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Metal–organic frameworks such as ZIF-8, grown in situ on nanocellulose (NC), have gained significant attention in recent years due to the versatility of the processing route and multifaceted application in the field of environmental remediation and biomedical applications. However, insights into the interactions between NC and MOF precursors and MOF structure evolution during in situ synthesis are

Inhibition of adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK) is under increasing investigation for its therapeutic potential in many diseases. Existing AMPK inhibitors are however limited, with poor selectivity and substantial off-target effects. Here, we provide mechanistic insights and describe the cellular selectivity of the recently identified AMPK inhibitor BAY-3827. A 2.5-Å

Amplification of the MYCN proto-oncogene serves as a key marker of aggressive disease and poor treatment outcomes in certain pediatric tumors originating from the nervous system, including neuroblastoma and medulloblastoma. However, the complex nature of the challenging MYCN protein underscores the urgent need for additional targets and therapies to tackle neuroblastoma and medulloblastoma. In thi

This paper examines how gender inclusion in transboundary water governance can be transformed from symbolic to substantive participation in the Brahmaputra Basin. Using qualitative methods including literature review, stakeholder mapping, and 11 semi-structured interviews across India and Bangladesh, the study applies an integrated conceptual framework linking transboundary water governance, multi

ObjectivesDespite regularly being found in blood cultures, there are few studies of bloodstream infection (BSI) with Citrobacter. In this population-based study, the aim was to explore patient characteristics, outcome, and incidence in a publicly funded single payer setting.MethodsPatients with growth of Citrobacter in blood culture were identified through the clinical microbiology laboratory in L

Learning a locomotion controller for a musculoskeletal system is challenging due to over-actuation and high-dimensional action space. While many reinforcement learning methods attempt to address this issue, they often struggle to learn human-like gaits because of the complexity involved in engineering an effective reward function. In this paper, we demonstrate that adversarial imitation learning c

Motion planning is a mature area of research in robotics with many well-established methods based on optimization or sampling the state space, suitable for solving kinematic motion planning. However, when dynamic motions under constraints are needed and computation time is limited, fast kinodynamic planning on the constraint manifold is indispensable. In recent years, learning-based solutions have

Genomgång av samtliga de tillfällen då Carl von Linné omnämns i Carl Tersmedens memoarer inklusive kommentarer, bland annat rörande kronologiska diskrepanser.

Machine learning methods have a groundbreaking impact in many application domains, but their application on real robotic platforms is still limited. Despite the many challenges associated with combining machine learning technology with robotics, robot learning remains one of the most promising directions for enhancing the capabilities of robots. When deploying learning-based approaches on real rob

Recent methods for imitation learning directly learn a Q-function using an implicit reward formulation rather than an explicit reward function.However, these methods generally require implicit reward regularization to improve stability and often mistreat absorbing states.Previous works show that a squared norm regularization on the implicit reward function is effective, but do not provide a theore

Quadruped robots are currently a widespread platform for robotics research, thanks to powerful Reinforcement Learning controllers and the availability of cheap and robust commercial platforms. However, to broaden the adoption of the technology in the real world, we require robust navigation stacks relying only on low-cost sensors such as depth cameras. This paper presents a first step towards a ro

Black-box policy optimization is a class of reinforcement learning algorithms that explores and updates the policies at the parameter level. This class of algorithms is widely applied in robotics with movement primitives or non-differentiable policies. Furthermore, these approaches are particularly relevant where exploration at the action level could cause actuator damage or other safety issues. H

Autonomous robots should operate in real-world dynamic environments and collaborate with humans in tight spaces. A key component for allowing robots to leave structured lab and manufacturing settings is their ability to evaluate online and real-time collisions with the world around them. Distance-based constraints are fundamental for enabling robots to plan their actions and act safely, protecting

Learning robot motions from demonstration requires models able to specify vector fields for the full robot pose when the task is defined in operational space. Recent advances in reactive motion generation have shown that learning adaptive, reactive, smooth, and stable vector fields is possible. However, these approaches define vector fields on a flat Euclidean manifold, while representing vector f

Reinforcement learning with sparse rewards is still an open challenge. Classic methods rely on getting feedback via extrinsic rewards to train the agent, and in situations where this occurs very rarely the agent learns slowly or cannot learn at all. Similarly, if the agent receives also rewards that create suboptimal modes of the objective function, it will likely prematurely stop exploring. More

Reinforcement learning (RL) has demonstrated its ability to solve high dimensional tasks by leveraging non-linear function approximators. However, these successes are mostly achieved by 'black-box' policies in simulated domains. When deploying RL to the real world, several concerns regarding the use of a 'black-box' policy might be raised. In order to make the learned policies more transparent, we