A CdZnTeSe Gamma Spectrometer Trained by Deep Convolutional Neural Network for Radioisotope Identification

We report the implementation of a deep convolutional neural network to train a high-resolution room-temperature CdZnTeSe based gamma ray spectrometer for accurate and precise determination of gamma ray energies for radioisotope identification. The prototype learned spectrometer consists of a NI PCI 5122 fast digitizer connected to a pre-amplifier to recognize spectral features in a sequence of data. We used simulated preamplifier pulses that resemble actual data for various gamma photon energies to train a CNN on the equivalent of 90 seconds worth of data and validated it on 10 seconds worth of simulated data

Specifying Goals to Deep Neural Networks with Answer Set Programming

Recently, methods such as DeepCubeA have used deep reinforcement learning to learn domain-specific heuristic functions in a largely domain-independent fashion. However, such methods either assume a predetermined goal or assume that goals will be given as fully-specified states. Therefore, specifying a set of goal states to these learned heuristic functions is often impractical. To address this issue, we introduce a method of training a heuristic function that estimates the distance between a given state and a set of goal states represented as a set of ground atoms in first-order logic. Furthermore, to allow for more expressive goal specification, we introduce techniques for specifying goals as answer set programs and using answer set solvers to discover sets of ground atoms that meet the specified goals. In our experiments with the Rubik's cube, sliding tile puzzles, and Sokoban, we show that we can specify and reach different goals without any need to re-train the heuristic function. Our code is publicly available at https://github.com/forestagostinelli/SpecGoal

Explainable Pathfinding for Inscrutable Planners with Inductive Logic Programming

The complexity of the solutions that artificial intelligence can learn to solve problems currently surpasses its ability to explain these solutions. In many domains, explainable solutions are a necessary condition while optimality is not. Therefore, we seek to constrain solutions to the space of solutions that can be explained to a human. To do this, we build on inductive logic programming (ILP) techniques that allow us to define robust background knowledge and inductive biases. By combining ILP with a given inscrutable planner, we are able to construct an explainable graph representing solutions to all states in the state space. This graph can then be summarized using a variety of methods such as hierarchical representations and simple if/else rules. We test our approach on Towers of Hanoi and discuss future work for applications to the Rubik’s cube

Synthesis of CdZnTeSe Single Crystals for Room Temperature Radiation Detector Fabrication: Mitigation of Hole Trapping Effects Using a Convolutional Neural Network

We report the growth of Cd0.9Zn0.1Te0.97Se0.03 (CZTS) wide bandgap semiconductor single crystals for room temperature gamma-ray detection using a modified vertical Bridgman method. Charge transport properties measured in the radiation detectors, fabricated from the grown CZTS crystals, indicated signs of hole trapping. Hole traps inhibit high-resolution radiation detection especially for energetic gamma rays. In this article, we describe a deep convolutional neural network (CNN) that has demonstrated remarkable efficiency in identifying the energy of a gamma photon detected by a CZTS detector. The CNN has been trained using simulated data that resemble output pulses from actual CZTS detectors when exposed to 662-keV gamma photons. The device properties required for the simulation have been derived from radiation detection measurements on a real Cd0.9Zn0.1Te0.97Se0.03 detector fabricated in our laboratory. The CNN has been trained with detector pulses arising through photoelectric (PE) and Compton scattering (CS) separately. The percentage error in predicting the detected energies, within an extremely small duration of 0.28 ms, was found to be lower than 0.1% for gamma energies above 50 keV and for training datasets containing PE and CS events separately. The CNN was also validated for a mixed PE and CS dataset to obtain a prediction error of 1%. The effect of detector resolution on the efficiency of the CNN was also explored