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The low-light image enhancement task aims to improve the visibility of information in the dark to obtain more data and utilize it, while also improving the visual quality of the image. In this paper, we propose a dual cross-attention multi-stage embedding network (DCMENet) for fast and accurate enhancement of low-light images into high-quality images with high visibility. The problem that enhanced images tend to have more noise in them, which affects the image quality, is improved by introducing an attention mechanism in the encoder–decoder structure. In addition, the encoder–decoder can focus most of its attention on the dark areas of the image and better attend to the detailed features in the image that are obscured by the dark areas. In particular, the poor performance of the Transformer when the dataset size is small is solved by fusing the CNN-Attention and Transformer in the encoder. Considering the purpose of the low-light image enhancement task, we raise the importance of recovering image detail information to the same level as reconstructing the lighting. For features such as texture details in images, cascade extraction using spatial attention and pixel attention can reduce the model complexity while the performance is also improved. Finally, the global features obtained by the encoder–decoder are fused into the shallow feature extraction structure to reconstruct the illumination while guiding the network for the focused extraction of information in the dark. The proposed DCMENet achieves the best results in both objective quality assessment and subjective evaluation, while for the computer vision tasks working in low-light environments as well, the enhanced images using the DCMENet proposed in this paper show the best performance. [ABSTRACT FROM AUTHOR]

Purpose: During COVID-19 restrictions, people spent more time in cyberspace and consuming health-related information. An increase was also observed in mediated caring messages or health-relevant information sent to one another. This study aims to explore how the information and interactions around COVID-19 can provide a good learning opportunity for public health, specifically related to eHealth literacy and eHealth promotion. Design/methodology/approach: While mainstream literature has concentrated on experimental designs and a priming effect, this study inspects psychological distance related to a health threat under real-life circumstances. The article adopted a survey approach and utilized PLS-SEM techniques to examine the proposed hypotheses. Findings: Results indicated that whereas closer social support correlates with closer psychological distance and less usage of the social media approach, more substantial COVID-19 impacts were associated with closer psychological distance but greater use of social media. Since both closer psychological distance and social media approach contribute to eHealth literacy, social support from closer and virtual social networks should be embraced but utilized through different routes and for different purposes. The timing of messages but not psychological distance affects people's social media approach, indicating that morning messages should be employed. Moreover, eHealth literacy mediates timing preferences and leads to a preference for eHealth communication earlier in the day. Overall, morning messages create a virtuous circle during a health crisis. Originality/value: This paper establishes a mechanism of virtuous cycles for eHealth communication during a health threat. Additionally, it bridges existing research gaps by expanding chronopsychology and CLT in the health domain using an empirical approach, a real-life case and an extension of performance regarding information-seeking and utilization. [ABSTRACT FROM AUTHOR]

Many design problems involve reasoning about points in high-dimensional space. A common strategy is to first embed these high-dimensional points into a low-dimensional latent space. We propose that a good embedding should be isometric--i.e., preserving the geodesic distance between points on the data manifold in the latent space. However, enforcing isometry is non-trivial for common neural embedding models such as autoencoders. Moreover, while theoretically appealing, it is unclear to what extent is enforcing isometry necessary for a given design analysis. This paper answers these questions by constructing an isometric embedding via an isometric autoencoder, which we employ to analyze an inverse airfoil design problem. Specifically, the paper describes how to train an isometric autoencoder and demonstrates its usefulness compared to non-isometric autoencoders on the UIUC airfoil dataset. Our ablation study illustrates that enforcing isometry is necessary for accurately discovering clusters through the latent space. We also show how isometric autoencoders can uncover pathologies in typical gradient-based shape optimization solvers through an analysis on the SU2-optimized airfoil dataset, wherein we find an over-reliance of the gradient solver on the angle of attack. Overall, this paper motivates the use of isometry constraints in neural embedding models, particularly in cases where researchers or designers intend to use distance-based analysis measures to analyze designs within the latent space. While this work focuses on airfoil design as an illustrative example, it applies to any domain where analyzing isometric design or data embeddings would be useful. [ABSTRACT FROM AUTHOR]

A key challenge for the virtual characterization of components manufactured using short fiber-reinforced thermoplastics (SFRTs) is the inherent anisotropy which stems from the manufacturing process. To address this, a multi-scale approach is necessary, leveraging deep material networks (DMNs) as a micromechanical surrogate, for a one-stop solution when simulating SFRTs under highly nonlinear long-term load cases like creep and fatigue. Therefore, we extend the a priori fiber orientation tensor interpolation for quasi-static loading (Liu et al. in Intelligent multi-scale simulation based on process-guided composite database. arXiv:2003.09491, 2020; Gajek et al. in Comput Methods Appl Mech Eng 384:113,952, 2021; Meyer et al. in Compos Part B Eng 110,380, 2022) using DMNs with a posteriori approach. We also use the trained DMN framework to simulate the stiffness degradation under fatigue loading with a linear fatigue-damage law for the matrix. We evaluate the effectiveness of the interpolation approach for a variety of load classes using a dedicated fully coupled plasticity and creep model for the polymer matrix. The proposed methodology is validated through comparison with composite experiments, revealing the limitations of the linear fatigue-damage law. Therefore, we introduce a new power-law fatigue-damage model for the matrix in the micro-scale, leveraging the quasi-model-free nature of the DMN, i.e., it models the microstructure independent of the material models attached to the constituents of the microstructure. The DMN framework is shown to effectively extend material models and inversely identify model parameters based on composite experiments for all possible orientation states and variety of material models. [ABSTRACT FROM AUTHOR]

With the expansion of social media and the multiple possibilities for communication they offer, flirting is increasingly developing online. The aim of this article is to analyze how the adolescent population uses Instagram to initiate and develop courtship. For this, the results of 14 focus groups held in Secondary Education Institutes in Castilla-La Mancha (Spain), in which a total of 114 students between the ages of 12 and 17 participated, are analyzed. The results show that the online courtship game has its own rules and strategies. The most common Instagram strategies are: "best friends," the use of songs, and direct appeal for audience response. The results show that social interactions continue to be influenced by regulated and stereotyped social patterns. [ABSTRACT FROM AUTHOR]

The ever-increasing use of artificial intelligence (AI) to optimize embedding a watermark or find the optimal location in the host image for inserting a watermark might solve some problems in this field. However, the main problem, which is finding the optimum trade-off point among several watermarking criteria, has still not been investigated by researchers in this field, especially for speech signals. This paper aims to find the best trade-off among the watermarking requirements such as capacity, inaudibility, and robustness by applying an AI model. Moreover, a novel watermarking technique is proposed by modification of the probability density function (PDF) of the linear predictive (LP) residual and wavelet detail coefficient. For this method, a mathematical model is developed based on applying higher-order statistics for embedding and extracting the watermark. Sinh-arcsinh is used to shape the skewness and kurtosis of normal distribution for the LP residual or the wavelet high-frequency sub-bands, respectively, based on the watermark bits. Experimental results will show that although LP residual is not robust and it shows random behavior for modeling its PDF, the wavelet high-frequency band is quite robust and it can model the PDF of the wavelet. Moreover, it is demonstrated that AI has the capability to compromise among the watermarking criteria. Conclusions are drawn based on theoretical (maximum likelihood) and AI (machine learning) approaches, which confirm the effectiveness of the proposed model. Finally, in conclusion, several potential areas are discussed for further exploration. [ABSTRACT FROM AUTHOR]

The expected acceleration in sea level rise (SLR) throughout this century poses significant threats to coastal cities and low-lying regions. Since the early 1990s, high-precision multi-mission satellite altimetry (SA) has enabled the routine measurement of sea levels, providing a continuous 30-year record from which the mean sea level rise (global and regional) and its variability can be computed. The latest reprocessed product from CMEMS span the period from 1993 to 2020, and have enabled the acquisition of accurate sea level data within the coastal range of 0–20 km. In order to fully utilize this new dataset, we establish a global virtual network consisting of 184 virtual SA stations. We evaluate the impact of different stochastic noises on the estimation of the velocity of the sea surface height (SSH) time series using BIC_tp information criterion. In the second step, the principal component analysis (PCA) allows the common mode noise in the SSH time series to be mitigated. Finally, we analyzed the spatiotemporal characteristics and accuracy of sea level change derived from SA. Our results suggest that the stochasticity of the SSH time series is not well described by a combination of random, flicker, and white noise, but is best described by an ARFIM/ARMA/GGM process. After removing the common mode noise with PCA, about 96.7% of the times series' RMS decreased, and most of the uncertainty associated with the computed SLR decreased. We confirm that the spatiotemporal correlations should be accounted for to yield trustworthy trends and reliable uncertainties. Our estimated SLR is 2.75 ± 0.89 mm/yr, which aligns closely with recent studies, emphasizing the robustness and consistency of our method using virtual SA stations. We additionally introduce open-source software (SA_Tool V1.0) to process the SA data and reduce noise in surface height time series to the community. [ABSTRACT FROM AUTHOR]

In the last two decades, we are witnessing a huge increase of valuable big data structured in the form of graphs or networks. To apply traditional machine learning and data analytic techniques to such data it is necessary to transform graphs into vector‐based representations that preserve the most essential structural properties of graphs. For this purpose, a large number of graph embedding methods have been proposed in the literature. Most of them produce general‐purpose embeddings suitable for a variety of applications such as node clustering, node classification, graph visualization and link prediction. In this article, we propose two novel graph embedding algorithms based on random walks that are specifically designed for the node classification problem. Random walk sampling strategies of the proposed algorithms have been designed to pay special attention to hubs–high‐degree nodes that have the most critical role for the overall connectedness in large‐scale graphs. The proposed methods are experimentally evaluated by analyzing the classification performance of three classification algorithms trained on embeddings of real‐world networks. The obtained results indicate that our methods considerably improve the predictive power of examined classifiers compared with currently the most popular random walk method for generating general‐purpose graph embeddings (node2vec). [ABSTRACT FROM AUTHOR]

Co-clustering is the simultaneous clustering of the samples and attributes of a data matrix that provides deeper insight into data than traditional clustering. However, there is a lack of representation learning algorithms that serve this mechanism of co-clustering, and the current representation learning algorithms are limited to the sample perspective and lack the use of information in the attribute perspective. To solve this problem, in this article, ctSNE, a co-representation learning model based on t-distributed stochastic neighbor embedding, is proposed for unsupervised co-clustering, where ctSNE makes the dataset representation outputted more discriminative of row and column clusters (i.e. co-discrimination). On the basis of t-distributed stochastic neighbor embedding retaining the sample data distribution and local data structure, the philosophy of collaboration is introduced (i.e., row and column hidden relationship information) so that the ctSNE model is equipped with co-representation learning capability, which can effectively improve the performance of co-clustering. To prove the effectiveness of the ctSNE model, several classic co-clustering algorithms are used to check the co-representation performance of ctSNE, and a novel internal index based on an internal clustering index, known as total inertia, is proposed to demonstrate the effect of co-clustering. The numerous experimental results show that ctSNE has tremendous co-representation capability and can significantly improve the performance of co-clustering algorithms. [ABSTRACT FROM AUTHOR]

In recent years, the growth of data has promoted the development of parallel and distributed systems. Graph embedding is of great importance in improving parallel and distributed system performance. The quality of an embedding can be measured by many important metrics, and wirelength is one of the critical metrics related to communication performance and layout costs of physical systems. The hierarchical cubic network is a well-performing interconnection network and the k -rooted complete binary tree is a data structure with a hierarchical relationship among its various elements in algorithms and programming. In this paper, we solve the problem of the embedding of hierarchical cubic networks into k -rooted complete binary trees with minimum wirelength. We first study the optimal set of the hierarchical cubic network, and propose algorithms to give embedding h e t which is an embedding scheme of hierarchical cubic networks into k -rooted complete binary trees with minimum wirelength. Moreover, we give the exact minimum wirelength of this embedding. Finally, we conduct comparative experiments to evaluate the performance of embedding h e t. [ABSTRACT FROM AUTHOR]

Kohn–Sham (KS) inversion, in which the effective KS mean-field potential is found for a given density, provides insights into the nature of exact density functional theory (DFT) that can be exploited for the development of density functional approximations. Unfortunately, despite significant and sustained progress in both theory and software libraries, KS inversion remains rather difficult in practice, especially in finite basis sets. The present work presents a KS inversion method, dubbed the "Lieb-response" approach, that naturally works with existing Fock-matrix DFT infrastructure in finite basis sets, is numerically efficient, and directly provides meaningful matrix and energy quantities for pure-state and ensemble systems. Some additional work yields potential. It thus enables the routine inversion of even difficult KS systems, as illustrated in a variety of problems within this work, and provides outputs that can be used for embedding schemes or machine learning of density functional approximations. The effect of finite basis sets on KS inversion is also analyzed and investigated. [ABSTRACT FROM AUTHOR]

Many successful researchers in the biomedical sciences have benefitted from mentors and networks earlier in their career. However, early-career researchers from minoritized and underrepresented groups do not have the same access to potential mentors and networks as many of their peers. In this article we describe how ‘cold emails’ and social media platforms – notably Twitter/X and LinkedIn – can be used to build virtual networks, and stress the need to invest in maintaining networks once they have been established. [ABSTRACT FROM AUTHOR]

Significance: There is a significant need for the generation of virtual histological information from coronary optical coherence tomography (OCT) images to better guide the treatment of coronary artery disease (CAD). However, existing methods either require a large pixel-wise paired training dataset or have limited capability to map pathological regions. Aim: The aim of this work is to generate virtual histological information from coronary OCT images, without a pixel-wise paired training dataset while capable of providing pathological patterns. Approach: We design a structurally constrained, pathology-aware, transformer generative adversarial network, namely structurally constrained pathology-aware convolutional transformer generative adversarial network (SCPAT-GAN), to generate virtual stained H&E histology from OCT images. We quantitatively evaluate the quality of virtual stained histology images by measuring the Fréchet inception distance (FID) and perceptual hash value (PHV). Moreover, we invite experienced pathologists to evaluate the virtual stained images. Furthermore, we visually inspect the virtual stained image generated by SCPAT-GAN. Also, we perform an ablation study to validate the design of the proposed SCPAT-GAN. Finally, we demonstrate 3D virtual stained histology images. Results: Compared to previous research, the proposed SCPAT-GAN achieves better FID and PHV scores. The visual inspection suggests that the virtual histology images generated by SCPAT-GAN resemble both normal and pathological features without artifacts. As confirmed by the pathologists, the virtual stained images have good quality compared to real histology images. The ablation study confirms the effectiveness of the combination of proposed pathological awareness and structural constraining modules. Conclusions: The proposed SCPAT-GAN is the first to demonstrate the feasibility of generating both normal and pathological patterns without pixel-wisely supervised training. We expect the SCPAT-GAN to assist in the clinical evaluation of treating the CAD by providing 2D and 3D histopathological visualizations. [ABSTRACT FROM AUTHOR]

Viruses are the most abundant biological entities on earth and are important components of microbial communities. A metagenome contains all microorganisms from an environmental sample. Correctly identifying viruses from these mixed sequences is critical in viral analyses. It is common to identify long viral sequences, which has already been passed thought pipelines of assembly and binning. Existing deep learning-based methods divide these long sequences into short subsequences and identify them separately. This makes the relationships between them be omitted, leading to poor performance on identifying long viral sequences. In this paper, VirGrapher is proposed to improve the identification performance of long viral sequences by constructing relationships among short subsequences from long ones. VirGrapher see a long sequence as a graph and uses a Graph Convolutional Network (GCN) model to learn multilayer connections between nodes from sequences after a GCN-based node embedding model. VirGrapher achieves a better AUC value and accuracy on validation set, which is better than three benchmark methods. [ABSTRACT FROM AUTHOR]

In this paper, we outline an approach for automatically generating challenging road networks for virtual testing of an automated lane‐keeping system. Based on a set of control points, we construct a parametric curve representing a road network, defining the dynamic driving task an automated lane‐keeping system‐equipped vehicle must perform. Changing control points has a global influence on the resulting road geometry. Our approach uses search to find control‐point sets that result in a challenging road, eventually forcing the vehicle to leave the intended path. We apply our approach in different search variants to evaluate their performance regarding test efficiency and the diversity of failing tests. In addition, we evaluate different genetic algorithm control parameter configurations to investigate the most influential parameters and if specific configurations can be seen as optimal, leading to better results than others. For both studies, we consider another search‐based test method and two different random test generators as a baseline for comparison. The empirical results indicate that specific control parameter settings increase the overall performance for each search variant. While the population size is the most influential control parameter for all methods, the performance improvement when using optimal settings is only significant for one method. [ABSTRACT FROM AUTHOR]

With recent developments in cloud computing, massive unplanned traffic loads are submitted to cloud platforms. High traffic load variations lead to uncertainty in resource utilization. Therefore, efficient data-driven mechanisms for automatic resource management become crucial. These mechanisms enable complex and distributed systems to anticipate and efficiently react to workload fluctuations. They rely on accurate resource utilization prediction techniques to satisfy the resource needs, in order to fulfill the service level objective for cloud applications and infrastructures. In this paper, we propose a deep learning model to predict the resource consumption (e.g., CPU, memory) in network function virtualization infrastructures. We model an augmented graphical neural network (GNN) that exploit neighbouring relationships between virtual network functions (VNF) composing various service function chains (SFC), and use an augmented feature vector allowing to capture the consumption evolution of a VNF. The model enables to predict the resource needs of VNFs by identifying the multidimensional dependencies according to the graph structure of an SFC. The proposed GNN model has been compared with MLP, LSTM, hybrid LSTM and CNN models to evaluate its accuracy and efficiency. Real word datasets have been used to evaluate the proposed model using five performance metrics. The performance analysis reveals that our graph-features based GNN model outperforms the other models for SFCs with high traffic load variation. [ABSTRACT FROM AUTHOR]

With the emergence of various delay-sensitive applications, how to improve the system's delay performance has be come a hot topic in academic. However, it is difficult for existing multi-domain embedding algorithms to meet the delay per- formance requirements of virtual networks. Therefore, to solve this problem, this paper proposed a TDS-VNE algorithm. In the node embedding phase, it defined a propagation delay evaluation function(D). In the link embedding phase, it defined a path delay sensing parameter. Simulation results show that the proposed embedding algorithm reduces the average network propaga tion delay and has good results on the virtual network request acceptance rate and long-term benefit-cost ratio [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an important virtual network that allows remote users to access linked multimedia devices. The development of IoT and its ubiquitous application across various domains of everyday life has led to continuous research efforts. Security is a perceptual concern for researchers involved in IoT as it is a key factor in the acceptance of any innovative technology. Numerous research studies have been conducted concentrating on the level of IoT security on a particular mechanism, on specific applications, or on categorizing vulnerabilities, in order to address a defined situation of securing an IoT network. This present paper aims to comprehensively review potential solutions for securing IoT, between emerging and traditional mechanisms, such as blockchain, machine learning, cryptography, and quantum computing. This study provides a comparative analysis of related papers with their characteristics, pros and cons. Accordingly, it taxonomizes relevant solutions based on their achieved security requirements. Furthermore, the potential benefits and challenges of each of the four mechanisms are discussed. [ABSTRACT FROM AUTHOR]

We conducted an online questionnaire-based cross-sectional study to clarify psychiatrists' perspectives on virtual networking events. We compared two groups of respondents: those who had participated in virtual networking events (experienced group, n = 85) and those who had not (inexperienced group, n = 13). The experienced group had a greater level of agreement than the inexperienced group that virtual events were generally useful and helped with forming professional relationships and improving professional skills. Respondents in the experienced group considered the ease of participation and low financial burden to be advantages of virtual networking meetings and difficulties in building friendships and socialising to be disadvantages. [ABSTRACT FROM AUTHOR]

Analyzing substrate noise for an n-channel MOSFET (NMOS) substrate has been developed using various concepts such as virtual separation concept, p-contact to p-contact model and transistor switching noise model. A design is necessary for the substrate design development to minimize noise in FoMs (Figure Of Merits) and reduce threshold voltage developed noise. In the proposed model, a substrate-induced chip used for finding the noise in drain current has been improved with the increased doping density of an NMOS device. From a substrate noise coupling network with the proposed low signal equivalent circuit, the system measures noise at NMOS substrate using a hot carrier injection model. The proposed concept has been implemented in TCAD-SILVACO software, the design was made in ATHENA, and its corresponding electrical characteristics are simulated by the ATLAS software tool. This model extracts parameters such as overall noise in FoMs, drain current-gate current bias analysis, cross-correlation, and minimized threshold value. After capacitive coupling is achieved over different operation conditions and dimensions, excellent output is from simulated noise data. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is rapidly growing, with an estimated 14.4 billion active endpoints in 2022 and a forecast of approximately 30 billion connected devices by 2027. This proliferation of IoT devices has come with significant security challenges, including intrinsic security vulnerabilities, limited computing power, and the absence of timely security updates. Attacks leveraging such shortcomings could lead to severe consequences, including data breaches and potential disruptions to critical infrastructures. In response to these challenges, this research paper presents the IoT Proxy, a modular component designed to create a more resilient and secure IoT environment, especially in resource-limited scenarios. The core idea behind the IoT Proxy is to externalize security-related aspects of IoT devices by channeling their traffic through a secure network gateway equipped with different Virtual Network Security Functions (VNSFs). Our solution includes a Virtual Private Network (VPN) terminator and an Intrusion Prevention System (IPS) that uses a machine learning-based technique called oblivious authentication to identify connected devices. The IoT Proxy's modular, scalable, and externalized security approach creates a more resilient and secure IoT environment, especially for resource-limited IoT devices. The promising experimental results from laboratory testing demonstrate the suitability of IoT Proxy to secure real-world IoT ecosystems. [ABSTRACT FROM AUTHOR]

Online reviews have a significant influence on consumers' purchasing decisions. Unfortunately, many sellers exploit these reviews by employing a large number of spammers who strategically craft fake reviews to enhance their own reputation or tarnish their competitors'. Although several models have been proposed to address this issue in recent years, they often overlook the importance of considering a combination of structural and behavioural features, focusing solely on either structural or textual aspects. To overcome these limitations, we propose a novel model for detecting organized spammer groups called the Fake Reviewers Groups Detection Model. This model comprehensively considers the network structure and reviewer behavioural features to identify such groups. By extracting user, product, review time, and rating information, we construct a heterogeneous information network using a Meta-graph, which explores user relationships. Then apply the Node Ranking Walk2Vec algorithm to generate random walks within this heterogeneous information network, enabling us to obtain low-dimensional vector representations of the user nodes. Subsequently, utilize the Gaussian fuzzy Cluster Means algorithm for clustering, thereby identifying candidate groups of reviewers. The dynamic weight of each detection indicator is determined using the entropy approach, allowing us to assign an appropriate rank to reviewer groups based on their level of suspiciousness and identify them as fake reviewer groups. [ABSTRACT FROM AUTHOR]

Social media is a technology that supports the sharing of content, multimedia, and information through virtual networks and communities. The aim of this study was to determine the role of Twitter in oral health information and promotion. This systematic review was carried out using a transparent methodology in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. An extensive literature search was carried out in PubMed, Trip database, and Google Scholar database from the year 2006 to 2023. The search generated 1901 articles. Five articles were selected for this review. The majority of the articles in this review demonstrated a positive association between Twitter and the promotion of oral health by assessing oral health campaign tweets and emotional experiences shared regarding people's dental health. [ABSTRACT FROM AUTHOR]

This study substantiates the feasibility of implementing Web 2.0 services in practical educational activities and classifies the main types of Web 2.0 services adaptive to educational goals. A survey of 58 teachers from the First Moscow State Medical University, Kuban State Technological University, the Herzen State Pedagogical University of Russia, and Yelabuga Institute of Kazan (Volga Region) Federal University was conducted; their pedagogical experiences interacting with Web 2.0 services were considered in order to provide effective technological support for educational interactions in the context of distance learning in the era of COVID-19. The survey results showed that the most popular Web 2.0 services teachers use in distance learning include social networking services and virtual meeting and conference services. The study confirmed that using Web 2.0 services in educational processes can ensure high-quality digital pedagogy. [ABSTRACT FROM AUTHOR]

Modern hydrologic models have extraordinary capabilities for representing complex process in surface‐subsurface systems. These capabilities have revolutionized the way we conceptualize flow systems, but how to represent uncertainty in simulated flow systems is not as well developed. Currently, characterizing model uncertainty can be computationally expensive, in part, because the techniques are appended to the numerical methods rather than seamlessly integrated. The next generation of computers, however, presents opportunities to reformulate the modeling problem so that the uncertainty components are handled more directly within the flow system simulation. Misconceptions about quantum computing abound and they will not be a "silver bullet" for solving all complex problems, but they might be leveraged for certain kinds of highly uncertain problems, such as groundwater (GW). The point of this issue paper is that the GW community could try to revise the foundations of our models so that the governing equations being solved are tailored specifically for quantum computers. The goal moving forward should not just be to accelerate the models we have, but also to address their deficiencies. Embedding uncertainty into the models by evolving distribution functions will make predictive GW modeling more complicated, but doing so places the problem into a complexity class that is highly efficient on quantum computing hardware. Next generation GW models could put uncertainty into the problem at the very beginning of a simulation and leave it there throughout, providing a completely new way of simulating subsurface flows. [ABSTRACT FROM AUTHOR]

A growing body of experimental evidence suggests that microRNAs (miRNAs) are closely associated with specific human diseases and play critical roles in their development and progression. Therefore, identifying miRNA related to specific diseases is of great significance for disease screening and treatment. In the early stages, the identification of associations between miRNAs and diseases demanded laborious and time-consuming biological experiments that often carried a substantial risk of failure. With the exponential growth in the number of potential miRNA-disease association combinations, traditional biological experimental methods face difficulties in processing massive amounts of data. Hence, developing more efficient computational methods to predict possible miRNA-disease associations and prioritize them is particularly necessary. In recent years, numerous deep learning-based computational methods have been developed and have demonstrated excellent performance. However, most of these methods rely on external databases or tools to compute various auxiliary information. Unfortunately, these external databases or tools often cover only a limited portion of miRNAs and diseases, resulting in many miRNAs and diseases being unable to match with these computational methods. Therefore, there are certain limitations associated with the practical application of these methods. To overcome the above limitations, this study proposes a multi-view computational model called MVNMDA, which predicts potential miRNA-disease associations by integrating features of miRNA and diseases from local views, global views, and semantic views. Specifically, MVNMDA utilizes known association information to construct node initial features. Then, multiple networks are constructed based on known association to extract low-dimensional feature embedding of all nodes. Finally, a cascaded attention classifier is proposed to fuse features from coarse to fine, suppressing noise within the features and making precise predictions. To validate the effectiveness of the proposed method, extensive experiments were conducted on the HMDD v2.0 and HMDD v3.2 datasets. The experimental results demonstrate that MVNMDA achieves better performance compared to other computational methods. Additionally, the case study results further demonstrate the reliable predictive performance of MVNMDA. [ABSTRACT FROM AUTHOR]

Low-speed internet can negatively impact incident response by causing delayed detection, ineffective response, poor collaboration, inaccurate analysis, and increased risk. Slow internet speeds can delay the receipt and analysis of data, making it difficult for security teams to access the relevant information and take action, leading to a fragmented and inadequate response. All of these factors can increase the risk of data breaches and other security incidents and their impact on IoT-enabled communication. This study combines virtual network function (VNF) technology with software -defined networking (SDN) called virtual network function software-defined networking (VNFSDN). The adoption of the VNFSDN approach has the potential to enhance network security and efficiency while reducing the risk of cyberattacks. This approach supports IoT devices that can analyze large volumes of data in real time. The proposed VNFSDN can dynamically adapt to changing security requirements and network conditions for IoT devices. VNFSDN uses threat filtration and threat-capturing and decision-driven algorithms to minimize cyber risks for IoT devices and enhance network performance. Additionally, the integrity of IoT devices is safeguarded by addressing the three risk categories of data manipulation, insertion, and deletion. Furthermore, the prioritized delegated proof of stake (PDPoS) consensus variant is integrated with VNFSDN to combat attacks. This variant addresses the scalability issue of blockchain technology by providing a safe and adaptable environment for IoT devices that can quickly be scaled up and down to pull together the changing demands of the organization, allowing IoT devices to efficiently utilize resources. The PDPoS variant provides flexibility to IoT devices to proactively respond to potential security threats, preventing or mitigating the impact of cyberattacks. The proposed VNFSDN dynamically adapts to the changing security requirements and network conditions, improving network resiliency and enabling proactive threat detection. Finally, we compare the proposed VNFSDN to existing state-of-the-art approaches. According to the results, the proposed VNFSDN has a 0.08 ms minimum response time, a 2% packet loss rate, 99.5% network availability, a 99.36% threat detection rate, and a 99.77% detection accuracy with 1% malicious nodes. [ABSTRACT FROM AUTHOR]

Peripheral venous hypertension has emerged as a prominent characteristic of venous disease (VD). This disease causes lower limb edema due to impaired blood transport in the veins. The phlebotonic drugs in use showed moderate evidence for reducing edema slightly in the lower legs and little or no difference in the quality of life. To enhance the probability of favorable experimental results, a virtual screening procedure was employed to identify molecules with potential therapeutic activity in VD. Compounds obtained from multiple databases, namely AC Discovery, NuBBE, BIOFACQUIM, and InflamNat, were compared with reference compounds. The examination of structural similarity, targets, and signaling pathways in venous diseases allows for the identification of compounds with potential usefulness in VD. The computational tools employed were rcdk and chemminer from R-Studio and Cytoscape. An extended fingerprint analysis allowed us to obtain 1846 from 41,655 compounds compiled. Only 229 compounds showed pharmacological targets in the PubChem server, of which 84 molecules interacted with the VD network. Because of their descriptors and multi-target capacity, only 18 molecules of 84 were identified as potential candidates for experimental evaluation. We opted to evaluate the berberine compound because of its affordability, and extensive literature support. The experiment showed the proposed activity in an acute venous hypertension model. [ABSTRACT FROM AUTHOR]

Ancient murals are precious cultural heritages. They suffer from various damages due to man-made destruction and long-time exposure to the environment. It is urgent to protect and restore the damaged ancient murals. Virtual restoration of ancient murals aims to fill damaged mural regions by using modern computer techniques. Most existing restoration approaches fail to fill the loss mural regions with rich details and complex structures. In this paper, we propose a virtual restoration network of ancient murals based on global–local feature extraction and structural information guidance (GLSI). The proposed network consists of two major sub-networks: the structural information generator (SIG) and the image content generator (ICG). In the first sub-network, SIG can predict the structural information and the coarse contents for the missing mural regions. In the second sub-network, ICG utilizes the predicted structural information and the coarse contents to generate the refined image contents for the missing mural regions. Moreover, we design an innovative BranchBlock module that can effectively extract and integrate the local and global features. We introduce a Fast Fourier Convolution (FFC) to improve the color restoration for the missing mural regions. We conduct experiments over simulated and real damaged murals. Experimental results show that our proposed method outperforms other three comparative state-of-the-art approaches in terms of structural continuity, color harmony and visual rationality of the restored mural images. In addition, the mural restoration results of our method can achieve comparatively high quantitative evaluation metrics. [ABSTRACT FROM AUTHOR]

Trajectory-user linking (TUL) aims to link trajectories to users who generate them, based on the historical trajectories of a set of users (e.g., social media users or drivers). It is a fundamental task for many downstream applications of trajectory data. However, there is a lack of a general method to solve the TUL problem for different types of trajectory data. Existing studies usually focus on specific trajectory data, such as check-in data. Unlike subjectively self-selected check-in data, objectively recorded trajectory data is more prevalent and voluminous in real life. To address the issue, we propose a mobility signature augmentation model named SAMLink to solve the TUL problem for different types of trajectory data. It contains three components: feature embedding, mobility signature learning, and linking. Specifically, there are three modules in the key component of mobility signature learning, i.e., local mobility signature learning, attention-based signature augmentation, and adaptive fusion. The first module encodes the input trajectory to learn the local mobility signature, while the second module generates a global mobility signature from the historical trajectories using an attention mechanism. Furthermore, the last module adaptively fuses local and global mobility signatures to yield better identification results. We evaluate our proposed model on three real-world trajectory datasets, i.e., the check-in dataset, the TCD dataset, and the ERI taxi dataset. Our results demonstrate the effectiveness and generalization of our proposed model for solving the TUL problem across different types of trajectory data. [ABSTRACT FROM AUTHOR]

Green Mobile Edge Networks（GMENs） is an emerging distributed network that uses renewable green energy as the main energy source for edge nodes to operate, thereby reducing energy acquisition from the power grid. In GMENs, network service providers can form a service function chain（SFC） of multiple virtual network functions（VNFs） in a specific order to process user requests. These VNFs can flexibly deploy in the form of software instances on edge nodes to efficiently utilize green energy. To ensure the latency and reliability requirements of the service, it is necessary to deploy multiple backup VNFs（BVNF） on nodes within a k-hop range of VNF, to replace it in providing services that meet the latency requirements in the event of VNF failure. However, the green energy collected by each geographically dispersed edge node is dynamically changing. If the collected energy of nodes around the VNF decreases and is less than the energy consumption required to operate the BVNF, the node needs to harvest additional energy from the grid to guarantee its operation. Therefore, developing an online joint deployment plan for VNF and BVNF based on the green energy collected by nodes and the energy rate consumed by computing, communication, and other resources is an NP-hard problem, which minimizes the energy obtained by edge nodes from the power grid while ensuring service latency and reliability requirements. At present, there have been many studies on this issue and certain achievements have been made, but there are still some problems in practical applications. To further promote research in this field, this article analyzes, summarizes, and compares the latest research achievements in recent years, their advantages and disadvantages, and provides prospects for future work. [ABSTRACT FROM AUTHOR]

In the rapidly evolving urban advanced mobility (UAM) sphere, Vehicular Ad Hoc Networks (VANETs) are crucial for robust communication and operational efficiency in future urban environments. This paper quantifies VANETs to improve their reliability and availability, essential for integrating UAM into urban infrastructures. It proposes a novel Stochastic Petri Nets (SPN) method for evaluating VANET-based Vehicle Communication and Control (VCC) architectures, crucial given the dynamic demands of UAM. The SPN model, incorporating virtual machine (VM) migration and Edge Computing, addresses VANET integration challenges with Edge Computing. It uses stochastic elements to mirror VANET scenarios, enhancing network robustness and dependability, vital for the operational integrity of UAM. Case studies using this model offer insights into system availability and reliability, guiding VANET optimizations for UAM. The paper also applies a Design of Experiments (DoE) approach for a sensitivity analysis of SPN components, identifying key parameters affecting system availability. This is critical for refining the model for UAM efficiency. This research is significant for monitoring UAM systems in future cities, presenting a cost-effective framework over traditional methods and advancing VANET reliability and availability in urban mobility contexts. [ABSTRACT FROM AUTHOR]

In the realm of Network Function Virtualization (NFV), Virtual Network Functions (VNFs) are crucial software entities that require execution on virtualized hardware infrastructure. Deploying a Service Function Chain (SFC) requires multiple steps for instantiating VNFs to analyze, request, deploy, and monitor resources. It is well recognized that the sharing of infrastructure resources among different VNFs will enhance resource utilization. However, conventional mechanisms for VNF sharing often neglect the interests of both VNF instances and infrastructure providers. In this context, this paper presents a blockchain-based framework that focuses on resource sharing and access control, with a particular emphasis on ensuring profitability during VNF instantiation. Additionally, a resource sharing game model and a novel greedy matching algorithm are introduced to optimize the benefits for both VNF instances and infrastructure resource providers. Furthermore, a blockchain-based access control mechanism is designed to securely store keys and provide fine-grained access control. The experimental results demonstrate that the proposed resource sharing game model and greedy matching algorithm promote healthy competition among resource owners and facilitate effective bargaining between resource owners and infrastructure providers. In comparison to the standard Stackelberg game solution, our proposed method achieves up to an 8.1 times performance improvement while sacrificing fewer optimal social utility values. Furthermore, compared to other CP-ABE methods, the proposed approach enhances security within a blockchain-based framework while maintaining an excellent encryption efficiency and a moderate decryption efficiency. [ABSTRACT FROM AUTHOR]

In the modern era, with the emergence of the Internet of Things (IoT), big data applications, cloud computing, and the ever-increasing demand for high-speed internet with the aid of upgraded telecom network resources, users now require virtualization of the network for smart handling of modern-day challenges to obtain better services (in terms of security, reliability, scalability, etc.). These requirements can be fulfilled by using software-defined networking (SDN). This research article emphasizes one of the major aspects of the practical implementation of SDN to enhance the QoS of a virtual network through the load management of network servers. In an SDN-based network, several servers are available to fulfill users' hypertext transfer protocol (HTTP) requests to ensure dynamic routing under the influence of the SDN controller. However, if the number of requests is directed to a specific server, the controller is bound to follow the user-programmed instructions, and the load on that server is increased, which results in (a) an increase in end-to-end user delay, (b) a decrease in the data transfer rate, and (c) a decrease in the available bandwidth of the targeted server. All of the above-mentioned factors will result in the degradation of network QoS. With the implementation of the proposed algorithm, dynamic active sensing server load management (DASLM), on the SDN controller, the load on the server is shared based on QoS control parameters (throughput, response time, round trip time, etc.). The overall delay is reduced, and the bandwidth utilization along with throughput is also increased. [ABSTRACT FROM AUTHOR]

Social capital (or the ways in which people in an organization collaborate and to a greater extent the quality of the professional community in that context) has a variety of implications for organizations, specifically schools. This study examined the relationship between the attitudes of administrators and faculty at the secondary level toward Internet-based technology and virtual networking and the development and support of social capital within school settings. A mixed-methods design was employed consisting of a questionnaire, focus group discussion, and site observations conducted in six secondary schools. Findings demonstrate a significant relationship between (teacher and administrative) perceptions of Internet-based technology and virtual networking and the development of social capital. Evidence suggests that school improvement as demonstrated by student outcomes (e.g., achievement and a sense of community welfare or connectedness) can be achieved by using Internet-based technology for virtual networking and professional community building. Given the proliferation of technology within school settings, this study provides (1) insights into those concerned with 21st-century learning priorities and (2) knowledge that can inform decisionmaking regarding attitudes toward Internet-based technology and virtual networking. [ABSTRACT FROM AUTHOR]

The transition towards full network virtualization will see services for smart ecosystems including smart metering, healthcare and transportation among others, being deployed as Service Function Chains (SFCs) comprised of an ordered set of virtual network functions. However, since such services are usually deployed in remote cloud networks, the SFCs may transcend multiple domains belonging to different Infrastructure Providers (InPs), possibly with differing policies regarding billing and Quality-of-service (QoS) guarantees. Therefore, efficiently allocating the exhaustible network resources to the different SFCs while meeting the stringent requirements of the services such as delay and QoS among others, remains a complex challenge, especially under limited information disclosure by the InPs. In this work, we formulate the SFC deployment problem across multiple domains focusing on delay constraints, and propose a framework for SFC orchestration which adheres to the privacy requirements of the InPs. Then, we propose a reinforcement learning (RL)-based algorithm for partitioning the SFC request across the different InPs while considering service reliability across the participating InPs. Such RL-based algorithms have the intelligence to infer undisclosed InP information from historical data obtained from past experiences. Simulation results, considering both online and offline scenarios, reveal that the proposed algorithm results in up to 10% improvement in terms of acceptance ratio and provisioning cost compared to the benchmark algorithms, with up to more than 90% saving in execution time for large networks. In addition, the paper proposes an enhancement to a state-of-the-art algorithm which results in up to 5% improvement in terms of provisioning cost. [ABSTRACT FROM AUTHOR]

A coupled oscillator network may be able to perform an energy-efficient associative memory operation. However, its realization has been difficult because inhomogeneities unavoidably arise among the oscillators during fabrication and lead to an unreliable operation. This issue could be resolved if the oscillator network were able to be formed from a single oscillator. Here, we performed numerical simulations and theoretical analyses on an associative memory operation that uses a virtual oscillator network based on a spin-torque oscillator. The virtual network combines the concept of coupled oscillators with that of feedforward neural networks. Numerical experiments demonstrate successful associations of 60-pixel patterns with various memorized patterns. Moreover, the origin of the associative memory is shown to be forced synchronization driven by feedforward input, where phase differences among oscillators are fixed and correspond to the colors of the pixels in the pattern. [ABSTRACT FROM AUTHOR]

Virtual network embedding (VNE), as the key challenge of network resource management technology, lies in the contradiction between online embedding decision and pursuing long-term average revenue goals. Most of the previous work ignored the dynamics in Virtual Network (VN) modeling, or could not automatically detect the complex and time-varying network state to provide a reasonable network embedding scheme. In view of this, we model a network embedding framework where the topology and resource allocation change dynamically with the number of network users and workload, and then introduce a deep reinforcement learning method to solve the VNE problem. Further, a dynamic virtual network embedding algorithm based on Deep Reinforcement Learning (DRL), named DVNE-DRL, is proposed. In DVNE-DRL, VNE is modeled as a Markov Decision Process (MDP), and then deep learning is introduced to perceive the current network state through historical data and embedded knowledge, while utilizing reinforcement learning decision-making capabilities to implement the network embedding process. In addition, we improve the method of feature extraction and matrix optimization, and consider the characteristics of virtual network and physical network together to alleviate the problem of redundancy and slow convergence. The simulation results show that compared with the existing advanced algorithms, the acceptance rate and average revenue of DVNE-DRL are increased by about 25% and 35%, respectively. [ABSTRACT FROM AUTHOR]

Allocating and routing resources in optical networks is a critical process that has a major bearing on the efficiency and scalability of the network's resources. There has been a lot of study put into it, and several high-quality algorithms have been developed. The authors of this paper propose a novel approach to distributing optical network channels and assessing their spectrum for 5G uses. In this scenario, network channel allocation is performed by means of a reinforcement Markov Fourier spatial convolutional network. By analysing the channel allocation, the spectral efficiency of the network may be increased. Software defined networking, network function virtualization, and network slicing (NS) are just a few examples of the technologies gaining traction in academia and the IT industry that will fuel 5G. Network slicing (NS) is a network management technique that allows a physical topology to be partitioned into many independent virtual networks. Network resources, such as bandwidth, may be dynamically delivered on demand in line with specific quality of service and service level agreement requirements, and many service function chains can be established within a single slice. An experimental analysis of SNR, spectral efficiency, throughput, and packet delivery ratio is conducted based on the number of optical channels, users, and radio networks. [ABSTRACT FROM AUTHOR]

Knowledge representation models have been extensively studied and they provide an important foundation for artificial intelligence. However, the existing knowledge representation models or related knowledge embedding methods mostly aim at static or temporal knowledge, which are not suitable for highly spatio-temporal relevant knowledge, such as the cyber security knowledge. In this paper, we propose a knowledge embedding framework called TSEE to handle this problem, which builds on the MDATA model to represent and utilize dynamic knowledge for cyber security. TSEE is composed of knowledge extraction module, knowledge representation module, knowledge embedding module, and situational awareness module. There modules can obtain, transform, and embed cyber security knowledge from different sources, improving the detection capabilities of various complicated attacks. We conduct experiments on the cyber range for evaluation, and the experimental results validate the higher prediction accuracy and stronger extendability than existing embedding methods. The framework can effectively improve the cyber security defense capabilities in the future. [ABSTRACT FROM AUTHOR]

Simple Summary: The cartilage-to-bone transition is an essential process in healthy bone development and repair. Our previous work has shown that when the cells found within the human periosteum (the membrane surrounding the bone) are cultured in human serum (HS) as opposed to the standard animal serum (FBS), these cells have greater bone-forming capacity as assessed in an ectopic assay in nude mice. What is not understood is the molecular interactions that permitted this enhanced biological potency. Herein, virtual networks are created to identify the key proteins driving increased bone formation from these cells. Key signalling factors were identified through a network analysis, where FGFR3 was pinpointed as a major differential regulator between cells grown in HS and cells grown in FBS. This analysis was validated through an analysis of human-derived periosteal progenitor cells (PDCs) containing a constitutively active (ca) FGFR3. Following removal and analysis, we found that the FGFR3-ca cells that were implanted on bone void filler scaffolds in mice had an abundance of bone and cartilage that were present compared to the scaffold containing normal/healthy cells. This suggests that these cells were undergoing enhanced cartilage-to-bone transitions and that this protein may be a potentially novel therapeutic target for diseases where the cartilage-to-bone transition is affected such as during poor fracture healing. Human periosteum-derived progenitor cells (hPDCs) have the ability to differentiate towards both the chondrogenic and osteogenic lineages. This coordinated and complex osteochondrogenic differentiation process permits endochondral ossification and is essential in bone development and repair. We have previously shown that humanised cultures of hPDCs enhance their osteochondrogenic potentials in vitro and in vivo; however, the underlying mechanisms are largely unknown. This study aimed to identify novel regulators of hPDC osteochondrogenic differentiation through the construction of miRNA-mRNA regulatory networks derived from hPDCs cultured in human serum or foetal bovine serum as an alternative in silico strategy to serum characterisation. Sixteen differentially expressed miRNAs (DEMis) were identified in the humanised culture. In silico analysis of the DEMis with TargetScan allowed for the identification of 1503 potential miRNA target genes. Upon comparison with a paired RNAseq dataset, a 4.5% overlap was observed (122 genes). A protein–protein interaction network created with STRING interestingly identified FGFR3 as a key network node, which was further predicted using multiple pathway analyses. Functional analysis revealed that hPDCs with the activating mutation FGFR3N540K displayed increased expressions of chondrogenic gene markers when cultured under chondrogenic conditions in vitro and displayed enhanced endochondral bone formation in vivo. A further histological analysis uncovered known downstream mediators involved in FGFR3 signalling and endochondral ossification to be upregulated in hPDC FGFR3N540K-seeded implants. This combinational approach of miRNA-mRNA-protein network analysis with in vitro and in vivo characterisation has permitted the identification of FGFR3 as a novel mediator of hPDC biology. Furthermore, this miRNA-based workflow may also allow for the identification of drug targets, which may be of relevance in instances of delayed fracture repair. [ABSTRACT FROM AUTHOR]

Spreading digitalization, flexibility, and autonomy of technological processes in cyber-physical systems entails high security risks corresponding to negative consequences of the destructive actions of adversaries. The paper proposes a comprehensive technique that represents a distributed functional cyber-physical system's infrastructure as graphs: a functional dependencies graph and a potential attacks graph. Graph-based representation allows us to provide dynamic detection of the multiple compromised nodes in the functional infrastructure and adapt it to rolling intrusions. The experimental modeling with the proposed technique has demonstrated its effectiveness in the use cases of advanced persistent threats and ransomware. [ABSTRACT FROM AUTHOR]

Physical sensing is increasingly implemented in modern industries to improve information visibility, which generates real-time signals that are spatially distributed and temporally varying. These signals are often nonlinear and nonstationary in the high-dimensional space, which pose significant challenges to monitoring and control of complex systems. Therefore, this article presents a new "virtual sensing" approach that places imaginary sensors at different locations in signaling trajectories to monitor evolving dynamics within the signal space. First, we propose self-organizing principles to investigate distributional and topological features of nonlinear signals for optimal placement of imaginary sensors. Second, we design and develop the network model to represent real-time flux dynamics among these virtual sensors, in which each node represents a virtual sensor, while edges signify signal flux among sensors. Third, the establishment of a network model as well as the notion of transition uncertainty enable a fine-grained view into system dynamics and then extend a new Flux Rank (FR) algorithm for process monitoring. Experimental results show that the network FR methodology not only delineate real-time flux patterns in nonlinear signals, but also effectively monitor spatiotemporal changes in the dynamics of nonlinear dynamical systems. [ABSTRACT FROM AUTHOR]

Media servicesmust ensure an enhanced user’s perceived quality during content playback to attract and retain audiences, especially while the streams are distributed remotely via networks. Thus,media streaming services rely heavily on good and predictable network performance when delivered to a large number of people. Furthermore, as the quality ofmedia content gets high, the network performance demands are also increasing, and meeting them is challenging. Network functions devoted to improving media streaming services become essential to cope with the high dynamics of network performance and user mobility. Furthermore, new networking paradigms and architectures under the 5G networks umbrella are bringing new possibilities to deploy smart network functions, which monitor the media streaming services through live and objective metrics and boost them in real-time. This survey overviews the state-of-the-art technologies and solutions proposed to apply new network functions for enhancing media streaming. [ABSTRACT FROM AUTHOR]

Purpose: Early clinical trials are the first step into clinical therapies for new drugs. Within the six Bavarian university-based hospitals (Augsburg, Erlangen, Regensburg, Munich (LMU and TU), Würzburg) we have enrolled a virtual network platform for patient discussion. Methods: The virtual Early Clinical Trial Unit Tumor Board (ECTU Tumor Board) is a secured web-based meeting to evaluate early clinical trial options for patients, where representatives from local ECTUs participate. We retrospectively analyzed patient cases discussed between November 2021 and November 2022. Results: From November 2021 to November 2022, a total of 43 patients were discussed in the ECTU Tumor Board. Median age at diagnosis was 44.6 years (range 10–76 years). The median number of previous lines of therapies was 3.7 (range 1–9 therapies) including systemic treatment, surgery, and radiation therapy. A total of 27 different tumor entities were presented and 83.7% (36/43) patients received at least one trial recommendation. In total, 21 different active or shortly recruiting clinical trials were recommended: ten antibody trials, four BiTE (bispecific T cell engager) trials, six CAR (chimeric antigen receptor) T-cell trials, and one chemotherapy trial. Only six trials (28.6%) were recommended on the basis of the previously performed comprehensive genetic profiling (CGP). Conclusion: The ECTU Tumor Board is a feasible and successful network, highlighting the force of virtual patient discussions for improving patient care as well as trial recruitment in advanced diseases. It can provide further treatment options after local MTB presentation, aiming to close the gap to access clinical trials. [ABSTRACT FROM AUTHOR]

In the multi-domain network scenario, in order to improve the survivability of service function chain (SFC) in the face of network failure, most methods solve this problem through virtual network function (VNF) backup mechanism. However, the traditional multi-domain SFC deployment method lacks a SFC partition mechanism for backup resource consumption and does not consider the isolation and privacy requirements between different network domains. In view of the above problems, this paper proposes a reliability partition scheme based on reinforcement learning in SFC partition stage, which can ensure that VNF is backed up while maintaining good load balancing and low inter-domain transmission delay, and improve the reliability of SFC. Then, this paper proposes a VNF backup mechanism with minimum resource fluctuation in the VNF mapping stage and uses the integer linear programming (ILP) model to determine the backup scheme of each VNF, so as to ensure the minimum fluctuation of resource occupancy of the entire network. Finally, this paper proposes a multi-domain SFC deployment and backup algorithm based on Federated learning (FA-MSDB). The experimental results indicate that FA-MSDB can effectively improve the survival rate of SFC, reduce the overall transmission delay, and ensure good inter-domain and intra-domain load balance. [ABSTRACT FROM AUTHOR]

This paper proposes a learning-based visual peg-in-hole that enables training with several shapes in simulation and adapting to arbitrary unseen shapes in the real world with minimal sim-to-real cost. The core idea is to decouple the generalization of the sensory-motor policy from the design of a fast-adaptable perception module and a simulated generic policy module. The framework consists of a segmentation network (SN), a virtual sensor network (VSN), and a controller network (CN). Concretely, the VSN is trained to measure the pose of the unseen shape from a segmented image. After that, given the shape-agnostic pose measurement, the CN is trained to achieve a generic peg-in-hole. Finally, when applying to real unseen holes, we only have to fine-tune the SN required by the simulated VSN + CN. To further minimize the transfer cost, we propose to automatically collect and annotate the data for the SN after one-minute human teaching. Simulated and real-world results are presented under the configuration of eye-to/in-hand. An electric vehicle charging system with the proposed policy inside achieves a 10/10 success rate in 2–3 s, using only hundreds of auto-labeled samples for the SN transfer. [ABSTRACT FROM AUTHOR]