This paper studies the design of strategyproof distributed mechanisms for a constrained location problem involving two heterogeneous facilities under the max-variant cost model. A set of agents with private locations on the real line is partitioned into disjoint groups, and the two facilities must be placed at locations drawn from a given multiset of candidate locations, with each candidate location hosting at most one facility. Each agent requires access to both facilities, and her individual cost is defined as the distance from her location to the farther facility. Each such mechanism operates in two stages. First, it selects a pair of candidate locations as representatives for each group based solely on the reports of that group's members. It then selects the final locations of the two facilities from the aggregated multiset of group representatives. We investigate deterministic strategyproof mechanisms within this distributed framework and derive constant lower and upper bounds on their distortion with respect to four social objectives: the Average-of-Average, Max-of-Max, Max-of-Average, and Average-of-Max costs.

Spatial transcriptomics (ST) technologies offer an unprecedented opportunity to resolve complex tissue microenvironments. The accurate identification of spatial domains is still a pivotal and challenging task in spatial transcriptomics studies. Although numerous computational methods have been developed for spatial domain detection, prevailing methods struggle with multi-modal data fusion, noise robustness, and clustering stability. To address these limitations, we introduce DPST, an end-to-end deep learning model, which integrates gene expression, spatial coordinates, and histology images with an attention mechanism. DPST leverages the self-supervised bootstrap your own latent (BYOL) framework to extract robust feature embeddings from histology images without requiring negative samples. At its core, DPST employs a MASK-REMASK dual-view decoding strategy that simultaneously corrects for noise in masked data while recovering details from unmasked data. Furthermore, we use the breaking the reclustering barriers mechanism. This mechanism incorporates weight resets, reclustering, and momentum resets. It helps deep embedded clustering algorithms overcome performance bottlenecks. The experimental results show that DPST outperforms state-of-the-art methods consistently in several tasks, including spatial clustering and trajectory inference.

Humans constantly interact with objects in daily life tasks. Capturing such processes and subsequently conducting visual inferences from a fixed viewpoint suffers from occlusions, shape and texture ambiguities, motions, etc. To mitigate the problem, it is essential to build a training dataset that captures free-viewpoint interactions. We construct a dense multi-view dome to acquire a complex human object interaction dataset, named HODome, that consists of ~71 M frames on 10 subjects interacting with 23 objects. To process the HODome dataset, we develop NeuralDome, a layer-wise neural processing pipeline tailored for multi-view video inputs to conduct accurate tracking, geometry reconstruction and free-view rendering, for both human subjects and objects. Extensive experiments on the HODome dataset demonstrate the effectiveness of NeuralDome on a variety of inference, modeling, and rendering tasks. Both the dataset and the NeuralDome tools will be disseminated to the community for further development, which can be found at https://juzezhang.github.io/NeuralDome

Gallbladder carcinoma (GBC), an aggressive malignant tumor of the biliary system, is characterized by high cellular heterogeneity and poor prognosis. Fewer data have been reported in GBC than other common cancer types. Multi-omics data will contribute to the understanding of the molecular mechanisms of cancer, cancer diagnosis and prognosis. Herein, to provide better understanding of the molecular events in GBC pathogenesis, we developed GBCdb ( http://tmliang.cn/gbc/ ), a user-friendly interface for the query and browsing of GBC-associated genes and RNA interaction networks using published multi-omics data, which also included experimentally supported data from different molecular levels. GBCdb will help to elucidate the potential biological roles of different RNAs and allow for the exploration of RNA interactions in GBC. These resources will provide an opportunity for unraveling the potential molecular features of Gallbladder carcinoma.