), isothermality (28.52-80.49%), annual precipitation (780.13-3863.13 mm), precipitation of the warmest quarter (≥ 204.37 mm), and nighttime light (≥ 3.38) were important limiting environmental variables for the distribution of B. indica. Under current climate conditions, the projected potential suitable habitats for B. indica were mainly in India, China, Myanmar, Thailand, and Vietnam, which cover a total area of 301.70 × 104 km2 . The potentially suitable areas of B. indica in the world will expand under different future climate change scenarios by 1.61-17.65%. CONCLUSIONS: These results validate the potential influence of climate change on the distribution of B. indica and aid in understanding the linkages between B. indica niches and the relevant environment, thereby identifying urgent management areas where interventions may be necessary to develop feasible early warning and prevention strategies to protect against this rodent's spread. © 2023 Society of Chemical Industry.

YOLOH: You Only Look One Hourglass for Real-Time Object Detection.

Multi-scale detection based on Feature Pyramid Networks (FPN) has been a popular approach in object detection to improve accuracy. However, using multi-layer features in the decoder of FPN methods entails performing many convolution operations on high-resolution feature maps, which consumes significant computational resources. In this paper, we propose a novel perspective for FPN in which we directly use fused single-layer features for regression and classification. Our proposed model, You Only Look One Hourglass (YOLOH), fuses multiple feature maps into one feature map in the encoder. We then use dense connections and dilated residual blocks to expand the receptive field of the fused feature map. This output not only contains information from all the feature maps, but also has a multi-scale receptive field for detection. The experimental results on the COCO dataset demonstrate that YOLOH achieves higher accuracy and better run-time performance than established detector baselines, for instance, it achieves an average precision (AP) of 50.2 on a standard 3× training schedule and achieves 40.3 AP at a speed of 32 FPS on the ResNet-50 model. We anticipate that YOLOH can serve as a reference for researchers to design real-time detection in future studies. Our code is available at https://github.com/wsb853529465/YOLOH-main.

Relation-Aware Heterogeneous Graph Network for Learning Intermodal Semantics in Textbook Question Answering.

Textbook question answering (TQA) task aims to infer answers for given questions from a multimodal context, including text and diagrams. The existing studies have aggregated intramodal semantics extracted from a single modality but have yet to capture the intermodal semantics between different modalities. A major challenge in learning intermodal semantics is maintaining lossless intramodal semantics while bridging the gap of semantics caused by heterogeneity. In this article, we propose an intermodal relation-aware heterogeneous graph network (IMR-HGN) to extract the intermodal semantics for TQA, which aggregates different modalities while learning features rather than representing them independently. First, we design a multidomain consistent representation (MDCR) to eliminate semantic gaps by capturing intermodal features while maintaining lossless intramodal semantics in multidomains. Furthermore, we present neighbor-based relation inpainting (NRI) to reduce semantic ambiguity via repairing fuzzy relations with correlations of relations. Finally, we propose hierarchical multisemantics aggregation (HMSA) to guarantee the completeness of semantics by aggregating features of nodes and relations with a reconstruction network (RN). Experimental results show that IMR-HGN could extract the intermodal semantics of answers, achieving a 2.16% improvement on the validation set of the TQA dataset and a 3.04% increase on the test set of the AI2D dataset.

Transcriptome and Differentially Expressed Gene Profiles in Mycelium, Primordium and Fruiting Body Development in Stropharia rugosoannulata.

Stropharia rugosoannulata uses straw as a growth substrate during artificial cultivation and has been widely promoted in China. However, its fruiting body formation and development processes have not been elucidated. In this study, the developmental transcriptomes were analyzed at three stages: the mycelium (G-S), primordium (P-S) and fruiting body (M-F) stages. A total of 9690 differentially expressed genes (DEGs) were identified in the different developmental stages. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that these DEGs were involved mainly in hydrolase activity, structural molecule activity and oxidoreductase activity as well as xenobiotic biodegradation and metabolism and energy metabolism pathways. We further found that the higher expression of most carbohydrate enzyme (i.e., GH, CE, CBM, AA and PL) genes in the hyphal (i.e., G-S) stage was related mainly to substrate degradation, while the upregulation of glycosyltransferase (GT) gene expression in the P-S and M-F stages may be related to cell wall synthesis. In addition, we found that CO2-sensing-related genes (i.e., CA-2, CA-3, PKA-1 and PKA-2) were upregulated in the P-S and M-F stages, heat shock protein genes (HSP60 and HSP90) were significantly downregulated in the P-S stage and upregulated in the M-F stage and the transcription factors (i.e., steA, MYB, nosA, HAP1, and GATA-4/5/6) involved in growth and development were significantly upregulated in the P-S stage. These results suggest that environmental factors (i.e., CO2 and temperature) and transcription factors may play a key role in primordium formation. In short, this study provides new insights into the study of stimulating primordia formation affecting the development of fruiting bodies of S. rugosoannulata.

Analyzing Heterogeneous Networks With Missing Attributes by Unsupervised Contrastive Learning.

Heterogeneous information networks (HINs) are potent models of complex systems. In practice, many nodes in an HIN have their attributes unspecified, resulting in significant performance degradation for supervised and unsupervised representation learning. We developed an unsupervised heterogeneous graph contrastive learning approach for analyzing HINs with missing attributes (HGCA). HGCA adopts a contrastive learning strategy to unify attribute completion and representation learning in an unsupervised heterogeneous framework. To deal with a large number of missing attributes and the absence of labels in unsupervised scenarios, we proposed an augmented network to capture the semantic relations between nodes and attributes to achieve a fine-grained attribute completion. Extensive experiments on three large real-world HINs demonstrated the superiority of HGCA over several state-of-the-art methods. The results also showed that the complemented attributes by HGCA can improve the performance of existing HIN models.

Whole-genome sequencing reveals the genetic mechanisms of domestication in classical inbred mice.

BACKGROUND: The laboratory mouse was domesticated from the wild house mouse. Understanding the genetics underlying domestication in laboratory mice, especially in the widely used classical inbred mice, is vital for studies using mouse models. However, the genetic mechanism of laboratory mouse domestication remains unknown due to lack of adequate genomic sequences of wild mice. RESULTS: We analyze the genetic relationships by whole-genome resequencing of 36 wild mice and 36 inbred strains. All classical inbred mice cluster together distinctly from wild and wild-derived inbred mice. Using nucleotide diversity analysis, Fst, and XP-CLR, we identify 339 positively selected genes that are closely associated with nervous system function. Approximately one third of these positively selected genes are highly expressed in brain tissues, and genetic mouse models of 125 genes in the positively selected genes exhibit abnormal behavioral or nervous system phenotypes. These positively selected genes show a higher ratio of differential expression between wild and classical inbred mice compared with all genes, especially in the hippocampus and frontal lobe. Using a mutant mouse model, we find that the SNP rs27900929 (T>C) in gene Astn2 significantly reduces the tameness of mice and modifies the ratio of the two Astn2 (a/b) isoforms. CONCLUSION: Our study indicates that classical inbred mice experienced high selection pressure during domestication under laboratory conditions. The analysis shows the positively selected genes are closely associated with behavior and the nervous system in mice. Tameness may be related to the Astn2 mutation and regulated by the ratio of the two Astn2 (a/b) isoforms.

Geometrical Consistency Modeling on B-Spline Parameter Domain for 3D Face Reconstruction From Limited Number of Wild Images.

A number of methods have been proposed for face reconstruction from single/multiple image(s). However, it is still a challenge to do reconstruction for limited number of wild images, in which there exists complex different imaging conditions, various face appearance, and limited number of high-quality images. And most current mesh model based methods cannot generate high-quality face model because of the local mapping deviation in geometric optics and distortion error brought by discrete differential operation. In this paper, accurate geometrical consistency modeling on B-spline parameter domain is proposed to reconstruct high-quality face surface from the various images. The modeling is completely consistent with the law of geometric optics, and B-spline reduces the distortion during surface deformation. In our method, 0th- and 1st-order consistency of stereo are formulated based on low-rank texture structures and local normals, respectively, to approach the pinpoint geometric modeling for face reconstruction. A practical solution combining the two consistency as well as an iterative algorithm is proposed to optimize high-detailed B-spline face effectively. Extensive empirical evaluations on synthetic data and unconstrained data are conducted, and the experimental results demonstrate the effectiveness of our method on challenging scenario, e.g., limited number of images with different head poses, illuminations, and expressions.