June 2022 - computational.science

CFP-Due July 11: AHPC 2022 High-Performance Computing, Oct 3-6
by Pierre F. Tiako 20 Jun '22

20 Jun '22

[Apologies if you receive multiple copies] --- Call for Abstracts and Papers ------------- 2022 OkIP International Conference on Advances in High-Performance Computing (AHPC) Downtown Oklahoma City, OK, USA & Online October 3-6, 2022 https://eventutor.com/e/AHPC002 OkIP Published & SCOPUS/WoS Indexed Submission Deadline: July 11, 2022 Extended versions of the best papers will be considered for journal publication. * Contribution Types (One-Column IEEE Format Style): - Full Paper: Accomplished research results (10 pages) - Short Paper: Work in progress/fresh developments (6 pages) - Extended Abstract/Poster/Journal First: Displayed/Oral presented (3 pages) * Areas: >> HPC Concepts - High-Performance Computing (HPC) - Performance | Scalability | Virtualization - Visualization | Simulation | Modelisation - Prediction | Program Optimization | Workloads - Algorithms | Networks | Efficiency |Scheduling - Compiler Design and Technologies - HPC Components and Technologies - Data Management and Transport | Compilation - Load Balancing | Resource Management - Fault Tolerance | Reliability | Availability - Multicore Algorithms | Operating System - Software Design and Implementations - Asynchronous Methods | Interconnects - Interconnections | Distributed Computing - High Scalability Computing | Accelerators - High-Performance Modeling/Software/Tools - Routing Protocols | Quality of Service - Allocation Framework | Benchmarks |... >> Parallel Computing - Parallelism | Automatic Parallelization - Programming Languages/Libraries/Tools - Programing Models/Environments - Parallel Metaheuristics/Application-Behavior - Parallel/Distributed Algorithms/Architectures - Energy Factors/Technological Factors - Partitioning and Optimal Scheduling - Systems Performance/Potential/Efficiency - Distributed/Heterogeneous Systems - System Synergy and Optimization - Advances in Parallel Models/Systems/Tools >> Mobile & Wireless Computing - Communication Modeling/Designing/Analysis - Mobile Communication Prediction/Control - Mobile/Wireless Systems-of-Systems - Mobile/Wireless Software and Hardware - Heterogeneous Wireless Networks - Medical Alert Devices/Privacy Consideration - Wireless Sensor Network | IoT Platforms - Mobile Network Loss Issues - Ultra-Dense Networks | Mobile Performance - Sensor Networks and Embedded Applications - Innovative Mobile/Wireless Networks/Systems >> Network Architecture/System - Interconnection Networks and Architectures - Memory Architecture Evaluation - I/O, File Systems, and Memory Systems - Power-Efficiency | Systems-on-Chip - System Modeling Methodologies - Fault-Tolerant Algorithms/Systems - Scalable Servers and Systems - Shared Memory Implementation - Memory Exploitation/Management Techniques - Network Storage Systems and Services - Cluster Management/Architecture - Heterogeneous Architectures/Accelerators - Multi/Many Core/CPU Framework/Systems - Micro-Architecture Techniques - Reconfigurable Architectures/Systems >> HPC AI/Simulation/Security - Surrogate Modeling | Machine Learning - Neural Architecture Performance - Deep Learning | Agent-based Modeling - Intelligent Algorithm| Artificial Neural Network - Distributed Deep Learning Systems - Hybrid/Complex System Modeling/Simulation - Advanced/Stochastic Simulation Frameworks - Security, Policy, and Management Issues - Configuration, Policy, and Management issues - Graph Partitioning/Analysis/Analytics - Spiking Neural P Systems | Data Integrity - Enabling Blockchain-as-a-Service - Fuzzy Logic Approach | Streaming Algorithms - Association Rule Mining | Anomaly Detection - Big Data Analytics and Applications >> HPC Applications - HPC Applications and Case Studies - Grid/Cloud/Fog/Bio-inspired Computing - Scientific/Engineering/Commercial Workloads - Augmented/Virtual Reality - Biotechnology, Nanotechnology, and Finance - Space Research, Urban Planning, and Energy - Teleimmersion | Collaborative Applications - Media/Entertainment Industry | Visualization - Workflow Management | HPC Resiliency - Advanced Computational Methods/Applications >> Important Dates: - Submission Deadline: July 11, 2022 - Notification Due: August 01, 2022 - Camera-ready Due: August 22, 2022 >> Technical Program Committee https://eventutor.com/event/20/page/58-committee Please feel free to contact us for any inquiries at: info(a)okipublishing.com -------- Pierre Tiako General Chair

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A research grant is available within the RIPARTI regional projects
by Massimo Cafaro 17 Jun '22

17 Jun '22

A research grant is available at the University of Salento, Lecce, Italy. The research shall be developed in close collaboration with Echolight (https://www.echolightmedical.com, 12 months out of 18 in total), and is related to the following topic: The main goal of the project is to improve the tuning and calibration process of noninvasive diagnostic imaging devices used for imaging. One of the most critical steps during the implementation of a diagnostic imaging device is its calibration. In fact, poor calibration can lead to unreliable instrument performance with noisy images and the presence of unwanted artefacts that could mislead the diagnosis made by the physician. The calibration phase involves a repeated try-and-check procedure during which the instrument parameters are repeatedly changed in order to obtain images that are sharp and as closely matched as possible to the target reference. This phase often requires considerable time expenditure and expert supervision; moreover, if one considers that calibration is carried out both following the production of the diagnostic instrument but also after several months of its use in the operational context, it is easy to deduce that automating this process on the one hand would improve the diagnostic yield, and on the other hand would reduce downtime and recalibration. The project aims to improve and automate the calibration process by introducing machine learning techniques for image classification. The results of the project find application on all instruments used for imaging, whether they are based on MRI, computed tomography, X-ray or ultrasound techniques. In fact, the goal is to relate the configuration parameters of the instrument to the images it produces in order to eliminate noise and artefacts produced by misconfiguration. Despite this, in the project we will consider as a case study the images produced by an ultrasound-based device produced by Echolight S.p.A. Medical devices produced by Echolight S.p.A. exploit images derived from ultrasound scans (B-Mode) to automatically identify anatomical reference targets (lumbar vertebrae bone interfaces of the L1-L4 tract and proximal femur bone interface). Once the regions of interest (ROIs) are identified, a proprietary algorithm evaluates the spectral characteristics of selected portions of the raw ultrasound signal related to the analyzed bone tissues. From the analysis of the raw signal characteristics, a measure of the bone mineral density (BMD) of the analyzed anatomical sites is determined. In order to provide reliable, repeatable, and accurate BMD measurements, special calibration and testing procedures have been developed, however, which require several manual measurements and checks, resulting in a high human-time commitment and, consequently, introducing a risk of human error on the collection and interpretation of the collected measurements and results. Leveraging the image processing and image classification techniques developed within the project, the algorithm will provide output indicative of the presence of artefacts or other alterations in the performance of the ultrasound system in production in order to possibly intervene with further modifications and calibrations. As part of the project, standard conditions for conducting tests will also be defined through the use of specific ultrasound phantoms provided by the company. Prof. Italo Epicoco (italo.epicoco(a)unisalento.it) is the scientific responsible for this research grant. DEADLINE: June 24, 2022 ALL INCLUSIVE GROSS AMOUNT (for 18 months): 29050,50 euro (i.e., 19367 euro annual gross amount) NOTE: Foreign candidates are strongly encouraged to contact me by email if they need help/support in order to prepare their application: I will be glad to assist. Here are, attached, an unofficial English translation of the call and the corresponding application and self declaration forms, translated in English. NOTE: Foreign candidates are strongly encouraged to contact Prof. Epicoco by email if they need help/support in order to prepare their application: hewill be glad to assist. ******************************************************************************************* Prof. Massimo Cafaro, Ph.D. Associate Professor of Parallel Algorithms and Data Mining/Machine Learning Head of HPC Lab https://hpc-lab.unisalento.it Director of Master in Applied Data Science Department of Engineering for Innovation University of Salento, Lecce, Italy Via per Monteroni 73100 Lecce, Italy Voice/Fax +39 0832 297371 Web https://www.massimocafaro.it Web https://www.unisalento.it/people/massimo.cafaro E-mail massimo.cafaro(a)unisalento.it E-mail cafaro(a)ieee.org E-mail cafaro(a)acm.org INGV National Institute of Geophysics and Volcanology Via di Vigna Murata 605 Roma CMCC Foundation Euro-Mediterranean Center on Climate Change Via Augusto Imperatore, 16 - 73100 Lecce massimo.cafaro(a)cmcc.it ******************************************************************************************* --

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two research grants available within the RIPARTI regional projects
by Massimo Cafaro 15 Jun '22

15 Jun '22

The first research grant shall be developed in close collaboration with Planetek Italia (12 months out of 18 in total), and is related to the following topic: Machine Learning for Space Weather The proposed research project is concerned with the study of "Space Weather Phenomena" and the development of knowledge about the mechanisms and effects of solar-derived perturbative phenomena developing in circumterrestrial space and impacting the ionized atmosphere (ionosphere). In the project emphasis is given to the study and modeling of the dynamics of the ionospheric plasma and the electron density irregularities in it on a global scale, in order to improve the capability of long-term (24-48 hours in advance) nowcasting and forecasting of the ionospheric response to Space Weather events over the Mediterranean area. The modeling approach is developed through innovative "machine learning" techniques, recently introduced (Cesaroni et al 2020), the results of which point to this as a strategy to extend the time horizon of ionospheric forecasting, a fundamental requirement for increasing knowledge of Space Weather phenomena in near-Earth space. In addition, the growing demand for semi-empirical approaches for real-time mitigation of errors introduced by the ionosphere on positioning and navigation systems makes the proposed topic a significant contribution in the area of "services and research for society" in relation to the strategic objective "Development of a National Space Weather Service" in the context of developing countermeasures to contain the negative effect that the irregular and disturbed ionosphere can have on technological systems in use in modern society such as, for example, navigation and positioning satellite systems (GNSS, GLobal Navigation Satellite Systems), trans-horizon HF radio communications, and L-band satellite communication systems. Such systems are of interest to a variety of end users who can be identified as users of the service in which the developed products may be embedded. Examples of users may include: precision agriculture operators, operators in the field of mapping, aviation, and radio communications operators for emergency management in civil defense. Cesaroni, C., Spogli, L., Aragon-Angel, A., Fiocca, M., Dear, V., De Franceschi, G., & Romano, V. (2020). Neural network based model for global Total Electron Content forecasting. Journal of Space Weather and Space Climate, 10, 11. The second research grant shall be developed in close collaboration with GE Avio (12 months out of 18 in total), and is related too the following topic: Operative Framework For HPC (Off-HPC) High-performance computing (HPC) clouds are becoming a complement or, in some cases, an alternative to on-premise clusters for running scientific-technical, engineering, and business analytics service applications. Most research efforts in the area of cloud HPC aim to analyze and understand the cost-benefit of migrating computationally intensive applications from on-premise environments to public cloud platforms. Industry trends show that on-premise/cloud hybrid environments are the natural path to get the best out of on-premise and cloud resources. Workloads that are stable from the point of view of required computing resources and sensitive from the point of view of the need to protect processed information can be performed on on-premise resources, while peak computational loads can take advantage of remote computing resources available in the cloud typically under a "pay-as-you-go" consumption mode. The main difficulties in using cloud solutions to run HPC applications stem from their characteristics and properties compared to traditional cloud services to handle, for example, standard enterprise applications, Web applications, data storage or backup, or business intelligence. HPC applications tend to require more computing power than application services typically delivered in cloud environments. These processing requirements arise not only from the characteristics of the CPUs (Central Processing Units), but also from the amount of memory and network speed to support their proper execution. In addition, such applications may have a particular and different execution mechanism than dedicated cloud application services that instead run 24/7. HPC applications tend to run in batch mode. Users execute a series of computational jobs, consisting of instances of the application with different inputs, and wait until results are generated to decide whether new computational tasks need to be submitted and executed. Therefore, moving HPC applications to cloud platforms requires not only a focus on resource allocation in the infrastructure in use and its optimization, but also on how users interact with this new environment. Research in the area of cloud HPC can be classified into three broad categories: (i) feasibility studies on adopting the cloud to replace or complement on-premise computing clusters to run HPC applications; (ii) performance optimization of cloud resources for running HPC applications; and (iii) services to simplify the use of cloud HPC, particularly for users who are not specialized in data and information processing and processing technologies. This research project intends to focus on study activities within the first category, in which, more specifically, there are four main aspects that should be considered: (i) metrics used to assess how feasible the use of HPC cloud is; (ii) resources used in computational experiments; (iii) computational infrastructure; and (iv) software, which includes both well-known HPC benchmarks and computational tools, algorithms, or methodologies related to specific business application cases. Currently, the company uses HPC applications running mostly on on-premise systems but faces issues related to the need for greater computational resources that can be met through flexible and scalable architectures provided by cloud technologies. The need is to build clear technology and governance references for cloud or hybrid infrastructures. The research project will therefore aim to carefully analyze the state of the art of hybrid HPC solutions, define criteria for benchmarking different solutions, develop an operational framework that includes the operational and economic management aspects of a hybrid HPC solution, and finally implement one or more industrial pilots. DEADLINE: June 24, 2022 ALL INCLUSIVE GROSS AMOUNT (for 18 months): 29050,50 euro (i.e., 19367 euro annual gross amount) NOTE: Foreign candidates are strongly encouraged to contact me by email if they need help/support in order to prepare their application: I will be glad to assist. Here you can download an unofficial English translation of the call: RIPARTI-call ******************************************************************************************* Prof. Massimo Cafaro, Ph.D. Associate Professor of Parallel Algorithms and Data Mining/Machine Learning Head of HPC Lab https://hpc-lab.unisalento.it Director of Master in Applied Data Science Department of Engineering for Innovation University of Salento, Lecce, Italy Via per Monteroni 73100 Lecce, Italy Voice/Fax +39 0832 297371 Web http://sara.unisalento.it/~cafaro Web https://www.unisalento.it/people/massimo.cafaro E-mail massimo.cafaro(a)unisalento.it E-mail cafaro(a)ieee.org E-mail cafaro(a)acm.org INGV National Institute of Geophysics and Volcanology Via di Vigna Murata 605 Roma CMCC Foundation Euro-Mediterranean Center on Climate Change Via Augusto Imperatore, 16 - 73100 Lecce massimo.cafaro(a)cmcc.it ******************************************************************************************* --

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Expression of interest PhD Positions in Edge Computing
by Carlo Vallati 03 Jun '22

03 Jun '22

Dear all, we are looking for bright and highly motivated student for one PhD position at the Department of Information Engineering at the University of Pisa. The position is funded within the framework of the "Crosslab: Innovation for Industry 4.0" project. The research activities will be carried out in the "Cloud Computing, Big Data & Cybersecurity" laboratory ( https://crosslab.dii.unipi.it/cloud-computing-big-data-cybersecurity-lab) A short description of the research topic can be found below. Interested people are requested to send an expression of interest by submitting a curriculum vitae, a one-page research statement showing motivation and understanding of the topic of the position, and the official Transcript of Record. The expression of interest must be sent by email to Carlo Vallati at carlo.vallati(a)unipi.it with the reference [PhD expression of interest] in the subject of the email. Applications will be reviewed continuously until 5th July 2022. The starting date of the PhD position is Fall 2022. The duration of the PhD is three years. The compensation is a standard Italian Ph.D. student fare, about 1150 Euro/month net. ================================================ Edge Computing 2.0: Efficient Deep Learning at the Edge ================================================ Abstract: Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial intelligence (AI), including computer vision, natural language processing, and speech recognition. However, their superior performance comes at the considerable cost of computational complexity, which greatly hinders their applications in many resource-constrained devices, such as Edge computing nodes and Internet of Things (IoT) devices. Therefore, methods and techniques that can lift the efficiency bottleneck while preserving the high accuracy of DNNs are in great demand to enable numerous edge AI applications. The proposed research plan involves the analysis and identification of the challenges related to DNNs for time series prediction both at training time, on the GPU-enabled resource-constrained devices, and at inference time, on microcontrollers, leveraging available open-source software such as Tensorflow and Pytorch. The final goal of the research activity will be the definition, design, implementation, and testing of novel algorithms to improve the efficiency of DNNs on the edge and on IoT devices, on real-case scenarios. Reference contact: Carlo Vallati, email: carlo.vallati(a)unipi.it -- -- ------------------------------ ------- Carlo Vallati, PhD Associate Professor Computer Networking Group Department of Information Engineering University of Pisa Via Diotisalvi 2, 56122 Pisa - Italy Ph. : (+39) 050-2217.572 (direct) .599 (switch) Fax : (+39) 050-2217.600 Skype: warner83 E-mail: carlo.vallati@iet.unipi.ithttp://www.iet.unipi.it/c.vallati/ -----------------------------------------------

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