Computational Evolutionary Embryogeny

Evolutionary and developmental processes are used to evolve the configurations of 3-D structures in silico to achieve desired performances. Natural systems utilize the combination of both evolution and development processes to produce remarkable performance and diversity. However, this approach has not yet been applied extensively to the design of continuous 3-D load-supporting structures. Beginning with a single artificial cell containing information analogous to a DNA sequence, a structure is grown according to the rules encoded in the sequence. Each artificial cell in the structure contains the same sequence of growth and development rules, and each artificial cell is an element in a finite element mesh representing the structure of the mature individual. Rule sequences are evolved over many generations through selection and survival of individuals in a population. Modularity and symmetry are visible in nearly every natural and engineered structure. An understanding of the evolution and expression of symmetry and modularity is emerging from recent biological research. Initial evidence of these attributes is present in the phenotypes that are developed from the artificial evolution, although neither characteristic is imposed nor selected-for directly. The computational evolutionary development approach presented here shows promise for synthesizing novel configurations of high-performance systems. The approach may advance the system design to a new paradigm, where current design strategies have difficulty producing useful solutions

How well do DRGs for appendectomy explain variations in resource use? : An analysis of patient-level data from 10 European countries

Appendectomy is a common and relatively simple procedure to remove an inflamed appendix, but the rate of appendectomy varies widely across Europe. This paper investigates factors that explain differences in resource use for appendectomy. We analysed 106,929 appendectomy patients treated in 939 hospitals in ten European countries. In stage one, we tested the performance of three models in explaining variation in the (log of) cost of the inpatient stay (seven countries) or length-of-stay (three countries). The first model used only the Diagnosis Related Groups (DRGs) to which patients were coded; the second used a core set of general patient-level and appendectomy-specific variables; and the third model combined both sets of variables. In stage two, we investigated hospital-level variation. In classifying appendectomy patients, most DRG systems take account of complex diagnoses and comorbidities, but use different numbers of DRGs (range: 2 to 8). The capacity of DRGs and patient-level variables to explain patient-level cost variation ranges from 34% in Spain to over 60% in England and France. All DRG systems can make better use of administrative data such as the patient’s age, diagnoses and procedures, and all countries have outlying hospitals that could improve their management of resources for appendectomy

A Method to Visualize Patient Flow Using Virtual Reality and Serious Gaming Techniques

This paper proposes a method to visualize Emergency Department patient flow data in a Virtual Reality (VR) Serious Game (SG) environment. Visualizing the patient flow data will allow patterns and trends that hospitals can use to reduce alternative level of care (ALC) days and increase the acute capacity of the hospital. The method proposes to use Unity to develop two VR visualisations of patient flow to a hospital ED such that hospital staff can determine which of the two visualizations will be the most usable, immersive, and playable. This paper also presents future work that will look at the whole system of a hospital using one years’ worth of patient flow data to develop a usable, immersive and playable Virtual Environment (VE)

Engineering by fundamental elements of evolution

The method presented in this note mimics two fundamental mechanisms from nature, growth, and development, for the synthesis of new three-dimensional structures. The structures were synthesized to support a load generated by a wind. Every structure grows from a single artificial cell following a set of genes, encoded in an artificial genome shared by all cells. Genes are a set of commands that control the growth process. Genes are regulated by interaction with the environment. The environment is both external and internal to the structure. The performance each structure is measured by its ability to hold the load and other additional engineering criteria. A population of structures is evolved using a genetic algorithm, which alters the genome of two mating individuals. We will present evolved phenotypes with high degrees of modularity and symmetry which evolved according to engineering criteria. Neither one of these two characteristics has been directly imposed as the fitness evaluation, but rather spontaneously emerge as a consequence of natural selection. We will argue that the types of rules we are using in this model are not biased toward any of these characteristics, but rather basic rules for growth and development

Demonstration of three- and four-body interactions between trapped-ion spins

Quantum processors use the native interactions between effective spins to simulate Hamiltonians or execute quantum gates. In most processors, the native interactions are pairwise, limiting the efficiency of controlling entanglement between many qubits. Here we experimentally demonstrate a new class of native interactions between trapped-ion qubits, extending conventional pairwise interactions to higher order. We realize three- and four-body spin interactions as examples, showing that high-order spin polynomials may serve as a new toolbox for quantum information applications

A Modulo-Based Architecture for Analog-to-Digital Conversion

Systems that capture and process analog signals must first acquire them through an analog-to-digital converter. While subsequent digital processing can remove statistical correlations present in the acquired data, the dynamic range of the converter is typically scaled to match that of the input analog signal. The present paper develops an approach for analog-to-digital conversion that aims at minimizing the number of bits per sample at the output of the converter. This is attained by reducing the dynamic range of the analog signal by performing a modulo operation on its amplitude, and then quantizing the result. While the converter itself is universal and agnostic of the statistics of the signal, the decoder operation on the output of the quantizer can exploit the statistical structure in order to unwrap the modulo folding. The performance of this method is shown to approach information theoretical limits, as captured by the rate-distortion function, in various settings. An architecture for modulo analog-to-digital conversion via ring oscillators is suggested, and its merits are numerically demonstrated

General Partially Fair Multi-Party Computation with VDFs

Gordon and Katz, in Partial Fairness in Secure Two-Party Computation , present a protocol for two-party computation with partial fairness which depends on presumptions on the size of the input or output of the functionality. They also show that for some other functionalities, this notion of partial fairness is impossible to achieve. In this work, we get around this impossibility result using verifiable delay functions, a primitive which brings in an assumption on the inability of an adversary to compute a certain function in a specified time. We present a gadget using VDFs which allows for any MPC to be carried out with ≈ 1/R partial fairness, where R is the number of communication rounds