Point cloud geometry compression using neural implicit representations

openIn recent years, the increasing prominence of 3D point clouds in various applications has led to an escalating need for efficient storage and transmission methods. The sheer size of these point cloud datasets presents challenges in rendering, transmission, and general usability. This thesis introduces a novel approach to point cloud geometry compression leveraging neural implicit representations, specifically through the use of a DiGS network model. By training this model on a single point cloud, we achieve a compact neural representation of its geometry. Notably, this representation allows for the reconstruction of the point cloud with an arbitrary resolution. After training a reconstructing network, dynamic quantization is applied on the trained weights, significantly reducing its overall bitrate without strongly compromising the quality of the reconstructed point cloud. A dequantization is then used to rebuild a high-fidelity representation of the original point cloud. Our experimental results demonstrate the efficacy of this approach in terms of compression ratios and reconstruction quality, assessed using PSNR relative to the bitrate. This research provides a promising direction for efficient point cloud geometry storage and transmission, addressing some of the growing demands of the 3D data era

The Impact of Oil Rent, Currency Overvaluation, and Institution Quality, on Economic Growth of Oil-Rich Countries: A Heterogeneous Panel Data Study

This paper investigates several key challenges faced by oil-rich countries regarding their economic growth and development. First, it discusses how to determine currency overvaluation for these countries (if any). To determine the overvaluation, the real exchange rate (RER) is calculated and the Balassa–Samuelson effect is estimated via a regression model. Next, the study presents an empirical model for assessing the impact of oil rent on economic growth in the context of currency overvaluation and the institutional quality in every country. As a dynamic model, both endogeneity and heterogeneity are expected across cross-sections because countries are different in culture, customs, and political institutions. Consequently, heterogeneous panel data analysis is undertaken using the error correction model cointegration technique and the mean group estimation method in an autoregressive distributed lag model. Finally, the study concludes the findings and provides policy recommendations by offering a new perspective on an ongoing dilemma, discussing the challenges and limitations facing developing oil-rich countries and how their path to success may differ from other countries

Iranian Philosophy of Religion and the History of Political Thought

The subject of this dissertation is the relationship between Iranian philosophy of religion and the history of political thought. Major turning points in this history can be attributed to the ideas of the ancient Iranian poet, Zoroaster. He preached against raiding nomads who would steal animals for sacrificial purposes. His moral and ethical philosophy is known as Zoroastrianism.As the first monotheist philosophy, Zoroastrianism inspired a unique form of religious toleration. The ancient Zoroastrians opposed the political rivalries attributed to belligerent city-state gods. This played a vital role in the rise of the Persian Empire. During the height of the Empire, Zoroastrianism was the largest religion in the world. The belief in a single Creator also changed the idea of history itself. By rejecting the existence of conflicting spirits, some human beings no longer perceived themselves as the irrelevant victims of a cosmological struggle. Although Zoroastrianism endured a sharp and dramatic decline after the Muslim conquest of Iran, its legacy lives on. This legacy has been unjustly ignored by historians of political ideas. It played a major role in the development of numerous cultural philosophies, and it had more influence on the history of human religion than any other faith. In this study, I will argue that Zoroastrianism is relevant to virtually every philosophy of history. I will also demonstrate how the study of Zoroastrianism by European travelers and philosophers from the sixteenth to the nineteenth centuries influenced the history of German nationalism and helped set into motion the unfortunate events that culminated in the Aryan Supremacy movement. The rise of the Nazi party disgraced the Aryan identity and stigmatized many Oriental traditions, but the remnants of ancient Iranian culture are still admired by the Iranians of today. In the concluding section of this study, I will argue that Zoroastrianism still plays an important role in Iranian political affairs. While Zoroastrians are a recognized minority in Iran, all Iranians, including Muslims and Jews, celebrate Zoroastrian traditions and holidays. Since the establishment of the Islamic Republic of Iran these traditions have been used to defy the Islamic policies of the state

Multi-Objective Task Scheduling Using Smart MPI-Based Cloud Resources

Task Scheduling and Resource Allocation (TSRA) is the key focus of cloud computing. This paper utilizes Smart Message Passing Interface based Approach (SMPIA) and the Roulette Wheel selection method in order to determine the best Alternative Virtual Machine (AVM). To do so, the Virtual MPI Bus (VMPIB) is employed for efficient communication among Virtual Machines (VMs) using SMPIA. In this matter, SMPIA is applied on different resource allocation and task scheduling strategies. MakeSpan (MS) was chosen as an optimization factor and solutions with minimum MS value as the best task mapping performance and reduced cloud consumption. The simulation is conducted using MATLAB. The analysis proves that applying SMPIA reduced the Total Execution Time (TET) of resource allocation, maximum MS time, and increase the Resource Utilization (RU), as compared to non-SMPIA for Greedy, Max-Min, Min-Min algorithms. It is observed that SMPIA can outperform non-SMPIA. The effect of SMPIA is more obvious as change in the MS and the number of cloud workloads increase. Furthermore, regarding the TET and MS of the tasks, the SMPIA can significantly reduce the starvation problem as well as the lack of sufficient resources. In addition, this approach improves the system's performance more than the previous methods, what reflects effectiveness of the proposed approach concerning the Message Passing Interface (MPI) communication time in the network virtualization. The mentioned text mining work was prepared concurrently after practical evaluation

Software reliability prediction using SPN

Reliability is an important software quality parameter. In this research for computation of software reliability, component reliability model based on SPN would be proposed. An isomorphic markov chain is obtained from component SPN model. A quantitative reliability prediction method is proposed. The component reliability value is calculated according to the transition cumulative probability distribution of markov chain, obtained from the software SPN model. By means of reliability prediction of the whole software, we'll introduce CRMPN. In CRMPN states are component reliability model and transition are marked with components reliability. With this research more complex software could be simplified and reliability of the software could be evaluated effectively. An example is provided for demonstrating the feasibility and applicability of our method.Keywords: Reliability, SPN, Markov Chain, Component based-softwar

Hypermatrix Based on Krasner Hypervector Spaces

In this paper we extend a very specific class of hypervector spaces called Krasner hypervector spaces in order to obtain a hypermatrix. For reaching to this goal, we will define dependent and independent vectors in this kind of hypervector space and define basis and dimension for it. Also, by using multivalued linear transformations, we examine the possibility of existing a free object here. Finally, we study the fundamental relation on Krasner hypervector spaces and we define a functor

TWO CIRCULAR CYLINDERS IN TURBULENT CROSS FLOW

The effects of turbulence intensity (Tu = 0.5% to 10%), normalized integral length scale (f⌐/D = 0.56 to 1.28, where D is the diameter of the cylinder), and Reynolds number (6.7~103 ≲ Re ≲ 1.2~104) on the drag and lift force coefficients, CD and CL, of two staggered circular cylinders were experimentally investigated. The longitudinal and transverse spacing between the centres of two identical cylinders, L/D and T/D, were kept constant at 4 and 1, respectively. At Tu = 4%, increasing f⌐/D dramatically changes CD. However, the difference diminished when Tu ≳ 7%. f⌐/D = 0.56 has an insignificant effect on CL. Increasing Re has a negligible effect on the force coefficients. When f⌐/D ≲ 1, with increasing Tu from 4% to 7%, the CD increases and remains constant beyond 7%. When f⌐/D = 1.28, with increasing Tu from 4% to 7%, the CD decreases and remains constant beyond 7%

PSO for multi-objective problems: Criteria for leader selection and uniformity distribution

This paper proposes a method to solve multi-objective problems using improved Particle Swarm Optimization. We propose leader particles which guide other particles inside the problem domain. Two techniques are suggested for selection and deletion of such particles to improve the optimal solutions. The first one is based on the mean of the m optimal particles and the second one is based on appointing a leader particle for any n founded particles. We used an intensity criterion to delete the particles in both techniques. The proposed techniques were evaluated based on three standard tests in multi-objective evolutionary optimization problems. The evaluation criterion in this paper is the number of particles in the optimal-Pareto set, error, and uniformity. The results show that the proposed method searches more number of optimal particles with higher intensity and less error in comparison with basic MOPSO and SIGMA and CMPSO and NSGA-II and microGA and PAES and can be used as proper techniques to solve multi-objective optimization problems

Formal approach on modeling and predicting of software system security: Stochastic petri net

To evaluate and predict component-based software security, a two-dimensional model of software security is proposed by Stochastic Petri Net in this paper. In this approach, the software security is modeled by graphical presentation ability of Petri nets, and the quantitative prediction is provided by the evaluation capability of Stochastic Petri Net and the computing power of Markov chain. Each vulnerable component is modeled by Stochastic Petri net and two parameters, Successfully Attack Probability (SAP) and Vulnerability Volume of each component to another component. The second parameter, as a second dimension of security evaluation, is a metric that is added to modeling to improve the accuracy of the result of system security prediction. An isomorphic Markov chain is obtained from a corresponding SPN model. The security prediction is calculated based on the probability distribution of the MC in the steady state. To identify and trace back to the critical points of system security, a sensitive analysis method is applied by derivation of the security prediction equation. It provides the possibility to investigate and compare different solutions with the target system in the designing phase