Higher Order BLG Supersymmetry Transformations from 10-Dimensional Super Yang Mills

We study a Simple Route for constructing the higher order Bagger-Lambert-Gustavsson theory - both supersymmetry transformations and Lagrangian - starting from knowledge of only the $10$-dimensional Super Yang Mills Fermion Supersymmetry transformation. We are able to uniquely determine the four-derivative order corrected supersymmetry transformations, to lowest non-trivial order in Fermions, for the most general three-algebra theory. For the special case of Euclidean three-algbera, we reproduce the result presented in arXiv:$1207.1208$, with significantly less labour. In addition, we apply our method to calculate the quadratic fermion terms in the higher order BLG fermion supersymmetry transformation.Comment: 15 page

Stacking-based Deep Neural Network: Deep Analytic Network on Convolutional Spectral Histogram Features

Stacking-based deep neural network (S-DNN), in general, denotes a deep neural network (DNN) resemblance in terms of its very deep, feedforward network architecture. The typical S-DNN aggregates a variable number of individually learnable modules in series to assemble a DNN-alike alternative to the targeted object recognition tasks. This work likewise devises an S-DNN instantiation, dubbed deep analytic network (DAN), on top of the spectral histogram (SH) features. The DAN learning principle relies on ridge regression, and some key DNN constituents, specifically, rectified linear unit, fine-tuning, and normalization. The DAN aptitude is scrutinized on three repositories of varying domains, including FERET (faces), MNIST (handwritten digits), and CIFAR10 (natural objects). The empirical results unveil that DAN escalates the SH baseline performance over a sufficiently deep layer.Comment: 5 page

Ethanol: Implications for Rural Communities

This paper presents an overview of the U.S. ethanol industry, its location, and the public policy umbrella that supports its growth. Then the paper analyzes what happens when a county adds an ethanol plant, demonstrates what must be done to modify input-output models to capture those effects realistically, and applies the approach to proposed plants in three counties.Resource /Energy Economics and Policy,

Stacking-Based Deep Neural Network: Deep Analytic Network for Pattern Classification

Stacking-based deep neural network (S-DNN) is aggregated with pluralities of basic learning modules, one after another, to synthesize a deep neural network (DNN) alternative for pattern classification. Contrary to the DNNs trained end to end by backpropagation (BP), each S-DNN layer, i.e., a self-learnable module, is to be trained decisively and independently without BP intervention. In this paper, a ridge regression-based S-DNN, dubbed deep analytic network (DAN), along with its kernelization (K-DAN), are devised for multilayer feature re-learning from the pre-extracted baseline features and the structured features. Our theoretical formulation demonstrates that DAN/K-DAN re-learn by perturbing the intra/inter-class variations, apart from diminishing the prediction errors. We scrutinize the DAN/K-DAN performance for pattern classification on datasets of varying domains - faces, handwritten digits, generic objects, to name a few. Unlike the typical BP-optimized DNNs to be trained from gigantic datasets by GPU, we disclose that DAN/K-DAN are trainable using only CPU even for small-scale training sets. Our experimental results disclose that DAN/K-DAN outperform the present S-DNNs and also the BP-trained DNNs, including multiplayer perceptron, deep belief network, etc., without data augmentation applied.Comment: 14 pages, 7 figures, 11 table

Simulating Metal Mixing of Both Common and Rare Enrichment Sources in a Low-mass Dwarf Galaxy

One-zone models constructed to match observed stellar abundance patterns have been used extensively to constrain the sites of nucleosynthesis with sophisticated libraries of stellar evolution and stellar yields. The metal mixing included in these models is usually highly simplified, although it is likely to be a significant driver of abundance evolution. In this work we use high-resolution hydrodynamics simulations to investigate how metals from individual enrichment events with varying source energies E_(ej) mix throughout the multiphase interstellar medium (ISM) of a low-mass (M_(gas) = 2 × 10⁶ M_⊙), low-metallicity, isolated dwarf galaxy. These events correspond to the characteristic energies of both common and exotic astrophysical sites of nucleosynthesis, including asymptotic giant branch winds (E_(ej) ~ 10⁴⁶ erg), neutron star–neutron star mergers (E_(ej) ~ 10⁴⁹ erg), supernovae (E_(ej) ~ 10⁵¹ erg), and hypernovae (E_(ej) ~ 10⁵² erg). We find the mixing timescales for individual enrichment sources in our dwarf galaxy to be long (100 Myr–1 Gyr), with a clear trend of increasing homogeneity for the more energetic events. Given these timescales, we conclude that the spatial distribution and frequency of events are important drivers of abundance homogeneity on large scales; rare, low-E_(ej) events should be characterized by particularly broad abundance distributions. The source energy E_(ej) also correlates with the fraction of metals ejected in galactic winds, ranging anywhere from 60% at the lowest energy to 95% for hypernovae. We conclude by examining how the radial position, local ISM density, and global star formation rate influence these results

Doctor of Philosophy

dissertationBone diseases range from degenerative osteoporosis to bone cancers and metastasized malignancies. They can be congenital or developed late in life and represent a major cause of decreased quality of life and increased mortality. Large molecule drug delivery systems have been in development and are aimed at delivering targeted drugs to bone. They do this by increasing accumulation with targeting ligands to attach to bone, with large size increasing accumulation in inflamed and malignant tissue and extending circulation half-life due to their reduced clearance by the kidneys. Unfortunately, they often have large dispersities, making consistency among batches more complicated. Small molecules, on the other hand, can be replicated predictably for each batch but lack in extended circulation time and good accumulation due to their size. Micelles have the capability of bridging this gap. By conjugating a high-affinity bone- targeting ligand, aspartic acid octapeptide, to a hydrophobic drug by a degradable linker, a monodisperse unimer is formed that can self-assemble into a large molecule micelle. This dissertation describes the development of a bone-targeted micelle and the incorporation of two different drugs, doxorubicin and 6-bromoindirubin-3’-oxime (6BIO), for the application of treating osteosarcoma and bone fractures, respectively. Four doxorubicin-containing micelles were designed and synthesized. These micelles demonstrated micellar aggregation at low concentrations, increasing the overall size of the delivery system. The micelles also were able to retain their binding to hydroxyapatite and release unmodified drug for treatment of osteosarcoma. From osteosarcoma, efforts were made to examine the versatility of the micellar drug delivery system by applying it to bone fractures. We adapted the delivery system by conjugating it to the GSK3β inhibitor, 6BIO. The modified micelle retained its micelle-assembling capabilities and was able to release drug over several days. Animal studies demonstrated the micelles’ high affinity to bone fractures and ability to increase the bone density of fractured femurs. We have developed a drug delivery system that can be adapted for multiple applications. The micelle has demonstrated its capabilities both in vitro and in vivo