Should State Land in Southern California Be Allocated to Warehousing Goods or Housing People? Analyzing Transportation, Climate, and Unintended Consequences of Supply Chain Solutions

In response to COVID-19 pandemic supply chain issues, the State of California issued Executive Order (EO) N-19-21 to use state land to increase warehousing capacity. This highlights a land-use paradox between economic and environmental goals: adding warehouse capacity increases climate pollution and traffic congestion around the ports and warehouses, while there is a deficit of affordable housing and high homeless rates in port-adjacent underserved communities. This study aims to inform regional policymakers and community stakeholders about these trade-offs by identifying current and future supply of and demand for warehousing and housing in Southern California through 2040. The study uses statistical analysis and forecasting, and evaluates across numerous scenarios the environmental impact of meeting demand for both with the Community LINE Source Model. Warehousing and housing are currently projected to be in high demand across Southern California in future decades, despite short-run adjustments in the post-pandemic period of inflation and net declines in population. Using state land for warehousing creates environmental justice concerns, as the number of air pollution hotspots increases even with electrifying trucking fleets, especially when compared against low-impact affordable housing developments. However, low-income housing demand appears to be positively correlated with unemployment, suggesting that the jobs provided by warehousing development might help to ameliorate that concern

Mask-Free Video Instance Segmentation

The recent advancement in Video Instance Segmentation (VIS) has largely been driven by the use of deeper and increasingly data-hungry transformer-based models. However, video masks are tedious and expensive to annotate, limiting the scale and diversity of existing VIS datasets. In this work, we aim to remove the mask-annotation requirement. We propose MaskFreeVIS, achieving highly competitive VIS performance, while only using bounding box annotations for the object state. We leverage the rich temporal mask consistency constraints in videos by introducing the Temporal KNN-patch Loss (TK-Loss), providing strong mask supervision without any labels. Our TK-Loss finds one-to-many matches across frames, through an efficient patch-matching step followed by a K-nearest neighbor selection. A consistency loss is then enforced on the found matches. Our mask-free objective is simple to implement, has no trainable parameters, is computationally efficient, yet outperforms baselines employing, e.g., state-of-the-art optical flow to enforce temporal mask consistency. We validate MaskFreeVIS on the YouTube-VIS 2019/2021, OVIS and BDD100K MOTS benchmarks. The results clearly demonstrate the efficacy of our method by drastically narrowing the gap between fully and weakly-supervised VIS performance. Our code and trained models are available at https://github.com/SysCV/MaskFreeVis.Comment: Accepted in CVPR 2023; Code: https://github.com/SysCV/MaskFreeVis; Project page: http://vis.xyz/pub/maskfreevi

Segment Anything Meets Point Tracking

The Segment Anything Model (SAM) has established itself as a powerful zero-shot image segmentation model, enabled by efficient point-centric annotation and prompt-based models. While click and brush interactions are both well explored in interactive image segmentation, the existing methods on videos focus on mask annotation and propagation. This paper presents SAM-PT, a novel method for point-centric interactive video segmentation, empowered by SAM and long-term point tracking. SAM-PT leverages robust and sparse point selection and propagation techniques for mask generation. Compared to traditional object-centric mask propagation strategies, we uniquely use point propagation to exploit local structure information agnostic to object semantics. We highlight the merits of point-based tracking through direct evaluation on the zero-shot open-world Unidentified Video Objects (UVO) benchmark. Our experiments on popular video object segmentation and multi-object segmentation tracking benchmarks, including DAVIS, YouTube-VOS, and BDD100K, suggest that a point-based segmentation tracker yields better zero-shot performance and efficient interactions. We release our code that integrates different point trackers and video segmentation benchmarks at https://github.com/SysCV/sam-pt

The Best, the Worst, and the Hardest to Find: How People, Mobiles, and Social Media Connect Migrants In(to) Europe

For displaced people, migrating into Europe has highly complex information needs about the journey and destination. Each new need presents problems of where to seek information, how to trust or distrust information, and financial and other costs. The outcomes of receiving poor or false information can cause bodily harm or death, loss of family, or financial ruin. We aim to make two major contributions: First, provide rich insights into digital literacy, information needs, and strategies among Syrian and Iraqi refugees who entered Europe in 2015, a topic rarely dealt with in the literature. Second, we seek to change the dominant perspective on migrants and refugees as passive victims of international events and policies by showing their capacities and skills to navigate the complex landscape of information and border regimes en route to Europe. Building on research at Za’atari refugee camp (Jordan), we surveyed 83 Arab refugees in two centers in Berlin. Analyses address refugees’ temporal information worlds, focusing on the importance and difficulty in finding specific information, how migrants identify mis- and disinformation, and the roles of information and technology mediaries. Findings illustrate the digital capacities refugees employ during and after their journey to Europe; they show social support via social media and highlight the need for a radical shift in thinking about and researching migration in the digital age

Segment Anything in High Quality

The recent Segment Anything Model (SAM) represents a big leap in scaling up segmentation models, allowing for powerful zero-shot capabilities and flexible prompting. Despite being trained with 1.1 billion masks, SAM's mask prediction quality falls short in many cases, particularly when dealing with objects that have intricate structures. We propose HQ-SAM, equipping SAM with the ability to accurately segment any object, while maintaining SAM's original promptable design, efficiency, and zero-shot generalizability. Our careful design reuses and preserves the pre-trained model weights of SAM, while only introducing minimal additional parameters and computation. We design a learnable High-Quality Output Token, which is injected into SAM's mask decoder and is responsible for predicting the high-quality mask. Instead of only applying it on mask-decoder features, we first fuse them with early and final ViT features for improved mask details. To train our introduced learnable parameters, we compose a dataset of 44K fine-grained masks from several sources. HQ-SAM is only trained on the introduced detaset of 44k masks, which takes only 4 hours on 8 GPUs. We show the efficacy of HQ-SAM in a suite of 9 diverse segmentation datasets across different downstream tasks, where 7 out of them are evaluated in a zero-shot transfer protocol. Our code and models will be released at https://github.com/SysCV/SAM-HQ.Comment: We propose HQ-SAM to upgrade SAM for high-quality zero-shot segmentation. Github: https://github.com/SysCV/SAM-H

Cascade-DETR: Delving into High-Quality Universal Object Detection

Object localization in general environments is a fundamental part of vision systems. While dominating on the COCO benchmark, recent Transformer-based detection methods are not competitive in diverse domains. Moreover, these methods still struggle to very accurately estimate the object bounding boxes in complex environments. We introduce Cascade-DETR for high-quality universal object detection. We jointly tackle the generalization to diverse domains and localization accuracy by proposing the Cascade Attention layer, which explicitly integrates object-centric information into the detection decoder by limiting the attention to the previous box prediction. To further enhance accuracy, we also revisit the scoring of queries. Instead of relying on classification scores, we predict the expected IoU of the query, leading to substantially more well-calibrated confidences. Lastly, we introduce a universal object detection benchmark, UDB10, that contains 10 datasets from diverse domains. While also advancing the state-of-the-art on COCO, Cascade-DETR substantially improves DETR-based detectors on all datasets in UDB10, even by over 10 mAP in some cases. The improvements under stringent quality requirements are even more pronounced. Our code and models will be released at https://github.com/SysCV/cascade-detr.Comment: Accepted in ICCV 2023. Our code and models will be released at https://github.com/SysCV/cascade-det

Chimpanzees Extract Social Information from Agonistic Screams

Chimpanzee (Pan troglodytes) agonistic screams are graded vocal signals that are produced in a context-specific manner. Screams given by aggressors and victims can be discriminated based on their acoustic structure but the mechanisms of listener comprehension of these calls are currently unknown. In this study, we show that chimpanzees extract social information from these vocal signals that, combined with their more general social knowledge, enables them to understand the nature of out-of-sight social interactions. In playback experiments, we broadcast congruent and incongruent sequences of agonistic calls and monitored the response of bystanders. Congruent sequences were in accordance with existing social dominance relations; incongruent ones violated them. Subjects looked significantly longer at incongruent sequences, despite them being acoustically less salient (fewer call types from fewer individuals) than congruent ones. We concluded that chimpanzees categorised an apparently simple acoustic signal into victim and aggressor screams and used pragmatics to form inferences about third-party interactions they could not see

Sensitivity of inferred climate model skill to evaluation decisions: a case study using CMIP5 evapotranspiration

Confrontation of climate models with observationally-based reference datasets is widespread and integral to model development. These comparisons yield skill metrics quantifying the mismatch between simulated and reference values and also involve analyst choices, or meta-parameters, in structuring the analysis. Here, we systematically vary five such meta-parameters (reference dataset, spatial resolution, regridding approach, land mask, and time period) in evaluating evapotranspiration (ET) from eight CMIP5 models in a factorial design that yields 68 700 intercomparisons. The results show that while model–data comparisons can provide some feedback on overall model performance, model ranks are ambiguous and inferred model skill and rank are highly sensitive to the choice of meta-parameters for all models. This suggests that model skill and rank are best represented probabilistically rather than as scalar values. For this case study, the choice of reference dataset is found to have a dominant influence on inferred model skill, even larger than the choice of model itself. This is primarily due to large differences between reference datasets, indicating that further work in developing a community-accepted standard ET reference dataset is crucial in order to decrease ambiguity in model skill

Effect of floor type on the performance, physiological and behavioural responses of finishing beef steers

peer-reviewedBackground:The study objective was to investigate the effect of bare concrete slats (Control), two types of mats [(Easyfix mats (mat 1) and Irish Custom Extruder mats (mat 2)] fitted on top of concrete slats, and wood-chip to simulate deep bedding (wood-chip placed on top of a plastic membrane overlying the concrete slats) on performance, physiological and behavioral responses of finishing beef steers. One-hundred and forty-four finishing steers (503 kg; standard deviation 51.8 kg) were randomly assigned according to their breed (124 Continental cross and 20 Holstein–Friesian) and body weight to one of four treatments for 148 days. All steers were subjected to the same weighing, blood sampling (jugular venipuncture), dirt and hoof scoring pre study (day 0) and on days 23, 45, 65, 86, 107, 128 and 148 of the study. Cameras were fitted over each pen for 72 h recording over five periods and subsequent 10 min sampling scans were analysed. Results: Live weight gain and carcass characteristics were similar among treatments. The number of lesions on the hooves of the animals was greater (P < 0.05) on mats 1 and 2 and wood-chip treatments compared with the animals on the slats. Dirt scores were similar for the mat and slat treatments while the wood-chip treatment had greater dirt scores. Animals housed on either slats or wood-chip had similar lying times. The percent of animals lying was greater for animals housed on mat 1 and mat 2 compared with those housed on concrete slats and wood chips. Physiological variables showed no significant difference among treatments. Conclusions: In this exploratory study, the performance or welfare of steers was not adversely affected by slats, differing mat types or wood-chip as underfoot material