This paper describes a flexible C++ software framework, called GRINS, for simulating complex multiphysics systems of partial differential equations using the finite element method. GRINS is designed to facilitate the ...
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In recent years, research on neuromporphic computing platforms has focused on variable-structure, spiking network models. An important methodology for programming these networks is evoluationary optimization (EO), whe...
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ISBN:
(纸本)9781509061839
In recent years, research on neuromporphic computing platforms has focused on variable-structure, spiking network models. An important methodology for programming these networks is evoluationary optimization (EO), where thousands of networks are generated and then evaluated by determining fitness scores on specific tasks. Fitness scores guide the generation of new networks until a target fitness is achieved. One source of performance overhead during EO is the simulation of the task on each network to determing its fitness. To mitigate this source of overhead, we formulate the Static Fitness Prediction Task (SFPT), for predicting a network's fitness without direct simulation. Our hypothesis is that we can use SFPT to predict a network's fitness sufficiently accurately to reject a significant portion of networks during EO without having to simulate them, thereby making the EO more efficient. We propose a data-driven approach to the SFPT on the neuromorphic model DANNA [1]. Our approach transforms networks into directed graphs and extracts structural features to train an ancillary model for predicting the fitness of new networks. We analyze the extracted features and evaluate several predictive models to predict the fitness of networks for five tasks. Our results demonstrate a predictive capacity in these features and models. Our primary contribution is to demonstrate the utility of graph-level features extracted from variable-structure networks to predict network fitness and circumvent expensive simulations.
The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum f...
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The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse d
Global warming affects the Earth system in complex ways, often preventing a functional understanding of the underlying processes. Disentangling these processes between abiotic drivers and single species or entire comm...
Global warming affects the Earth system in complex ways, often preventing a functional understanding of the underlying processes. Disentangling these processes between abiotic drivers and single species or entire communities is, however, essential for an in-depth understanding of the impacts of climate change on the ecosystem. Using a high-resolution time series on heat waves and cold spells in an Arctic fjord system, we demonstrate that AI-supported digital data processing, which is based on state-of-the-art observatory technology, has the potential to provide new insights into the effects of abiotic factors on biotic communities, which would not be possible with traditional expedition-based sampling methods. Furthermore, our study shows that short-term, event-driven anomalies in key ocean variables not only alter a system's hydrography but also have the potential to impact the entire community across the trophic chain from benthos and zooplankton to fish. We found a significant positive correlation between hydrographic temperature anomalies and biota abundance, with high biota abundances linked to 'Atlantic' phases with frequent heat waves and low biota abundances correlated with 'Arctic' phases dominated by cold spells. The study also revealed that hydrographic anomalies can not only influence overall biota abundance in an area but also trigger complex shifts in species composition. This leads to fluctuating interannual abundance peaks in specific biotic groups, such as jellyfish, fish, or chaetognaths, depending on trigger factors that are not yet fully understood.
作者:
Chen, KeLi, QinWang, LiMathematics Department
University of Wisconsin-Madison 480 Lincoln Dr. MadisonWI53705 United States Department of Mathematics
Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo 244 Mathematics Building BuffaloNY14206 United States
We consider the inverse problem of reconstructing the optical parameters for stationary radiative transfer equation (RTE) from velocity-averaged measurement. The RTE often contains multiple scales characterized by the...
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We consider a class of tumor growth models under the combined effects of density-dependent pressure and cell multiplication, with a free boundary model as its singular limit when the pressure-density relationship beco...
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Strong and supportive social relationships are fundamental to our well-being. However, there are costs to their maintenance, resulting in a trade-off between quality and quantity, a typical strategy being to put a lot...
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Reproducibility of computational studies is a hallmark of scientific methodology. It enables researchers to build with confidence on the methods and findings of others, reuse and extend computational pipelines, and th...
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作者:
Li, QinShu, RuiwenWang, LiMathematics Department
University of Wisconsin-Madison 480 Lincoln Dr. MadisonWI53705 United States Department of Mathematics
Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo 244 Mathematics Building BuffaloNY14206 United States
The inverse radiative transfer problem finds broad applications in medical imaging, atmospheric science, astronomy, and many other areas. This problem intends to recover the optical properties, denoted as absorption a...
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