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Reliable and Efficient Application Scheduling on Edge, Fog and Cloud
Cloud computing has emerged in the last decade as a popular distributed computing service
offered by commercial providers. Public Clouds offer pay-as-you-go access to elastic resources
that can be acquired and released ...
Epistasis Detection and Phenotype Prediction in GWAS Using Machine Learning Methods
Genome-wide association studies (GWAS) are used to find the association between genetic variants, Single Nucleotide Polymorphisms (SNPs), and phenotypic traits or diseases in a population. The number of GWAS has increased ...
Deep Convolutional and Generative Networks for Ocean Synoptic Feature Extraction and Super Resolution from Remotely Sensed Images
Accurate extraction of Synoptic Ocean Features and Downscaling of Ocean Features is crucial
for climate studies and the operational forecasting of ocean systems. With the advancement of
space and sensor technologies, the ...
Mitigating Domain Shift via Self-training in Single and Multi-target Unsupervised Domain Adaptation
Though deep learning has achieved significant successes in many computer vision tasks, the state-of-the-art approaches rely on the availability of a large amount of labeled data for supervision, collection of which is ...
Improving photoacoustic imaging with model compensating and deep learning methods
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
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
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
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
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
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 ...