Boiler for generating high quality vapor Patent

Vapor generating boiler system for turbine moto

AdS Strings with Torsion: Non-complex Heterotic Compactifications

Combining the effects of fluxes and gaugino condensation in heterotic supergravity, we use a ten-dimensional approach to find a new class of four-dimensional supersymmetric AdS compactifications on almost-Hermitian manifolds of SU(3) structure. Computation of the torsion allows a classification of the internal geometry, which for a particular combination of fluxes and condensate, is nearly Kahler. We argue that all moduli are fixed, and we show that the Kahler potential and superpotential proposed in the literature yield the correct AdS radius. In the nearly Kahler case, we are able to solve the H Bianchi using a nonstandard embedding. Finally, we point out subtleties in deriving the effective superpotential and understanding the heterotic supergravity in the presence of a gaugino condensate.Comment: 42 pages; v2. added refs, revised discussion of Bianchi for N

On the Sample Complexity of Predictive Sparse Coding

The goal of predictive sparse coding is to learn a representation of examples as sparse linear combinations of elements from a dictionary, such that a learned hypothesis linear in the new representation performs well on a predictive task. Predictive sparse coding algorithms recently have demonstrated impressive performance on a variety of supervised tasks, but their generalization properties have not been studied. We establish the first generalization error bounds for predictive sparse coding, covering two settings: 1) the overcomplete setting, where the number of features k exceeds the original dimensionality d; and 2) the high or infinite-dimensional setting, where only dimension-free bounds are useful. Both learning bounds intimately depend on stability properties of the learned sparse encoder, as measured on the training sample. Consequently, we first present a fundamental stability result for the LASSO, a result characterizing the stability of the sparse codes with respect to perturbations to the dictionary. In the overcomplete setting, we present an estimation error bound that decays as \tilde{O}(sqrt(d k/m)) with respect to d and k. In the high or infinite-dimensional setting, we show a dimension-free bound that is \tilde{O}(sqrt(k^2 s / m)) with respect to k and s, where s is an upper bound on the number of non-zeros in the sparse code for any training data point.Comment: Sparse Coding Stability Theorem from version 1 has been relaxed considerably using a new notion of coding margin. Old Sparse Coding Stability Theorem still in new version, now as Theorem 2. Presentation of all proofs simplified/improved considerably. Paper reorganized. Empirical analysis showing new coding margin is non-trivial on real dataset

Determinants of Student Debt in New England

This paper examines the determinants of average student debt in New England based on financial, institutional and demographic variables. The dataset is derived from CollegeInsight and measures 15 variables across 71 institutions during the 2011-2014 academic school years. Between 2011 and 2014, average student debt increased 7%, tuition and room and board increased 10%, the percentage of Hispanic students increased 20% and the percentage of international students increased 26%. The estimated model, ln(AVDEBT) = 7.401 – 0.090ln(TUITION) – 0.042ln(BOOKS) + 0.433ln(ROOMBOARD) + 0.07ln(ENROLLMENT) – 0.200PUBLIC – 4.106ASIAN – 1.992AFAMERICAN + 0.254HISPANIC + 0.007WHITE + 0.641INTERNATIONAL – 0.676PERCENTFEDDEBT + 1.018PERCENTPELL, indicates that the primary determinants of average student debt in the region are: room and board costs, enrollment, public vs. private classification, the percentage of Asian, African American and international students, the percentage of student debt that comes from federal loans and the percentage of Pell Grant recipients. Other regions in the country could leverage a similar study to understand where the student debt burden is most likely coming from. The resulting information can stir policy discussions amongst institutions and governmental organizations to decrease the burden and ease the so-called student debt crisis

A novel method to construct stationary solutions of the Vlasov-Maxwell system : the relativistic case

A method to derive stationary solutions of the relativistic Vlasov-Maxwell system is explored. In the non-relativistic case, a method using the Hermite polynomial series to describe the deviation from the Maxwell-Boltzmann distribution is found to be successful in deriving a few stationary solutions including two dimensional one. Instead of the Hermite polynomial series, two special orthogonal polynomial series, which are appropriate to expand the deviation from the Maxwell-J\"uttner distribution, are introduced in this paper. By applying this method, a new two-dimensional equilibrium is derived, which may provide an initial setup for investigations of three-dimensional relativistic collisionless reconnection of magnetic fields.Comment: 15pages, 2 figures, to appear in Phys. Plasma

One-pass adaptive universal vector quantization

The authors introduce a one-pass adaptive universal quantization technique for real, bounded alphabet, stationary sources. The algorithm is set on line without any prior knowledge of the statistics of the sources which it might encounter and asymptotically achieves ideal performance on all sources that it sees. The system consists of an encoder and a decoder. At increasing intervals, the encoder refines its codebook using knowledge about incoming data symbols. This codebook is then described to the decoder in the form of updates on the previous codebook. The accuracy to which the codebook is described increases as the number of symbols seen, and thus the accuracy to which the codebook is known, grows