Spatial transcriptomics (ST) technology enables the simultaneous capture of gene expression profile and spatial information within 2D tissue slices. However, conventional analyses that process each individual slice independently often overlook shared features across multiple slices, limiting comprehensive biological insights. To address this, we introduce GRASS, a deep graph representation learning-based framework designed for the integration and alignment of multislice ST data. GRASS consists of two core modules: GRASS_Integration, which employs a heterogeneous graph architecture integrating contrastive learning and a multi-expert collaboration strategy to fully utilize both shared and unique information, enabling multislice integration, clustering, and various downstream analyses; and GRASS_Alignment, which uses a dual-perception similarity metric to guide spot-level alignment, supporting downstream tasks such as imputation and 3D reconstruction. Experimental results on seven ST datasets from five different platforms demonstrate that GRASS consistently outperforms eight state-of-the-art methods in both integration and alignment tasks. By comprehensively addressing multi-level information integration, GRASS emerges as an ideal solution for the joint analysis of multislice ST data.

Land surface temperature (LST) is an important physical parameter at the interface between the Earth’s surface and the atmosphere. Accurately quantifying LST uncertainty is essential for the generation of a long-term and consistent LST Climate Data Record (CDR) or Earth System Data Record (ESDR) from either multiple sensors or algorithms. In this study, a physical-based method was proposed to quantify the uncertainty of LST retrieval from satellite thermal infrared (TIR) data using the generalized split-window (GSW) algorithm. LST uncertainties were parameterized as a function of brightness temperature at the top of the atmosphere (TOA) and surface emissivity in two split-window channels, which are two key input parameters in the GSW algorithm, as well as their uncertainties. The performance of the parameterized uncertainty model was evaluated according to the simulation dataset at six prescribed viewing zenith angles (VZAs) of 0°, 33.56°, 44.42°, 51.32°, 56.25°, and 60°, with a root mean squared error (RMSE) of 0.001 K. The coefficients of the parameterized uncertainty model at arbitrary VZA within a sensor’s field of view (FOV) can be obtained by linear interpolation of the coefficients at the six prescribed VZAs. Once the coefficients of the parameterized uncertainty model for each pixel are available, total LST uncertainties can be quantified on a pixel-by-pixel basis. As an example, the parameterized uncertainty model was applied to actual MODIS data for displaying the spatial distribution of LST uncertainties. The results indicate that the parameterized uncertainty model can characterize the spatial variation in LST uncertainties well over various land cover types.

Auctions are among the best-known market-based tools to solve the problem of dynamic spectrum redistribution. In recent years, a good number of strategy-proof auction mechanisms have been proposed to improve spectrum utilization and to prevent market manipulation. However, the issue of privacy preservation in spectrum auctions remains open. On the one hand, truthful bidding reveals bidders' private valuations of the spectrum. On the other hand, coverage/interference areas of the bidders may be revealed to determine conflicts. In this paper, we present PISA, which is a PrIvacy preserving and Strategy-proof Auction mechanism for spectrum allocation. PISA provides protection for both bid privacy and coverage/interference area privacy leveraging a privacy-preserving integer comparison protocol, which is well applicable in other contexts. We not only theoretically prove the privacy-preserving properties of PISA, but also extensively evaluate its performance. Evaluation results show that PISA achieves good spectrum allocation efficiency with light computation and communication overheads.

Bandwidth reservation has been recognized as a value-added service to the cloud provider in recent years. We consider an open market of cloud bandwidth reservation, in which cloud providers offer bandwidth reservation services to cloud tenants, especially online streaming service providers, who have strict requirements on the amount of bandwidth to guarantee their quality of services. In this paper, we model the open market as a double-sided auction, and propose the first family of STrategy-proof double Auctions for multi-cloud, multi-tenant bandwidth Reservation (STAR). STAR contains two auction mechanisms. The first one, STAR-Grouping, divides the tenants into groups by a bid-independent way, and carefully matches the cloud providers with the tenant groups to form good trades. The second one, STAR-Padding, greedily matches the cloud providers with the tenants, and fills the partially reserved cloud provider(s) with a novel virtual padding tenant who can be a component of the auctioneer. Our analysis shows that both of the two auction mechanisms achieve strategy-proofness and ex-post budget balance. Our evaluation results show that they achieve good performance in terms of social welfare, cloud bandwidth utilization, and tenant satisfaction ratio.

A novel approach is presented for color image segmentation. By incorporating the advantages of mean shift (MS) segmentation and spectral clustering (SC) method, the proposed approach provides effective and robust segmentation. Firstly, input image is transformed from a pixel-based to a region-based model by using the MS algorithm. The input image after MS segmentation is composed of multiple disjoint regions that preserve the desirable discontinuity characteristics of the image. Then the regions are treated as nodes in the image plane and a graph structure is applied to represent them. The final step is to apply the improved SC to perform globally optimal clustering. To avoid some incorrect partitioning when considering each region as one graph node, we assign different numbers of nodes to represent the regions according to area ratios among the regions. In addition, K-harmonic means (KHM) instead of K-means is applied in the improved SC procedure in order to enhance its stability and performance. The superiority of the proposed approach is demonstrated and examined through a mass of experiments using color images.