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Where Research Becomes Reality Through Strategic Application

FROM PHILOSOPHY TO ARTIFICIAL INTELLIGENCE

Scientific Research at the Intersection of Perception, Recognition, and Intelligent Computing

Every breakthrough begins with a question invisible to the eye. These works explore the boundary where philosophy, neuroscience, artificial intelligence, and computer vision converge into a unified language of perception. Beyond recognizing faces or bodies, they examine how machines learn identity, distinguish permanence from appearance, and transform observation into understanding. Together, they represent an ongoing pursuit to bridge human cognition with intelligent systems, revealing that perception is not merely the act of seeing, it is the architecture through which knowledge, recognition, and future infrastructure emerge.

Rousix Translucent Portals Inc.

Education, Experience & Research Excellence

Dr. Blake Austin Myers is Chief Technology Officer of Rousix Inc. and a multidisciplinary researcher whose work bridges philosophy, neuroscience, artificial intelligence, biometrics, and computer vision. He earned a Ph.D. in Philosophy with a minor in Neuroscience from the University of Wisconsin–Madison before conducting postdoctoral research at the University of Texas at Dallas, where he developed advanced deep learning architectures for long-range body and facial recognition under projects supported by IARPA and the Office of the Director of National Intelligence (ODNI). 


His research spans machine learning, computer vision, natural language processing, and cognitive science, resulting in peer-reviewed publications, international conference presentations, and collaborations with leading academic and government institutions. Dr. Myers has also mentored emerging researchers while advancing intelligent systems that transform perception, recognition, and decision-making into scalable real-world technologies.

Body Shape Recognition Through Deep Learning

  • The following research presents a deep learning approach for identifying people by body shape when faces are not visible. By combining image-based features with linguistic descriptions of body shape, the proposed method improves identification accuracy across long distances and challenging viewing conditions.

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Unconstrained Body Recognition At Altitude And Range

  • This paper compares four deep learning approaches for long-term person identification using body shape under real-world conditions, including long distances, aerial viewpoints, and clothing changes. The results demonstrate that body shape provides a robust biometric for identification in challenging unconstrained environments, with the proposed Swin-BIDDS Vision Transformer achieving the best overall performance.

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Understanding Deep Representations For Body Identification

  • This publication examines what deep learning models learn when identifying people by body shape. It analyzes the information encoded in body recognition networks, showing that they capture identity along with facial information, gender, viewpoint, and other image characteristics, while demonstrating that simple modifications to the learned embedding space can further improve identification accuracy without additional training.

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PERCEPTION BEYOND THE FACE

Advancing Body-Based Person Identification Using Images and Language

This conference presentation introduces a deep learning approach to person identification by body shape that combines image-based features with human-interpretable linguistic body descriptors. Using challenging real-world datasets encompassing long viewing distances, multiple viewpoints, and aerial imagery, the research demonstrates that integrating linguistic and visual representations improves identification performance beyond either approach alone.

Deep Learning - Artificial Intelligence - Body Recognition - Neural Networks - DLT

Deep Learning - Artificial Intelligence - Body Recognition - Neural Networks - DLT

Deep Learning - Artificial Intelligence - Body Recognition - Neural Networks - DLT

Deep Learning - Artificial Intelligence - Body Recognition - Neural Networks - DLT

Deep Learning - Artificial Intelligence - Body Recognition - Neural Networks - DLT

Deep Learning - Artificial Intelligence - Body Recognition - Neural Networks - DLT

The Impossible Architecture of Belief

  • This paper introduces a new paradox concerning belief and other representational states, demonstrating that certain propositions cannot be truly represented without generating contradiction. It argues that the paradox cannot be resolved by standard responses to the Liar Paradox and extends the underlying reasoning to a broad class of representational activities, including assertion, imagination, and hope.

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The Separation of Knowing and Believing

  • This publication challenges the traditional assumption that knowledge requires belief by presenting five cases in which individuals appear to know a proposition without believing it. Supported by philosophical analysis and empirical evidence, it proposes that knowledge and belief are distinct cognitive concepts, defining knowledge as a capacity and belief as a dispositional tendency.

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The Ventromedial Prefrontal Cortex Reconsidered

  • This review critically examines the role of the ventromedial prefrontal cortex (vmPFC) in mood and anxiety disorders, arguing that the prevailing model of vmPFC as a simple inhibitor of amygdala activity is incomplete. Drawing on evidence from animal studies, neuroimaging, and lesion research, it proposes a more nuanced functional organization in which distinct vmPFC subregions differentially contribute to positive and negative affect.

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The Acoustic Architecture of Human Emotion

  • This paper demonstrates that certain human speech sounds possess an inherent emotional quality independent of word meaning. Through behavioral experiments, it shows that specific acoustic patterns in phoneme formant transitions systematically influence whether nonsense words are perceived as positive or negative, challenging the traditional view that the relationship between speech sounds and meaning is entirely arbitrary.

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Graded Propositional Knowledge and Mental Effort

  • This paper presents three experiments examining whether propositional knowledge can meaningfully be described in comparative terms. It finds that statements claiming one person “knows better” than another become more acceptable as the perceived mental effort required to understand the proposition increases, suggesting that knowledge may admit of degrees based partly on how well a person grasps a proposition’s content.

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The Structure of Understanding-Why

  • The enclosed dissertation develops a unified account of knowledge and understanding by examining three dimensions of understanding-why. It argues that understanding-why is a more developed, quantitative form of knowing-why, introduces “causal guiding explanations” to explain empirical understanding, and analyzes how logical grounding contributes to understanding non-empirical truths.

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Perceptual Experience Beyond the Brain

  • This paper examines whether minimal supervenience can resolve the dispute between brain-centered and extended theories of perceptual experience. It argues that even if brain properties form a minimal supervenience base, environmental, historical, and brain–world interactive properties may form alternative minimal bases, so minimality alone cannot determine what constitutes perceptual experience.

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Knowledge In Degrees

  • The enclosed academic work challenges the traditional view that propositional knowledge is all-or-nothing and develops a formal account of knowledge as coming in degrees. Drawing on virtue epistemology, it proposes that a person’s degree of knowledge depends on the strength of their justification, how closely their actual confidence matches the confidence warranted by their evidence, and whether their belief is properly based on that evidence.

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The Architecture of Representational Limits

  • This paper develops a classical response to Myers’ paradox by arguing that the contradiction arises from an impossible global arrangement of representational states, not from a failure of classical logic. It introduces “self-excluding strengthenings” and argues that no possible distribution of truth-assessable attitudes can exhaust both sides of a contingent divide with such contents, revealing a general structural limit on representation. 

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The Convergence of Unified Intelligence and Commerce

The Evolution of Global Exchanges

The journeys of Rousix founders Adam Hamid and Dr. Blake Austin Myers followed remarkably different paths before converging around a shared vision for the future of digital infrastructure. As Chief Technology Officer, Dr. Myers brings a distinguished background in philosophy, neuroscience, artificial intelligence, and computer vision, including postdoctoral research at The University of Texas at Dallas focused on deep learning, body and facial recognition, and intelligent systems. As Founder and Chief Executive Officer, Adam Hamid has led the strategic development of Rousix's long-term vision, including the concepts behind Intermetanet, TexasSwap, Teacher's Partner, and Rousix's incentive-based K–12 education initiatives. 


Together, they are working to advance an integrated commerce and technology ecosystem that combines centralized coordination with decentralized technologies, supported by income-producing compute infrastructure engineered to power scalable applications for education, finance, artificial intelligence, and digital commerce.

Copyright © 2017 Rousix ICCF Inc. All rights reserved.

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