adaptations in electronic structure calculations in heterogeneous environments

Modern quantum chemistry deals with electronic structure calculations of unprecedented complexity and accuracy. They demand full power of high-performance computing and must be in tune with the given architecture for superior efficiency. To make such applications resource-aware, it is desirable to enable their static and dynamic adaptations using some external software (middleware), which may monitor both system availability and application needs, rather than mix science with system-related calls inside the application. The present work investigates scientific application interlinking with middleware based on the example of the computational chemistry package GAMESS and middleware NICAN. The existing synchronous model is limited by the possible delays due to the middleware processing time under the sustainable runtime system conditions. Proposed asynchronous and hybrid models aim at overcoming this limitation. When linked with NICAN, the fragment molecular orbital (FMO) method is capable of adapting statically and dynamically its fragment scheduling policy based on the computing platform conditions. Significant execution time and throughput gains have been obtained due to such static adaptations when the compute nodes have very different core counts. Dynamic adaptations are based on the main memory availability at run time. NICAN prompts FMO to postpone scheduling certain fragments, if there is not enough memory for their immediate execution. Hence, FMO may be able to complete the calculations whereas without such adaptations it aborts

ML-Based User Authentication Through Mouse Dynamics

Increasing reliance on digital services and the limitations of traditional authentication methods have necessitated the development of more advanced and secure user authentication methods. For user authentication and intrusion detection, mouse dynamics, a form of behavioral biometrics, offers a promising and non-invasive method. This paper presents a comprehensive study on ML-Based User Authentication Through Mouse Dynamics. This project proposes a novel framework integrating sophisticated techniques such as embeddings extraction using Transformer models with cutting-edge machine learning algorithms such as Recurrent Neural Networks (RNN). The project aims to accurately identify users based on their distinct mouse behavior and detect unauthorized access by utilizing the hybrid models. Using a mouse dynamics dataset, the proposed framework’s performance is evaluated, demonstrating its efficacy in accurately identifying users and detecting intrusions. In addition, a comparative analysis with existing methodologies is provided, highlighting the enhancements made by the proposed framework. This paper contributes to the development of more secure, reliable, and user-friendly authentication systems that leverage the power of machine learning and behavioral biometrics, ultimately augmenting the privacy and security of digital services and resources

Improving the Accuracy and Robustness of Self-Tuning Histograms by Subspace Clustering

Self-tuning histograms are a type of histograms very popular these days, as they allow the usage of multidimensional datasets. The main advantage of them is that they have a low computational cost due to their capacity to understand the dataset. Also, they proposed a better approach as they stay up-to-date and have adaptability to query patterns. According to the above, many researchers have worked on improving the accuracy of these type of histograms, which has led to the use of subspace clustering methods as initialization values. Following this approach in this study, a self-tuning histogram code was developed with the objective of comparing two different subclustering methods (Proclus and Mineclus) for the initialization values. The script was tested with two different datasets (2-D and 4-D). It was found that the Proclus algorithm performed better than the Mineclus. Also, it was proved that the size of the bucket was crucial to achieve more accuracy (Khachatryan, Clustering-initialized adaptive histograms and probabilistic cost estimation for query optimization, 2012)

Vakuumbasierte Abscheidung von funktionellen Nanokompositen und deren Modifikation durch Ionenstrahlbehandlung

Nanocomposite thin film coatings with a wide range of metal volume fractions were prepared by co–sputtering of TiO2/Teflon and Ag/Au from two different magnetron sources simultaneously in a home made deposition chamber under high vacuum conditions. Two different types of host materials a polymeric (PTFE) and a ceramic (TiO2) were studied in this work. Morphology, optical and antibacterial properties of these nanocomposites were examined. The formation of metallic nanoparticles upon vapor phase co–deposition of a metal and a dielectric matrix component can be understood in terms of the high cohesive energy of the metal and the low metal-matrix interaction energy which lead to high metal atom mobility on the growing composite surface and metal aggregation whenever metal atoms encounter each other or a metal cluster. Unlike the case of polymers, in the case of Ag nanoparticles on TiO2, segregation of the clusters on the surface also provides a fast pathway for Ostwald ripening without any restrictions by elastic distortions at least for those clusters which are in direct contact with the surface. 3D electron tomography was employed on the TiO2 based nanocomposite thin films to explain the two step model for the particle size distribution. First step involved the formation of small nanoparticles during vacuum phase deposition or on the growing surface. Second step after the deposition process involved the formation of larger particles through particle coarsening by Ostwald ripening and surface segregation. In bimetallic nanocomposites based on sandwich geometry in polymer system, the changes in the particle plasmon spectra of sandwiched Au nanoclusters as a result of the presence of Ag nanoclusters in their vicinity and vice versa was studied. Also, the optimum dielectric barrier thickness for the observation of equal intensity double plasmon resonance was reported. Also efforts towards tuning of the double plasmon resonances by tailoring the dielectric separation were carried out. Special attention was laid on the swift heavy ion irradiation (SHI) of the nanocomposites. The SHI beamlines from both the Hahn–Meitner–Institute in Berlin, Germany and the Inter University Accelerator Center in New–Delhi, India, were employed in this work. The TiO phase formation on SHI irradiation with increasing fluence was understood by the interaction of two different counteracting mechanisms, where at lower fluences, the tendency towards the formation of TiO existed with the larger unaffected areas and at higher fluences, the destruction of the evolved TiO phase into fragments was evident. This served as an evidence for the counter play between "hit" and "no–hit", "single–hit" and "multiple–hit" processes. A comparative study involving the in–situ heating of the TiO2 based nanocomposites in the TEM confirms the absence of the formation of TiO. Changes of the microstructure of the nanocomposite film upon annealing allowed demonstrating the absence of the formation of TiO but rather only the crystallization of the TiO2. SHI irradiation of Ag nanoparticles embedded in PTFE matrix shows a marginal dissolution of Ag nanoparticles along with a slight agglomeration of nanoparticles. At higher fluences, carbon rich areas were observed, which were as a result of the carbonization along the ion tracks. Functionality of the nanocomposites in terms of the antibacterial properties was studied. Cultures of B.megaterium, S.aureus, S.epidermidis and E.coli were used to study the effect on the Ag–TiO2 nanocomposites. Additionally, silver ion release studies were carried out at dfferent MVFs by using X-ray photoelectron and UV-Vis/NIR spectroscopies. Enhancement of the silver ion release after SHI irradiation at a fluence was observed to the fact that the ion trajectories after irradiation provide better silver ion release

Stock market predictions using machine learning

2021 Spring.Includes bibliographical references.In this thesis, an attempt is made to try and establish the impact of news articles and correlated stocks on any one stock. Stock prices are dependent on many factors, some of which are common for most stocks, and some are specific to a type of company. For instance, a product-based company's stocks are dependent on sales and profit, while a research-based company's stocks are based on the progress made in their research over a specified time period. The main idea behind this thesis is that using news articles, we can potentially estimate how much each of these factors can impact the stock prices and how much of it is based on common factors like momentum. This thesis is split into three parts. The first part is finding the correlated stocks for a selected stock ticker. Correlated stocks can have a significant impact on stock prices; having a diverse portfolio of non-correlated stocks is very important for a stock trader, and yet very little research has been done on this part from a computer science point of view. The second part is to use Long-Short Term Memory on a pre-compiled list of news articles for the selected stock ticker; this enables us to understand which articles might have some influence on the stock prices. The third part is to combine the two and compare the result to stock predictions made using the deep neural network on the stock prices during the same period. The selected companies for the experiment are - Microsoft, Google, Netflix, Apple, Nvidia, AMD, Amazon. The companies were selected based on their popularity on the Internet, which makes it easier to get more articles on the companies. If we look at the day to day movement in stock prices, a typical regression approach can give reasonably accurate results on stock prices, but where this method fails is in predicting the significant changes in prices that are not based on trends or momentum. For instance, if a company releases a faulty product but the hype for the product is high prior to the release, the trends would show a positive direction for the stocks and a regression approach would most likely not predict the fall in the prices right after the news of the fault is made public. It will eventually correct itself, but it would not be instantaneous. Using a news-based approach, it is possible to predict the fall in stocks before the change is noticed in the actual stock price. This approach seems to show success to a varying degree with Microsoft showing the best accuracy of 91.46%, and AMD had the lowest at 40.59% on the test dataset. This was probably because of the volatility of AMD's stock prices, and this volatility could be caused by factors other than the news such as the impact of some other third-party companies. While the news articles can help predict specific stock movements, we still need a trend based regression approach for the day to day stock movements. The second part of the thesis is focused on this part of the stock predictions. It incorporates the results from these news articles into another neural network to predict the actual stock prices of each of the companies. The second neural network takes the percentage change in stock price from one day to the next as the input along with the predicted values from the news articles to predict the value of the stock for the next day. This approach seems to produce mixed results. AMD's predicted values seem to be worse when incorporated with only the news articles

Development of a compact, IoT-enabled electronic nose for breath analysis

In this paper, we report on an in-house developed electronic nose (E-nose) for use with breath analysis. The unit consists of an array of 10 micro-electro-mechanical systems (MEMS) metal oxide (MOX) gas sensors produced by seven manufacturers. Breath sampling of end-tidal breath is achieved using a heated sample tube, capable of monitoring sampling-related parameters, such as carbon dioxide (CO2), humidity, and temperature. A simple mobile app was developed to receive real-time data from the device, using Wi-Fi communication. The system has been tested using chemical standards and exhaled breath samples from healthy volunteers, before and after taking a peppermint capsule. Results from chemical testing indicate that we can separate chemical standards (acetone, isopropanol and 1-propanol) and different concentrations of isobutylene. The analysis of exhaled breath samples demonstrate that we can distinguish between pre- and post-consumption of peppermint capsules; area under the curve (AUC): 0.81, sensitivity: 0.83 (0.59–0.96), specificity: 0.72 (0.47–0.90), p-value: <0.001. The functionality of the developed device has been demonstrated with the testing of chemical standards and a simplified breath study using peppermint capsules. It is our intention to deploy this system in a UK hospital in an upcoming breath research study

A numerical model for the fractional condensation of pyrolysis vapours

Experimentation on the fast pyrolysis process has been primarily focused on the pyrolysis reactor itself, with less emphasis given to the liquid collection system (LCS). More importantly, the physics behind the vapour condensation process in LCSs has not been thoroughly researched mainly due to the complexity of the phenomena involved. The present work focusses on providing detailed information of the condensation process within the LCS, which consists of a water cooled indirect contact condenser. In an effort to understand the mass transfer phenomena within the LCS, a numerical simulation was performed using the Eulerian approach. A multiphase multi-component model, with the condensable vapours and non-condensable gases as the gaseous phase and the condensed bio-oil as the liquid phase, has been created. Species transport modelling has been used to capture the detailed physical phenomena of 11 major compounds present in the pyrolysis vapours. The development of the condensation model relies on the saturation pressures of the individual compounds based on the corresponding states correlations and assuming that the pyrolysis vapours form an ideal mixture. After the numerical analysis, results showed that different species condense at different times and at different rates. In this simulation, acidic components like acetic acid and formic acids were not condensed as it was also evident in experimental works, were the pH value of the condensed oil is higher than subsequent stages. In the future, the current computational model can provide significant aid in the design and optimization of different types of LCSs