Single-cell multimodal data can simultaneously provide cellular features at different levels, such as gene expression, chromatin accessibility, and spatial location. The integration of multimodal data can efficiently utilize the information from various views, thereby enhancing the reliability and accuracy of cellular research. The current integration methods mainly focus on obtaining the representation of cells but neglect the representation of genes, not beneficial to cell type-specific gene module analyses. Besides, some integration algorithms only can integrate multi-omics data and cannot be applied to spatial transcriptome data for integrating transcriptomic data and spatial location. To this end, we propose MISF, a Multimodal data Integration algorithm based on adaptive Similarity network learning and matrix Factorization. MISF integrates multimodal data and learns the lower-dimensional representations of cells and genes. We validate the feasibility of MISF on multiple single-cell multi-omics data and spatial transcriptome data and compare it with the existing multi-omics data integration methods as well as spatial transcriptome data analysis algorithms. The results demonstrate that MISF can effectively integrate multimodal data, localize different types of cell clusters, and outline cellular spatial distribution pattern. Furthermore, MISF facilitates cell clustering and cell type-specific gene module analyses, providing new insights for the study of cellular heterogeneity.

Understanding the nature of user-device interactions (e.g., who is using the device and what he/she is doing with it) is critical for many applications including time management, user profiles, and privacy protection. However, in scenarios where mobile devices are shared among family members or multiple employees in a company, conventional account-based statistics are not meaningful. This poses an even bigger problem when dealing with sensitive data. Moreover, fingerprint readers and front-facing cameras were not designed to continuously identify users. In this study, we developedMagPrint++, a novel approach to fingerprint users based on unique patterns in the electromagnetic (EM) signals associated with the specific use patterns of users. Initial experiments showed that time-varying EM patterns are unique to individual users. They are also temporally and spatially consistent, which makes them suitable for fingerprinting.MagPrint++has a number of advantages over existing schemes: i) Non-intrusive fingerprinting, ii) implementation both on COTS mobile phones and a small and easy-to-deploy device, and iii) high accuracy thanks to the proposed classification algorithm. In experiments involving 30 users,MagPrint++achieves$94.3\%$accuracy in classifying users from these traces, which represents a$10.9\%$improvement over the state-of-the-art classification method.

Elastic Block Storage (EBS) has a pivotal role in modern data center infrastructure, providing reliable, high-performance and flexible block storage service to users. In Alibaba Cloud, EBS is the most widely used service and has been supporting the operation of millions of virtual disks. However, even with layers of load balancing and caching, we still observe significant traffic skewness across the EBS stack. This motivates us to comprehensively investigate symptoms and root causes behind the traffic patterns and, more importantly, explore the fixes for the identified issues.

Class-Incremental Learning (CIL) has gained considerable attention due to its capacity to accommodate new classes during learning. Replay-based methods demonstrate state-of-the-art performance in CIL but suffer from high memory consumption to save a set of old exemplars for revisiting. To address this challenge, many memory-efficient replay methods have been developed by exploiting image compression techniques. However, the gains are often bittersweet when pixel-level compression methods are used. Here, we present a simple yet efficient approach that employs tensor decomposition to address these limitations. This method fully exploits the low intrinsic dimensionality and pixel correlation of images to achieve high compression efficiency while preserving sufficient discriminative information, significantly enhancing performance. We also introduce a hybrid exemplar selection strategy to improve the representativeness and diversity of stored exemplars. Extensive experiments across datasets with varying resolutions consistently demonstrate that our approach substantially boosts the performance of baseline methods, showcasing strong generalization and robustness.

With the sheer volume of data in today’s world, archive storage systems play a significant role in persisting the cold data. Due to stringent cost concerns, one popular design is to organize disks into groups and periodically switch them to be powered on for serving user requests. Scheduling thus becomes critical for both CapEx and performance. Unfortunately, field results indicate that existing schedulers can be often suboptimal. Our further analysis suggests that the main reason is the mismatch between the ever-changing workloads and the fixed set of coarsely-configured parameters in current heuristic-based schedulers. In this article, we propose MasterPlan , a reinforcement learning (RL) based scheduler for archive storage systems. By identifying the unique characteristics of archive storage service, we design a state space and reward function for the RL agent. MasterPlan includes a continuous action encoding approach to guarantee efficient exploration, and a meta adaptation module to extract features of workload series. Experiments show that MasterPlan can achieve 1.25× throughput, 2.16× 99 th latency and 1.47× power draw improvement compared to existing solutions.

The deep-learning-based detection of planetary craters provides significant assistance for in-orbit vehicles in space exploration. However, most object detection researches to date tended to focus on the single-source data collected by mono-type sensors, and their performances were severely hindered by the disability of using diverse source data from various sensors. For effectually using distinct sources in resource-limited vehicles, we present a compact framework to learn unified representation from elevation and visual sources basing on tensor decomposition. In particular, the vital elevation is extracted to obtain the deficient information in visuals. Furthermore, for accommodating limited computing resource, a compound tensor decomposition layer is proposed based on the special structure of decomposition, which extracts the source-specific and source-independent information, and at the same time crafts unified representation for terrains. Comprehensive comparisons with recent methods demonstrate the effective representation learning of the proposed method on different planetary terrains and in insufficient data learning setting, revealing its potential for real-world applications.

Robust tensor completion (RTC) aims to recover a low-rank tensor from its incomplete observations with outlier corruption. The recently proposed tensor ring (TR) model has demonstrated superiority in solving the RTC problem. However, the methods using the TR model either require a preassigned TR rank or aggressively pursue the minimum TR rank, where the latter often leads to biased solutions in the presence of noise. To tackle these bottlenecks, a Bayesian robust TR decomposition (BRTR) method is proposed to give a more accurate solution for the RTC problem, which can avoid exquisite selection of the TR rank and penalty parameters. A variational Bayesian (VB) algorithm is developed to infer the probability distribution of posteriors. During the learning process, BRTR can prune off zero components of core tensors, resulting in automatic TR rank determination. Extensive experiments show that BRTR can achieve significantly improved performance than other state-of-the-art methods.

Deep multitask learning (MTL) shares beneficial knowledge across participating tasks, alleviating the impacts of extreme learning conditions on their performances such as the data scarcity problem. In practice, participators stemming from different domain sources often have varied complexities and input sizes, for example, in the joint learning of computer vision tasks with RGB and grayscale images. For adapting to these differences, it is appropriate to design networks with proper representational capacities and construct neural layers with corresponding widths. Nevertheless, most of the state-of-the-art methods pay little attention to such situations, and actually fail to handle the disparities. To work with the dissimilitude of tasks' network designs, this article presents a distributed knowledge-sharing framework called tensor ring multitask learning (TRMTL), in which the relationship between knowledge sharing and original weight matrices is cut up. The framework of TRMTL is flexible, which is not only capable of sharing knowledge across heterogenous networks but also able to jointly learn tasks with varied input sizes, significantly improving performances of data-insufficient tasks. Comprehensive experiments on challenging datasets are conducted to empirically validate the effectiveness, efficiency, and flexibility of TRMTL in dealing with the disparities in MTL.