Effective Physical Processes and Active Information in Quantum Computing

The recent debate on hypercomputation has arisen new questions both on the computational abilities of quantum systems and the Church-Turing Thesis role in Physics. We propose here the idea of "effective physical process" as the essentially physical notion of computation. By using the Bohm and Hiley active information concept we analyze the differences between the standard form (quantum gates) and the non-standard one (adiabatic and morphogenetic) of Quantum Computing, and we point out how its Super-Turing potentialities derive from an incomputable information source in accordance with Bell's constraints. On condition that we give up the formal concept of "universality", the possibility to realize quantum oracles is reachable. In this way computation is led back to the logic of physical world.Comment: 10 pages; Added references for sections 2 and

Constructing Seifert surfaces from n-bridge link projections

This paper presents a new algorithm "A" for constructing Seifert surfaces from n-bridge projections of links. The algorithm produces minimal complexity surfaces for large classes of braids and alternating links. In addition, we consider a family of knots for which the canonical genus is strictly greater than the genus, (g_c(K) > g(K)), and show that A builds surfaces realizing the knot genus g(K). We also present a generalization of Seifert's algorithm which may be used to construct surfaces representing arbitrary relative second homology classes in a link complement.Comment: 19 pages, 15 figure

Logical openness in Cognitive Models

It is here proposed an analysis of symbolic and sub-symbolic models for studying cognitive processes, centered on emergence and logical openness notions. The Theory of logical openness connects the Physics of system/environment relationships to the system informational structure. In this theory, cognitive models can be ordered according to a hierarchy of complexity depending on their logical openness degree, and their descriptive limits are correlated to Gödel-Turing Theorems on formal systems. The symbolic models with low logical openness describe cognition by means of semantics which fix the system/environment relationship (cognition in vitro), while the sub-symbolic ones with high logical openness tends to seize its evolutive dynamics (cognition in vivo). An observer is defined as a system with high logical openness. In conclusion, the characteristic processes of intrinsic emergence typical of “bio-logic” - emerging of new codes-require an alternative model to Turing-computation, the natural or bio-morphic computation, whose essential features we are going here to outline

Radiation Monitoring with Diamond Sensors for the Belle-II Vertex Detector

The Belle II detector is currently under construction at the SuperKEKB electron-positron high-luminosity collider, that will provide an instantaneous luminosity 40 times higher than that of KEKB. There- fore the Belle-II VerteX Detector (VXD) will operate in a very harsh environment. A radiation monitoring and beam abort system is needed to safely operate the VXD detector in these conditions. This system is based on 20 single crystal CVD diamond sensors placed in 20 key positions in the vicinity of the VXD and interaction region. In this contribution we describe the system design and we present the procedures followed for the characterisation and calibration of the diamond sensors. We discuss also the performance of the prototype system during the first SuperKEKB commissioning phase in February-June 2016

Evolutionary Neural Gas (ENG): A Model of Self Organizing Network from Input Categorization

Despite their claimed biological plausibility, most self organizing networks have strict topological constraints and consequently they cannot take into account a wide range of external stimuli. Furthermore their evolution is conditioned by deterministic laws which often are not correlated with the structural parameters and the global status of the network, as it should happen in a real biological system. In nature the environmental inputs are noise affected and fuzzy. Which thing sets the problem to investigate the possibility of emergent behaviour in a not strictly constrained net and subjected to different inputs. It is here presented a new model of Evolutionary Neural Gas (ENG) with any topological constraints, trained by probabilistic laws depending on the local distortion errors and the network dimension. The network is considered as a population of nodes that coexist in an ecosystem sharing local and global resources. Those particular features allow the network to quickly adapt to the environment, according to its dimensions. The ENG model analysis shows that the net evolves as a scale-free graph, and justifies in a deeply physical sense- the term gas here used.Comment: 16 pages, 8 figure