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IEEE World Engineering Education Conference (EDUNINE)

作者： Prateek Shekhar Md Tarique Hasan Khan Sanjeet Gajjar School of Applied Engineering and Technology New Jersey Institute of Technology Newark NJ USA School of Math Engineering & Technology Navajo Technical University Crownpoint NM USA Department of Computer Science New Jersey Institute of Technology Newark NJ USA

Students’ grade point average (GPA) is an important indicator of students’ academic success. In our work-in-progress study, we utilized decision tree analysis to investigate patterns in predicting the GPA of engineering students, considering various demographic, socioeconomic, and academic aspects. Our analysis of the dataset consisting of engineering students’ academic records revealed several key insights. First, SAT scores emerged as a central factor in GPA prediction, with higher scores predicting better GPAs. Second, socioeconomic status also became evident among students with high SAT scores, reflecting the impact of background characteristics on academic achievements. Lastly, parent education level also stood out as significant, showing that students with highly educated parents generally achieved higher GPAs, underlining family educational background's role in success. Overall, our research adds to the existing literature by illuminating the intricate factors influencing engineering students’ GPA and provides an example of utilizing decision tree-based quantitative methods in engineering education research.

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Crystallography of honeycomb formation under geometric frustration

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Proceedings of the National Academy of sciences of the United States of America 2022年第48期119卷 e2205043119-e2205043119页

作者： Fard, Golnar Gharooni Zhang, Daisy Jiménez, Francisco López Peleg, Orit Department of Computer Science University of Colorado Boulder 80309 CO United States Department of Computer Science Princeton University 08544 NJ United States Ann and H.J. Smead Department of Aerospace Engineering Sciences University of Colorado Boulder 80309 CO United States Department of Physics University of Colorado Boulder 80309 CO United States Department of Applied Math University of Colorado Boulder 80309 CO United States Department of Ecology and Evolutionary Biology University of Colorado Boulder 80309 CO United States BioFrontiers Institute University of Colorado Boulder 80309 CO United States Santa Fe Institute Santa Fe 87501 NM United States

As honeybees build their nests in preexisting tree cavities, they must deal with the presence of geometric constraints, resulting in nonregular hexagons and topological defects in the comb. In this work, we study how bees adapt to their environment in order to regulate the comb structure. Specifically, we identify the irregularities in honeycomb structure in the presence of various geometric frustrations. We 3D-print experimental frames with a variety of constraints imposed on the imprinted foundations. The combs constructed by the bees show clear evidence of recurring patterns in response to specific geometric frustrations on these starter frames. Furthermore, using an experimental-modeling framework, we demonstrate that these patterns can be successfully modeled and replicated through a simulated annealing process, in which the minimized potential is a variation of the Lennard-Jones potential that considers only first-neighbor interactions according to a Delaunay triangulation. Our simulation results not only confirm the connection between honeycomb structures and other crystal systems such as graphene, but also show that irregularities in the honeycomb structure can be explained as the result of analogous interactions between cells and their immediate surroundings, leading to emergent global order. Additionally, our computational model can be used as a first step to describe specific strategies that bees use to effectively solve geometric mismatches while minimizing cost of comb building. Copyright © 2022 the Author(s). Published by PNAS.

关键词： behavioral assays collective behavior honey bees honeycomb mathematical modelling

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Numerical and analytical calculations of orbit-wise synthetic spectra from two-step gamma-ray fusion reactions 50

Numerical and analytical calculations of orbit-wise syntheti...

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50th EPS Conference on Plasma Physics, EPS 2024

作者： Valentini, A. Nocente, M. Eriksson, J. Järleblad, H. Moseev, D. Reman, B.C.G. Rud, M. Snicker, A. Stagner, L. Salewski, M. Department of Physics Technical University of Denmark Kgs Lyngby2800 Denmark Department of Physics University of Milano-Bicocca Milan20126 Italy Department of Physics and Astronomy Uppsala University Uppsala75120 Sweden Department of Appl. Math. & Computer Science Technical University of Denmark Kgs Lyngby2800 Denmark Max-Planck-Institut fur Plasmaphysik Wendelsteinstr. 1 Greifswald17491 Germany Laboratory for Plasma Physics LPP-ERM/KMS BrusselsB-1000 Belgium Department of Applied Physics Aalto University AaltoFI-00076 Finland General Atomics P.O. Box 85608 San DiegoCA92186-5609 United States

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A graph-based convolutional neural network stock price prediction with leading indicators

A graph-based convolutional neural network stock price predi...

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作者： Wu, Jimmy Ming-Tai Li, Zhongcui Srivastava, Gautam Tasi, Meng-Hsiun Lin, Jerry Chun-Wei College of Computer Science and Engineering Shandong University of Science and Technology Shandong China Department of Math and Computer Science Brandon University Brandon Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Department of Management Information Systems National Chung Hsing University Taichung Taiwan Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

The stock market is a capitalistic haven where the issued shares are transferred, traded, and circulated. It bases stock prices on the issue market, however, the structure and trading activities of the stock market are much more complicated than the issue market itself. Therefore, making an accurate prediction becomes an intricate as well as highly difficult task. On the other hand, because of the potential benefits of stock prediction, it attracts generation after generation of scholars as well as investors to continuously develop various prediction methods from different perspectives, a myriad of theories, a multitude of investment strategies, and different practical experiences. In this article, aiming at the task of time series (financial) feature extraction and prediction of price movements, a new convolutional novel neural network that can be called a framework to improve the prediction accuracy of stock trading is proposed. The method that is proposed is called SSACNN, a short form of stock sequence array convolutional neural network. SSACNN collects data including historical data of prices and its leading indicators (options/futures) for a stock to take an array as the input graph of the convolutional neural network framework. In our experimental results, five Taiwanese and American stocks were used as a benchmark to compare with the previous algorithms and proposed algorithm, the motion prediction performance of SSACNN has been improved significantly and proved that it has the potential to be applied in the real financial market. © 2020 John Wiley & Sons Ltd

关键词： Forecasting

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Semi-Implicit Lagrangian Voronoi Approximation for Compressible Viscous Fluid Flows

arXiv

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arXiv 2024年

作者： Kincl, Ondřej Peshkov, Ilya Boscheri, Walter Laboratory of Applied Mathematics DICAM University of Trento Trento38123 Italy Laboratoire de Mathématiques UMR 5127 CNRS Université Savoie Mont Blanc Le Bourget du Lac73376 France Department of Mathematics and Computer Science University of Ferrara Ferrara44121 Italy

This paper contributes to the recent investigations of Lagrangian methods based on Voronoi meshes. The aim is to design a new conservative numerical scheme that can simulate complex flows and multi-phase problems with more accuracy than SPH (Smoothed Particle Hydrodynamics) methods but, unlike diffuse interface models on fixed grid topology, does not suffer from the deteriorating quality of the computational grid. The numerical solution is stored at particles, which move with the fluid velocity and also play the role of the generators of the computational mesh, that is efficiently re-constructed at each time step. The main novelty stems from combining a Lagrangian Voronoi scheme with a semi-implicit integrator for compressible flows. This allows to model low-Mach number flows without the extremely stringent stability constraint on the time step and with the correct scaling of numerical viscosity. The implicit linear system for the unknown pressure is obtained by splitting the reversible from the irreversible (viscous) part of the dynamics, and then using entropy conservation of the reversible sub-system to derive an auxiliary elliptic equation. The method, called SILVA (Semi-Implicit Lagrangian Voronoi Approximation), is validated in a variety of test cases that feature diverse Mach numbers, shocks and multi-phase flows. Copyright © 2024, The Authors. All rights reserved.

关键词： Topology

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SCORE: A Second-Order Conic Initialization for Range-Aided SLAM

SCORE: A Second-Order Conic Initialization for Range-Aided S...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Alan Papalia Joseph Morales Kevin J. Doherty David M. Rosen John J. Leonard Computer Science and Artificial Intelligence Laboratory (CSAIL) Massachusetts Institute of Technology (MIT) Cambridge MA USA Department of Applied Ocean Physics & Engineering Woods Hole Oceanographic Institution Woods Hole MA USA Department of Electrical & Computer Engineering Northeastern University Boston MA USA Department of Math Northeastern University Boston MA USA

We present a novel initialization technique for the range-aided simultaneous localization and mapping (RA-SLAM) problem. In RA-SLAM we consider measurements of point-to-point distances in addition to measurements of rigid transformations to landmark or pose variables. Standard formulations of RA-SLAM approach the problem as non-convex optimization, which requires a good initialization to obtain quality results. The initialization technique proposed here relaxes the RA-SLAM problem to a convex problem which is then solved to determine an initialization for the original, non-convex problem. The relaxation is a second-order cone program (SOCP), which is derived from a quadratically constrained quadratic program (QCQP) formulation of the RA-SLAM problem. As a SOCP, the method is highly scalable. We name this relaxation Second-order COnic RElaxation for RA-SLAM (SCORE). To our knowledge, this work represents the first convex relaxation for RA-SLAM. We present real-world and simulated experiments which show SCORE initialization permits the efficient recovery of quality solutions for a variety of challenging single- and multi-robot RA-SLAM problems with thousands of poses and range measurements.

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Radial kinks in a Schwarzschild-like geometry

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Physical Review D 2024年第12期110卷 125025-125025页

作者： Jean-Guy Caputo Tomasz Dobrowolski Jacek Gatlik Panayotis G. Kevrekidis Laboratoire de Mathématiques INSA de Rouen Avenue de l’Université 76801 Saint-Etienne du Rouvray France Department of Computer Physics and Quantum Computing University of the National Education Commission in Krakow Podchora¸żych 2 30-084 Cracow Poland AGH University of Krakow Faculty of Physics and Applied Computer Science 30-059 Krakow Poland Department of Mathematics and Statistics University of Massachusetts Amherst Massachusetts 01003-4515 USA

We study the propagation of a domain wall (kink) of the ϕ4 model in a radially symmetric environment defined by a gravity source. This source introduces a Schwarzschild-like geometry. We introduce an effective model that accurately describes the dynamics of the kink center. This description works well even outside the perturbation region, i.e., even for large masses of the gravitating object. We observed that such a spherical domain wall surrounding a star-type object inevitably “collapses,” i.e., shrinks in radius toward the origin and offer an understanding of the latter phenomenology. The relevant analysis is presented for a circular domain wall and a spherical one.

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Across Scales and Across Dimensions: Temporal Super-Resolution Using Deep Internal Learning 1

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16th European Conference on computer Vision, ECCV 2020

作者： Zuckerman, Liad Pollak Naor, Eyal Pisha, George Bagon, Shai Irani, Michal Department of Computer Science and Applied Math The Weizmann Institute of Science Rehovot Israel Rehovot Israel Technion Israel Institute of Technology Haifa Israel

ISBN: (数字)9783030585716

ISBN: (纸本)9783030585709

When a very fast dynamic event is recorded with a low-framerate camera, the resulting video suffers from severe motion blur (due to exposure time) and motion aliasing (due to low sampling rate in time). True Temporal Super-Resolution (TSR) is more than just Temporal-Interpolation (increasing framerate). It can also recover new high temporal frequencies beyond the temporal Nyquist limit of the input video, thus resolving both motion-blur and motion-aliasing – effects that temporal frame interpolation (as sophisticated as it may be) cannot undo. In this paper we propose a "Deep Internal Learning" approach for true TSR. We train a video-specific CNN on examples extracted directly from the low-framerate input video. Our method exploits the strong recurrence of small space-time patches inside a single video sequence, both within and across different spatio-temporal scales of the video. We further observe (for the first time) that small space-time patches recur also across-dimensions of the video sequence – i.e., by swapping the spatial and temporal dimensions. In particular, the higher spatial resolution of video frames provides strong examples as to how to increase the temporal resolution of that video. Such internal video-specific examples give rise to strong self-supervision, requiring no data but the input video itself. This results in Zero-Shot Temporal-SR of complex videos, which removes both motion blur and motion aliasing, outperforming previous supervised methods trained on external video datasets. © 2020, Springer Nature Switzerland AG.

关键词： Interpolation

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Semi-implicit Lagrangian Voronoi Approximation for the incompressible Navier-Stokes equations

arXiv

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arXiv 2024年

作者： Kincl, Ondřej Peshkov, Ilya Boscheri, Walter Laboratory of Applied Mathematics DICAM University of Trento via Mesiano 77 Trento38123 Italy Laboratoire de Mathématiques UMR 5127 CNRS Université Savoie Mont Blanc Le Bourget du Lac73376 France Department of Mathematics and Computer Science University of Ferrara Ferrara Italy

We introduce Semi-Implicit Lagrangian Voronoi Approximation (SILVA), a novel numerical method for the solution of the incompressible Euler and Navier-Stokes equations, which combines the efficiency of semi-implicit time marching schemes with the robustness of time-dependent Voronoi tessellations. In SILVA, the numerical solution is stored at particles, which move with the fluid velocity and also play the role of the generators of the computational mesh. The Voronoi mesh is rapidly regenerated at each time step, allowing large deformations with topology changes. As opposed to the reconnection-based Arbitrary-Lagrangian-Eulerian schemes, we need no remapping stage. A semi-implicit scheme is devised in the context of moving Voronoi meshes to project the velocity field onto a divergence-free manifold. We validate SILVA by illustrative benchmarks, including viscous, inviscid, and multi-phase flows. Compared to its closest competitor, the Incompressible Smoothed Particle Hydrodynamics (ISPH) method, SILVA offers a sparser stiffness matrix and facilitates the implementation of no-slip and free-slip boundary conditions. Copyright © 2024, The Authors. All rights reserved.

关键词： Stiffness matrix

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A Robust Detection Framework for Intelligent Growth Monitoring in Greenhouse Rose Cultivation

A Robust Detection Framework for Intelligent Growth Monitori...

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Advanced Algorithms and Control Engineering (ICAACE), International Conference on

作者： Danhua Liu Boheng Li Sicheng You Qingyang Wu Jiaqi Wang Zhiyan Ren Yongying Liu Jianghan Tao Zhijie Zhang Shan Sun Zhiben Yin Chenyu Zhang Feng Xue Yijia Chen Graduate School of Business and Finance Waseda University Tokyo Japan Department of Applied Informatics Hosei University Tokyo Japan Faculty of Data Science City University of Macau Macau China Department of Environmental Health Sciences University of California Los Angeles CA US Graduate School of Frontier Sciences The University of Tokyo Kashiwa Japan Graduate School of Global Environmental Studies Sophia University Tokyo Japan Department of Computer Science and Technology Xinjiang College of Science & Technology Xinjiang China Graduate School of Information Production and Systems Waseda University Kitakyushu Japan Department of Math and Applied Mathematics China University of Petroleum-Beijing Karamay China Graduate School of Frontier Science The University of Tokyo Kashiwa Japan

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Accurately detecting roses in UAV-captured greenhouse imagery presents significant challenges due to occlusions, scale variability, and complex environmental conditions. This study introduces ROSE-MAMBA-YOLO, a hybrid detection framework that combines the efficiency of YOLOv11 with Mamba-inspired state-space modeling to enhance feature extraction, multi-scale fusion, and contextual representation. The model achieves a mAP@50 of 87.5%, precision of 90.4%, and recall of 83.1%, surpassing state-of-the-art object detection models. This framework offers a practical approach for precision floriculture and sets the stage for integrating advanced detection technologies into real-time crop monitoring systems, advancing intelligent, data-driven agriculture.

关键词： Deep learning Accuracy Green products Object detection Self-supervised learning Feature extraction Real-time systems Data models Monitoring Context modeling

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