Shared Network Research Articles

LIVE PROCESSING AND FEEDBACK ON A COMBINED LASERSCANNING AND PHOTOGRAMMETRIC DOCUMENTATION WORKFLOW

Abstract. The use of a combined terrestrial laserscanning (TLS) and structure-from-motion (SFM) surveying approach has proved to be able to deliver advantageous benefits in terms of coverage and surface rendition. The elaboration of such approaches entails a succession of processes and are without many options for on-site feedback largely dependent on the experience of the surveyor. In support of this approach a combined automated processing workflow with augmented reality feedback is introduced, allowing operators to get a better understanding of their progress and project success during surveying. To do so native TLS and SFM software is connected to a shared network and data storage environment and controlled within an active framework. Within this, scans and images are aligned within RealityCapture and resulting registration is visualised to the operator using augmented reality (AR). To achieve complete independence of pre-scanned environments, the augmented reality application presented within this research uses a GNSS supported visual SLAM algorithm. With this the application is able to position virtual objects – such as markers representing the positions of registered cameras – within a physical space using only their geoposition as reference. The workflow presented here should be understood as a proof of concept to determine possible improvements that would enable such interactive means of surveying and would allow deeper integration of components into a combined AR supported documentation workflow.

Distributed Swarm Trajectory Planning for Autonomous Surface Vehicles in Complex Sea Environments

In this paper, a swarm trajectory-planning method is proposed for multiple autonomous surface vehicles (ASVs) in an unknown and obstacle-rich environment. Specifically, based on the point cloud information of the surrounding environment obtained from local sensors, a kinodynamic path-searching method is used to generate a series of waypoints in the discretized control space at first. Next, after fitting B-spline curves to the obtained waypoints, a nonlinear optimization problem is formulated to optimize the B-spline curves based on gradient-based local planning. Finally, a numerical optimization method is used to solve the optimization problems in real time to obtain collision-free, smooth and dynamically feasible trajectories relying on a shared network. The simulation results demonstrate the effectiveness and efficiency of the proposed swarm trajectory-planning method for a network of ASVs.

Asymmetrical cortical surface area decrease in epilepsy patients with postictal generalized electroencephalography suppression.

Postictal generalized electroencephalographic suppression is a possible electroencephalographic marker for sudden unexpected death in epilepsy. We aimed to investigate the cortical surface area abnormalities in epilepsy patients with postictal generalized electroencephalographic suppression. We retrospectively included 30 epilepsy patients with postictal generalized electroencephalographic suppression (PGES+), 21 epilepsy patients without postictal generalized electroencephalographic suppression (PGES-), and 30 healthy controls. Surface-based analysis on high-resolution T1-weighted images was conducted and cortical surface areas were compared among the three groups, alongside correlation analyses with seizure-related clinical variables. Compared with PGES- group, we identified reduced surface area in the bilateral insula with more extensive distribution in the right hemisphere in PGES+ group. The reduced right insular surface area was associated with younger seizure-onset age. When compared with healthy controls, PGES- group presented reduced surface area in the left caudal middle frontal gyrus; PGES+ group presented more widespread surface area reductions in the right posterior cingulate gyrus, left postcentral gyrus, middle frontal gyrus, and middle temporal gyrus. Our results suggested cortical microstructural impairment in patients with postictal generalized electroencephalographic suppression. The significant surface area reductions in the insular cortex supported the autonomic network involvement in the pathology of postictal generalized electroencephalographic suppression, and its right-sided predominance suggested the potential shared abnormal brain network for postictal generalized electroencephalographic suppression and sudden unexpected death in epilepsy.

Analysis of MicroRNA-Transcription Factors Co-Regulatory Network Linking Depression and Vitamin D Deficiency.

Depression and vitamin D deficiency are often co-occurring pathologies, the common pathogenetic ground of which includes an augmented inflammatory response. However, the molecular details of this relationship remain unclear. Here, we used a bioinformatic approach to analyze GEO transcriptome datasets of major depressive disorder (MDD) and vitamin D deficiency (VDD) to identify the hub genes within the regulatory networks of commonly differentially expressed genes (DEGs). The MDD-VDD shared regulatory network contains 100 DEGs (71 upregulated and 29 downregulated), with six hub genes (PECAM1, TLR2, PTGS2, LRRK2, HCK, and IL18) all significantly upregulated, of which PTGS2 (also known as COX2) shows the highest inference score and reference count. The subsequent analysis of the miRNA-transcription factors network identified COX2, miR-146a-5p, and miR-181c-5p as key co-regulatory actors in the MDD-VDD shared molecular pathogenic mechanisms. Subsequent analysis of published MDD and VDD transcriptome data confirmed the importance of the identified hub genes, further validating our bioinformatic analytical pipeline. Our study demonstrated that PTGS2 was highly upregulated in both depressive patients and patients with low vitamin D plasma levels. Therefore, regulators targeting PTGS2, like miR-146a-5p and miR181c-5p, may have great potential in controlling both diseases simultaneously, accentuating their role in future research.

Dissipative filtering for uncertain positive Roesser system under the try-once-discard protocol

This article addresses the problem of dissipative filtering for the delayed positive Roesser system with uncertainties under the try-once-discard communication protocol. To estimate the output of the addressed two-dimensional system, both positive lower- and upper-bounding filters are constructed, where the signals from the sensor nodes to the remote filters are transmitted through a shared communication network. To avoid the undesired data congestion phenomenon, the try-once-discard protocol is implemented to dynamically schedule the transmission sequence of the sensor nodes. Then, sufficient conditions are derived to design appropriate filters ensuring that the filtering error systems are positive, two-dimensional [Formula: see text]- γ-dissipative, and asymptotically stable. Finally, one numerical example is presented to demonstrate effectiveness of the designed filters.

A Soft Sensor for Multirate Quality Variables Based on MC-CNN.

In recent years, data-driven soft sensor modeling methods have been widely used in industrial production, chemistry, and biochemical. In industrial processes, the sampling rates of quality variables are always lower than those of process variables. Meanwhile, the sampling rates among quality variables are also different. However, few multi-input multi-output (MIMO) sensors take this temporal factor into consideration. To solve this problem, a deep-learning (DL) model based on a multitemporal channels convolutional neural network (MC-CNN) is proposed. In the MC-CNN, the network consists of two parts: the shared network used to extract the temporal feature and the parallel prediction network used to predict each quality variable. The modified BP algorithm makes the blank values generated at unsampled moments not participate in the backpropagation (BP) process during training. By predicting multiple quality variables of two industrial cases, the effectiveness of the proposed method is verified.

Robust Remote Estimation over Shared Networks in the Presence of Denial-of-Service Attacks

Robust Remote Estimation over Shared Networks in the Presence of Denial-of-Service Attacks

Poland ∙ Competition Authority Fines Distribution Network for RPM and Market Sharing

Poland ∙ Competition Authority Fines Distribution Network for RPM and Market Sharing

Variation and convergence in the morpho-functional properties of the mammalian neocortex.

Man's natural inclination to classify and hierarchize the living world has prompted neurophysiologists to explore possible differences in brain organisation between mammals, with the aim of understanding the diversity of their behavioural repertoires. But what really distinguishes the human brain from that of a platypus, an opossum or a rodent? In this review, we compare the structural and electrical properties of neocortical neurons in the main mammalian radiations and examine their impact on the functioning of the networks they form. We discuss variations in overall brain size, number of neurons, length of their dendritic trees and density of spines, acknowledging their increase in humans as in most large-brained species. Our comparative analysis also highlights a remarkable consistency, particularly pronounced in marsupial and placental mammals, in the cell typology, intrinsic and synaptic electrical properties of pyramidal neuron subtypes, and in their organisation into functional circuits. These shared cellular and network characteristics contribute to the emergence of strikingly similar large-scale physiological and pathological brain dynamics across a wide range of species. These findings support the existence of a core set of neural principles and processes conserved throughout mammalian evolution, from which a number of species-specific adaptations appear, likely allowing distinct functional needs to be met in a variety of environmental contexts.

Data-assisted radio resource allocation in shared spectrum multi-RAT heterogeneous network

Data-assisted radio resource allocation in shared spectrum multi-RAT heterogeneous network

Screening of Diagnostic Biomarkers and Immune Infiltration Characteristics Linking Rheumatoid Arthritis and Rosacea Based on Bioinformatics Analysis.

Both rheumatoid arthritis (RA) and rosacea represent common chronic systemic autoimmune conditions. Recent research indicates a heightened RA risk among individuals with rosacea. However, the molecular mechanisms linking these diseases remain largely unknown. This study aims to uncover shared molecular regulatory networks and immune cell infiltration patterns in both rosacea and RA. The gene expression profiles of RA (GSE12021, GSE55457), and the rosacea gene expression profile (GSE6591), were downloaded from Gene Expression Omnibus (GEO) databases, and obtained to screen differentially expressed genes (DEGs) by using "limma" package in R software. Various analyses including GO, KEGG, protein-protein interaction (PPI) network, and weighted gene co-expression network analyses (WGCNA) were conducted to explore potential biological functions and signaling pathways. CIBERSORT was used to assess the abundance of immune cells. Pearson coefficients were used to calculate the correlations between overlapped genes and the leukocyte gene signature matrix. Flow cytometry (FCM) analysis confirmed the most abundant immune cells detected in rheumatoid arthritis and rosacea. Receiver operator characteristic (ROC) analysis, enzyme-linked immunosorbent assay (ELISA), and qRT-PCR were used to confirm biomarkers and functions. Two hundred seventy-seven co-expressed DEGs were identified from these datasets. Functional enrichment analysis indicated that these DEGs were associated with immune processes and chemokine-mediated signaling pathways. Fourteen and 17 hub genes overlapped between cytoHubba and WGCNA were identified in RA and rosacea, respectively. Macrophages and dendritic cells were RA and rosacea's most abundant immune cells, respectively. The ROC curves demonstrated potential diagnostic values of CXCL10 and CCL27, showing higher levels in the serum of patients with RA or rosacea, and suggesting possible regulation in the densities and functions of macrophages and dendritic cells from RA and rosacea, which were validated by FCM and qRT-PCR. Importantly, our findings may contribute to the scientific basis for biomarkers and therapeutic targets for patients with RA and rosacea in the future.

A Dual-Feature-Based Adaptive Shared Transformer Network for Image Captioning

A Dual-Feature-Based Adaptive Shared Transformer Network for Image Captioning

Across Sessions and Subjects Domain Adaptation for Building Robust Myoelectric Interface.

Gesture interaction via surface electromyography (sEMG) signal is a promising approach for advanced human-computer interaction systems. However, improving the performance of the myoelectric interface is challenging due to the domain shift caused by the signal's inherent variability. To enhance the interface's robustness, we propose a novel adaptive information fusion neural network (AIFNN) framework, which could effectively reduce the effects of multiple scenarios. Specifically, domain adversarial training is established to inhibit the shared network's weights from exploiting domain-specific representation, thus allowing for the extraction of domain-invariant features. Effectively, classification loss, domain diversence loss and domain discrimination loss are employed, which improve classification performance while reduce distribution mismatches between the two domains. To simulate the application of myoelectric interface, experiments were carried out involving three scenarios (intra-session, inter-session and inter-subject scenarios). Ten non-disabled subjects were recruited to perform sixteen gestures for ten consecutive days. The experimental results indicated that the performance of AIFNN was better than two other state-of-the-art transfer learning approaches, namely fine-tuning (FT) and domain adversarial network (DANN). This study demonstrates the capability of AIFNN to maintain robustness over time and generalize across users in practical myoelectric interface implementations. These findings could serve as a foundation for future deployments.

Boosting On-Policy Actor-Critic With Shallow Updates in Critic.

Deep reinforcement learning (DRL) benefits from the representation power of deep neural networks (NNs), to approximate the value function and policy in the learning process. Batch reinforcement learning (BRL) benefits from stable training and data efficiency with fixed representation and enjoys solid theoretical analysis. This work proposes least-squares deep policy gradient (LSDPG), a hybrid approach that combines least-squares reinforcement learning (RL) with online DRL to achieve the best of both worlds. LSDPG leverages a shared network to share useful features between policy (actor) and value function (critic). LSDPG learns policy, value function, and representation separately. First, LSDPG views deep NNs of the critic as a linear combination of representation weighted by the weights of the last layer and performs policy evaluation with regularized least-squares temporal difference (LSTD) methods. Second, arbitrary policy gradient algorithms can be applied to improve the policy. Third, an auxiliary task is used to periodically distill the features from the critic into the representation. Unlike most DRL methods, where the critic algorithms are often used in a nonstationary situation, i.e., the policy to be evaluated is changing, the critic in LSDPG is working on a stationary case in each iteration of the critic update. We prove that, under some conditions, the critic converges to the regularized TD fixpoint of current policy, and the actor converges to the local optimal policy. The experimental results on challenging Procgen benchmark illustrate the improvement of sample efficiency of LSDPG over proximal policy optimization and phasic policy gradient (PPG).

Wind power forecasting based on manifold learning and a double-layer SWLSTM model

With the increase of wind power installed capacity, wind energy is playing an increasingly important role in power grids. However, this also aggravates the volatility of a power grid. Accurate wind power prediction is particularly important for grid dispatching and grid connection modes. However, the existing forecasting methods mostly consider the meteorological environmental factors and historical power data and do not consider the impact of wind turbine generator parameters. Therefore, this study proposes a double-layer Shared Weight Long Short-Term Memory Network (SWLSTM) prediction model based on manifold learning considering meteorological environment factors, wind turbine generator parameters, and historical wind power. First, 17 indicators such as meteorological environment factors and wind turbine parameters are determined as influencing factors to construct high-dimensional data sets. Second, manifold learning is used to reduce the dimension of high-dimensional data sets and determine the best dimension reduction. The data after dimensionality reduction and historical wind power are used as the input of the prediction model. Finally, considering the influence of errors on the prediction results, a double-layer SWLSTM prediction model is constructed. The double-layer model is constructed simultaneously, and the final power prediction result is obtained by adding the prediction error of the lower model and the prediction wind power of the upper model. Moreover, a wind farm in Xinjiang is used to verify the effectiveness of the proposed prediction model. The results show that the prediction strategy and the model proposed in this paper have high prediction accuracy.

Security Issues on Industrial Internet of Things: Overview and Challenges

The Industrial Internet of Things (IIoT), where numerous smart devices associated with sensors, actuators, computers, and people communicate with shared networks, has gained advantages in many fields, such as smart manufacturing, intelligent transportation, and smart grids. However, security is becoming increasingly challenging due to the vulnerability of the IIoT to various malicious attacks. In this paper, the security issues of the IIoT are reviewed from the following three aspects: (1) security threats and their attack mechanisms are presented to illustrate the vulnerability of the IIoT; (2) the intrusion detection methods are listed from the attack identification perspectives; and (3) some defense strategies are comprehensively summarized. Several concluding remarks and promising future directions are provided at the end of this paper.

Shared and distinct pathways and networks genetically linked to coronary artery disease between human and mouse

Mouse models have been used extensively to study human coronary artery disease (CAD) or atherosclerosis and to test therapeutic targets. However, whether mouse and human share similar genetic factors and pathogenic mechanisms of atherosclerosis has not been thoroughly investigated in a data-driven manner. We conducted a cross-species comparison study to better understand atherosclerosis pathogenesis between species by leveraging multiomics data. Specifically, we compared genetically driven and thus CAD-causal gene networks and pathways, by using human GWAS of CAD from the CARDIoGRAMplusC4D consortium and mouse GWAS of atherosclerosis from the Hybrid Mouse Diversity Panel (HMDP) followed by integration with functional multiomics human (STARNET and GTEx) and mouse (HMDP) databases. We found that mouse and human shared >75% of CAD causal pathways. Based on network topology, we then predicted key regulatory genes for both the shared pathways and species-specific pathways, which were further validated through the use of single cell data and the latest CAD GWAS. In sum, our results should serve as a much-needed guidance for which human CAD-causal pathways can or cannot be further evaluated for novel CAD therapies using mouse models.

Shared and distinct pathways and networks genetically linked to coronary artery disease between human and mouse.

Mouse models have been used extensively to study human coronary artery disease (CAD) or atherosclerosis and to test therapeutic targets. However, whether mouse and human share similar genetic factors and pathogenic mechanisms of atherosclerosis has not been thoroughly investigated in a data-driven manner. We conducted a cross-species comparison study to better understand atherosclerosis pathogenesis between species by leveraging multiomics data. Specifically, we compared genetically driven and thus CAD-causal gene networks and pathways, by using human GWAS of CAD from the CARDIoGRAMplusC4D consortium and mouse GWAS of atherosclerosis from the Hybrid Mouse Diversity Panel (HMDP) followed by integration with functional multiomics human (STARNET and GTEx) and mouse (HMDP) databases. We found that mouse and human shared >75% of CAD causal pathways. Based on network topology, we then predicted key regulatory genes for both the shared pathways and species-specific pathways, which were further validated through the use of single cell data and the latest CAD GWAS. In sum, our results should serve as a much-needed guidance for which human CAD-causal pathways can or cannot be further evaluated for novel CAD therapies using mouse models.

Visible Light Integrated Positioning and Communication: A Multi-Task Federated Learning Framework

Recently, visible light positioning and visible light communication are becoming a promising technology for integrated sensing and communication. However, the isolated design of positioning and communication has limited the system efficiency and performance. In this paper, a visible light integrated positioning and communication (VIPAC) framework is formulated, in which the positioning task for the sensing service and the channel estimation task for the communication service are integrated into a unified architecture. Firstly, a multi-task learning architecture, which is composed of a sparsity-aware shared network and two task-oriented sub-networks, is proposed to fully exploit the inherent sparse features of visible light channels, and achieve mutual benefits between the two tasks. The depth of the shared network can be adaptively adjusted to extract the optimal shared features, and the two sub-networks are further optimized for the two tasks, respectively. Moreover, the emerging federated learning technique is introduced to devise a multi-user cooperative VIPAC scheme, which further improves the generalization ability in spatiotemporally nonstationary environments while preserving data privacy. It is shown by theoretical analysis and simulation results that, the proposed scheme can significantly improve the performance of positioning and channel estimation in spatiotemporally nonstationary environments compared with existing benchmark schemes.

Joint localization and classification of breast masses on ultrasound images using an auxiliary attention-based framework.

Multi-task learning (MTL) methods have been extensively employed for joint localization and classification of breast lesions on ultrasound images to assist in cancer diagnosis and personalized treatment. One typical paradigm in MTL is a shared trunk network architecture. However, such a model design may suffer information-sharing conflicts and only achieve suboptimal performance for individual tasks. Additionally, the model relies on fully-supervised learning methodologies, imposing heavy burdens on data annotation. In this study, we propose a novel joint localization and classification model based on attention mechanisms and a sequential semi-supervised learning strategy to address these challenges. Our proposed framework offers three primary advantages. First, a lesion-aware network with multiple attention modules is designed to improve model performance on lesion localization. An attention-based classifier explicitly establishes correlations between the two tasks, alleviating information-sharing conflicts while leveraging location information to assist in classification. Second, a two-stage sequential semi-supervised learning strategy is designed for model training to achieve optimal performance on both tasks and substantially reduces the need for data annotation. Third, the asymmetric and modular model architecture allows for the flexible interchangeability of individual components, rendering the model adaptable to various applications. Experimental results from two different breast ultrasound image datasets under varied conditions have demonstrated the effectiveness of the proposed method. Furthermore, we conduct comprehensive investigations into the impacts of various factors on model performance, gaining in-depth insights into the mechanism of our proposed framework. The code is available at https://github.com/comp-imaging-sci/lanet-bus.git.