Artificial Intelligence
This is a part of the COVID-CTset dataset for testing and training the network. This dataset contains 12058 CT slices. It was gathered from Negin medical center that is located at Sari in Iran. This medical center uses a SOMATOM Scope model and syngo CT VC30-easyIQ software version for capturing and visualizing the lung HRCT radiology images from the patients. The format of the exported radiology images was 16-bit grayscale DICOM format with 512*512 pixels resolution.
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In the data set acquisition phase, the system will automatically record the following data: frontal video recording: the frame rate of the video is 10Hz per second, and the video contains the patient's movement, posture and facial information; hemodynamic data: the acquisition frequency is 10Hz per second, covering the area of the prefrontal lobe of the brain, and including the hemodynamic information of 22 channels; kinematic data: the acquisition frequency is 10Hz per second, and including the hand velocity, shoulder angular velocity, elbow angular velocity and sitting posture information
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Register allocation is an important phase in compiler optimization. Often, its resolution involves graph coloring, which is an NP-complete problem. Because of their significance, numerous heuristics have been suggested for their resolution. Heuristic development is a complex process that requires specialized domain expertise. Recently, several machine learning based approaches have been proposed to solve compiler optimization problems.
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As the field of human-computer interaction continues to evolve, there is a growing need for new methods of gesture recognition that can be used in a variety of applications, from gaming and entertainment to healthcare and robotics. While traditional methods of gesture recognition rely on cameras or other optical sensors, these systems can be limited by factors such as lighting conditions and occlusions.
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The operator controls the vehicle to drive in the environment with dense distribution of obstacles, collects the spatial environment data through LIDAR and camera in the process of driving, and then processes the exercisable area map according to the change of longitudinal gradient, which is used to display the distribution of exercisable area in the current driving space of the vehicle, and then carries out the research of humanoid driving according to the distribution of the exercisable area and the data of driving behaviors.
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The operator controls the vehicle to drive in the environment with dense distribution of obstacles, collects the spatial environment data through LIDAR and camera in the process of driving, and then processes the exercisable area map according to the change of longitudinal gradient, which is used to display the distribution of exercisable area in the current driving space of the vehicle, and then carries out the research of humanoid driving according to the distribution of the exercisable area and the data of driving behaviors.
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The operator controls the vehicle to drive in the environment with dense distribution of obstacles, collects the spatial environment data through LIDAR and camera in the process of driving, and then processes the exercisable area map according to the change of longitudinal gradient, which is used to display the distribution of exercisable area in the current driving space of the vehicle, and then carries out the research of humanoid driving according to the distribution of the exercisable area and the data of driving behaviors.
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The study included 50 epilepsy patients undergoing long-term video-EEG monitoring at the Epilepsy Center of Guangdong 999 Brain Hospital. The inclusion criteria for patients were as follows: (1) VEEG reports confirming definite epileptic seizures, (2) complete video data containing both seizure and non-seizure periods, (3) no intentional interference during patient seizures, and no occlusion of the patient, such as patients were covered by quilts.
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The dataset encompasses a diverse array of electrical signals representing Power Quality Disturbances (PQD), both in single and combined forms, meticulously generated in adherence to the IEEE 1159 guideline. Crucially, the dataset includes both raw data and corresponding labels, facilitating supervised learning tasks and enabling the development and evaluation of classification algorithms.
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Our DeepCoAST dataset specifically explores the vulnerabilities of various traffic-splitting Website Fingerprinting (WF) Defenses, such as TrafficSliver, HyWF, and CoMPS. Our dataset comprises defended traces generated from the BigEnough dataset, which includes Tor cell trace instances of 95 websites, each represented by 200 instances collected under the standard browser security level. We simulated the traffic-splitting defenses assuming there are two split traces from the vanilla trace.
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