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    Improving photoacoustic imaging with model compensating and deep learning methods 

    Gutta, Sreedevi
    Photoacoustic imaging is a hybrid biomedical imaging technique combining optical ab- sorption contrast with ultrasonic resolution. It is a non-invasive technique that is scalable to reveal structural, functional, and ...

    Coarse-grained dynamics derived structural ensemble for prediction of metal binding sites of protein and phenotypic effects of variants 

    Garg, Aditi
    Structures of proteins play a key role in determining their functions. Knowledge of structure, especially the details of specific sites of a protein can help us understand their contribution to the overall activity. ...

    Learning Across Domains: Applications to Text-based Person Search and Multi-Source Domain Adaptation 

    Aggarwal, Surbhi
    With rapid development in technology and ubiquitous presence of diverse types of sensors, a large amount of data from different modalities (e.g., text, audio, images etc.) describing the same person/ object/event has ...

    Numerical Analysis of Some Preconditioners and Associated Error Estimators for Solving Linear Systems 

    Dugar, Abhishek
    Convergence of iterative algorithms in solving large linear systems is largely affected by the condition number of the matrix. Preconditioners reduce the condition number of the system matrix, thereby letting the linear ...

    Global control of mechanics on Riemannian manifolds, and applications to under-actuated aerial vehicles 

    Simha, Ashutosh
    We consider the problem of designing trajectory tracking feedback control laws for La- grangian mechanical systems in a Riemannian geometric framework. Classical nonlinear control techniques that rely on Euclidean ...

    Towards Learning Adversarially Robust Deep Learning Models 

    Vivek, B S
    Deep learning models have shown impressive performance across a wide spectrum of computer vision applications, including medical diagnosis and autonomous driving. One of the major concerns that these models face is their ...

    Scalability Bottleneck Analysis of High Performance Applications 

    Halder, Soham
    Obtaining high performance and scalability for high performance applications are challenging. There are various bottlenecks including, higher rate of memory access, complex algorithm, high rate of communication, big ...

    Improving Data Center Utilisation by Reducing Fragmentation 

    Shravan, S K
    Virtualization enables better server consolidation and utilisation compared to stand-alone servers running a single workload. This enabled wide-spread cloud adoption among many organizations. Data center utilisation is ...

    A Divide and Conquer Framework For Graph Processing in Distributed Heterogeneous Systems 

    Panja, Rintu
    In many fields of science and engineering graph data structures are used to represent real-world information. As these graphs scale in size, it becomes very inefficient to process these graphs on a single core CPU using ...

    EMF: System Design and Challenges for Disaggregated GPUs in datacenters for Efficiency, Modularity and Flexibility 

    Guleria, Anubhav
    With Dennard Scaling phasing out in the mid-2000s, architectural scaling and hardware specialization take centre stage to provide performance bene fits with already stalling Moore's law. An outcome from this hardware ...
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    AuthorKumar, Vikash (2)Abhishek, A (1)Addepalli, Sravanti (1)Aggarwal, Surbhi (1)Agrawal, Harish (1)Ahmad, Touseef (1)Alladin, Muttaqi Ahmad (1)Anandh, Thivin (1)Arun, I (1)Avasarala, Srikanth (1)... View MoreSubject
    TECHNOLOGY (68)
    Deep Learning (11)INTERDISCIPLINARY RESEARCH AREAS (6)Computer Vision (5)Machine Learning (4)Deep learning (3)Distributed Systems (3)GPGPU (3)Adversarial Robustness (2)Arterial Input Function (2)... View MoreHas File(s)Yes (67)No (1)

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