School of Mathematics and Statistics - Research Publications

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Now showing 1 - 10 of 29
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    Benchmarking algorithm portfolio construction methods
    Muñoz, MA ; Soleimani, H ; Kandanaarachchi, S (Association for Computing Machinery, 2022-07-09)
    A portfolio is a set of algorithms, which run concurrently or interchangeably, whose aim is to improve performance by avoiding a bad selection of a single algorithm. Despite its high error tolerance, a carefully constructed portfolio, i.e., the smallest set of complementary algorithms, is expected to perform better than an arbitrarily constructed one. In this paper, we benchmark five algorithm portfolio construction methods, using as benchmark problems the ASLib scenarios, under a cross-validation regime. We examine the performance of each portfolio in terms of its riskiness, i.e., the existence of unsolved problems on the test set, and its robustness, i.e., the existence of an algorithm that solves most instances. The results demonstrate that two of these methods produce portfolios with the lowest risk, albeit with different levels of robustness.
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    Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition
    Zhu, A ; Ke, Q ; Gong, M ; Bailey, J (IEEE COMPUTER SOC, 2023)
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    Progressive Video Summarization via Multimodal Self-supervised Learning
    Li, H ; Ke, Q ; Gong, M ; Drummond, T (IEEE, 2023-01-01)
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    Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation
    Xu, Y ; Xie, S ; Wu, W ; Zhang, K ; Gong, M ; Batmanghelich, K (IEEE COMPUTER SOC, 2022-01-01)
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    Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint
    Guo, J ; Li, J ; Fu, H ; Gong, M ; Zhang, K ; Tao, D (IEEE COMPUTER SOC, 2022-01-01)
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    Off-lattice and parallel implementations of the pivot algorithm
    Clisby, N ; Ho, DTC (IOP Publishing, 2021-12-09)
    Abstract The pivot algorithm is the most efficient known method for sampling polymer configurations for self-avoiding walks and related models. Here we introduce two recent improvements to an efficient binary tree implementation of the pivot algorithm: an extension to an off-lattice model, and a parallel implementation.
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    Semiglobal Practical Stability of a Class of Parameterized Networked Control Systems
    Wang, B ; Nesic, D (IEEE, 2012-01-01)
    This paper studies a class of parameterized networked control systems that are designed via an emulation procedure. In the first step, a controller is designed ignoring network so that semiglobal practical stability is achieved for the closed-loop. In the second step, it is shown that if the same controller is emulated and implemented over a large class of networks, then the networked control system is also semiglobally practically asymptotically stable; in this case, the controller parameter needs to be sufficiently small and communication bandwidth sufficiently high.
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    Differential operators on modular forms mod p
    Ghitza, A (RIMS, 2019)
    We give a survey of recent work on the construction of differential operators on various types of modular forms (mod p) . We also discuss a framework for determining the effect of such operators on the mod p Galois representations attached to Hecke eigenforms.
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    Analytic evaluation of Hecke eigenvalues for Siegel modular forms of degree two
    Ghitza, A ; Colman, O ; Ryan, NC (Mathematical Sciences Publishers, 2019)
    The standard approach to evaluate Hecke eigenvalues of a Siegel modular eigenform F is to determine a large number of Fourier coefficients of F and then compute the Hecke action on those coefficients. We present a new method based on the numerical evaluation of F at explicit points in the upper half-space and of its image under the Hecke operators. The approach is more efficient than the standard method and has the potential for further optimization by identifying good candidates for the points of evaluation, or finding ways of lowering the truncation bound. A limitation of the algorithm is that it returns floating point numbers for the eigenvalues; however, the working precision can be adjusted at will to yield as close an approximation as needed.
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    Logic and the 2-Simplicial Transformer
    Murfet, D ; Clift, J ; Doyrn, D ; Wallbridge, J (International Conference on Learning Representations, 2020)
    We introduce the 2-simplicial Transformer, an extension of the Transformer which includes a form of higher-dimensional attention generalising the dot-product attention, and uses this attention to update entity representations with tensor products of value vectors. We show that this architecture is a useful inductive bias for logical reasoning in the context of deep reinforcement learning.