COEL: A Web-based Chemistry Simulation Framework

The chemical reaction network (CRN) is a widely used formalism to describe macroscopic behavior of chemical systems. Available tools for CRN modelling and simulation require local access, installation, and often involve local file storage, which is susceptible to loss, lacks searchable structure, and does not support concurrency. Furthermore, simulations are often single-threaded, and user interfaces are non-trivial to use. Therefore there are significant hurdles to conducting efficient and collaborative chemical research. In this paper, we introduce a new enterprise chemistry simulation framework, COEL, which addresses these issues. COEL is the first web-based framework of its kind. A visually pleasing and intuitive user interface, simulations that run on a large computational grid, reliable database storage, and transactional services make COEL ideal for collaborative research and education. COEL's most prominent features include ODE-based simulations of chemical reaction networks and multicompartment reaction networks, with rich options for user interactions with those networks. COEL provides DNA-strand displacement transformations and visualization (and is to our knowledge the first CRN framework to do so), GA optimization of rate constants, expression validation, an application-wide plotting engine, and SBML/Octave/Matlab export. We also present an overview of the underlying software and technologies employed and describe the main architectural decisions driving our development. COEL is available at http://coel-sim.org for selected research teams only. We plan to provide a part of COEL's functionality to the general public in the near future.Comment: 23 pages, 12 figures, 1 tabl

The Unreasonable Success of Local Search: Geometric Optimization

What is the effectiveness of local search algorithms for geometric problems in the plane? We prove that local search with neighborhoods of magnitude $1/\epsilon^c$ is an approximation scheme for the following problems in the Euclidian plane: TSP with random inputs, Steiner tree with random inputs, facility location (with worst case inputs), and bicriteria $k$-median (also with worst case inputs). The randomness assumption is necessary for TSP

Adaptive end-to-end optimization of mobile video streaming using QoS negotiation

Video streaming over wireless links is a non-trivial problem due to the large and frequent changes in the quality of the underlying radio channel combined with latency constraints. We believe that every layer in a mobile system must be prepared to adapt its behavior to its environment. Thus layers must be capable of operating in multiple modes; each mode will show a different quality and resource usage. Selecting the right mode of operation requires exchange of information between interacting layers. For example, selecting the best channel coding requires information about the quality of the channel (capacity, bit-error-rate) as well as the requirements (latency, reliability) of the compressed video stream generated by the source encoder. In this paper we study the application of our generic QoS negotiation scheme to a specific configuration for mobile video transmission. We describe the results of experiments studying the overall effectiveness, stability, and dynamics of adaptation of our distributed optimization approach

Privacy-Preserving Outsourcing of Large-Scale Nonlinear Programming to the Cloud

The increasing massive data generated by various sources has given birth to big data analytics. Solving large-scale nonlinear programming problems (NLPs) is one important big data analytics task that has applications in many domains such as transport and logistics. However, NLPs are usually too computationally expensive for resource-constrained users. Fortunately, cloud computing provides an alternative and economical service for resource-constrained users to outsource their computation tasks to the cloud. However, one major concern with outsourcing NLPs is the leakage of user's private information contained in NLP formulations and results. Although much work has been done on privacy-preserving outsourcing of computation tasks, little attention has been paid to NLPs. In this paper, we for the first time investigate secure outsourcing of general large-scale NLPs with nonlinear constraints. A secure and efficient transformation scheme at the user side is proposed to protect user's private information; at the cloud side, generalized reduced gradient method is applied to effectively solve the transformed large-scale NLPs. The proposed protocol is implemented on a cloud computing testbed. Experimental evaluations demonstrate that significant time can be saved for users and the proposed mechanism has the potential for practical use.Comment: Ang Li and Wei Du equally contributed to this work. This work was done when Wei Du was at the University of Arkansas. 2018 EAI International Conference on Security and Privacy in Communication Networks (SecureComm

VIRTUE : integrating CFD ship design

Novel ship concepts, increasing size and speed, and strong competition in the global maritime market require that a ship's hydrodynamic performance be studied at the highest level of sophistication. All hydrodynamic aspects need to be considered so as to optimize trade-offs between resistance, propulsion (and cavitation), seakeeping or manoeuvring. VIRTUE takes a holistic approach to hydrodynamic design and focuses on integrating advanced CFD tools in a software platform that can control and launch multi-objective hydrodynamic design projects. In this paper current practice, future requirements and a potential software integration platform are presented. The necessity of parametric modelling as a means of effectively generating and efficiently varying geometry, and the added-value of advanced visualization, is discussed. An illustrating example is given as a test case, a container carrier investigation, and the requirements and a proposed architecture for the platform are outlined