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Parametric design systems serve as powerful assistive technologies in the design process by aiding the generation of a vast number of design alternatives. However, with their focus on low-level engineering and structural form, parametric systems do not automatically fulfil cognitively founded people-centred behavioural design criteria. We propose a precedent-based design approach for parametric sy
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
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
Reinforcement learning methods for robotics are increasingly successful due to the constant development of better policy gradient techniques. A precise (low variance) and accurate (low bias) gradient estimator is crucial to face increasingly complex tasks. Traditional policy gradient algorithms use the likelihood-ratio trick, which is known to produce unbiased but high variance estimates. More mod
Hur skapar man samsyn och samarbete i en kommunal koncern där förvaltningar tidigare tänkte i stuprör? I Höganäs växte en kultur av gemensamt ansvar fram genom vardagliga berättelser och budgetprocessen. Den här artikeln berättar historien om hur kommunen etablerade en koncernledningsgrupp för att styra sin samlade verksamhet. Eller var det egentligen en koncernkultur som växte fram?
Railways are vital for climate mitigation as a low-carbon mode of transport but must also be resilient to climate change impacts. This study explores dynamic adaptation strategies for coastal railway infrastructure in Sweden, focusing on Trelleborg. Stakeholder workshops informed adaptation pathways using a Dynamic Adaptive Pathways Planning Light (DAPP)-light approach, which enhances stakeholder
Although a great variety of strategies to suppress Li dendrite have been proposed for lithium metal batteries (LMBs), a deeper understanding of the factors playing a crucial role during extended electrochemical cycling is often lacking. Herein, the morphological reversibility of the Li-based anode for next-generation batteries under three prevalent strategies, i.e., the use of Li-Al alloys, polyme
This paper addresses the path-tracking problem of time-optimal trajectories under model uncertainties, by proposing a real-time predictive scaling algorithm. The algorithm is formulated as a convex optimization problem, designed to balance the trade-off between improving feasibility and time optimality of a trajectory. The predicted trajectory is scaled based on the presence of path segments that
PLCs are the core of most industrial automation systems (IAS). Modern IAS development emphasizes hardware unifica-tion and increased software significance. Remote PLC programming boosts efficiency by reducing time and travel costs, while supporting advanced training. Using safety controllers in the example of IAS, this paper presents hardware and PLC-specific software tools enabling remote PLC pro
RATIONALE: Caffeine is commonly used in preterm infants to improve respiratory function. However, the optimal strategies for caffeine initiation have not been defined. OBJECTIVES: To evaluate the relative benefits and harms of different indications and timings for caffeine initiation in preterm infants. SEARCH METHODS: We searched MEDLINE, Embase, CENTRAL, and trial registries up to 23 April 2025.
The package ParSeq is a Python software library for Parallel execution of Sequential data analysis. It implements a general analysis framework that consists of transformation nodes - intermediate stops along the analysis propagation to visualize data, display status and provide user input - and transformations that connect the nodes. ParSeq imports data into an adjustable data model - a collection
Putin kan välja att avsluta kriget i Ukraina – frågan är om han har råd med det. Fred är farligt, i alla fall om man är president i Ryssland. Och respekten för den som tappar makten försvinner snabbt. Att eskalera spänningar är därför en ren självbevarelsedrift. Ukraina är en huvudvärk för Putin, men den är inte hans värsta, skriver historikern Tomas Sniegon vid Lunds universitet.
RATIONALE: Acute otitis media (AOM) is one of the most common bacterial infections in children worldwide, and the most common reason for prescribing antibiotics. Although serious complications (e.g. mastoiditis or meningitis) are rare, the infection causes acute pain and can lead to hearing impairment. Since AOM often resolves spontaneously, current treatment guidelines recommend an approach of wa
Few-shot segmentation (FSS) tackles data scarcity using multiple priors, but its simplicity limits handling base and novel classes with limited data access. Generalized few-shot semantic segmentation (GFSS) enhances model performance for base classes with abundant data, while novel classes have limited data access, improving generalization with scarce data. Building on the design of query-based se
The strong shell effects observed experimentally and theoretically at high spins and moderate deformations are explained in terms of the interference between two effects. Thus, the effect of the quadrupole deformation to destroy the spherical shell degeneracy can be largely compensated by the orbital separation due to rotational motion. In the resulting coupling scheme, the subshell degeneracy of
Many audio applications, including echo-cancellation and active noise control, rely on the availability of accurately estimated room impulse responses (RIRs). For these applications, it is common that the source signal is short and primarily consists of speech or music, which may cause the estimation of the RIR to be poorly conditioned. Although priors on the amplitudes of the RIR could in princip
This paper introduces a novel diffusion-based method for learning representations from categorical data. Conditional diffusion models have demonstrated their potential to extract meaningful representations from input samples. However, they often struggle to yield versatile, multi-task information, limiting their adaptability to unforeseen tasks. To address this, we propose a cycle conditioning app
