While stability analysis is a mainstay for control science, especially computing regions of attraction of equilibrium points, until recently most stability analysis tools always required explicit knowledge of the model or a high-fidelity simulator represen ...
The rise of high-throughput first-principles calculations of materials since the turn of the millennium has gifted the fields of physics, quantum chemistry, and materials science with a mountainous and ever-growing pile of data ready to be mined for hidden ...
While convolution and self-attention mechanisms have dominated architectural design in deep learning, this survey examines a fundamental yet understudied primitive: the Hadamard product. Despite its widespread implementation across various applications, th ...
The detailed feeding and feedback mechanisms of active galactic nuclei (AGNs) are not yet well known. For low-luminosity AGNs, obscured AGNs, and late-type galaxies, the masses of their central black holes (BH) are difficult to determine precisely. Our goa ...
This work investigates adversarial training in the context of margin-based linear classifiers in the high-dimensional regime where the dimension d and the number of data points n diverge with a fixed ratio α = n/d. We introduce a tractable mathematical mod ...
Despite widespread helmet usage in high-speed sports, the incidence of brain injuries continues to rise, indicating some deficiencies in current helmet technologies. Existing helmet safety standards only address linear impacts, failing to account adequatel ...
The uncanny ability of over-parameterised neural networks to generalise well has been explained using various ‘simplicity biases’. These theories postulate that neural networks avoid overfitting by first fitting simple, linear classifiers before learning m ...
Structured interviews often include past-behavior questions inviting applicants to recount a past work experience. While optimal responses to these questions should take the form of a story, applicants struggle to produce them extemporaneously. Asynchronou ...
Synthetic magnetic resonance spectra (MRS) are mathematically generated spectra which can be used to investigate the assumptions of data analysis strategies, optimize experimental design, and as training data for the development and validation of machine l ...
The shift from traditional classroom settings to technology-supported learning environments has led to the adoption of learning analytics and artificial intelligence (AI) in education. These technologies promise to support personalized learning by analyzin ...
Ruthenium and osmium half-sandwich complexes with hydrazinocurcuminoid ligands, 4,4′-((1E,1′E)-(1-(pyridin-2-yl)-1H-pyrazole-3,5-diyl)bis(ethene-2,1-diyl))bis-(2-methoxyphenol) (HZPcurc) and 4,4′-((1E,1′E)-(1-(pyridin-2-yl)-1H-pyrazole-3,5-diyl)bis(ethene- ...
This thesis sets itself the goal of investigating function learning approaches. The topic is extremely relevant today, since the abundance of data, jointly with the technological progress which has enhanced computing power, now allow to represent functions ...
Planning for diverse real-world robotic tasks necessitates to know and write all constraints. However, instances exist where these constraints are either unknown or challenging to specify accurately. A possible solution is to infer the unknown constraints ...
Metal additive manufacturing is a recent breakthrough technology that promises automated production of complex geometric shapes at low operating costs. However, its potential is not yet fully exploited due to the low reproducibility of quality in mass prod ...
We introduce a new task, novel view synthesis for LiDAR sensors. While traditional model-based LiDAR simulators with style-transfer neural networks can be applied to render novel views, they fall short of producing accurate and realistic LiDAR patterns bec ...
Recent advancements in recommender systems have focused on integrating knowledge graphs (KGs) to leverage their auxiliary information. The core idea of KG-enhanced recommenders is to incorporate rich semantic information for more accurate recommendations. ...
The rapid advancement of artificial intelligence (AI) has greatly influenced numerous research areas, leading to significant breakthroughs in human face-related technologies, particularly in face recognition and deepfake detection. While offering substanti ...
This contribution takes on the incomprehensibility of human transformation of the Earth's surface, framing it as a "Constructocene" dominated by architecture and infrastructure. Based on an idea first proposed by Jan Zalasiewicz, it then looks at the Anthr ...
Constrained Markov decision processes (CMDPs) are a common way to model safety constraints in reinforcement learning. State-of-the-art methods for efficiently solving CMDPs are based on primal-dual algorithms. For these algorithms, all currently known regr ...
