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Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 104
Author(s):  
Marko Jercic ◽  
Ivan Jercic ◽  
Nikola Poljak

The properties of decays that take place during jet formation cannot be easily deduced from the final distribution of particles in a detector. In this work, we first simulate a system of particles with well-defined masses, decay channels, and decay probabilities. This presents the “true system” for which we want to reproduce the decay probability distributions. Assuming we only have the data that this system produces in the detector, we decided to employ an iterative method which uses a neural network as a classifier between events produced in the detector by the “true system” and some arbitrary “test system”. In the end, we compare the distributions obtained with the iterative method to the “true” distributions.


Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2889
Author(s):  
Vassilis G. Kaburlasos ◽  
Chris Lytridis ◽  
Eleni Vrochidou ◽  
Christos Bazinas ◽  
George A. Papakostas ◽  
...  

Social robots keep proliferating. A critical challenge remains their sensible interaction with humans, especially in real world applications. Hence, computing with real world semantics is instrumental. Recently, the Lattice Computing (LC) paradigm has been proposed with a capacity to compute with semantics represented by partial order in a mathematical lattice data domain. In the aforementioned context, this work proposes a parametric LC classifier, namely a Granule-based-Classifier (GbC), applicable in a mathematical lattice (T,⊑) of tree data structures, each of which represents a human face. A tree data structure here emerges from 68 facial landmarks (points) computed in a data preprocessing step by the OpenFace software. The proposed (tree) representation retains human anonymity during data processing. Extensive computational experiments regarding three different pattern recognition problems, namely (1) head orientation, (2) facial expressions, and (3) human face recognition, demonstrate GbC capacities, including good classification results, and a common human face representation in different pattern recognition problems, as well as data induced granular rules in (T,⊑) that allow for (a) explainable decision-making, (b) tunable generalization enabled also by formal logic/reasoning techniques, and (c) an inherent capacity for modular data fusion extensions. The potential of the proposed techniques is discussed.


2021 ◽  
Vol 9 ◽  
Author(s):  
Leslie A. Brandt ◽  
Gary R. Johnson ◽  
Eric A. North ◽  
Jack Faje ◽  
Annamarie Rutledge

Urban trees play an important role in helping cities adapt to climate change, but also are vulnerable to changes in climate themselves. We developed an approach for assessing vulnerability of urban tree species and cultivars commonly planted in cities in the United States Upper Midwest to current and projected climate change through the end of the 21st century. One hundred seventy-eight tree species were evaluated for their adaptive capacity to a suite of current and future-projected climate and urban stressors using a weighted scoring system based on an extensive literature review. These scores were then evaluated and adjusted by leading experts in arboriculture in the region. Each species or cultivar’s USDA Hardiness Zone and American Horticultural Society Heat Zone tolerance was compared to current and future heat and hardiness zones for 14 municipalities across Michigan, Wisconsin, and Minnesota using statistically downscaled climate data. Species adaptive capacity and zone tolerance was combined to assign each species one of five vulnerability categories for each location. We determined the number of species and trees in each category based on the most recent municipal street tree data for each location. Under a scenario of less climate change (RCP 4.5), fewer than 2% of trees in each municipality were considered highly vulnerable across all 14 municipalities. Under a scenario of greater change (RCP 8.5), upward of 25% of trees were considered highly vulnerable in some locations. However, the number of vulnerable trees varied greatly by location, primarily because of differences in projected summer high temperatures rather than differences in species composition. Urban foresters can use this information as a complement to other more traditional considerations used when selecting trees for planting.


Author(s):  
Mathias Aloui ◽  
Gaëtan Duhamel ◽  
Manon Frédout ◽  
Olivier Rovellotti

It is now well known that a healthy urban ecosystem is a crucial element to healthier citizens (Astell-Burt and Feng 2019), better air (Ning et al. 2016) and water quality (Livesley et al. 2016), and overall, to a more resilient urban environment (Huff et al. 2020). With ecoTeka, an open-source platform for tree management, we leverage the power of OpenStreetMap (Mooney 2015), Mappilary, and open data to allow decision makers to improve their urban forestry practices. To have the most comprehensive data about the ecosystems, we plan use all available sources from satellite imagery to LIDAR (light detection and ranging) and compute them with the DeepForest (Weinstein et al. 2020) learning algorithm. We also teamed with the French government to build an open standard for tree data to improve the interoperability of the system. Finally, we calculate a Shannon-Wiener diversity index (used by ecologists to estimate species diversity by their relative abundance in a habitat) to inform the decision making of urban ecosystems.


2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Jingjing Guo ◽  
Jiacong Sun

Group nearest neighbor (GNN) query enables a group of location-based service (LBS) users to retrieve a point from point of interests (POIs) with the minimum aggregate distance to them. For resource constraints and privacy concerns, LBS provider outsources the encrypted POIs to a powerful cloud server. The encryption-and-outsourcing mechanism brings a challenge for the data utilization. However, as previous work from k − anonymity technique leaks all contents of POIs and returns an answer set with redundant communication cost, the LBS system cannot work properly with those privacy-preserving schemes. In this paper, we illustrate a secure group nearest neighbor query scheme, which is referred to as SecGNN. It supports the GNN query with n n ≥ 3 LBS users and assures the data privacy and query privacy. Since SecGNN only achieves linear search complexity, an efficiency enhanced scheme (named Sec GNN + ) is introduced by taking advantage of the KD-tree data structure. Specifically, we convert the GNN problem to the nearest neighbor problem for their centroid, which can be computed by anonymous veto network and Burmester–Desmedt conference key agreement protocols. Furthermore, the Sec GNN + scheme is introduced from the KD-tree data structure and a designed tool, which supports the computation of inner products over ciphertexts. Finally, we run experiments on a real-database and a random database to evaluate the performance of our SecGNN and Sec GNN + schemes. The experimental results show the high efficiency of our proposed schemes.


2021 ◽  
Vol 5 (1) ◽  
pp. 1-13
Author(s):  
Anis Rahmawati ◽  
Syifa Nur Rakhmah ◽  
Lusa Indah Prahartiwi

AbstractThere are many ways that each service provider company does, especially services to win the competition, among others, by increasing service productivity targets. One service provider company that is committed to increasing service productivity targets is PT. Sanggar Sarana Baja. This study aims to predict service productivity system targets using the application of Algortima C4.5 at PT. Sanggar Sarana Baja. The attributes of working time input in this study include area, performance, efficiency, and productivity. In this study, it was found that the results obtained came from several input attributes which resulted in a causal relationship in classifying the results of service productivity targets at PT. Sanggar Sarana Baja. This research is expected to help PT. Sanggar Sarana Baja in increasing customer satisfaction to retain customers and increase profits of PT. Sanggar Sarana Baja. Based on the classification results using the C4.5 Algorithm, it shows that the accuracy reaches 95.00%, which indicates that the C4.5 algorithm is suitable for measuring the target level at PT. Sanggar Sarana Baja. Keywords: Accuracy, Validation, Decision Tree, Data mining, KDD, C4.5 Algorithm, Services Companies, Target Services Productivity Systems  Banyak cara yang dilakukan oleh masing - masing perusahaan penyedia jasa, khususnya servis untuk memenangkan persaingan, antara lain dengan meningkatkan target produktivitas servis. Salah satu perusahaan penyedia jasa servis yang berkomitmen dalam meningkatkan target produktivitas servis adalah PT. Sanggar Sarana Baja. Penelitian ini bertujuan untuk memperdiksi target sistem produktivitas servis menggunakan penerapan Algoritma C4.5 pada PT. Sanggar Sarana Baja. Atribut masukan waktu kerja dalam penelitian ini mencangkup daerah, kinerja, efisiensi, dan produktivitas.  Dalam penelitian ini, didapatkan bahwa hasil yang didapatkan berasal dari beberapa atribut masukan menghasilkan hubungan sebab -akibat dalam mengklasifikasikan hasil dari target produktivitas servis pada PT. Sanggar Sarana Baja. Penelitian ini diharapkan dapat membantu pihak PT. Sanggar Sarana Baja dalam meningkatkan kepuasan konsumen untuk mempertahankan pelanggan dan meningkatkan laba PT. Sanggar Sarana Baja tersebut. Berdasarkan hasil klasifikasi menggunakan Algoritma C4.5 menunjukkan bahwa diperoleh akurasi mencapai 95,00%, yang menunjukkan bahwa algoritma C4.5 cocok digunakan untuk mengukur tingkat target pada PT. Sanggar Sarana Baja. Kata kunci: Akurasi, Validasi, Decision Tree, Data mining, KDD, Algoritma C4.5, Perusahaan Jasa, Target Sistem Productivity ServicesReferensi[1]        Yulia and N. Azwanti, “Data Mining Prediksi Besarnya Penggunaan Listrik Rumah Tangga di Kota Batam Dengan Menggunakan Algoritma C4.5,” Semin. Nas. Ilmu Sos. dan Teknol., vol. 1, no. 1, pp. 175–180, 2018.[2]      R. Novita, “Teknik Data Mining?: Algoritma C 4 . 5,” pp. 1–12, 2016.


Author(s):  
Sardar Anisul Haque

This paper describes a polynomial time algorithm for solving graph isomorphism and automorphism. We introduce a new tree data structure called Walk Length Tree. We show that such tree can be both constructed and compared with another in polynomial time. We prove that graph isomorphism and automorphism can be solved in polynomial time using Walk Length Trees.


2021 ◽  
Vol 11 (15) ◽  
pp. 6767
Author(s):  
Evgenii Maltsev ◽  
Dmitry Popov ◽  
Svyatoslav Chugunov ◽  
Alexander Pasko ◽  
Iskander Akhatov

Complex 3D objects with microstructures can be modelled using the function representation (FRep) approach and then manufactured. The task of modelling a geometric object with a sophisticated microstructure based on unit cell repetition is often too computationally expensive for CAD systems. FRep provides efficient tools to solve this problem. However, even for FRep the slicing step required for manufacturing can take a significant amount of time. An accelerated slicing algorithm for FRep 3D objects is proposed in this paper. This algorithm allows the preparation of FRep models for 3D printing without surface generation stage. The spatial index is employed to accelerate the slicing process. A novel compound adaptive criterion and a novel acceleration criterion are proposed to speed up the evaluation of the defining function of an FRep object. The use of these criteria is significantly reducing the computational time for contour construction during the slicing process. The k-d tree and R-tree data structures are used as spatial indexes. The performance of the accelerated slicing algorithm was tested. The contouring time was reduced 100-fold due to using the novel compound adaptive criterion with the novel acceleration criterion.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
David Kerk ◽  
Jordan F. Mattice ◽  
Mario E. Valdés-Tresanco ◽  
Sergei Yu Noskov ◽  
Kenneth K.-S. Ng ◽  
...  

AbstractPhosphoprotein phosphatase (PPP) enzymes are ubiquitous proteins involved in cellular signaling pathways and other functions. Here we have traced the origin of the PPP sequences of Eukaryotes and their radiation. Using a bacterial PPP Hidden Markov Model (HMM) we uncovered “BacterialPPP-Like” sequences in Archaea. A HMM derived from eukaryotic PPP enzymes revealed additional, unique sequences in Archaea and Bacteria that were more like the eukaryotic PPP enzymes then the bacterial PPPs. These sequences formed the basis of phylogenetic tree inference and sequence structural analysis allowing the history of these sequence types to be elucidated. Our phylogenetic tree data strongly suggest that eukaryotic PPPs ultimately arose from ancestors in the Asgard archaea. We have clarified the radiation of PPPs within Eukaryotes, substantially expanding the range of known organisms with PPP subtypes (Bsu1, PP7, PPEF/RdgC) previously thought to have a more restricted distribution. Surprisingly, sequences from the Methanosarcinaceae (Euryarchaeota) form a strongly supported sister group to eukaryotic PPPs in our phylogenetic analysis. This strongly suggests an intimate association between an Asgard ancestor and that of the Methanosarcinaceae. This is highly reminiscent of the syntrophic association recently demonstrated between the cultured Lokiarchaeal species Prometheoarchaeum and a methanogenic bacterial species.


Author(s):  
Rashmi V. Varade ◽  
Blessy Thankanchan

Data mining is a technique for extracting meaningful information or patterns from large amounts of data. These techniques are frequently utilised for analysis and prediction in practically all fields around the world. It's employed in a variety of fields, including education, business, health care, fraud detection, financial banking, and manufacturing engineering. This study explores the Decision Tree data mining methodology for predicting undergraduate students' academic performance.


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