Refinement Approach Research Articles

Instrument-model refinement in normalized reciprocal-vector space for X-ray Laue diffraction.

A simple yet efficient instrument-model refinement method for X-ray diffraction data is presented and discussed. The method is based on least-squares minimization of differences between respective normalized (i.e. unit length) reciprocal vectors computed for adjacent frames. The approach was primarily designed to work with synchrotron X-ray Laue diffraction data collected for small-molecule single-crystal samples. The method has been shown to work well on both simulated and experimental data. Tests performed on simulated data sets for small-molecule and protein crystals confirmed the validity of the proposed instrument-model refinement approach. Finally, examination of data sets collected at both BioCARS 14-ID-B (Advanced Photon Source) and ID09 (European Synchrotron Radiation Facility) beamlines indicated that the approach is capable of retrieving goniometer parameters (e.g. detector distance or primary X-ray beam centre) reliably, even when their initial estimates are rather inaccurate.

Finite element procedures for computing normals and mean curvature on triangulated surfaces and their use for mesh refinement

In this paper we consider finite element approaches to computing the mean curvature vector and normal at the vertices of piecewise linear triangulated surfaces. In particular, we adopt a stabilization technique which allows for first order L2-convergence of the mean curvature vector and apply this stabilization technique also to the computation of continuous, recovered, normals using L2-projections of the piecewise constant face normals. Finally, we use our projected normals to define an adaptive mesh refinement approach to geometry resolution where we also employ spline techniques to reconstruct the surface before refinement. We compare our results to previously proposed approaches.

Antibody escape by polyomavirus capsid mutation facilitates neurovirulence.

JCPyV polyomavirus, a member of the human virome, causes progressive multifocal leukoencephalopathy (PML), an oft-fatal demyelinating brain disease in individuals receiving immunomodulatory therapies. Mutations in the major viral capsid protein, VP1, are common in JCPyV from PML patients (JCPyV-PML) but whether they confer neurovirulence or escape from virus-neutralizing antibody (nAb) in vivo is unknown. A mouse polyomavirus (MuPyV) with a sequence-equivalent JCPyV-PML VP1 mutation replicated poorly in the kidney, a major reservoir for JCPyV persistence, but retained the CNS infectivity, cell tropism, and neuropathology of the parental virus. This mutation rendered MuPyV resistant to a monoclonal Ab (mAb), whose specificity overlapped the endogenous anti-VP1 response. Using cryo-EM and a custom sub-particle refinement approach, we resolved an MuPyV:Fab complex map to 3.2 Å resolution. The structure revealed the mechanism of mAb evasion. Our findings demonstrate convergence between nAb evasion and CNS neurovirulence in vivo by a frequent JCPyV-PML VP1 mutation.

Enhancing Employability and e-Business Capacities for Arabic-Speaking Residents of Australia through START Online Training

Background: Arabic minority groups in Australia face language barriers and shortage of computer skills, which cause unemployment and/or an inability to establish their own businesses. The unemployment rate for this group is ~ 20.5%, which is 3-times higher than the average unemployment rate in Australia (~7%). The unemployment will get worse due to COVID-19 pandemic. The current provision of computer and language training in Australia is in English, which results in longer training times and higher chances of non-completion.
 Objective: The Smart Training for Arabic Residents on Technology (START) is an interventional online bi-lingual training that assists Arabic-speaking residents of Australia to establish an online business with minimum resources (money, space, and infrastructure) or at least help them find suitable employment.
 Methods: START uses Design-Based Research DBR, as it has its own progressive refinement approach. Both qualitative methods (skills assessment interviews, semi-guided observation, and final follow-up interviews) and quantitative methods (practical tests, log analysis/learning analytics, feedback surveys) contribute to evaluation and improvement cycles.
 Discussion: DBR has not been applied to vocational immigrant education previously. This research project contributes to a better understanding of the relationships between educational theory, designed learning and outcomes, to help advance learning and teaching environments by refining critical factors that lead to success for trainees. Practically, Arabic residents are provided opportunities to master computer and English skills for establishing their own online businesses. This research, however, has some limitations. Usually a team of teacher, learning designer, and researcher is recommended for DBR, but that is not possible in this PhD study. It is also acknowledged that although this study aims for optimal refinement of the START program, through multiple cycles of improvement, realistically it will be difficult to “recreate” the exact learning environment in future programs.

The need for transformative changes in the use of Indigenous knowledge along with science for environmental decision‐making in the Arctic

Abstract Recent attention to the role of Indigenous knowledge (IK) in environmental monitoring, research and decision‐making is likely to attract new people to this field of work. Advancing the bringing together of IK and science in a way that is desirable to IK holders can lead to successful and inclusive research and decision‐making. We used the Delphi technique with 18 expert participants who were IK holders or working closely with IK from across the Arctic to examine the drivers of progress and limitations to the use of IK along with science to inform decision‐making related to wildlife, reindeer herding and the environment. We also used this technique to identify participants' experiences of scientists' misconceptions concerning IK. Participants had a strong focus on transformative change relating to the structure of institutions, politics, rights, involvement, power and agency over technical issues advancing or limiting progress (e.g. new technologies and language barriers). Participants identified two modes of desirable research: coproducing knowledge with scientists and autonomous Indigenous‐led research. They highlighted the need for more collaborative and coproduction projects to allow further refinement of approaches and more funding to support autonomous, Indigenous‐led research. Most misconceptions held by scientists concerning IK that were identified by participants related to the spatial, temporal and conceptual scope of IK, and the perceived need to validate IK using Western science. Our research highlights some of the issues that need to be addressed by all participants in research and decision‐making involving IK and science. While exact approaches will need to be tailored to specific social‐ecological contexts, consideration of these broader concerns revealed by our analysis are likely to be central to effective partnerships. A free Plain Language Summary can be found within the Supporting Information of this article.

Crystal structures, Hirshfeld atom refinements and Hirshfeld surface analyses of tris-(4,5-di-hydro-furan-2-yl)methyl-silane and tris-(4,5-di-hydro-furan-2-yl)phenyl-silane.

The title compounds, C13H18O3Si (1) and C18H20O3Si (2), represent functional-izable di-hydro-furan-ylsilanes, which permit substitution by a variety of nucleophiles. The crystal structures of 1 and 2 display weak inter-molecular C-H⋯O hydrogen-bonding inter-actions (qu-anti-fied by Hirshfeld surface analysis), leading to a two-dimensional supra-molecular network for 1 and a one-dimensional supra-molecular network for 2. The crystal structures of 1 and 2 were refined both on the basis of the independent atom model (IAM) and the Hirshfeld atom refinement (HAR) approach, and the results are comparatively discussed.

Experimental and numerical investigations of tip vortex cavitation for the propeller of a research vessel, “The Princess Royal”

The present study includes experimental and numerical investigations on propeller cavitation phenomena, particularly the tip vortex cavitation, for the test cases conducted with the model propeller of the Newcastle University research vessel, “The Princess Royal”. These test cases were recommended recently as the benchmark data for cavitation observations and noise measurements by the ITTC, and have been tested in a major round-robin campaign by eight cavitation tunnel/basin facilities around the world. This round-robin test campaign included the Newcastle Emerson Cavitation Tunnel where the first set of tests was conducted.This study presents further systematic measurements conducted in the Shanghai Jiao Tong University (SJTU) cavitation tunnel with this benchmark propeller in the open water conditions, including the cavitation observations, and tests for the tip vortex cavitation inception and desinence. Also, the study includes computational fluid dynamics (CFD) investigation to discuss the results of the experiments, especially the tip vortex cavitation, in a comparative manner. To simulate tip vortex cavitation, the newly developed adaptive mesh approach MARCS (Mesh Adaption Refinement Approach for Cavitation Simulations) was further enhanced. The results of the CFD computations using this approach showed good agreement with the results of the tunnel tests for the open water propeller performance characteristics, cavitation observations and tip vortex cavitation pattern, including its extent.

Temporally Coherent General Dynamic Scene Reconstruction

Existing techniques for dynamic scene reconstruction from multiple wide-baseline cameras primarily focus on reconstruction in controlled environments, with fixed calibrated cameras and strong prior constraints. This paper introduces a general approach to obtain a 4D representation of complex dynamic scenes from multi-view wide-baseline static or moving cameras without prior knowledge of the scene structure, appearance, or illumination. Contributions of the work are: an automatic method for initial coarse reconstruction to initialize joint estimation; sparse-to-dense temporal correspondence integrated with joint multi-view segmentation and reconstruction to introduce temporal coherence; and a general robust approach for joint segmentation refinement and dense reconstruction of dynamic scenes by introducing shape constraint. Comparison with state-of-the-art approaches on a variety of complex indoor and outdoor scenes, demonstrates improved accuracy in both multi-view segmentation and dense reconstruction. This paper demonstrates unsupervised reconstruction of complete temporally coherent 4D scene models with improved non-rigid object segmentation and shape reconstruction and its application to various applications such as free-view rendering and virtual reality.

A Clustering Refinement Approach for Revealing Urban Spatial Structure from Smart Card Data

Facilitated by rapid development of the data-intensive techniques together with communication and sensing technology, we can take advantage of smart card data collected through Automatic Fare Collection (AFC) systems to establish connections between public transit and urban spatial structure. In this paper, with a case study on Shenzhen metro system in China, we investigate the agglomeration pattern of passenger flow among subway stations. Specifically, leveraging inbound and outbound passenger flows at subway stations, we propose a clustering refinement approach based on cluster member stability among multiple clusterings produced by isomorphic or heterogeneous clusterers. Furthermore, we validate and elaborate five clusters of subway stations in terms of regional functionality and urban planning by comparing station clusters with reference to government planning policies and regulations of Shenzhen city. Additionally, outlier stations with ambiguous functionalities are detected using proposed clustering refinement framework.

The benefits of a Bayesian analysis for the characterization of magnetic nanoparticles

Magnetic nanoparticles offer a unique potential for various biomedical applications, but prior to commercial usage a standardized characterization of their structural and magnetic properties is required. For a thorough characterization, the combination of conventional magnetometry and advanced scattering techniques has shown great potential. In the present work, we characterize a powder sample of high-quality iron oxide nanoparticles that are surrounded with a homogeneous thick silica shell by DC magnetometry and magnetic small-angle neutron scattering (SANS). To retrieve the particle parameters such as their size distribution and saturation magnetization from the data, we apply standard model fits of individual data sets as well as global fits of multiple curves, including a combination of the magnetometry and SANS measurements. We show that by combining a standard least-squares fit with a subsequent Bayesian approach for the data refinement, the probability distributions of the model parameters and their cross correlations can be readily extracted, which enables a direct visual feedback regarding the quality of the fit. This prevents an overfitting of data in case of highly correlated parameters and renders the Bayesian method as an ideal component for a standardized data analysis of magnetic nanoparticle samples.

Structural interpretation of cryo-EM image reconstructions

The productivity of single-particle cryo-EM as a structure determination method has rapidly increased as many novel biological structures are being elucidated. The ultimate result of the cryo-EM experiment is an atomic model that should faithfully represent the computed image reconstruction. Although the principal approach of atomic model building and refinement from maps resembles that of the X-ray crystallographic methods, there are important differences due to the unique properties resulting from the 3D image reconstructions. In this review, we discuss the practiced work-flow from the cryo-EM image reconstruction to the atomic model. We give an overview of (i) resolution determination methods in cryo-EM including local and directional resolution variation, (ii) cryo-EM map contrast optimization including complementary map types that can help in identifying ambiguous density features, (iii) atomic model building and (iv) refinement in various resolution regimes including (v) their validation and (vi) discuss differences between X-ray and cryo-EM maps. Based on the methods originally developed for X-ray crystallography, the path from 3D image reconstruction to atomic coordinates has become an integral and important part of the cryo-EM structure determination work-flow that routinely delivers atomic models.

Scalable Mesh Refinement for Canonical Polygonal Schemas of Extremely High Genus Shapes.

Any closed manifold of genus g can be cut open to form a topological disk and then mapped to a regular polygon with 4g sides. This construction is called the canonical polygonal schema of the manifold, and is a key ingredient for many applications in graphics and engineering, where a parameterization between two shapes with same topology is often needed. The sides of the 4g-gon define on the manifold a system of loops, which all intersect at a single point and are disjoint elsewhere. Computing a shortest system of loops of this kind is NP-hard. A computationally tractable alternative consists of computing a set of shortest loops that are not fully disjoint in polynomial time using the greedy homotopy basis algorithm proposed by Erickson and Whittlesey and then detach them in post processing via mesh refinement. Despite this operation is conceptually simple, known refinement strategies do not scale well for high genus shapes, triggering a mesh growth that may exceed the amount of memory available in modern computers, leading to failures. In this article we study various local refinement operators to detach cycles in a system of loops, and show that there are important differences between them, both in terms of mesh complexity and preservation of the original surface. We ultimately propose two novel refinement approaches: the former greatly reduces the number of new elements in the mesh, possibly at the cost of a deviation from the input geometry. The latter allows to trade mesh complexity for geometric accuracy, bounding deviation from the input surface. Both strategies are trivial to implement, and experiments confirm that they allow to realize canonical polygonal schemas even for extremely high genus shapes where previous methods fail.

Phase-field-lattice Boltzmann simulation of dendrite motion using an immersed boundary method

A phase-field-lattice Boltzmann model (PF-LBM) coupled with immersed boundary method is developed to simulate the growth and motion of dendrites against convection. A conservative phase-field model is employed to maintain mass conservation. A parallel adaptive mesh refinement (Para-AMR) approach is developed to improve the computational efficiency. Multi-dendrite growth and motion against 3-D cavity flow is simulated and simulation results reveal the morphological transition of moving dendrites, together with collision and coalescence. The interaction between dendrite and flow is accurately restored, enabling one to grasp and understand the microstructure evolution in practical solidification condition.

Putting Geological Focus Back into Rock Engineering Design

The trend today to ever increasing modelling sophistication demands that much more attention be paid by practitioners to achieving better appreciation and characterization of geology and rockmass variability, so that rock–structure interaction effects can be analysed more realistically in better calibrated models. This paper is thus directed towards focussing attention on risk-based geological characterization as basis for helping modellers and designers improve calibration of their models. A sequential approach of appropriate input parameter refinement is outlined as a path forward methodology for consistently achieving maximum reliability in modelling. Processes that are needed for identifying key controlling geological structural features and rockmass domain characteristics that may be critical influences on true rockmass behaviour are explored so that rationalization steps can be followed in model building to ensure that actual behaviour drivers are not only properly represented, but are reliably characterized through rigorous calibration. Suggestions for the use of the observational and quantitative GSI charts at various scales appropriate to specific geological domains are presented as a means for achieving such calibration. Illustration is then given of how quantification can be achieved of rock quality variability throughout the complete range of rock competence, from intact pseudo-homogeneous high strength rockmasses subject to brittle spalling, through blocky, folded or foliated rockmasses, where kinematic controls are typically of paramount importance, through to completely degraded, fault process core zones and saprolites, where material matrix strength almost entirely dominates behaviour. Guidelines are given for suggested ranges of classification applicability for use with the Hoek–Brown failure criterion.

Pin Assignment Optimization for Multi-2.5D FPGA-Based Systems With Time-Multiplexed I/Os

As 2.5-D field-programmable gate arrays (FPGAs) have larger logic capacity and higher pin counts compared to conventional FPGAs, they are already deployed in some multi-FPGA systems. 2.5-D FPGA consists of multiple dies connected through an interposer. Since the interposer provides only a fraction of the amount of interconnect resources with an increased delay compared to that within individual dies, it is important to reduce the number of signal crossings between dies both for routability and timing consideration when a circuit is mapped to a 2.5-D FPGA. In a multi-2.5D FPGA system with time-multiplexed hardwired inter-FPGA connections, there can be tens of thousands of inter-FPGA signals incident with each FPGA and their pin assignment can greatly affect the amount of die-crossing signals within the FPGAs. In this article, we formulate the pin assignment problem for such systems with the objective of minimizing signal crossings between dies within the individual FPGAs. Taking into consideration of the multi-die structure of 2.5-D FPGA, we propose two different iterative refinement approaches to the problem, one based on integer linear programming (ILP) and the other algorithm C-2MCF based on clustering optimization and minimum cost flow optimization. Compared to a greedy pin assignment heuristic for minimization of signal crossings between dies, C-2MCF produced 27.1% less crossings on average. While the ILP-based algorithm has up to 1% quality advantage over C-2MCF for moderate-sized instances, C-2MCF is orders of magnitude faster and can comfortably handle very large problem instances. In addition, the proposed minimum cost flow techniques within C-2MCF can also be used in conjunction with any other possible pin assignment method to enhance the final result.

Deterministic finite automata learning using counterexample guided abstraction refinement

Subject of Research. The paper studies minimum-sized deterministic finite automata inferring problem. A hybrid method is developed and implemented reducing the given problem to Boolean satisfiability (SAT) technique and at the same time applying a counterexample guided abstraction refinement approach. Method. It is proposed to use not all given behavior examples as training data but start with some subset of them and build a consistent automaton, using SAT- based automata inferring method. Then the built automaton is checked against the complete set of behavior examples. The examples not consistent with the automaton are counterexamples. Some subset of counterexamples is added to the current training data and the process is being repeated. Main Results. The proposed method is implemented as a part of deterministic finite automata inferring tool in Python language. Experimental comparison of the developed method and the SAT-based method without abstraction refinement is carried out. Practical Relevance. Experimental research has shown that the developed method is reasonable for application if the number of behavior examples is large enough, at least, two hundred times exceeds the number of the automaton states, and, therefore, the Boolean formula being created contains tens and hundreds of millions of clauses.

Adaptive in situ model refinement for surrogate-augmented population-based optimization

In surrogate-based optimization (SBO), the deception issues associated with the low fidelity of the surrogate model can be dealt with in situ model refinement that uses infill points during optimization. However, there is a lack of model refinement methods that are both independent of the choice of surrogate model (neural networks, radial basis functions, Kriging, etc.) and provides a methodical approach to preserve the fidelity of the search dynamics, especially in the case of population-based heuristic optimization processes. This paper presents an adaptive model refinement (AMR) approach to fill this important gap. Therein, the question of when to refine the surrogate model is answered by a novel hypothesis testing concept that compares the distribution of model error and distribution of function improvement over iterations. These distributions are respectively computed via a probabilistic cross-validation approach and by leveraging the probabilistic improvement information uniquely afforded by population-based algorithms such as particle swarm optimization. Moreover, the AMR method identifies the size of the batch of infill points needed for refinement. Numerical experiments performed on multiple benchmark functions and an optimal (building energy) planning problem demonstrate AMR’s ability to preserve computational efficiency of the SBO process while providing solutions of more attractive fidelity than those provisioned by a standard SBO approach.

Design of composite measure schemes for comparative severity assessment in animal-based neuroscience research: A case study focussed on rat epilepsy models.

Comparative severity assessment of animal models and experimental interventions is of utmost relevance for harm-benefit analysis during ethical evaluation, an animal welfare-based model prioritization as well as the validation of refinement measures. Unfortunately, there is a lack of evidence-based approaches to grade an animal’s burden in a sensitive, robust, precise, and objective manner. Particular challenges need to be considered in the context of animal-based neuroscientific research because models of neurological disorders can be characterized by relevant changes in the affective state of an animal. Here, we report about an approach for parameter selection and development of a composite measure scheme designed for precise analysis of the distress of animals in a specific model category. Data sets from the analysis of several behavioral and biochemical parameters in three different epilepsy models were subjected to a principal component analysis to select the most informative parameters. The top-ranking parameters included burrowing, open field locomotion, social interaction, and saccharin preference. These were combined to create a composite measure scheme (CMS). CMS data were subjected to cluster analysis enabling the allocation of severity levels to individual animals. The results provided information for a direct comparison between models indicating a comparable severity of the electrical and chemical post-status epilepticus models, and a lower severity of the kindling model. The new CMS can be directly applied for comparison of other rat models with seizure activity or for assessment of novel refinement approaches in the respective research field. The respective online tool for direct application of the CMS or for creating a new CMS based on other parameters from different models is available at https://github.com/mytalbot/cms. However, the robustness and generalizability needs to be further assessed in future studies. More importantly, our concept of parameter selection can serve as a practice example providing the basis for comparable approaches applicable to the development and validation of CMS for all kinds of disease models or interventions.

Sentiment aware word embeddings using refinement and senti-contextualized learning approach

Most pre-trained word embeddings are achieved from context-based learning algorithms trained over a large text corpus. This leads to learning similar vectors for words that share most of their contexts, while expressing different meanings. Therefore, the complex characteristics of words cannot be fully learned by using such models. One of the natural language processing applications that suffers from this problem is sentiment analysis. In this task, two words with opposite sentiments are not distinguished well by using common pre-trained word embeddings. This paper addresses this problem and proposes two simple, but empirically effective, approaches to learn word embeddings for sentiment analysis. The both approaches exploit sentiment lexicons and take into account the polarity of words in learning word embeddings. While the first approach encodes the sentiment information of words into existing pre-trained word embeddings, the second one builds synthetic sentimental contexts for embedding models along with other semantic contexts. The word embeddings obtained from the both approaches are evaluated on several sentiment classification tasks using Skip-gram and GloVe models. Results show that both approaches improve state-of-the-art results using basic deep learning models over sentiment analysis benchmarks.

Non-Negative Iterative Convex Refinement Approach for Accurate and Robust Reconstruction in Cerenkov Luminescence Tomography.

Cerenkov luminescence tomography (CLT) is a promising imaging tool for obtaining three-dimensional (3D) non-invasive visualization of the in vivo distribution of radiopharmaceuticals. However, the reconstruction performance remains unsatisfactory for biomedical applications because the inverse problem of CLT is severely ill-conditioned and intractable. In this study, therefore, a novel non-negative iterative convex refinement (NNICR) approach was utilized to improve the CLT reconstruction accuracy, robustness as well as the shape recovery capability. The spike and slab prior information was employed to capture the sparsity of Cerenkov source, which could be formalized as a non-convex optimization problem. The NNICR approach solved this non-convex problem by refining the solutions of the convex sub-problems. To evaluate the performance of the NNICR approach, numerical simulations and in vivo tumor-bearing mice models experiments were conducted. Conjugated gradient based Tikhonov regularization approach (CG-Tikhonov), fast iterative shrinkage-thresholding algorithm based Lasso approach (Fista-Lasso) and Elastic-Net regularization approach were used for the comparison of the reconstruction performance. The results of these experiments demonstrated that the NNICR approach obtained superior reconstruction performance in terms of location accuracy, shape recovery capability, robustness and in vivo practicability. It was believed that this study would facilitate the preclinical and clinical applications of CLT in the future.