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  1. Using Eye Tracking to Elucidate the Mechanisms Underlying Stimulation-Enhanced Visual Target Detection

    Trumbo, Michael Christopher Stefan ; Jones, Aaron Paul ; Robert, Bradley Monroe ; ... - International Journal of Cognitive Sciences
    Transcranial direct current stimulation (tDCS) is a noninvasive form of brain stimulation that involves passing a weak electrical current between electrodes on the scalp to modulate underlying neural tissue. TDCS has been shown to modulate cognition in a variety of domains, including memory, attention, and visual processing. Prior work from our laboratory has shown positive effects of tDCS on learning to detect target objects hidden in complex naturalistic visual scenes and learn rules for categorizing images, though the mechanism for these benefits remains unknown. One possibility is that tDCS optimizes visual search by modulating visual attention or via the reductionmore » in search errors. One method of quantifying visual attention is to use eye tracking to record search patterns to determine if and how visual search is adjusted under verum stimulation conditions. Eye tracking data allows classification of errors into error types, including sampling errors (failing to look in the relevant region), recognition errors (looking at the critical portion of a scene, but failing to recognize it as such as evidenced by visual fixation), and decision-making errors (fixating on the relevant portion of a scene, but making the wrong determination). Our results indicate that the benefit tDCS confers on visual search for targets stems from the reduction in decision-making errors when targets are present (Cohen’s d = 0.86). Also reported is a replication of previous findings showing a tDCS-dependent improvement in learning this task, learning score (Cohen’s d = 0.88); d’ (Cohen’s d = 1.00). This provides support for moving tDCS into the application space by pairing it with analysts who are concerned with the type of search error that is corrected via stimulation.« less

  2. Stochastic Unit Commitment: Model Reduction via Learning

    Liu, Xuan ; Conejo, Antonio J. ; Constante Flores, Gonzalo E. - Current Sustainable/Renewable Energy Reports
    As weather-dependent renewable generation increases its share in the generation mix of most electric energy systems, a stochastic unit commitment becomes the natural day-ahead scheduling tool. However, such a tool is generally computationally intractable if a detailed uncertainty description is considered. Taking this into account, we proposed a learning method to make the stochastic unit commitment problem tractable. Here, recent advances in statistical learning and machine learning to address optimization problems can be advantageously applied to the rather intractable stochastic unit commitment problem. Considering these advances, we explore simple learning techniques to drastically reduce the size of a stochastic unitmore » commitment problem without significantly altering its optimal solution. The considered stochastic unit commitment problem is formulated as a two-stage stochastic programming problem. The first stage represents commitment decisions, while the second one represents the operation conditions under different scenarios. Taking into account historical solved instances (or proxies for them), we reduce the size (measured by numbers of constraints and variables) of the stochastic unit commitment problem by (i) fixing unchanged binary variables and by (ii) eliminating inactive inequality constraints. Our numerical results show that the reduced problem generally requires significantly less time to solve while obtaining high-quality solutions, which are very close to or indistinguishable from the one obtained by solving the original problem. We use an Illinois 200-bus system to illustrate and characterize the performance of the proposed problem-reduction method.« less

  3. Learning Distribution Grid Topologies: A Tutorial

    Deka, Deepjyoti ; Kekatos, Vassilis ; Cavraro, Guido - IEEE Transactions on Smart Grid
    Unveiling feeder topologies from data is of paramount importance to advance situational awareness and proper utilization of smart resources in power distribution grids. This tutorial summarizes, contrasts, and establishes useful links between recent works on topology identification and detection schemes that have been proposed for power distribution grids. The primary focus is to highlight methods that overcome the limited availability of measurement devices in distribution grids, while enhancing topology estimates using conservation laws of power-flow physics and structural properties of feeders. Grid data from phasor measurement units or smart meters can be collected either passively in the traditional way, ormore » actively, upon actuating grid resources and measuring the feeder's voltage response. Analytical claims on feeder identifiability and detectability are reviewed under disparate meter placement scenarios. Such topology learning claims can be attained exactly or approximately so via algorithmic solutions with various levels of computational complexity, ranging from least-squares fits to convex optimization problems, and from polynomial-time searches over graphs to mixed-integer programs. Although the emphasis is on radial single-phase feeders, extensions to meshed and/or multiphase circuits are sometimes possible and discussed. Here this tutorial aspires to provide researchers and engineers with knowledge of the current state-of-the-art in tractable distribution grid learning and insights into future directions of work.« less

  4. Online Learning and Pricing for Service Systems with Reusable Resources

    Jia, Huiwen ; Shi, Cong ; Shen, Siqian - Operations Research
    We consider a price-based revenue management problem with finite reusable resources over a finite time horizon T. Customers arrive following a price-dependent Poisson process, and each customer requests one unit of c homogeneous reusable resources. If there is an available unit, the customer gets served within a price-dependent exponentially distributed service time; otherwise, the customer waits in a queue until the next available unit. In this paper, we assume that the firm does not know how the arrival and service rates depend on posted prices, and thus it makes adaptive pricing decisions in each period based only on past observationsmore » to maximize the cumulative revenue. Given a discrete price set with cardinality P, we propose two online learning algorithms, termed batch upper confidence bound (BUCB) and batch Thompson sampling (BTS), and prove that the cumulative regret upper bound is O˜(√PT) , which matches the regret lower bound. In establishing the regret, we bound the transient system performance upon price changes via a novel coupling argument, and also generalize bandits to accommodate subexponential rewards. Here, we also extend our approach to models with balking and reneging customers and discuss a continuous price setting. Our numerical experiments demonstrate the efficacy of the proposed BUCB and BTS algorithms.« less

  5. Permutationless many-jet event reconstruction with symmetry preserving attention networks

    Fenton, Michael James ; Shmakov, Alexander ; Ho, Ta-Wei ; ... - Physical Review D

  6. Physical learning beyond the quasistatic limit

    Stern, Menachem ; Dillavou, Sam ; Miskin, Marc Z. ; ... - Physical Review Research

  7. Efficient emulation of relativistic heavy ion collisions with transfer learning

    Liyanage, D. ; Ji, Y. ; Everett, D. ; ... - Physical Review. C
    Measurements from the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider (RHIC) can be used to study the properties of quark-gluon plasma. Systematic constraints on these properties must combine measurements from different collision systems and methodically account for experimental and theoretical uncertainties. Such studies require a vast number of costly numerical simulations. While computationally inexpensive surrogate models (“emulators”) can be used to efficiently approximate the predictions of heavy ion simulations across a broad range of model parameters, training a reliable emulator remains a computationally expensive task. We use transfer learning to map the parameter dependencies of one modelmore » emulator onto another, leveraging similarities between different simulations of heavy ion collisions. By limiting the need for large numbers of simulations to only one of the emulators, this technique reduces the numerical cost of comprehensive uncertainty quantification when studying multiple collision systems and exploring different models.« less

  8. Quantified limits of the nuclear landscape

    Neufcourt, Léo ; Cao, Yuchen ; Giuliani, Samuel A. ; ... - Physical Review C
    The chart of the nuclides is limited by particle drip lines beyond which nuclear stability to proton or neutron emission is lost. Predicting the range of particle-bound isotopes poses an appreciable challenge for nuclear theory as it involves extreme extrapolations of nuclear masses well beyond the regions where experimental information is available. Still, quantified extrapolations are crucial for a wide variety of applications, including the modeling of stellar nucleosynthesis. We use microscopic nuclear global mass models, current mass data, and Bayesian methodology to provide quantified predictions of proton and neutron separation energies as well as Bayesian probabilities of existence throughoutmore » the nuclear landscape all the way to the particle drip lines. Here, we apply nuclear density functional theory with several energy density functionals. We also consider two global mass models often used in astrophysical nucleosynthesis simulations. To account for uncertainties, Bayesian Gaussian processes are trained on the separation-energy residuals for each individual model, and the resulting predictions are combined via Bayesian model averaging. This framework allows to account for systematic and statistical uncertainties and propagate them to extrapolative predictions. We establish and characterize the drip-line regions where the probability that the nucleus is particle- bound decreases from 1 to 0. In these regions, we provide quantified predictions for one- and two-nucleon separation energies. According to our Bayesian model averaging analysis, 7759 nuclei with Z ≤ 119 have a probability of existence ≥ 0.5. The extrapolation results obtained in this study will be put through stringent tests when new experimental information on existence and masses of exotic nuclei becomes available. In this respect, the quantified landscape of nuclear existence obtained in this study should be viewed as a dynamical prediction that will be fine-tuned when new experimental information and improved global mass models become available.« less

  9. Creative Outcome as Implausible Utility

    Tsao, J. Y. ; Ting, C. L. ; Johnson, C. M. - Review of General Psychology
    Two perspectives are used to reframe Simonton’s recent three-factor definition of creative outcome. The first perspective is functional: that creative ideas are those that add significantly to knowledge by providing both utility and learning. The second perspective is calculational: that learning can be estimated by the change in probabilistic beliefs about an idea’s utility before and after it has played out in its environment. The results of the reframing are proposed conceptual and mathematical definitions of (a) creative outcome as the product of two overarching factors (utility and learning) and (b) learning as a function of two subsidiary factors (blindnessmore » reduction and surprise). Learning will be shown to depend much more strongly on surprise than on blindness reduction, so creative outcome may then also be defined as “implausible utility.”« less

  10. The economics of numbering up a chemical process enterprise

    Weber, Robert S. ; Snowden‐Swan, Lesley J. - Journal of Advanced Manufacturing and Processing
    Chemical–processing plants that can be numbered up by installing and operating many replicate facilities are economically and technically well suited for the conversion of geographically distributed sources of renewable or waste carbon into fuels or chemicals. Examples from the manufacture of chemicals and the installation of flue gas treatment technology suggest that the relative cost diminution should correlate through a power law (Cost/Cost1 ∝ E–a) with E, a measure of the experience of operating those facilities and/or the number of units that are mass manufactured and installed. In conclusion, the exponent, a, can be related to the complexity of themore » process and the characteristics of the products.« less
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