23 papers · ranked by Valyu relevance
Bestoun S. Ahmed
—The rapidly changing landscapes of modern optimization problems require algorithms that can be adapted in real-time. This paper introduces an Adaptive Metaheuristic Framework (AMF) designed for dynamic environments. It is capable of intelligently adapting to changes in the problem parameters. The AMF combines a…
Claire Gillum, Kara Tureski, Joseph Msofe
The SBC Adaptive Management Framework, tested under the USAID Tulonge Afya project in Tanzania, offers a systematic, evidence-informed, and participatory approach to strengthen program design and implementation and enhance outcomes.
Daniel Gaspar-Figueiredo
The continuous adaptation of software systems to meet the evolving needs of users is very important for enhancing user experience (UX). User interface (UI) adaptation, which involves adjusting the layout, navigation, and content presentation based on user preferences and contextual conditions, plays an important role…
Kaarin J. Anstey
Many of the challenges facing ageing societies involve an interaction of factors from many domains and levels, including person-level age-related changes in function through to the physical environment, economy, urban design and social policies. The pace of change in our societies is accelerated by climate change and…
Tina L. Y. Wu, Anna Murphy, Chao Chen, Dana Kulić
People with Parkinson's (PwP) experience gait impairments that can be improved through cue training, where visual, auditory, or haptic cues are provided to guide the walker's cadence or step length. There are two types of cueing strategies: open and closed-loop. Closed-loop cueing may be more effective in addressing…
Authors not listed
This conceptual paper introduces the Adaptive Multi-Resolution Modeling Framework (AMRMF), a novel technique designed to revolutionize chemical engineering by integrating multi-scale simulations, quantum-inspired algorithms, advanced uncertainty quantification, and Bayesian inference. The framework bridges theoretical…
Oliver Ott, Lennart Ralfs, Robert Weidner
The fifth industrial revolution and the accompanying influences of digitalization are presenting enterprises with significant challenges. Regardless of the trend, however, humans will remain a central resource in future factories and will continue to be required to perform manual tasks. Against the backdrop of, e.g.…
MANDY PAAUW, MURRAY SCOWN, ANNISA TRIYANTI, HAOMIAO DU + 1 more
'AHJOND GARMESTANI'] Many deltas are increasingly threatened by environmental change, including climate change-induced sea-level rise, land subsidence and reduced sediment delivery. Dealing with these challenges is a pressing necessity because deltas are home to many people and are important centres for economic and…
Kit Gallagher, Maximillian Strobl, Robert Gatenby, Philip Maini + 1 more
Standard-of-care treatment regimes have long been designed to for maximal cell kill, yet these strategies often fail when applied to treatment–resistant tumors, resulting in patient relapse. Adaptive treatment strategies have been developed as an alternative approach, harnessing intra-tumoral competition to suppress…
Ging-Jehli, Nadja R., Childers, Russell K. + 5 more
How do we learn when to persist, when to let go, and when to shift gears? Gearshift Fellowship (GF) is the prototype of a new Supertask paradigm designed to model how humans and artificial agents adapt to shifting environment demands. Grounded in cognitive neuroscience, computational psychiatry, economics, and…
Eddy Truyen
The Adaptable TeaStore specification provides a microservice-based case study for implementing self-adaptation through a control loop. We argue that implementations of this specification should be informed by key properties of self-adaptation: system-wide consistency (coordinated adaptations across replicas), planning…
Hua-Dong Xiong, Li Ji-An, Robert C. Wilson, Marcelo G. Mattar
A hallmark of intelligence is the ability to adapt behavior to changing environments, which requires adapting one’s own learning strategies. This phenomenon is known as learning to learn in cognitive science and meta-learning in artificial intelligence. While this phenomenon is well-established in humans and animals…
Authors not listed
Designing molecules with specific target properties remains a fundamental challenge in computational chemistry. While existing approaches show promise, most rely on simplified representations like SMILES strings or 2D graphs that lack essential three-dimensional geometric information. We present EvoDiffMol, a…
Maneeshika M. Madduri, Momona Yamagami, Si Jia Li, Sasha Burckhardt + 2 more
Neural interfaces can restore or augment human sensorimotor capabilities by converting high-bandwidth biological signals into control signals for an external device via a decoder algorithm. Leveraging user and decoder adaptation to create co-adaptive interfaces presents opportunities to improve usability and…
Hua-Dong Xiong, Li Ji-An, Robert C. Wilson, Marcelo G. Mattar
A hallmark of intelligence is the ability to adapt behavior to changing environments, which requires adapting one’s own learning strategies. This phenomenon is known as learning to learn or meta-learning. Although well established in humans and animals, a computational framework that characterizes how biological agents…
Tarik A. Rashid, Bryar A. Hassan, Abeer Alsadoon, Shko Qader + 3 more
'S. Vimal' 'Amit Chhabra' 'Zaher Mundher Yaseen\u202c\u202c\u202c\u202c\u202c\u202c\u202c\u202c\u202c\u202c\u202c\u202c\u202c\u202c\u202c\u202c'] 1 Computer Science and Engineering Department, School of Science and Engineering, University of Kurdistan Hewler, Erbil, Kurdistan Region, Iraq 2 Department of Information…
Ana Petrovska, Guan Erjiage, Stefan Kugele
—In the last two decades, the popularity of selfadaptive systems in the field of software and systems engineering has drastically increased. However, despite the extensive work on self-adaptive systems, the literature still lacks a common agreement on the definition of these systems. To this day, the notion of…
Miranda Ramírez-Cruz, Luis Enrique Sucar, Eduardo F. Morales, Jesús Joel Rivas
Static rehabilitation protocols often struggle to keep pace with the dynamic, non-linear reality of human motor recovery. Traditional therapy models frequently assume stationarity, relying on fixed performance schemes that fail to capture the highly individualized nature of human recovery and skill acquisition. This…
Finlay Clark, Graeme Robb, Daniel Cole, Julien Michel
Alchemical absolute binding free energy (ABFE) calculations have substantial potential in drug discovery, but are often prohibitively computationally expensive. To unlock their potential, efficient automated ABFE workflows are required to reduce both computational cost and human intervention. We present a…
Li Ji-An, Marcus K. Benna, Marcelo G. Mattar
Normative modeling frameworks such as Bayesian inference and reinforcement learning provide valuable insights into the fundamental principles governing adaptive behavior. While these frameworks are valued for their simplicity and interpretability, their reliance on few parameters often limits their ability to capture…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Chengyuan Wu, Carol A. Seger, Canhuang Luo, Ying Zhou + 2 more
In dynamic environments, flexible cognitive control adaptively adjusts processing through proactive mechanisms deployed in advance and reactive mechanisms engaged upon conflict. Previous studies have primarily focused on identifying neural networks supporting specific control components, while less is known about how…
Authors not listed
Artificial intelligence (AI) is reshaping chemical engineering. Still, its role in safety-critical operations is limited because we rarely see tools that link physical models with data-driven methods. This study brings together three elements: physics-constrained neural networks, uncertainty quantification, and a…