168,135 research outputs found
Container-Based Orchestration in Cloud: State of the Art and Challenges
No full textHow to effectively manage increasingly complex enterprise computing environments is one of the hardest challenges that most organizations have to face in the era of cloud computing, big data and IoT. Advanced automation and orchestration systems are the most valuable solutions helping IT staff to handle large-scale cloud data centers. Containers are the new revolution in the cloud computing world, they are more lightweight than VMs, and can radically decrease both the start up time of instances and the processing and storage overhead with respect to traditional VMs. The aim of this paper is to provide a comprehensive description of cloud orchestration approaches with containers, analyzing current research efforts, existing solutions and presenting issues and challenges facing this topic
Performance analysis of WRF simulations in a public cloud and HPC environment
No full textThe Weather Research and Forecasting (WRF) Model is a numerical weather prediction system designed for both atmospheric research and operational forecasting needs. WRF requires a large amount of CPU power which increases drastically if WRF is used to model a big geographical area with a high resolution. To satisfy the computational demand WRF requires large number of computing resources through infrastructures such as clusters in grid or cloud. In this paper the performance analysis of different WRF simulations to the Amazon Web Services (AWS) cloud computing environment (single node and cluster) compared to that of a HCP cluster is presented
Tricyclic Pyrazoles. 3. Synthesis, biological evaluation, and molecular modeling of analogues of the cannabinoid antagonist 8-chloro-1-(2',4'-dichlorophenyl)-N-piperidin-1-yl-1,4,5,6- tetrahydrobenzo[6,7]cyclohepta[1,2-c]pyrazole-3-carboxamide
No full textA series of analogues of 8-chloro-1-(2′,4′-dichlorophenyl)-AT- piperidin-1-yl-1,4,5,6-tetrahydrobenzo-[6,7]cyclohepta[1,2-c] pyrazole-3-carboxamide 4a (NESS 0327) (Ruiu, S.; Pinna, G. A.; Marchese, G.; Mussinu, J. M.; Saba, P.; Tambaro, S.; Casti, P.; Vargiu, R.; Pani, L. Synthesis and Characterization of NESS 0327: A Novel Putative Antagonist of CB 1 Cannabinoid Receptor. J. Pharmacol. Exp. Ther. 2003, 306, 363-370) was synthesized and evaluated for their affinity to cannabinoid receptors. Depending on the chemical modification of the lead structure that was chosen, compounds 4b, 4c, 4i, 4l, and 4m still proved to be potent binders of the CB1 receptor. Moreover, several analogues (4c, 4d, 4e, and 4m) demonstrated superior CB2 receptor binding affinities compared to the parent ligand. Compounds 4b, 4c, 4i, and 4l displayed the most promising pharmacological profiles, having the highest selectivity for CB1 receptors with Ki(CB2) to Ki(CB1) ratios of 11 250, 2000, 3330 and 4625, respectively. Compound 4c increased the intestinal propulsion in mice and antagonized the effect induced by the CB 1 receptor agonist WIN 55,212-2. Finally, molecular modeling studies were carried out on a set of tricyclic pyrazoles (2a-4a) and on rimonabant 1 (SR141716A), indicating that high CB1 receptors affinities were consistent for the tricyclic derivatives, both with a nonplanar geometry of the tricyclic cores and with a precise orientation of the substituent (chlorine) on this ring system. © 2005 American Chemical Society
Comparative efficacy trials with two different Bacillus thuringiensis serovar kurstaki strains against gypsy moth in mediterranean cork oak forests
No full textThe efficacy of two formulations (Foray® 76B AVIO and Rapax® AS AIR) containing different Bacillus thuringiensis kurstaki (Btk) strains (ABTS-351 and EG-2348, respectively) was evaluated against Lymantria dispar larval populations in cork oak forests in Sardinia (Italy), in 2018 and 2019. The experimental design involved the following treatments: (I) untreated control; (II) Foray® 76B at the dose of 2.0 L/ha; (III) Foray® 76B at the dose of 2.5 L/ha; (IV) Rapax® AS AIR at the dose of 2.0 L/ha. Aerial applications were carried out using a helicopter equipped with four electronic rotary atomizers adjusted to sprinkle 160 micron-sized drops. Btk efficacy was evaluated by assessing the larval density reduction 7, 14, and 21 days after the application in each experimental plot in comparison with an untreated check. In addition to field surveys, the mortality of second and third instar larval samples, randomly collected from each plot after treatment and fed with foliage from the same plot, was determined in the laboratory. All Btk treatments were similarly effective, and no differences in larval density reduction among Btk strains and doses were found in either year. Twenty-one days after application, the average larval density reduction in the field was approximately 70% in all treated plots in 2018, whereas in 2019 it reached 80% only in areas treated with Foray 76B at 2.5 L/ha. Laboratory observations showed that all Btk-based products were effective against gypsy moth larvae, with significant differences in mortality between untreated control and the different Btk treatments. Our results shed light on the possibility of alternating different Btk strains for resistance management purposes and of applying lower doses than labeled, in order to achieve cost savings for product shipment and distribution and to reduce the environmental impact
Power comparison of cloud data center architectures
No full textPower consumption is a primary concern for cloud computing data centers. Being the network one of the non-negligible contributors to energy consumption in data centers, several architectures have been designed with the goal of improving network performance and energy-efficiency. In this paper, we provide a comparison study of data center architectures, covering both classical two- and three-tier design and state-of-art ones as Jupiter, recently disclosed by Google. Specifically, we analyze the combined effect on the overall system performance of different power consumption profiles for the IT equipment and of different resource allocation policies. Our experiments, performed in small and large scale scenarios, unveil the ability of network-aware allocation policies in loading the the data center in a energy-proportional manner and the robustness of classical two- and three-tier design under network-oblivious allocation strategies
Automatic Dynamic Allocation of Cloud Storage for Scientific Applications
No full textParticularly in scientific community the size of digital data to be stored is ramping up. For those application characterized by very dynamic workloads is difficult to estimate the real size of storage to be allocated and avoid over-provisioning, also in extremely elastic environments as cloud computing. DAViS (Dynamic Allocation of Virtual Storage) is a prototype of a system for dynamically providing virtual block storage to Virtual Machines (VMs), optimizing the physical resources utilization, through dynamic and autonomous resizing of the storage
Exploiting Face Recognizability with Early Exit Vision Transformers
No full textFace recognition with Deep Learning is generally approached as a problem of capacity. The field has seen progressively deeper, more complex models or larger, more highly variant datasets. However, the carbon footprint of machine learning (ML) is a concern. A real push is developing to reduce the energy consumption of ML as we strive for a more eco-friendly society. Lower energy consumption or compute budget is always desirable, if accuracy is not reduced below a usable level. We present an approach using the state of the art Vision Transformer and Early Exits for reducing compute budget without significantly affecting performance. We develop a system for face recognition and identification with a closed-set gallery and show that with a small reduction in performance, a reasonable reduction in FLOPs can be obtained using our method
Forensic Biometrics: Challenges, Innovation and Opportunities. In: Francese, S., S. P. King, R. (eds) Driving Forensic Innovation in the 21st Century. Springer, Cham.
No full textForensic science has always benefited from the adoption and exploitation of novel technologies to perform and analyze measurements at a crime scene and in the laboratory. Modern information technologies boosted many forensic procedures, such as accelerating and automating the comparison of fingerprints and fingermarks, and, recently, the analysis and comparison of images from human faces. Moreover, the recent advent of fast and performant Machine Learning (often dubbed AI) models, greatly improved the applicability of automatic face recognition to operational scenarios. However, even though technology has enabled the development of such systems, there are several hindering factors which must be taken into account. In this chapter the technological, legal and societal factors potentially enabling and fostering the development and application of automatic face recognition in forensic procedures are described and discussed. Also, the current issues and main concerns, restricting the mass-adoption of automatic face recognition technologies in forensic cases are presented. This chapter attempts not only to document both enablers and roadblockers of forensic face recognition, but also provides some promising research avenues and suggestions for a better application of these technologies in today’s society
- …
