1,721,143 research outputs found
MuReQua Chain: Multiscale Relativistic Quantum Blockchain
No full textIn this paper, we introduce a new approach to fix the validation of a block and the assignment of a new block in a blockchain infrastructure by using a novel negotiation procedure. The block validation and assignment are reached thanks to negotiation procedures based on an extended probability environment. Also, by using a multiscale approach (typical of Complexity Theory) and Quantum and Relativistic Mechanics, the result appears to solve some of the most relevant questions in the Blockchain context, which are the democracy and the randomness of the validator of a block and the assignment of the new one. The selection of actors to mine is invariant concerning the number of addresses, i.e., the coins of owners, which have more chance to be selected generally. This work is the companion of CQKD (Computational Quantum Key Distribution), as we will see in the introduction, where we considered the infrastructural question of the key distribution; also, it is a very effective application of the decision and reasoning in incompleteness or uncertainty conditions as described in the previous and prodromic paper as described in the introduction too
Numerical modelling of two-phase piezocomposites with interface mechanical anisotropic effects
No full textDecision Support System Driven by Thermo-Complexity: Scenario Analysis and Data Visualization
No full textThe present modelling aims to construct a computational information representation system useful for decision support system (DSS) solutions in the realization of intelligent systems or complex systems analysis solutions. Starting from an n-dimensional space (with n ≥ 7) represented by problem variables (referred to as CSF—Critical Success Factors), a dimensional embedding procedure is used to transition to a two-dimensional space. In the two-dimensional space, thanks to new lattice motion algorithms, the decision support system can determine the optimal solution with a lower computational cost based on the decision-maker’s preferences. Finally, thanks to an algorithm that takes into account the hierarchical order of importance of the seven CSFs as per the expert’s liking or according to his optimization logics, a return is made to the n-dimensional space and the final solution in the original space. As we will see, the starting and ending states in the n-dimensional space (referred to as micro-states) when projected into the two-dimensional space generate states (referred to as macro-states) which are degenerate. In other words, the correspondence between micro-states and macro-states is not one-to-one, as multiple micro-states correspond to one macro-state. Therefore, in relation to the decision-maker’s preferences, it will be the responsibility of the decision support system to provide the decision-maker with the micro-state of interest in the n-dimensional space (dimensional emergence procedure), starting from the obtained optimal macro-state. This result can be achieved starting from a flat chain of sensors capable of measuring/emulating certain specific parameters of interest. As we will see, it emerges that by considering random–exhaustive rolling value paths in order to track and potentially intervene to rebalance a dynamic system representing the state of stress/sensing of a system of interest, we are using the most general and, therefore, complex hypotheses of ergodic theory. In this work, we will focus on the representation of information in n-dimensional and two-dimensional spaces, as well as construct evaluation scenarios. We will also show the results of the decision support system in some cases of specific interest, thanks to a specific lattice motion algorithm of the realized decision-making environment
Keplerian motion on a carousel: a research teaching project
No full textItalian ministerial programs have introduced the teaching of modern physics in the last year of high school (students of 18 years). The textbooks also introduce, obviously in an elementary way, some current problems as, for example, the mysterious galactic rotation curves. It is explained that the cause of their non-Keplerian motion is due to the presence of a non-luminous form of matter and, for this reason, called Dark Matter. To stimulate the curiosity of students who have an aptitude for research, it might be useful to observe that "dark particles" are not provided by the standard model of particle physics and most cosmologists believe in their existence only because they very well explain a series of experimental observations. For this reason, in our opinion, it is interesting to highlight that, until a dark particle is found, it is not wrong to look for an alternative explanation of the experimental data. For example, to simulate the strange behavior of galaxies rotation using elementary and well-known concepts, we have built with students, during a series of lectures in the classroom, a Galilean composition between a Keplerian motion and a very slow circular motion
Coordinate velocity and desynchronization of clocks
No full textIn this letter, starting from recent experiments about the circular motion of a rotor with an absorber of photons emitted by a Mossbauer source, we want to underline some mathematical aspects in General Relativity framework. We do not want to discuss in detail the different physical interpretations of the experimental results proposed during the recent years and we do not want to propose a new one. Indeed, starting from a paper awarded to Gravity Research Competition 2018, the aim of our work is to analyze three different types of time involved in this experiment linking a term introduced in the above mentioned paper, to the difference between coordinate and physical velocity of light. (C) 2019 Elsevier Inc. All rights reserved
Probing the nature of compact dark object at the Galactic Center by gravitational lensing
No full textCognitiveNet: Enriching Foundation Models with Emotions and Awareness
No full textFoundation models are gaining considerable interest for their capacity of solving many downstream tasks without fine-tuning parameters on specific datasets. The same solutions can connect visual and linguistic representations through image-text contrastive learning. These abilities allow an artificial agent to act similarly to a human, but significant cognitive processes still need to be introduced in the learning process. The present study proposes an advancement to more human-like artificial intelligence by introducing CognitiveNet, a learnable architecture integrating foundation models. Starting from the latest studies in the field of Artificial Consciousness, a hierarchy of cognitive layers has been modeled and pre-trained for estimating the emotional content of images. By employing CLIP as the backbone model, significant concordant emotional activity was produced. Furthermore, the proposed model overcomes the accuracy of CLIP in classifying CIFAR-10 and -100 datasets through supervised optimization, suggesting CognitiveNet as a promising solution for solving classification tasks through online meta-learning
- …
