Classical novae are cataclysmic binary star systems in which the matter of a companion star is accreted on a white dwarf1,2. Accumulation of hydrogen in a layer eventually causes a thermonuclear explosion on the surfa...
Classical novae are cataclysmic binary star systems in which the matter of a companion star is accreted on a white dwarf1,2. Accumulation of hydrogen in a layer eventually causes a thermonuclear explosion on the surface of the white dwarf3, brightening the white dwarf to ~105 solar luminosities and triggering ejection of the accumulated matter. Novae provide the extreme conditions required to accelerate particles, electrons or protons, to high energies. Here we present the detection of gamma rays by the MAGIC telescopes from the 2021 outburst of RS Ophiuchi, a recurrent nova with a red giant companion, which allowed us to accurately characterize the emission from a nova in the 60 GeV to 250 GeV energy range. The theoretical interpretation of the combined Fermi LAT and MAGIC data suggests that protons are accelerated to hundreds of gigaelectronvolts in the nova shock. Such protons should create bubbles of enhanced cosmic ray density, of the order of 10 pc, from the recurrent *** of the 2021 outburst of the nova RS Oph in very-high-energy gamma rays by the MAGIC telescopes is reported. Investigation of the gamma-ray emission provides evidence for acceleration of protons within the nova shock, which then propagate outwards to create bubbles of enhanced cosmic ray density.
Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raise...
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This article proposes a fast Coding Unit (CU) partition decision for use in HEVC encoders based on Decision Tree classifiers. The trees are employed in a modified low-complexity encoder that implements a fast CU parti...
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ISBN:
(纸本)9781538646595
This article proposes a fast Coding Unit (CU) partition decision for use in HEVC encoders based on Decision Tree classifiers. The trees are employed in a modified low-complexity encoder that implements a fast CU partition decision algorithm. Using the proposed method, an average complexity reduction of 47.8% is achieved with a Bjontegaard Delta bitrate (BD-BR) loss of 0.24% in the Random Access coding configuration, and a 42.8% complexity reduction with a 0.19% BD-BR loss in the Low Delay B configuration. A decision threshold analysis is also presented to assess the rate-distortion-complexity trade-off of the proposed method at different complexity points, varying the complexity reduction from 28% (with a 0.04% loss in BD-BR) up to 60% (with a 3.6% BD-BR loss) using the Random Access configuration. A comparison with related works shows that the proposed method outperforms competing solutions in terms of both rate-distortion efficiency and complexity reduction.
High abstraction level models can be used within the system-level simulation to allow rapid evaluations of architectural aspects in early Design Space Exploration (DSE) and direct the development decisions. Further, e...
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ISBN:
(纸本)9781728102412;9781728102405
High abstraction level models can be used within the system-level simulation to allow rapid evaluations of architectural aspects in early Design Space Exploration (DSE) and direct the development decisions. Further, early DSE is of paramount importance in the specification of future Embedded Systems (ES) and its evaluation for applications with high computing demands and energy restrictions. This paper presents the exploration of Heterogeneous Task-Level Parallelism (HTLP) in a Block-Matching Algorithm (BMA) video coding application. HTLP means the creation and execution of simultaneous threads of kernels defined for different types of Processing Elements (PE) - e.g., CPU and GPU - but all for an equal purpose. We employ a BMA implementation as a case study, and its characteristics are used to explore the HTLP - in particular, its kernels for data preparation, SAD (sum of absolute differences) criteria calculation, and SAD values grouping. For the exploration, a system-level simulation framework (SAVE-htlp) is augmented, being able to support the HTLP. In the performed experiments, SAVE-htlp simulates workload and architecture models and explores 22 settings varying the PE type employed during the tasks' execution and the number of concurrent threads for each kernel. Execution time, performance, energy, and power results show HTLP settings overcoming CPU-only ones as well as those with solely GPUs to process its tasks.
This paper presents a hardware design for the 3D-High Efficiency video Coding (3D-HEVC) Disparity Estimation (DE) based on the Unidirectional Disparity Search (UDS) algorithm. The architecture was developed to process...
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This paper presents a hardware design for the 3D-High Efficiency video Coding (3D-HEVC) Disparity Estimation (DE) based on the Unidirectional Disparity Search (UDS) algorithm. The architecture was developed to process the four square-shaped Prediction Unit (PU) sizes rather than using all 24 possible PU sizes. These two design approaches caused a coding efficiency degradation of only 0.554% in the BD-rate when compared to the traditional approach whereas allowed a significant reduction in the required calculations and also in the memory accesses, contributing to generate a fast and energy-efficient design. The architecture has a high level of parallelism to reach the desired high throughput and explored clock gating to reduce the power dissipation. As the best of the author's knowledge, this is the first work on literature presenting a hardware design for the 3D-HEVC DE. The architecture was ASIC synthesized and can reach up to 1.457 GHz. This result allows a processing rate of five views of UHD 2160p videos at 52 frames per second. When compared with related works targeting the HEVC Motion Estimation, the developed architecture uses a smaller area, requires a smaller memory, reaches a higher throughput and dissipates at least 92% less power.
The 3D-High Efficiency video Coding (3D-HEVC) is an extension of the High Efficiency video Coding (HEVC) standard targeting 3D-video encoding. The use of this extension leads to a significant computational-effort incr...
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ISBN:
(纸本)9781538674321
The 3D-High Efficiency video Coding (3D-HEVC) is an extension of the High Efficiency video Coding (HEVC) standard targeting 3D-video encoding. The use of this extension leads to a significant computational-effort increase since new tools are introduced to efficiently encode 3D videos. Therefore, the real-time processing of 3D-videos using 3D-HEVC is highly challenging, and it requires the development of efficient hardware solutions, mainly when mobile devices with energy constraints are considered. This paper focuses on the Depth Modeling Mode 4 (DMM4) encoding mode which is one of the novelties introduced by 3D-HEVC. A low-power and high-throughput architecture was designed and presented in this paper targeting the DMM4, and this architecture was synthesized targeting FPGA and ASIC. The FPGA synthesis was focused on an Altera Stratix V, and the ASIC synthesis was focused on the 45nm Nangate technology. The FPGA and ASIC results showed that the architecture can process UHD 2160p 3D-videos (two views) at 60 fps or to process five views of HD 1080p 3D-videos at 30 frames per second, surpassing the related works in area usage, power dissipation, and processing rate.
作者：
Dutt, NikilRegazzoni, Carlo S.Rinner, BernhardYao, XinNikil Dutt (Fellow
IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member
IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and
Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In c...
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Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.
In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhous...
In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A
BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is r...
BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.
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