377 research outputs found
Towards autonomic virtual machine management
No full textVirtual machine technologies are gaining wide acceptance in today’s era due to invaluable services in system management, server consolidation, and secure resource containment along with providing requisite application execution environment. Every virtual machine platform reduces dependence on hardware by fully or partially abstracting operating systems enabling flexible control of manipulation or migration of guest machines by manual system administration or reactive/proactive approaches to management. This dissertation focuses on resolving the resource reservation problem to help define a mathematical model and study interference within multiple virtual machines while trying to achieve load balancing and improve performance efficiency. Our goal is three-pronged. Firstly, we aim to understand the underlying support available for virtual machine migration and pursue new technologies or abstractions to improve efficiency and speed of the data transfer. Secondly, we carefully evaluate all the resources used by VMs for proper functioning and study the synchronization and multiplexing processes underneath which delineate when and where to migrate a virtual machine. Finally, we attempt to deduce the action to perform on running VMs (manipulation or resource configuration) so as to resolve the issue at hand. To achieve these goals, we follow a step-by-step procedure limiting the number of variable parameters and analyze the outcome of focal experiments. The results show that, using RDMA (Remote Direct Memory Access) to perform virtual machine migration can be used only in scenarios where the underlying hardware offers support for such transactions (eg. InfiniBand architecture) and such an abstraction over TCP/IP does not ameliorate efficiency of VM transfers. Further, a utility based function designed to analyze environment and application metrics and project an area of good/bad states on a map would require a plethora of parameters increasing its complexity. Considering VM re-distribution, one can predict the ideal number and time of migration of guest virtual machines on any configuration by gathering statistics from parallel migration for graphical analysis. Parallel VM migration gives us shorter average transfer time and higher latencies per VM. Pinning of virtual CPUs to VMs improves the performance efficiency of applications compared to sharing of CPUs.M.S.Includes bibliographical referencesby Siddharth Wag
Special Session: STT-MRAMs: Technology, Design and Test
Full text linkSTT-MRAM has long been a promising non-volatile memory solution for the embedded application space owing to its attractive characteristics such as non-volatility, low leakage, high endurance, and scalability. However, the operating requirements for high-performance computing (HPC) and low power (LP) applications involve different challenges. This paper addresses different aspects of STT-MRAM; it will cover state-of-the-art, some new results and future challenges related to technology, design and test. While STT-MRAM devices have shown encouraging performance metrics at device-level, a key challenge has been achieving backend-of-line (BEOL) CMOS compatibility, while retaining the benefits of low power operation. Scaling demands to improve data densities have placed additional challenges in terms of addressing the impact of process-induced damage on device performance at CD < 100 nm. In addition, the paper discusses the design of reliable read mechanism considering the variability effects. Moreover, the failure of traditional fault modeling and test approaches in model STT-MRAM unique defects for appropriate test solutions is demonstrated in this paper based on silicon data. Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Computer EngineeringQuantum & Computer Engineerin
Computer vision methods for large-scale online clustering and quantitative dermatology
No full textIn modern computer vision applications where datasets are large and updates with new data may be ongoing, methods of online clustering are extremely important. Online clustering algorithms incrementally cluster the data points, use a fraction of the dataset memory, and update the clustering decisions when new data comes in. In this thesis we adapt a classic online clustering algorithm called Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) to incrementally cluster large datasets of features commonly used in computer vision, e.g., 840K color SIFT descriptors, 1.09 million color patches, 60K outlier corrupted grayscale patches, and 700K grayscale SIFT descriptors. We use the algorithm to cluster datasets consisting of non-convex clusters, e.g., Hopkins 155 3-D motion segmentation dataset. We call the adapted version modified-BIRCH (m-BIRCH). BIRCH was originally developed by the database management community. Modifications made in m-BIRCH enable data driven parameter selection and effectively handle varying density regions in the feature space. Data driven parameter selection automatically controls the level of coarseness of the data summarization. Effective handling of varying density regions is necessary to well represent the different density regions in the data summarization. Our implementation of the algorithm provides a useful clustering tool and is made publicly available. In the second part of the thesis, we present a micro-level feature based approach to register time-lapse skin images acquired over an extended period of time and multimodal skin images acquired in quick succession. Misregistration between the images makes it difficult to perform quantitative analysis and track the progression of skin disease. We demonstrate the utility of both types of registration, by employing the results for two quantitative dermatology tasks: 1) automatic detection of acne-like regions, and 2) separation of surface and subsurface reflection. Additionally, we have created a time-lapse video showing the registered time-lapse images, which clearly brings out the evolution of acne lesions with time.Ph. D.Includes bibliographical referencesby Siddharth K. Mada
MFA-MTJ Model: Magnetic-Field-Aware Compact Model of pMTJ for Robust STT-MRAM Design
Full text linkThe popularity of perpendicular magnetic tunnel junction (pMTJ)-based spin-transfer torque magnetic random access memories (STT-MRAMs) is growing very fast. The performance of such memories is very sensitive to magnetic fields, including both internal and external ones. This article presents a magnetic-field-aware compact model of pMTJ, named the MFA-magnetic tunnel junction (MTJ) model, for magnetic/electrical co-simulation of MTJ/CMOS circuits. Magnetic measurement data of MTJ devices, with diameters ranging from 35 to 175 nm, are used to calibrate an in-house magnetic coupling model. This model is subsequently integrated into our developed compact pMTJ model, which is implemented in Verilog-A. The superiority of the proposed MFA-MTJ model for device/circuit co-design of STT-MRAM is demonstrated by simulating a single pMTJ as well as STT-MRAM full circuits. The design space is explored under PVT variations and various configurations of magnetic fields.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Computer EngineeringQuantum & Computer Engineerin
Budhan Stories S2E8: We wanted to go back
No full textEpisode 8 of Season 2 - In this episode we spoke with migrant workers who walked thousands of kilometers to reach their homes in different part of India. We also spoke with labourers who travelled by train and faced hardships. When there was no livelihood options open due to lockdown, millions of labourers walked to their homes. This episode is an effort to capture the gist of their huge suffering on the way and in their hometowns. Created (Author) by: Siddharth Garange. Participants: Budhan Theatre, Dakxin Chhara, Atish Indrekar, Ruchika Kodekar, Chetna Rathod, Kushal Batunge, Keyur Bajrange, Anish Garange, Siddharth Garange, Alice Tilche, Akshay Khanna, Yashodara Udupa, Labour exodus, Labour on foot, Harsh Shodhan, Machan Theatre company, Nasim Akhtar.Supplementary materials include short clips, poster and subtitles.</p
Author Correction: Deficiency of TET3 leads to a genome-wide DNA hypermethylation episignature in human whole blood (npj Genomic Medicine, (2021), 6, 1, (92), 10.1038/s41525-021-00256-y):Deficiency of TET3 leads to a genome-wide DNA hypermethylation episignature in human whole blood (npj Genomic Medicine, (2021), 6, 1, (92), 10.1038/s41525-021-00256-y)
Full text linkIn this article the author name Siddharth Banka was incorrectly written as Sidharth Banka. The original article has been corrected.</p
Applications of U-net to diffuse optical tomography data: Image reconstruction and superresolution
Full text linkDi use optical tomography (DOT) is being investigated for effective functional brain imaging. It serves as a cheaper, less bulky and safer alternative to fMRI imaging which is the current gold standard. However, due to the complicated nature of the system, analytical reconstruction approaches begin to fail. Currently functional signal activations are reconstructed using an analytical approach but the quality of the image, especially in terms of resolution, is low. This is one of the primary barriers to making DOT the gold standard for functional brain imaging. This thesis presents the theory behind the inverse problem and discusses possible solutions to image reconstruction and superresolution problems in the DOT brain imaging space. With the growth in deep learning and its applications in medical imaging, a U-net based architecture is proposed to learn the mapping and estimate a higher resolution image. This work shows that the proposed deep learning model trained on simulated images from real-world fMRI images of the human brain can reconstruct higher resolution images while reducing the number of hallucinations.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-05-01The student, Siddharth Muralidaran, accepted the attached license on 2021-04-28 at 13:55.The student, Siddharth Muralidaran, submitted this Thesis for approval on 2021-04-28 at 14:03.This Thesis was approved for publication on 2021-04-28 at 14:12.DSpace SAF Submission Ingestion Package generated from Vireo submission #16605 on 2021-09-16 at 17:06:40Made available in DSpace on 2021-09-17T02:34:50Z (GMT). No. of bitstreams: 2
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Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I Onl
Rapid time-resolved brain imaging with multiple clinical contrasts using wave-shuffling
No full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 41-42).Magnetic Resonance Imaging (MRI) is a non-invasive but slow biomedical imaging modality that produces images of multiple desirable contrasts. Currently, multiple receive channels are used in MRI to exploit redundancy and sample fewer data points for faster acquisition. Wave-CAIPI is a recently introduced technique that modifies the acquisition scheme in a manner that improves the effectiveness of the receive channels, allowing for higher rates of acceleration. In this work, the principle behind Wave-CAIPI is applied to a compressed-sensing technique called Shuffling. In Shuffling, the spatial and temporal axis are randomly sampled and the data is reconstructed with temporal subspace constraints, yielding a time-series of images with desirable contrast. Combining Wave-CAIPI and Shuffling achieves rapid, time-resolved imaging of brain at 1mm-isotropic spatial resolution in clinically feasible times.by Siddharth Iyer.S.M.S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienc
A data driven approach towards understanding deficits in olfactory and motor function and identifying applications of smartphone motor testing in prodromal and manifest Parkinson’s
Full text linkBackground:
Significant disease heterogeneity in Parkinson’s means that identifying ways of applying observations made at a group level, to specific individuals, remains challenging. Standardised clinical assessments, critical for capturing the scientific evidence that informs clinical practice, are rarely used outside of research due to the time required for their administration.
Methods:
Since 2010, the Oxford Discovery longitudinal cohort study has recruited over 1600 individuals with Parkinson’s, Rapid Eye Movement Sleep Behaviour disorder (RBD) (a condition associated with a high risk of developing Parkinson’s or another neurodegenerative disorder) and controls. Detailed clinical assessments, including clinical tests of olfactory and motor function and smartphone motor testing, were performed at 18-month intervals. Machine learning algorithms (chiefly random forests) were used to predict clinical scores and outcomes using clinical data or smartphone motor testing data alone.
Results:
The use of three Sniffin’ sticks (Anise, Liquorice, Banana) allowed the identification of individuals with a poor sense of smell (ordinarily requiring assessment with all 16 sticks) with excellent accuracy (area under the curve (AUCs) values of over 0.90 in development and validation cohorts). Summation of scores from 6 tasks of the Movement Disorders Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) motor examination: repetitive hand movements, finger tapping, pronation/supination, leg movements, constancy of rest tremor and bradykinesia, yielded an abbreviated score whose correlation coefficient with the total 18 task score was high at 0.91. Three approaches to using smartphone motor testing to quantify motor impairment were explored. Smartphone motor testing was also used to predict dopaminergic deficit in RBD with AUCs>0.73 and the new future onset of motor, cognitive and functional disability in Parkinson’s with AUCs>0.75.
Conclusion:
A data-driven approach can be used to promote understanding of existing clinical tests in prodromal and manifest Parkinson’s, and to drive their refinement. Smartphone motor testing can be used to quantify motor impairment and provide individual estimates of risk; however, both are likely to benefit from further evaluation within the context of clinical trials and routine clinical practice
Association of single nucleotide polymorhism in microrna 196a2 and 146a gene towards risk for lung cancer in north Indian population
No full textMaster of Science-BiotechnologyMicroRNAs (miRNAs) are small RNA molecules that regulate the expression of corresponding messenger RNAs (mRNAs). Variations in the level of expression of distinct miRNAs have been observed in the genesis, progression and prognosis of multiple human malignancies. The present study was aimed to investigate the association between two highly studied miRNA polymorphisms (mir-146a rs2910164, mir-196a2 rs11614913) and cancer risk. A case-control study was performed on North Indian population. Odds ratio (OR) and 95% confidence interval (95% CI) were used to investigate the strength of the association.
Participants who possessed CC (mutant type) genotypes for miRNA 146a gene showed high risk for lung cancer (OR=5.648, 95%C.I; 0.64-49.8, p=0.08) especially for SQCC and SCLC compared to those who possessed GC or GG genotypes. The association also persisted among non-smokers.
On the other hand TT (mutant genotype for miRNA 196 a2 gene also showed high risk for lung cancer (OR=3.78, 95%C.I; 0.94-15.2, p=0.04) especially SCLC and ADCC. Smokers with mutant genotype were more susceptible for lung cancer risk as compared to nonsmokers.
Individuals having combined mutant or variant genotype for both the genes (OR=3.8, 95%C.I; 1.2-12.1 and p=0.02) were at much higher risk for lung cancer as compared to those having single nucleotide polymorphism for either of the two genes.
The present study suggests an important role of mir-196a2 rs11614913 and rs291014 polymorphism with overall lung cancer risk especially in North Indian population. Further studies with large sample size are needed to evaluate and confirm this association.Biotechnology and Environmental Sciences, Thapar University, Patial
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