1,045 research outputs found
Hypothesis Disparity Regularized Mutual Information Maximization
We propose a hypothesis disparity regularized mutual information
maximization~(HDMI) approach to tackle unsupervised hypothesis transfer -- as
an effort towards unifying hypothesis transfer learning (HTL) and unsupervised
domain adaptation (UDA) -- where the knowledge from a source domain is
transferred solely through hypotheses and adapted to the target domain in an
unsupervised manner. In contrast to the prevalent HTL and UDA approaches that
typically use a single hypothesis, HDMI employs multiple hypotheses to leverage
the underlying distributions of the source and target hypotheses. To better
utilize the crucial relationship among different hypotheses -- as opposed to
unconstrained optimization of each hypothesis independently -- while adapting
to the unlabeled target domain through mutual information maximization, HDMI
incorporates a hypothesis disparity regularization that coordinates the target
hypotheses jointly learn better target representations while preserving more
transferable source knowledge with better-calibrated prediction uncertainty.
HDMI achieves state-of-the-art adaptation performance on benchmark datasets for
UDA in the context of HTL, without the need to access the source data during
the adaptation.Comment: Accepted to AAAI 202
Software mitigation of coherent two-qubit gate errors
Two-qubit gates are important components of quantum computing. However, unwanted interactions between qubits (so-called parasitic gates) can be particularly problematic and degrade the performance of quantum applications. In this work, we present two software methods to mitigate parasitic two-qubit gate errors. The first approach is built upon the Cartan's KAK decomposition and keeps the original unitary decomposition for the error-free native two-qubit gate. It counteracts a parasitic two-qubit gate by only applying single-qubit rotations and therefore has no two-qubit gate overhead. We show the optimal choice of single-qubit mitigation gates. The second approach applies a numerical optimisation algorithm to re-compile a target unitary into the error-parasitic two-qubit gate plus single-qubit gates. We demonstrate these approaches on the CPhase-parasitic iSWAP-like gates. The KAK-based approach helps decrease unitary infidelity by a factor of 3 compared to the noisy implementation without error mitigation. When arbitrary single-qubit rotations are allowed, recompilation could completely mitigate the effect of parasitic errors but may require more native gates than the KAK-based approach. We also compare their average gate fidelity under realistic noise models, including relaxation and depolarising errors. Numerical results suggest that different approaches are advantageous in different error regimes, providing error mitigation guidance for near-term quantum computers
Software mitigation of coherent two-qubit gate errors
Two-qubit gates are important components of quantum computing. However, unwanted interactions between qubits (so-called parasitic gates) can be particularly problematic and degrade the performance of quantum applications. In this work, we present two software methods to mitigate parasitic two-qubit gate errors. The first approach is built upon the Cartan's KAK decomposition and keeps the original unitary decomposition for the error-free native two-qubit gate. It counteracts a parasitic two-qubit gate by only applying single-qubit rotations and therefore has no two-qubit gate overhead. We show the optimal choice of single-qubit mitigation gates. The second approach applies a numerical optimisation algorithm to re-compile a target unitary into the error-parasitic two-qubit gate plus single-qubit gates. We demonstrate these approaches on the CPhase-parasitic iSWAP-like gates. The KAK-based approach helps decrease unitary infidelity by a factor of 3 compared to the noisy implementation without error mitigation. When arbitrary single-qubit rotations are allowed, recompilation could completely mitigate the effect of parasitic errors but may require more native gates than the KAK-based approach. We also compare their average gate fidelity under realistic noise models, including relaxation and depolarising errors. Numerical results suggest that different approaches are advantageous in different error regimes, providing error mitigation guidance for near-term quantum computers
Towards General-Purpose Representation Learning of Polygonal Geometries
Neural network representation learning for spatial data is a common need for
geographic artificial intelligence (GeoAI) problems. In recent years, many
advancements have been made in representation learning for points, polylines,
and networks, whereas little progress has been made for polygons, especially
complex polygonal geometries. In this work, we focus on developing a
general-purpose polygon encoding model, which can encode a polygonal geometry
(with or without holes, single or multipolygons) into an embedding space. The
result embeddings can be leveraged directly (or finetuned) for downstream tasks
such as shape classification, spatial relation prediction, and so on. To
achieve model generalizability guarantees, we identify a few desirable
properties: loop origin invariance, trivial vertex invariance, part permutation
invariance, and topology awareness. We explore two different designs for the
encoder: one derives all representations in the spatial domain; the other
leverages spectral domain representations. For the spatial domain approach, we
propose ResNet1D, a 1D CNN-based polygon encoder, which uses circular padding
to achieve loop origin invariance on simple polygons. For the spectral domain
approach, we develop NUFTspec based on Non-Uniform Fourier Transformation
(NUFT), which naturally satisfies all the desired properties. We conduct
experiments on two tasks: 1) shape classification based on MNIST; 2) spatial
relation prediction based on two new datasets - DBSR-46K and DBSR-cplx46K. Our
results show that NUFTspec and ResNet1D outperform multiple existing baselines
with significant margins. While ResNet1D suffers from model performance
degradation after shape-invariance geometry modifications, NUFTspec is very
robust to these modifications due to the nature of the NUFT.Comment: 58 pages, 20 figures, Accepted to GeoInformatic
Novel Ionic Liquid with Both Lewis and Brønsted Acid Sites for Michael Addition
Ionic liquid with both Lewis and Brønsted acid sites has been synthesized and its catalytic activities for Michael addition were carefully studied. The novel ionic liquid was stable to water and could be used in aqueous solution. The molar ratio of the Lewis and Brønsted acid sites could be adjusted to match different reactions. The results showed that the novel ionic liquid was very efficient for Michael addition with good to excellent yields within several min. Operational simplicity, high stability to water and air, small amount used, low cost of the catalyst used, high yields, chemoselectivity, applicability to large-scale reactions and reusability are the key features of this methodology, which indicated that this novel ionic liquid also holds great potential for environmentally friendly processes
Cohort profile: The Guangzhou Biobank Cohort Study, a Guangzhou-Hong Kong-Birmingham collaboration
postprin
To Broadcast or Not to Broadcast: Decision-Making Strategies for Mining Empty Blocks
Resource efficiency in blockchain systems remains a pivotal concern in their design. While Ethereum often experiences network congestion, leading to rewarding opportunities for miners through transaction inclusions, a significant amount of block space remains underutilized. Remarkably, instances of entirely unutilized blocks contribute to resource wastage within the Ethereum ecosystem. This study delves into the incentives driving miners to produce empty blocks. We ascertain that the immediate rewards of mining empty blocks often lead miners to forego potential benefits from transaction inclusions. Moreover, our investigation reveals a marked reduction in empty blocks after the Ethereum\u27s Merge, highlighting that the Proof-of-Stake (PoS) consensus mechanism enhances block space efficiency in the blockchain sphere
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