6,686 research outputs found
TRiP: a transfer learning based rice disease phenotype recognition platform using SENet and microservices
Classification of rice disease is one significant research topics in rice phenotyping. Recognition of rice diseases such as Bacterialblight, Blast, Brownspot, Leaf smut, and Tungro are a critical research field in rice phenotyping. However, accurately identifying these diseases is a challenging issue due to their high phenotypic similarity. To address this challenge, we propose a rice disease phenotype identification framework which utilizing the transfer learning and SENet with attention mechanism on the cloud platform. The pre-trained parameters are transferred to the SENet network for parameters optimization. To capture distinctive features of rice diseases, the attention mechanism is applied for feature extracting. Experiment test and comparative analysis are conducted on the real rice disease datasets. The experimental results show that the accuracy of our method reaches 0.9573. Furthermore, we implemented a rice disease phenotype recognition platform based microservices architecture and deployed it on the cloud, which can provide rice disease phenotype recognition task as a service for easy usage
Efficient Visual Computing with Camera RAW Snapshots
Conventional cameras capture image irradiance (RAW) on a sensor and convert it to RGB images using an image signal
processor (ISP). The images can then be used for photography or visual computing tasks in a variety of applications, such as public
safety surveillance and autonomous driving. One can argue that since RAW images contain all the captured information, the conversion
of RAW to RGB using an ISP is not necessary for visual computing. In this paper, we propose a novel Ï-Vision framework to perform
high-level semantic understanding and low-level compression using RAW images without the ISP subsystem used for decades.
Considering the scarcity of available RAW image datasets, we first develop an unpaired CycleR2R network based on unsupervised
CycleGAN to train modular unrolled ISP and inverse ISP (invISP) models using unpaired RAW and RGB images. We can then flexibly
generate simulated RAW images (simRAW) using any existing RGB image dataset and finetune different models originally trained in
the RGB domain to process real-world camera RAW images. We demonstrate object detection and image compression capabilities in
RAW-domain using RAW-domain YOLOv3 and RAW image compressor (RIC) on camera snapshots. Quantitative results reveal that
RAW-domain task inference provides better detection accuracy and compression efficiency compared to that in the RGB domain.
Furthermore, the proposed Ï-Vision generalizes across various camera sensors and different task-specific models. An added benefit of
employing the Ï-Vision is the elimination of the need for ISP, leading to potential reductions in computations and processing times
Resource-aware scheduling for 2D/3D multi-/many-core processor-memory systems
This dissertation addresses the complexities of 2D/3D multi-/many-core processor-memory systems, focusing on two key areas: enhancing timing predictability in real-time multi-core processors and optimizing performance within thermal constraints. The integration of an increasing number of transistors into compact chip designs, while boosting computational capacity, presents challenges in resource contention and thermal management. The first part of the thesis improves timing predictability. We enhance shared cache interference analysis for set-associative caches, advancing the calculation of Worst-Case Execution Time (WCET). This development enables accurate assessment of cache interference and the effectiveness of partitioned schedulers in real-world scenarios. We introduce TCPS, a novel task and cache-aware partitioned scheduler that optimizes cache partitioning based on task-specific WCET sensitivity, leading to improved schedulability and predictability. Our research explores various cache and scheduling configurations, providing insights into their performance trade-offs. The second part focuses on thermal management in 2D/3D many-core systems. Recognizing the limitations of Dynamic Voltage and Frequency Scaling (DVFS) in S-NUCA many-core processors, we propose synchronous thread migrations as a thermal management strategy. This approach culminates in the HotPotato scheduler, which balances performance and thermal safety. We also introduce 3D-TTP, a transient temperature-aware power budgeting strategy for 3D-stacked systems, reducing the need for Dynamic Thermal Management (DTM) activation. Finally, we present 3QUTM, a novel method for 3D-stacked systems that combines core DVFS and memory bank Low Power Modes with a learning algorithm, optimizing response times within thermal limits. This research contributes significantly to enhancing performance and thermal management in advanced processor-memory systems
Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches
Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly
Current Challenges and Advances in Cataract Surgery
This reprint focuses on new trials related to cataract surgery, intraocular lens power calculations for cataracts after refractive surgery, problems related to high myopia, toric IOL power calculations, etc. Intraoperative use of the 3D Viewing System and OCT, studies on the spectacle dependence of EDOF, IOL fixation status and visual function, and dry eye after FLAC are also discussed. Proteomic analysis of aqueous humor proteins is also discussed
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks
Transformer is a deep neural network that employs a self-attention mechanism
to comprehend the contextual relationships within sequential data. Unlike
conventional neural networks or updated versions of Recurrent Neural Networks
(RNNs) such as Long Short-Term Memory (LSTM), transformer models excel in
handling long dependencies between input sequence elements and enable parallel
processing. As a result, transformer-based models have attracted substantial
interest among researchers in the field of artificial intelligence. This can be
attributed to their immense potential and remarkable achievements, not only in
Natural Language Processing (NLP) tasks but also in a wide range of domains,
including computer vision, audio and speech processing, healthcare, and the
Internet of Things (IoT). Although several survey papers have been published
highlighting the transformer's contributions in specific fields, architectural
differences, or performance evaluations, there is still a significant absence
of a comprehensive survey paper encompassing its major applications across
various domains. Therefore, we undertook the task of filling this gap by
conducting an extensive survey of proposed transformer models from 2017 to
2022. Our survey encompasses the identification of the top five application
domains for transformer-based models, namely: NLP, Computer Vision,
Multi-Modality, Audio and Speech Processing, and Signal Processing. We analyze
the impact of highly influential transformer-based models in these domains and
subsequently classify them based on their respective tasks using a proposed
taxonomy. Our aim is to shed light on the existing potential and future
possibilities of transformers for enthusiastic researchers, thus contributing
to the broader understanding of this groundbreaking technology
Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation
Trustworthy Artificial Intelligence (AI) is based on seven technical
requirements sustained over three main pillars that should be met throughout
the system's entire life cycle: it should be (1) lawful, (2) ethical, and (3)
robust, both from a technical and a social perspective. However, attaining
truly trustworthy AI concerns a wider vision that comprises the trustworthiness
of all processes and actors that are part of the system's life cycle, and
considers previous aspects from different lenses. A more holistic vision
contemplates four essential axes: the global principles for ethical use and
development of AI-based systems, a philosophical take on AI ethics, a
risk-based approach to AI regulation, and the mentioned pillars and
requirements. The seven requirements (human agency and oversight; robustness
and safety; privacy and data governance; transparency; diversity,
non-discrimination and fairness; societal and environmental wellbeing; and
accountability) are analyzed from a triple perspective: What each requirement
for trustworthy AI is, Why it is needed, and How each requirement can be
implemented in practice. On the other hand, a practical approach to implement
trustworthy AI systems allows defining the concept of responsibility of
AI-based systems facing the law, through a given auditing process. Therefore, a
responsible AI system is the resulting notion we introduce in this work, and a
concept of utmost necessity that can be realized through auditing processes,
subject to the challenges posed by the use of regulatory sandboxes. Our
multidisciplinary vision of trustworthy AI culminates in a debate on the
diverging views published lately about the future of AI. Our reflections in
this matter conclude that regulation is a key for reaching a consensus among
these views, and that trustworthy and responsible AI systems will be crucial
for the present and future of our society.Comment: 30 pages, 5 figures, under second revie
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