An Inverse Procedural Modeling Pipeline for SVBRDF Maps

Procedural modeling is now the de facto standard of material modeling in industry. Procedural models can be edited and are easily extended, unlike pixel-based representations of captured materials. In this article, we present a semi-automatic pipeline for general material proceduralization. Given Spatially Varying Bidirectional Reflectance Distribution Functions (SVBRDFs) represented as sets of pixel maps, our pipeline decomposes them into a tree of sub-materials whose spatial distributions are encoded by their associated mask maps. This semi-automatic decomposition of material maps progresses hierarchically, driven by our new spectrum-aware material matting and instance-based decomposition methods. Each decomposed sub-material is proceduralized by a novel multi-layer noise model to capture local variations at different scales. Spatial distributions of these sub-materials are modeled either by a by-example inverse synthesis method recovering Point Process Texture Basis Functions (PPTBF) [ 30 ] or via random sampling. To reconstruct procedural material maps, we propose a differentiable rendering-based optimization that recomposes all generated procedures together to maximize the similarity between our procedural models and the input material pixel maps. We evaluate our pipeline on a variety of synthetic and real materials. We demonstrate our method’s capacity to process a wide range of material types, eliminating the need for artist designed material graphs required in previous work [ 38 , 53 ]. As fully procedural models, our results expand to arbitrary resolution and enable high-level user control of appearance.

Abstract 1122‐000047: Machine Learning to Predict Stroke Outcomes after Mechanical Thrombectomy

Introduction : Prognostication is an integral part of clinical decision‐making in stroke care. Machine learning (ML) methods have gained increasing popularity in the medical field due to their flexibility and high performance. Using a large comprehensive stroke center registry, we sought to apply various ML techniques for 90‐day stroke outcome predictions after thrombectomy. Methods : We used individual patient data from our prospectively collected thrombectomy database between 09/2010 and 03/2020. Patients with anterior circulation strokes (Internal Carotid Artery, Middle Cerebral Artery M1, M2, or M3 segments and Anterior Cerebral Artery) and complete records were included. Our primary outcome was 90‐day functional independence (defined as modified Rankin Scale score 0–2). Pre‐ and post‐procedure models were developed. Four known ML algorithms (support vector machine, random forest, gradient boosting, and artificial neural network) were implemented using a 70/30 training‐test data split and 10‐fold cross‐validation on the training data for model calibration. Discriminative performance was evaluated using the area under the receiver operator characteristics curve (AUC) metric. Results : Among 1248 patients with anterior circulation large vessel occlusion stroke undergoing thrombectomy during the study period, 1020 had complete records and were included in the analysis. In the training data (n = 714), 49.3% of the patients achieved independence at 90‐days. Fifteen baseline clinical, laboratory and neuroimaging features were used to develop the pre‐procedural models, with four additional parameters included in the post‐procedure models. For the preprocedural models, the highest AUC was 0.797 (95%CI [0.75‐ 0.85]) for the gradient boosting model. Similarly, the same ML technique performed best on post‐procedural data and had an improved discriminative performance compared to the pre‐procedure model with an AUC of 0.82 (95%CI [0.77‐ 0.87]). Conclusions : Our pre‐and post‐procedural models reliably estimated outcomes in stroke patients undergoing thrombectomy. They represent a step forward in creating simple and efficient prognostication tools to aid treatment decision‐making. A web‐based platform and related mobile app are underway.

DIGITAL MODEL OF SURVIVABILITY TO SUPPORT THE LIFE CYCLE OF BAR STRUCTURES

Рассматриваются структура и содержание цифровых моделей живучести на примере стержневых конструкциях. Разработаны функциональная и процедурная модели анализа живучести, выполнен численный анализ повреждаемого объекта с оценкой показателей живучести. Специфической чертой цифровых моделей живучести является то, что в основу дискретизации пространства входных данных и результатов предлагаются сценарии накопления повреждений в стержневых конструкциях. The structure and content of digital models of survivability are considered on the example of bar structures. Functional and procedural models of survivability analysis were developed, numerical analysis of the damaged object was carried out with an assessment of survivability indicators. A specific feature of digital survivability models is that scenarios of damage accumulation in bar structures are proposed as the basis for sampling the space of input data and results.

Prosecutors and Trials

This chapter captures the role of the prosecutor or an equivalent authority during the trial phase. The object of the analysis—the trial phase—is generally divided into the following stages: opening stage, presentation of evidence, closing stage, and judgment/sentence. In exceptional cases, the stage of jury selection is added. The perspective of the analysis is twofold: in the first half of the chapter, the prosecutor’s role at these stages is described from a normative perspective. Here, the stages mentioned are broken down into selected prosecutorial measures and procedural facets in various legal systems and procedural models. The legal systems and procedural models analyzed have been chosen to represent either the common law tradition (the United States, England, and Wales) or civil law tradition (Germany and France). In the second part of the chapter, the prosecutor’s role is analyzed from an ethical perspective. It displays both the features and normative foundations of prosecutorial ethics and describes certain types of prosecutorial misconduct that may occur in the previously mentioned stages of a trial. The findings and observations of the chapter are combined in a conclusion at the end of the chapter. Upon identifying the multiple roles of the prosecutor at trial, the chapter proves that prosecutorial work at trial is influenced by two role-related factors: role duality and role ambiguity.

Let’s Join: A Pavilion Inspired by the Weaire and Phelan Space Tessellation

The pavilion presented in this paper is an articulation of space based on the Weaire and Phelan tessellation. In this pavilion a second generative pattern is partially engraved and cut out of the polygonal surfaces that bind the polyhedron. This pattern is made up of cubic curves that aim to spatially engage with the tessellation. The design and prototyping process was implemented and controlled using parametric and procedural models. The use of these models made it possible to define the shape, orientation, and size of each element of the pavilion: from the polyhedron and the pattern’s curves, to smaller components like the shape of the panels’ joints, holes, and the countersinks that hold the screws. This research illustrates the geometric, formal, and procedural design that was used to shape the pavilion in its current spatial configuration, to draw the pattern cut and engraved into the polyhedron’s surface, and finally, to determine the shape and scale of the joints that hold the faces of the polyhedron together and characterize the internal space. Regarding the topic of Patterns and Spatial Organization, the paper investigates shape design methodologies, which are based on historical practices but updated using new technologies.

Impacts of Metacognition Management System (MMS) Training Course on Metacognitive Competencies

Metacognition is one of the vital competencies to seize the opportunities and overcome the challenges of twenty-first century; however, there is no precise definition of metacognition and it is a fuzzy concept. While classic, descriptive and procedural models try to describe the nature and ingredients of metacognition, theoretical clarity in terms of better definition and representation of its components is needed. This study by adopting theoretical models of metacognition through the Plan-Do-Check-Act principles (as a management instrument) proposes a conceptual framework, “Metacognition Management System (MMS)” that consolidates components, functions, and processes of metacognition in a single window. Then, impacts of a multidimensional intervention designed based on the MMS concept (MMS Training Course) provided in 12 hours, on 31 students’ metacognitive competencies was investigated using quasi-experimental pre-test, post-test design. The large effect size (Partial η2 = .939, 95% confidence interval) implied that MMS training course has a statistically significant impact on metacognitive competencies. This study has implications for further theoretical and experimental researches on the configuration and application of the MMS as well as designing multidimensional metacognitive intervention.

Game Based Learning: Design a Multimedia with DDD-E Model for Mathematics Education

The purpose of this article is to provide insight into how to design game-based learning. In the 21st century learning focuses on the process of skills in applying materials using technology, and collaborating. Mathematics learning is learning that requires students to not only memorize and read but also be directly involved in the process of finding concepts. Learning media is an alternative to creating interactive and fun learning. In this study the research design adaptation of the multimedia development research model: Decide, Design, Development and Evaluation (DDD-E). The results of this study are in the form of conceptual and procedural models of steps in developing game-based learning for mathematics subjects using the DDD-E model design.

Development Educational Material Topeng Malang with the Augmented Reality for Supporting Character

Strengthening the quality of education, especially in the arts and culture, in Indonesia is very important. This can be achieved via the application of technology. Malang is a city that has a lot of traditional cultures, such as masks (Topeng), of which there are about 67 designs. It is a challenge in developing learning materials for Topeng Malang. The influence of the inclusion of modern styles is one of the factors stimulating the decline of Malang people’s insights related to traditional culture such as Topeng Malang. This research is related to the strengthening of Topeng Malang education media material by using AR technology. This research focuses on insightful content relating to characters in Topeng Malang, especially for supporting models. This research is carried out through descriptive procedural models, which must be followed to produce a development product — data was obtained by interview, data observation, and documentation. Observation data consisted of reference data for the description of supporting character models namely Pranajiwa and Emban, visuals in the form of Topeng Malang, library data and surveys from the target audience. Keywords: Topeng Malang, Augmented Reality, Support character model