1,457 research outputs found
Leveraging Semantic Web Technologies for Managing Resources in a Multi-Domain Infrastructure-as-a-Service Environment
This paper reports on experience with using semantically-enabled network
resource models to construct an operational multi-domain networked
infrastructure-as-a-service (NIaaS) testbed called ExoGENI, recently funded
through NSF's GENI project. A defining property of NIaaS is the deep
integration of network provisioning functions alongside the more common storage
and computation provisioning functions. Resource provider topologies and user
requests can be described using network resource models with common base
classes for fundamental cyber-resources (links, nodes, interfaces) specialized
via virtualization and adaptations between networking layers to specific
technologies.
This problem space gives rise to a number of application areas where semantic
web technologies become highly useful - common information models and resource
class hierarchies simplify resource descriptions from multiple providers,
pathfinding and topology embedding algorithms rely on query abstractions as
building blocks.
The paper describes how the semantic resource description models enable
ExoGENI to autonomously instantiate on-demand virtual topologies of virtual
machines provisioned from cloud providers and are linked by on-demand virtual
connections acquired from multiple autonomous network providers to serve a
variety of applications ranging from distributed system experiments to
high-performance computing
Enabling Technology in Optical Fiber Communications: From Device, System to Networking
This book explores the enabling technology in optical fiber communications. It focuses on the state-of-the-art advances from fundamental theories, devices, and subsystems to networking applications as well as future perspectives of optical fiber communications. The topics cover include integrated photonics, fiber optics, fiber and free-space optical communications, and optical networking
State of the Art in Dense Monocular Non-Rigid 3D Reconstruction
3D reconstruction of deformable (or non-rigid) scenes from a set of monocular2D image observations is a long-standing and actively researched area ofcomputer vision and graphics. It is an ill-posed inverse problem,since--without additional prior assumptions--it permits infinitely manysolutions leading to accurate projection to the input 2D images. Non-rigidreconstruction is a foundational building block for downstream applicationslike robotics, AR/VR, or visual content creation. The key advantage of usingmonocular cameras is their omnipresence and availability to the end users aswell as their ease of use compared to more sophisticated camera set-ups such asstereo or multi-view systems. This survey focuses on state-of-the-art methodsfor dense non-rigid 3D reconstruction of various deformable objects andcomposite scenes from monocular videos or sets of monocular views. It reviewsthe fundamentals of 3D reconstruction and deformation modeling from 2D imageobservations. We then start from general methods--that handle arbitrary scenesand make only a few prior assumptions--and proceed towards techniques makingstronger assumptions about the observed objects and types of deformations (e.g.human faces, bodies, hands, and animals). A significant part of this STAR isalso devoted to classification and a high-level comparison of the methods, aswell as an overview of the datasets for training and evaluation of thediscussed techniques. We conclude by discussing open challenges in the fieldand the social aspects associated with the usage of the reviewed methods.<br
State of the Art in Dense Monocular Non-Rigid 3D Reconstruction
3D reconstruction of deformable (or non-rigid) scenes from a set of monocular
2D image observations is a long-standing and actively researched area of
computer vision and graphics. It is an ill-posed inverse problem,
since--without additional prior assumptions--it permits infinitely many
solutions leading to accurate projection to the input 2D images. Non-rigid
reconstruction is a foundational building block for downstream applications
like robotics, AR/VR, or visual content creation. The key advantage of using
monocular cameras is their omnipresence and availability to the end users as
well as their ease of use compared to more sophisticated camera set-ups such as
stereo or multi-view systems. This survey focuses on state-of-the-art methods
for dense non-rigid 3D reconstruction of various deformable objects and
composite scenes from monocular videos or sets of monocular views. It reviews
the fundamentals of 3D reconstruction and deformation modeling from 2D image
observations. We then start from general methods--that handle arbitrary scenes
and make only a few prior assumptions--and proceed towards techniques making
stronger assumptions about the observed objects and types of deformations (e.g.
human faces, bodies, hands, and animals). A significant part of this STAR is
also devoted to classification and a high-level comparison of the methods, as
well as an overview of the datasets for training and evaluation of the
discussed techniques. We conclude by discussing open challenges in the field
and the social aspects associated with the usage of the reviewed methods.Comment: 25 page
Machine Learning Small Molecule Properties in Drug Discovery
Machine learning (ML) is a promising approach for predicting small molecule
properties in drug discovery. Here, we provide a comprehensive overview of
various ML methods introduced for this purpose in recent years. We review a
wide range of properties, including binding affinities, solubility, and ADMET
(Absorption, Distribution, Metabolism, Excretion, and Toxicity). We discuss
existing popular datasets and molecular descriptors and embeddings, such as
chemical fingerprints and graph-based neural networks. We highlight also
challenges of predicting and optimizing multiple properties during hit-to-lead
and lead optimization stages of drug discovery and explore briefly possible
multi-objective optimization techniques that can be used to balance diverse
properties while optimizing lead candidates. Finally, techniques to provide an
understanding of model predictions, especially for critical decision-making in
drug discovery are assessed. Overall, this review provides insights into the
landscape of ML models for small molecule property predictions in drug
discovery. So far, there are multiple diverse approaches, but their
performances are often comparable. Neural networks, while more flexible, do not
always outperform simpler models. This shows that the availability of
high-quality training data remains crucial for training accurate models and
there is a need for standardized benchmarks, additional performance metrics,
and best practices to enable richer comparisons between the different
techniques and models that can shed a better light on the differences between
the many techniques.Comment: 46 pages, 1 figur
Resource Management in Softwarized Networks
Communication networks are undergoing a major transformation through softwarization, which is changing the way networks are designed, operated, and managed. Network Softwarization is an emerging paradigm where software controls the treatment of network flows, adds value to these flows by software processing, and orchestrates the on-demand creation of customized networks to meet the needs of customer applications. Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Network Virtualization are three cornerstones of the overall transformation trend toward network softwarization. Together, they are empowering network operators to accelerate time-to-market for new services, diversify the supply chain for networking hardware and software, bringing the benefits of agility, economies of scale, and flexibility of cloud computing to networks. The enhanced programmability enabled by softwarization creates unique opportunities for adapting network resources in support of applications and users with diverse requirements. To effectively leverage the flexibility provided by softwarization and realize its full potential, it is of paramount importance to devise proper mechanisms for allocating resources to different applications and users and for monitoring their usage over time.
The overarching goal of this dissertation is to advance state-of-the-art in how resources are allocated and monitored and build the foundation for effective resource management in softwarized networks. Specifically, we address four resource management challenges in three key enablers of network softwarization, namely SDN, NFV, and network virtualization. First, we challenge the current practice of realizing network services with monolithic software network functions and propose a microservice-based disaggregated architecture enabling finer-grained resource allocation and scaling. Then, we devise optimal solutions and scalable heuristics for establishing virtual networks with guaranteed bandwidth and guaranteed survivability against failure on multi-layer IP-over-Optical and single-layer IP substrate network, respectively. Finally, we propose adaptive sampling mechanisms for balancing the overhead of softwarized network monitoring and the accuracy of the network view constructed from monitoring data
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