Unsupervised Domain Adaptation by Backpropagation

Top-performing deep architectures are trained on massive amounts of labeled data. In the absence of labeled data for a certain task, domain adaptation often provides an attractive option given that labeled data of similar nature but from a different domain (e.g. synthetic images) are available. Here, we propose a new approach to domain adaptation in deep architectures that can be trained on large amount of labeled data from the source domain and large amount of unlabeled data from the target domain (no labeled target-domain data is necessary). As the training progresses, the approach promotes the emergence of "deep" features that are (i) discriminative for the main learning task on the source domain and (ii) invariant with respect to the shift between the domains. We show that this adaptation behaviour can be achieved in almost any feed-forward model by augmenting it with few standard layers and a simple new gradient reversal layer. The resulting augmented architecture can be trained using standard backpropagation. Overall, the approach can be implemented with little effort using any of the deep-learning packages. The method performs very well in a series of image classification experiments, achieving adaptation effect in the presence of big domain shifts and outperforming previous state-of-the-art on Office datasets

Length Dependence of Band Structure in Carbon Nanotubes of Ultra Small Diameter

The paper presents results of a study of the band structure and related parameters and also the bond energy of single-walled carbon nanotubes carried out using semiempirical methods and ab initio density functional theory implemented in Gaussian 2003 framework. Much attention is paid to the dependency of the values mentioned on the length and on the chirality of the tubes. Both the infinite and the finite open-ended nanotubes are considered. It was found that the dependency of the band gap on the diameter has os-cillating character for infinite zigzag semiconducting tubes. It was also found that finite armchair nano-tubes have non-zero band gap which decreases showing oscillations with the length and decreases mono-tonically with the diameter. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3556

Sustainable CO2 adsorbents prepared by coating chitosan onto mesoporous silicas for large-scale carbon capture technology

In this article, we report a new sustainable synthesis procedure for manufacturing chitosan/silica CO2 adsorbents. Chitosan is a naturally abundant material and contains amine functionality, which is essential for selective CO2 adsorptions. It is, therefore, ideally suited for manufacturing CO2 adsorbents on a large scale. By coating chitosan onto high-surface-area mesoporous silica supports, including commercial fumed silica (an economical and accessible reagent) and synthetic SBA-15 and MCF silicas, we have prepared a new family of CO2 adsorbents, which have been fully characterised with nitrogen adsorption isotherms, thermogravimetric analysis/differential scanning calorimetry, TEM, FTIR spectroscopy and Raman spectroscopy. These adsorbents have achieved a significant CO2 adsorption capacity of up to 0.98 mmol g−1 at ambient conditions (P=1 atm and T=25 °C). The materials can also be fully regenerated/recycled on demand at temperatures as low as 75 °C with a >85 % retention of the adsorption capacity after 4 cycles, which makes them promising candidates for advanced CO2 capture, storage and utilisation technology

STRUM - An Interactive Computer System for Modeling Binary Relations

System identification and specification is essential in every systems study. The development of structural models (which describe the geometric relationships between the elements of the system) is an important part of this procedure. However, when the system is very complex or the number of elements is large it becomes difficult to construct such models without some technical assistance. In this paper, the authors describe an interactive computer system called STRUM which facilitates the structural modeling process. The use of the system (which is implemented on the IIASA VAX 11/780 computer) is illustrated by application to a specific example. This paper is a contribution to research currently underway in the System and Decision Sciences Program

Exact Results for Spectra of Overdamped Brownian Motion in Fixed and Randomly Switching Potentials

The exact formulae for spectra of equilibrium diffusion in a fixed bistable piecewise linear potential and in a randomly flipping monostable potential are derived. Our results are valid for arbitrary intensity of driving white Gaussian noise and arbitrary parameters of potential profiles. We find: (i) an exponentially rapid narrowing of the spectrum with increasing height of the potential barrier, for fixed bistable potential; (ii) a nonlinear phenomenon, which manifests in the narrowing of the spectrum with increasing mean rate of flippings, and (iii) a nonmonotonic behaviour of the spectrum at zero frequency, as a function of the mean rate of switchings, for randomly switching potential. The last feature is a new characterization of resonant activation phenomenon.Comment: in press in Acta Physica Polonica, vol. 35 (4), 200

Resguardo del derecho ambiental frente a los procedimientos de tratamiento de residuos y reciclaje.

Tesis (Licenciado en Ciencias Jurídicas)INTRODUCCIÓN: El cuidado del medio ambiente es la preocupación más latente de la sociedad en este último tiempo. Son varios los temas que afectan el ambiente, y que nosotros como ciudadanos, luchamos para evitar cualquier impacto a éste e incentivamos a la sociedad para que día a día, el cuidado medioambiental sea una nueva tarea para todos. Así encontramos que los municipios son los principales encargados de que la tarea ambiental sea implementada en proyectos y buenas prácticas, tendientes a desarrollar y mejorar la gestión ambiental. La educación ambiental está frecuentemente dirigida a esta nueva sociedad, respecto de un contexto de calidad de vida de manera más integral, donde el vínculo del medio ambiente es fundamental para el desarrollo social, y que tiene como base el principio de las 3R: Reducir, Reutilizar y Reciclar. Esta manera de aprovechar los residuos trae múltiples beneficios y el cuál nos proporcionaría a largo plazo, un buen vivir, cumpliéndose nuestro derecho constitucional, al “derecho a vivir en un ambiente libre de contaminación”. Es importante recalcar que nosotros como ciudadanos no somos los principales invasores de esta contaminación, ya que no alcanzamos a impactar el ambiente, no así las industrias, quiénes forman parte del porcentaje más alto de contaminación ambiental en el planeta, y en ellos es donde debiera recaer el análisis ambiental más drástico, siendo a nuestro parecer quienes no tienen un mínimo de conciencia ambiental. El sector exportador, tal como la minería, pesca, agrícola y forestal, han provocado problemas de contaminación y degradación de los recursos en todas las regiones del país, afectando los ecosistemas, la salud y la calidad de vida de las personas, vulnerando sus derechos de acceso a recursos básicos para una vida digna. Estos problemas resultan agravados por la deficiente fiscalización de las actividades industriales y sus impactos; por el retraso de políticas públicas orientadas a la protección del medio ambiente; y la falta de voluntad política de las autoridades para enfrentar y resolver estos problemas. La política gubernamental ha priorizado la explotación y comercialización de recursos naturales para Son estos los motivos que nos lleva a realizar un análisis exhaustivo en material ambiental para conocer y valorar los múltiples beneficios que nos proporcionan los..

Zero-Shot Deep Domain Adaptation

Domain adaptation is an important tool to transfer knowledge about a task (e.g. classification) learned in a source domain to a second, or target domain. Current approaches assume that task-relevant target-domain data is available during training. We demonstrate how to perform domain adaptation when no such task-relevant target-domain data is available. To tackle this issue, we propose zero-shot deep domain adaptation (ZDDA), which uses privileged information from task-irrelevant dual-domain pairs. ZDDA learns a source-domain representation which is not only tailored for the task of interest but also close to the target-domain representation. Therefore, the source-domain task of interest solution (e.g. a classifier for classification tasks) which is jointly trained with the source-domain representation can be applicable to both the source and target representations. Using the MNIST, Fashion-MNIST, NIST, EMNIST, and SUN RGB-D datasets, we show that ZDDA can perform domain adaptation in classification tasks without access to task-relevant target-domain training data. We also extend ZDDA to perform sensor fusion in the SUN RGB-D scene classification task by simulating task-relevant target-domain representations with task-relevant source-domain data. To the best of our knowledge, ZDDA is the first domain adaptation and sensor fusion method which requires no task-relevant target-domain data. The underlying principle is not particular to computer vision data, but should be extensible to other domains.Comment: This paper is accepted to the European Conference on Computer Vision (ECCV), 201