Machine Learning Classification of SDSS Transient Survey Images

We show that multiple machine learning algorithms can match human performance in classifying transient imaging data from the Sloan Digital Sky Survey (SDSS) supernova survey into real objects and artefacts. This is a first step in any transient science pipeline and is currently still done by humans, but future surveys such as the Large Synoptic Survey Telescope (LSST) will necessitate fully machine-enabled solutions. Using features trained from eigenimage analysis (principal component analysis, PCA) of single-epoch g, r and i-difference images, we can reach a completeness (recall) of 96 per cent, while only incorrectly classifying at most 18 per cent of artefacts as real objects, corresponding to a precision (purity) of 84 per cent. In general, random forests performed best, followed by the k-nearest neighbour and the SkyNet artificial neural net algorithms, compared to other methods such as na\"ive Bayes and kernel support vector machine. Our results show that PCA-based machine learning can match human success levels and can naturally be extended by including multiple epochs of data, transient colours and host galaxy information which should allow for significant further improvements, especially at low signal-to-noise.Comment: 14 pages, 8 figures. In this version extremely minor adjustments to the paper were made - e.g. Figure 5 is now easier to view in greyscal

Network structure and dynamics of effective models of non-equilibrium quantum transport

Across all scales of the physical world, dynamical systems can often be usefully represented as abstract networks that encode the system's units and inter-unit interactions. Understanding how physical rules shape the topological structure of those networks can clarify a system's function and enhance our ability to design, guide, or control its behavior. In the emerging area of quantum network science, a key challenge lies in distinguishing between the topological properties that reflect a system's underlying physics and those that reflect the assumptions of the employed conceptual model. To elucidate and address this challenge, we study networks that represent non-equilibrium quantum-electronic transport through quantum antidot devices -- an example of an open, mesoscopic quantum system. The network representations correspond to two different models of internal antidot states: a single-particle, non-interacting model and an effective model for collective excitations including Coulomb interactions. In these networks, nodes represent accessible energy states and edges represent allowed transitions. We find that both models reflect spin conservation rules in the network topology through bipartiteness and the presence of only even-length cycles. The models diverge, however, in the minimum length of cycle basis elements, in a manner that depends on whether electrons are considered to be distinguishable. Furthermore, the two models reflect spin-conserving relaxation effects differently, as evident in both the degree distribution and the cycle-basis length distribution. Collectively, these observations serve to elucidate the relationship between network structure and physical constraints in quantum-mechanical models. More generally, our approach underscores the utility of network science in understanding the dynamics and control of quantum systems.Comment: 37 pages, including supplementary materia

Spin-Dependent Quantum Emission in Hexagonal Boron Nitride at Room Temperature

Optically addressable spins associated with defects in wide-bandgap semiconductors are versatile platforms for quantum information processing and nanoscale sensing, where spin-dependent inter-system crossing (ISC) transitions facilitate optical spin initialization and readout. Recently, the van der Waals material hexagonal boron nitride (h-BN) has emerged as a robust host for quantum emitters (QEs), but spin-related effects have yet to be observed. Here, we report room-temperature observations of strongly anisotropic photoluminescence (PL) patterns as a function of applied magnetic field for select QEs in h-BN. Field-dependent variations in the steady-state PL and photon emission statistics are consistent with an electronic model featuring a spin-dependent ISC between triplet and singlet manifolds, indicating that optically-addressable spin defects are present in h-BN $-$ a versatile two-dimensional material promising efficient photon extraction, atom-scale engineering, and the realization of spin-based quantum technologies using van der Waals heterostructures.Comment: 38 pages, 34 figure

Dynamic reconfiguration of human brain networks during learning

Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes -- flexibility and selection -- must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we explore the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.Comment: Main Text: 19 pages, 4 figures Supplementary Materials: 34 pages, 4 figures, 3 table

Network architecture of energy landscapes in mesoscopic quantum systems

Mesoscopic quantum systems exhibit complex many-body quantum phenomena, where interactions between spins and charges give rise to collective modes and topological states. Even simple, non-interacting theories display a rich landscape of energy states --- distinct many-particle configurations connected by spin- and energy-dependent transition rates. The collective energy landscape is difficult to characterize or predict, especially in regimes of frustration where many-body effects create a multiply degenerate landscape. Here we use network science to characterize the complex interconnection patterns of these energy-state transitions. Using an experimentally verified computational model of electronic transport through quantum antidots, we construct networks where nodes represent accessible energy states and edges represent allowed transitions. We then explore how physical changes in currents and voltages are reflected in the network topology. We find that the networks exhibit Rentian scaling, which is characteristic of efficient transportation systems in computer circuitry, neural circuitry, and human mobility, and can be used to measure the interconnection complexity of a network. Remarkably, networks corresponding to points of frustration in quantum transport (due, for example, to spin-blockade effects) exhibit an enhanced topological complexity relative to networks not experiencing frustration. Our results demonstrate that network characterizations of the abstract topological structure of energy landscapes can capture salient properties of quantum transport. More broadly, our approach motivates future efforts to use network science in understanding the dynamics and control of complex quantum systems.Comment: 26 pages, including supplementary materia

Bayesian estimation applied to multiple species

Observed data are often contaminated by undiscovered interlopers, leading to biased parameter estimation. Here we present BEAMS (Bayesian estimation applied to multiple species) which significantly improves on the standard maximum likelihood approach in the case where the probability for each data point being “pure” is known. We discuss the application of BEAMS to future type-Ia supernovae (SNIa) surveys, such as LSST, which are projected to deliver over a million supernovae light curves without spectra. The multiband light curves for each candidate will provide a probability of being Ia (pure) but the full sample will be significantly contaminated with other types of supernovae and transients. Given a sample of N supernovae with mean probability, ⟨P⟩, of being Ia, BEAMS delivers parameter constraints equal to N⟨P⟩ spectroscopically confirmed SNIa. In addition BEAMS can be simultaneously used to tease apart different families of data and to recover properties of the underlying distributions of those families (e.g. the type-Ibc and II distributions). Hence BEAMS provides a unified classification and parameter estimation methodology which may be useful in a diverse range of problems such as photometric redshift estimation or, indeed, any parameter estimation problem where contamination is an issue

Integrating EEG and MEG signals to improve motor imagery classification in brain-computer interfaces

We propose a fusion approach that combines features from simultaneously recorded electroencephalographic (EEG) and magnetoencephalographic (MEG) signals to improve classification performances in motor imagery-based brain-computer interfaces (BCIs). We applied our approach to a group of 15 healthy subjects and found a significant classification performance enhancement as compared to standard single-modality approaches in the alpha and beta bands. Taken together, our findings demonstrate the advantage of considering multimodal approaches as complementary tools for improving the impact of non-invasive BCIs

COOKING TIME AND SENSORY ANALYSIS OF A DRY BEAN DIVERSITY PANEL

INTRODUCTION - Cooking time and sensory quality are two important traits when selecting dry beans for consumption, but have largely been overlooked by breeders in favor of yield and other traits. Dry beans are an affordable, nutrient-rich food, but often require long cooking times, particularly without prior soaking. They also display a range of sensory characteristics, with consumers preferring cooked beans that are sweet and soft1. Increased interest in dry beans to make new products necessitates studies assessing the diversity of sensory traits in beans, which would allow beans to be selected for specific products. In this study, the Andean Diversity Panel2 (ADP) was assessed for cooking time and sensory characteristics in order to identify diversity for these traits. MATERIALS AND METHODS - Cooking Time Evaluation: 398 genotypes of the ADP were harvested in Hawassa, Ethiopia in 2015, six months prior to evaluation. Prior to cooking, each sample was soaked for 12 hours in 250 ml distilled water after ensuring moisture content was between 10-14%. Two replicates per genotype of 25 seeds each were cooked in random order in boiling distilled water using the Mattson cooker method for determining cooking time3. The Mattson cooker uses twenty-five 85g stainless steel rods with 2mm diameter pins that pierce beans loaded in wells when sufficiently cooked. For this study, the 50% and 80% cooking times were recorded, and the 80% cook time is regarded as the time required to cook each genotype to completion. The cooking time data was analyzed using the MIXED procedure in SAS with genotype as a fixed effect and rep as a random effect