The denoising problem of multidimensional seismic data can be regarded as a low-rank tensor decomposition problem. However, seismic data only has many ranks close to zero, which does not meet the definition of low-rank data. Adding low-rank constraints to the data will result in the loss of some features of the data. To overcome this problem, this letter proposes a nearly low-rank tensor decomposition method (NLRTD) for seismic data denoising. First, differing from existing low-rank-based methods, we consider a nearly low-rank approximation, which is closer to the latent low-rank structure of the clean data. Then, we exploit a simultaneously sparse and low-rank tensor representation model to enhance the capability of dictionary learning and use the alternating direction method of multipliers (ADMMs) algorithm to obtain denoising results. In the experimental part, we test the 3-D seismic data. Compared with the higher-order singular value decomposition (HOSVD) and low-rank tensor dictionary learning (LTDL) methods, the method in this letter achieves better denoising results.

Understanding the functions of proteins is of great importance for deciphering the mechanisms of life activities. To date, there have been over 200 million known proteins, but only 0.2% of them have well-annotated functional terms. By measuring the contacts among residues, proteins can be described as graphs so that the graph leaning approaches can be applied to learn protein representations. However, existing graph-based methods put efforts in enriching the residue node information and did not fully exploit the edge information, which leads to suboptimal representations considering the strong association of residue contacts to protein structures and to the functions. In this article, we propose SuperEdgeGO, which introduces the supervision of edges in protein graphs to learn a better graph representation for protein function prediction. Different from common graph convolution methods that uses edge information in a plain or unsupervised way, we introduce a supervised attention to encode the residue contacts explicitly into the protein representation. Comprehensive experiments demonstrate that SuperEdgeGO achieves state-of-the-art performance on all three categories of protein functions. Additional ablation analysis further proves the effectiveness of the devised edge supervision strategy. The implementation of edge supervision in SuperEdgeGO resulted in enhanced graph representations for protein function prediction, as demonstrated by its superior performance across all the evaluated categories. This superior performance was confirmed through ablation analysis, which validated the effectiveness of the edge supervision strategy. This strategy has a broad application prospect in the study of protein function and related fields.

Numerical vector aggregation plays a crucial role in privacy-sensitive applications, such as distributed gradient estimation in federated learning and statistical analysis of key-value data. In the context of local differential privacy, this study provides a tight minimax error bound of$O(\frac{ds}{n\epsilon ^{2}})$, where$d$represents the dimension of the numerical vector and$s$denotes the number of non-zero entries. By converting the conditional/unconditional numerical mean estimation problem into a frequency estimation problem, we develop an optimal and efficient mechanism called Collision. In contrast, existing methods exhibit sub-optimal error rates of$O(\frac{d^{2}}{n\epsilon ^{2}})$or$O(\frac{ds^{2}}{n\epsilon ^{2}})$. Specifically, for unconditional mean estimation, we leverage the negative correlation between two frequencies in each dimension and propose the CoCo mechanism, which further reduces estimation errors for mean values compared to Collision. Moreover, to surpass the error barrier in local privacy, we examine privacy amplification in the shuffle model for the proposed mechanisms and derive precisely tight amplification bounds. Our experiments validate and compare our mechanisms with existing approaches, demonstrating significant error reductions for frequency estimation and mean estimation on numerical vectors.

Distributed key-value storage and computation are essential components of cloud services. As the demand for high-performance systems has increased significantly, a new architecture has been motivated to separate computing and storage nodes and connect them using RDMA-enabled networks. Existing RDMA-enabled systems use client-side cached indexes to reduce communication overhead and improve performance. However, such approaches could result in high server CPU contention due to heavy dynamic workloads (i.e.,inserts), and cause a large accuracy gap because of the different indexes between client-side and server-side. These drawbacks limit the performance of RDMA-enabled systems. In this paper, to deal with these issues, we introduce AStore to achieve high performance with low memory footprint. AStore employs a new uniformed architecture, utilizing an adaptive learned index as both the server-side learned index and the client-side cached index, to handle dynamic and static workloads. We propose several optimization techniques to optimize dynamic and static workload procedures and design the leaf node lock mechanism to support high concurrent access. Extensive evaluations on YCSB, LGN, and OSM datasets demonstrate that AStore achieves competitive performance on read-only workloads by up to 75.2%, 107.3% and 57.7%, as well as improving performance on write-read workloads by up to 65.7%, 108.7% and 74.3% than XStore.

In many mobile applications, user-generated data are presented as set-valued data. To tackle potential privacy threats in analyzing these valuable data, local differential privacy has been attracting substantial attention. However, existing approaches only provide sub-optimal utility and are expensive in computation and communication for set-valued data distribution estimation and heavy-hitter identification. In this paper, we propose a utility-optimal and efficient set-valued data publication method (i.e.,Wheel mechanism). On the user side, the computational complexity is only$O(\min \lbrace m\log m, m e^\epsilon \rbrace )$and communication costs are$O(\epsilon +\log m)$bits, where$m$is the number of items,$d$is the domain size and$\epsilon$is the privacy budget, while existing approaches usually depend on$O(d)$or$O(\log d)$($d \gg m$). Our theoretical analyses reveal the estimation errors have been reduced from the previously known$O(\frac{m^{2} d}{n\epsilon ^{2}})$to the optimal rate$O(\frac{m d}{n\epsilon ^{2}})$. Additionally, for heavy-hitter identification, we present a variant of the Wheel mechanism as an efficient frequency oracle, entailing only$O(\sqrt{n})$computational complexity. This heavy-hitter protocol achieves an identification bar of$\tilde{O}(\frac{1}{\epsilon }\sqrt{\frac{m}{n} \log d})$, reducing by a factor of$\sqrt{m}$relative to existing protocols. Extensive experiments demonstrate our methods are 3-100x faster than existing approaches and have optimized statistical efficiency.

Knowledge distillation has emerged as a scalable and effective way for privacy-preserving machine learning. One remaining drawback is that it consumes privacy in a client-level manner. In order to attain fine-grained privacy accountant and improve utility, this work proposes a model-free reverse k-NN labeling method towards record-level private knowledge distillation, where each private record is employed for labeling at most k queries. Theoretically, we provide bounds of labeling error rate under the centralized/local model of differential privacy. Experimentally, we demonstrate that it achieves new state-of-the-art accuracy in MNIST/SVHN/CIFAR-10 dataset with one order of magnitude lower of privacy loss.

BACKGROUND: Identifying differentially abundant features between different experimental groups is a common goal for many metabolomics and proteomics studies. However, analyzing data from mass spectrometry (MS) is difficult because the data may not be normally distributed and there is often a large fraction of zero values. Although several statistical methods have been proposed, they either require the data normality assumption or are inefficient. RESULTS: We propose a new semi-parametric differential abundance analysis (SDA) method for metabolomics and proteomics data from MS. The method considers a two-part model, a logistic regression for the zero proportion and a semi-parametric log-linear model for the possibly non-normally distributed non-zero values, to characterize data from each feature. A kernel-smoothed likelihood method is developed to estimate model coefficients and a likelihood ratio test is constructed for differential abundant analysis. The method has been implemented into an R package, SDAMS, which is available at https://www.bioconductor.org/packages/release/bioc/html/SDAMS.html . CONCLUSION: By introducing the two-part semi-parametric model, SDA is able to handle both non-normally distributed data and large fraction of zero values in a MS dataset. It also allows for adjustment of covariates. Simulations and real data analyses demonstrate that SDA outperforms existing methods.