Fast Sequences of Non-spatial State Representations in Humans

AbstractAs a new multiplexing dimension, spatial modes are catching increasing attentions nowadays. It is a fundamental task to establish an appropriate theoretical model to describe these spatial modes, especially higher-order spatial modes. However, existing theoretical models are only able to explain some special higher-order spatial states in fiber. The basic problem in these models is that their discussed dimensions are not enough. Indeed, to describe a higher-order spatial state, at least four dimensions are needed. In this paper, we present an expanded Jones complex space model, which is four-dimensional when a single higher-order state is discussed. The expanded Jones model is based on the discussion of an arbitrary combination of four degenerated higher-order modes. As a result, arbitrary spatial states are described. Because the number of used dimensions matches that of the problem, the descriptions of higher-order modes are more complete than other models. Also, we have verified the reliability of the expanded Jones model in our experiment. This model has the potential to simplify many analyses related to spatial modes in fiber.

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Riemannian Spatio-Temporal Features of Locomotion for Individual Recognition

Sensors ◽

10.3390/s19010056 ◽

2018 ◽

Vol 19 (1) ◽

pp. 56 ◽

Cited By ~ 1

Author(s):

Jianhai Zhang ◽

Zhiyong Feng ◽

Yong Su ◽

Meng Xing ◽

Wanli Xue

Keyword(s):

Riemannian Manifold ◽

Individual Recognition ◽

Metric Learning ◽

Influence Factors ◽

Feature Representation ◽

Geometric Feature ◽

Major Influence ◽

Temporal Features ◽

Spatio Temporal ◽

Spatial State

Individual recognition based on skeletal sequence is a challenging computer vision task with multiple important applications, such as public security, human–computer interaction, and surveillance. However, much of the existing work usually fails to provide any explicit quantitative differences between different individuals. In this paper, we propose a novel 3D spatio-temporal geometric feature representation of locomotion on Riemannian manifold, which explicitly reveals the intrinsic differences between individuals. To this end, we construct mean sequence by aligning related motion sequences on the Riemannian manifold. The differences in respect to this mean sequence are modeled as spatial state descriptors. Subsequently, a temporal hierarchy of covariance are imposed on the state descriptors, making it a higher-order statistical spatio-temporal feature representation, showing unique biometric characteristics for individuals. Finally, we introduce a kernel metric learning method to improve the classification accuracy. We evaluated our method on two public databases: the CMU Mocap database and the UPCV Gait database. Furthermore, we also constructed a new database for evaluating running and analyzing two major influence factors of walking. As a result, the proposed approach achieves promising results in all experiments.

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Modified Mohr-Coulomb Theory for Triaxial State of Stress

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.809-810.658 ◽

2015 ◽

Vol 809-810 ◽

pp. 658-663 ◽

Cited By ~ 2

Author(s):

Paul Doru Barsanescu ◽

Ana Maria Comanici ◽

Liviu Andrusca ◽

Adriana Savin

Keyword(s):

Major Drawback ◽

Plane State ◽

State Of Stress ◽

Failure Theory ◽

Spatial State

The Mohr-Coulomb failure theory presents the major drawback that can be applied only for plane state of stress. In this paper, we propose a modification of the Mohr-Coulomb theory that might eliminate this disadvantage. It is proposed to replace the three Mohr's circles for spatial state of stress with one “virtual circle” with diameter σ1-σ3±2ρ, concentric with the circle that has the diameter σ1-σ3. The triaxial state of stress and biaxial state of stress are considered to be equivalent dangerous.

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Analysis of spatial state of multistory building with ceiling without beams

EPJ Web of Conferences ◽

10.1051/epjconf/201922101049 ◽

2019 ◽

Vol 221 ◽

pp. 01049

Author(s):

Sergey Turovsky ◽

Oleg Pakhmurin

Keyword(s):

Internal Stress ◽

Wind Loads ◽

Existing Building ◽

Computer Complex ◽

Frame Building ◽

Multistory Building ◽

Spatial State

In this paper, we discuss a spatial work of construction structures under an influence of pulsating wind loads. There are examples of deformations those arise from incorrect arrangement of rigid structures. We consider the forms of oscillation of a frame building and show the result of their incorrect sequence. There are calculations in software and computer complex and their results are shown in the form of isofields of internal stress of enclosing structures. The results of calculations are compared with real deformations of structures. The work of a structure is considered on the example of an existing building.

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FAST sequences optimization for contrast media pharmacokinetic quantification in tissue

Journal of Magnetic Resonance Imaging ◽

10.1002/jmri.10009 ◽

2001 ◽

Vol 14 (6) ◽

pp. 771-778 ◽

Cited By ~ 15

Author(s):

Marko K. Ivancevic ◽

Ivan Zimine ◽

François Lazeyras ◽

David Foxall ◽

Jean-Paul Vallée

Keyword(s):

Contrast Media ◽

Fast Sequences

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Fragmented Spatial Maps: State Abstraction and Efficient Planning from Surprisal

10.1101/2021.10.29.466499 ◽

2021 ◽

Author(s):

Mirko Klukas ◽

Sugandha Sharma ◽

Yilun Du ◽

Tomas Lozano-Perez ◽

Leslie Pack Kaelbling ◽

...

Keyword(s):

Spatial Clustering ◽

Place Cells ◽

Complex Environments ◽

Chinese Restaurant Process ◽

Chinese Restaurant ◽

Electrophysiological Recordings ◽

Efficient Planning ◽

State Abstraction ◽

Spatial State

When animals explore spatial environments, their representations often fragment into multiple maps. What determines these map fragmentations, and can we predict where they will occur with simple principles? We pose the problem of fragmentation of an environment as one of (online) spatial clustering. Taking inspiration from the notion of a "contiguous region" in robotics, we develop a theory in which fragmentation decisions are driven by surprisal. When this criterion is implemented with boundary, grid, and place cells in various environments, it produces map fragmentations from the first exploration of each space. Augmented with a long-term spatial memory and a rule similar to the distance-dependent Chinese Restaurant Process for selecting among relevant memories, the theory predicts the reuse of map fragments in environments with repeating substructures. Our model provides a simple rule for generating spatial state abstractions and predicts map fragmentations observed in electrophysiological recordings. It further predicts that there should be "fragmentation decision" or "fracture" cells, which in multicompartment environments could be called "doorway" cells. Finally, we show that the resulting abstractions can lead to large (orders of magnitude) improvements in the ability to plan and navigate through complex environments.

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