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Fundamental functional differences between gyri and sulci: implications for brain function, cognition, and behavior

Psychoradiology 2021年第1期1卷 23-41页

作者： Xi Jiang Tuo Zhang Shu Zhang Keith MKendrick Tianming Liu School of Life Science and Technology MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengdu 611731China School of Automation Northwestern Polytechnical UniversityXi’an 710129China Center for Brain and Brain-Inspired Computing Research School of Computer ScienceNorthwestern Polytechnical UniversityXi’an 710129China Cortical Architecture Imaging and Discovery Laboratory Department of Computer Science and Bioimaging Research CenterThe University of GeorgiaAthensGA 30605USA

Folding of the cerebral cortex is a prominent characteristic of mammalian *** or deficits in cortical folding are strongly correlated with abnormal brain function,cognition,and ***,a precise mapping between the anatomy and function of the brain is critical to our understanding of the mechanisms of brain structural architecture in both health and *** and sulci,the standard nomenclature for cortical anatomy,serve as building blocks to make up complex folding patterns,providing a window to decipher cortical anatomy and its relation with brain *** efforts have been devoted to this research topic from a variety of disciplines including genetics,cell biology,anatomy,neuroimaging,and neurology,as well as involving computational approaches based on machine learning and artificial intelligence ***,despite increasing progress,our understanding of the functional anatomy of gyro-sulcal patterns is still in its *** this review,we present the current state of this field and provide our perspectives of the methodologies and conclusions concerning functional differentiation between gyri and sulci,as well as the supporting information from genetic,cell biology,and brain structure *** particular,we will further present a proposed framework for attempting to interpret the dynamic mechanisms of the functional interplay between gyri and ***,this review will provide a comprehensive summary of anatomo-functional relationships in the cortical gyro-sulcal system together with a consideration of how these contribute to brain function,cognition,and behavior,as well as to mental disorders.

关键词： cortical folding gyro-sulcal pattern brain anatomo-function

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Optimal Design of Heliostat Field Using Random Search Algorithm 6

Optimal Design of Heliostat Field Using Random Search Algori...

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6th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Mou, Chenglin Wu, Kaiqi Du, Pengcheng Yang, Kaibo School of Civil Engineering and Architecture Henan University of Science and Technology Department of Engineering Mechanics Luoyang471099 China School of Information Engineering Henan University of Science and Technology Department of Computer Science and Technology Luoyang471099 China School of Information Engineering Henan University of Science and Technology Department of Automation Luoyang471099 China School of International Education Henan University of Science and Technology Department of Civil Engineering Luoyang471099 China

ISBN: (纸本)9798350374315

Tower solar thermal power generation is a new type of low-carbon and environmentally friendly clean energy technology. In this paper, a single-objective optimization model is established with the maximum annual average thermal output per unit mirror area of the heliostat field as the objective function. By optimizing the heliostat width, heliostat height and heliostat coordinate, the annual average output thermal power can be maximized while meeting the rated power. The heliostat field is distributed in a circular shape. As the seasons change, the light intensity received by the heliostat field is uneven, so the optical efficiency of the heliostats is also different. We simplify the heliostat field into S area (area with higher optical efficiency) and W area (area with lower optical efficiency) based on the optical efficiency distribution of heliostats. At different locations, According to the heliostat width, heliostat height and heliostat coordinate generated by random functions, substituting these parameters into the efficiency model in this paper, and the random search algorithm is used to traverse the maximum annual output thermal power and the corresponding heliostat parameters. In the shadow occlusion loss model, we have grid to set up the heliostat and use the coordinate conversion to solve the shadow occlusion loss efficiency. © 2024 IEEE.

关键词： Heliostats (instruments)

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Data hiding algorithm for hevc using intra-coded frames 50

Data hiding algorithm for hevc using intra-coded frames

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50th Summer computer Simulation Conference, SCSC 2018, Part of the 2018 Summer Simulation Multi-Conference, SummerSim 2018

作者： Rodríguez, David Botella, Guillermo Del Barrio, Alberto A. Cuesta, David Computer Science Faculty Universidad Complutense de Madrid Prof. José García Santesmases 9 Madrid Spain La Paz University Hospital Spain Department of Computer Architecture and Automation Universidad Complutense de Madrid Prof. José García Santesmases 9 Madrid Spain Technical Direction Admr. Infraestructuras Ferroviarias Sor Ángela de la Cruz 3 Madrid Spain

ISBN: (纸本)9781510860209

This paper proposes a method to hide information in a video and to recover it after having been encoded under the standard H.265/HEVC. The process is based on manipulating the luminance properties of the YUV video sequence, without the need to modify the HEVC standard. Results show that the quality loss due to embedding the message is negligible in comparison with the original video. Besides, the proposed recovery method is able to extract all the information from the encoded video. © 2018 Society for Modeling & Simulation International (SCS).

关键词： Steganography

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Latent source mining in FMRI data via deep neural network

Latent source mining in FMRI data via deep neural network

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IEEE International Symposium on Biomedical Imaging

作者： Heng Huang Xintao Hu Junwei Han Jinglei Lv Nian Liu Lei Guo Tianming Liu School of Automation Northwestern Polytechnical University Xi'an China Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center The University of Georgia US

ISBN: (纸本)9781479923519

Independent component analysis (ICA) and its variants have been the dominant methods to the problem of blind source separation (BSS) for functional magnetic resonance imaging (fMRI) data. However, the functional interactions among spatially distributed brain regions and concurrent brain networks deteriorate the basic assumption in ICA-based BSS, that is, the spatial independence of the sources. In this paper, we proposed a novel method for BSS based on recently advanced deep neural network (DNN) algorithm, aiming to detect both internal and functional interaction-induced latent sources simultaneously. We used the motor task fMRI data in the Human Connectome Project (HCP) as a test-bed in the experiments. The results demonstrated the feasibility and effectiveness of the proposed method and its outperformance compared with ICA.

关键词： Training Correlation Data mining Biological neural networks Blind source separation Visualization Signal reconstruction

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Species-Shared and -Specific Structural Connections Revealed by Dirty Multi-task Regression 23rd

Species-Shared and -Specific Structural Connections Revealed...

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Zhang, Tuo He, Zhibin Jiang, Xi Guo, Lei Hu, Xiaoping Liu, Tianming Du, Lei School of Automation Northwestern Polytechnical University Xian China School of Life Science and Technology MOE Key Lab for Neuroinformation University of Electronic Science and Technology of China Chengdu China Department of Bioengineering UC Riverside RiversideCA United States Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center The University of Georgia AthensGA United States

ISBN: (纸本)9783030597276

Comparative studies across species, such as primates in this work, reveal the shared structural or functional patterns that might be inherited from the common ancestors and the specific ones that might be related to individualized evolution strategies. Both the shared or specific patterns could help promote the understanding of mechanisms of brain structural and functional architectures and brain dynamics. Many previous studies can be found to report the comparative results on species pairs. However, very few studies were found to perform a comparison of large-scale connectomes across multiple species via a data-driven method. To this end, we construct brain connectomes for three primates, macaque, chimpanzee and human, by using Brodmann areas as graphic nodes and diffusion MRI derived white matter fibers to define edges and edge weights. On these connectomes, a novel dirty multi-task regression method is developed in the attempt to automatically identified the species-shared and -specific connections. The concordance of the findings via our method and previous reports demonstrate the effectiveness and the promise of this framework. © 2020, Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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Adiabatic training for Variational Quantum Algorithms

arXiv

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arXiv 2024年

作者： Acosta, Ernesto Gutiérrez, Carlos Cano Botella, Guillermo Campos, Roberto Dpt. Computer Science and AI University of Granada Granada18071 Spain Computer Architecture and Automation Department Complutense University of Madrid Madrid28040 Spain Theoretical Physics Department Complutense University of Madrid Madrid28040 Spain

This paper presents a new hybrid Quantum Machine Learning (QML) model composed of three elements: a classical computer in charge of the data preparation and interpretation;a Gate-based Quantum computer running the Variational Quantum Algorithm (VQA) representing the Quantum Neural Network (QNN);and an adiabatic Quantum computer where the optimization function is executed to find the best parameters for the VQA. As of the moment of this writing, the majority of QNNs are being trained using gradient-based classical optimizers having to deal with the barren-plateau effect [5]. Some gradient-free classical approaches such as Evolutionary Algorithms have also been proposed to overcome this effect [4]. However, to the knowledge of the authors, adiabatic quantum models have not been used to train VQAs. The paper compares the results of gradient-based classical algorithms against adiabatic optimizers showing the feasibility of integration for gate-based and adiabatic quantum computing models, opening the door to modern hybrid QML approaches for High Performance Computing. © 2024, CC BY-NC-SA.

关键词： Quantum computers

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Quasi-Z-Source Cascaded H-Bridge Multilevel Inverters for PV Applications with Fault

Quasi-Z-Source Cascaded H-Bridge Multilevel Inverters for PV...

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International Conference on Environment and Electrical Engineering (EEEIC)

作者： David Carrasco-González Pablo Horrillo-Quintero Pablo García-Triviño Raúl Sarrias-Mena Carlos Andrés García-Vázquez Luis M. Fernández-Ramirez Department of Electrical Engineering SURET Research Group University of Cadiz Algeciras Spain Department of Engineering in Automation Electronics and Computer Architecture and Networks SURET Research Group University of Cadiz Algeciras Spain

Nowadays, the use of quasi-Z-source cascaded H-bridge multilevel inverters (qZS-CHBMLI) in photovoltaic (PV) has gained their attention. They permit the single-stage power transformation of a PV generator adjusting the DC voltage independently in each module to operate each PV generator at maximum power. This paper develops a new control strategy that allows the system to continue operating after a fault in a module, by increasing the DC voltage in the operating modules. The control strategy is employed to a grid-connected qZS-CHBMLI, which consists of three cascaded qZSI powered by independent 4.8 kW PV cells. Under several operating circumstances, the proposed control system is evaluated. The results demonstrate how the control strategy applied to the system successfully reacts to the failure of a module.

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Distributed Control for a Microgrid Cluster: Implementation and Experimental Validation

Distributed Control for a Microgrid Cluster: Implementation ...

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International Conference on Environment and Electrical Engineering (EEEIC)

作者： David Carrasco-González Raúl Sarrias-Mena Pablo Horrillo-Quintero Ehsan Hosseini Francisco Llorens-Iborra Luis M. Fernández-Ramírez SURET Research Group Department of Electrical Engineering University of Cadiz Algeciras Spain SURET Research Group Department of Engineering in Automation Electronics and Computer Architecture and Networks University of Cadiz Algeciras Spain

ISBN: (数字)9798350355185

ISBN: (纸本)9798350355192

In response to growing energy demands and concerns about climate change, microgrid clusters (MGCs) are gaining widespread attention. Their ability to integrate technologies for energy consumption, generation, and storage in AC, DC or a compound of both offer a high degree of flexibility and resiliency. This research introduces a new control strategy for an MGC composed of two MGs interconnected to a local grid via a point of common coupling. The first one is a DC MG comprising an ultracapacitor (UC), wind turbine (WT), DC loads and hydrogen system. The second is an AC MG with a battery bank, AC loads and photovoltaic (PV) generator. The control strategy employs local controllers for each device and distributed control via two control agents, coordinating the power distribution among the energy storage systems (ESSs) of the MGC. This system is tested and validated on a real-time experimental setup, using two Raspberry Pi microcontrollers and an OPAL-RT unit, under different working conditions. The results obtained in this work show that the control strategy implemented in the MGC works correctly.

关键词： Energy consumption Microcontrollers Wind speed Decentralized control Hydrogen Microgrids Supercapacitors Real-time systems Wind turbines Solar radiation

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Particle swarm optimisation of memory usage in embedded systems

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International Journal of High Performance Systems architecture 2008年第4期1卷 209-219页

作者： Risco-Martín, Jose L. Garnica, Oscar Lanchares, Juan Hidalgo, J. Ignacio Atienza, David Department of Computer Architecture and Automation Facultad de Informática Universidad Complutense de Madrid C/Prof. José García Santesmases s/n 28040 Madrid Spain 1015 Lausanne Switzerland

In this paper, we propose a dynamic, non-dominated sorting, multi-objective particle-swarm-based optimiser, named hierarchical non-dominated sorting particle swarm optimiser (H-NSPSO), for memory usage optimisation in embedded systems. It significantly reduces the computational complexity of others multi- objective particle swarm optimisation (MOPSO) algorithms. Concretely, it first uses a fast non-dominated sorting approach with O(mN2) computational complexity. Second, it maintains an external archive to store a fixed number of non-dominated particles, which is used to drive the particle population towards the best non-dominated set over many iteration steps. Finally, the proposed algorithm separates particles into multi sub-swarms, building several tree networks as the neighbourhood topology. H-NSPSO has been made adaptive in nature by allowing its vital parameters (inertia weight and learning factors) to change within iterations. The method is evaluated using two real world examples in embedded applications and compared with existing covering methods. Copyright © 2008, Inderscience Publishers.

关键词： Multiobjective optimization

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Discover mouse gene coexpression landscape using dictionary learning and sparse coding 1

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1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2016

作者： Li, Yujie Chen, Hanbo Jiang, Xi Li, Xiang Lv, Jinglei Peng, Hanchuan Tsien, Joe Z. Liu, Tianming Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center The University of Georgia AthensGA United States School of Automation Northwestern Polytechnical University Xi’an China Allen Institute for Brain Science SeattleWA United States Brain and Behavior Discovery Institute Medical College of Georgia at Augusta University AugustaGA United States

ISBN: (数字)9783319467207

ISBN: (纸本)9783319467191

Gene coexpression patterns carry rich information of complex brain structures and functions. Characterization of these patterns in an unbiased and integrated manner will illuminate the higher order transcriptome organization and offer molecular foundations of functional circuitry. Here we demonstrate a data-driven method that can effectively extract coexpression networks from transcriptome profiles using the Allen Mouse Brain Atlas dataset. For each of the obtained networks,both genetic compositions and spatial distributions in brain volume are learned. A simultaneous knowledge of precise spatial distributions of specific gene as well as the networks the gene plays in and the weights it carries can bring insights into the molecular mechanism of brain formation and functions. Gene ontologies and the comparisons with published data reveal interesting functions of the identified coexpression networks,including major cell types,biological functions,brain regions,and/or brain diseases. © Springer International Publishing AG 2016.

关键词： Genes

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