Nucleon and hadron structure changes in the nuclear medium and impact on observables

We review the effect of hadron structure changes in a nuclear medium using the quark-meson coupling (QMC) model, which is based on a mean field description of non-overlapping nucleon (or baryon) bags bound by the self-consistent exchange of scalar and vector mesons. This approach leads to simple scaling relations for the changes of hadron masses in a nuclear medium. It can also be extended to describe finite nuclei, as well as the properties of hypernuclei and meson-nucleus deeply bound states. It is of great interest that the model predicts a variation of the nucleon form factors in nuclear matter. We also study the empirically observed, Bloom-Gilman (quark-hadron) duality. Other applications of the model include subthreshold kaon production in heavy ion collisions, D and D-bar meson production in antiproton-nucleus collisions, and J/Psi suppression. In particular, the modification of the D and D-bar meson properties in nuclear medium can lead to a large J/Psi absorption cross section, which explains the observed J/Psi suppression in relativistic heavy ion collisions.Comment: 143 pages, 77 figures, references added, a review article accepted in Prog. Part. Nucl. Phy

Transport-theoretical Description of Nuclear Reactions

In this review we first outline the basics of transport theory and its recent generalization to off-shell transport. We then present in some detail the main ingredients of any transport method using in particular the Giessen Boltzmann-Uehling-Uhlenbeck (GiBUU) implementation of this theory as an example. We discuss the potentials used, the ground state initialization and the collision term, including the in-medium modifications of the latter. The central part of this review covers applications of GiBUU to a wide class of reactions, starting from pion-induced reactions over proton and antiproton reactions on nuclei to heavy-ion collisions (up to about 30 AGeV). A major part concerns also the description of photon-, electron- and neutrino-induced reactions (in the energy range from a few 100 MeV to a few 100 GeV). For this wide class of reactions GiBUU gives an excellent description with the same physics input and the same code being used. We argue that GiBUU is an indispensable tool for any investigation of nuclear reactions in which final-state interactions play a role. Studies of pion-nucleus interactions, nuclear fragmentation, heavy ion reactions, hyper nucleus formation, hadronization, color transparency, electron-nucleus collisions and neutrino-nucleus interactions are all possible applications of GiBUU and are discussed in this article.Comment: 173 pages, review article. v2: Text-rearrangements in sects. 2 and 3 (as accepted for publication in Physics Reports

Heuristic model for configurable polymer wire synaptic devices

Recently, there has been considerable research on nonvolatile analog devices for artificial intelligence (AI); however, it focuses on all-coupled neural networks. In contrast, polymer wire-type synaptic devices, which can be expected to be arbitrarily wired similar to a biological neural network, have already been proposed and demonstrated. In this study, we model a polymer wire synaptic device based on the results of previous research, and demonstrate an example of applying simple perceptron (AI) to the model. The results of our study show that it is possible to predict effective methods of using polymer wire synaptic elements in AI

Evolving conductive polymer neural networks on wetware

Neural networks in the brain are structured in three-dimensional (3D) space, and the networks evolve through development and learning, whereas two-dimensional (2D) crossbars have essentially been optimized for a fully connected neural network, which results in a significant increase in unused memristors. Here, we present a prototype of molecular neural networks on wetware consisting of a space-free synaptic medium immersed in monomer solution. In the medium, conductive polymer wires are grown between multiple electrodes through learning only when necessary, i.e. no polymer wire is pre-placed, unlike present 2D crossbar devices. Through experiments, we found the necessary growth conditions for synaptic polymer wires. We first demonstrated the learning of simple Boolean functions and then data-encoding tasks by using our system comprising the synaptic media and their external controllers. These results are valuable for expanding the concept of space-free synapse development, i.e. extending our 2D synaptic media to 3D is possible in principle

Long- and Short-Term Conductance Control of Artificial Polymer Wire Synapses

Networks in the human brain are extremely complex and sophisticated. The abstract model of the human brain has been used in software development, specifically in artificial intelligence. Despite the remarkable outcomes achieved using artificial intelligence, the approach consumes a huge amount of computational resources. A possible solution to this issue is the development of processing circuits that physically resemble an artificial brain, which can offer low-energy loss and high-speed processing. This study demonstrated the synaptic functions of conductive polymer wires linking arbitrary electrodes in solution. By controlling the conductance of the wires, synaptic functions such as long-term potentiation and short-term plasticity were achieved, which are similar to the manner in which a synapse changes the strength of its connections. This novel organic artificial synapse can be used to construct information-processing circuits by wiring from scratch and learning efficiently in response to external stimuli

Highly-integrable analogue reservoir circuits based on a simple cycle architecture

Abstract Physical reservoir computing is a promising solution for accelerating artificial intelligence (AI) computations. Various physical systems that exhibit nonlinear and fading-memory properties have been proposed as physical reservoirs. Highly-integrable physical reservoirs, particularly for edge AI computing, has a strong demand. However, realizing a practical physical reservoir with high performance and integrability remains challenging. Herein, we present an analogue circuit reservoir with a simple cycle architecture suitable for complementary metal-oxide-semiconductor (CMOS) chip integration. In several benchmarks and demonstrations using synthetic and real-world data, our developed hardware prototype and its simulator exhibit a high prediction performance and sufficient memory capacity for practical applications, showing promise for future applications in highly integrated AI accelerators