Spatial transcriptomics (ST) integrates spatial data with transcriptomic information, providing high-resolution maps of gene expression within tissue contexts. It has revolutionized studies on cellular function and disease mechanisms, particularly in cancer and immunology. We conducted a bibliometric analysis of 1197 publications from the Web of Science (2015-24), focusing on publication trends, journal distribution, and keyword analysis to identify key research areas in ST. ST publications surged from 2021, with 500 papers in 2023. Five of the top 10 journals are from the Nature Publishing Group. Keyword analysis identified emerging trends like "tumor microenvironment," "immune infiltration," and "biomarker," highlighting ST's expanding role in cancer and immunology. International collaboration among multidisciplinary teams is crucial for maximizing ST's potential, and understanding its trends will guide its future impact. Large language models can further enrich the results of bibliometric research, making the findings of bibliometrics more comprehensive and specific.

A primary design objective for user-facing services for cloud and edge computing is to maximize query throughput, while meeting query tail latency Service Level Objectives (SLOs) for individual queries. Unfortunately, the existing solutions fall short of achieving this design objective, which we argue, is largely attributed to the fact that they fail to take the query fanout explicitly into account. In this paper, we propose TailGuard based on a Tail-latency-SLO-and-Fanout-aware Earliest-Deadline-First Queuing policy (TF-EDFQ) for task queuing at individual task servers the query tasks are fanned out to. With the task pre-dequeuing time deadline for each task being derived based on both query tail latency SLO and query fanout, TailGuard takes an important first step towards achieving the design objective. A query admission control scheme is also developed to provide tail latency SLO guarantee in the presence of resource shortages. TailGuard is evaluated against First-In-First-Out (FIFO) task queuing, task PRIority Queuing (PRIQ) and Tail-latency-SLO-aware EDFQ (T-EDFQ) policies by both simulation and testing in the Amazon EC2 cloud. It is driven by three types of applications in the Tailbench benchmark suite, featuring web search, in-memory key-value store, and transactional database applications. The results demonstrate that TailGuard can significantly improve resource utilization (e.g., up to 80% compared to FIFO), while also meeting the targeted tail latency SLOs, as compared with the other three policies. TailGuard is also implemented and tested in a highly heterogeneous Sensing-$a$s-a-Service (SaS) testbed for a data sensing service, demonstrating performance gains of up to 33% . These results are consistent with both the simulation and Amazon EC2 results.

Hyperspectral image (HSI) has fine spectral resolution and abundant spatial information to detect subtle differences between targets. However, it is heavily contaminated with noise due to sensor design and atmospheric radiative transfer, resulting in spectral shifts and spatial discontinuities. Current denoising methods usually establish constraints directly on the ground truth and denoised image, lacking supervision of intermediate parameters of the network, resulting in insufficient model constraints and poor convergence. In addition, existing methods do not consider spatial-spectral compensation, so the denoising results have obvious spatial-spectral distortion. To this end, we propose a novel multiscale spatial-spectral invertible compensation network (MSIC-Net) for HSI denoising. The method constructs an invertible spatial-spectral compensation (ISSC) module, which supervises intermediate features through inverse constraints, realizes the circulation of multiscale information, and improves the stability of the model. At the same time, we also introduce style transfer for spatial-spectral compensation, which uses its superior fine feature control ability to precisely compensate for the lost spatial and spectral detail features. The method is extensively validated experimentally and categorically on simulated and real datasets. The experimental results show that MSIC-Net outperforms other state-of-the-art denoising methods in quantitative and qualitative evaluations.

The hyperspectral imaging instrument makes a trade-off by sacrificing spatial resolution to achieve high spectral resolution. This compromise leads to a low signal-to-noise ratio, and hyperspectral images (HSIs) are often heavily contaminated with mixed noise, which is an inherent challenge. Previous research has achieved satisfactory results for natural image denoising; hyperspectral denoising has remained a formidable task. In this article, we introduce an innovative method called the multistage hybrid-denoising network for satellite hyperspectral images (SUC-MSDN). SUC-MSDN initially decomposes the noisy HSI into multiple scales and constructs a multistage denoising network by analyzing the spatial spectrum texture distribution characteristics of noise signals. Instead of simply stacking the output results from each scale, SUC-MSDN uses the denoising results from the low-scale network as prior knowledge for the high-scale denoising network to more accurately remove the final noise components. Extensive experimental datasets are used to validate the performance of SUC-MSDN. Experimental results show that SUC-MSDN outperforms benchmark methods and significantly enhances the accuracy of land cover mapping.

A primary design objective for Data-intensive User-facing (DU) services for cloud and edge computing is to maximize query throughput, while meeting query tail latency Service Level Objectives (SLOs) for individual queries. Unfortunately, the existing solutions fall short of achieving this design objective, which we argue, is largely attributed to the fact that they fail to take the query fanout explicitly into account. In this paper, we propose TailGuard based on a Tail-latency-SLO-and-Fanout-aware Earliest-Deadline-First Queuing policy (TF-EDFQ) for task queuing at individual task servers the query tasks are fanned out to. With the task queuing deadline for each task being derived based on both query tail latency SLO and query fanout, TailGuard takes an important first step towards achieving the design objective. TailGuard is evaluated against First-In-First-Out (FIFO) task queuing, task PRIority Queuing (PRIQ) and Tail-latency-SLO-aware EDFQ (T-EDFQ) policies by simulation. It is driven by three types of applications in the Tailbench benchmark suite. The results demonstrate that TailGuard can improve resource utilization by up to 80%, while meeting the targeted tail latency SLOs, as compared with the other three policies. TailGuard is also implemented and tested in a highly heterogeneous Sensing-as-a-Service (SaS) testbed for a data sensing service, with test results in line with the other ones.

In today's datacenters, job heterogeneity makes it difficult for schedulers to simultaneously meet latency requirements and maintain high resource utilization. The state-of-the-art datacenter schedulers, including centralized, distributed, and hybrid schedulers, fail to ensure low latency for short jobs in large-scale and highly loaded systems. The key issues are the scalability in centralized schedulers, ineffective and inefficient probing and resource sharing in both distributed and hybrid schedulers.

For the past few years, Internet of Things (IoT) has developed rapidly and been extensively used. However, its transmission security and privacy protection are insufficient, which limits the development of IoT to a certain extent. As a technology of IoT information transmission, anonymous communication technology comes into being as an important means to ensure the security of healthcare data, which can better protect users’ privacy in some ways. Nowadays, a variety of attack techniques for anonymous communication systems have been proposed by the academic community to track senders and receivers or discover communications between two users. Thus, the MSFA (Multiple System Fingerprint Attack) scheme for anonymous communication systems is presented in this paper where the MSFA scheme architecture, implementation in the Tor environment, and experimental data processing are described. Through a comparative analysis between two traces of visiting the same website based on the edit distance, it is shown that the longer the length of the site traffic data, the greater the edit distance of the site access traffic and the larger the range.

As automation increasingly takes over functions requiring humans' skill- and rule-based behaviors, effective human-automation interaction increasingly depends upon human reasoning about when the automation should be disengaged. This paper uses the aircraft traffic alert and collision avoidance system (TCAS) as a case study where the pilot needs to decide how to respond to decision automation that generates collision avoidance advisories commanding vertical maneuvers. Of note, recent installations of TCAS can now automatically execute the advisories, potentially to improve compliance with TCAS advisories. This paper describes a Human-in-the-loop flight simulator study examining pilots' interaction with the automatic execution of advisories, including situations in which pilots chose to disengage it. While overall automatic execution appears to improve compliance to TCAS advisories in the aggregate, it was also disengaged in some of the same situations in which pilots historically had not manually flown compliant avoidance maneuvers. Further, the autopilot's subsequent reversionary mode settings after following the TCAS advisory may not be appropriate for returning to air traffic clearances. These results highlight the importance of designing automation to support effective pilot reasoning about traffic events, a task that remains in place while the pilot remains ultimately responsible for safety.