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0111 · Dec 200319922001200920172026
12 results for rotors

Rotors were introduced in Graph Theory by W.Tutte. The concept was adapted to Knot Theory as a generalization of mutation by Anstee, Przytycki and Rolfsen in 1987. In this paper we show that Tristram-Levine signature is preserved by orientation-preserving rotations. Moreover, we show that any link invariant obtained fr…

2004-07-11abs ↗pdf ↗

Anstee, Przyticki and Rolfsen introduced the idea of rotants, pairs of links related by a generalised form of link mutation. We exhibit infinitely many pairs of rotants which can be distinguished by Khovanov homology, but not by the Jones polynomial.

2015-01-05abs ↗pdf ↗

This work reveals a new scaling law for optimal design of multirotor aerial vehicles.

problem Designing optimal configurations for fully-actuated multirotor aerial vehicles.
method Formulated on the product manifold of Projective Lines \RP^2^N, minimizing a coordinate-invariant Log-Volume isotropy metric.
result The topology of the global optima is governed by the symmetry of the chassis, leading to a N-5 Scaling Law.

Simplifies neural network models by explicitly enforcing constraints in Cartesian coordinates.

problem Learning dynamics of complex systems efficiently and accurately.
method Embedding systems into Cartesian coordinates and using Lagrange multipliers to enforce constraints.
result Explicitly enforcing constraints leads to a 100x improvement in accuracy and data efficiency.

TorsionNet uses reinforcement learning to efficiently generate conformers of flexible molecules.

problem Efficiently generating diverse and representative conformer sets for flexible molecules.
method Sequential conformer search technique based on reinforcement learning under the rigid rotor approximation, trained via curriculum learning.
result TorsionNet outperforms chemoinformatics methods by 4x on large branched alkanes and several orders of magnitude on biopolymer lignin.

This paper is based on my talks (`Skein modules with a cubic skein relation: properties and speculations' and `Symplectic structure on colorings, Lagrangian tangles and its applications') given in Kyoto (RIMS), September 11 and September 18 respectively, 2001. The first three sections closely follow the talks: starting…

2003-12-31abs ↗pdf ↗

Bayesian optimization improved for high-dimensional outputs using randomized priors.

problem Efficient global optimization of high-dimensional black-box functions.
method Deep learning framework with bootstrapped ensembles of neural architectures with randomized priors.
result Superior performance in tasks with high-dimensional outputs compared to state-of-the-art methods.