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...

Phylogenetic hidden Markov models, or phylo-HMMs, are probabilistic models that consider not only the way substitutions occur through evolutionary history at each site of a genome, but also the way this process changes from one site to the next. By treating molecular evolution as a combination of two Markov processes—one that operates in the dimension of space (along a genome) and one that oper...

background: myocardial infarction (mi) occurs due to heart muscle death that costs like human life, which is higher than the treatment costs. this study aimed to present an mi prediction model using classification data mining methods, which consider the imbalance nature of the problem. methods: we enrolled 455 healthy and 295 myocardial infarction cases of visitors to shahid madani specialized ...