The Arithmetic Progression Graph Labeling is an NP-complete problem with various applications, including optimizing scheduling problems. This paper presents Quadratic Unconstrained Boolean Optimization solutions for the version of the problem with fixed vertex labels and the original problem. We use and compare standard (D-Wave Advantage and Advantage 2 Prototype) quantum annealers and hybrid (D-Wave Leap Hybrid Solver Service) methods to solve these problems with D-Wave quantum machines. Our experiments suggest that the hybrid methods outperform the standard ones.
In this work, we present a comparison of the effectiveness of biological systems and modern digital AI solutions. Life, from its simplest forms such as slime molds to complex biological systems with central nervous systems, operates as a physical process counteracting entropy. This paper explores the relationship between energy and life, focusing on how living organisms minimise and optimise energy consumption to maintain their highly ordered states. Additionally, it compares the effectiveness of biological systems in energy optimization with digital artificial intelligence (AI) systems. By analysing mechanisms across various scales of biological complexity, this paper highlights the principles of negentropy in living organisms and evaluates how these principles are totally not used in modern AI systems making them ineffective and outdated.
A major challenge for our uncertain future is to develop such electronic devices and sensors that will be based on commonly available materials, will be easy to use, and will consume little energy. The presented work describes the concepts of using biomaterials for applications in such areas as healthcare and biomedical devices, energy storage and harvesting, or security and authentication. The first part of the presented work contains a literature review of the devices based on biomaterials developed so far. It shows not only the advantages of such devices but also the technological challenges that need to be overcome. The second part describes a system based on cadmium sulphide - multiwalled carbon nanotubes, which can be successfully used to detect alkali metal cations in aqueous solutions.
In ongoing work combining theory, simulation and biological experiments, we experimentally demonstrated how self-propelled molecular "agents" moving stochastically through a specially designed nanofabricated network can solve an NP-complete problem (SUBSET SUM) in a massively parallel fashion and, theoretically, in polynomial time. Here, we formally introduce a new computing model, of the distributed parallel type, based on this principle. The computing device, which we call an "N-system", consists of (a) an appropriately designed digraph encoding a specific instance of a decision problem and (b) a population of agents that evolve on the digraph by jumping between connected nodes at each step of the computation, starting at one or more "input nodes". The computation terminates when the occupancy status for all nodes is stable, or after some maximum time has elapsed, at which point the result of the computation is completely described by the occupancy status of one or more "output nodes". The evolution takes place in parallel for all agents that are able to evolve at each step. We show, with an example, how such a system can enumerate the natural numbers as well as how to solve SUBSET SUM in general with this approach. We consider the computational power of this system, describing the power and limitations of N-systems without "stickers" (i.e. without information about agents paths) as well as briefly considering the additional power conferred by stickers. Finally, some issues connected with the physical implementation of N-systems are discussed, as well as their similarities with neural networks, some open problems and future research directions.
We implemented a chemically inspired cellular automaton using a model of an artificial chemical system as a molecular ensemble, in which the degree of self-aggregation and self-dispersion interacts with the degree of activation. This molecular ensemble is designed to periodically repeat self-aggregation (clustering) and dispersion (de-clustering) in the absence of noise. However, it was shown to exhibit stationary fluctuations under noise conditions, particularly 1/f noise, which is characteristic of critical phenomena. Critical phenomena are suggested to have high computational power. Here, the trade-off between computational universality and computational efficiency was evaluated. As a result, it was shown that while ordinary cellular automata display a clear trade-off between computational universality and computational efficiency as defined here, the chemically inspired cellular automata break this trade-off. This is one of the powerful advantages of unconventional computing.
The structure-function relationship is the basis of quantitative analysis of living organisms whose fundamental unit is a cell. Cellular structural and functional complexity is a challenge to our understanding of responses to various environmental signals and stimuli. Electrical and electromagnetic interactions within and between cells are particularly poorly understood. Experimental investigations in parallel with computational modelling using modern methods and tools are gradually developing towards an integrated model of the cell as bioelectric circuitry. A complete quantitative bioelectric model of various subcellular components and a whole cell could allow us to reverse engineer the underlying bio-electrodynamic design principles and use this information for the construction of novel bioelectric devices. As a result, controllable use of such devices for the development of hybrid technologies and within biological systems can allow us to manipulate cell differentiation, cell division and other processes. While much is known about the electric properties of cell membranes, explorations of the cytoskeleton are still nebulous. Key cytoskeleton components, actin filaments and microtubules, play essential roles in cell motility, mitosis, cell differentiation, transport and signaling. Their elementary protein building blocks self-assemble into cell-spanning filaments, and are strongly affected by temperature, ionic concentrations, pH and other factors such as pharmacological agents. These factors, affecting cellular structure formation, also affect cellular responses to electric and electromagnetic fields in a largely unexplored way. Future research in this area of investigations may have major implications for the development of novel therapeutic modalities and for a range of nanotechnology applications such as nano-sensors and biocomputing elements.
Responsive materials have attracted considerable interest because of their capacity to modify and alter their properties in reaction to external stimuli. This research explores the possibility of kombucha mats, which are a byproduct of the kombucha fermentation process, as a new and responsive material. By regulating the fermentation conditions and postprocessing methods, one can adjust the distinctive electrical, mechanical and optical properties of kombucha mats. In this study, we employ mechanical testing, electrical measurements and optical stimulation to evaluate the responsive behaviour of kombucha mats. We examine the potential applications of kombucha mats as responsive materials, focusing on smart textiles, wearable devices, biosensors, soft robotics, and biomedical devices. We provide multiple case studies that demonstrate the successful incorporation of kombucha mats into practical and adaptable systems.This study demonstrates that kombucha mats have great potential as a sustainable and effective platform for creating responsive materials with specific qualities and a wide range of uses.
Roles of three important properties of the neuromorphic systems are considered in this paper: effect of noise, cross-talk of elements and energy efficiency. The positive effect of noise, directly demonstrated theoretically and experimentally during recent years, allows the system to reach a global energy level, providing effective classification and forecasting tasks execution. Cross-talk of elements is important for the realization of bio-realistic learning algorithms and mimicking cognitive and creative processes. Energy efficiency is an obvious important parameter for all modern systems. In this paper we underline the necessity of applying new hardware approaches for the effective solving of this problem.
Fungi have existed on earth for at least hundreds of millions of years suggesting that mats and networks of mycelium could have formed an early source of protocognitive activity. Amino acids are a fungal cell wall permeable source of nutrition for many present day fungi and this relationship may have been initiated in certain early earth conditions fuelling metabolic processes that could not occur otherwise. Particular kinds of peptides called thermal proteins or proteinoids could have played an important role in this geological period due to their assembly at high temperatures (150+degrees C) and ability to form electrical junctions with biological organisms via attachment of microspheres. In artificial cultures proteinoids could be used as a method to interface with intracellular and extracellular electrophysiology. To investigate these conditions and the resulting dynamics of electrical signaling, we studied the characteristics of an in-vitro culture of mycelium cultivated with proteinoids. Scanning electron micrographs show growth of the basidiomycete Schizophyllum commune with proteinoid microspheres attached to hyphae suggesting that this fungal species grows successfully in proteinoid solution. Further, we show evidence for the existence of integration of electrical signaling between the abiotic peptides and fungus in via analysis of the spontaneous low frequency extracellular oscillations.
The dream of space exploration is approximately a century old, and still sparks admiration and awe among humanity, being supported by a growing economy that nurtures a fertile field where private and public cooperate towards the achievement of unexplored frontiers. Yet, manned exploration of distant worlds represents a far objective, and autonomous, robotic probes are developed to survive the harsh conditions found in space and to send back to Earth images and measurements of celestial bodies. Liquid spaceships here devised can offer substantial advantages, when compared to conventional architectures. A description of future hypothetical spaceships and missions is presented, based on the recent discoveries made in the field of liquid cybernetic systems.
This work explores the complexity and nonlinearity of seven differ-ent colloidal suspensions-Au, ferrofluid, TiO2, ZnO, g-C3N4, MXene,and PEDOT:PSS-when electrically stimulated with fractal, chaotic,and random binary signals. The recorded electrical responses were analyzed using entropy, file compression, fractal dimension, and Fisher information measures to quantify complexity. The nonlinearity introduced by each colloid was evaluated by the deviation of the output from the best-fit hyperplane of the input-output mapping. The results showedthat TiO2 was the most complex colloid across all inputs, exhibiting high entropy, poor compressibility, and an unpredictable response pat-tern. The colloids also exhibited significant nonlinearity, making them promising candidates for reservoir computation, where the mapping of inputs into high-dimensional nonlinear states is advantageous. This study provides insight into the dynamics of colloids and their potential for unconventional computational applications that exploit their inher-\ent complexity and nonlinearity, and it provides a rapid method for assessing the suitability of a particular material for use as a computational substrate before others
The complex electrodynamic properties of dendrites play a crucial role in information processing within neurons. This manuscript delves into the fundamental components of dendrites, focusing on the cytoskeleton and spines. By exploring the interactions between microtubules, actin filaments, ion channels, and dendritic spines, the aim is to put forward a model of unconventional computing taking place in dendrites, that contributes to neural computation and signaling. The hypothesis is that the dendritic cytoskeleton, including both microtubules and actin filaments, plays an active and central role in computations affecting neuronal function. These cytoskeletal elements are affected by, and in turn regulate, ion channels and spines activity. An integrated view of these phenomena in a bottom-up scheme is provided. We outline substantial evidence to support our model and suggest that ionic wave propagation and processing along cytoskeletal structures impact ion channels' function, and thus computational capabilities of the dendritic tree and neuronal function as a whole.
We investigate the potential of bio-inspired evolutionary algorithms for designing quantum circuits with specific goals, focusing on two particular tasks. The first one is motivated by the ideas of Artificial Life that are used to reproduce stochastic cellular automata with given rules. We test the robustness of quantum implementations of the cellular automata for different numbers of quantum gates The second task deals with the sampling of quantum circuits that generate highly entangled quantum states, which constitute an important resource for quantum computing. In particular, an evolutionary algorithm is employed to optimize circuits with respect to a fitness function defined with the Mayer-Wallach entanglement measure. We demonstrate that, by balancing the mutation rate between exploration and exploitation, we can find entangling quantum circuits for up to five qubits. We also discuss the trade-off between the number of gates in quantum circuits and the computational costs of finding the gate arrangements leading to a strongly entangled state. Our findings provide additional insight into the trade-off between the complexity of a circuit and its performance, which is an important factor in the design of quantum circuits.
A primary challenge in utilizing in-vitro biological neural networks for computations is finding good encoding and decoding schemes for inputting and decoding data to and from the networks. Furthermore, identifying the optimal parameter settings for a given combination of encoding and decoding schemes adds additional complexity to this challenge. In this study we explore stimulation timing as an encoding method, i.e. we encode information as the delay between stimulation pulses and identify the bounds and acuity of stimulation timings which produce linearly separable spike responses. We also examine the optimal readout parameters for a linear decoder in the form of epoch length, time bin size and epoch offset. Our results suggest that stimulation timings between 36 and 436ms may be optimal for encoding and that different combinations of readout parameters may be optimal at different parts of the evoked spike response.
This article further develops the Space Element Reduction Duplication (SERD) model, a dynamic self generating topological information transmission network model for a background independent discrete space-time. Evidence is provided for the satisfaction of Newtons Laws under a specific extrinsic curvature reducing embedding algorithm applied to the background independent observed states evolution of the model at large scales. From this a specific definition of inertial rest mass is strengthened. Details relating to the specific update operations corresponding to matter flows are presented, leading to a resulting hypothesis regarding the internal structure of particles as local massive stable equilibrium states resulting from the update operations. The SERD model provides an emergent biologically analogous discrete space-time topology with a construct for an observer, transmission of topological information along space filaments and a capacity to define a fundamental unit of observer, and thereby begin to define the idea of observer complexity from first principles. Due to the specificity of the rules at play appropriate methods can be utilised to test physical analogies and to align emergent properties and behaviours of the model with observed physical systems.
Data-intensive application tasks have always fueled research and development towards more powerful computing systems. In this context, the recently proposed framework of hyper-dimensional computing (HDC) is rapidly emerging to open new opportunities for the development of systems that perform cognitive tasks in hardware. The highly memory-centric nature of HDC was the key motivation for the in-memory computing hardware implementation approaches explored recently where memristive devices were used to locally implement logic operations. In this work, we explore using memristive devices to implement one of the fundamental modules of an HDC system, the "associative memory" (AM). We designed and simulated an HDC system in MATLAB software using a behavioral model for memristive devices and explored the performance of the HDC system in image classification tasks, using different AM implementations to enrich the representation of the image classes in the AM when they included up to 25% of noise. The simulation results also explored the impact of nonidealities of memristive devices and demonstrate the critical system design aspects to consider in such an implementation approach.
Mass-spring networks are a popular choice to numerically simulate physical reservoir computing systems. As these kinds of networks are also widely used in the simulation of different types of fabric, in this paper, we evaluate the use of conductive fabrics as a reservoir. Stretching such a fabric changes its conductivity. Then, by measuring this change in resistance, we are able to exploit a piece of fabric as a computational resource by encoding input data into forces that pull on the cloth. Our numerical experiments support the use of conductive fabrics as a computational reservoir. Further, we estimated the memory capacity of a hypothetical cloth reservoir. Our results suggest that conductive fabrics have the potential to be exploited for computation and thus can serve as a viable reservoir for reservoir computing.
This study focuses on realizing reservoir computing applications using low-density cellular automata networks. The innovation lies in generto arbitrary network structures, with text classification serving as the test case. The primary objective is to optimize system performance by leveraging the interplay between update rules and network topology. By exploring various network configurations, the study aims to uncover structural characteristics that facilitate efficient information propagation and decision-making within the cellular automata framework. Observations from the study indicate the existence of advantageous combinations of network structures and update rules that enhance reservoir quality. The cellular automata networks considered are categorized into different groups based on their performance, providing a solid foundation for further research.
We consider the role of a Turing machine in controlling measurement experiments and the corresponding revision of Measurement Theory, incorporating the notion of physical time in a theory we show to be realised by all types of measurements of extensive quantities found in the scientific literature. Surprisingly, when we try to mechanise certain aspects of the experimental procedures with Turing machines, we uncover that quantities have an inherent measurement complexity. We demonstrate that there is a relationship between the structure of a real number and the amount of time required to measure its digits, which leads to the emergence of complexity classes associated with measuring of the digits of a real number and a new form of uncertainty: When algorithms govern experiments in Physics, then, even in the limit of the application of the theory, even in the absence of measurement errors, precise measurements of quantities cannot always be made.
