Finito: A Faster, Permutable Incremental Gradient Method for Big Data Problems

Recent advances in optimization theory have shown that smooth strongly convex finite sums can be minimized faster than by treating them as a black box "batch" problem. In this work we introduce a new method in this class with a theoretical convergence rate four times faster than existing methods, for sums with sufficiently many terms. This method is also amendable to a sampling without replacement scheme that in practice gives further speed-ups. We give empirical results showing state of the art performance

Clamping improves TRW and mean field approximations

We examine the effect of clamping variables for approximate inference in undirected graphical models with pairwise relationships and discrete variables. For any number of variable labels, we demonstrate that clamping and summing approximate sub-partition functions can lead only to a decrease in the partition function estimate for TRW, and an increase for the naive mean field method, in each case guaranteeing an improvement in the approximation and bound. We next focus on binary variables, add the Bethe approximation to consideration and examine ways to choose good variables to clamp, introducing new methods. We show the importance of identifying highly frustrated cycles, and of checking the singleton entropy of a variable. We explore the value of our methods by empirical analysis and draw lessons to guide practitioners.NICTA is funded by the Australian Government through the Department of Communications and the Australian Research Council through the ICT Centre of Excellence Program.This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by MIT Press

Study of cost/benefit tradeoffs for reducing the energy consumption of the commercial air transportation system

Economic studies were conducted for three general fuel conserving options: (1) improving fuel consumption characteristics of existing aircraft via retrofit modifications; (2) introducing fuel efficient derivations of existing production aircraft and/or introducing fuel efficient, current state-of-the-art new aircraft; and (3) introducing an advanced state-of-the-art turboprop airplane. These studies were designed to produce an optimum airline fleet mix for the years 1980, 1985 and 1990. The fleet selected accommodated a normal growth market by introducing somewhat larger aircraft while solving for maximum departure frequencies and a minimum load factor corresponding to a 15% investment hurdle rate. Fuel burnt per available-seat-mile flown would drop 22% from 1980 to 1990 due to the use of more fuel efficient aircraft designs, larger average aircraft size, and increased seating density. An inflight survey was taken to determine air traveler attitudes towards a new generation of advanced turboprops

The Beam Conditions Monitor of the LHCb Experiment

The LHCb experiment at the European Organization for Nuclear Research (CERN) is dedicated to precision measurements of CP violation and rare decays of B hadrons. Its most sensitive components are protected by means of a Beam Conditions Monitor (BCM), based on polycrystalline CVD diamond sensors. Its configuration, operation and decision logics to issue or remove the beam permit signal for the Large Hadron Collider (LHC) are described in this paper.Comment: Index Terms: Accelerator measurement systems, CVD, Diamond, Radiation detector

Energy-based temporal neural networks for imputing missing values

Imputing missing values in high dimensional time series is a difficult problem. There have been some approaches to the problem [11,8] where neural architectures were trained as probabilistic models of the data. However, we argue that this approach is not optimal. We propose to view temporal neural networks with latent variables as energy-based models and train them for missing value recovery directly. In this paper we introduce two energy-based models. The first model is based on a one dimensional convolution and the second model utilizes a recurrent neural network. We demonstrate how ideas from the energy-based learning framework can be used to train these models to recover missing values. The models are evaluated on a motion capture dataset

Utilization of Golf Course Facilities by Residents of Golf Course Communities in Myrtle Beach

The number of golf courses in the Myrtle Beach area is constantly changing. So too, are the number of new residents who decide to buy or build homes or condominiums near these golf courses. This thesis explores the utilization of golf courses by residents of the particular golf course community. Primary research is performed and analyzed concerning four Myrtle Beach area golf course communities. The study shows that primary home ownership, retirement, membership of the golf course, and prestige of the golf course are all positively associated with usage of the golf course. The results of the study may be used for marketers, golf course developers, and managers to better identify golf course community homeowners, a target market often overlooked

The Impact of Attractions Demand on Lodging Demand

This three-year study of the relationship between attractions demand and lodging demand indicate that increasing attendance at recreation and entertainment-related attractions is associated with heightened lodging demand. However, it is also clear that the relationship between attractions attendance and lodging demand may vary from destination to destination

Revisiting loss-specific training of filter-based MRFs for image restoration

It is now well known that Markov random fields (MRFs) are particularly effective for modeling image priors in low-level vision. Recent years have seen the emergence of two main approaches for learning the parameters in MRFs: (1) probabilistic learning using sampling-based algorithms and (2) loss-specific training based on MAP estimate. After investigating existing training approaches, it turns out that the performance of the loss-specific training has been significantly underestimated in existing work. In this paper, we revisit this approach and use techniques from bi-level optimization to solve it. We show that we can get a substantial gain in the final performance by solving the lower-level problem in the bi-level framework with high accuracy using our newly proposed algorithm. As a result, our trained model is on par with highly specialized image denoising algorithms and clearly outperforms probabilistically trained MRF models. Our findings suggest that for the loss-specific training scheme, solving the lower-level problem with higher accuracy is beneficial. Our trained model comes along with the additional advantage, that inference is extremely efficient. Our GPU-based implementation takes less than 1s to produce state-of-the-art performance.Comment: 10 pages, 2 figures, appear at 35th German Conference, GCPR 2013, Saarbr\"ucken, Germany, September 3-6, 2013. Proceeding

Agri-Tainment: Combining Agriculture and Entertainment Along the Grand Strand

This paper provides an overview of the benefits sought by consumers from Agri-tainment venues and the challenges of establishing such venues. The paper begins by defining Agri-tainment, and providing examples of established Agri-tainment venues across the United States and within the Grand Strand area of South Carolina. It then provides a series of Supporting Statements regarding likely benefits and Cautionary Statements associated with Agri-tainment, and concludes with potential resources for expanding the offering of Agri-tainment venues along the Grand Strand