288 research outputs found

    Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras

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    We introduce Kapre, Keras layers for audio and music signal preprocessing. Music research using deep neural networks requires a heavy and tedious preprocessing stage, for which audio processing parameters are often ignored in parameter optimisation. To solve this problem, Kapre implements time-frequency conversions, normalisation, and data augmentation as Keras layers. We report simple benchmark results, showing real-time on-GPU preprocessing adds a reasonable amount of computation.Comment: ICML 2017 machine learning for music discover

    PokerKit: A Comprehensive Python Library for Fine-Grained Multi-Variant Poker Game Simulations

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    PokerKit is an open-source Python library designed to overcome the restrictions of existing poker game simulation and hand evaluation tools, which typically support only a handful of poker variants and lack flexibility in game state control. In contrast, PokerKit significantly expands this scope by supporting an extensive array of poker variants and it provides a flexible architecture for users to define their custom games. This paper details the design and implementation of PokerKit, including its intuitive programmatic API, multi-variant game support, and a unified hand evaluation suite across different hand types. The flexibility of PokerKit allows for applications in diverse areas, such as poker AI development, tool creation, and online poker casino implementation. PokerKit's reliability has been established through static type checking, extensive doctests, and unit tests, achieving 97\% code coverage. The introduction of PokerKit represents a significant contribution to the field of computer poker, fostering future research and advanced AI development for a wide variety of poker games.Comment: 6 pages, 1 figure, submission to IEEE Transactions on Game

    PadChannel: Improving CNN Performance through Explicit Padding Encoding

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    In convolutional neural networks (CNNs), padding plays a pivotal role in preserving spatial dimensions throughout the layers. Traditional padding techniques do not explicitly distinguish between the actual image content and the padded regions, potentially causing CNNs to incorrectly interpret the boundary pixels or regions that resemble boundaries. This ambiguity can lead to suboptimal feature extraction. To address this, we propose PadChannel, a novel padding method that encodes padding statuses as an additional input channel, enabling CNNs to easily distinguish genuine pixels from padded ones. By incorporating PadChannel into several prominent CNN architectures, we observed small performance improvements and notable reductions in the variances on the ImageNet-1K image classification task at marginal increases in the computational cost. The source code is available at https://github.com/AussieSeaweed/pad-channelComment: 7 pages, 4 figures, submitted to the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognitio

    Stretching-induced conductance variations as fingerprints of contact configurations in single-molecule junctions

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    Molecule-electrode contact atomic structures are a critical factor that characterizes molecular devices, but their precise understanding and control still remain elusive. Based on combined first-principles calculations and single-molecule break junction experiments, we herein establish that the conductance of alkanedithiolate junctions can both increase and decrease with mechanical stretching and the specific trend is determined by the S-Au linkage coordination number (CN) or the molecule-electrode contact atomic structure. Specifically, we find that the mechanical pulling results in the conductance increase for the junctions based on S-Au CN two and CN three contacts, while the conductance is minimally affected by stretching for junctions with the CN one contact and decreases upon the formation of Au monoatomic chains. Detailed analysis unravels the mechanisms involving the competition between the stretching-induced upshift of the highest occupied molecular orbital-related states toward the Fermi level of electrodes and the deterioration of molecule-electrode electronic couplings in different contact CN cases. Moreover, we experimentally find a higher chance to observe the conductance enhancement mode under a faster elongation speed, which is explained by ab initio molecular dynamics simulations that reveal an important role of thermal fluctuations in aiding deformations of contacts into low-coordination configurations that include monoatomic Au chains. Pointing out the insufficiency in previous notions of associating peak values in conductance histograms with specific contact atomic structures, this work resolves the controversy on the origins of ubiquitous multiple conductance peaks in S-Au-based single-molecule junctions.Comment: 11 pages, 4 figures; to be published in J. Am. Chem. So

    Outage Probability and Outage-Based Robust Beamforming for MIMO Interference Channels with Imperfect Channel State Information

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    In this paper, the outage probability and outage-based beam design for multiple-input multiple-output (MIMO) interference channels are considered. First, closed-form expressions for the outage probability in MIMO interference channels are derived under the assumption of Gaussian-distributed channel state information (CSI) error, and the asymptotic behavior of the outage probability as a function of several system parameters is examined by using the Chernoff bound. It is shown that the outage probability decreases exponentially with respect to the quality of CSI measured by the inverse of the mean square error of CSI. Second, based on the derived outage probability expressions, an iterative beam design algorithm for maximizing the sum outage rate is proposed. Numerical results show that the proposed beam design algorithm yields better sum outage rate performance than conventional algorithms such as interference alignment developed under the assumption of perfect CSI.Comment: 41 pages, 14 figures. accepted to IEEE Transactions on Wireless Communication
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