Computing vs. Genetics

This chapter first presents the interrelations between computing and genetics, which both are based on information and, particularly, self-reproducing artificial systems. It goes on to examine genetic code from a computational viewpoint. This raises a number of important questions about genetic code. These questions are stated in the form of an as yet unpublished working hypothesis. This hypothesis suggests that many genetic alterations are caused by the last base of certain codons. If this conclusive hypothesis were to be confirmed through experiementation if would be a significant advance for treating many genetic diseases

Parametric Design Synthesis of Distributed Embedded Systems

This paper presents a design synthesis method for distributed embedded systems. In such systems, computations can flow through long pipelines of interacting software components, hosted on a variety of resources, each of which is managed by a local scheduler. Our method automatically calibrates the local resource schedulers to achieve the system's global end-to-end performance requirements. A system is modeled as a set of distributed task chains (or pipelines), where each task represents an activity requiring nonzero load from some CPU or network resource. Task load requirements can vary stochastically, due to second-order effects like cache memory behavior, DMA interference, pipeline stalls, bus arbitration delays, transient head-of-line blocking, etc. We aggregate these effects -- along with a task's per-service load demand -- and model them via a single random variable, ranging over an arbitrary discrete probability distribution. Load models can be obtained via profiling tasks in isolation, or simply by using an engineer's hypothesis about the system's projected behavior. The end-to-end performance requirements are posited in terms of throughput and delay constraints. Specifically, a pipeline's delay constraint is an upper bound on the total latency a computatation can accumulate, from input to output. The corresponding throughput constraint mandates the pipeline's minimum acceptable output rate -- counting only outputs which meet their delay constraints. Since per-component loads can be generally distributed, and since resources host stages from multiple pipelines, meeting all of the system's end-to-end constraints is a nontrivial problem. Our approach involves solving two sub-problems in tandem: (A)~finding an optimal proportion of load to allocate each task and channel; and (B)~deriving the best combination of service intervals over which all load proportions can be guaranteed. The design algorithms use analytic approximations to quickly estimate output rates and propagation delays for candidate solutions. When all parameters are synthesized, the estimated end-to-end performance metrics are re-checked by simulation. The per-component load reservations can then be increased, with the synthesis algorithms re-run to improve performance. At that point the system can be configured according to the synthesized scheduling parameters -- and then re-validated via on-line profiling. In this paper we demonstrate our technique on an example system, and compare the estimated performance to its simulated on-line behavior. (Also cross-referenced as UMIACS-TR-98-18

Computational Natural Philosophy: A Thread from Presocratics through Turing to ChatGPT

Modern computational natural philosophy conceptualizes the universe in terms of information and computation, establishing a framework for the study of cognition and intelligence. Despite some critiques, this computational perspective has significantly influenced our understanding of the natural world, leading to the development of AI systems like ChatGPT based on deep neural networks. Advancements in this domain have been facilitated by interdisciplinary research, integrating knowledge from multiple fields to simulate complex systems. Large Language Models (LLMs), such as ChatGPT, represent this approach's capabilities, utilizing reinforcement learning with human feedback (RLHF). Current research initiatives aim to integrate neural networks with symbolic computing, introducing a new generation of hybrid computational models.Comment: 17 page

Transforming High School Physics With Modeling And Computation

The Engage to Excel (PCAST) report, the National Research Council\u27s Framework for K-12 Science Education, and the Next Generation Science Standards all call for transforming the physics classroom into an environment that teaches students real scientific practices. This work describes the early stages of one such attempt to transform a high school physics classroom. Specifically, a series of model-building and computational modeling exercises were piloted in a ninth grade Physics First classroom. Student use of computation was assessed using a proctored programming assignment, where the students produced and discussed a computational model of a baseball in motion via a high-level programming environment (VPython). Student views on computation and its link to mechanics was assessed with a written essay and a series of think-aloud interviews. This pilot study shows computation\u27s ability for connecting scientific practice to the high school science classroom

Computational steering and the SCIRun integrated problem solving environment

Journal ArticleSCIRun is a problem solving environment that allows the interactive construction, debugging, and steering of large-scale scientific computations. We review related systems and introduce a taxonomy that explores different computational steering solutions. Considering these approaches, we discuss why a tightly integrated problem solving environment, such as SCIRun, simplifies the design and debugging phases of computational science applications and how such an environment aids in the scientific discovery process

Quantum Communication Systems: Vision, Protocols, Applications, and Challenges

The growth of modern technological sectors have risen to such a spectacular level that the blessings of technology have spread to every corner of the world, even to remote corners. At present, technological development finds its basis in the theoretical foundation of classical physics in every field of scientific research, such as wireless communication, visible light communication, machine learning, and computing. The performance of the conventional communication systems is becoming almost saturated due to the usage of bits. The usage of quantum bits in communication technology has already surpassed the limits of existing technologies and revealed to us a new path in developing technological sectors. Implementation of quantum technology over existing system infrastructure not only provides better performance but also keeps the system secure and reliable. This technology is very promising for future communication systems. This review article describes the fundamentals of quantum communication, vision, design goals, information processing, and protocols. Besides, quantum communication architecture is also proposed here. This research included and explained the prospective applications of quantum technology over existing technological systems, along with the potential challenges of obtaining the goal.Comment: 23 pages, 11 Figure

ICASE

This report summarizes research conducted at the Institute for Computer Applications in Science and Engineering in the areas of (1) applied and numerical mathematics, including numerical analysis and algorithm development; (2) theoretical and computational research in fluid mechanics in selected areas of interest, including acoustics and combustion; (3) experimental research in transition and turbulence and aerodynamics involving Langley facilities and scientists; and (4) computer science

State-of-the-art Assessment For Simulated Forces

Summary of the review of the state of the art in simulated forces conducted to support the research objectives of Research and Development for Intelligent Simulated Forces