Adjacency Networks Research Articles

Population identification and genetic diversity analysis of Fritillaria ussuriensis (Fritillaria) based on chloroplast genes atpF and petB.

The chloroplast genomes of five Fritillaria ussuriensis materials from different production areas were comparatively analyzed, atpF and petB were screened as specific DNA barcodes, and the population identification and genetic diversity of F. ussuriensis were analyzed based on them. The F. ussuriensis chloroplast genome showed a total length of 151 515-151 548bp with a typical tetrad structure and encoded 130 genes. atpF and petB were used to amplify 183 samples from 13 populations, and they could identify 6 and 9 haplotypes, respectively. Joint analysis of the two sequences revealed 18 haplotypes, named H1-H18, with the most widely distributed and most abundant being H4. Ten haplotypes were unique for 7 populations that they could be used to distinguish from others. Haplotype diversity and nucleotide diversity were 0.99 and 2.09 × 10-3, respectively, indicating the genetic diversity was relatively rich. The results of the intermediary adjacency network showed that H5 was the oldest haplotype, and stellate radiation was centered around it, indicating that population expansion occurred in genuine production areas. This study lays a theoretical foundation for the population identification, genetic evolution, and breed selection of F. ussuriensis.

Geospatial Structure and Evolution Analysis of National Terrestrial Adjacency Network Based on Complex Network

The first law of geography is one of the most important concepts in geographical analysis, revealing the significant role of spatial proximity. At present, some current international relation studies or geographic network analysis studies tend to build corresponding network models according to different themes, but the most basic level of geographic neighborhoods is intentionally or unintentionally neglected in those processes. Based on the adjacency relationship between the terrestrial countries in the world, the model of the terrestrial adjacency network (TAN) is constructed. The model includes almost all land-based countries and is divided into three main regions, respectively, Eurasia, Africa, and America. On the mathematical model of these regions, we analyze the geospatial structure and network evolution of the adjacent networks utilizing statistical methods and network analysis methods. This study helps to map and understand the geographical attributes and characteristics of countries from the perspective of holistic structure, aiming to provide a quantitative reference for subsequent research on international relations and geographic computing. Moreover, despite some limitations, TAN represents a new advance in geographical network analysis that can be further applied by overlaying more attribute data.

False statements in Egan et al.’s defence of their word adjacency network method

False statements in Egan et al.’s defence of their word adjacency network method

“I would I had that corporal soundness”: Pervez Rizvi's Analysis of the Word Adjacency Network Method of Authorship Attribution

Abstract In his two-part article ‘An Analysis of the Word Adjacency Network Method—Part 1—The evidence of its unsoundness’ and ‘Part 2—A true understanding of the method’ Digital Scholarship in the Humanities, 38: 347-78 (2022), Pervez Rizvi attempts to replicate the Word Adjacency Network (WAN) method for authorship attribution and show that it does not produce the new knowledge that we, its inventors, claim for it. In the present essay, we will show that Rizvi misrepresents fundamental aspects of the WAN method, that his attempted replication fails not because the method is flawed but because he erred in replicating it, and that Rizvi misunderstands key aspects of the mathematics of Information Theory that the method uses.

Adjacency Constraint for Efficient Hierarchical Reinforcement Learning.

Goal-conditioned Hierarchical Reinforcement Learning (HRL) is a promising approach for scaling up reinforcement learning (RL) techniques. However, it often suffers from training inefficiency as the action space of the high-level, i.e., the goal space, is large. Searching in a large goal space poses difficulty for both high-level subgoal generation and low-level policy learning. In this article, we show that this problem can be effectively alleviated by restricting the high-level action space from the whole goal space to a k-step adjacent region of the current state using an adjacency constraint. We theoretically prove that in a deterministic Markov Decision Process (MDP), the proposed adjacency constraint preserves the optimal hierarchical policy, while in a stochastic MDP the adjacency constraint induces a bounded state-value suboptimality determined by the MDP's transition structure. We further show that this constraint can be practically implemented by training an adjacency network that can discriminate between adjacent and non-adjacent subgoals. Experimental results on discrete and continuous control tasks including challenging simulated robot locomotion and manipulation tasks show that incorporating the adjacency constraint significantly boosts the performance of state-of-the-art goal-conditioned HRL approaches.

Geometry of the skeleton graph of the no-signaling polytope for the 2223 Bell scenario and beyond

The study of no-signaling polytopes is addressed under an alternative approach, focusing on the skeleton graph of the polytopes rather than on the Bell inequalities solely. The paper starts with the study of the Bell scenario where one of the parties (Bob) has three output options (2223 scenario). We found that in this polytope the Popescu-Rohrlich (PR) vertices are interconnected, making up two networks around the polytope of local correlations, which is a new feature in relation to the 2222 polytope. We also found that in this scenario each CHSH inequality is violated by five PR vertices: the PR vertex associated to that inequality and its four adjacent PR vertices. Later, we study the facet structure of the corresponding polytope of local correlations. What we obtain is a one-to-one correspondence between some two-dimensional faces of the no-signaling polytope defined by specific loops in the adjacency networks of PR vertices, the lifting Bell functions, and the nontrivial facets of the local polytope. Finally, by studying the skeleton graphs of the polytopes for the general $222{v}_{B}$ scenarios, where ${v}_{B}$ is the number of possible Bob's outcomes, a striking high degeneracy is unveiled. We show that the set of local vertices exhibits a much higher degeneracy than the PR vertices, which in turn are more degenerate than a regular vertex.

Construction and application of data-driven knowledge adjacency network for product CMF design

To solve the problems of over-reliance on personal experience, lack of thinking about users' perceptual preferences, and neglect of the overall coupling characteristics of CMF design, the construction and application methods of product CMF design knowledge adjacency network are proposed based on the multi-layer network, intuitionistic fuzzy set, and gray correlation method theory. Firstly, the construction of the product CMF design multilayer network is completed by extracting and selecting the design resources of product families with similar imagery. Secondly, considering the product CMF design characteristics, a multi-layer network association division method based on leader nodes is proposed, and the construction of the CMF design knowledge adjacency network is further completed. Thirdly, based on intuitionistic fuzzy set theory, user preferences are considered to further improve the science of evaluation data. Finally, the calculation of association coefficients is combined with the gray correlation method, and the optimal matching between the adjacency network and the product surface assignment solution space is completed to assist designers to improve the design efficiency. The proposed method is applied to the CMF design work of a smart tractor, and perceptual experiments are set up to evaluate the design results, and the results show that this design meets the target requirements, which proves the usefulness of the method to assist the designer's work.

Identifying Firm Significance and Positions in the Patent Innovation Based on Centrality Measures’ Clustering Approach

In the current era of inevitable technological progress, organizations strive to achieve technological competence. One way to measure the adaptability of firms to huge technological shifts is through various parameters, including patenting activities. This study presents a method for identifying the significance of firms in an innovation network using patent citation analysis and centrality measures. Specifically, the study employs k-means clustering to classify firms into similar clusters based on Network-based centrality measures such as betweenness, closeness, and eigenvector centrality. The study then develops a cluster relational network by establishing a cluster adjacency network and identifying firm positions within and between clusters. By examining the relation between clusters, the cluster network identifies the significance of firms. The study identifies four positions, namely, leader, follower, knowledge inertia, and significantly emerging, that align with the status of firms in patenting innovation capability. The method is implemented using blockchain technology as a case study. The novelty of the study lies in the structured approach to identifying firm significance by adding another layer of adjacency network to existing patent citation analysis techniques.

COMMUNITY CONTEXT OF OLDER ADULT CARE: A CASE STUDY DURING THE COVID-19 PANDEMIC

Abstract Introduction Expectations for older adults (OA) to live in the community and prevent costly long-term care assume OAs’ informal network members are available, able, and willing to fill this need. Yet, little is known about the processes whereby OAs construct care networks, especially during COVID-19. Methods A longitudinal case study of one OA male who participated in the ethnographic community Older Adult Care study in one urban Chicago neighborhood is described. The OA male described his network on three occasions: 2/2018, 1/2021 and 9/2021. The care networks were described by size, density, and transitivity. Data analysis was performed using the R programming language. Adjacency networks were constructed using the network package, then visualized using the sna package. Results The OA’s network went from 23 members pre-pandemic in time one to 13 in time two and 8 members in time three. As network size contracted, the network density increased from 25% in time two to 46% at time three, indicating a more interconnected network. Clustering varied over time and was at its lowest in time 2 (27%) and increased by time 3 (67%). Friends and church connections were 72% of his network in time one, whereas 71% of his network were family and neighbors in time three. Conclusions Our OA’s large, relatively disconnected social network tightened to fewer, more closely connected members during COVID-19 months. Contextual variables (environment, health guidelines, fear, pets) influenced the networks. It is essential to understand OA care networks to promote healthy aging in community.

Multi-Scale 2D Temporal Adjacency Networks for Moment Localization With Natural Language.

We address the problem of retrieving a specific moment from an untrimmed video by natural language. It is a challenging problem because a target moment may take place in the context of other temporal moments in the untrimmed video. Existing methods cannot tackle this challenge well since they do not fully consider the temporal contexts between temporal moments. In this paper, we model the temporal context between video moments by a set of predefined two-dimensional maps under different temporal scales. For each map, one dimension indicates the starting time of a moment and the other indicates the duration. These 2D temporal maps can cover diverse video moments with different lengths, while representing their adjacent contexts at different temporal scales. Based on the 2D temporal maps, we propose a Multi-Scale Temporal Adjacency Network (MS-2D-TAN), a single-shot framework for moment localization. It is capable of encoding the adjacent temporal contexts at each scale, while learning discriminative features for matching video moments with referring expressions. We evaluate the proposed MS-2D-TAN on three challenging benchmarks, i.e., Charades-STA, ActivityNet Captions, and TACoS, where our MS-2D-TAN outperforms the state of the art.

Learning-Based Adaptive IRS Control With Limited Feedback Codebooks

Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms, which can change the wireless propagation environment through design of their reflection coefficients. We consider a practical setting where (i) the IRS reflection coefficients are configured by adjusting tunable elements embedded in the meta-atoms, (ii) the IRS reflection coefficients are affected by the incident angles of the incoming signals, (iii) the IRS is deployed in multi-path, time-varying channels, and (iv) the feedback link from the base station to the IRS has a low data rate. Conventional optimization-based IRS control protocols, which rely on channel estimation and conveying the optimized variables to the IRS, are not applicable in this setting due to the difficulty of channel estimation and the low feedback rate. Therefore, we develop a novel adaptive codebook-based limited feedback protocol where only a codeword index is transferred to the IRS. We propose two solutions for adaptive codebook design, random adjacency (RA) and deep neural network policy-based IRS control (DPIC), both of which only require the end-to-end compound channels. We further develop several augmented schemes based on RA and DPIC. Numerical evaluations show that the data rate and average data rate over one coherence time are improved substantially by our schemes.

Network Structure Features and Influencing Factors of Tourism Flow in Rural Areas: Evidence from China

Exploring the spatial network structure of tourism flow and its influencing factors is of great significance to the transmission of characteristic culture and the sustainable development of tourism in tourist destinations, especially in backward rural areas. Taking Qiandongnan Miao and Dong Autonomous Prefecture (hereinafter referred to as Qiandongnan Prefecture) as an example, this paper adopts social network analysis and Quadratic Assignment Procedure regression analysis to study the network structural characteristics and influencing factors of tourism flow using online travel blog data. The results show that: (1) There are seasonal changes in tourism flow, but the attractions that tourists pay attention to do not change with the seasons. (2) The tightness of the tourism flow network structure is poor. The core nodes are unevenly distributed, and there are obvious structural holes. (3) The density of the tourism flow network is low. There is a clear core–periphery structure in the network, and the core area has a weak driving effect on the periphery area. There are more cohesive subgroups in the network, but the degree of connectedness between the subgroups varies greatly. (4) Geographical adjacency, transportation accessibility, and tourism resource endowment influence tourism flow network structure. The study found that the influencing factors of tourism flow in rural areas are different from those in urban areas. These results provide useful information for the marketing and development of tourism management departments in rural areas.

An analysis of the Word Adjacency Network method—Part 1—The evidence of its unsoundness

AbstractA new mathematical method for authorship attribution, known as the Word Adjacency Network method, has emerged and its inventors have claimed a high success rate with it. They have announced that their method has shown that Christopher Marlowe wrote some scenes in the Henry VI trilogy of plays, an attribution which has made newspaper headlines. This first part of a two-part paper replicates the method’s success in attributing complete plays but demonstrates that it cannot be relied on to attribute individual scenes correctly, particularly where Marlowe is a candidate author. It shows by examining the method’s calculations that its unreliability is due to its mass exclusion of relevant evidence, that exclusion being necessitated by its use of a mathematical formula which can now be seen to be unsuited to the texts of early modern plays.

An analysis of the Word Adjacency Network method—Part 2—A true understanding of the method

AbstractThis second of a two-part examination of the Word Adjacency Network (WAN) method considers the idea behind the method, which is that authors place function words in proximity to each other in such distinctive ways that the distinctions can be used for authorship attribution. By doing control experiments, it shows that the attributions made by the method are not due to distinctive word adjacencies at all, contrary to the claim of its inventors. It shows that the method is in fact another word-counting method, the counting hidden by superfluously complicated mathematics. It concludes that the claim that the placements of function words in a text can be used for attributing its authorship remains unsubstantiated. It also demonstrates that the claimed theoretical basis for the method, that WAN are Markov chains, is false.

Superpixel segmentation algorithm based on local network modularity increment

Superpixel segmentation is a kind of image preprocessing technology and a popular research direction in image processing. The purpose of superpixel segmentation is to reduce the complexity of image processing. The most widely applied Simple Linear Iterative Clustering (SLIC) superpixel segmentation algorithm has high operating efficiency. However, under-segmentation is prone to occur when the number of given superpixel regions is too small. In order to improve the segmentation accuracy, the superpixel segmentation algorithm based on local network modularity increment (LocalNet) from the perspective of network community detection is proposed here. The adjacency network is constructed according to the colour similarity of image pixels, the local community centre is found by the degree of network nodes, and the local network structure of the community is constructed. The modularity increment is employed as the boundary constraint to improve the segmentation accuracy of superpixel segmentation. Through the experimental comparison with the SLIC algorithm, its improved algorithm, and the algorithm proposed in recent years, the results show that our LocalNet algorithm significantly improves in segmentation accuracy Furthermore, the segmentation effect has obvious advantages under the premise that the segmentation speed is not much different from that of the other five algorithms.

A two-stage text detection approach using gradient point adjacency and deep network

A two-stage text detection approach using gradient point adjacency and deep network

A two-stage text detection approach using gradient point adjacency and deep network

A two-stage text detection approach using gradient point adjacency and deep network

How the Word Adjacency Network (WAN) works

Abstract The Word Adjacency Network (WAN) method is a newly invented method for attributing the authorship of texts based on internal evidence about the proximities of high-frequency words in those texts. The method has been the subject of mathematically rigorous explanations in scientific journals and here is offered instead a relatively non-technical account for humanist readers unfamiliar with advanced mathematical nomenclature. The description will refer to the operation of the algorithm as it is implemented in a newly completed open-source, open-access version that the authors have made available for free unlimited download on the WorldWide Web.

Advances of bioelectrocatalysis by oxidoreductases

<p indent=0mm>Bioelectrocatalysis attracts much attention with the advantages of high selectivity, high efficiency, no byproduct and environmental friendliness. Electrification of industrial biotechnology is challenging for the different operating conditions and catalysts of electrochemical and biochemical reactions. In this review, we present the state-of-the-art information on enzymatic electrosynthesis for the production of fine chemicals. Multidisciplinary strategies of electrode surface modification and enzyme immobilization have been integrated in electrocatalytic devices with various applications in biosensors and bioelectrocatalysis. However, the efficiency of bioelectrosynthesis is determined by the intramolecular electron transfer rate, and interfacial electron transfer rate between the oxidoreductases and electrode. This review aims to summarize recent progresses on the mechanism studies of intramolecular electron transferof redox proteins, including tunneling, hopping, proton-coupled electron transfer, etc. Modular origin of electron transfer chain was also discussed from the perspective of the evolution of spatial adjacency network. Novel and efficient oxidoreductases with enhanced intramolecular and interfacial electron transfer rate can be obtained by protein engineering. The strategies for facilitating intramolecular electron transfer are addressed, which includes the regulation of co-evolved electron transfer network, the designs of molecular switch and the assembly of conductive modules. Oxidoreductases are engineered to improve their biocatalytic performance by using the tools of molecular evolution, modeling, structure prediction, and mutation. Co-evolved molecular switches controll proton-coupled electron transfer and regulate electron transfer inside the multi-center redox proteins. Assembly of surface-binding conductive peptides to oxidoreductases facilitates electricity-driven catalysis. Moreover, the modifications of oxidoreductases allow their predictable immobilization on functionalized electrode surface with improved stability, controlled orientation, surface coverage and enhanced electron transfer. After that, electron transfer within a series of well-defined peptides with orientation-controlled and surface-confined enzymes was addressed. Strategies also developed to increase the biocompatible active surface of electrodes and to promote charge transfer reactions. Nano- and macroporous electrodes based on metal nanoparticles, nanocarbon tubes, graphene, and metallic inverse opals have been designed and fabricated with predictable surface functionalities and electrochemical properties. Electrode/mediator/cofactor/enzyme conjugates can enhance the <italic>in vitro</italic> bioelectricity generation of cofactor. Screening of redox mediators for electrochemical regeneration of NADH was also summarized. Multifunctional surfaces of nano or meso-porous electrodes, where oxidoreductases were bounded to structured electrodes with necessary components (e.g., mediators, cofactor, etc.) with tailored properties, were summarized. Optimizations of functional electrode materials and surfaces can improve the efficiency of cofactor regeneration in the electrochemical reactor modules. Key challenges and future research for better bioelectrocatalysis are discussed and expected briefly. Mining and engineering of novel and robust enzymes from genomic and metagenomic libraries would benefit the bioelectrocatalysis. Based on genome mining, discovery of novel oxidoreductases from extreme microorganism provide thermophilic, alkalophilic, halophilic and organic solvent tolerant oxidoreductases. Reactors for bioelectrocatalysis are optimized to provide a platform for the production of chiral chemicals, which brings great promises for biomanufacturing. Further study of bio-inspired multi-enzyme immobilization which mimics quick electron transfer in multi-enzyme complex is suggested. A variety of approaches for bioelectrocatalysis have been successfully applied for the synthesis of chiral chemicals which are intermediates of fine chemicals and pharmaceuticals. Bioelectrosynthesis processes could replace many chemically catalyzed routes in the future, and aims to construct a new platform for more efficient and green biomanufacturing.

Research Spotlights

We have two Research Spotlights papers in the lineup this issue. The first of these, “Persistent Homology of Geospatial Data: A Case Study with Voting," written by Michelle Feng and Mason Porter, is quite timely. The article begins with a gentle introduction into the what and why of persistent homology (PH): after converting the point cloud of data into a series of simplicial complexes at different scales, the homology groups are computed at each scale, with homological features tracked across scales. It is generally assumed that persistence of features can then be used to distinguish signal (important patterns) from noise. Of concern is the method of converting the data into the simplicial complexes. The authors argue that PH using the simplicial complexes computed via the distance-based approach can lead to erroneous assumptions or missed characteristics about the voting data they consider. They propose to counter these deficiencies by developing two new methods, one based on adjacency networks and one that utilizes level sets to capture the manifold nature of the data, for building a filtered simplicial complex. The dramatic differences achieved by varying the complexes on the resulting PH are colorfully illustrated and interpreted for real voter data. Readers may appreciate not only the tools used and results obtained, but also the broad range of problems---transportation networks, spatial demography, granular materials, biological structures---identified by the authors for which the new approaches may prove relevant. An animal's ability to localize odor sources and follow trails of odor is key to many aspects of its survival. Authors Nour Riman, Jonathan D. Victor, Sebastian D. Boie, and Bard Ermentrout focus their article, “The Dynamics of Bilateral Olfactory Search and Navigation," on providing a comprehensive mathematical analysis of the dynamics of the differential equations that model the way that animals use bilateral information to navigate odor sources. When odor sensors are located at two different positions of the body, it enables comparison of odor concentrations and thus allows the animal to determine whether to move toward or away from the stimulus. Tropotaxis in the present context refers to the movement of the animal in response to an odor directly toward or away from the source of the odor. The sensor angles, which the animal can control, play an important role in enabling the animal to find and follow a trail. The other model parameters include sensor length and sensitivity to concentration change. The authors investigate tropotaxis as functions of the model parameters under varying conditions for the odor sources and trails. In some cases, the authors are able to reduce the dynamics to a planar system. Graphical illustrations compliment the discussion of the behavior. The paper concludes with a discussion of future research, particularly with respect to exploring the effects of noise on the models. It is the hope that this new “understanding [of] the underlying dynamics of the bilateral model will help in building models that use bilateral information together with other strategies."