Template Skeleton Research Articles

Improvement of the specific surface area of biochar by calcium-precipitated nanoparticles synthesized by microbial induction as a template skeleton: Removal mechanism of tetracycline in water

The template method is an effective means to improve the specific surface area and porosity of biochar, but the synthesis of template agents and the way they are integrated with biomass materials still need further development. Therefore, the free Pseudomonas sp. Y1 was used to synthesize calcium-precipitated nanoparticles (CPN) on sludge as a fused template skeleton to enlarge the surface area of sludge biochar facilitating the adsorption of tetracycline (TC) in this work. The modified biochar (FBC) showed excellent specific surface area (448.55 m2 g−1) and porosity (0.0053 cm³ g−1), stable morphological structure, abundant active functional groups, and appreciable adsorption capacity (65.43 mg g−1) based on several characterization and adsorption experiments. Moreover, the adsorption model postulated that the removal of TC is mainly a chemisorption-based heat-trapping, disordered multilayer interaction. In detail, this process involved the joint contribution from electrostatic interactions, ligand exchange, hydrogen bonding, π-π bonding, complexation, and pore filling. Meanwhile, the adaptability and stability of FBC were examined by pH and coexisting substances. This template skeleton induced by microorganisms can provide new insight into the modification of biochar with the template method.

TARig: Adaptive template-aware neural rigging for humanoid characters

We present TARig, a template-aware neural rigging method, to automatically generates industry-standard and high-quality skeleton and skin weights for humanoid characters. The resulting skeleton consists of a widely-used humanoid template with 21 template joints and associate secondary joint sets which are generated adaptively for handling auxiliary components. To explicitly utilize the anatomic consistency among humanoid characters, we incorporate the manually annotated anatomic label of each template joint to fully supervise the rigging process in an end-to-end training scheme. Together with a shared graph neural network backbone, such semantic constraint ensures to generate precise joint positions and accurate skin weights simultaneously. Beyond the pre-defined topology of the template skeleton, we additionally learn a boneflow field to further determine the inner connections for each secondary joint set and avoid counter-anatomical skeleton construction. Extensive experiments demonstrate TARig generates high-quality rigging results and outperforms other state-of-the-art auto-rigging methods in terms of skeleton generation and skin weight estimation for humanoid characters with 8∼20 times speedup.

Multifunctional Open-Cell Copper Foam with Sphere Pores by a Modified Sintering–Dissolution Process

In this study, open-cell copper foam with completely interconnected sphere pores was prepared by a modified sintering–dissolution process using a preformed calcium chloride template skeleton as space holder. Compared with the traditional foaming process method, the open-cell copper foam prepared by this method has fewer impurities and better surface morphology. In addition, the pore distribution can be controlled by adjusting the distribution of calcium chloride particles in the skeleton. The compression performance, airflow resistance, and filtration performance of the prepared open-cell copper foam were studied. The results show that with the increase in porosity, the bearing capacity of open-cell copper foam decreases, but the width of the stress platform increases. The prepared open-cell copper foam exhibits excellent energy absorption efficiency, reaching nearly 90% at a porosity of 85%. When the porosity is 85%, the static airflow resistance of the structure is as high as 9 KPa·s·m−2. Moreover, the structure has a filtration efficiency of more than 90% as the filtration thickness exceeds 20 mm, which demonstrates the excellent filtration ability. Such open-cell copper foam shows multi-functional potential as an impact damper, sound absorber, and impurity filter.

MoCap-solver

In a conventional optical motion capture (MoCap) workflow, two processes are needed to turn captured raw marker sequences into correct skeletal animation sequences. Firstly, various tracking errors present in the markers must be fixed ( cleaning or refining ). Secondly, an agent skeletal mesh must be prepared for the actor/actress, and used to determine skeleton information from the markers ( re-targeting or solving ). The whole process, normally referred to as solving MoCap data, is extremely time-consuming, labor-intensive, and usually the most costly part of animation production. Hence, there is a great demand for automated tools in industry. In this work, we present MoCap-Solver, a production-ready neural solver for optical MoCap data. It can directly produce skeleton sequences and clean marker sequences from raw MoCap markers, without any tedious manual operations. To achieve this goal, our key idea is to make use of neural encoders concerning three key intrinsic components: the template skeleton, marker configuration and motion, and to learn to predict these latent vectors from imperfect marker sequences containing noise and errors. By decoding these components from latent vectors, sequences of clean markers and skeletons can be directly recovered. Moreover, we also provide a novel normalization strategy based on learning a pose-dependent marker reliability function, which greatly improves system robustness. Experimental results demonstrate that our algorithm consistently outperforms the state-of-the-art on both synthetic and real-world datasets.

Symmetry-Aware Semantic Segmentation of a 3D Human Body Model

<p indent="0mm">An automatic segmentation method for a 3D human model is proposed for the symmetry-aware semantic segmentation. Firstly, a symmetry-aware kinematic skeleton is extracted from the input human body model via template skeleton embedding. Secondly, the number of the segments is determined by the kinematic skeleton; some key points corresponding to the segments are extracted meanwhile. Thirdly, while taking the key points as the boundary constraints, the harmonic fields are constructed to obtain a set of isolines as the potential cutting boundaries. Finally, the key points are taken as the clustering centers to conduct a spectral clustering operation, which guides the selections of the cutting boundaries. In this way, the semantic information in the kinematic skeleton can be directly transferred to the segments, and the number of segments can be controlled by the structure of the kinematic skeleton. Results on the SCAPE dataset, the MPI-FAUST dataset and the Princeton Segmentation Benchmark show that the proposed method can automatically achieve symmetry-aware semantic segmentation, and is robust to the shape and the pose of the human body model.

Test Case Design and Test Case Prioritization using Machine Learning

Designing and prioritizing test cases is a very tedious task. Given all the advancements in the world of software testing, on any given day engineers spend several man-hours to identify all possible testing scenarios and preconditions attached with it. Test engineers then use the scenarios and preconditions to write multiple test cases. Every test case has a template skeleton to follow - expected results, actual results, priority, test suite category classification (regression, sanity, smoke, integration, etc.), and the respective software (i.e. version, build, release etc.) that has to be tested. Until now there have been efforts to make test case designing simpler by providing software test engineers with tools and processes. But these tools and processes still needs considerable amount of manual intervention in terms of understanding the requirements, analyzing the quality risks and documentation of all possible test scenarios in order to ensure a high quality software delivery. Man-hours spent on test case design and test case prioritization is directly proportional to the cost involved in building software. Our goal was to make sure that the manual intervention in test case design and test case prioritization is reduced to minimum without imposing any software quality risks. So, that the cost to ship and build software is reduced. With this paper we are presenting a solution to this problem. Our goal here was to use machine learning [5] to do automated test case prioritization and creation of test cases for software. In order to achieve this goal we used supervised machine learning [6] approach based on K-Nearest Neighbor classification model for test case design and test case prioritization [4]. On experimenting with other linear and non-linear classifiers we learnt that they did not prove to be as accurate as K-Nearest Neighbor. Our method of machine learning based automated test case design and test case prioritization can be used by any software development organization to reduce it’s software development cost and time taken to ship software to their respective consumers. This aims to benefit the software development industry as a whole.

Three-dimensional poly(lactic-co-glycolic acid)/silica colloidal crystal microparticles for sustained drug release and visualized monitoring

In this paper, a three-dimensional (3D) poly(lactic-co-glycolic acid) (PLGA)/silica colloidal crystal drug delivery system with sustained drug release and visualized release monitoring was developed. This system had employed silica colloidal crystal microparticles as template skeleton, PLGA as drug carrier and dexamethasone (DEX) as therapeutic agent. The fabrication of the microparticle-based system included droplet formation based-on microfluidics, silica nanoparticle self-assembly and layer-by-layer deposition of PLGA containing DEX. In 370 μm droplets, the silica colloidal nanoparticles could self-assemble orderly into microparticles with a diameter of 187 μm, featuring red structure color. During the deposition of PLGA with the drug into the voids of the template microparticles, the reflection peak red-shifted and weakened until the voids were completely filled. Owing to the gradual degradation of PLGA, the release of DEX was triggered and sustained for 4 weeks with a cumulative release of 94.9%, while the structure color of the microparticles recovered during the release process. The color change could be recognized by the naked eyes, which would benefit the non-invasive monitoring of the drug release. The in vitro cytotoxicity and long-term inhibiting proliferation were investigated on retinal pigment epithelial cells. The inhibition effect of DEX released from the microparticles showed concentration-dependence from 40 to 200 μg mL−1 and time-dependence within 7 days. As a sustained drug delivery system with self-reporting drug release, the particles have potential applications in treatment of intraocular diseases.

Observation-oriented silhouette-aware fast full body tracking with Kinect

We present a novel approach to track full human body mesh with a single depth camera, e.g. Microsoft Kinect, using a template body model. The proposed observation-oriented tracking mainly targets at fitting the body mesh silhouette to the 2D user boundary in video stream by deforming the body. It is fast to be integrated into real-time or interactive applications, which is impossible with traditional iterative optimization based approaches. Our method is a composite of two main stages: user-specific body shape estimation and on-line body tracking. We first develop a novel method to fit a 3D morphable human model to the actual body shape of the user in front of the depth camera. A strategy, making use of two constrains, i.e. point clouds from depth images and correspondence between foreground user mask contour and the boundary of projected body model, is designed. On-line tracking is made possible in successive steps. At each frame, the joint angles of template skeleton are optimized towards the captured Kinect skeleton. Then, the aforementioned contour correspondence is adopted to adjust the projected body model vertices towards the contour points of foreground user mask, using a Laplacian deformation technique. Experimental results show that our method achieves fast and high quality tracking. We also show that the proposed method is benefit to three applications: virtual try-on, full human body scanning and applications in manufacturing systems.

Development of Porous Template Carbons from Montmorillonite Clays and Evaluation of Their Toluene Adsorption Behaviors

Carbon replicas were developed with two montmorillonites as the template and sucrose as the carbon source. The fabrication procedures included sucrose doping, polymerization with H2SO4, carbonization at 500–900°C, and liberation of carbon replicas from the template skeleton. N2 adsorption isotherms indicated the all of the resulting carbon replicas contained both micropores and mesopores. A higher carbonization temperature typically led to greater initial N2 adsorption, suggesting the presence of a greater number of micropores. Since no measurable micropores were found in the parent montmorillonites, those observed in the carbon replicas may have developed via activation of inherent water due to dehydration of montmorillonite clay or sucrose during the high temperature carbonization. XRD examination confirmed the formation of both graphite-like structure and amorphous carbon. The C content, C/H molar ratio, and the extent of πelectron resonance of the carbon replicas increased as the carbonization temperature rose, indicating that a higher temperature caused more thorough carbonization in the montmorillonite templates. Toluene adsorption experiments at 1000–8000 ppmv and 30–90°C demonstrated the effectiveness of carbon replicas with regard to capturing toluene. Model simulations further suggest that pore filling of toluene inside the mesopores of template carbon may occur, because the adsorption data showed better agreement with the DR model.

A novel template‐based automatic rigging algorithm for articulated‐character animation

ABSTRACTRigging is a process for creating skeletons used to animate articulated characters. In conventional computer‐animation software, this process must be performed manually. Although several automatic rigging algorithms have been proposed, these methods still require user intervention. This paper proposes an automatic algorithm that generates an inverse kinematic skeleton for a character by locating an appropriate template skeleton on the extracted curve skeleton of the input 3D character model. After the curve skeleton is extracted, it is analyzed and classified into an appropriate category. The classification conditions are developed from the characteristics of each kind of real animal. We also develop an algorithm to extract the anatomical meaning of each skeleton segment. On the basis of the classification result, a suitable template skeleton is retrieved from the database. Each bone of the template skeleton can then be located on the appropriate skeleton segment of the input skeleton graph by using the extracted anatomical meanings. In contrast to previous methods, the algorithm does not require the input 3D character models to have certain poses or orientations. Moreover, all processes can be completed without user intervention. Copyright © 2012 John Wiley &amp; Sons, Ltd.