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Fluorescence Lifetime of NV Centers for Temperature-Insensitive All-Optical Magnetic Field Sensing

Fluorescence Lifetime of NV Centers for Temperature-Insensit...

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Ludwig Horsthemke Jens Pogorzelski Dennis Stiegekötter Frederik Hoffmann Ann-Sophie Bülter Lutz Langguth Robert Staacke Sarah Trinschek Markus Gregor Peter Glösekötter Department of Electrical Engineering and Computer Science FH Münster—University of Applied Sciences Steinfurt Germany Quantum Technologies GmbH Leipzig Germany Department of Engineering Physics FH Münster—University of Applied Sciences Steinfurt Germany

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

We investigate the combined temperature and magnetic field dependent behavior of high NV-density microdiamonds in an all-optical frequency-domain based setup, utilizing the change in fluorescence lifetime. We use these frequency-domain data to train neural networks for the prediction of temperatures and magnetic fields. At zero magnetic field, we show the ability to predict temperatures in the range of 0° C to 100°C, with a standard deviation of 1.24 °C. Furthermore, we show the ability to predict applied magnetic fields, while varying the ambient temperature. We achieve a higher accuracy, compared to the approach of observing of the fluorescence intensity at a single excitation frequency.

关键词： Temperature sensors Temperature distribution Temperature dependence Accuracy scientific computing Frequency-domain analysis Fluorescence Sensors Magnetic fields Standards

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How to Tackle Fake Medical Content with Clustering and Transformers

How to Tackle Fake Medical Content with Clustering and Trans...

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Radu Razvan Slavescu Marina Bianca Trif Anca Marginean Kinga Cristina Slavescu Department of Computer Science Technical University of Cluj-Napoca Cluj-Napoca Romania 2nd Pediatrics Clinic Iuliu Hatieganu University of Medicine and Pharmacy Cluj-Napoca Romania

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

Spreading fake medical information / news online might threat the public health, e.g., by decreasing the vaccination rates or the level of trust in the healthcare process. the system presented in our paper aims to address this by determining in an automatic manner whether a text either contains or implies some claims known to be false. To this end, it relies on the Monant dataset, which comprises medical posts and claims whose veracity has been established by experts in the field. the dataset is used in two ways. First, the set of false claims aimed to be detected was obtained from it. these false claims are clustered, allowing the next steps to be performed against one cluster only, thus reducing the response time. Second, to detect a post's falsehood, this gets summarized using transformers, then the summary is compared similarity-wise with each element of the chosen cluster of false claims, using all-MiniLM-L6-v2 and a variation of BioBERT. A high similarity score is interpreted as the presence of the false claim in the summary. the results obtained from the experiments demonstrate, through manually checked examples, that the system manages to identify about 77% of the false claims within the text at hand (with an F1-value of 0.73).

关键词： Hands scientific computing Clustering algorithms Transformers Vaccines Time factors Public healthcare Fake news

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Enhanced Anomaly Detection in Automotive Systems Using SAAD: Statistical Aggregated Anomaly Detection

Enhanced Anomaly Detection in Automotive Systems Using SAAD:...

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Dacian Goina Eduard Hogea George Maties Faculty of Mathematics and Computer Science West University of Timisoara Timisoara Timis Faculty of Electrical Engineering Gheorghe Asachi Technical University of Iasi Iasi

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

this paper presents a novel anomaly detection methodology termed Statistical Aggregated Anomaly Detection (SAAD). the SAAD approach integrates advanced statistical techniques with machine learning, and its efficacy is demonstrated through validation on real sensor data from a Hardware-in-the-Loop (HIL) environment within the automotive domain, where physical components of the vehicle are interfaced with simulation models. the HIL setup provides a controlled, real-time testing framework that replicates actual driving conditions, allowing for the validation of the vehicle's control systems under various operational scenarios without the need for a physical vehicle prototype. the key innovation of SAAD lies in its ability to significantly enhance the accuracy and robustness of anomaly detection when combined with Fully Connected Networks (FCNs) augmented by dropout layers. Comprehensive experimental evaluations indicate that the standalone statistical method achieves an accuracy of 72.1%, whereas the deep learning model alone attains an accuracy of 71.5%. In contrast, the aggregated method achieves a superior accuracy of 88.3% and an F1 score of 0.921, thereby outperforming the individual models. these results underscore the effectiveness of SAAD, demonstrating its potential for broad application in various domains, including automotive systems.

关键词： Deep learning Technological innovation Accuracy Statistical analysis scientific computing Robustness Data models Anomaly detection Automotive engineering Testing

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Hierarchical Scheduling in on-demand GPU-as-a-Service Systems

Hierarchical Scheduling in on-demand GPU-as-a-Service System...

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Federica Filippini Marco Lattuada Arezoo Jahani Michele Ciavotta Danilo Ardagna Edoardo Amaldi Politecnico di Milano Milan Italy Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Università degli studi di Milano-Bicocca Milan Italy

ISBN: (数字)9781728176284

ISBN: (纸本)9781728176291

Deep learning (DL) methods have recently gained popularity. Training this class of models is, however, computing-intensive, and frequently GPUs are used to boost performance. Although the costs of GPU-based systems are gradually reducing due to the high demand, they are still prohibitive: in public clouds, GPU-powered virtual machines (VMs) time unit price is 5-8x higher than CPU-only VMs. While the cloud remains the most cost-effective and flexible deployment, operation costs can be reduced, in large settings, by rightsizing and sharing resources among multiple processes. this work addresses the online joint capacity planning and job scheduling with due dates problem and proposes alternative matheuristic solution methods. Our objective is to optimize operation costs by: i) rightsizing the VM capacities at each node, ii) partitioning the set of GPUs among multiple concurrent jobs on the same VM, and iii) determining a due-date-aware job schedule. the effectiveness of the proposed hierarchical approach, coupled with an appropriate Mixed Integer Linear Programming formulation, is validated against first-principle methods by relying on simulation. the experiments prove that the efficiency of GPU-based systems evaluated in terms of costs can be improved by 50-70%. Finally, scalability analyses show that the proposed approach enables to solve problem instances with up to 100 nodes in less than one minute on average, making it suitable for practical scenarios.

关键词： Training Cloud computing Schedules scientific computing Scalability Virtual machining Scheduling

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Exploring Sum-Product Networks for Image Classification. Applications

Exploring Sum-Product Networks for Image Classification. App...

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Maria-Alexandra Badea Elena Pelican Raluca Vernic Doctoral School of Mathematics Ovidius University Constanta Romania Faculty of Mathematics and Informatics Ovidius University Constanta Romania

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

Sum-Product Networks (SPN) represent a significant and novel development within the probabilistic graphical models domain that unites the representational capabilities of deep learning with effective probabilistic inference. Our focus is on presenting these unique structural properties of SPNs along with their theoretical foundations and putting them into application on real-life diverse datasets. Despite its promising prospects, only a few existing applications are using this technology. To illustrate how this method can be used in practice, we have applied the model to four different datasets: the popular MNIST dataset, the Fashion MNIST dataset, the Fast, Furious and Insured dataset, and the Garbage Classification dataset, respectively. In the first two datasets, we used SPN s to classify hand-written digits and fashion products respectively displaying their effectiveness within a standard image classification task. the models' architecture incorporates SPN-specific layers together with conventional convolutional layers thereby achieving remarkable performance measures. the results obtained for the last two datasets outperformed the results obtained by the classical deep learning methods. All these show that SPNs form a strong basis for theoretical exploration and practical application to probabilistic modeling along with deep learning. this paper adds to the collective understanding of SPN applications and, at the same time, highlights possible further areas of study within the field.

关键词： Deep learning Support vector machines Adaptation models Uncertainty scientific computing Probabilistic logic Stability analysis Convolutional neural networks Standards Image classification

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Synthetic Dataset Generation for Edge Drone Inference and Training

Synthetic Dataset Generation for Edge Drone Inference and Tr...

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Bogdan Nedelcu Adina Magda Florea Faculty of Automatic Control and Computers Politehnica University of Bucharest

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

this research investigates the generation of synthetic aerial images for training deep neural networks., particularly targeting the task of object detection in mostly top-down views. the experiment leveraged a simulation environment to create a complex dataset., addressing the challenges associated with collecting real-world aerial images. the work encompassed several critical components., including algorithm parameter variation for ground truth annotation., the exploration of bounding box generation techniques and an investigation into camera orientations and altitude to simulate real-world scenarios. Additionally., the research delved into the training of neural network models., specifically tailored for low-power devices., particularly the Coral Edge TPU. the findings of this research indicate the feasibility of utilizing synthetic environments and datasets to train neural networks for aerial image analysis. the trained model showcased promising results., with the potential for broader applications in object detection. the study also addressed safety considerations., emphasizing the need for high-resolution cameras in drone operations to detect objects., such as human bodies., from safe distances. Furthermore., the paper outlines a roadmap for future work., highlighting areas such as multi-class object detection., integration with autopilot systems., and real-world testing. this research serves as a foundational step toward harnessing synthetic data and deep learning for enhanced drone capabilities in diverse scenarios., with a commitment to safety and compliance with aviation regulations.

关键词： Training scientific computing Image edge detection Object detection Cameras Regulation Safety Drones Synthetic data Testing

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Infrastructure as Code for Heterogeneous computing

Infrastructure as Code for Heterogeneous Computing

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Daniel Vladusic Dragan Radolovic XLAB Slovenia

ISBN: (数字)9781728176284

ISBN: (纸本)9781728176291

Setting up an infrastructure for application deployment is a non-trivial task. We usually simplify the task by using the Infrastructure as Code (IaC) approach. through IaC we set-up software defined infrastructure, able to run applications. Several tools and platforms have been developed to describe the system and to implement the actual deployment of the application (e.g., Puppet, Chef, Ansible, Terraform, etc.). When the infrastructure is heterogeneous (e.g., combination of Cloud and HPC) the challenges to set up a collaborative infrastructure is even harder, as the paradigms and environments differ significantly. Cloud infrastructure is focusing on servers, events, functions, while the HPC infrastructure is procedural and takes the application and merely executes it, usually on bare metal. As expected, the applications deployed on the respective systems differ in their dynamics and longevity. Whilst deployment of applications on the Cloud can be solved with the aforementioned tools, the HPC systems are in this respect in their infancy - the application is usually scheduled to be executed on a pre-defined set of processors and from that point on, the scripts merely gather input data, execute the application and then organize the output data. Merging the two approaches is thus currently rather hard and requires an explicit boundary between the Cloud and HPC parts of the application, posing a significant issue for the overall modelling and thus set-up of the system with the IaC approach. SODALITE, an H2020 project, is targeting simplification of the application deployment complexity while retaining or improving application performance on targeted HPC heterogeneous and cloud systems. the application deployment is abstracted through modelling of application's component relationships, policies and performance. the application is deployed using appropriate container technologies, matching the targeted heterogeneous HPC and cloud-based platforms. the starting point is th

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Preemptive Phased Execution Models with Scratchpad Memory Requirements

Preemptive Phased Execution Models with Scratchpad Memory Re...

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Vlad Rădulescu Albert M.K. Cheng Ştefan Andrei Department of Computer Science A. I. Cuza University of Iaşi Iaşi Romania Department of Computer Science University of Houston Houston TX USA

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

there is currently a widespread interest in using phased execution models in the design of real-time systems, stimulated by their ability to reduce contention in accessing shared resources. the main beneficiary of such approaches is the wide category of Commercial Off-the-Shelf (COTS) multi-core systems, where complexity is an underlying issue. However, the advantage of predictability comes at a cost, which often resides in the additional decisions that are usually made when using phased execution models. One such decision is employing non-preemptive scheduling; as is well known, this leads to NP-hardness for most instances of the scheduling problem, while preemption can avoid this level of complexity in many cases. It is no surprise, then, that recent efforts have attempted to explore the possibilities offered by preemptive scheduling in combination with phased execution models. Unlike existing work, the current paper focuses on the scratchpad memory consumption in preemptive phased execution models. the starting point, the model introduced by the authors of a previous article, was meant for the general analysis of the efficiency of preemptive scheduling in phased execution models. Notably, two methods for handling intermediate data are introduced, namely, the Waiting Time Minimizing Preemption (WMPM) and the Overhead Minimizing Preemption (OMPM). the current paper analyses the requirements of each approach in terms of scratchpad memory. It also introduces a hybrid method, in order to achieve a finer balance between memory consumption and schedulability constraints.

关键词： Analytical models scientific computing Processor scheduling Computational modeling Memory management Predictive models Scheduling Real-time systems numerical models Complexity theory

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A multiprocessor computer system for integrated numeric and symbolic computation

A multiprocessor computer system for integrated numeric and ...

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IEEE international symposium on Intelligent Control (ISIC)

作者： T.J. Goblick P.G. Harmon J.I. Leivent J.J. Olsen D.S. Cogen J.J. DiTuri P.G. McHugh R.L. Walton Lincoln Laboratory Massachusetts Institute of Technology Lexington MA USA

A class of problems, called signal understanding (SU) systems, that involves intensive numeric, as well as symbolic testing, is considered. A shared-memory multiprocessor computer system designed specifically to facilitate the development and evaluation of parallel SU algorithms, which would be a basic tool for developing real-time SU systems, is described. this machine, called the MX-1, runs Common LISP, which has been augmented with extensions for user-directed parallelism. It is, in essence, a parallel LISP machine with digital signal processors (DSPs) integrated into the architecture to provide powerful numeric computational capability. the MX-1 has 16 crossbar interconnected processing nodes, each with a Motorola 68020 CPU operating at 16 MHz, 8 Mbytes of dynamic random-access memory and its own Weitek DSP coprocessor that can do 10 million multiply-accumulate operations per second. High-speed I/O ports are provided for direct entry of sensor data into the DSPs. Two 4-node systems and a 16-node system have been fabricated, and several applications have been run on the systems.< >

关键词： Digital signal processing Concurrent computing System testing Algorithm design and analysis Signal processing algorithms Real time systems Parallel processing Digital signal processors Computer architecture Power system interconnection

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symbolic execution and deductive verification approaches to verifythis 2017 challenges 8th

Symbolic execution and deductive verification approaches to ...

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8th international symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2018

作者： Luo, Ziqing Siegel, Stephen F. Verified Software Laboratory Department of Computer and Information Sciences University of Delaware NewarkDE19716 United States

ISBN: (纸本)9783030034207

We present solutions to the Verifythis 2017 program verification challenges using the symbolic execution tool CIVL. Comparing these to existing solutions using deductive verification tools such as Why3 and KeY, we analyze the advantages and disadvantages of the two approaches. the issues include scalability;the ability to handle challenging programming language constructs, such as expressions with side-effects, pointers, and concurrency;the ability to specify complex properties;and usability and automation. We conclude with a presentation of a new CIVL feature that attempts to bridge the gap between the two approaches by allowing a user to incorporate loop invariants incrementally into a symbolic execution framework. © Springer Nature Switzerland AG 2018.

关键词： Model checking

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