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2221
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2222por Benavides-Lara, Mario Alberto, Pompa Mansilla, Maura, de Agüero Servín, Mercedes, Sánchez-Mendiola, Melchor, Rendón Cazales, Víctor JesúsEnlace del recurso
Publicado 2022
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Online Artículo -
2223por Castillo Ramírez, Mtro. Arturo, Izar Landeta, Dr. Juan Manuel, Hernándz García, Mtro. VicenteEnlace del recurso
Publicado 2012
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2224
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2225por Ramiro Marentes, Mtra. Fabiola, Arcos Vega, Mtro. José Luis, Sevilla García, Dr. Juan José, Conde Maldonado, Sergio PascualEnlace del recurso
Publicado 2012
Enlace del recurso
Online Artículo -
2226
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2227
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2228“…Six different genome-wide association statistical models (GLM, MLM, MLMM, FarmCPU, BLINK, and SUPER) were utilized to search for reasonable models to analyze soft winter wheat populations with increased markers and/or breeding lines going forward. …”
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2229“…However, existing methods that employ a subsequence dynamic time warping (sDTW) algorithm for this problem are too computationally intensive that a massive workstation with dozens of CPU cores still struggles to keep up with the data rate of a mobile phone–sized MinION sequencer. …”
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2230por Singh, Rupesh, Singuri, Srinidhi, Batoki, Julia, Lin, Kimberly, Luo, Shiming, Hatipoglu, Dilara, Anand-Apte, Bela, Yuan, Alex“…Graded scans were used to label training data for the convolution neural network (CNN) development and training. RESULTS: On a single CPU system, the best performing CNN training took ∼35 mins. …”
Publicado 2023
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2231por Huang, Jiaxin, Kelber, Florian, Vogginger, Bernhard, Liu, Chen, Kreutz, Felix, Gerhards, Pascal, Scholz, Daniel, Knobloch, Klaus, Mayr, Christian G.“…The potential low-energy feature of the spiking neural network (SNN) engages the attention of the AI community. Only CPU-involved SNN processing inevitably results in an inherently long temporal span in the cases of large models and massive datasets. …”
Publicado 2023
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2232“…We performed 494498 random hard-sphere packing simulations representing 206 CPU days’ worth of computational overhead. Simulations required nine input parameters with linear constraints and two discrete fidelities each with continuous fidelity parameters. …”
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2233por Zhang, Yichen, He, Gan, Ma, Lei, Liu, Xiaofei, Hjorth, J. J. Johannes, Kozlov, Alexander, He, Yutao, Zhang, Shenjian, Kotaleski, Jeanette Hellgren, Tian, Yonghong, Grillner, Sten, Du, Kai, Huang, Tiejun“…This GPU-based method performs with 2-3 orders of magnitude higher speed than that of the classic serial Hines method in the conventional CPU platform. We build a DeepDendrite framework, which integrates the DHS method and the GPU computing engine of the NEURON simulator and demonstrate applications of DeepDendrite in neuroscience tasks. …”
Publicado 2023
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2234“…Based on the prediction module, the decision module analyzes the environment information and uses the Adaptive Particle Swarm Optimization algorithm and Genetic Algorithm Operator (APSO-GA) algorithm to select the most suitable configuration plan for each function, including CPU, memory, and edge platforms. In this way, it is possible to effectively minimize the financial overhead while fulfilling the Service Level Objectives (SLOs). …”
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2235por Alvi, Sohaib Bin Khalid, Nayyer, Muhammad Ziad, Jamal, Muhammad Hasan, Raza, Imran, de la Torre Diez, Isabel, Velasco, Carmen Lili Rodriguez, Brenosa, Jose Manuel, Ashraf, Imran“…The proposed method reduces training time by 53.1%, optimizes CPU and memory utilization by 17.5% and 33.6%, and maintains accurate COVID-19 detection capabilities without compromising the F1 score, demonstrating the efficiency and effectiveness of the lightweight convolutional neural network-based edge federated learning architecture. …”
Publicado 2023
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2236“…Yet, simulation-driven design may be problematic due to the incurred CPU expenses. Addressing the high-cost issues stimulated the development of surrogate modeling methods. …”
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2237“…Compared with the original method, Structural Similarity (SSIM) increases by 1.3%, Learned Perceptual Image Patch Similarity (LPIPS) decreases by 1.7%, Mean Squared Error (MSE) decreases by 2.5%, and it runs faster on GPU and CPU. Additionally, we evaluate the results of E2VIDX with application to image classification, object detection, and instance segmentation. …”
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2238por Burke, Jamie, Engelmann, Justin, Hamid, Charlene, Reid-Schachter, Megan, Pearson, Tom, Pugh, Dan, Dhaun, Neeraj, Storkey, Amos, King, Stuart, MacGillivray, Tom J., Bernabeu, Miguel O., MacCormick, Ian J. C.“…RESULTS: DeepGPET achieved excellent agreement with GPET on data from three clinical studies (AUC = 0.9994, Dice = 0.9664; Pearson correlation = 0.8908 for choroidal thickness and 0.9082 for choroidal area), while reducing the mean processing time per image on a standard laptop CPU from 34.49 ± 15.09 seconds using GPET to 1.25 ± 0.10 seconds using DeepGPET. …”
Publicado 2023
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2239“…Its accuracy is similar to Combinatorial Extension (CE) and works over 243,000 times faster, searching 34,000 proteins in 0.34 sec with a 3.2-GHz CPU. SARST provides statistically meaningful expectation values to assess the retrieved information. …”
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2240“…RESULTS: Our new heuristic produces significantly better alignments, especially on globally related sequences, without increasing the CPU time and memory consumption exceedingly. The new method is based on a guide tree; to detect possible spurious sequence similarities, it employs a vertex-cover approximation on a conflict graph. …”
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