 School of Mathematics and Statistics  Research Publications
School of Mathematics and Statistics  Research Publications
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ItemNo Preview AvailableBenchmarking algorithm portfolio construction methodsMuñoz, MA ; Soleimani, H ; Kandanaarachchi, S (Association for Computing Machinery, 20220709)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 crossvalidation 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.

ItemNo Preview AvailableAdaptive LocalComponentaware Graph Convolutional Network for Oneshot Skeletonbased Action RecognitionZhu, A ; Ke, Q ; Gong, M ; Bailey, J (IEEE COMPUTER SOC, 2023)

ItemNo Preview AvailableProgressive Video Summarization via Multimodal Selfsupervised LearningLi, H ; Ke, Q ; Gong, M ; Drummond, T (IEEE, 20230101)

ItemNo Preview AvailableMaximum Spatial Perturbation Consistency for Unpaired ImagetoImage TranslationXu, Y ; Xie, S ; Wu, W ; Zhang, K ; Gong, M ; Batmanghelich, K (IEEE COMPUTER SOC, 20220101)

ItemNo Preview AvailableAlleviating Semantics Distortion in Unsupervised LowLevel ImagetoImage Translation via Structure Consistency ConstraintGuo, J ; Li, J ; Fu, H ; Gong, M ; Zhang, K ; Tao, D (IEEE COMPUTER SOC, 20220101)

ItemNo Preview AvailableOfflattice and parallel implementations of the pivot algorithmClisby, N ; Ho, DTC (IOP Publishing, 20211209)Abstract The pivot algorithm is the most efficient known method for sampling polymer configurations for selfavoiding walks and related models. Here we introduce two recent improvements to an efficient binary tree implementation of the pivot algorithm: an extension to an offlattice model, and a parallel implementation.

ItemSemiglobal Practical Stability of a Class of Parameterized Networked Control SystemsWang, B ; Nesic, D (IEEE, 20120101)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 closedloop. 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.

ItemDifferential operators on modular forms mod pGhitza, 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.

ItemAnalytic evaluation of Hecke eigenvalues for Siegel modular forms of degree twoGhitza, 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 halfspace 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.

ItemLogic and the 2Simplicial TransformerMurfet, D ; Clift, J ; Doyrn, D ; Wallbridge, J (International Conference on Learning Representations, 2020)We introduce the 2simplicial Transformer, an extension of the Transformer which includes a form of higherdimensional attention generalising the dotproduct 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.
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