In this paper, we propose Scene Splatter, a momentum-based paradigm for video diffusion to generate generic scenes from single image. Existing methods, which employ video generation models to synthesize novel views, suffer from limited video length and scene inconsistency, leading to artifacts and distortions during further reconstruction. To address this issue, we construct noisy samples from original features as momentum to enhance video details and maintain scene consistency. However, for latent features with the perception field that spans both known and unknown regions, such latent-level momentum restricts the generative ability of video diffusion in unknown regions. Therefore, we further introduce the aforementioned consistent video as a pixel-level momentum to a directly generated video without momentum for better recovery of unseen regions. Our cascaded momentum enables video diffusion models to generate both high-fidelity and consistent novel views. We further finetune the global Gaussian representations with enhanced frames and render new frames for momentum update in the next step. In this manner, we can iteratively recover a 3D scene, avoiding the limitation of video length. Extensive experiments demonstrate the generalization capability and superior performance of our method in high-fidelity and consistent scene generation.

Satisfiability Modulo Counting (SMC) is a recently proposed general language to reason about problems integrating statistical and symbolic Artificial Intelligence. An SMC problem is an extended SAT problem in which the truth values of a few Boolean variables are determined by probabilistic inference. Approximate solvers may return solutions that violate constraints. Directly integrating available SAT solvers and probabilistic inference solvers gives exact solutions but results in slow performance because of many back-and-forth invocations of both solvers. We propose KOCO-SMC, an integrated exact SMC solver that efficiently tracks lower and upper bounds in the probabilistic inference process. It enhances computational efficiency by enabling early estimation of probabilistic inference using only partial variable assignments, whereas existing methods require full variable assignments. In the experiment, we compare KOCO-SMC with currently available approximate and exact SMC solvers on large-scale datasets and real-world applications. The proposed KOCO-SMC finds exact solutions with much less time.

Satisfiability Modulo Counting (SMC) encompasses problems that require both symbolic decision-making and statistical reasoning. Its general formulation captures many real-world problems at the intersection of symbolic and statistical AI. SMC searches for policy interventions to control probabilistic outcomes. Solving SMC is challenging because of its highly intractable nature (NP^PP-complete), incorporating statistical inference and symbolic reasoning. Previous research on SMC solving lacks provable guarantees and/or suffers from suboptimal empirical performance, especially when combinatorial constraints are present. We propose XOR-SMC, a polynomial algorithm with access to NP-oracles, to solve highly intractable SMC problems with constant approximation guarantees. XOR-SMC transforms the highly intractable SMC into satisfiability problems by replacing the model counting in SMC with SAT formulae subject to randomized XOR constraints. Experiments on solving important SMC problems in AI for social good demonstrate that XOR-SMC outperforms several baselines both in solution quality and running time.

Recent advances in spatial transcriptomics (ST) have enabled comprehensive profiling of gene expression with spatial information in the context of the tissue microenvironment. However, with the improvements in the resolution and scale of ST data, deciphering spatial domains precisely while ensuring efficiency and scalability is still challenging. Here, we develop SGCAST, an efficient auto-encoder framework to identify spatial domains. SGCAST adopts a symmetric graph convolutional auto-encoder to learn aggregated latent embeddings via integrating the gene expression similarity and the proximity of the spatial spots. This framework in SGCAST enables a mini-batch training strategy, which makes SGCAST memory-efficient and scalable to high-resolution spatial transcriptomic data with a large number of spots. SGCAST improves the overall accuracy of spatial domain identification on benchmarking data. We also validated the performance of SGCAST on ST datasets at various scales across multiple platforms. Our study illustrates the superior capacity of SGCAST on analyzing spatial transcriptomic data.

Simultaneous measurements of multi-target vital signs have been quite challenging for radar-based non-contact vital signs monitoring. This paper presents a novel cumulative phase gradient approach for accurate estimates of multi-target respirations without either range or beam discrimination in a single-input single-output (SISO) radar sensor. The signal model of multi-target respiration is first provided, and then a cumulative phase gradient approach is developed to accurately estimate multi-target respirations from the observed mixtures by exploiting the underlying periodicity of the respiration waveforms. The proposed method provides average estimated error rates around 3% in the experiment with closely located three human targets within range resolution, and demonstrates its significant superiorities over other methods.

Lactic acid bacteria consortia are commonly present in food, and some of these bacteria possess probiotic properties. However, discovery and experimental validation of probiotics require extensive time and effort. Therefore, it is of great interest to develop effective screening methods for identifying probiotics. Advances in sequencing technology have generated massive genomic data, enabling us to create a machine learning-based platform for such purpose in this work. This study first selected a comprehensive probiotics genome dataset from the probiotic database (PROBIO) and literature surveys. Then, k-mer (from 2 to 8) compositional analysis was performed, revealing diverse oligonucleotide composition in strain genomes and apparently more probiotic (P-) features in probiotic genomes than non-probiotic genomes. To reduce noise and improve computational efficiency, 87 376 k-mers were refined by an incremental feature selection (IFS) method, and the model achieved the maximum accuracy level at 184 core features, with a high prediction accuracy (97.77%) and area under the curve (98.00%). Functional genomic analysis using annotations from gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Rapid Annotation using Subsystem Technology (RAST) databases, as well as analysis of genes associated with host gastrointestinal survival/settlement, carbohydrate utilization, drug resistance and virulence factors, revealed that the distribution of P-features was biased toward genes/pathways related to probiotic function. Our results suggest that the role of probiotics is not determined by a single gene, but by a combination of k-mer genomic components, providing new insights into the identification and underlying mechanisms of probiotics. This work created a novel and free online bioinformatic tool, iProbiotics, which would facilitate rapid screening for probiotics.