Extreme events in stochastic transport on networks. (arXiv:1911.09335v1 [physics.soc-ph])

Extreme events are emergent phenomena in multi-particle transport processes on complex networks. In practice, such events could range from power blackouts to call drops in cellular networks to traffic congestion on roads. All the earlier studies of extreme events on complex networks have focused only on the nodal events. If random walks are used to…

GPU-Accelerated Event Reconstruction for the COMET Phase-I Experiment. (arXiv:1911.09340v1 [physics.ins-det])

A parallelization scheme with GPU is introduced for event reconstruction in the cylindrical drift chamber of COMET Phase-I experiment. The experiment is aiming for a discovery of charged lepton flavor violation by observing 104.97 MeV electrons from neutrinoless muon to electron conversion in muonic atoms. Helical trajectories of signal electrons, inside the cylindrical drift chamber,…

Charge fluctuations from molecular simulations in the constant-potential ensemble. (arXiv:1911.09351v1 [cond-mat.stat-mech])

We revisit the statistical mechanics of charge fluctuations in capacitors. In constant-potential classical molecular simulations, the atomic charge of electrode atoms are treated as additional degrees of freedom which evolve in time so as to satisfy the constraint of fixed electrostatic potential for each configuration of the electrolyte. The present work clarifies the role of…

Heart Segmentation From MRI Scans Using Convolutional Neural Network. (arXiv:1911.09332v1 [eess.IV])

Heart is one of the vital organs of human body. A minor dysfunction of heart even for a short time interval can be fatal, therefore, efficient monitoring of its physiological state is essential for the patients with cardiovascular diseases. In the recent past, various computer assisted medical imaging systems have been proposed for the segmentation…

Convolutional Mixture Density Recurrent Neural Network for Predicting User Location with WiFi Fingerprints. (arXiv:1911.09344v1 [cs.LG])

Predicting smartphone users activity using WiFi fingerprints has been a popular approach for indoor positioning in recent years. However, such a high dimensional time-series prediction problem can be very tricky to solve. To address this issue, we propose a novel deep learning model, the convolutional mixture density recurrent neural network (CMDRNN), which combines the strengths…

A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data. (arXiv:1911.09355v1 [cs.LG])

Discovering human mobility patterns with geo-location data collected from smartphone users has been a hot research topic in recent years. In this paper, we attempt to discover daily mobile patterns based on GPS data. We view this problem from a probabilistic perspective in order to explore more information from the original GPS data compared to…

On the separation of shape and temporal patterns in time series — Application to signature authentication. (arXiv:1911.09360v1 [cs.DM])

In this article we address the problem of separation of shape and time components in time series. The concept of shape that we tackle is termed temporally “neutral” to consider that it may possibly exist outside of any temporal specification, as it is the case for a geometric form. We propose to exploit and adapt…

A semiparametric instrumental variable approach to optimal treatment regimes under endogeneity. (arXiv:1911.09260v1 [stat.ME])

There is a fast-growing literature on estimating optimal treatment regimes based on randomized trials or observational studies under a key identifying condition of no unmeasured confounding. Because confounding by unmeasured factors cannot generally be ruled out with certainty in observational studies or randomized trials subject to noncompliance, we propose a general instrumental variable approach to…

Robustness Certificates for Sparse Adversarial Attacks by Randomized Ablation. (arXiv:1911.09272v1 [cs.LG])

Recently, techniques have been developed to provably guarantee the robustness of a classifier to adversarial perturbations of bounded L_1 and L_2 magnitudes by using randomized smoothing: the robust classification is a consensus of base classifications on randomly noised samples where the noise is additive. In this paper, we extend this technique to the L_0 threat…

Computer Model Emulation with High-Dimensional Functional Output in Large-Scale Observing System Uncertainty Experiments. (arXiv:1911.09274v1 [stat.AP])

The observing system uncertainty experiments (OSUEs) have been widely used as a cost-effective way to make retrieval quality assessment in NASA’s Orbiting Carbon Observatory-2 (OCO-2) mission. One important component in the OCO-2 retrieval algorithm is a full-physics forward model that describes the relationship between the atmospheric variables such as carbon dioxide and radiances measured by…