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Automated evaluation of normal uptake in different skeletal parts in 18F-sodium fluoride (NaF) PET/CT using a new convolutional neural network method

Introduction: Understanding normal skeletal uptake of 18F-sodium fluoride (18F-NaF) in positron emission tomography/computed tomography (PET/CT) is important for clinical interpretation. Quantification of tracer uptake in PET/CT is often performed by placing a volume of interest (VOI) to measure standard uptake values (SUVs). Manual placement of this VOI requires a subjective decision and can only

Barnens tid och framtid : En studie av den svenska asylbyråkratin efter 2015

Syftet med denna studie är att analysera asylbyråkratins tidssyn, tidsanvändning och tidsstyrning samt peka på konsekvenserna av detta för skyddssökande familjer och rätten till asyl: Hur används tid och vilka perspektiv på tid tonar fram i asylprövningen på Migrationsverket, den myndighet som genomför svensk asylpolitik? Hur fördelas makten över tiden, mellan myndigheter och domstolar som fattar Syftet med denna studie är att analysera asylbyråkratins tidssyn, tidsanvändning och tidsstyrning samt peka på konsekvenserna av detta för skyddssökande familjer och rätten till asyl: Hur används tid och vilka perspektiv på tid tonar fram i asylprövningen på Migrationsverket, den myndighet som genomför svensk asylpolitik? Hur fördelas makten över tiden, mellan myndigheter och domstolar som fattar

A survey of robot manipulation in contact

In this survey, we present the current status on robots performing manipulation tasks that require varying contact with the environment, such that the robot must either implicitly or explicitly control the contact force with the environment to complete the task. Robots can perform more and more manipulation tasks that are still done by humans, and there is a growing number of publications on the t

Motion Prediction Based on Multiple Futures for Dynamic Obstacle Avoidance of Mobile Robots

The ability to decide and adjust actions according to motion prediction of dynamic obstacles offers a flexible planning scheme and ampler reaction time to avoid potential impact. Prediction-based collision avoidance implies a two-stage decision-making process from motion prediction to action planning. One of the challenges in motion prediction is the movements of objects are usually non-determinis

Modelling and Learning Dynamics for Robotic Food-Cutting

Interaction dynamics are difficult to model analytically, making data-driven controllers preferable for contact-rich manipulation tasks. In this work, we approximate the intricate dynamics of food-cutting with a Long Short-Term Memory (LSTM) model to apply a Model Predictive Controller (MPC). We propose a problem formulation that allows velocity-controlled robots to learn the interaction dynamics

Monte Carlo Filtering Objectives

Learning generative models and inferring latent trajectories have shown to be challenging for time series due to the intractable marginal likelihoods of flexible generative models. It can be addressed by surrogate objectives for optimization. We propose Monte Carlo filtering objectives (MCFOs), a family of variational objectives for jointly learning parametric generative models and amortized adapt

Jakt på papperslösa gör oss till en polisstat

Regeringen föreslår nio åtgärder för att hitta och utvisa papperslösa. Det kommer att slå hårt och främst gå ut över redan svaga och jagade människor. Vi uppmanar därför regeringen att ta tillbaka åtgärderna, skriver 43 forskare.

Human-robot collaborative object transfer using human motion prediction based on Cartesian pose Dynamic Movement Primitives

In this work, the problem of human-robot collaborative object transfer to unknown target poses is addressed. The desired pattern of the end-effector pose trajectory to a known target pose is encoded using DMPs (Dynamic Movement Primitives). During transportation of the object to new unknown targets, a DMP-based reference model and an EKF (Extended Kalman Filter) for estimating the target pose and

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Deformable object manipulation tasks have long been regarded as challenging robotic problems. However, until recently very little work has been done on the subject, with most robotic manipulation methods being developed for rigid objects. Deformable objects are more difficult to model and simulate, which has limited the use of model-free Reinforcement Learning (RL) strategies, due to their need fo

Interpretability in Contact-Rich Manipulation via Kinodynamic Images

Deep Neural Networks (NNs) have been widely utilized in contact-rich manipulation tasks to model the complicated contact dynamics. However, NN-based models are often difficult to decipher which can lead to seemingly inexplicable behaviors and unidentifiable failure cases. In this work, we address the interpretability of NN-based models by introducing the kinodynamic images. We propose a methodolog

ReForm: A Robot Learning Sandbox for Deformable Linear Object Manipulation

Recent advances in machine learning have triggered an enormous interest in using learning-based approaches for robot control and object manipulation. While the majority of existing algorithms are evaluated under the assumption that the involved bodies are rigid, a large number of practical applications contain deformable objects. In this work we focus on Deformable Linear Objects (DLOs) which can

Temporal Coupling of Dynamical Movement Primitives for Constrained Velocities and Accelerations

The framework of Dynamical Movement Primitives (DMPs) has become a popular method for trajectory generation in robotics. Most robotic systems are subject to saturation and/or kinematic constraints on motion variables, but DMPs do not inherently encode constraints and this may lead to poor tracking performance. Temporal coupling (online temporal scaling) of DMPs represents a possible way for handli

Task-Based Role Adaptation for Human-Robot Cooperative Object Handling

In this letter, we propose a task-based role allocation control scheme for the cooperative manipulation of a rigid-body object held jointly by a human and a robot. The task-based allocation scheme allows an assistive robot to take an active role when the task is known. To deal with the translation/rotation problem, we define rotation and translation as distinct parameterized tasks. The task that d

Study protocol : The daicy trial—dual versus single-antibiotic impregnated cement in primary hemiarthroplasty for femoral neck fracture—a register-based cluster-randomized crossover-controlled trial

Background and purpose — Older patients with a displaced femoral neck fracture (FNF) are often treated with a cemented primary hemiarthroplasty (HA). The DAICY trial investigates whether high-dose dual-impregnated antibiotic-loaded cement (DIAC) including gentamicin and clindamycin can reduce the risk of periprosthetic joint infection (PJI) in comparison with low-dose single-impregnated gentamicin