Energy disaggregation is the task of taking a whole-home energy signal and separating it into its component appliances. Studies have shown that having devicelevel energy information can cause users to conserve significant amounts of energy, but current electricity meters only report whole-home data. Thus, developing algorithmic methods for disaggregation presents a key technical challenge in th...

Performance is one of the key features of parallel and distributed computing systems. Therefore, in the past a significant research effort was invested in the development of approaches for performance modeling and prediction of parallel and distributed computing systems. In this paper we identify the trends, contributions, and drawbacks of the state of the art approaches. We describe a wide ran...

Existing approaches to non-rigid 3D reconstruction either are specifically designed for feature point correspondences, or require a good shape initialization to exploit more complex image likelihoods. In this paper, we formulate reconstruction as inference in a graphical model, where the variables encode the rotations and translations of the facets of a surface mesh. This lets us exploit comple...

The Predictive Performance Equation (PPE) is a mathematical model of learning and forgetting developed to capture performance effectiveness across training histories, and to generate precise, quantitative point predictions of performance by extrapolating the unique mathematical regularities indicative of the learner. This equation is implemented in the Predictive Performance Optimizer (PPO) cog...

The task of predicting query performance has received much attention over the past decade. However, many of the frameworks and approaches to predicting query performance are more heuristic than not. In this paper, we develop a principled framework based on modelling the document score distribution to predict query performance directly. In particular, we (1) show how a standard performance measu...

Despite intensive studies during the last 30 years researchers are yet far from the “holy grail” of blind structure prediction of the three dimensional native state of a protein from its sequence of amino acids. We introduce here a Multimeme Algorithm which is robust across a range of protein structure models and instances. New benchmark sequences for the triangular lattice in the HP model and ...

In problems where input features have varying amounts of noise, using distinct regularization hyperparameters for different features provides an effective means of managing model complexity. While regularizers for neural networks and support vector machines often rely on multiple hyperparameters, regularizers for structured prediction models (used in tasks such as sequence labeling or parsing) ...

there is an increasing demand for reliable large-scale soil datato meet the requirements of models for planning of land-usesystems, characterization of soil pollution, and prediction ofland degradation. cation exchangecapacity (cec) is among the most important soil propertiesthat are required in soil databases. this paper applied a novel method for whole-soil profile predictions of cec (to 1 m)...

Short-term water demand modeling plays a key role in urban water resources planning and management. The importance of demand prediction is even greater in countries like Iran with frequent periods of drought. Short-term water demand estimation is useful for planning and management of water and wastewater facilities such as pump scheduling, control of reservoirs and tanks volume, pressure manage...