Does the mood-as-input model explain task persistence when goals are activated automatically?

AbstractMachine learning has recently emerged as a fruitful area for finding potential quantum computational advantage. Many of the quantum-enhanced machine learning algorithms critically hinge upon the ability to efficiently produce states proportional to high-dimensional data points stored in a quantum accessible memory. Even given query access to exponentially many entries stored in a database, the construction of which is considered a one-off overhead, it has been argued that the cost of preparing such amplitude-encoded states may offset any exponential quantum advantage. Here we prove using smoothed analysis that if the data analysis algorithm is robust against small entry-wise input perturbation, state preparation can always be achieved with constant queries. This criterion is typically satisfied in realistic machine learning applications, where input data is subjective to moderate noise. Our results are equally applicable to the recent seminal progress in quantum-inspired algorithms, where specially constructed databases suffice for polylogarithmic classical algorithm in low-rank cases. The consequence of our finding is that for the purpose of practical machine learning, polylogarithmic processing time is possible under a general and flexible input model with quantum algorithms or quantum-inspired classical algorithms in the low-rank cases.

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Unraveling the time-dependent relevance of input model uncertainties for a lumped hydrologic model of a pre-alpine karst system

Hydrogeology Journal ◽

10.1007/s10040-021-02377-1 ◽

2021 ◽

Author(s):

Daniel Bittner ◽

Beatrice Richieri ◽

Gabriele Chiogna

Keyword(s):

Groundwater Recharge ◽

Temporal Variations ◽

Hydrologic Model ◽

Time Dependent ◽

Model Parameters ◽

Lumped Parameter ◽

Hydrodynamic Processes ◽

Input Model ◽

Karst Model ◽

Input Uncertainties

AbstractUncertainties in hydrologic model outputs can arise for many reasons such as structural, parametric and input uncertainty. Identification of the sources of uncertainties and the quantification of their impacts on model results are important to appropriately reproduce hydrodynamic processes in karst aquifers and to support decision-making. The present study investigates the time-dependent relevance of model input uncertainties, defined as the conceptual uncertainties affecting the representation and parameterization of processes relevant for groundwater recharge, i.e. interception, evapotranspiration and snow dynamic, on the lumped karst model LuKARS. A total of nine different models are applied, three to compute interception (DVWK, Gash and Liu), three to compute evapotranspiration (Thornthwaite, Hamon and Oudin) and three to compute snow processes (Martinec, Girons Lopez and Magnusson). All the input model combinations are tested for the case study of the Kerschbaum spring in Austria. The model parameters are kept constant for all combinations. While parametric uncertainties computed for the same model in previous studies do not show pronounced temporal variations, the results of the present work show that input uncertainties are seasonally varying. Moreover, the input uncertainties of evapotranspiration and snowmelt are higher than the interception uncertainties. The results show that the importance of a specific process for groundwater recharge can be estimated from the respective input uncertainties. These findings have practical implications as they can guide researchers to obtain relevant field data to improve the representation of different processes in lumped parameter models and to support model calibration.

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Enhanced Accuracy of Continuous Glucose Monitoring during Exercise through Physical Activity Tracking Integration

Sensors ◽

10.3390/s19173757 ◽

2019 ◽

Vol 19 (17) ◽

pp. 3757 ◽

Cited By ~ 1

Author(s):

Alejandro José Laguna Sanz ◽

José Luis Díez ◽

Marga Giménez ◽

Jorge Bondia

Keyword(s):

Physical Activity ◽

Estimation Error ◽

Glucose Monitoring ◽

Linear Regression Models ◽

Activity Tracking ◽

Metabolic Equivalent ◽

Optimal Inputs ◽

Input Model ◽

Continuous Glucose Monitors ◽

Monitoring Devices

Current Continuous Glucose Monitors (CGM) exhibit increased estimation error during periods of aerobic physical activity. The use of readily-available exercise monitoring devices opens new possibilities for accuracy enhancement during these periods. The viability of an array of physical activity signals provided by three different wearable devices was considered. Linear regression models were used in this work to evaluate the correction capabilities of each of the wearable signals and propose a model for CGM correction during exercise. A simple two-input model can reduce CGM error during physical activity (17.46% vs. 13.8%, p < 0.005) to the magnitude of the baseline error level (13.61%). The CGM error is not worsened in periods without physical activity. The signals identified as optimal inputs for the model are “Mets” (Metabolic Equivalent of Tasks) from the Fitbit Charge HR device, which is a normalized measurement of energy expenditure, and the skin temperature reading provided by the Microsoft Band 2 device. A simpler one-input model using only “Mets” is also viable for a more immediate implementation of this correction into market devices.

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