Spatial coherence in DNA barcode networks

With the continuous development of satellite technology, to achieve a complete and reliable information transmission has become a hot space information technology research, building space information network is imperative. Aiming at the characteristics of the existing spatial information network structure and the complexity of technical problems, the spatial network is studied and analyzed according to the method of functional classification and structure stratification. Firstly, the networking structure of spatial comprehensive information network is introduced. Then the existing spatial information network is classified according to the functional purpose, and the existing spatial network system research status is introduced according to the category. Finally, the spatial network is divided by the idea of analog stratification. The research hotspots of each layer are introduced, and the key technologies of spatial routing are analyzed.

Functional Split between Parietal and Entorhinal Cortices in the Rat

Since the mid-2010s, the ALICE experiment at CERN has seen significant changes in its software, especially with the introduction of the Online-Offline (O²) computing system during Long Shutdown 2. This evolution required continuous adaptation of the Quality Control (QC) framework responsible for online Data Quality Monitoring (DQM) and offline Quality Assurance (QA). After a general overview of the system, this talk delves into the evolving user requirements that shaped the QC framework from its initial prototyping phase to its current state. We will explore the changing landscape of performance needs and feature demands, highlighting which initial requirements persisted, which emerged later, and which features ultimately proved unnecessary. Additionally, we will trace the framework’s development in relation to other software components within the ALICE ecosystem, offering valuable insights and lessons learned throughout the process. Finally, we will also discuss the challenges encountered in balancing development team resources with the evolving project scope.

Recently, sparse representation-based classification (SRC), which assigns a test sample to the class with minimum representation error via a sparse linear combination of all the training samples, has successfully been applied to hyperspectral imagery. Meanwhile, spatial information, that means the adjacent pixels belong to the same class with a high probability, is a valuable complement to the spectral information. In this paper, we have presented a new spatial-neighborhood-integrated SRC method, abbreviated as SN-SRC, to jointly consider the spectral and spatial neighborhood information of each pixel to explore the spectral and spatial coherence by the SRC method. Experimental results have shown that the proposed SN-SRC approach could achieve better performance than the other state-of-the-art methods, especially with limited training samples.

In this paper, we present a novel framework for quality control in cinematic VR (360-video) based on Voronoi patches and saliency which can be used in post-production workflows. Our approach first extracts patches in stereoscopic omnidirectional images (ODI) using the spherical Voronoi diagram. The subdivision of the ODI into patches allows an accurate detection and localization of regions with artifacts. Further, we introduce saliency in order to weight detected artifacts according to the visual attention of end-users. Then, we propose different artifact detection and analysis methods for sharpness mismatch detection (SMD), color mismatch detection (CMD) and disparity distribution analysis. In particular, we took two state of the art approaches for SMD and CMD, which were originally developed for conventional planar images, and extended them to stereoscopic ODIs. Finally, we evaluated the performance of our framework with a dataset of 18 ODIs for which saliency maps were obtained from a subjective test with 17 participants.

An algorithm is presented for finding the k nearest neighbors in a spatial network in a best-first manner using network distance. The algorithm is based on precomputing the shortest paths between all possible vertices in the network and then making use of an encoding that takes advantage of the fact that the shortest paths from vertex u to all of the remaining vertices can be decomposed into subsets based on the first edges on the shortest paths to them from u. Thus, in the worst case, the amount of work depends on the number of objects that are examined and the number of links on the shortest paths to them from q, rather than depending on the number of vertices in the network. The amount of storage required to keep track of the subsets is reduced by taking advantage of their spatial coherence which is captured by the aid of a shortest path quadtree. In particular, experiments on a number of large road networks as well as a theoretical analysis have shown that the storage has been reduced from O(N3) to O(N1.5) (i.e., by an order of magnitude equal to the square root). The precomputation of the shortest paths along the network essentially decouples the process of computing shortest paths along the network from that of finding the neighbors, and thereby also decouples the domain S of the query objects and that of the objects from which the neighbors are drawn from the domain V of the vertices of the spatial network. This means that as long as the spatial network is unchanged, the algorithm and underlying representation of the shortest paths in the spatial network can be used with different sets of objects.

Higher-order interactions exist widely in mobile populations and are extremely important in spreading epidemics, such as influenza. However, research on high-order interaction modeling of mobile crowds and the propagation dynamics above is still insufficient. Therefore, this study attempts to model and simulate higher-order interactions among mobile populations and explore their impact on epidemic transmission. This study simulated the spread of the epidemic in a spatial high-order network based on agent-based model modeling. It explored its propagation dynamics and the impact of spatial characteristics on it. Meanwhile, we construct state-specific rate equations based on the uniform mixing assumption for further analysis. We found that hysteresis loops are an inherent feature of high-order networks in this space under specific scenarios. The evolution curve roughly presents three different states with the initial value change, showing different levels of the endemic balance of low, medium, and high, respectively. Similarly, network snapshots and parameter diagrams also indicate these three types of equilibrium states. Populations in space naturally form components of different sizes and isolations, and higher initial seeds generate higher-order interactions in this spatial network, leading to higher infection densities. This phenomenon emphasizes the impact of high-order interactions and high-order infection rates in propagation. In addition, crowd density and movement speed act as protective and inhibitory factors for epidemic transmission, respectively, and depending on the degree of movement weaken or enhance the effect of hysteresis loops.

This paper presents a novel satellite autonomous navigation and orbit determination method in spatial data chains and network, using wireless measurements of the. distances from each other. The satellite position and velocity in ECEF Cartesian coordinates, as a function of the six Keplerian elements, are used to estimate the instantaneous orbital elements by the extended Kalman filter algorithm. The problems confronted in the spatial network are simply analyzed, such as high data transmission between satellites, accurate wireless distance measurement and network management. In the case of the low Earth orbit satellite (LEO), the estimated position error was controlled to around ten meters. Two important problems to be studied further are discussed.

Recently, sparse representation-based classification (SRC), which assigns a test sample to the class with minimum representation error via a sparse linear combination of all the training samples, has successfully been applied to hyperspectral imagery. Alternatively, spatial information, which means the adjacent pixels belong to the same class with a high probability, is a valuable complement to the spectral information. In this paper, we have presented a new spectral-spatial-combined SRC method, abbreviated as SSSRC or $$\mathrm{S}^{3}\mathrm{RC}$$S3RC, to jointly consider the spectral and spatial neighborhood information of each pixel to explore the spectral and spatial coherence by the SRC method. Furthermore, a fast interference-cancelation operation is adopted to accelerate the classification procedure of $$\mathrm{S}^{3}\mathrm{RC}$$S3RC, named $$\mathrm{FS}^{3}\mathrm{RC}$$FS3RC. Experimental results have shown that both the proposed SRC-based approaches, $$\mathrm{S}^{3}\mathrm{RC}$$S3RC and $$\mathrm{FS}^{3}\mathrm{RC}$$FS3RC, could achieve better performance than the other state-of-the-art methods.

In vivo application of short-lag spatial coherence and harmonic spatial coherence imaging in fetal ultrasound.

It is well known that routing strategies based on global topological information is not a good choice for the enhancement of traffic throughput in large-scale networks due to the heavy communication cost. On the contrary, acquiring spatial information, such as spatial distances among nodes, is more feasible. In this paper, we propose a novel distance-based routing strategy in spatial scale-free networks, called LDistance strategy. The probability of establishing links among nodes obeys the power-law in the spatial network under study. Compared with the LDegree strategy (Wang et al., 2006) and the mixed strategy (a strategy combining both greedy routing strategy and random routing strategy), results show that our proposed LDistance strategy can further enhance traffic capacity. Besides, the LDistance strategy can also achieve a much shorter delivering time than the LDegree strategy. Analyses reveal that the superiority of our strategy is mainly due to the interdependent relationship between topological and spatial characteristics in spatial scale-free networks. Furthermore, along transporting path in the LDistance strategy, the spatial distance to destination decays more rapidly, and the degrees of routers are higher than those in the LDegree strategy.

In the big data era, spatial-network data has become increasingly important and popular in many real-world objects, ranging from micro-scale (e.g., molecule structures), to middle-scale (e.g., biological neural networks), to macro-scale (e.g., mobility networks). Spatial networks consist of nodes and edges that are embedded in a geometric space. Although, it is critical to model and understand the generative process of spatial networks, this task remains largely under-explored due to the significant difficulty in automatically modeling and distinguishing the dependency and relevance among various spatial and network semantic factors. In addition, containing both spatial and network information makes the modeling of spatial networks bear large time and memory cost, especially for large graphs. To address the aforementioned challenges, we first propose a novel objective for joint spatial-network disentangled representation learning from the perspective of information bottleneck as well as a novel progressive optimization algorithm to optimize the intractable objective. Based on this, a Spatial-Network Disentangled Variational Autoencoder (SND-VAE) is proposed to discover the independent and dependent latent factors of spatial and networks. To reduce the time complexity, an efficient version SND-VAE-light is proposed, which is based on a novel Efficient Spatial-Network Message Passing Neural Network (ES-MPNN). Qualitative and quantitative experiments on both synthetic and real-world datasets with various scales of graph size demonstrate the superiority of the proposed model over the state-of-the-arts by up to 66.9% for graph generation and 37.3% for interpretability. In addition, the ES-MPNN is also proved to reduce the time complexity of the encoder in the generative model from cubic to linear growth (The implementation of this work can be found at https://github.com/xguo7/SND-VAE ).

Commercial Transport During a Pandemic: Network Analysis to Reconcile COVID-19 Diffusion and Vital Supply Chain Resilience.

Previous studies have suggested that the degree of social anhedonia reflects the vulnerability for developing schizophrenia. However, only few studies have investigated how functional network changes are related to social anhedonia. The aim of this fMRI study was to classify subjects according to their degree of social anhedonia using supervised machine learning. More specifically, we extracted both spatial and temporal network features during a social cognition task from 70 subjects, and used support vector machines for classification. Since impairment in social cognition is well established in schizophrenia-spectrum disorders, the subjects performed a comic strip task designed to specifically probe theory of mind (ToM) and empathy processing. Features representing both temporal (time series) and network dynamics were extracted using task activation maps, seed region analysis, independent component analysis (ICA), and a newly developed multi-subject archetypal analysis (MSAA), which here aimed to further bridge aspects of both seed region analysis and decomposition by incorporating a spotlight approach.We found significant classification of subjects with elevated levels of social anhedonia when using the times series extracted using MSAA, indicating that temporal dynamics carry important information for classification of social anhedonia. Interestingly, we found that the same time series yielded the highest classification performance in a task classification of the ToM condition. Finally, the spatial network corresponding to that time series included both prefrontal and temporal-parietal regions as well as insula activity, which previously have been related schizotypy and the development of schizophrenia.

The interference pattern resulting from a double slit (or pinhole pair) is commonly used to measure the spatial coherence properties of a light source in the visible1, as well as in the extreme ultraviolet (EUV)2,3 regions of the spectrum. This interference pattern results from the path difference of the incident light field from each slit to the observation point. In a standard spatial coherence measurement, the incident field is assumed to be quasi-monochromatic, so that temporal coherence effects are isolated from a measurement of spatial coherence. However, under conditions where a broad bandwidth source illuminates a pinhole pair, the interference pattern will contain both temporal and spatial coherence information of the source, as well as the power spectrum. In this talk we show that the spectrum of a beam can be extracted from a measurement of a double-pinhole interference pattern and the geometry of the set-up4. Such a spectral measurement is unique in that it determines the absolute wavelength and relative spectral intensity of the source, which is a great advantage in the EUV region where calibration is very difficult.