Presented by: Samuel Mathew

Date: Tuesday, July 2, 2024
Time: 1:00 pm
Place: Zoom, link below.

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Abstract: A model of the interaction of a Rayleigh particle with a Bethe aperture in terms of the electric dipole of the particle and the effective magnetic dipole of the aperture reveals a polarizability-dependent enhancement in both the trapping potential and the transmission through the Bethe aperture. Since it is known that polarizability is a geometry-dependent material parameter, it is hypothesized that protein conformation changes may be associated with a change in material polarizability. It is further hypothesized that if point mutations, impact the conformation of a protein, then they may necessarily alter the material polarizability of a protein too and, through this, the transmission through the aperture of a nano-optical tweezer such as the double nanohole (DNH) aperture tweezer, making it possible to use the optical trapping signals of the DNH aperture tweezer to study the impact of such mutations on a protein such as PR65 – the α-subunit of protein phosphatase 2A (PP2A), which serves as an elastic bridge between the structural and catalytic subunits of PP2A – which has been shown in a previous in silico study to undergo conformation changes in response to mechanical forces such as may be present in an optical tweezer. Thus, DNH apertures were fabricated in a structure of gold-on-glass by colloidal lithography and used to trap PR65 wild type and six of its mutants at a laser power of 22 mW. The resulting optical signals were captured using an avalanche photodiode (APD) connected to a digital USB-4771A data acquisition module and analysed using MATLAB in terms of parameters including the median transition time between the characteristic jump steps shown by the acquired signals and the root-mean-squared-deviation (RMSD) and corner frequency of the acquired signals. Comparison of these parameters for the mutants with those of PR65 wild type shows that some mutations conferred more stability on PR65 while other mutations had a destabilizing effect on PR65. This conclusion is further supported by correlation of these parameters with in silico mean contour lengths of PR65 wild type and its six mutants studied. Based on these results, it can be concluded that PR65 undergoes conformation changes that are impacted by substitution mutations. This might have potential for the detection of mutant proteins and for tracking protein mutations and conformational dynamics. The technique developed in the work may also be extended to study protein-ligand binding, although further research to model and characterize conformation changes induced by ligand-binding in the absence of thermo-optical forces would be necessary to extract binding-specific conformation changes from the overall conformation change in this case.

Presented by: Yuhao Chen

Date: Thursday, July 18, 2024
Time: 11:00 am
Place: EOW 430

Abstract: Artificial intelligence and vehicle communication have been a very hot topic in recent years. A large number of related industries have emerged around the relevant technologies. Currently, self-driving systems such as Tesla Autopilot have begun to change human life. To help graduate students understand the principles of autonomous driving and practice the technologies, the F1Tenth platform was born at the University of Pennsylvania. It relies on a one-tenth scale racing car to simulate autonomous driving competitions, helping students and autonomous driving enthusiasts explore the hardware, algorithms, and more of autonomous driving. In this seminar, I will introduce the construction of the F1Tenth system from scratch, including the principles of the LiDAR-based autonomous driving algorithms and their implementation in ROS2. I will also showcase the latest progress of our Connected and Autonomous Vehicle Club. I hope this can inspire more engineering students to develop an interest in autonomous driving and join our community to enjoy the thrilling F1Tenth competitions. 

Presented by: Yixiang Jia

Date: Friday, July 26, 2024
Time: 8:00 am
Place: Zoom, link below.

Location: Remote via Zoom 

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ABSTRACT

Ultrasonic nondestructive evaluation (NDE) is an essential tool in various industries, including aerospace, energy, and civil engineering, for assessing the structural integrity of manufactured products without damaging them. This thesis is focused on the automated analysis of ultrasonic NDE data by means of low-cost machine learning (ML) techniques, particularly in the context of inline pipeline inspection. We propose two lightweight neural network architectures for efficient multi-attribute classification to characterize surface-breaking crack defects in terms of their location, size, and tilt. Our networks have under 2M parameters and incorporate novel design elements inspired by the latest MobileNet models. Their computational footprint is also small, not exceeding 100M floating-point operations (FLOPs) per data sample. The proposed models process raw channel data acquired by a transducer array, as opposed to multi-view beamformed image patches utilized in related works, thus eliminating the computational burden associated with image reconstruction. Our evaluation results, based on a public-domain NDE dataset, demonstrate that our networks offer a balanced combination of their competitively high classification performance and low cost. These findings highlight the potential of lightweight deep learning models in ultrasonic NDE data analysis, which contributes to the development of more advanced and intelligent inspection systems. Our future research will focus on refining the proposed models to enhance their spatio-temporal feature learning, interpretability, generalization capability, and applicability to other fields, such as biomedical imaging and computer vision.

Presented by: Zahra Nikdel

Date: Friday, July 26, 2024
Time: 12:00 pm
Place: Zoom, link below.

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ABSTRACT

This research presents a detailed investigation into the run-time performance predictability of software-centric systems using Monte Carlo simulation. The focus is on assessing statistical performance measures in systems ranging from small-scale in-house deployed to large-scale cloud deployed environments, including containers and virtual machines. By introducing an OMNet++ based simulations, that incorporates various workload scenarios, OS scheduling algorithms, and deployment configurations, we aim to provide insights into the factors affecting performance predictability. Moreover, by simulating a real-world, in-production Large-scale Distributed Software System (LDSS) under statistically identical service workloads and deployment regimes, we contrast the impacts of reliable and non-reliable protocols. The results reveal that the event recovery actions triggered by reliable protocols substantially decrease the LDSS's run-time performance predictability. Key findings highlight variations in systems’ run-time behavior under similar conditions and provide practical insights for industry applications. This research contributes to a deeper understanding of performance predictability in modern software systems, offering guidance for improving the predictability and reliability of software systems.

Presented by: Najme Sahami

Date: Tuesday, July 30, 2024
Time: 10:00 am
Place: Zoom, link below.

Abstract: 

A Legendre sequence s of length p, where p is an odd prime, is used to create a circulant matrix S. An alternative Legendre sequence, ˜s, is employed to form another circulant matrix ˜S. By concatenating these two matrices, we obtain the matrix D′, which is subsequently used to form a bordered double-circulant code with length 2p + 2 and dimension k = p + 1 over GF(q), q is a prime and gcd(p, q) = 1. We demonstrate that for p = 2qm − 1 the code generated by

                                          D =11 1^t |1^t

                                                 10 S|˜S

over GF(q) is self-dual. We introduce the decomposition of these codes, emphasizing their self-dual properties. Theoretical proofs are provided to support the orthogonality and self-orthogonality of the rows of these codes. Additionally, we discuss the rank of the circulant matrices formed by Legendre sequences of length p = 4kq−1 over GF(q). We demonstrate that specific row-column permutations in D′ lead to non-singular matrices, revealing that these codes can be defined as direct sums of codes generated by S and ˜ S.

Time: Jul 30, 2024 10:00 AM Vancouver

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