A Stochastic ADMM Algorithm for Large-Scale Ptychography with Weighted Difference of Anisotropic and Isotropic Total Variation

Ptychography, a prevalent imaging technique in fields such as biology and optics, poses substantial challenges in its reconstruction process, characterized by nonconvexity and large-scale requirements. This paper presents a novel approach by introducing a class of variational models that incorporate the weighted difference of anisotropic--isotropic total variation. This formulation enables the handling of measurements corrupted by Gaussian or Poisson noise, effectively addressing the nonconvex challenge. To tackle the large-scale nature of the problem, we propose an efficient stochastic alternating direction method of multipliers, which guarantees convergence under mild conditions. Numerical experiments validate the superiority of our approach by demonstrating its capability to successfully reconstruct complex-valued images, especially in recovering the phase components even in the presence of highly corrupted measurements.Comment: submitte

Robust Efficiency Evaluation of NextCloud and GoogleCloud

Cloud storage services such as GoogleCloud and NextCloud have become increasingly popular among Internet users and businesses. Despite the many encrypted file cloud systems being implemented worldwide today for different purposes, we are still faced with the problem of their usage, security, and performance. Although some cloud storage solutions are very efficient in communication across different clients, others are better in file encryption, such as images, videos, and text files. Therefore, it is evident that the efficiency of these algorithms varies based on the purpose and type of encryption and compression. This paper focuses on the comparative analysis of NextCloud with composed end-to-end solutions that use both an unencrypted cloud storage and an encrypted solution. In this paper, we measured the network use, file output size, and computation time of given workloads for two different services to thoroughly evaluate the efficiency of NextCloud and GoogleCloud. Our findings concluded that there is similar network usage and synchronization time. However, GoogleCloud had more CPU utilization than NextCloud. On the other hand, NextCloud had a longer delay when uploading files to their cloud service. Our experimental results show that the evaluation model is considered robust if its output and forecasts are consistently accurate, even if one or more of the input variables or assumptions are drastically changed due to unforeseen circumstances

Development Of Robot-Based Cognitive And Motor Assessment Tools For Stroke And Hiv Neurorehabilitation

Stroke and HIV are leading causes of disability worldwide. HIV is an independent risk factor for stroke, resulting in an emerging population dealing with both but without guidelines on how to manage the co-presentation of these conditions. There is a need for solutions to combat functional decline that results from the cognitive and motor dysfunction associated with these conditions. Rehabilitation robotics has been explored as a solution to provide therapy in the stroke population, but its application to people living with HIV has not yet been examined. Additionally, current technology-based approaches generally tend to treat cognitive and motor impairments in isolation. As such, a major barrier to the clinical utility of these approaches is that improvements on robotic rehabilitation tasks do not transfer to activities of daily living. In this thesis, I combine rehabilitation robotics, cognitive neuroscience, and bioengineering principles to design robot-based assessment tasks capable of measuring both cognitive and motor impairment. I use clinical assessment and robotic tools to first explore the impact of cognitive impairment on motor performance in the chronic stroke population. The results from this investigation demonstrate that motor performance on a robotic task is sensitive to cognitive impairment due to stroke. I then tested additional assessment tasks against standard clinical assessments of cognitive and motor function relevant in both HIV and stroke. These results showed the ability of robot-based metrics to capture differences in performance between varying levels of impairment among people living with HIV. After demonstrating the concurrent validity of this approach in the U.S., I implemented this approach in Botswana. The preliminary results demonstrated that robotic assessment was feasible in this context and that some of our models had good predictive value. This work expands the application of rehabilitation robotics to new populations, including people living with HIV, those with cognitive impairments, and people residing in LMICs. My hope is that the work presented in this thesis will lead to future efforts that can overcome the barriers to better health by enabling the development of more effective and accessible rehabilitation technologies

Extracting biologically significant patterns from short time series gene expression data

<p>Abstract</p> <p>Background</p> <p>Time series gene expression data analysis is used widely to study the dynamics of various cell processes. Most of the time series data available today consist of few time points only, thus making the application of standard clustering techniques difficult.</p> <p>Results</p> <p>We developed two new algorithms that are capable of extracting biological patterns from short time point series gene expression data. The two algorithms, <it>ASTRO </it>and <it>MiMeSR</it>, are inspired by the <it>rank order preserving </it>framework and the <it>minimum mean squared residue </it>approach, respectively. However, <it>ASTRO </it>and <it>MiMeSR </it>differ from previous approaches in that they take advantage of the relatively few number of time points in order to reduce the problem from NP-hard to linear. Tested on well-defined short time expression data, we found that our approaches are robust to noise, as well as to random patterns, and that they can correctly detect the temporal expression profile of relevant functional categories. Evaluation of our methods was performed using Gene Ontology (GO) annotations and chromatin immunoprecipitation (ChIP-chip) data.</p> <p>Conclusion</p> <p>Our approaches generally outperform both standard clustering algorithms and algorithms designed specifically for clustering of short time series gene expression data. Both algorithms are available at <url>http://www.benoslab.pitt.edu/astro/</url>.</p