Iterative Expansion Research Articles

A Hybrid Recognition Framework for Highly Interacting Machining Features Based on Primitive Decomposition, Learning and Reconstruction

For the highly interacting machining features, Layered Projection Decomposition Method presents inferior recognition efficiency and accuracy, due to its high-cost 3D projection and failures in determining projection faces for internal occluded faces. To address these issues, we propose a potential hybrid recognition framework. We first introduce a straightforward adjacent projection wire (APW) over UV wires, automatically restoring the full projection wires from highly interacting features. Building on APWs, an efficient hybrid boundary representation and its corresponding unambiguous primitive definitions are proposed by combing with graph-based boundary representations. Subsequently, we design an efficient primitive decomposition method by introducing primitive boundary matching to decide the initial projection faces, and introducing iterative projection boundary expansion to complete the full primitives from occluded faces. Moreover, we establish an efficient Graph Neural Network to learn the distinguishable distributions over the decomposed primitives. Specifically, an Adjacency Attention Unit is proposed to automatically perceive the influence weight of adjacent nodes, leading to more discriminative self-adaptive shape embedding for efficient primitive recognition. Finally, we summarize convenient reconstruction rules to correct the wrong predictions of feature faces with indistinguishable adjacent relationships. To evaluate the effectiveness of the proposed recognition framework, CAD models of complex aircraft structural parts are collected to present a challenging machining feature dataset. Extensive numerical experiments demonstrate that the proposed hybrid recognition framework enables significant improvements over the state-of-the-art machining feature recognition techniques.

Single-shot 20-fold expansion microscopy

Expansion microscopy (ExM) is in increasingly widespread use throughout biology because its isotropic physical magnification enables nanoimaging on conventional microscopes. To date, ExM methods either expand specimens to a limited range (~4–10× linearly) or achieve larger expansion factors through iterating the expansion process a second time (~15–20× linearly). Here, we present an ExM protocol that achieves ~20× expansion (yielding <20-nm resolution on a conventional microscope) in a single expansion step, achieving the performance of iterative expansion with the simplicity of a single-shot protocol. This protocol, which we call 20ExM, supports postexpansion staining for brain tissue, which can facilitate biomolecular labeling. 20ExM may find utility in many areas of biological investigation requiring high-resolution imaging.

γ-tubulin complex controls the nucleation of tubulin-based structures in Apicomplexa.

Apicomplexan parasites rely on tubulin structures throughout their cell and life cycles, particularly in the polymerization of spindle microtubules to separate the replicated nucleus into daughter cells. Additionally, tubulin structures, including conoid and subpellicular microtubules, provide the necessary rigidity and structure for dissemination and host cell invasion. However, it is unclear whether these tubulin structures are nucleated via a highly conserved γ-tubulin complex or through a specific process unique to apicomplexans. This study demonstrates that Toxoplasma γ-tubulin is responsible for nucleating spindle microtubules, akin to higher eukaryotes, facilitating nucleus division in newly formed parasites. Interestingly, γ-tubulin colocalizes with nascent conoid and subpellicular microtubules during division, potentially nucleating these structures as well. Loss of γ-tubulin results in significant morphological defects due to impaired nucleus scission and the loss of conoid and subpellicular microtubule nucleation, crucial for parasite shape and rigidity. Additionally, the nucleation process of tubulin structures involves a concerted action of γ-tubulin and Gamma Tubulin Complex proteins (GCPs), recapitulating the localization and phenotype of γ-tubulin. This study also introduces new molecular markers for cytoskeletal structures and applies iterative expansion microscopy to reveal microtubule-based architecture in Cryptosporidium parvum sporozoites, further demonstrating the conserved localization and probable function of γ-tubulin in Cryptosporidium.

Service innovations, value-driven business model, and institute growth: insights from a higher-education institute

PurposeExternal pressures, like the pandemic, influence industry and academia. ICT can assist in creating service innovations and better responses to external pressures. When higher education institutes combine service innovations with an appropriate business model, they can better understand educational transformations and marketing and aim for productivity, effectiveness, and sustainability. This paper aims to provide insights into transformations by organizing innovations and business models and creating a positive influence.Design/methodology/approachThis paper uses a case study method to examine the educational transformation in its settings. The method followed is direct non-participant observation, and the selected case is a marginalized business school of an Indian public institution. Combining observation and case study provides deeper insights into individuals/groups in a social context. The direct non-participant allows the researcher to get closer to the field of research while retaining the position of an outsider.FindingsThis paper defines service innovations with four conceptualizations – ICT, socio-techno-organization, demand, and orchestration. The findings provide insights into business models and educational marketing, with three suggestions for sustainability and economic growth. The suggestions are (1) operationalization based on incremental, iterative, and spiral expansion for the first two, (2) orchestrating educational marketing and value elements for a value-driven business model, and (3) social reengineering based on human values and leadership commitment.Research limitations/implicationsIt uses a single marginalized negative case study and is not bereft of limitations. Selecting positive cases representing geographically dispersed units, hybrid lectures, and diverse participants provides further insights into the operationalization of ICT and socio-techno-organizational aspects.Practical implicationsAs educational institutions decide the appropriate strategy for their institution, the findings provide practitioners with insights to create value, expansion, growth, and insights into educational marketing.Originality/valueThe three steps in findings and their contribution to economic growth and sustainability enhance the body of knowledge – transformations in educational institutions. It provides insights into business models and educational marketing.

Restricted Open-Shell Hartree-Fock Method for a General Configuration State Function Featuring Arbitrarily Complex Spin-Couplings.

In this work, we present a general spin restricted open-shell Hartree-Fock (ROHF) implementation that is able to generate self-consistent field (SCF) wave functions for an arbitrary configuration state function (CSF). These CSFs can contain an arbitrary number of unpaired electrons in arbitrary spin-couplings. The resulting method is named CSF-ROHF. We demonstrate that starting from the ROHF energy expression, for example, the one given by Edwards and Zerner, it is possible to obtain the values of the ROHF vector-coupling coefficients by setting up an open-shell for each group of consecutive parallel-coupled spins dictated by the unique spin-coupling pattern of any given CSF. To achieve this important and nontrivial goal, we employ the machinery of the iterative configuration expansion configuration interaction (ICE-CI) method, which is able to tackle general CI problems on the basis of spin-adapted CSFs. This development allows for the efficient generation of SCF spin-eigenfunctions for systems with complex spin-coupling patterns, such as polymetallic chains and metal clusters, while maintaining SCF scaling with system size (quadratic or less, depending on the specific algorithm and approximations chosen).

Co-dependent formation of the Toxoplasma gondii sub-pellicular microtubules and inner membrane skeleton.

One of the defining features of apicomplexan parasites is their cytoskeleton composed of alveolar vesicles, known as the inner membrane complex (IMC) undergirded by intermediate-like filament network and an array of subpellicular microtubules (SPMTs). In Toxoplasma gondii, this specialized cytoskeleton is involved in all aspects of the disease-causing lytic cycle, and notably acting as a scaffold for parasite offspring in the internal budding process. Despite advances in our understanding of the architecture and molecular composition, insights pertaining to the coordinated assembly of the scaffold are still largely elusive. Here, T. gondii tachyzoites were dissected by advanced, iterative expansion microscopy (pan-ExM) revealing new insights into the very early sequential formation steps of the tubulin scaffold. A comparative study of the related parasite Sarcocystis neurona revealed that different MT bundling organizations of the nascent SPMTs correlate with the number of central and basal alveolar vesicles. In absence of a so far identified MT nucleation mechanism, we genetically dissected T. gondii γ-tubulin and γ-tubulin complex protein 4 (GCP4). While γ-tubulin depletion abolished the formation of the tubulin scaffold, a set of MTs still formed that suggests SPMTs are nucleated at the outer core of the centrosome. Depletion of GCP4 interfered with the correct assembly of SPMTs into the forming daughter buds, further indicating that the parasite utilizes the γ-tubulin complex in tubulin scaffold formation .

Quantum Davidson algorithm for excited states

Excited state properties play a pivotal role in various chemical and physical phenomena, such as charge separation and light emission. However, the primary focus of most existing quantum algorithms has been the ground state, as seen in quantum phase estimation and the variational quantum eigensolver (VQE). Although VQE-type methods have been extended to explore excited states, these methods grapple with optimization challenges. In contrast, the quantum Krylov subspace (QKS) method has been introduced to address both ground and excited states, positioning itself as a cost-effective alternative to quantum phase estimation. However, conventional QKS methodologies depend on a pre-generated subspace through real or imaginary-time evolutions. This subspace is inherently expansive and can be plagued with issues like slow convergence or numerical instabilities, often leading to relatively deep circuits. Our research presents an economic QKS algorithm, which we term the quantum Davidson (QDavidson) algorithm. This innovation hinges on the iterative expansion of the Krylov subspace and the incorporation of a pre-conditioner within the Davidson framework. By using the residues of eigenstates to expand the Krylov subspace, we manage to formulate a compact subspace that aligns closely with the exact solutions. This iterative subspace expansion paves the way for a more rapid convergence in comparison to other QKS techniques, such as the quantum Lanczos. Using quantum simulators, we employ the novel QDavidson algorithm to delve into the excited state properties of various systems, spanning from the Heisenberg spin model to real molecules. Compared to the existing QKS methods, the QDavidson algorithm not only converges swiftly but also demands a significantly shallower circuit. This efficiency establishes the QDavidson method as a pragmatic tool for elucidating both ground and excited state properties on quantum computing platforms.

Universal approximation property of stochastic configuration networks for time series

For the purpose of processing sequential data, such as time series, and addressing the challenge of manually tuning the architecture of traditional recurrent neural networks (RNNs), this paper introduces a novel approach-the Recurrent Stochastic Configuration Network (RSCN). This network is constructed based on the random incremental algorithm of stochastic configuration networks. Leveraging the foundational structure of recurrent neural networks, our learning model commences with a modest-scale recurrent neural network featuring a single hidden layer and a solitary hidden node. Subsequently, the node parameters of the hidden layer undergo incremental augmentation through a random configuration process, with corresponding weights assigned structurally. This iterative expansion continues until the network satisfies predefined termination criteria. Noteworthy is the adaptability of this algorithm to handle time series data, exhibiting superior performance compared to traditional recurrent neural networks with similar architectures. The experimental results presented in this paper underscore the efficacy of the proposed RSCN for sequence data processing, showcasing its advantages over conventional recurrent neural networks in the context of the performed experiments.

IU-ExM: nanoscopy of organelles and tissues with iterative ultrastructure expansion microscopy

Expansion microscopy (ExM) is a highly effective technique for super-resolution fluorescence microscopy that enables imaging of biological samples beyond the diffraction limit with conventional fluorescence microscopes. Despite the development of several enhanced protocols, ExM has not yet demonstrated the ability to achieve the precision of nanoscopy techniques such as Single Molecule Localization Microscopy (SMLM). Here, to address this limitation, we have developed an iterative ultrastructure expansion microscopy (iU-ExM) approach that achieves SMLM-level resolution. With iU-ExM, it is now possible to visualize the molecular architecture of gold-standard samples, such as the eight-fold symmetry of nuclear pores or the molecular organization of the conoid in Apicomplexa. With its wide-ranging applications, from isolated organelles to cells and tissue, iU-ExM opens new super-resolution avenues for scientists studying biological structures and functions.

WordPPR: A Researcher-Driven Computational Keyword Selection Method for Text Data Retrieval from Digital Media

ABSTRACT Despite the increasing use of digital media data in communication research, a central challenge persists – retrieving data with maximal accuracy and coverage. Our investigation of keyword-based data collection practices in extant communication research reveals a one-step process, whereas our cross-disciplinary literature review suggests an iterative query expansion process guided by human knowledge and computer intelligence. Hence, we introduce the WordPPR method for keyword selection and text data retrieval, which entails four steps: 1) collecting an initial dataset using core/seed keyword(s); 2) constructing a word graph based on the dataset; 3) applying the Personalized PageRank (PPR) algorithm to rank words in proximity to the seed keyword(s) and selecting new keywords that optimize retrieval precision and recall; 4) repeating steps 1–3 to determine if additional data collection is needed. Without requiring corpus-wide sampling/analysis or extensive manual annotation, this method is well suited for data collection from large-scale digital media corpora. Our simulation studies demonstrate its robustness against parameter choice and its improvement upon other methods in suggesting additional keywords. Its application in Twitter data retrieval is also provided. By advancing a more systematic approach to text data retrieval, this study contributes to improving digital media data retrieval practices in communication research and beyond.

An iteration-based interactive attention network for 3D point cloud registration

Several learning-based methods dedicated to 3D point cloud registration have been developed recently. However, the performance of such methods is limited due to a lack of effective feature interaction mechanisms. To overcome this problem, an iteration-based interactive attention network is proposed here for 3D point cloud registration that can effectively learn the overlapping features of point cloud pairs and improve the registration accuracy. Concretely, we first designed a condensed global information extractor to achieve aggregated global features. Meanwhile, to reduce the amount of calculation required for subsequent modules while maintaining high-level characteristic information, the number of point cloud features is reduced. Furthermore, an overlapping feature iterative attention module and a discriminant feature iterative expansion module are designed. The main function of both modules is to implement feature interaction in the form of cross-attention and self-attention as well as to deepen interactions iteratively. The former captures overlapping information by iteratively interacting with the features of the condensed global features of one point cloud and the local geometric features of another point cloud. The latter can expand the number of features back to the initial number of point clouds through the interaction of overlapping features and local geometric features. Finally, matching features with high discriminative ability are obtained and used to formulate a soft correspondence matrix. Furthermore, weighted singular-value decomposition is adopted to obtain the rigid transformation matrix. Extensive experiments demonstrate that the proposed method achieves state-of-the-art performance.

Flow Cytometry: The Next Revolution.

Unmasking the subtleties of the immune system requires both a comprehensive knowledge base and the ability to interrogate that system with intimate sensitivity. That task, to a considerable extent, has been handled by an iterative expansion in flow cytometry methods, both in technological capability and also in accompanying advances in informatics. As the field of fluorescence-based cytomics matured, it reached a technological barrier at around 30 parameter analyses, which stalled the field until spectral flow cytometry created a fundamental transformation that will likely lead to the potential of 100 simultaneous parameter analyses within a few years. The simultaneous advance in informatics has now become a watershed moment for the field as it competes with mature systematic approaches such as genomics and proteomics, allowing cytomics to take a seat at the multi-omics table. In addition, recent technological advances try to combine the speed of flow systems with other detection methods, in addition to fluorescence alone, which will make flow-based instruments even more indispensable in any biological laboratory. This paper outlines current approaches in cell analysis and detection methods, discusses traditional and microfluidic sorting approaches as well as next-generation instruments, and provides an early look at future opportunities that are likely to arise.

KICE: A Knowledge Consolidation and Expansion Framework for Relation Extraction

Machine Learning is often challenged by insufficient labeled data. Previous methods employing implicit commonsense knowledge of pre-trained language models (PLMs) or pattern-based symbolic knowledge have achieved great success in mitigating manual annotation efforts. In this paper, we focus on the collaboration among different knowledge sources and present KICE, a Knowledge-evolving framework by Iterative Consolidation and Expansion with the guidance of PLMs and rule-based patterns. Specifically, starting with limited labeled data as seeds, KICE first builds a Rule Generator by prompt-tuning to stimulate the rich knowledge distributed in PLMs, generate seed rules, and initialize the rules set. Afterwards, based on the rule-labeled data, the task model is trained in a self-training pipeline where the knowledge in rules set is consolidated with self-learned high-confidence rules. Finally, for the low-confidence rules, KICE solicits human-enlightened understanding and expands the knowledge coverage for better task model training. Our framework is verified on relation extraction (RE) task, and the experiments on TACRED show that the model performance (F1) grows from 33.24% to 79.84% with the enrichment of knowledge, outperforming all the baselines including other knowledgeable methods.

Adaptive goal-oriented data sampling in Data-Driven Computational Mechanics

Data-Driven (DD) computing is an emerging field of Computational Mechanics, motivated by recent technological advances in experimental measurements, the development of highly predictive computational models, advances in data storage and data processing, which enable the transition from a material data-scarce to a material data-rich era. The predictive capability of DD simulations is contingent on the quality of the material data set, i.e. its ability to closely sample all the strain–stress states in the phase space of a given mechanical problem. In this study, we develop a methodology for increasing the quality of an existing material data set through iterative expansions. Leveraging the formulation of the problems treated with the DD paradigm as distance minimization problems, we identify regions in phase space with poor data coverage, and target them with additional experiments or lower-scale simulations. The DD solution informs the additional experiments so that they can provide better coverage of the phase space of a given application.We first illustrate the convergence properties of the approach through a DD finite element simulation of a linear elastic cylinder under triaxial compression. The same numerical experiment is then performed on a specimen of Hostun sand, a material with complex history-dependent behavior. Data sampling is performed with Level-Set Discrete Element Method (LS-DEM) calculations of unit cells representative of this granular material, subjected to loading paths determined by the proposed method. It is shown that this adaptive expansion of the data set, tailored for a particular application, leads to convergent and accurate DD predictions, without the computational cost of using large databases with potentially redundant or low-quality data.

Convergence rate to the Tracy–Widom laws for the largest eigenvalue of sample covariance matrices

We establish a quantitative version of the Tracy–Widom law for the largest eigenvalue of high-dimensional sample covariance matrices. To be precise, we show that the fluctuations of the largest eigenvalue of a sample covariance matrix X∗X converge to its Tracy–Widom limit at a rate nearly N−1/3, where X is an M×N random matrix whose entries are independent real or complex random variables, assuming that both M and N tend to infinity at a constant rate. This result improves the previous estimate N−2/9 obtained by Wang (2019). Our proof relies on a Green function comparison method (Adv. Math. 229 (2012) 1435–1515) using iterative cumulant expansions, the local laws for the Green function and asymptotic properties of the correlation kernel of the white Wishart ensemble.

A novel approach of unsupervised feature selection using iterative shrinking and expansion algorithm

An major constraint in the realm of feature selection is that users choose the ideal number of characteristics that must be picked. In this article, an effort is made to automate the process of determining a suitable value for the appropriate the quantity of characteristics that must be chosen for better recognition tasks. To estimate the ideal amount of features that should be maintained for properly describing the data, we use the dense subgraph discovery approach for this goal. Notably, the existing methods uses a similar kind of approach called the dense subgraph finding. But the earlier approach obtains a single dense subgraph, while the task of dense subgraphs finding obtains a number of dense subgraphs that are important for learning the internal structure of any network. Thus, dense subgraphs finding is likely to be adopted as a natural choice for realizing the complex relations among the nodes of massive real-life networks, such as biological networks, social networks.

Unsupervised Feature Selection Using Iterative Shrinking and Expansion Algorithm

In this article, we propose an unsupervised feature selection algorithm based on the recently developed shrinking and expansion algorithm (SEA). The SEA is already an established, efficient algorithm to identify dense subgraphs within a weighted graph. To conduct SEA on a weighted graph, the dataset is first mapped onto an equivalent graph notation where each vertex corresponds to a feature, and the weight of each edge represents the normalized mutual information between the associated two features that form the edge. As a result, the feature selection problem is presented as the best way to obtain several dense sub-feature spaces by using the SEA on a weighted feature graph. The proposed feature selection algorithm consists of a two-stage structure, in which the first stage is utilized to find a number of dense feature subgraphs, whereas the second stage is used to identify a representative feature from each of these feature subgraphs. The main advantage of the proposed algorithm is that the users are not required to provide the number of features to be selected, which is a major flaw in most of the existing algorithms. The current findings from the researchers' method are better than the other state-of-the-art algorithms because the feature selection process identifies approximately one-fifth to one-third of the number of original features while providing a high accuracy score. The superiority of the proposed algorithm over the other conventional methods of feature selection is established for a number of real-life datasets. In addition, the performance of the proposed feature selection algorithm is also found to be better than other algorithms in one of the innovative application areas of computational intelligence such as ambient intelligence.

Genome sequence assembly algorithms and misassembly identification methods.

The sequence assembly algorithms have rapidly evolved with the vigorous growth of genome sequencing technology over the past two decades. Assembly mainly uses the iterative expansion of overlap relationships between sequences to construct the target genome. The assembly algorithms can be typically classified into several categories, such as the Greedy strategy, Overlap-Layout-Consensus (OLC) strategy, and de Bruijn graph (DBG) strategy. In particular, due to the rapid development of third-generation sequencing (TGS) technology, some prevalent assembly algorithms have been proposed to generate high-quality chromosome-level assemblies. However, due to the genome complexity, the length of short reads, and the high error rate of long reads, contigs produced by assembly may contain misassemblies adversely affecting downstream data analysis. Therefore, several read-based and reference-based methods for misassembly identification have been developed to improve assembly quality. This work primarily reviewed the development of DNA sequencing technologies and summarized sequencing data simulation methods, sequencing error correction methods, various mainstream sequence assembly algorithms, and misassembly identification methods. A large amount of computation makes the sequence assembly problem more challenging, and therefore, it is necessary to develop more efficient and accurate assembly algorithms and alternative algorithms.

Revealing nanostructures in brain tissue via protein decrowding by iterative expansion microscopy.

Many crowded biomolecular structures in cells and tissues are inaccessible to labelling antibodies. To understand how proteins within these structures are arranged with nanoscale precision therefore requires that these structures be decrowded before labelling. Here we show that an iterative variant of expansion microscopy (the permeation of cells and tissues by a swellable hydrogel followed by isotropic hydrogel expansion, to allow for enhanced imaging resolution with ordinary microscopes) enables the imaging of nanostructures in expanded yet otherwise intact tissues at a resolution of about 20 nm. The method, which we named 'expansion revealing' and validated with DNA-probe-based super-resolution microscopy, involves gel-anchoring reagents and the embedding, expansion and re-embedding of the sample in homogeneous swellable hydrogels. Expansion revealing enabled us to use confocal microscopy to image the alignment of pre-synaptic calcium channels with post-synaptic scaffolding proteins in intact brain circuits, and to uncover periodic amyloid nanoclusters containing ion-channel proteins in brain tissue from a mouse model of Alzheimer's disease. Expansion revealing will enable the further discovery of previously unseen nanostructures within cells and tissues.

Multi-users interaction anomalous subgraph detection for event mining

Event detection on networks is an important research task in data mining. Most previous approaches usually detect a particular type of event that satisfies the predefined rules in the model, i.e., data-driven methods. However, few methods consider the diverse interests of multiple experts during the detection process to discover unexplored events. In this paper, we regard interactive event detection on attributed networks as Multi-users Interaction Anomalous Subgraph Detection (MIASD), where events are represented as connected subgraphs on networks. The core of our method is automatically maximizing a non-parametric scan statistic over connected subgraphs to identify the most anomalous subgraph as the event and integrating the interactions from multiple experts simultaneously, i.e., under both data-driven and user-driven paradigm. In our approach, we first define individual and shared environments of experts on attributed networks to model the interests of experts, including the interactive operations and Recommended Interaction Domain. Afterwards, we propose an efficient anomalous subgraph detection algorithm in which each expert gives feedback separately on the vertices in the Recommended Interaction Domain; then, the non-parametric scan statistic over the connected subgraph is approximately maximized by integrating various feedback from experts into the iterative subgraph expansion process. In this way, our method retrieves the most anomalous subgraph on the network as the final event, which contains the potential unexplored information. We have conducted extensive experiments on three real-world datasets and proved that our algorithm could achieve better performance compared with several competitive baselines. Furthermore, the case study shows that our method could detect global abnormal events effectively.