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X-WR-CALDESC:Events for Northeastern University College of Engineering
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20210317
DTEND;VALUE=DATE:20210422
DTSTAMP:20260427T034339
CREATED:20210318T134829Z
LAST-MODIFIED:20210318T134829Z
UID:25081-1615939200-1619049599@coe.northeastern.edu
SUMMARY:Study Recruitment: Ancient Techniques and Mental Health Today
DESCRIPTION:Northeastern Department of Philosophy & Religion  \nHave you been experiencing stress and anxiety? \nYou may be eligible to participate in our study! \nHelp us investigate the impact of mindfulness on various life outcomes! All components of this study will take place virtually; participants will be asked to attend two 30-minute Zoom sessions in addition to up to 5 weeks of short\, daily smartphone tasks. \nYou must be 18 years or older\, a Boston-based Northeastern undergraduate student\, and a native English speaker to be eligible to participate. \nParticipants will receive $80 in compensation. \nContact us at pwolstudy@gmail.com if you’re interested and to see if you are eligible! \nThis study has been reviewed and approved by the Northeastern University Institutional Review Board (#21-02-21).
URL:https://coe.northeastern.edu/event/study-recruitment-ancient-techniques-and-mental-health-today/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210420T100000
DTEND;TZID=America/New_York:20210420T110000
DTSTAMP:20260427T034339
CREATED:20210420T140838Z
LAST-MODIFIED:20210420T140838Z
UID:25503-1618912800-1618916400@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Danton Zhao
DESCRIPTION:MS Thesis Defense: LiDAR with a Silicon Photomultiplier for Applications in Adverse Weather \nDanton Zhao \nLocation: Zoom Link \nAbstract: As Light Detection and Ranging (LiDAR) integration becomes more widespread in the field of remote sensing for autonomous navigation\, the impact of degraded visual environments will quickly need to be addressed. The particles responsible for the degradation not only reduce the reflected signal from targets of interest but can also trigger false returns given sufficient density. Of particular interest for solutions to this problem are Geiger-mode avalanche photodiodes\, as these detectors provide high photon sensitivity and high time accuracy with a caveat. In this thesis\, I will be discussing the work that I have done in modeling and addressing artifacts that were generated in the data as a result of using Geiger-mode detectors.
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-danton-zhao/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210420T110000
DTEND;TZID=America/New_York:20210420T120000
DTSTAMP:20260427T034339
CREATED:20210412T144906Z
LAST-MODIFIED:20210412T144906Z
UID:25386-1618916400-1618920000@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Yize Li
DESCRIPTION:MS Thesis Defense: Supervised Classification on Deep Neural Network Attack Toolchains \nYize Li \nLocation: Zoom Link \nAbstract: Deep learning\, while an important machine learning technique\, is susceptible to adversarial example attacks. Adversarial examples generated by adding perturbations on clean images/video frames can lead to mis-predictions of deep neural networks. Moreover\, deep learning/machine learning can be used to deceive humans by generating adversarial falsified media e.g.\, deepfake attacks. The thesis work will study the above two attack scenarios\, i.e.\, machine-centric adversary and human-centric adversary\, with targets to fool ML decisions and human decisions\, respectively. We aim to build a generalizable and scalable supervised learning system for classifying attack attributes behind the machine-centric attacks as well as the human-centric attacks. We start from building an integrated Attack Toolchain Library (ATL) with a broad coverage of both machine-centric and human-centric adversaries\, as well as through an integrated user interface for great flexibility and extensibility to serve our downstream tasks. Based on the developed ATL\, we further design a meta-classifier pipeline architecture for predicting attack attributes. The proposed overall meta-classifier shows effectiveness in dealing with false alarms and data distribution shift\, and generalization to both machine-centric and human-centric attacks.
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-yize-li/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210420T120000
DTEND;TZID=America/New_York:20210420T130000
DTSTAMP:20260427T034339
CREATED:20210414T173115Z
LAST-MODIFIED:20210414T173115Z
UID:25445-1618920000-1618923600@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Ashutosh Singh
DESCRIPTION:MS Thesis Defense: Variation is the Norm: Brain State Dynamics Evoked By Emotional Video Clips \nAshutosh Singh \nLocation: Zoom Link \nAbstract: Past affective neuroscience studies have attempted to identify a “biomarker” or consistent pattern of brain activity (as measured externally using\, for instance\, fMRI) to indicate the presence of a single pre-defined category of emotion (e.g.\, fear) that remains consistent throughout all instances of that category for an individual across contexts and even across individuals. In this thesis\, we investigated variation rather than consistency during emotional experiences. Using fMRI data acquired while individuals watched affect-invoking video clips that have been normed for their evoked emotion categories in prior population studies. Towards this end\, we developed a probabilistic model of the temporal dynamics associated with the hypothetical affect-related brain states\, fitted to the measured brain activity of the participants. We characterized brain states traversed while individuals watched these clips as distinct state occupancy periods between state transitions\, inferred by blood oxygen level-dependent (BOLD) signal patterns captured in fMRI measurements. We found substantial variability in the state occupancy probability distributions across individuals watching the same video\, hence supporting the hypothesis that when it comes to the brain correlates of emotional experience\, variation may indeed be the norm. Studying the mean activation pattern associated with each state\, as well as covariance (in the Gaussian conditional measurement model we assumed)\, we further improve our understanding of the variability between instances of these brain states. Additionally\, we analyzed the presence of potential clusters of brain state trajectories among participants who showed less divergence in their response to each of these videos and checked for their consistency throughout all the video clips.
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-ashutosh-singh/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210420T133000
DTEND;TZID=America/New_York:20210420T143000
DTSTAMP:20260427T034339
CREATED:20210420T135838Z
LAST-MODIFIED:20210420T135838Z
UID:25487-1618925400-1618929000@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Peng Chang
DESCRIPTION:PhD Dissertation Defense: Model-Based Manipulation of Linear Flexible Objects \nPeng Chang \nLocation: Teams Meetings \nAbstract: Manipulation of deformable objects plays an important role in various scenarios such as manufacturing\, service\, healthcare\, and security. Linear flexible objects such as cables\, wires\, and ropes are common in these scenarios. However\, the high dimensionality of the linear flexible objects brings challenges to the modeling and planning in manipulation tasks\, and automatic manipulation of these objects is computationally expensive due to their infinite degrees of freedom in the free spaces. In this dissertation\, we investigate model-based manipulation of linear flexible objects such as cables. We contribute to different models including geometrical and physical models to represent the linear flexible objects. With these models\, we then develop manipulation plans and strategies to achieve the automation of the linear flexible object manipulation tasks in both simulation and real-world. Besides\, we also investigate human-robot collaboration to complete a sample assembly task involving linear flexible object manipulation.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-peng-chang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210420T140000
DTEND;TZID=America/New_York:20210420T150000
DTSTAMP:20260427T034339
CREATED:20210420T135556Z
LAST-MODIFIED:20210420T135556Z
UID:25482-1618927200-1618930800@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Griffin Knipe
DESCRIPTION:MS Thesis Defense: Unifying Performance and Security Evaluation for Microarchitecture Design Exploration \nGriffin Knipe \nLocation: Zoom Link \nAbstract: Computer architects develop microarchitectural features that boost instruction-level parallelism to improve CPU performance. While performance may be improved\, adding new features increases the CPU’s design complexity. This further compounds the effort required to complete design verification. Trustworthy design verification is paramount to microarchitecture design\, as silicon chips cannot easily be patched in the field.\nDespite the best efforts for security verification\, researchers have created transient execution side-channel attacks which can exploit microarchitecture performance features to leak data across ISA-prescribed security boundaries. This motivates the unification of performance evaluation and security verification techniques to ensure that new microarchitectural features are understood from multiple design perspectives.\nThis thesis presents Yori\, a RISC-V microarchitecture simulator that aims to enable computer architects to evaluate microarchitecture performance and security using a single framework. As Yori is a work-in-progress\, this thesis presents the work-to-date\, focusing on a detailed model of the reference microarchitecture and evaluation of the current model accuracy. We describe a viable methodology to interface between the Yori simulator and an existing security verification tool. We conclude the thesis\, laying out a plan to complete this marriage of performance and security.
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-griffin-knipe/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210420T160000
DTEND;TZID=America/New_York:20210420T170000
DTSTAMP:20260427T034339
CREATED:20210414T173500Z
LAST-MODIFIED:20210414T173500Z
UID:25439-1618934400-1618938000@coe.northeastern.edu
SUMMARY:MS Thesis Defense: Hao Chen
DESCRIPTION:MS Thesis Defense: Reconstruction of Sulcal Geometry in Brain Stimulation Models using Spherical Harmonics \nHao Chen \nLocation: Zoom Link \nAbstract: Over the past few years\, there has been increasing interest in transcranial electrical stimulation (tCS) and thus it has been the subject of a growing number of simulation studies. Indeed\, some federal agencies in the US now require model-based simulations to be included as part of tCS grant proposal. In order to obtain more accurate simulation results and guide the relevant research\, it is of important to assess the impact of the accuracy of the anatomical 3D brain model that these studies depend on. However\, due to the partial volume problem\, many 3D reconstruction results based on MR images are inaccurate with respect to the details of the geometry of the sulci. Specifically\, when the sulci are on the scale of\, or even smaller than\, the voxel resolution of the MRI\, these models generally really in a binary approximation\, either making the sulcus wider in the model than in reality or eliminating it altogether. In this thesis\, we describe a method for modeling the 3D reconstruction of the brain that may facilitate controlled study of the effect of these approximations. The general approach is to model the brain surface using a spherical harmonic expansion\, then modify the expansion coefficients in an attempt to selectively and smoothly control sulcal width. In the first part of the thesis\, we describe and evaluate an approach in which we experimentally selected two groups of spherical harmonic coefficients within a specified range that could simultaneously affect a chosen sample of the gyri. For the coefficients in the first group\, the widths of all gyri in the sample were increased by enlarging the corresponding coefficients for each spherical harmonic. Conversely\, for each coefficient in the second group\, this adjustment caused the widths of the sampled gyri to decrease simultaneously. We evaluated the method by alternately increasing / decreasing the coefficients in the first group\, and decreasing / increasing those in the second\, by a chosen range of factors\, and observing the effects on the model cortical surface. Experimental results showed that the widths of most of the sulci and gyri were simultaneously adjusted according to the desired effect.\nIn the second part of the thesis\, we tried to build a volume mesh starting from the modified spherical harmonic surfaces. It turned out that this problem was particularly challenging because most of the surface models in our study had self-intersection points. We used a well-known software package for mesh processing\, iso2mesh\, to successfully remove the self-intersection points on all surfaces were removed finally\, but this process seemed to create small holes in the surfaces of the models. Despite these holes\, with a few exceptions\, the widths of most sulci (gyri) were still simultaneously increased (decreased) with the coefficient adjustments. This result provides a direction for further study towards controlled study of the influence of the partial volume problem on modeling of tCS.
URL:https://coe.northeastern.edu/event/ms-thesis-defense-hao-chen/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210420T173000
DTEND;TZID=America/New_York:20210420T183000
DTSTAMP:20260427T034339
CREATED:20210329T173703Z
LAST-MODIFIED:20210329T173812Z
UID:25249-1618939800-1618943400@coe.northeastern.edu
SUMMARY:Making a Graduate School Plan
DESCRIPTION:In this intensive workshop\, we’ll walk through the steps of identifying a right fit graduate program and help you plan out the who\, what\, when\, where\, and how of applying to graduate schools. Bring a laptop if you have one\, a notebook\, and come prepared with some thoughts about what’s next! If you can’t make it\, contact URF@Northeastern.edu to access our Canvas workshop site (graduating seniors only or alumni only please). \nRegistration
URL:https://coe.northeastern.edu/event/making-a-graduate-school-plan/
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