The increasing quality and availability of Quantum Processing Units (QPUs) is fueling a growing interest in quantum computing across many technological areas. The resulting increase in demand for QPU resources necessi...
详细信息
ISBN:
(纸本)9798331541378
The increasing quality and availability of Quantum Processing Units (QPUs) is fueling a growing interest in quantum computing across many technological areas. The resulting increase in demand for QPU resources necessitates Quantum computing as a Service (QCaaS) providers to support a high throughput of quantum workloads. A major runtime bottleneck in current QCaaS software stacks is the computationally-intensive compilation step which requires significant compute. To address this, Oxford Quantum Circuits has introduced distributed compilation whereby quantum programs are compiled in parallel and stored until the QPU is available. This has replaced our previous serial compilation approach where each program was compiled immediately prior to execution. From experiments using our production compilers and a simulated backend representing the QPU, we show that distributed compilation has resulted in a 78% reduction in processing time as compared to serial compilation. This demonstrates that there are sizeable performance gains to program throughput attainable through the introduction of distributed compilation into a QCaaS architecture. We posit that the usefulness of this feature will only grow given the increasing complexity of quantum programs and the growing popularity of quantum -classical hybrid algorithms.
Developing tools in the context of autonomous systems [22, 24], such as self-driving cars (SDCs), is time-consuming and costly since researchers and practitioners rely on expensive computing hardware and simulation so...
详细信息
ISBN:
(纸本)9798350363982;9798400705878
Developing tools in the context of autonomous systems [22, 24], such as self-driving cars (SDCs), is time-consuming and costly since researchers and practitioners rely on expensive computing hardware and simulation software. We propose SensoDat, a dataset of 32,580 executed simulation-based SDC test cases generated with state-of-the-art test generators for SDCs. The dataset consists of trajectory logs and a variety of sensor data from the SDCs (e.g., rpm, wheel speed, brake thermals, transmission, etc.) represented as a time series. In total, SensoDat provides data from 81 different simulated sensors. Future research in the domain of SDCs does not necessarily depend on executing expensive test cases when using SensoDat. Furthermore, with the high amount and variety of sensor data, we think SensoDat can contribute to research, particularly for AI development, regression testing techniques for simulation-based SDC testing, flakiness in simulation, etc.
The outstanding progress in the smart devices as well as the noticeable reduction in their cost has led to the invasion of the Internet of Things (IoT) in our real lives. The multiplicity of the IoT applications and t...
详细信息
ISBN:
(纸本)9798350333398
The outstanding progress in the smart devices as well as the noticeable reduction in their cost has led to the invasion of the Internet of Things (IoT) in our real lives. The multiplicity of the IoT applications and the diversity and heterogeneity of the IoT devices present a real challenge in the IoT development. There is hence a need to have an intermediate layer between the physical layer including the smart devices and the application layer to facilitate the integration and the usage of the IoT devices in the IoT applications. This layer is the middleware layer. Most of the existing middleware are suitable for small-scale IoT networks including limited number of smart things. The performance of these middleware is reduced when the number of IoT devices increases. The main reason of the performance deterioration is the unsuitable middleware architecture that is unable to bear the increasing number of IoT devices. We present in this paper a distributed middleware architecture based on Chord protocol. Our architecture transforms any centralized middleware to a distributed one which improves its performance and makes it scalable. The performance evaluation have shown that our proposed approach has the ability to cope successfully with the increasing number of IoT devices and requests.
Motivation: Large scale deep neural network models use a lot of training data, evident from the large datasets curated for their training, such as ImageNet. Thus it is necessary to use high performance computing (HPC)...
详细信息
Robot mapping usually benefits from the combination of several sensors. However, most algorithms for Simultaneous Localization And Mapping using point clouds only consider a single point cloud as input. There are also...
详细信息
ISBN:
(纸本)9798350352351;9798350352344
Robot mapping usually benefits from the combination of several sensors. However, most algorithms for Simultaneous Localization And Mapping using point clouds only consider a single point cloud as input. There are also scenarios where SLAM is not needed and a simple local map representation without probabilities is preferred, for example due to throughput requirements. For these scenarios, software is needed that combines multiple point clouds into a unified, short-lived, robot-centric representation. Mapping software is only one tool in the software stack and should not compromise other components, which is why its performance-oriented design is of utmost importance. CUPREDS presents a ROS package for local mapping that prioritizes robustness and performance, with minimal overhead for its internal mechanisms.
The paper proposes approach to use new tools for malware detection in corporate networks, which are distributedsystems with partial centralization. To make decision about malware presence, the components which includ...
详细信息
Every year, the amount of data created by Internet of Things (IoT) devices increases;therefore, data processing is carried out by edge devices in close proximity. To ensure Quality of Service (QoS) throughout these op...
详细信息
ISBN:
(纸本)9798350304367;9798350304374
Every year, the amount of data created by Internet of Things (IoT) devices increases;therefore, data processing is carried out by edge devices in close proximity. To ensure Quality of Service (QoS) throughout these operations, systems are supervised and adapted with the help of Machine Learning (ML). However, as long as ML models are not retrained, they fail to capture gradual shifts in the variable distribution, leading to an inaccurate view of the system state and poor inference. In this paper, we present a novel ML paradigm that is constructed upon Active Inference (ACI) - a concept from neuroscience that describes how the brain constantly predicts and evaluates sensory information to decrease long-term surprise. We implemented a use case, in which an ACI-based agent continuously optimized the operation on a smart manufacturing engine according to QoS requirements. The agent used causal knowledge to gradually develop an understanding of how its actions are related to requirements fulfillment, and which configurations to favor. As a result, our agent required 5 cycles to converge to the optimal solution.
Since the sensor nodes (SNs) have limited resources, developing an energy-aware routing method for extending the total lifespan of the network has become one of the most vital developments in WSNs. Cluster based routi...
详细信息
Federated learning (FL) is an emerging distributed machine learning paradigm, which has shown great potential in collaborative learning with privacy preservation. However, FL clients usually have disparate system reso...
详细信息
ISBN:
(纸本)9798350339864
Federated learning (FL) is an emerging distributed machine learning paradigm, which has shown great potential in collaborative learning with privacy preservation. However, FL clients usually have disparate system resource capabilities (e.g., data, computation, and communication) for model training and aggregation, which can cause a series of system heterogeneity issues with performance degradation. To this end, we propose FedPKD, a Prototype-based Knowledge Distillation framework for FL. FedPKD integrates knowledge distillation and prototype learning with FL, which enables heterogeneous clients and the server to learn collaboratively, with different model architectures and resource capability adaptations. Specifically, FedPKD proposes to transfer dual knowledge of clients including the model output logits and prototypes to the server, and a prototype-based ensemble distillation mechanism is proposed to aggregate the logits and prototypes from clients, which can be used to train the server model with an unlabeled public dataset. The server model knowledge is then transferred back to clients to improve the performance of client models. Moreover, to improve learning performance and reduce communication overhead, we propose a prototype-based data filter mechanism to filter out the samples with low-quality knowledge. Extensive experiments under various settings demonstrate the superiority of FedPKD in learning performance and communication efficiency when compared to state-of-the-art benchmarks.
The SARS-CoV-2 virus causes coronary artery disease (COVID-19). The majority of persons who are infected with the virus will have mild to severe respiratory illness and recover without the need for therapy. Some, on t...
详细信息
ISBN:
(纸本)9781665495127
The SARS-CoV-2 virus causes coronary artery disease (COVID-19). The majority of persons who are infected with the virus will have mild to severe respiratory illness and recover without the need for therapy. Some, on the other hand, will become critically unwell and require medical assistance. People over the age of 65, as well as those with underlying medical diseases such as cardiovascular disease, diabetes, chronic respiratory disease, or cancer, are at a higher risk of developing serious illness. Being thoroughly informed on the disease and how it spreads is the best strategy to avoid and slow down transmission. Stay at least 1 metre apart from other people to avoid infection. In this research work, we focus on how non-contact sensing technology and deep learning technique are being used to detect COVID-19 and assist healthcare workers in caring for COVID-19 patients. The proposed system captures images from the patient using non -contact sensing technologies and feeds the data into deep learning convolutional neural network architectures such as VGG16, VGG19, ResNet101, NASNet, DenseNet121, MobileNet, Xception, EfficientNet, and InceptionV3. In comparison to other architectures, the VGG16 architecture delivers superior accuracy.
暂无评论