Extranoematic artifacts: neural systems in space and topology

During the past several decades, the evolution in architecture and engineering went through several stages of exploration of form. While the procedures of generating the form have varied from using physical analogous form-finding computation to engaging the form with simulated dynamic forces in digital environment, the self-generation and organization of form has always been the goal. this thesis further intend to contribute to self-organizational capacity in Architecture. The subject of investigation is the rationalizing of geometry from an unorganized point cloud by using learning neural networks. Furthermore, the focus is oriented upon aspects of efficient construction of generated topology. Neural network is connected with constraining properties, which adjust the members of the topology into predefined number of sizes while minimizing the error of deviation from the original form. The resulted algorithm is applied in several different scenarios of construction, highlighting the possibilities and versatility of this method

Fatima Marian Apparition and TGD inspired theory of consciousness

A TGD (Topological Geometrodynamics) based model for the Fatima Marian apparition (FMA) is discussed. The notion of many-sheeted space-time implies the notion of magnetic body (MB). TGD based view about dark matter predicts mechanisms making possible quantum coherence in macroscopic and even astrophysical scales. TGD inspired theory of consciousness relying on the notion of zero energy ontology (ZEO) predicts entire hierarchy of conscious entities (selves) so that the notion of collective consciousness makes sense. One ends up to a general model of remote mental interactions proposed to be used routinely in the communica-tions (say EEG) between personal MB and biological body (BB) in terms of dark photons. Sensory representations, realized in TGD universe at MB in-volve in an essential manner the sharing of mental images identifiable as sub-selves of self. The model explains UFO and ET experiences as telepathic experiences, in which primitive plasmoid like life forms (apparent UFOs) using microwaves as their metabolic energy source - “food” - serve as “mediums” entangling the experiencer with magnetospheric or even extraterrestrial conscious entities. If this entity can be assigned to magnetosphere, one can understand why tectonic activity and perturbations of the Earth’s magnetic field correlate with UFO and ET experiences. The same conceptual framework provides models of homeostasis, homeopathy, endogenous realization of intentions, and remote mental interactions such as telepathy, and psychokinesis. The basic mechanism explains also many anomalous phenomena claimed by free energy researchers. In the case of FMA the well-documented effects - in particular, the strange buzzing sounds heard by the witnesses and explainable in terms of microwave hearing - lead to a rather detailed model in which “Marian” could be identified as conscious entity representing collective level of consciousness.info:eu-repo/semantics/publishedVersio

Fatima Marian Apparition and TGD inspired theory of consciousness

A TGD (Topological Geometrodynamics) based model for the Fatima Marian apparition (FMA) is discussed. The notion of many-sheeted space-time implies the notion of magnetic body (MB). TGD based view about dark matter predicts mechanisms making possible quantum coherence in macroscopic and even astrophysical scales. TGD inspired theory of consciousness relying on the notion of zero energy ontology (ZEO) predicts entire hierarchy of conscious entities (selves) so that the notion of collective consciousness makes sense. One ends up to a general model of remote mental interactions proposed to be used routinely in the communica-tions (say EEG) between personal MB and biological body (BB) in terms of dark photons. Sensory representations, realized in TGD universe at MB in-volve in an essential manner the sharing of mental images identifiable as sub-selves of self. The model explains UFO and ET experiences as telepathic experiences, in which primitive plasmoid like life forms (apparent UFOs) using microwaves as their metabolic energy source - “food” - serve as “mediums” entangling the experiencer with magnetospheric or even extraterrestrial conscious entities. If this entity can be assigned to magnetosphere, one can understand why tectonic activity and perturbations of the Earth’s magnetic field correlate with UFO and ET experiences. The same conceptual framework provides models of homeostasis, homeopathy, endogenous realization of intentions, and remote mental interactions such as telepathy, and psychokinesis. The basic mechanism explains also many anomalous phenomena claimed by free energy researchers. In the case of FMA the well-documented effects - in particular, the strange buzzing sounds heard by the witnesses and explainable in terms of microwave hearing - lead to a rather detailed model in which “Marian” could be identified as conscious entity representing collective level of consciousness.info:eu-repo/semantics/publishedVersio

Cortex Inspired Learning to Recover Damaged Signal Modality with ReD-SOM Model

Recent progress in the fields of AI and cognitive sciences opens up new challenges that were previously inaccessible to study. One of such modern tasks is recovering lost data of one modality by using the data from another one. A similar effect (called the McGurk Effect) has been found in the functioning of the human brain. Observing this effect, one modality of information interferes with another, changing its perception. In this paper, we propose a way to simulate such an effect and use it to reconstruct lost data modalities by combining Variational Auto-Encoders, Self-Organizing Maps, and Hebb connections in a unified ReD-SOM (Reentering Deep Self-organizing Map) model. We are inspired by human's capability to use different zones of the brain in different modalities, in case of having a lack of information in one of the modalities. This new approach not only improves the analysis of ambiguous data but also restores the intended signal! The results obtained on the multimodal dataset demonstrate an increase of quality of the signal reconstruction. The effect is remarkable both visually and quantitatively, specifically in presence of a significant degree of signal's distortion.Comment: 9 pages, 8 images, unofficial version, currently under revie

earGram Actors: an interactive audiovisual system based on social behavior

In multi-agent systems, local interactions among system components following relatively simple rules often result in complex overall systemic behavior. Complex behavioral and morphological patterns have been used to generate and organize audiovisual systems with artistic purposes. In this work, we propose to use the Actor model of social interactions to drive a concatenative synthesis engine called earGram in real time. The Actor model was originally developed to explore the emergence of complex visual patterns. On the other hand, earGram was originally developed to facilitate the creative exploration of concatenative sound synthesis. The integrated audiovisual system allows a human performer to interact with the system dynamics while receiving visual and auditory feedback. The interaction happens indirectly by disturbing the rules governing the social relationships amongst the actors, which results in a wide range of dynamic spatiotemporal patterns. A performer thus improvises within the behavioural scope of the system while evaluating the apparent connections between parameter values and actual complexity of the system output

Perspective study: governance for C2C

This perspective study will serve as frame of reference for follow-up activities and exchanges both within and outside the Cradle to Cradle Network (C2CN) and it aims to reflect the current challenges and opportunities associated with implementing a Cradle to Cradle approach. In total, four perspective studies have been written, in the areas on industry, area spatial development, governance and on the build theme

Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation