Retrospective Uncertainties for Deep Models using Vine Copulas

Despite the major progress of deep models as learning machines, uncertainty estimation remains a major challenge. Existing solutions rely on modified loss functions or architectural changes. We propose to compensate for the lack of built-in uncertainty estimates by supplementing any network, retrospectively, with a subsequent vine copula model, in an overall compound we call Vine-Copula Neural Network (VCNN). Through synthetic and real-data experiments, we show that VCNNs could be task (regression/classification) and architecture (recurrent, fully connected) agnostic while providing reliable and better-calibrated uncertainty estimates, comparable to state-of-the-art built-in uncertainty solutions.The research leading to these results has received funding from the Horizon Europe Programme under the SAFEXPLAIN Project (www.safexplain.eu), grant agreement num. 101069595 and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 772773). Additionally, this work has been partially supported by Grant PID2019-107255GB-C21 funded by MCIN/AEI/ 10.13039/501100011033.Peer ReviewedPostprint (published version

A decision forest based feature selection framework for action recognition from RGB-Depth cameras

In this paper, we present an action recognition framework leveraging data mining capabilities of random decision forests trained on kinematic features. We describe human motion via a rich collection of kinematic feature time-series computed from the skeletal representation of the body in motion. We discriminatively optimize a random decision forest model over this collection to identify the most effective subset of features, localized both in time and space. Later, we train a support vector machine classifier on the selected features. This approach improves upon the baseline performance obtained using the whole feature set with a significantly less number of features (one tenth of the original). On MSRC-12 dataset (12 classes), our method achieves 94% accuracy. On the WorkoutSU-10 dataset, collected by our group (10 physical exercise classes), the accuracy is 98%. The approach can also be used to provide insights on the spatiotemporal dynamics of human actions

A Bayesian data assimilation framework for lake 3D hydrodynamic models with a physics-preserving particle filtering method using SPUX-MITgcm v1

We present a Bayesian inference for a three-dimensional hydrodynamic model of Lake Geneva with stochastic weather forcing and high-frequency observational datasets. This is achieved by coupling a Bayesian inference package, SPUX, with a hydrodynamics package, MITgcm, into a single framework, SPUX-MITgcm. To mitigate uncertainty in the atmospheric forcing, we use a smoothed particle Markov chain Monte Carlo method, where the intermediate model state posteriors are resampled in accordance with their respective observational likelihoods. To improve the uncertainty quantification in the particle filter, we develop a bi-directional long short-term memory (BiLSTM) neural network to estimate lake skin temperature from a history of hydrodynamic bulk temperature predictions and atmospheric data. This study analyzes the benefit and costs of such a state-of-the-art computationally expensive calibration and assimilation method for lakes.</p

Learning summary statistics for Bayesian inference with autoencoders

For stochastic models with intractable likelihood functions, approximate Bayesian computation offers a way of approximating the true posterior through repeated comparisons of observations with simulated model outputs in terms of a small set of summary statistics. These statistics need to retain the information that is relevant for constraining the parameters but cancel out the noise. They can thus be seen as thermodynamic state variables, for general stochastic models. For many scientific applications, we need strictly more summary statistics than model parameters to reach a satisfactory approximation of the posterior. Therefore, we propose to use a latent representation of deep neural networks based on Autoencoders as summary statistics. To create an incentive for the encoder to encode all the parameter-related information but not the noise, we give the decoder access to explicit or implicit information on the noise that has been used to generate the training data. We validate the approach empirically on two types of stochastic models

Photocurrent analysis of AgIn5S8 crystal

The photocurrent (PC) spectrum of AgIn5S8 crystal consists of a single peak, which provides to determine the bandgap energy by applying the Moss rule. The temperature dependence of the bandgap energy was also calculated. The PC dramatically increased by pre-illumination with light having wavelength corresponding to the bandgap of AgIn5S8. The temperature-dependent PC of the sample was measured at different temperatures from 80 to 300 K and the PC spectrum consisted a single broad peak. Thermal quenching of the PC was observed to start at similar to 105 K and the PC completely quenched at similar to 180 K. The quenching mechanism was discussed in terms of the two-centre model. The height of the PC peak rised linearly with applied voltage up to 5.0 V under constant intensity of light. Similarly, the dark current-voltage characteristics consisted of a single region dominating an ohmic behaviour, and no space charge limited region was apparent at various temperatures up to 20 V

Capacitive Loading Element with High Selectivity

In this letter, a novel fourth order filter using dual mode open loop resonators is proposed. Dual mode open loop resonators are performed by using capacitive loading element. The main advantage of this loading element is that it controls the passband frequency and bandwidth, sensitively. Also, use of the dual mode open loop resonator provides a compact and narrowband bandpass filter. In order to demonstrate the validity the filter is designed, simulated and fabricated. Measured result shows good agreement with simulated frequency response

Capacitive Loading Element with High Selectivity

In this letter, a novel fourth order filter using dual mode open loop resonators is proposed. Dual mode open loop resonators are performed by using capacitive loading element. The main advantage of this loading element is that it controls the passband frequency and bandwidth, sensitively. Also, use of the dual mode open loop resonator provides a compact and narrowband bandpass filter. In order to demonstrate the validity the filter is designed, simulated and fabricated. Measured result shows good agreement with simulated frequency response

Trapping parameters of repulsive centers in SbSI single crystals

Charge trapping centers in antimony sulphide iodide (SbSI) single crystals have been investigated by the use of thermally stimulated current (TSC) technique. The TSC spectrum consists of only one apparent peak which is found to be associated with a single trapping level. Those traps are experimentally found to obey the monomolecular kinetics. The trapping parameters as the energy depth, temperature dependent frequency factor and capture cross section together with the concentrations of the corresponding discrete trapping level are determined. The TSC signal is found to be strongly dependent on illumination temperature of the sample and this is explained by the model in which the traps are considered to be surrounded by repulsive potential barriers