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Minimal-Perimeter Lattice Animals and the Constant-Isomer Conjecture

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arXiv 2022年

作者： Barequet, Gill Ben-Shachar, Gil Center for Graphics and Geometric Computing Dept. of Computer Science The Technion—Israel Inst. of Technology Haifa3200003 Israel

We consider minimal-perimeter lattice animals, providing a set of conditions which are sufficient for a lattice to have the property that inflating all minimal-perimeter animals of a certain size yields (without repetitions) all minimal-perimeter animals of a new, larger size. We demonstrate this result on the two-dimensional square and hexagonal lattices. In addition, we characterize the sizes of minimal-perimeter animals on these lattices that are not created by inflating members of another set of minimal-perimeter animals. © 2022, CC BY.

关键词： Isomers

Propagation of weakly advantageous mutations in cancer cell population

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arXiv 2023年

作者： Polanski, Andrzej Kania, Mateusz Gil, Jaroslaw Labaj, Wojciech Lach, Ewa Szczęsna, Agnieszka Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Department of Data Science and Engineering Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

Research into somatic mutations in cancer cell DNA and their role in tumour growth and progression between successive stages is crucial for improving our understanding of cancer evolution. Mathematical and computer modelling can provide valuable insights into the scenarios of cancer growth, the roles of somatic mutations, and the types and strengths of evolutionary forces they introduce. Previous studies have developed mathematical models of cancer evolution, incorporating driver and passenger somatic mutations. Driver mutations were assumed to have a strong advantageous effect on the growth of the cancer cell population, while passenger mutations were considered fully neutral or mildly deleterious. However, according to several studies, passenger mutations may have a weakly advantageous effect on tumour growth. In this paper, we develop models of cancer evolution with somatic mutations that introduce a weakly advantageous force to the evolution of cancer cells. The models used in this study can be classified into two categories: deterministic and stochastic. Deterministic models are based on systems of differential equations that balance the average number of cells and mutations during evolution. To verify the results of our deterministic modelling, we use a stochastic model based on the Gillespie algorithm. We compare the predictions of our modelling with some observational data on cancer evolution. © 2023, CC BY-NC-ND.

关键词： Cancer cells

Fuzzy Querying in the Cloud-based Environment for Data Stream-driven Predictive Maintenance in AGV-enabled Smart Factories

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Fuzzy Querying in the Cloud-based Environment for Data Strea...

IEEE International Conference on Big Data

作者： Bożena Małysiak-Mrozek Dominik Romanów Piotr Grzesik Paweł Benecki Alexandre Niyomugaba Theodore Habimana Daniel Kostrzewa Krzysztof Tokarz Che-Lun Hung Dariusz Mrozek Department of Distributed Systems and Informatics Devices Silesian University of Technology Gliwice Poland Department of Applied Informatics Silesian University of Technology Gliwice Poland Department of Computer Science INES Ruhengeri - Institute of Applied Sciences Rwanda Department of Graphics Computer Vision and Digital Systems Silesian University of Technology Gliwice Poland Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Fuzzy data processing enables data enrichment and increases data interpretation in industrial environments. In the cloud-based IoT data ingestion pipelines, fuzzy data processing can be implemented in several locations, closer to the IoT events gateways, stream processors, or the persistence layer before the data is visualized. Since Automated Guided Vehicles (AGV)-enabled manufacturing can produce vast amounts of data, the decision on the placement of the fuzzy data processing can be important for secondary processes performed on the enriched data, like the predictive maintenance inferencing. In this paper, we analyze two locations of fuzzy data processing in the cloud-based environment built for monitoring AGVs in smart factories - by formulating fuzzy queries against data streams on stream processing units and data at rest in a database. The querying scenarios cover fuzzy filtering with simple and complex criteria, fuzzy filtering through assignment to a linguistic variable, and joining data streams by representing joining attributes as fuzzy numbers. The experimental results show that querying the data stream can be more efficient and profitable in the scalable environment of many AGVs. However, the enrichment provided for the data at rest is also beneficial when gathering data for building future predictive maintenance models.

关键词： Remotely guided vehicles Program processors Filtering Pipelines Predictive models Logic gates Data processing Monitoring Predictive maintenance Smart manufacturing

Exploiting Correspondences with All-pairs Correlations for Multi-view Depth Estimation

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arXiv 2022年

作者： Cheng, Kai Chen, Hao Yin, Wei Xu, Guangkai Chen, Xuejin The National Engineering Laboratory for Brain Inspired Intelligence Technology and Application University of Science and Technology of China Hefei230026 China The Computer Graphics and Computer Assisted Design Laboratory Zhejiang University Hangzhou310058 China DJI Technology

Multi-view depth estimation plays a critical role in reconstructing and understanding the 3D world. Recent learning-based methods have made significant progress in it. However, multi-view depth estimation is fundamentally a correspondence-based optimization problem, but previous learning-based methods mainly rely on predefined depth hypotheses to build correspondence as the cost volume and implicitly regularize it to fit depth prediction, deviating from the essence of iterative optimization based on stereo correspondence. Thus, they suffer unsatisfactory precision and generalization capability. In this paper, we are the first to explore more general image correlations to establish correspondences dynamically for depth estimation. We design a novel iterative multi-view depth estimation framework mimicking the optimization process, which consists of 1) a correlation volume construction module that models the pixel similarity between a reference image and source images as all-to-all correlations;2) a flow-based depth initialization module that estimates the depth from the 2D optical flow;3) a novel correlation-guided depth refinement module that reprojects points in different views to effectively fetch relevant correlations for further fusion and integrate the fused correlation for iterative depth update. Without predefined depth hypotheses, the fused correlations establish multi-view correspondence in an efficient way and guide the depth refinement heuristically. We conduct sufficient experiments on ScanNet, DeMoN, ETH3D, and 7Scenes to demonstrate the superiority of our method on multi-view depth estimation and its best generalization ability. Copyright © 2022, The Authors. All rights reserved.

关键词： Optimization

Learning to reconstruct the bubble distribution with conductivity maps using Invertible Neural Networks and Error Diffusion

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arXiv 2023年

作者： Kumar, N. Krause, L. Wondrak, T. Eckert, S. Eckert, K. Gumhold, S. Chair of Computer Graphics and Visualization TU Dresden Germany Institute of Process Engineering and Environmental Technology TU Dresden Germany Institute of Fluid Dynamics Helmholtz-Zentrum Dresden-Rossendorf Dresden Germany

Electrolysis is crucial for eco-friendly hydrogen production, but gas bubbles generated during the process hinder reactions, reduce cell efficiency, and increase energy consumption. Additionally, these gas bubbles cause changes in the conductivity inside the cell, resulting in corresponding variations in the induced magnetic field around the cell. Therefore, measuring these gas bubble-induced magnetic field fluctuations using external magnetic sensors and solving the inverse problem of Biot-Savart’s Law allows for estimating the conductivity in the cell and, thus, bubble size and location. However, determining high-resolution conductivity maps from only a few induced magnetic field measurements is an ill-posed inverse problem. To overcome this, we exploit Invertible Neural Networks (INNs) to reconstruct the conductivity field. Our qualitative results and quantitative evaluation using random error diffusion show that INN achieves far superior performance compared to Tikhonov regularization. © 2023, CC BY.

关键词： Cells

Privacy-Preserving, Low-Storage, and High-Quality Image Processing in IoT and Clouds

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Privacy-Preserving, Low-Storage, and High-Quality Image Proc...

Intelligent Communication and computer Engineering (ICICCE), International Conference on

作者： Shushan Zhao Wes Chiang Department of Computer Electronics & Graphics Technology Central Connecticut State University New Britain CT US Department of Marketing International Business & Strategy Brock University St. Catharines ON Canada

In Internet of Things (IoT) and clouds, while many image processing tasks are outsourced to third party cloud computing platforms, image processing in encrypted domain is needed in many services for data confidentiality and privacy-preserving purposes. There are many solutions for image scaling, cropping and color correction in this context. However, most of the solutions are not compatible with each other and there are few solutions supporting all these operations. In this paper, we propose a scheme that supports image scaling, cropping, and color correction in encrypted domain with minimum storage overhead. An optional improvement solution is added for a conditional quality issue in certain circumstances. Analysis shows storage requirement is drastically reduced while performance otherwise remains unchanged.

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A Queueing-theoretic Analysis of the Performance of a Cloud Computing Infrastructure: Accounting for Task Reneging or Dropping

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A Queueing-theoretic Analysis of the Performance of a Cloud ...

2022 International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2022

作者： Kuaban, Godlove Suila Soodan, Bhavneet Singh Kumar, Rakesh Czekalski, Piotr Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland Chandigarh University Department of Mathematics Punjab 140413 India Namibia University of Science and Technology Department of Mathematics and Statistics Namibia Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

Cloud computing has revolutionized the information technology era. It offers high-speed computing, storage, and ICT resources on demand. One of the significant challenges in cloud computing is the impact of impatient users or request (task) reneging. When a request has been compromised, missed its execution deadline, or depends on other rejected ones, it must be removed from the queue without being processed. The reneging or removal of tasks from queues may trigger the reneging or removal of other tasks that depend of them. We refer to this reneging of requests from the load balancing or computing queues as correlated reneging. We presented the performance analysis of a network of queues that constitute a queueing model of a simplified cloud computing infrastructure. We show the relationship between the load, delay, and probability of buffer saturation (blocking probability). It is seen that at about 80% utilization, a small increase in utilization (due to a small increase in the arrival rate Δλ for a fixed service rate μ) results in a sharp increase in the delay experienced by the tasks, and on the probability of task rejection or blocking. © 2022 IEEE.

关键词： Cloud computing

Removing fences from sweep motion videos using global 3D reconstruction and fence-aware light field rendering

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Computational Visual Media 2019年第1期5卷 21-32页

作者： Chanya Lueangwattana Shohei Mori Hideo Saito Department of Science and Technology Keio University Institute of Computer Graphics and Vision Graz University of Technology

Diminishing the appearance of a fence in an image is a challenging research area due to the characteristics of fences(thinness, lack of texture, etc.) and the need for occluded background restoration. In this paper, we describe a fence removal method for an image sequence captured by a user making a sweep motion, in which occluded background is potentially observed. To make use of geometric and appearance information such as consecutive images, we use two well-known approaches: structure from motion and light field rendering. Results using real image sequences show that our method can stably segment fences and preserve background details for various fence and background combinations. A new video without the fence, with frame coherence, can be successfully provided.

关键词： video fence video repair diminished reality(DR) structure from motion(SfM) light field rendering(LFR)

End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGB

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End-to-end Trainable Deep Neural Network for Robotic Grasp D...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Stefan Ainetter Friedrich Fraundorfer Institute of Computer Graphics and Vision Graz University of Technology Graz Austria

In this work, we introduce a novel, end-to-end trainable CNN-based architecture to deliver high quality results for grasp detection suitable for a parallel-plate gripper, and semantic segmentation. Utilizing this, we propose a novel refinement module that takes advantage of previously calculated grasp detection and semantic segmentation and further increases grasp detection accuracy. Our proposed network delivers state-of-the-art accuracy on two popular grasp dataset, namely Cornell and Jacquard. As additional contribution, we provide a novel dataset extension for the OCID dataset, making it possible to evaluate grasp detection in highly challenging scenes. Using this dataset, we show that semantic segmentation can additionally be used to assign grasp candidates to object classes, which can be used to pick specific objects in the scene.

关键词： Deep learning Automation Conferences Semantics Feature extraction Convolutional neural networks Grippers