Predicting crystal structure is one of the most fundamental problems in materials science and a key early step in computational materials design. Ab initio simulation methods are a powerful tool for predicting crystal structure, but are too slow to explore the extremely large space of possible structures for new alloys. Here we describe ongoing work on a novel method (Data Mining of Quantum Cal...

We present a series of learning algorithms and theoretical guarantees for designing accurate ensembles of structured prediction tasks. This includes several randomized and deterministic algorithms devised by converting on-line learning algorithms to batch ones, and a boosting-style algorithm applicable in the context of structured prediction with a large number of labels. We give a detailed stu...

Context prediction is the approach to provide applications with information about future contexts. Some work has already been done on predicting future contexts based on the observed context history. The observed context timeline is analysed for typical patterns in the context history. Based on this knowledge the most probable future contexts are forecast. The authors of [1] and [2] study predi...

How to resolve the control flow breaking caused by the branch instructions is a major issue in modern deep pipeline processor design. Our project is based on the paper of J. Stark et. al. [1], a variable length path branch predictor. It uses the branch path information for prediction, and change the length of the path dynamically based on the profiling of the application. It shows that a “cleve...

Recent improvements in the accessibility of high-throughput genotyping have brought a great deal of attention to disease association studies[6]. It is believed that more accurate disease association is achieved with inferred haplotypes rather than with directly available genotypes. The main goal of disease association analysis is to identify gene variations or, in general, haplotypes which cont...

The amino acid sequence rules that correspond to beta-sheet structures in proteins are still not well understood. Current protein structure prediction methods are more accurate for alpha-helical structures than for beta-sheet structures. One reason that beta-strand structure prediction is more difficult is because of its high prevalence of nonlocal interactions between regions of the protein ch...

Perhaps surprisingly, it is possible to predict how long an algorithm will take to run on a previously unseen input, using machine learning techniques to build a model of the algorithm’s runtime as a function of problem-specific instance features. Such models have important applications to algorithm analysis, portfolio-based algorithm selection, and the automatic configuration of parameterized ...

Extended Abstract. Forecasting is one of the most important purposes of time series analysis. For many years, classical methods were used for this aim. But these methods do not give good performance results for real time series due to non-linearity and non-stationarity of these data sets. On one hand, most of real world time series data display a time-varying second order structure. On th...