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SSRN 2024年

作者： Verbel, Angel Augusto Schutesky, Maria Emilia Gregory, Daniel D. Verbel, Arturo University of Brasilia Institute of Geosciences Graduate Program in Geology DF Brasília70910-900 Brazil University of Toronto Department of Earth Sciences TorontoONM5S 3B1 Canada Universidad de los Andes Systems and Computing Engineering Department School of Engineering Bogotá111711 Colombia

Systematic sampling of gold grains and their characterization in abundance and morphology can be used as a vectoring tool for mineral deposits. However, the processes that lead to the formation of an ore deposit are multiscale and nonlinear. As a result, the elemental compositions recorded in gold grains during the mineralization process seem mostly irregular and unpredictable. Here, we took advantage of the capabilities of neural networks to approximate, a natural nonlinear function that better explains the chemical variability observed in gold allowing us to make predictions. this was achieved by using published data of trace elements in gold of four types of gold deposits (i.e., Orogenic, VMS, porphyry and epithermal) from 47 different localities, obtained by laser ablation-inductively coupled plasma-mass spectrometry (LA- ICP-MS). The model was optimized by training five different architectures on the most influential elements, determined by principal component analysis (PCA). As a result, it was found that two hidden layers with 10 neurons each, is the best architecture for using trace elements as predictors of the type of deposit that formed a natural gold grain. In order to encourage the use of the findings made in this paper, we introduce OreGenes, an app developed in Matlab2023b based on the best-obtained model. It allows any user who possesses compatible data, the ability to import and process it to obtain a prediction with an average accuracy level of 88.9% confidence to determine the mineral deposit type that an unknown gold grain came from which grants the user the ability to have a powerful tool for exploration or research. © 2024, The Authors. All rights reserved.

关键词： Deposits

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A Power-Efficient FFT Hardware Architecture Exploiting Approximate Adders

A Power-Efficient FFT Hardware Architecture Exploiting Appro...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Guilherme Ferreira Pedro T. L. Pereira Guilherme Paim Eduardo Costa Sergio Bampi Graduate Program in Microelectronics (PGMICRO) Federal University of Rio Grande do Sul (UFRGS) Brazil Graduate Program in Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Brazil

ISBN: (数字)9781728176703

ISBN: (纸本)9781728176710

This work presents an energy-efficient Fast Fourier Transform (FFT) hardware architecture exploiting approximate adder circuits. The FFT hardware architecture consists of a fixed-point fully sequential architecture with a radix-2 butterfly with decimation in time (DIT). In this paper, we explore a set of approximate adders (LOA, ETA-I, Copy-A, Copy-B, Trunc0, Trunc1) in the butterfly by varying the approximation level (K term). The Root-Mean-Square Error (RMSE) metric shows which approximate level term allows the FFT processing without widely signal losses. The results show that our best-proposed FFT employing Trunc0 approximate adder with K =10 saves up to 35% of power dissipation compared to the FFT with the original radix-2 butterfly using the synthesis tool operators.

关键词： Measurement Fast Fourier transforms Circuits and systems Hardware Energy efficiency Topology Power dissipation

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Quantum Kernel-Based Long Short-term Memory

arXiv

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arXiv 2024年

作者： Hsu, Yu-Chao Li, Tai-Yu Chen, Kuan-Cheng National Center for High-Performance Computing NARlabs Hsinchu Taiwan Cross College Elite Program National Cheng Kung University Tainan Taiwan National Synchrotron Radiation Research Center Hsinchu Taiwan Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Imperial College London London United Kingdom

—The integration of quantum computing into classical machine learning architectures has emerged as a promising approach to enhance model efficiency and computational capacity. In this work, we introduce the Quantum Kernel-Based Long Short-Term Memory (QK-LSTM) network, which utilizes quantum kernel functions within the classical LSTM framework to capture complex, non-linear patterns in sequential data. By embedding input data into a high-dimensional quantum feature space, the QK-LSTM model reduces the reliance on large parameter sets, achieving effective compression while maintaining accuracy in sequence modeling tasks. This quantum-enhanced architecture demonstrates efficient convergence, robust loss minimization, and model compactness, making it suitable for deployment in edge computing environments and resource-limited quantum devices (especially in the NISQ era). Benchmark comparisons reveal that QK-LSTM achieves performance on par with classical LSTM models, yet with fewer parameters, underscoring its potential to advance quantum machine learning applications in natural language processing and other domains requiring efficient temporal data processing. © 2024, CC BY.

关键词： Quantum computers

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A General Framework for Clustering and Distribution Matching with Bandit Feedback

arXiv

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arXiv 2024年

作者： Yavas, Recep Can Huang, Yuqi Tan, Vincent Y.F. Scarlett, Jonathan Descartes Program CNRS@CREATE 138602 Singapore Department of Information Systems and Analytics School of Computing National University of Singapore 119077 Singapore Department of Mathematics Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore Department of Computer Science Department of Mathematics Institute of Data Science National University of Singapore 119077 Singapore

We develop a general framework for clustering and distribution matching problems with bandit feedback. We consider a K-armed bandit model where some subset of K arms is partitioned into M groups. Within each group, the random variable associated to each arm follows the same distribution on a finite alphabet. At each time step, the decision maker pulls an arm and observes its outcome from the random variable associated to that arm. Subsequent arm pulls depend on the history of arm pulls and their outcomes. The decision maker has no knowledge of the distributions of the arms or the underlying partitions. The task is to devise an online algorithm to learn the underlying partition of arms with the least number of arm pulls on average and with an error probability not exceeding a pre-determined value δ. Several existing problems fall under our general framework, including finding M pairs of arms, odd arm identification, and N-ary clustering of K arms belong to our general framework. We derive a non-asymptotic lower bound on the average number of arm pulls for any online algorithm with an error probability not exceeding δ. Furthermore, we develop a computationally-efficient online algorithm based on the Track-and-Stop method and Frank–Wolfe algorithm, and show that the average number of arm pulls of our algorithm asymptotically matches that of the lower bound. Our refined analysis also uncovers a novel bound on the speed at which the average number of arm pulls of our algorithm converges to the fundamental limit as δ *** Codes 68T05 Copyright © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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Vision of the Seas: Open Visual Perception Framework for Autonomous Sailing Vessels

Vision of the Seas: Open Visual Perception Framework for Aut...

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International Conference on Systems, Signals and Image Processing, IWSSIP

作者： Araújo André P. D. Ganadev Prajapathy Chandrasekharan Esteban Walter Gonzalez Clua Phillipe Preux Eduardo Charles Vasconcellos Luiz Marcos Garcia Gonçalves Institute of Computing Universidade Federal Fluminense Niteroi Brazil Inria Université de Lille Villeneuve d’Ascq France Elec. and Comp. Eng. Graduate Program Univ. Fed. do Rio Grande do Norte Natal Brazil

We propose an Open Visual Perception framework for autonomous navigation and obstacle detection in marine sailing vessels. The proposed framework lays the groundwork for perceiving marine objects needed for vessel navigation and obstacle avoidance using only image processing as the primary method. Various methods are explored to extract features such as waterline, terrain, and other vessels. The proposed framework also incorporates image stabilization techniques without the need for an Inertial Measurement Unit to compensate for ocean wave oscillations. The work also introduces the system architecture of an autonomous marine vessel, besides the components of the open visual perception framework. Results demonstrate the capabilities of obstacle detection, horizon line identification, and water texture detection.

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Mixed Reality System for Teaching and Learning: A Systematic Literature Review 5

Mixed Reality System for Teaching and Learning: A Systematic...

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5th IEEE International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2021

作者： Suryodiningrat, Satrio Pradono Prabowo, Harjanto Meyliana Hidayanto, Achmad Nizar Bina Nusantara University Faculty Of Computing And Media Computer Science Department BINUS Graduate Program Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Computer Science Graduate Program Jakarta Indonesia Bina Nusantara University School Of Information Systems Information Systems Department Jakarta Indonesia Universitas Indonesia Faculty Of Computer Science Depok Indonesia

ISBN: (纸本)9781665401968

Mixed reality is becoming more popular, and a lot of technology companies trying to develop and enhance mixed reality technology and using it not just in manufactures or industries but lately the technology is used for teaching and learning, either in school or for training purposes. While traditional teaching and learning processes are not effective anymore and most of the time, the learning objectives are not reached. This paper has an objective to point out the benefits of using mixed reality for teaching and learning. 99 articles have been collected and reviewed carefully as a quality assessment process, there are 41 articles has been selected for further review and top 5 benefits will be discussed. This review informs educators and scholars that there are common benefits, and the most popular benefit is better learning engagement. © 2021 IEEE.

关键词： Mixed reality

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Assisted Multi-Frame Approach for Super-Resolution in UAV Photogrammetric Images

Assisted Multi-Frame Approach for Super-Resolution in UAV Ph...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Daniel Zanotta Vinicius Sales Ademir Marques Andre Luiz Durante Spigolon Luiz Gonzaga Maurício Roberto Veronez Vizlab — X-Reality and GeoInformatics Lab UNISINOS - São Leopoldo RS Brazil Graduate Program in Applied Computing at Vale do Rio dos Sinos University São Leopoldo RS Brazil PETROBRAS - Petrobras Research and Development Center (CENPES) RJ Brazil

Improvement in spatial resolution of remote sensing images will always be a hot-topic since the sharpening of image objects is mandatory for many appications. Even thought image instruments have received great improvements in the recent years, super-resolution techniques are welcome to increase even more the level of quality of the data for further interpretations. In this paper we present a multi-frame super-resolution approach that uses a set of UAV images acquired at the same spot, but with slight different perspectives caused by random fluctuations. The small off-sets between consecutive pixels of the low spatial resolution images are considered at subpixel level to feed an arithmetic system of equations able to produce high resolution pixels, which are the unknowns of the system. The results are soundness and could show visual enhancement. However, further developments is need to in deep undertanding and possible advancement of the designed approach.

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Evaluation of convolutional neural networks for COVID-19 classification on chest X-rays

arXiv

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arXiv 2021年

作者： Zeiser, Felipe André Da Costa, Cristiano André De Oliveira Ramos, Gabriel Bohn, Henrique Santos, Ismael Da Rosa Righi, Rodrigo Graduate Program in Applied Computing Universidade Do Vale Do Rio Dos Sinos São Leopoldo Brazil

Early identification of patients with COVID-19 is essential to enable adequate treatment and to reduce the burden on the health system. The gold standard for COVID-19 detection is the use of RT-PCR tests. However, due to the high demand for tests, these can take days or even weeks in some regions of Brazil. Thus, an alternative for detecting COVID-19 is the analysis of Digital Chest X-rays (XR). Changes due to COVID-19 can be detected in XR, even in asymptomatic patients. In this context, models based on deep learning have great potential to be used as support systems for diagnosis or as screening tools. In this paper, we propose the evaluation of convolutional neural networks to identify pneumonia due to COVID-19 in XR. The proposed methodology consists of a preprocessing step of the XR, data augmentation, and classification by the convolutional architectures DenseNet121, InceptionResNetV2, InceptionV3, MovileNetV2, ResNet50, and VGG16 pre-trained with the ImageNet dataset. The obtained results demonstrate that the VGG16 architecture obtained superior performance in the classification of XR for the evaluation metrics using the methodology proposed in this article. The obtained results for our methodology demonstrate that the VGG16 architecture presented a superior performance in the classification of XR, with an Accuracy of 85.11%, Sensitivity of 85.25%, Specificity of 85.16%, F1-score of 85.03%, and an AUC of 0.9758. © 2021, CC BY-NC-SA.

关键词： Convolutional neural networks

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Graphene-functionalized textile composites for wearable Joule heating applications

Advanced Nanocomposites

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Advanced Nanocomposites 2025年 2卷 108-123页

作者： Omar Faruk Abbas Ahmed Ashfaqul Hoque Khadem Lu Jia Luyi Sun Department of Materials Science and Engineering Binghamton University State University of New York at Binghamton NY 13902 USA Polymer Program Institute of Materials Science and Department of Chemical & Biomolecular Engineering University of Connecticut Storrs CT 06269 USA Department Mechanical Engineering College of Engineering Computing and Applied Science Clemson University Clemson SC 29634 USA College of Textile Engineering Taiyuan University of Technology Jinzhong Shanxi 030600 China

Thermal comfort is essential for maintaining physiological well-being, with textile materials traditionally serving as the primary medium for regulating heat exchange between the body and its environment. However, conventional textiles often fall short of maintaining optimal thermal balance. So, there is an increasing demand for advanced thermoregulation systems that can effectively reduce heat loss and enhance warmth, ensuring consistent comfort. Recent research has highlighted the promise of advanced functional materials, especially graphene and its composites, for modifying textiles to improve thermal properties. This article comprehensively reviews recent advancements in graphene-functionalized textile composites, focusing on their applications in wearable Joule heaters designed for personalized thermal comfort or thermal therapy. Various graphene-functionalized textile composites are reviewed from the perspectives of material properties, processing strategies and device fabrication methods. Key challenges and future opportunities are summarized for graphene-functionalized textile-based Joule heaters as innovative solutions in wearable thermal regulation.

关键词： Graphene Textile Composite Wearable electronics Joule heater

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Low-Complexity HEVC Transrating Based on Prediction Unit Mode Inheritance

Low-Complexity HEVC Transrating Based on Prediction Unit Mod...

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European Signal Processing Conference (EUSIPCO)

作者： Matheus Lindino Thiago Bubolz Bruno Zatt Daniel Palomino Guilherme Correa Video Technology Research Group (ViTech) Graduate Program in Computing (PPGC) Federal University of Pelotas (UFPel) Pelotas Brazil

ISBN: (数字)9789082797053

ISBN: (纸本)9781728150017

Video transcoding for bit rate adaptation has become mandatory for over-the-top applications that deliver multimedia content in heterogeneous environments under different network conditions and user capabilities. As transcoding requires sequentially decoding and re-encoding the video bitstream, the computational cost involved in the process is too high, especially when considering current state-of-the-art codecs, such as the High Efficiency Video Coding (HEVC). This work presents a fast HEVC transcoder for bit rate adaptation based on Prediction Unit (PU) mode inheritance, which uses information gathered from the HEVC decoding process to accelerate PU mode decision in the re-encoding process. Experimental results show that the proposed method achieves an average transrating time reduction of 42% at the cost of a bitrate increase of 0.54%.

关键词： Statistical analysis Bit rate Transcoding Streaming media Decoding Complexity theory High efficiency video coding

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