Edit Costs Research Articles

Measuring the similarity between shapes of buildings using graph edit distance

ABSTRACT Building data play a crucial role in geographic information science, and building shape similarity can be used for spatial inquiry, cartographic generalization, map updating, data quality assessment, and other spatial data mining applications. However, conventional shape representation and measurement methods use geometric and statistical measures and may not effectively capture the subtle differences among building shapes. Geospatial artificial intelligence methods often overlook nuanced differences in building contours, posing a challenge when precise fine-grained measurements are required. Furthermore, they have low interpretability. This study proposes a graph edit distance-based method for measuring the similarity between building shapes. It uses geometrics characteristics to construct a graph, matches and associates similar regional features between two building contours, and defines edit costs based on geometric and positional differences that align with the Gestalt principle of visual similarity. It converts the optimal edit path problem to the maximum weighted cluster solution problem and utilizes a heuristic approach to achieve the exact optimal solution within a moderately suitable timeframe. The experimental results demonstrated that the proposed method identifies detailed differences between building contours, providing accurate similarity measurement with high interpretability.

Moroccan Arabizi-to-Arabic conversion using rule-based transliteration and weighted Levenshtein algorithm

The rise of social media has contributed to the widespread of the Arabizi writing form, primarily used in colloquial communication. For Natural Language Processing (NLP) tools, processing texts in this form remains challenging due to the lack of suitable language resources. Additionally, there is a lack of standardized rules in the transliteration mapping between Arabizi and Arabic, resulting in variations across different dialectal groups. To address these limitations in the context of Moroccan Darija (MD), this work proposes a method for converting Arabizi to Arabic at the word level. This method involves a sequential combination of rule-based transliteration and weighted Levenshtein algorithm. The contributions of this approach include: (i) Building a large MD dataset that incorporates texts reflecting the characteristics of MD and the colloquial writing forms usually used in the Arabizi writing form. (ii) Generating transliteration rules tailored to MD. (iii) Adapting the edit costs within the weighted Levenshtein algorithm to enhance conversion performance. Successful tests have been conducted and the approach was applied to three datasets: two state-of-the-art Darija-Modern Standard Arabic (MSA) datasets and the MD dataset collected as part of this work. The proposed method achieved a Mean Reciprocal Rank (MRR) of 92.14% and an accuracy of 88.44%.

International commission on radiological protection report 152(2022): Radiation detriment calculation methodology

International commission on radiological protection report 152(2022): Radiation detriment calculation methodology

A Study on the Stability of Graph Edit Distance Heuristics

Graph edit distance (GED) is a powerful tool to model the dissimilarity between graphs. However, evaluating the exact GED is NP-hard. To tackle this problem, estimation methods of GED were introduced, e.g., bipartite and IPFP, during which heuristics were employed. The stochastic nature of these methods induces the stability issue. In this paper, we propose the first formal study of stability of GED heuristics, starting with defining a measure of these (in)stabilities, namely the relative error. Then, the effects of two critical factors on stability are examined, namely, the number of solutions and the ratio between edit costs. The ratios are computed on five datasets of various properties. General suggestions are provided to properly choose these factors, which can reduce the relative error by more than an order of magnitude. Finally, we verify the relevance of stability to predict performance of GED heuristics, by taking advantage of an edit cost learning algorithm to optimize the performance and the k-nearest neighbor regression for prediction. Experiments show that the optimized costs correspond to much higher ratios and an order of magnitude lower relative errors than the expert cost.

EmbAssi: embedding assignment costs for similarity search in large graph databases

The graph edit distance is an intuitive measure to quantify the dissimilarity of graphs, but its computation is mathsf {NP}-hard and challenging in practice. We introduce methods for answering nearest neighbor and range queries regarding this distance efficiently for large databases with up to millions of graphs. We build on the filter-verification paradigm, where lower and upper bounds are used to reduce the number of exact computations of the graph edit distance. Highly effective bounds for this involve solving a linear assignment problem for each graph in the database, which is prohibitive in massive datasets. Index-based approaches typically provide only weak bounds leading to high computational costs verification. In this work, we derive novel lower bounds for efficient filtering from restricted assignment problems, where the cost function is a tree metric. This special case allows embedding the costs of optimal assignments isometrically into ell _1 space, rendering efficient indexing possible. We propose several lower bounds of the graph edit distance obtained from tree metrics reflecting the edit costs, which are combined for effective filtering. Our method termed EmbAssi can be integrated into existing filter-verification pipelines as a fast and effective pre-filtering step. Empirically we show that for many real-world graphs our lower bounds are already close to the exact graph edit distance, while our index construction and search scales to very large databases.

Applying cognitive integration to oncology: A novel asynchronous foundational science curriculum for medical students during clerkships.

11028 Background: Cognitive integration (CI) connects foundational science (FS) to medical practice and improves clinical reasoning. While evidence supports CI in medical training, the clinical relevance of FS is often obscure to medical students, particularly in oncology, and few learning materials exist to support CI during clerkships. Providing effective and engaging tools to promote CI may enhance understanding of FS relevance to clinical medicine. We therefore aimed to create an online module to integrate FS into clerkships. Methods: Previous student feedback at our institution advocated for clinically relevant FS content. Applying Cognitive Theory of Multimedia Learning principles, we (an oncologist, a biochemist, and an instructional designer) created a cancer module grounded in the hallmarks of cancer (HoC), a key FS principle. To emphasize clinical relevance, we recorded patient interviews incorporating FS. The module, composed in Qualtrics, includes an introduction and four cases, each starting with a brief patient interview, followed by clinical text, images, questions, and explanations. Content co-created by the oncologist and biochemist combined clinical and FS perspectives and referenced the HoC throughout. We piloted a draft with a focus group of four students. The evaluation plan includes written learner feedback, a case-based pre/post-test, and review of clerkship assessments for evidence of CI. Results: We produced an FS oncology module with an estimated engagement time of two hours, which students may complete over multiple sittings. The development team met monthly over the course of one year. Filming and editing cost $100/hour. Overall, the project required approximately 60 faculty person-hours and 12 instructional designer person-hours. The patients interviewed for the module expressed gratitude for sharing their stories with students. The student focus group yielded unanimously positive feedback about the content, the multimedia format, the level of FS covered, and the use of authentic patient videos to enhance clinical relevance. Students strongly preferred this module compared to prior online learning activities. Conclusions: This novel interactive module can serve as a model for development of clerkship curricula promoting CI. Module creation based on real patients resulted in authentic story-telling and enhanced FS relevance. Content development involving a clinician and a basic scientist aligned with CI goals. Limitations include the need for funding and instructional design assistance. A randomized controlled trial comparing a series of novel FS modules to older recorded lectures may demonstrate improved outcomes. Involving learners in curriculum development may decrease faculty burden and increase student interest in FS.

On-line learning the graph edit distance costs

Abstract This paper presents the first on-line learning method to automatically deduce the insertion, deletion and substitution edit costs of the graph edit distance. The learning method is based on embedding the substitution and deletion operations into a Euclidean space. The points in this space are classified into the ones that represent substitution edit operations and the ones that represent deletion edit operations. Thus, the learning strategy is based on deducing the hyper-plane in this space that best splits these two types of points. Any linear classifier can be used to deduce this hyper-plane, for instance LDA or SVM. The on-line method has the advantage that learning the edit costs and computing the graph edit distance with the new updated costs can be done simultaneously. Experimental validation shows that the matching accuracy is competitive with the off-line methods but without the need of the whole learning set.

Answering Why-Questions for Subgraph Queries

Subgraph queries are routinely used to search for entities in richly attributed graphs e.g., social networks and knowledge graphs. With little knowledge of underlying data, users often need to rewrite queries multiple times to reach desirable answers. Why-questions are studied to clarify missing or unexpected query results. This paper makes a first step to answer Why-questions for entity search in attributed graphs. We consider three common types of Why-questions: Why-not, Why, and Why-rank, which suggest query manipulations that are responsible for user-specified missing, unexpected, and undesirably ranked entities, respectively. (1) We approach a general query rewriting paradigm that suggests to identify desired entities that are specified by Why-questions. We introduce measures that characterize good query rewrites by incorporating both query editing cost and answer closeness. (2) While computing optimal query rewrites is intractable, we develop feasible algorithms, from approximation to fast heuristics, and provide query rewrites with (near) optimality guarantees whenever possible, for Why, Why-not and Why-rank questions. We further show that our results remain intact for Why questions that (1) request a single query rewrite to clarify multiple types of entities, and (2) variants such as Why-empty and Why-so-many, by providing the matching algorithms. Using real-world graphs, we experimentally verify that our algorithms are effective and feasible for large graphs. Our case study also verifies their application in e.g., knowledge exploration.

A general model to define the substitution, insertion and deletion graph edit costs based on an embedded space

Abstract The paper presents a method to learn the substitution, deletion and insertion costs on nodes and edges applied to the graph edit distance. We model the learning strategy as a general model and then we concretise it in two different architectures: the first architecture is based on a neural network and the second architecture is based on a multivariate normal distribution, which have been previously trained. The insertion, deletion and substitution costs on nodes and edges are defined as functions that depend on the output of the machine learning architecture. Other machine learning architectures have been presented in the literature to define the graph edit distance costs. Nevertheless, the main feature of our method is that the insertion, deletion and substitution costs are learned together, training the same machine learning and generating only one model. Thus, these costs are influenced one to another, achieving a higher accuracy in the pattern recognition stage than previous methods.

Learning the Edit Costs of Graph Edit Distance Applied to Ligand-Based Virtual Screening.

Background Graph edit distance is a methodology used to solve error-tolerant graph matching. This methodology estimates a distance between two graphs by determining the minimum number of modifications required to transform one graph into the other. These modifications, known as edit operations, have an edit cost associated that has to be determined depending on the problem.Objective This study focuses on the use of optimization techniques in order to learn the edit costs used when comparing graphs by means of the graph edit distance.Methods Graphs represent reduced structural representations of molecules using pharmacophore-type node descriptions to encode the relevant molecular properties. This reduction technique is known as extended reduced graphs. The screening and statistical tools available on the ligand-based virtual screening benchmarking platform and the RDKit were used.ResultsIn the experiments, the graph edit distance using learned costs performed better or equally good than using predefined costs. This is exemplified with six publicly available datasets: DUD-E, MUV, GLL&GDD, CAPST, NRLiSt BDB, and ULS-UDS.Conclusion This study shows that the graph edit distance along with learned edit costs is useful to identify bioactivity similarities in a structurally diverse group of molecules. Furthermore, the target-specific edit costs might provide useful structure-activity information for future drug-design efforts.

Learning the Graph Edit Costs Based on a Learning Model Applied to Sub-optimal Graph Matching

Attributed graphs are used to represent patterns composed of several parts in pattern recognition. The nature of these patterns can be diverse, from images, to handwritten characters, maps or fingerprints. Graph edit distance has become an important tool in structural pattern recognition since it allows us to measure the dissimilarity of attributed graphs. It is based on transforming one graph into another through some edit operations such as substitution, deletion and insertion of nodes and edges. It has two main constraints: it requires an adequate definition of the costs of these operations and its computation cost is exponential with regard to the number of nodes. In this paper, we first present a general framework to automatically learn these edit costs considering graph edit distance is computed in a sub-optima way. Then, we specify this framework in two different models based on neural networks and probability density functions. An exhaustive practical validation on 14 public databases, which have different features such as the size of the graphs, the number of attributes or the number of graphs per class have been performed. This validation shows that with the learned edit costs, the accuracy is higher than with some manually imposed costs or other costs automatically learned by previous methods.

A hybrid heuristic for the overlapping cluster editing problem

Clustering problems are applicable to several areas of science. Approaches and algorithms are as varied as the applications. From a graph theory perspective, clustering can be generated by partitioning an input graph into a vertex-disjoint union of cliques (clusters) through addition and deletion of edges. Finding the minimum number of edges additions and deletions required to cluster data that can be represented as graphs is a well-known problem in combinatorial optimization, often referred to as cluster editing problem. However, many real-world clustering applications are characterized by overlapping clusters, that is, clusters that are non-disjoint. In these situations cluster editing cannot be applied to these problems. Literature concerning a relaxation of the cluster editing, where clusters can overlap, is scarce. In this work, we propose the overlapping cluster editing problem, a variation of the cluster editing where the goal is to partition a graph, also by editing edges, into maximal cliques that are not necessarily disjoint. In addition, we also present three slightly different versions of a hybrid heuristic to solve this problem. Each hybrid heuristic is based on coupling two metaheuristicsthat, together, generate a set of clusters; and one of three mixed-integer linear programming models, also introduced in this paper, that uses these clusters as input. The objective with the metaheuristics is to limit the solution exploration space in the models’ resolution, therefore reducing its computational time.Tests results show that the all proposed hybrid heuristic versions are able to generate good-quality overlapping cluster editing solutions. In particular, one version of the hybrid heuristic achieved, at a low computational cost, the best results in 51 of 112 randomly-generated graphs. Although the other two hybrid heuristic versions have harder to solve models, they obtained reasonable results in medium-sized randomly-generated graphs. In addition, the hybrid heuristic achieved good results identifying labeled overlapping clusters in a supervised data set experiment. Furthermore, we also show that, with our new problem definition, clustering a vertex in more than one cluster can reduce the edges editing cost.

Learning edit cost estimation models for graph edit distance

One of the most popular distance measures between a pair of graphs is the Graph Edit Distance. This approach consists of finding a set of edit operations that completely transforms a graph into another. Edit costs are introduced in order to penalize the distortion that each edit operation introduces. Then, one basic requirement when we design a Graph Edit Distance algorithm, is to define the appropriate edit cost functions. On the other hand, Machine Learning algorithms has been applied in many contexts showing impressive results, due, among other things, its ability to find correlations between input and output values. The aim of this paper is to bring the potentialities of Machine Learning to the Graph Edit Distance problem presenting a general framework in which this kind of algorithms are used to estimate the edit costs values for node substitutions.

Embedding the node-to-node mappings to learn the Graph edit distance parameters

This paper presents a learning method to automatically deduce the insertion, deletion and substitution costs of the Graph edit distance. The method is based on embedding the ground-truth node-to-node mappings into a Euclidean space and learning the edit costs through the hyperplane that splits the nodes into mapped ones and non-mapped ones in this new space. In this way, the algorithm does not need to compute any graph matching process, which is the main drawback of other methods due to its intrinsic exponential computational complexity. Nevertheless, our learning method has two main restrictions: 1) the insertion and deletion edit costs have to be constants; 2) the substitution edit costs have to be represented as inner products of two vectors. One vector represents certain weights and the other vector represents the distances between attributes. Experimental validation shows that the matching accuracy of this method outperforms the current methods. Furthermore, there is a significant reduction in the runtime in the learning process.

On the exact computation of the graph edit distance

The graph edit distance is a widely used distance measure for labelled graph. However, A★−GED, the standard approach for its exact computation, suffers from huge runtime and memory requirements. Recently, three better performing algorithms have been proposed: The general algorithms DF−GED and BIP−GED, and the algorithm CSI−GED, which only works for uniform edit costs. All newly proposed algorithms outperform the standard approach A★−GED. However, cross-comparisons are lacking. This paper consolidates and extends these recent advances. To this purpose, we present all existing algorithms in a unified way and show that the slightly different definitions of the graph edit distance underlying A★−GED and DF−GED, on the one side, and CSI−GED, on the other side, can be harmonised. This harmonisation allows us to develop a generalisation of CSI−GED to non-uniform edit cost. Moreover, we present a speed-up of A★−GED and DF−GED for uniform edit costs, which build upon the fact that, in the uniform case, a continuously used subroutine can be implemented to run in linear rather than cubic time. We also suggest an algorithm MIP−GED which builds upon a very compact new mixed integer linear programming formulation. Finally, we carry out a thorough empirical evaluation, which, for the first time, compares all existing exact algorithms.

Improved Lower Bounds for Graph Edit Distance

The problem of deriving lower and upper bounds for the edit distance between undirected, labeled graphs has recently received increasing attention. However, only one algorithm has been proposed that allegedly computes not only an upper but also a lower bound for non-uniform edit costs and incorporates information about both node and edge labels. In this paper, we demonstrate that this algorithm is incorrect. We present a corrected version $\mathsf {B\scriptstyle{RANCH}}$ that runs in $\mathcal{O}(n^2\Delta ^3+n^3)$ time, where $\Delta$ is the maximum of the maximum degrees of input graphs $G$ and $H$ . We also develop a speed-up $\mathsf {B\scriptstyle{RANCH}}\mathsf{F\scriptstyle{AST}}$ that runs in $\mathcal{O}(n^2\Delta ^2+n^3)$ time and computes an only slightly less accurate lower bound. The lower bounds produced by $\mathsf {B\scriptstyle{RANCH}}$ and $\mathsf {B\scriptstyle{RANCH}}\mathsf{F\scriptstyle{AST}}$ are shown to be pseudo-metrics on a collection of graphs. Finally, we suggest an anytime algorithm $\mathsf {B\scriptstyle{RANCH}}\mathsf{T\scriptstyle{IGHT}}$ that iteratively improves $\mathsf {B\scriptstyle{RANCH}}$ ’s lower bound. $\mathsf {B\scriptstyle{RANCH}}\mathsf{T\scriptstyle{IGHT}}$ runs in $\mathcal{O}(n^3\Delta ^2+I(n^2\Delta ^3+n^3))$ time, where the number of iterations $I$ is controlled by the user. A detailed experimental evaluation shows that all suggested algorithms are Pareto optimal, that they are very effective when used as filters for edit distance range queries, and that they perform excellently when used within classification frameworks.

Book Review: Book Reviews

Richard Hooker, Of the Laws of Ecclesiastical Polity: A Critical Edition with Modern Spelling. Edited by Arthur Stephen McGrade. Oxford: Oxford University Press, 2013. Three volumes, cxiv + 1100 pp. $450.00 (cloth).Richard Hooker and the Vision of God: Exploring the Origins of ?Anglicanism.' By Charles Miller. Cambridge: James Clarke & Co., 2013. 350 pp. $50.00 (paper).Recent decades have witnessed a resurgence in serious academic study of Richard Hooker's thought, largely thanks to the publication of the multivolume Folger edition of Hooker's collected works between 1977 and 1998. This resurgence has been matched, however, with an inexorable decline in popular reading of Hooker's Of the Laws of Ecclesiastical Polity : the exorbitant cost of the Folger edition has kept it the preserve of academic libraries, and the rarity of earlier critical editions has also led to a waning influence. The past decade has seen various attempts to remedy this situation, but they all share a similar weakness of providing only abridged selections. While affordable, these abridged selections are arranged to suit the interests of political philosophers or popular readers who want an easy arrangement of what Hooker had to say about isolated theological ideas. As a result, such selective editions variously obscure the context, complexity, and interdependence of Hookers thought across the Laws. A. S. McGrade's new critical edition of the Laws and Charles Millers introductory text both attempt, in their own ways, to provide a more comprehensive view of Hookers theological vision at an affordable price and in an accessible way.A. S. McGrade, Professor Emeritus of Philosophy at the University of Connecticut, caps a lifelong academic love affair with Hooker by providing a cheaper (although still relatively expensive) version of the definitive Folger edition of Hookers Laws. It includes all eight books, modernizes the Elizabethan spellings, introduces the structure and content of the Laws, and offers critical appendices. Without doubt, McGrade's achievements deserve high plaudits and his critical edition should become the standard reference work for years to come. McGrade acts as the consummate teacher in his introduction, written in accessible and yet academically rigorous language, expressive of his contagious joy in Hooker and yet cognizant of Hookers limitations as a writer and thinker. McGrade covers the historical provocation for the Laws, tours the reader around an itinerary which takes in the broad themes and structure of each book, and considers Hookers multiple audiences, including posterity and the modern reader. Briefly and gently, McGrade gives a strong apologia why Hooker remains pertinent not only as a beautiful writer, but also as a thinker with beautiful ideas about public life and the common good.McGrade s textual expertise and sensitivity remains consummate in the new critical edition. Spellings are modernized word-for-word, without paraphrase or substitution, thereby respecting Hookers original syntax and yet rendering it easier for the modem eye to parse. Although some purists may be alarmed at the modem spelling, translations of other major early modem texts (of various languages) into modem English should ameliorate such concerns, and certainly helps reduce the sense of distance between Hooker and the modem reader. McGrade translates non-English material into the English vernacular in both the main text and in Hookers footnotes; the original texts are either given on the page or in an appendix. The critical appendices themselves exhaustively provide a glossary of terms, a guide to sources and people mentioned in the Laws, indices of scriptural citations, persons, and subjects, as well as a thorough bibliography. These appendices mean that the text of the Laws remains uncrowded with immediate exegetical gloss, but still rigorously supported when the reader wants explanation or help navigating the text. …

575. High Content Analysis of CRISPR-Cas9 Gene-Edited Human Embryonic Stem Cells

Gene-edited human cells are important resources for drug target identification, regulatory science, regenerative medicine and basic biology. Recently, use of the CRISPR-Cas9 system drastically cut the time required to produce gene-edited cell lines down to a few months. Targeted gene disruption in a population of human cells followed by selection and next-generation sequencing can identify drug targets, however many of these methods destroy all mutant clones, so a subsequent gene-editing experiment is required to obtain living mutant cells for downstream analysis. Overall, there is a need to increase the speed, multiplexing and precision in generating CRISPR-Cas9 mutants. ArrayEdit, a simple approach utilizing surface-modified multiwell plates containing one-pot transcribed single-guide RNAs (see FigureFigure), separates thousands of edited cell populations for automated, live, high-content imaging and analysis. The approach lowers the time and cost of gene editing and produces edited human embryonic stem cells at high efficiencies. Edited genes can be expressed in both pluripotent stem cells and differentiated cells from all three major germ layers. This allows for the real-time observation of mutations causing phenotypic differences that can be measured in in vitro tissues and organoids during culture as opposed to defined end points. The live cell and tissue assays on ArrayEdit are compatible with standard screening platforms and a variety of human cell types, thus permitting robust and sensitive detection of differences in proliferation, differentiation and other downstream effects of gene editing. This preclinical platform adds important capabilities to observe editing and selection in situ within complex structures generated by human cells, ultimately enabling optical and other molecular perturbations in the editing workflow that could refine the specificity and versatility of gene editing.FigureArrayEdit provides a new window into the process of gene editing human cells. Overview of ArrayEdit assembly and key components. Top: Schematic of one-pot PCR and T7 transcription. Bottom: Surface modification to the bottom of multiwell plates generates cell-adhesive µFeatures on a glass bottom. Each µFeature can be tracked over time via high-content imaging and stitched together to form a time-lapse visualization of edited cell phenotypes.View Large Image | Download PowerPoint Slide

The economic security issues of periodicals by the example of women’s magazines

The authors consider the economic security issues of the modern periodical press by the example of women’s «glossy» publications. The article briefly describes the history of dissemination of women’s periodical in the world and in Russia. A comparative analysis of circulation and prices for advertising was carried out by the example of three magazines Vogue, Elle and Marie Claire. The authors note that the early XXI century marked the beginning of a protracted crisis for publishers of women’ gloss. First off, this is due to the development of Internet and digital technologies. Taking the above mentioned into considerations, we can conclude about the high level threats of economic safety for the modern periodicals, in particular, for the women’s magazines. First of all, this is drop in incomes from sales of circulations and advertise. In addition, a dollar volatility leads to an increase in the cost of magazine edition. The share of income received from the magazine sites still amounts to a miserable interests. The deliberate overstatement of circulations leads to the publishers’ reputational damage. All this results in the fact that the problems of raising level of economic security for the modern gloss enter into in the foreground.

High-Content Analysis of CRISPR-Cas9 Gene-Edited Human Embryonic Stem Cells.

SummaryCRISPR-Cas9 gene editing of human cells and tissues holds much promise to advance medicine and biology, but standard editing methods require weeks to months of reagent preparation and selection where much or all of the initial edited samples are destroyed during analysis. ArrayEdit, a simple approach utilizing surface-modified multiwell plates containing one-pot transcribed single-guide RNAs, separates thousands of edited cell populations for automated, live, high-content imaging and analysis. The approach lowers the time and cost of gene editing and produces edited human embryonic stem cells at high efficiencies. Edited genes can be expressed in both pluripotent stem cells and differentiated cells. This preclinical platform adds important capabilities to observe editing and selection in situ within complex structures generated by human cells, ultimately enabling optical and other molecular perturbations in the editing workflow that could refine the specificity and versatility of gene editing.