Data-efficient Neuroevolution with Kernel-Based Surrogate Models

Surrogate-assistance approaches have long been used in computationally expensive domains to improve the data-efficiency of optimization algorithms. Neuroevolution, however, has so far resisted the application of these techniques because it requires the surrogate model to make fitness predictions based on variable topologies, instead of a vector of parameters. Our main insight is that we can sidestep this problem by using kernel-based surrogate models, which require only the definition of a distance measure between individuals. Our second insight is that the well-established Neuroevolution of Augmenting Topologies (NEAT) algorithm provides a computationally efficient distance measure between dissimilar networks in the form of "compatibility distance", initially designed to maintain topological diversity. Combining these two ideas, we introduce a surrogate-assisted neuroevolution algorithm that combines NEAT and a surrogate model built using a compatibility distance kernel. We demonstrate the data-efficiency of this new algorithm on the low dimensional cart-pole swing-up problem, as well as the higher dimensional half-cheetah running task. In both tasks the surrogate-assisted variant achieves the same or better results with several times fewer function evaluations as the original NEAT.Comment: In GECCO 201

Dynamical tunneling in mushroom billiards

We study the fundamental question of dynamical tunneling in generic two-dimensional Hamiltonian systems by considering regular-to-chaotic tunneling rates. Experimentally, we use microwave spectra to investigate a mushroom billiard with adjustable foot height. Numerically, we obtain tunneling rates from high precision eigenvalues using the improved method of particular solutions. Analytically, a prediction is given by extending an approach using a fictitious integrable system to billiards. In contrast to previous approaches for billiards, we find agreement with experimental and numerical data without any free parameter.Comment: 4 pages, 4 figure

Achenbach syndrome as a rare cause of painful, blue finger

Paroxysmal finger hematoma, also known as Achenbach syndrome, is an underdiagnosed condition that causes apprehension in patients owing to the alarming appearance. It usually presents as a blue-purple discoloration of the volar aspect of one or more digits and can be associated with pain and paresthesia. This condition is benign and is usually self-limiting

Development of improved semi-organic structural adhesives for elevated temperature applications Technical summary report, 1 ~JUL. 1964 - 29 ~FEB. 1968

Titanium chelate polymer adhesive formulation for aluminum joint curing in high temperature application

A STOL airworthiness investigation using a simulation of an augmentor wing transport. Volume 2: Simulation data and analysis

A simulator study of STOL airworthiness was conducted using a model of an augmentor wing transport. The approach, flare and landing, go-around, and takeoff phases of flight were investigated. The simulation and the data obtained are described. These data include performance measures, pilot commentary, and pilot ratings. A pilot/vehicle analysis of glide slope tracking and of the flare maneuver is included

Transport properties of anyons in random topological environments

The quasi one-dimensional transport of Abelian and non-Abelian anyons is studied in the presence of a random topological background. In particular, we consider the quantum walk of an anyon that braids around islands of randomly filled static anyons of the same type. Two distinct behaviours are identified. We analytically demonstrate that all types of Abelian anyons localise purely due to the statistical phases induced by their random anyonic environment. In contrast, we numerically show that non-Abelian Ising anyons do not localise. This is due to their entanglement with the anyonic environment that effectively induces dephasing. Our study demonstrates that localisation properties strongly depend on non-local topological interactions and it provides a clear distinction in the transport properties of Abelian and non-Abelian statistics.Comment: 9 pages, 5 figure

Rethinking the appraisal and approval of drugs for type 2 diabetes

The process for approving new drugs for type 2 diabetes illustrates the significant shortcomings of the regulatory standards for licensing, reimbursing, and adopting new drugs. Regulatory reform is needed to improve the real-world therapeutic value of anti-diabetic drugs

The Case for Learned Index Structures

Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not. In this exploratory research paper, we start from this premise and posit that all existing index structures can be replaced with other types of models, including deep-learning models, which we term learned indexes. The key idea is that a model can learn the sort order or structure of lookup keys and use this signal to effectively predict the position or existence of records. We theoretically analyze under which conditions learned indexes outperform traditional index structures and describe the main challenges in designing learned index structures. Our initial results show, that by using neural nets we are able to outperform cache-optimized B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over several real-world data sets. More importantly though, we believe that the idea of replacing core components of a data management system through learned models has far reaching implications for future systems designs and that this work just provides a glimpse of what might be possible