How can we discern whether the covariance operator of a stochastic pro-cess is of reduced rank, and if so, what its precise rank is? And how can we do so at a given level of confidence? This question is central to a great deal of methods for functional dat ...
The Pollution Detection Algorithm (PDA) is an algorithm to identify and flag periods of primary polluted data in remote atmospheric time series in five steps. The first and most important step identifies polluted periods based on the gradient (time-derivat ...
We propose a statistically optimal approach to construct data-driven decisions for stochastic optimization problems. Fundamentally, a data-driven decision is simply a function that maps the available training data to a feasible action. It can always be exp ...
We test general relativity (GR) at the effective redshift (z) over tilde similar to 1.5 by estimating the statistic E-G, a probe of gravity, on cosmological scales 19 - 190 h(-1)Mpc. This is the highest redshift and largest scale estimation of E-G so far. ...
During the last twenty years, Random matrix theory (RMT) has produced numerous results that allow a better understanding of large random matrices. These advances have enabled interesting applications in the domain of communication. Although this theory can ...
Understanding how users navigate in a network is of high interest in many applications. We consider a setting where only aggregate node-level traffic is observed and tackle the task of learning edge transition probabilities. We cast it as a preference lear ...
A method to quantify the equivalent storage capacity inherent the operation of thermostatically controlled loads (TCLs) is developed. Equivalent storage capacity is defined as the amount of power and electricity consumption which can be deferred or anticip ...
The i-vector and Joint Factor Analysis (JFA) systems for text- dependent speaker verification use sufficient statistics computed from a speech utterance to estimate speaker models. These statis- tics average the acoustic information over the utterance ther ...
Physiological Brain connectivity and spontaneous interaction between regions of interest of the brain can be represented by a matrix (full or sparse) or equivalently by a complex network called connectome. This representation of brain connectivity is adopt ...
In this paper, we study how to extract visual concepts to understand landscape scenicness. Using visual feature representations from a Convolutional Neural Network (CNN), we learn a number of Concept Activation Vectors (CAV) aligned with semantic concepts ...
