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In this paper we propose a scalable framework for large-scale farm scene modeling that utilizes remote sensing data, specifically satellite images. Our approach begins by accurately extracting and categorizing the distributions of various scene elements from satellite images into four distinct layers: fields, trees, roads, and grasslands. For each layer, we introduce a set of controllable Parametric Layout Models (PLMs). These models are capable of learning layout parameters from satellite images, enabling them to generate complex, large-scale farm scenes that closely reproduce reality across multiple scales. Additionally, our framework provides intuitive control for users to adjust layout parameters to simulate different stages of crop growth and planting patterns. This adaptability makes our model an excellent tool for graphics and virtual reality applications. Experimental results demonstrate that our approach can rapidly generate a variety of realistic and highly detailed farm scenes with minimal inputs. 

PurposeFreezing extends the shelf life of food. Home freezing of fresh foods and the purchase of frozen foods have been advocated as approaches to reduce food waste in US households. This paper discusses how commonly US households apply these practices, quantifies frozen food waste and relates these practices to food waste. Design/methodology/approachWe add questions to the summer 2022 wave of the US Household Food Waste Tracking Survey. The novel survey data provide important baseline information and household behaviours, such as food waste, home freezing of fresh food and the purchase of frozen foods. We analyse the association among these behaviours from more than 1,000 US households. FindingsWe find that US household wastes about 26 g per person per week of food that was once frozen, which is about 6% of all household food waste. The finding indicates that a small portion of food waste in US households comes from frozen food. Vegetables and meats are the most commonly discarded frozen foods. Among the frozen items reported as discarded, about 30% were purchased as frozen rather than purchased fresh and then frozen at home by the consumer and about 30% more were reported as discarded from the refrigerator rather than directly from the freezer. The findings are important for informing strategies to reduce household food waste. Research limitations/implicationsWhile the data provide important baseline information and correlate the use of freezing with lower waste levels, more work is needed to understand if interventions encouraging frozen food purchase or home freezing would reduce household food waste. Originality/valueWe provide unique, detailed information about the quantity of frozen food waste in US households and the relationships between consumer food waste and the practices of frozen food purchasing and home freezing. 

Context. To explain the well-known tension between cosmological parameter constraints obtained from the primary cosmic microwave background (CMB) and those drawn from X-ray-selected galaxy cluster samples identified with early data, we propose a possible explanation for the incompleteness of detected clusters being higher than estimated. Specifically, we suggest that certain types of galaxy groups or clusters may have been overlooked in previous works. Aims. We aim to search for galaxy groups and clusters with especially extended surface brightness distributions by creating a new X-ray-selected catalog of extended galaxy clusters from the XMM-SpitzerExtragalactic Representative Volume Survey (XMM-SERVS) data, based on a dedicated source detection and characterization algorithm optimized for extended sources. Methods. Our state-of-the-art algorithm is composed of wavelet filtering, source detection, and characterization. We carried out a visual inspection of the optical image, and spatial distribution of galaxies within the same redshift layer to confirm the existence of clusters and estimated the cluster redshift with the spectroscopic and photometric redshifts of galaxies. The growth curve analysis was used to characterize the detections. Results. We present a catalog of extended X-ray galaxy clusters detected from the XMM-SERVS data. The XMM-SERVS X-ray eXtended Galaxy Cluster (XVXGC) catalog features 141 cluster candidates. Specifically, there are 53 clusters previously identified as clusters with intracluster medium (ICM) emission (class 3); 40 that were previously known as optical or infrared (IR) clusters, but detected as X-ray clusters for the first time (class 2); and 48 identified as clusters for the first time (class 1). Compared with the class 3 sample, the “class 1 + class 2” sample is systematically fainter and exhibits a flatter surface brightness profile. Specifically, the median flux in [0.5–2.0] keV band for “class 1 + class 2” and class 3 sample is 1.288 × 10⁻¹⁴erg/s/cm²and 1.887 × 10⁻¹⁴erg/s/cm², respectively. The median values ofβ(i.e., the slope of the cluster surface brightness profile) are 0.506 and 0.573 for the “class 1 + class 2” and class 3 samples, respectively. The entire sample is available at the CDS. 

Cryo-electron tomography (cryo-ET) provides 3D visualization of subcellular components in the near-native state and at sub-molecular resolutions in single cells, demonstrating an increasingly important role in structural biology in situ . However, systematic recognition and recovery of macromolecular structures in cryo-ET data remain challenging as a result of low signal-to-noise ratio (SNR), small sizes of macromolecules, and high complexity of the cellular environment. Subtomogram structural classification is an essential step for such task. Although acquisition of large amounts of subtomograms is no longer an obstacle due to advances in automation of data collection, obtaining the same number of structural labels is both computation and labor intensive. On the other hand, existing deep learning based supervised classification approaches are highly demanding on labeled data and have limited ability to learn about new structures rapidly from data containing very few labels of such new structures. In this work, we propose a novel approach for subtomogram classification based on few-shot learning. With our approach, classification of unseen structures in the training data can be conducted given few labeled samples in test data through instance embedding. Experiments were performed on both simulated and real datasets. Our experimental results show that we can make inference on new structures given only five labeled samples for each class with a competitive accuracy (> 0.86 on the simulated dataset with SNR = 0.1), or even one sample with an accuracy of 0.7644. The results on real datasets are also promising with accuracy > 0.9 on both conditions and even up to 1 on one of the real datasets. Our approach achieves significant improvement compared with the baseline method and has strong capabilities of generalizing to other cellular components.