Claims Browser

The proposed framework has been validated on population dynamics problems and industrial CFD applications involving Reynolds stress tensor prediction

"The proposed framework is validated on a population dynamics problem and on an industrial CFD application, which involves the prediction of Reynolds stress tensor components under the incompressibility constraint"

M-HySMap reduces routed multicast hops by 10.6-19.6% over Activity+QAP and 19.7-41.1% over Edge+QAP across a 115-job evidence suite on Potjans-inspired recurrent SNNs and mesh NoCs from 4x4 to 6x6, plus a 7x7 stress case

"Across a 115-job evidence suite on Potjans-inspired recurrent SNNs and mesh NoCs from 4 x 4 to 6 x 6, plus a 7 x 7 stress case, M-HySMap reduces routed multicast hops by 10.6-19.6% over Activity+QAP and 19.7-41.1% over Edge+QAP."

Incremental updates in M-HySMap accelerate refinement by 4.7-12.7x while matching full recomputation to numerical precision

"Incremental updates accelerate refinement by 4.7-12.7x while matching full recomputation to numerical precision."

Route-aware, activity-weighted multicast hypergraph mapping provides a better abstraction for mapping spiking neural networks onto neuromorphic many-core platforms than traditional graph partitioning with pairwise placement costs

"Mapping spiking neural networks (SNNs) onto neuromorphic many-core platforms is often formulated with graph partitioning and pairwise placement costs. That abstraction is convenient, but it does not match the physical communication event: one spike from a source neuron is delivered to a set of postsynaptic destinations, and routes to several destinations can share mesh links."

Kolmogorov-Arnold Networks with geometry-constrained edge activations match or outperform fixed-basis methods on symbolic regression tasks, achieving median NRMSE of 0.030 on AI Feynman benchmarks

"Across 50 symbolic-regression targets ($40$ from the AI Feynman benchmark plus $10$ synthetic stress tests), geometry-constrained KANs match or beat every fixed-basis baseline on median NRMSE (Banach-KAN $0.030$, tying Chebyshev and improving on splines)"

Geometry-constrained KANs are significantly more robust to measurement noise than unregularized splines, degrading only 3.7x compared to 21.6x as noise increases from 0 to 1

"as $σ$ grows from $0$ to $1$, $\ell^p$-KAN degrades only $3.7\times$ -- below even a cross-validated spline ($\approx 11\times$) -- while an unregularised spline degrades $21.6\times$"

Learned geometry exponents in KANs provide interpretable signals that reveal consistent, target-dependent geometric ordering across equation families

"Learned exponents provide an interpretable, relative signal: at a fixed initialisation they reveal a consistent, target-dependent geometric ordering across equation families and input dimensions."

Banach-KAN performs best in small-sample regimes, with fixed-basis models only catching up as training set size increases

"Banach-KAN also takes the most per-equation wins in the small-sample regime, with fixed-basis models catching up only as the training set grows."

Civil society needs to be centrally involved in devising AI governance plans, not just government and tech companies

"The importance of having civil society, and not just government and tech companies, centrally involved in devising that plan"

Driver behavior has three key properties that shape traffic phenomena: it is heterogeneous, context-dependent, and changes over time.

"Driver behavior is heterogeneous, context-dependent, and changes over time, and these properties shape the traffic phenomena we observe."

Most current traffic models fix in advance which behavioral variables interact and how, absorbing behavior outside that form as noise, while flexible models tend to lose interpretability.

"Most models, however, fix in advance which behavioral variables interact and how. Behavior outside that form is absorbed as noise, while models flexible enough to capture it tend to lose interpretability."

A quantum-inspired representation of driver behavior can combine properties usually treated separately: continuous, probabilistic, context-dependent, history-dependent representation with learned interactions among behavioral variables.

"We introduce a quantum-inspired representation of driver behavior that combines properties usually treated separately or in part: it is continuous, probabilistic, context-dependent, history-dependent, and represents interactions among behavioral variables as learned from data."

When trained without supervision on the I-24 MOTION dataset, the quantum-inspired framework recovers three interpretable driving profiles representing free flow, transition, and congestion regimes.

"Trained without supervision on the I-24 MOTION dataset, the framework recovers three interpretable driving profiles representing three regimes: free flow, transition, and congestion."

The quantum-inspired driver representation reproduces known macroscopic traffic phenomena, aligning with the fundamental diagram and reproducing hysteresis loops.

"The same representation also reproduces known macroscopic phenomena, aligning with the fundamental diagram and reproducing hysteresis loops."

There was a surge of public interest in AI between 2021 and 2023

"57 semi-structured interviews with AI professionals in 2021 and 2023--before and after the recent surge of public interest"

Technologists and policymakers must account for framing dynamics that will circumscribe beliefs, values, and actions in the era of transformative AI

"As humanity enters the era of transformative AI, technologists and policymakers must account for the framing dynamics that will circumscribe our beliefs, values, and actions."

There are three primary debates in AI: the method of development (top-down vs bottom-up), the nature of AI mind (passive tool vs humanlike), and the morality of AI development speed

"We develop a framework of three primary debates across which frames are adopted and contested: (i) the $\textit{method}$ of AI development, between frames of top-down expert systems and bottom-up emergent capabilities, (ii) the $\textit{mind}$ of an AI system, ranging from a passive tool to a humanlike "digital mind," and (iii) the $\textit{morality}$ of how AI is used, particularly the decision of whether to slow down or speed up AI development."

AI professionals use sociological frames to address cognitive challenges such as assigning responsibility for societal impacts of AI

"We identify a range of sociological frames (interpretive schemas that structure collective cognition) and show how AI professionals use frames to address significant cognitive challenges, such as assigning responsibility for societal impacts."

A matching lower bound of Ω(d log(1+T/d)) shows that the Ω(log T) factor is unavoidable for general OCO

"We also prove a matching lower bound of $Ω(d\log (1+T/d))$, showing that the $Ω(\log T)$ factor is unavoidable."