Computational Proximity

Peters, James F.

doi:10.1007/978-3-319-30262-1_1

Cited by 5 publications

(11 citation statements)

References 37 publications

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“…A topological view of this synergy between the brain and the environmental energy cycles exhibits strong proximity both spatially and descriptively. The spatial strong proximity [120] of the brain and environment is in the form of temporally overlapping cerebral energy and environmental energy readings ( e.g ., the occurrence energy highs and lows of the one set of readings overlaps with the occurrence similar readings of highs and lows in the other during the same timeframe). For example, let A and B represent nonempty sets of recordings of brain and environmental energy recordings that are temporally concomitant in situ .…”

Section: Thermodynamic Considerations Of Brain Activitiesmentioning

confidence: 99%

The thermodynamics of cognition: A mathematical treatment

Deli¹,

Peters

Kisvárday

2021

Computational and Structural Biotechnology Journal

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Graphical abstract The thermodynamics consequences of intelligent computation. The neural system uses substantial resources to maintain the resting state; therefore, the Carnot cycle can model the evoked cycle. The synaptic complexity during neural computation changes in parallel with the ability to respond to future challenges. Positive emotions (top) trigger the reversed Carnot cycle, which accumulates energy and entropy (the entropy at the end of the cycle is A′ where S (A′) > S (A)). Negative emotions initiate an exothermic Carnot cycle, which accumulates information and wastes energy (entropy at the end of the cycle is A′ and S (A′) < S (A)).

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Section: Thermodynamic Considerations Of Brain Activitiesmentioning

confidence: 99%

The thermodynamics of cognition: A mathematical treatment

Deli¹,

Peters

Kisvárday

2021

Computational and Structural Biotechnology Journal

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“…The analog of this mapping process is the drawing together separate antipodal free particles whose entangled states are correlated in a quantum mechanics view of BUT. This correlation of entangled states can be explained mathematically in terms of the descriptive proximity of a pair of antipodal particles [30,31]. In other words, thanks to a pair of companionable feature vectors that describe antipodal particles, the particles have a nonempty descriptive intersection [13, 14 [32].…”

Section: Topological Considerationsmentioning

confidence: 99%

A Topological Approach to Shrinking Higher Dimensions of Space to Observable Space-Time: Can the Dimensional Anisotropy of Space Satisfy Mach’s Principle?

Deli¹,

Peters²

2021

Preprint

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We create a model universe by equipping a topological surface (system) with compact dimensions insulated by an information blocking horizon. The insulated compact WF can produce entanglement independent of distance. Interaction between the system and the WF changes the curvature of the first and the quantum state (frequency) of the second in an interconnected relationship. Thus, the field curvature measures the evolution of the particle WF as time. Positive field curvature creates pressure, whereas negative field curvature generates a vacuum, satisfying the Borsuk-Ulam Theorem and the Page and Wootters mechanism of static time. The accumulation of pressure or vacuum generates poles with contrasting dimensionalities, two-dimensional black hole horizons (time infinite), and four-dimensional cosmic voids (time zero). The orthogonality of the field and the compact WF give rise to global self-regulation that fine-tunes the cosmic parameters and can promote fractal topology. The four-dimensional vacuum in cosmic voids can produce an accelerating expansion without dark energy. When gravity effects are eliminated, we find a new, so far unexplored, order-increasing side of entropy. The verifiable and elegant hypothesis satisfies Mach's principle.

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“…Descriptive topological spaces are a significant portion of the proposed visual search system, and, since the human FOV is simulated with digital images, this section introduces a descriptive topological framework defined in the context of digital images [6]. Recently, much work has been reported regarding descriptive topological spaces that are defined with respect to the descriptive intersection and union of open sets [5,6,39,7,40]. Keeping this in mind, a topology is defined as follows.…”

Section: Descriptive Topologiesmentioning

confidence: 99%

“…Keeping this in mind, a topology is defined as follows. [40]. For a given set, X, a topology, τ , on X is a family of subsets of X (called open sets) such that:…”

Section: Descriptive Topologiesmentioning

confidence: 99%

Descriptive Topological Spaces for Performing Visual Search

Henry

2019

Transactions on Rough Sets XXI

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This article presents an approach to performing the task of visual search in the context of descriptive topological spaces. The presented algorithm forms the basis of a descriptive visual search system (DVSS) that is based on the guided search model (GSM) that is motivated by human visual search. This model, in turn, consists of the bottom-up and top-down attention models and is implemented within the DVSS in three distinct stages. First, the bottom-up activation process is used to generate saliency maps and to identify salient objects. Second, perceptual objects, defined in the context of descriptive topological spaces, are identified and associated with feature vectors obtained from a VGG deep learning convolutional neural network. Lastly, the topdown activation process makes decisions on whether the object of interest is present in a given image through the use of descriptive patterns within the context of a descriptive topological space. The presented approach is tested with images from the ImageNet ILSVRC2012 and SIMPLIcity datasets. The contribution of this article is a descriptive pattern-based visual search algorithm.

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Computational Proximity

Cited by 5 publications

References 37 publications

The thermodynamics of cognition: A mathematical treatment

The thermodynamics of cognition: A mathematical treatment

A Topological Approach to Shrinking Higher Dimensions of Space to Observable Space-Time: Can the Dimensional Anisotropy of Space Satisfy Mach’s Principle?

Descriptive Topological Spaces for Performing Visual Search

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