225 research outputs found
Knowledge Reused Outlier Detection
Tremendous efforts have been invested in the unsupervised outlier detection research, which is conducted on unlabeled data set with abnormality assumptions. With abundant related labeled data available as auxiliary information, we consider transferring the knowledge from the labeled source data to facilitate the unsupervised outlier detection on target data set. To fully make use of the source knowledge, the source data and target data are put together for joint clustering and outlier detection using the source data cluster structure as a constraint. To achieve this, the categorical utility function is employed to regularize the partitions of target data to be consistent with source data labels. With an augmented matrix, the problem is completely solved by a K-means - a based method with the rigid mathematical formulation and theoretical convergence guarantee. We have used four real-world data sets and eight outlier detection methods of different kinds for extensive experiments and comparison. The results demonstrate the effectiveness and significant improvements of the proposed methods in terms of outlier detection and cluster validity metrics. Moreover, the parameter analysis is provided as a practical guide, and noisy source label analysis proves that the proposed method can handle real applications where source labels can be noisy
Secure Outsourced Computation of the Characteristic Polynomial and Eigenvalues of Matrix
Linear algebra plays an important role in computer science, especially in cryptography.Numerous cryptog-raphic protocols, scientific computations, and numerical computations are based on linear algebra. Many linear algebra tasks can be reduced to some core problems, such as matrix multiplication, determinant of matrix and the characteristic polynomial of matrix. However, it is difficult to execute these tasks independently for client whose computation abilities are weaker than polynomial-time computational ability. Cloud Computing is a novel economical paradigm which provides powerful computational resources that enables resources-constrained client to outsource their mass computing tasks to the cloud.
In this paper, we propose a new verifiable and secure outsourcing protocol for the problem of computing the characteristic polynomial and eigenvalues of matrix. These protocols are not only efficient and secure, but also unnecessary for any cryptographic assumption
Architectural Implications of GNN Aggregation Programming Abstractions
Graph neural networks (GNNs) have gained significant popularity due to the
powerful capability to extract useful representations from graph data. As the
need for efficient GNN computation intensifies, a variety of programming
abstractions designed for optimizing GNN Aggregation have emerged to facilitate
acceleration. However, there is no comprehensive evaluation and analysis upon
existing abstractions, thus no clear consensus on which approach is better. In
this letter, we classify existing programming abstractions for GNN Aggregation
by the dimension of data organization and propagation method. By constructing
these abstractions on a state-of-the-art GNN library, we perform a thorough and
detailed characterization study to compare their performance and efficiency,
and provide several insights on future GNN acceleration based on our analysis.Comment: 4 pages, to be published in IEEE Computer Architecture Letters (CAL
Robust MIMO Detection With Imperfect CSI: A Neural Network Solution
In this paper, we investigate the design of statistically robust detectors
for multi-input multi-output (MIMO) systems subject to imperfect channel state
information (CSI). A robust maximum likelihood (ML) detection problem is
formulated by taking into consideration the CSI uncertainties caused by both
the channel estimation error and the channel variation. To address the
challenging discrete optimization problem, we propose an efficient alternating
direction method of multipliers (ADMM)-based algorithm, which only requires
calculating closed-form solutions in each iteration. Furthermore, a robust
detection network RADMMNet is constructed by unfolding the ADMM iterations and
employing both model-driven and data-driven philosophies. Moreover, in order to
relieve the computational burden, a low-complexity ADMM-based robust detector
is developed using the Gaussian approximation, and the corresponding deep
unfolding network LCRADMMNet is further established. On the other hand, we also
provide a novel robust data-aided Kalman filter (RDAKF)-based channel tracking
method, which can effectively refine the CSI accuracy and improve the
performance of the proposed robust detectors. Simulation results validate the
significant performance advantages of the proposed robust detection networks
over the non-robust detectors with different CSI acquisition methods.Comment: 15 pages, 8 figures, 2 tables; Accepted by IEEE TCO
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