Synthesis and Assembly of Anisotropic Ellipsoidal Particles.

We create complex non-close packed assemblies from ellipsoidal anisotropic particles using external electric fields in this dissertation. Spherical colloidal particles have long been self-assembled into close-packed 2-D and 3-D ordered structures, while external fields have been used to accelerate their self-assembly as well as to alter the free energy landscape to create novel field driven ordered structures. In the first part of the dissertation, we develop a direct current (DC) electric field assembly method that accelerates the self-assembly of charged colloidal particles. In the second part of the dissertation, we use alternating current (AC) electric fields to alter the free energy landscape and actuate the self-assembled colloidal assemblies created from anisotropic particles.PHDMacromolecular Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107255/1/aayushs_1.pd

Applied Machine Learning for Games: A Graduate School Course

The game industry is moving into an era where old-style game engines are being replaced by re-engineered systems with embedded machine learning technologies for the operation, analysis and understanding of game play. In this paper, we describe our machine learning course designed for graduate students interested in applying recent advances of deep learning and reinforcement learning towards gaming. This course serves as a bridge to foster interdisciplinary collaboration among graduate schools and does not require prior experience designing or building games. Graduate students enrolled in this course apply different fields of machine learning techniques such as computer vision, natural language processing, computer graphics, human computer interaction, robotics and data analysis to solve open challenges in gaming. Student projects cover use-cases such as training AI-bots in gaming benchmark environments and competitions, understanding human decision patterns in gaming, and creating intelligent non-playable characters or environments to foster engaging gameplay. Projects demos can help students open doors for an industry career, aim for publications, or lay the foundations of a future product. Our students gained hands-on experience in applying state of the art machine learning techniques to solve real-life problems in gaming.Comment: The Eleventh Symposium on Educational Advances in Artificial Intelligence (EAAI-21

Identifying Trades Using Technical Analysis and ML/DL Models

The importance of predicting stock market prices cannot be overstated. It is a pivotal task for investors and financial institutions as it enables them to make informed investment decisions, manage risks, and ensure the stability of the financial system. Accurate stock market predictions can help investors maximize their returns and minimize their losses, while financial institutions can use this information to develop effective risk management policies. However, stock market prediction is a challenging task due to the complex nature of the stock market and the multitude of factors that can affect stock prices. As a result, advanced technologies such as deep learning are being increasingly utilized to analyze vast amounts of data and provide valuable insights into the behavior of the stock market. While deep learning has shown promise in accurately predicting stock prices, there is still much research to be done in this area.Comment: 14 pages, 9 figures, 5 table

Optimizing Multi-Domain Performance with Active Learning-based Improvement Strategies

Improving performance in multiple domains is a challenging task, and often requires significant amounts of data to train and test models. Active learning techniques provide a promising solution by enabling models to select the most informative samples for labeling, thus reducing the amount of labeled data required to achieve high performance. In this paper, we present an active learning-based framework for improving performance across multiple domains. Our approach consists of two stages: first, we use an initial set of labeled data to train a base model, and then we iteratively select the most informative samples for labeling to refine the model. We evaluate our approach on several multi-domain datasets, including image classification, sentiment analysis, and object recognition. Our experiments demonstrate that our approach consistently outperforms baseline methods and achieves state-of-the-art performance on several datasets. We also show that our method is highly efficient, requiring significantly fewer labeled samples than other active learning-based methods. Overall, our approach provides a practical and effective solution for improving performance across multiple domains using active learning techniques.Comment: 13 pages, 20 figures, draft work previously published as a medium stor

Liquid Crystal Order in Colloidal Suspensions of Spheroidal Particles by Direct Current Electric Field Assembly

D C electric fields are used to produce colloidal assemblies with orientational and layered positional order from a dilute suspension of spheroidal particles. These 3D assemblies, which can be visualized in situ by confocal microscopy, are achieved in short time spans ( t < 1 h) by the application of a constant voltage across the capacitor‐like device. This method yields denser and more ordered assemblies than had been previously reported with other assembly methods. Structures with a high degree of orientational order as well as layered positional order normal to the electrode surface are observed. These colloidal structures are explained as a consequence of electrophoretic deposition and field‐assisted assembly. The interplay between the deposition rate and the rotational Brownian motion is found to be critical for the optimal ordering, which occurs when these rates, as quantified by the Peclet number, are of order one. The results suggest that the mechanism leading to ordering is equilibrium self‐assembly but with kinetics dramatically accelerated by the application of the DC electric field. Finally, the crystalline symmetry of the densest structure formed is determined and compared with previously studied spheroidal assemblies. Rapid assembly of anisotropic colloidal particles is essential to create complex, uniform, and scalable crystal structures for applications. In this study, DC electric fields are used to accelerate the self‐assembly process of spheroidal particles. The image shows confocal microscopy images and renderings from image processing of the field‐induced 3D ordering. The assembly is shown to have high‐quality orientational order and previously unobserved periodic and dense layered ordering.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/92091/1/1551_ftp.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/92091/2/smll_201102265_sm_suppl.pd

PANC Study (Pancreatitis: A National Cohort Study): national cohort study examining the first 30 days from presentation of acute pancreatitis in the UK

Abstract Background Acute pancreatitis is a common, yet complex, emergency surgical presentation. Multiple guidelines exist and management can vary significantly. The aim of this first UK, multicentre, prospective cohort study was to assess the variation in management of acute pancreatitis to guide resource planning and optimize treatment. Methods All patients aged greater than or equal to 18 years presenting with acute pancreatitis, as per the Atlanta criteria, from March to April 2021 were eligible for inclusion and followed up for 30 days. Anonymized data were uploaded to a secure electronic database in line with local governance approvals. Results A total of 113 hospitals contributed data on 2580 patients, with an equal sex distribution and a mean age of 57 years. The aetiology was gallstones in 50.6 per cent, with idiopathic the next most common (22.4 per cent). In addition to the 7.6 per cent with a diagnosis of chronic pancreatitis, 20.1 per cent of patients had a previous episode of acute pancreatitis. One in 20 patients were classed as having severe pancreatitis, as per the Atlanta criteria. The overall mortality rate was 2.3 per cent at 30 days, but rose to one in three in the severe group. Predictors of death included male sex, increased age, and frailty; previous acute pancreatitis and gallstones as aetiologies were protective. Smoking status and body mass index did not affect death. Conclusion Most patients presenting with acute pancreatitis have a mild, self-limiting disease. Rates of patients with idiopathic pancreatitis are high. Recurrent attacks of pancreatitis are common, but are likely to have reduced risk of death on subsequent admissions. </jats:sec

Outcomes of transulnar and transradial percutaneous coronary intervention using ultrasound guided access in patients selected based on an ultrasound algorithm

We performed a prospective observational study of 215 patients (58 ± 11 years) and compared the outcomes of ultrasound guided ulnar (n = 98, 45.6%) vs. radial (n = 117, 54.4%) cardiac catheterization and percutaneous coronary intervention (PCI) in patients selected by an ultrasound based algorithm. Primary endpoints included the number of access attempts and conversion to femoral access. Secondary endpoints included all-cause mortality, cardiac mortality, myocardial infarction, stroke, repeat revascularization, stent thrombosis, in-stent restenosis, and access site complications.No significant difference was found in the primary endpoints between radial or ulnar. Ulnar access showed no significant hematomas. Therefore, ulnar PCI is a feasible alternative