22 papers · ranked by Valyu relevance
Jonas Kuckling, Thomas Stützle, Mauro Birattari, José Manuel Galán
Iterative improvement is an optimization technique that finds frequent application in heuristic optimization, but, to the best of our knowledge, has not yet been adopted in the automatic design of control software for robots. In this work, we investigate iterative improvement in the context of the automatic modular…
Ralph P. Lano
This paper presents the implementation of a self-replicating finite-state machine (FSM) and a self-replicating Turing Machine (TM) using bio-inspired mechanisms. Building on previous work that introduced self-replicating structures capable of sorting, copying, and reading information, this study demonstrates the…
Sierra Zoe Bennett-Manke, Sebastian Neumann, Roberta L. Dougherty
layout the states and transitions in a logical way, which may be different depending on the machine's behavior. Further, although using a static representation can be useful in the classroom, rendering a simulation of the machine by-hand takes valuable time, and updating the simulation's multiple data can be initially…
Sabah Al‐Fedaghi
The design of complex man-made systems mostly involves a conceptual modeling phase; therefore, it is important to ensure an appropriate analysis method for these models. A key concept for such analysis is the development of a diagramming technique (e.g., UML) because diagrams can describe entities and processes and…
Matthew Francis-Landau
This paper introduces MFST, a new Python library for working with Finite-State Machines based on OpenFST. MFST is a thin wrapper for OpenFST and exposes all of OpenFST's methods for manipulating FSTs. Additionally, MFST is the only Python wrapper for OpenFST that exposes OpenFST's ability to dene a custom semirings.…
Jun Wang, Xiaokang Zhang, Peijun Shi, Ben Cao + 2 more
'Keng-Shiang Huang'] Living organisms can produce corresponding functions by responding to external and internal stimuli, and this irritability plays a pivotal role in nature. Inspired by such natural temporal responses, the development and design of nanodevices with the ability to process time-related information…
Katherine E. Dunn, Martin A. Trefzer, Steven Johnson, Andy M. Tyrrell
'Andy M. Tyrrell'] DNA molecular machines have great potential for use in computing systems. Since Adleman originally introduced the concept of DNA computing through his use of DNA strands to solve a Hamiltonian path problem, a range of DNA-based computing elements have been developed, including logic gates, neural…
Katherine E. Dunn, Martin A. Trefzer, Steven Johnson, Andy M. Tyrrell
DNA molecular machines have great potential for use in computing systems. Since Adleman originally introduced the concept of DNA computing through his use of DNA strands to solve a Hamiltonian path problem, a range of DNA-based computing elements have been developed, including logic gates, neural networks, finite state…
Victor Yodaiken
State machines are usually presented in terms of a set of events E, a set of states S, and a map δ : S × A → S. Alternatively we can define a state variable as a function the set of finite sequences over E so that for any sequence s:
J. Silvestre-Ryan, Y. Wang, M. Sharma, S. Lin + 3 more
Many C++ libraries for using Hidden Markov Models in bioinformatics focus on inference tasks, such as likelihood calculation, parameter-fitting, and alignment. However, construction of the state machines can be a laborious task, automation of which would be time-saving and less error-prone. We present Machine Boss, a…
Alexandre P Francisco, Travis Gagie, Dominik Kempa, Leena Salmela + 3 more
Position weight matrices (PWMs) are the standard way to model binding site affinities in bioinformatics. However, they assume that symbol occurrences are position independent and, hence, they do not take into account symbols co-occurrence at different sequence positions. To address this problem, we propose to construct…
María T. López, Aurelio Bermúdez, Francisco Montero, José L. Sánchez + 1 more
'Antonio Fernández-Caballero'] Many researchers have explored the relationship between recurrent neural networks and finite state machines. Finite state machines constitute the best-characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving.…
Victor Yodaiken
A computer program or computing device changes state in discrete steps in response to discrete events. If E is the set of events and E ∗ is the set of finite sequences over E, then each element s ∈ E ∗ describes a sequence of events that drives a system from an inital state to some current state. An equation of the…
Thomas Kern
While automata theory often concerns itself with regular predicates, relations corresponding to acceptance by a finite state automaton, in this article I study the regular functions, such relations which are also functions in the set-theoretic sense. Here I present a small (but necessarily infinite) collection of…
Ben T. Larson, Jack Garbus, Jordan B. Pollack, Wallace F. Marshall
Cells are complex biochemical systems whose behavior emerges from interactions among myriad molecular components. The idea that cells execute computational processes is often invoked as a general framework for understanding cellular complexity. However, the manner in which cells might embody computational processes in…
Sabah Al‐Fedaghi
A system's behavior is typically specified through models such as state diagrams that describe how the system should behave. According to researchers, it is not clear what a state actually represents regarding the system to be modeled. Standards do not provide adequate definitions of or sufficient guidance on the use…
Authors not listed
Quantitative Structure Activity Relationship (QSAR) remains an effective tool for early-stage chemical modelling and virtual screening in drug design. The advancements in this field are led by two core paradigms, 1) descriptor engineering, where complex fixed-length vectors of compounds are generated and conventional…
Ian Holmes
We introduce a systematic method of approximating finite-time transition probabilities for continuous-time insertion-deletion models on sequences. The method uses automata theory to describe the action of an infinitesimal evolutionary generator on a probability distribution over alignments, where both the generator and…
Jorge M. Silva, Eduardo Pinho, Sérgio Matos, Diogo Pratas
Sources that generate symbolic sequences with algorithmic nature may differ in statistical complexity because they create structures that follow algorithmic schemes, rather than generating symbols from a probabilistic function assuming independence. In the case of Turing machines, this means that machines with the same…
James P. Crutchfield, Antonio M. Scarfone
We show that mixtures comprising multicomponent systems typically are much more structurally complex than the sum of their parts; sometimes, infinitely more complex. We contrast this with the more familiar notion of statistical mixtures, demonstrating how statistical mixtures miss key aspects of emergent hierarchical…
Authors not listed
Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent…
Authors not listed
Digital twins are virtual companions for the design, scale-up, and control of chemical processes. Equipping digital twins with mechanistic models of their mirrored unit operation expands their range of applicability compared to pure data-driven models. As constructing mechanistic models requires time, effort, and…