Eigenvalue Bifurcation in Doubly Nonlinear Problems with an **Application**
to Surface Plasmon Polaritons

5/10 relevant

arXiv

Methods and Experiences for Developing Abstractions for Data-intensive,
Scientific **Applications**

**application**, system, and infrastructure challenges of scientific

**applications**. Expand abstract.

**applications**that require the integration of diverse types of computing, instruments and data present challenges that are distinct from commercial software, due to scale, heterogeneity, and the need to integrate various programming and computational models with evolving and heterogeneous infrastructure. Pervasive and effective abstractions are thus critical. The process of developing abstractions for scientific

**applications**and infrastructures is not well understood. While theory-based approaches are suited for well-defined, closed environments, they have severe limitations for designing abstractions for complex, real-world systems. The design science research (DSR) method provides the basis for designing effective systems that can handle real-world complexities. DSR consists of two complementary phases: design and evaluation. This paper applies the DSR method to the development of abstractions for scientific

**applications**. Specifically, we address the critical problem of distributed resource management on heterogeneous infrastructure, a challenge that currently limits many scientific

**applications**. We use the pilot-abstraction, a widely used resource management abstraction for high-performance, high throughput, big data, and streaming applications, as a case study. We evaluate the activities of the process and extensively evaluate the artifacts using different methods, including conceptual modeling, performance characterizations, and modeling. We demonstrate the applicability of the DSR method for holistically handling the complexity of parallel and distributed computing environments addressing important application, system, and infrastructure challenges of scientific

**applications**. Finally, we capture our experiences and formulate different lessons learned.

9/10 relevant

arXiv

Using Deep Learning to Improve Ensemble Smoother: **Applications** to
Subsurface Characterization

**applications**. Here we show that the DL-based ES method, that is, ES$_\text{(DL)}$, is more general and flexible. In this new update scheme, a high volume of training data are generated from a relatively small-sized ensemble of model parameters and simulation outputs, and possible non-Gaussian features can be preserved in the training data and captured by an adequate DL model. This new variant of ES is tested in two subsurface characterization problems with or without Gaussian assumptions. Results indicate that ES$_\text{(DL)}$ can produce similar (in the Gaussian case) or even better (in the non-Gaussian case) results compared to those from ES$_\text{(K)}$. The success of ES$_\text{(DL)}$ comes from the power of DL in extracting complex (including non-Gaussian) features and learning nonlinear relationships from massive amounts of training data. Although in this work we only apply the ES$_\text{(DL)}$ method in parameter estimation problems, the proposed idea can be conveniently extended to analysis of model structural uncertainty and state estimation in real-time forecasting studies.

7/10 relevant

arXiv

Fixed Point Results for a New Class of Multi-Valued Mappings Under (θ; R)-Contractions With an **Application**

7/10 relevant

Preprints.org

A quantitative framework to define the end of an outbreak: **application** to Ebola Virus Disease

7/10 relevant

medRxiv

Holistic Slowdown Driven Scheduling and Resource Management for Malleable Jobs

**applications**as the key technology to reduce the average slowdown and response time of jobs. Expand abstract.

**applications**were highly tuned for static allocations, but offering zero flexibility to dynamic executions. This paper proposes a new holistic, dynamic job scheduling policy, Slowdown Driven (SD-Policy), which exploits the malleability of

**applications**as the key technology to reduce the average slowdown and response time of jobs. SD-Policy is based on backfill and node sharing. It applies malleability to running jobs to make room for jobs that will run with a reduced set of resources, only when the estimated slowdown improves over the static approach. We implemented SD-Policy in SLURM and evaluated it in a real production environment, and with a simulator using workloads of up to 198K jobs. Results show better resource utilization with the reduction of makespan, response time, slowdown, and energy consumption, up to respectively 7%, 50%, 70%, and 6%, for the evaluated workloads.

6/10 relevant

arXiv

The g-stable rank for tensors

**application**to the Cap Set Problem.

5/10 relevant

arXiv

Twisting Noncommutative Geometries with **Applications** to High Energy
Physics

7/10 relevant

arXiv

Titanium Dioxide Nanoparticles – Prospects and **Applications** in Medicine

**application**in the photodynamic therapy (PDT) for the treatment of a wide range of maladies, from psoriasis to cancer. Expand abstract.

**application**in the photodynamic therapy (PDT) for the treatment of a wide range of maladies, from psoriasis to cancer. Titanium dioxide NPs were studied as photosensitizing agents in the treatment of malignant tumors as well as in photodynamic inactivation of antibiotic-resistant bacteria. Both TiO2 NPs themselves, as well as their composites with other molecules, can be successfully used as photosensitizers in PDT. Moreover, various organic compounds can be grafted on TiO2 NPs, leading to hybrid materials. These nanostructures can reveal increased light absorption allowing their further use in targeted therapy in medicine. In order to improve efficient anticancer therapy, many approaches utilizing titanium dioxide were tested. The most significant studies are discussed in this review.

9/10 relevant

Preprints.org

Convergence of delay equations driven by a H\"older continuous function of order $\beta\in(\frac13,\frac12)$

**applications**to stochastic differential equations.

4/10 relevant

arXiv