Ameerika kirjanduse ajalugu

This e-course is a support for the course P2NC.00.137 “History of American Literature” (3 ECT). The e-course introduces the main periods and the key writers of American literature as well as provides an overview of the social processes and historic events that influenced the development of American literature.BeSt programmi toetusel loodud e-kursuse "Ameerika kirjanduse ajalugu" õppematerjalid

Improving the Processing of Dried Brown Algae of the Northern Basin Seas

Dried brown algae, abundant in the seas of the Northern basin, are additional sources of iodine, the lack of which affects more than two thirds of Russians. Brown algae of the Northern basin seas: sugar wrack and bady wrack, harvested in the Barents Sea were chosen as the object of the study. The use of cryoextrusion and freeze-drying will allow expanding the possibilities of brown algae processing. The use of frozen raw material allows processing it industrially far from the harvesting areas. The results presented in the paper confirm the possibility and expediency of applying cryoextrusion and freeze drying since they are advanced methods of resource-saving technology for the processing of the North basin brown algae. The modes for grinding of the frozen brown algae were developed on the basis of cryoextrusion with the use of dies with holes of ”cone-cone” type; the design of the unit is protected by a patent. The yield of ground semi-finished product obtained at different modes varies from 98.57 to 99.80% from the weight of the raw material. The resulting semi-finished product is of homogeneous structure. The use of freeze-drying, depending on the type, allows achieving the final content of product moisture from 5.24 to 10.6%

Neural networks and quantum many-body physics: exploring reciprocal benefits.

One of the main reasons why the physics of quantum many-body systems is hard lies in the curse of dimensionality: The number of states of such systems increases exponentially with the number of degrees of freedom involved. As a result, computations for realistic systems become intractable, and even numerical methods are limited to comparably small system sizes. Many efforts in modern physics research are therefore concerned with finding efficient representations of quantum states and clever approximations schemes that would allow them to characterize physical systems of interest. Meanwhile, Deep Learning (DL) has solved many non-scientific problems that have been unaccessible to conventional methods for a similar reason. The concept underlying DL is to extract knowledge from data by identifying patterns and regularities. The remarkable success of DL has excited many physicists about the prospect of leveraging its power to solve intractable problems in physics. At the same time, DL turned out to be an interesting complex many-body problem in itself. In contrast to its widespread empirical applications, the theoretical foundation of DL is strongly underdeveloped. In particular, as long as its decision-making process and result interpretability remain opaque, DL can not claim the status of a scientific tool. In this thesis, I explore the interface between DL and quantum many-body physics, and investigate DL both as a tool and as a subject of study. The first project presented here is a theory-based study of a fundamental open question about the role of width and the number of parameters in deep neural networks. In this work, we consider a DL setup for the image recognition task on standard benchmarking datasets. We combine controlled experiments with a theoretical analysis, including analytical calculations for a toy model. The other three works focus on the application of Restricted Boltzmann Machines as generative models for the task of wavefunction reconstruction from measurement data on a quantum many-body system. First, we implement this approach as a software package, making it available as a tool for experimentalists. Following the idea that physics problems can be used to characterize DL tools, we then use our extensive knowledge of this setup to conduct a systematic study of how the RBM complexity scales with the complexity of the physical system. Finally, in a follow-up study we focus on the effects of parameter pruning techniques on the RBM and its scaling behavior

Bounding generalization error with input compression: An empirical study with infinite-width networks

Estimating the Generalization Error (GE) of Deep Neural Networks (DNNs) is an important task that often relies on availability of held-out data. The ability to better predict GE based on a single training set may yield overarching DNN design principles to reduce a reliance on trial-and-error, along with other performance assessment advantages. In search of a quantity relevant to GE, we investigate the Mutual Information (MI) between the input and final layer representations, using the infinite-width DNN limit to bound MI. An existing input compression-based GE bound is used to link MI and GE. To the best of our knowledge, this represents the first empirical study of this bound. In our attempt to empirically falsify the theoretical bound, we find that it is often tight for best-performing models. Furthermore, it detects randomization of training labels in many cases, reflects test-time perturbation robustness, and works well given only few training samples. These results are promising given that input compression is broadly applicable where MI can be estimated with confidence.Comment: 12 pages main content, 26 pages tota

Use of open electronic courses in educational activity

The use of open electronic courses is a relatively new tendency in education and it is not yet fully discovered by researchers. With its introduction a change in the structure of the educational system happens. Therefore, the article studies the specifics of the introduction of online courses, including requirements to them, as well as factors that slow down their active development. Due to it, the following methods of investigation were used as: analysis, synthesis, deduction, induction. As an example, Kozma Minin Nizhny Novgorod State Pedagogical University - Minin University was used. Based on the study of the "Regulations on the use of open courses in the educational activities of Minin University", it was noted, in addition to the functions of the coordination group on open education, that for the trainee at the end of the course, in the case of unsatisfactory result, the possibility of the recalculation is established. This paragraph is used not by every university. In the article we make a conclusion that open courses are a new qualitative step in the development of world education, but online courses would be much more effective as narrow-minded, adult-oriented, and deeply motivated for learning. Taken as the basis of research, Minin University showed that the university is ready for the implementation of online courses. It is one of the few universities that actively use open courses in Russia.peer-reviewe