417 research outputs found
Scientific elite revisited: Patterns of productivity, collaboration, authorship and impact
Throughout history, a relatively small number of individuals have made a profound and lasting impact on science and society. Despite long-standing, multi-disciplinary interests in understanding careers of elite scientists, there have been limited attempts for a quantitative, career-level analysis. Here, we leverage a comprehensive dataset we assembled, allowing us to trace the entire career histories of nearly all Nobel laureates in physics, chemistry, and physiology or medicine over the past century. We find that, although Nobel laureates were energetic producers from the outset, producing works that garner unusually high impact, their careers before winning the prize follow relatively similar patterns as ordinary scientists, being characterized by hot streaks and increasing reliance on collaborations. We also uncovered notable variations along their careers, often associated with the Nobel prize, including shifting coauthorship structure in the prize-winning work, and a significant but temporary dip in the impact of work they produce after winning the Nobel. Together, these results document quantitative patterns governing the careers of scientific elites, offering an empirical basis for a deeper understanding of the hallmarks of exceptional careers in science
Significant wave height forecasting based on the hybrid EMD-SVM method
1957-1962Prediction of significant wave height (SWH) is considered an effective method in marine engineering and prevention of marine disasters. Support vector machine (SVM) model has limitations in processing nonlinear and non-stationary SWH time series. Fortunately, empirical mode decomposition (EMD) can effectively deal with the complicated series. So, the SWH prediction method based on EMD and SVM is proposed by combining the advantages of both methods. A statistical analysis was carried out to compare the results of two models i.e., between the hybrid EMD-SVM and SVM. In addition, two models are used for forecasting SWH with 3, 6, 12 and 24 hours lead times, respectively. A high R value of different prediction times for the hybrid model. Results indicate that SWH prediction of the hybrid EMD-SVM model is superior to the SVM model
A sketch-and-project method for solving the matrix equation AXB = C
In this paper, based on an optimization problem, a sketch-and-project method
for solving the linear matrix equation AXB = C is proposed. We provide a
thorough convergence analysis for the new method and derive a lower bound on
the convergence rate and some convergence conditions including the case that
the coefficient matrix is rank deficient. By varying three parameters in the
new method and convergence theorems, the new method recovers an array of
well-known algorithms and their convergence results. Meanwhile, with the use of
Gaussian sampling, we can obtain the Gaussian global randomized Kaczmarz
(GaussGRK) method which shows some advantages in solving the matrix equation
AXB = C. Finally, numerical experiments are given to illustrate the
effectiveness of recovered methods.Comment: arXiv admin note: text overlap with arXiv:1506.03296,
arXiv:1612.06013, arXiv:2204.13920 by other author
Covariance localization in the ensemble transform Kalman filter based on an augmented ensemble
With the increased density of available observation data, data assimilation has become an increasingly important tool in marine research. However, the success of the ensemble Kalman filter is highly dependent on the size of the ensemble. A small ensemble used in data assimilation could cause filter divergence, undersampling and spurious correlations. The primary method to alleviate these problems is localization. It can eliminate some spurious correlations and increase the rank of the forecast error covariance matrix. The ensemble transform Kalman filter has been widely used in various studies as a deterministic filter. Unfortunately, the covariance localization cannot be directly applied to ensemble transform Kalman filter. The new covariance localization needs to be presented to adapt the ensemble transform Kalman filter. Based on the method of expanded ensemble and eigenvalue decomposition, this study describes a variation of covariance localization that takes advantage of an unbiased covariance matrix from the expanded ensemble. Experiments described herein show that the new method outperforms the localization methods proposed by others when used in the ensemble transform Kalman filter. The new method yields an analysis estimate that is closer to the true state under different experimental conditions
Transport Model of Underground Sediment in Soils
Studies about sediment erosion were mainly concentrated on the river channel sediment, the terrestrial sediment, and the underground sediment. The transport process of underground sediment is studied in the paper. The concept of the flush potential sediment is founded. The transport equation with stable saturated seepage is set up, and the relations between the flush potential sediment and water sediment are discussed. Flushing of underground sediment begins with small particles, and large particles will be taken away later. The pore ratio of the soil increases gradually. The flow ultimately becomes direct water seepage, and the sediment concentration at the same position in the water decreases over time. The concentration of maximal flushing potential sediment decreases along the path. The underground sediment flushing model reflects the flushing mechanism of underground sediment
Dynamically configured physics-informed neural network in topology optimization applications
Integration of machine learning (ML) into the topology optimization (TO)
framework is attracting increasing attention, but data acquisition in
data-driven models is prohibitive. Compared with popular ML methods, the
physics-informed neural network (PINN) can avoid generating enormous amounts of
data when solving forward problems and additionally provide better inference.
To this end, a dynamically configured PINN-based topology optimization
(DCPINN-TO) method is proposed. The DCPINN is composed of two subnetworks,
namely the backbone neural network (NN) and the coefficient NN, where the
coefficient NN has fewer trainable parameters. The designed architecture aims
to dynamically configure trainable parameters; that is, an inexpensive NN is
used to replace an expensive one at certain optimization cycles. Furthermore,
an active sampling strategy is proposed to selectively sample collocations
depending on the pseudo-densities at each optimization cycle. In this manner,
the number of collocations will decrease with the optimization process but will
hardly affect it. The Gaussian integral is used to calculate the strain energy
of elements, which yields a byproduct of decoupling the mapping of the material
at the collocations. Several examples with different resolutions validate the
feasibility of the DCPINN-TO method, and multiload and multiconstraint problems
are employed to illustrate its generalization. In addition, compared to finite
element analysis-based TO (FEA-TO), the accuracy of the displacement prediction
and optimization results indicate that the DCPINN-TO method is effective and
efficient.Comment: 31 pages, 22 figure
A Resonance Model for Spontaneous Cortical Activity
How human brain function emerges from structure has intrigued researchers for
decades and numerous models have been put forward, yet none of them yields a
close structure-function relation. Here we present a resonance model based on
neuronal spike timing dependent plasticity (STDP) principle to describe the
spontaneous cortical activity by incorporating the dynamic interactions between
neuronal populations into a wave equation, which is able to accurately predict
the resting brain functional connectivity (FC), including the resting-state
networks. Besides, the proposed model provides strong theoretical and
experimental evidences that the spontaneous dynamic coupling between brain
regions fluctuates with a low frequency. Crucially, it is able to account for
how the negative functional correlations emerge during resonance. We test the
model with a large cohort of subjects (1038) from the Human Connectome Project
(HCP) S1200 release in both time and frequency domain, which exhibits superior
performance to existing eigen-decomposition models
Partial masquerading and background matching in two Asian box turtle species (<i>Cuora</i> spp.)
Animals living in heterogeneous natural environments adopt different camouflage strategies against different backgrounds, and behavioral adaptation is crucial for their survival. However, studies of camouflage strategies have not always quantified the effect of multiple strategies used together. In the present study, we used a human visual model to quantify similarities in color and shape between the carapace patterns of two Cuora species and their preferred habitats. Our results showed that the color of the middle stripe on the carapace of Cuora galbinifrons (Indochinese box turtle) was significantly similar to the color of their preferred substrates. Meanwhile, the middle stripe on the carapace of C. mouhotii (keeled box turtle) contrasted more with their preferred substrates, and the side stripe matched most closely with the environment. Furthermore, the carapace side stripe of C. galbinifrons and the carapace middle stripe of C. mouhotii highly contrasted with their preferred substrates. We quantified the similarity in shape between the high-contrast stripes of both Cuora species and leaves from their habitats. The carapace middle stripe of C. mouhotii was most similar in shape to leaves from the broad-leaves substrate, and the carapace side stripe of C. galbinifrons was the most similar in shape to leaves from the bamboo-leaves substrate. We determined that these species adopt partial masquerading when their entire carapace is exposed and partially match their background when they semi-cover themselves in leaf litter. To the best of our knowledge, this is the first study to demonstrate that partial masquerading and background matching improve the camouflage effect of Asian box turtles in their preferred habitats. This is a novel study focusing on the influence of the shape and color of individual carapace segments on reducing detectability and recognition.</p
Training and Tuning Generative Neural Radiance Fields for Attribute-Conditional 3D-Aware Face Generation
Generative Neural Radiance Fields (GNeRF) based 3D-aware GANs have
demonstrated remarkable capabilities in generating high-quality images while
maintaining strong 3D consistency. Notably, significant advancements have been
made in the domain of face generation. However, most existing models prioritize
view consistency over disentanglement, resulting in limited semantic/attribute
control during generation. To address this limitation, we propose a conditional
GNeRF model incorporating specific attribute labels as input to enhance the
controllability and disentanglement abilities of 3D-aware generative models.
Our approach builds upon a pre-trained 3D-aware face model, and we introduce a
Training as Init and Optimizing for Tuning (TRIOT) method to train a
conditional normalized flow module to enable the facial attribute editing, then
optimize the latent vector to improve attribute-editing precision further. Our
extensive experiments demonstrate that our model produces high-quality edits
with superior view consistency while preserving non-target regions. Code is
available at https://github.com/zhangqianhui/TT-GNeRF.Comment: 13 page
Surfactant Induced Reservoir Wettability Alteration: Recent Theoretical and Experimental Advances in Enhanced Oil Recovery
Reservoir wettability plays an important role in various oil recovery processes. The origin and evolution of reservoir wettability were critically reviewed to better understand the complexity of wettability due to interactions in crude oil-brine-rock system, with introduction of different wetting states and their influence on fluid distribution in pore spaces. The effect of wettability on oil recovery of waterflooding was then summarized from past and recent research to emphasize the importance of wettability in oil displacement by brine. The mechanism of wettability alteration by different surfactants in both carbonate and sandstone reservoirs was analyzed, concerning their distinct surface chemistry, and different interaction patterns of surfactants with components on rock surface. Other concerns such as the combined effect of wettability alteration and interfacial tension (IFT) reduction on the imbibition process was also taken into account. Generally, surfactant induced wettability alteration for enhanced oil recovery is still in the stage of laboratory investigation. The successful application of this technique relies on a comprehensive survey of target reservoir conditions, and could be expected especially in low permeability fractured reservoirs and forced imbibition process
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