Inside cowboy Charles Jesse "Buffalo" Jones's get-rich-quick scheme to restore the plains 100 years ago. By 1888, Charles Jesse "Buffalo" Jones had succeeded in crossbreeding a buffalo with cow, a hybrid he claimed would be as tasty as beef and as hardy as buffalo. Breakthroughs, discoveries, and DIY tips sent every weekday. The "cattalo" was a homely creature--stocky and shaggy, with a slight buffalo's hump and a cow's docile face. Charles "Buffalo" Jones invented the cow-buffalo hybrid in 1888.

Street scene datasets, collected from Street View or dashboard cameras, offer a promising means of detecting urban objects and incidents like street flooding. However, a major challenge in using these datasets is their lack of reliable labels: there are myriad types of incidents, many types occur rarely, and ground-truth measures of where incidents occur are lacking. Here, we propose BayFlood, a two-stage approach which circumvents this difficulty. First, we perform zero-shot classification of where incidents occur using a pretrained vision-language model (VLM). Second, we fit a spatial Bayesian model on the VLM classifications. The zero-shot approach avoids the need to annotate large training sets, and the Bayesian model provides frequent desiderata in urban settings - principled measures of uncertainty, smoothing across locations, and incorporation of external data like stormwater accumulation zones. We comprehensively validate this two-stage approach, showing that VLMs provide strong zero-shot signal for floods across multiple cities and time periods, the Bayesian model improves out-of-sample prediction relative to baseline methods, and our inferred flood risk correlates with known external predictors of risk. Having validated our approach, we show it can be used to improve urban flood detection: our analysis reveals 113,738 people who are at high risk of flooding overlooked by current methods, identifies demographic biases in existing methods, and suggests locations for new flood sensors. More broadly, our results showcase how Bayesian modeling of zero-shot LM annotations represents a promising paradigm because it avoids the need to collect large labeled datasets and leverages the power of foundation models while providing the expressiveness and uncertainty quantification of Bayesian models.

Near-real time estimation of damage to buildings and infrastructure, referred to as damage nowcasting in this study, is crucial for empowering emergency responders to make informed decisions regarding evacuation orders and infrastructure repair priorities during disaster response and recovery. Here, we introduce FloodDamageCast, a machine learning (ML) framework tailored for property flood damage nowcasting. The framework leverages heterogeneous data to predict residential flood damage at a resolution of 500 meters by 500 meters within Harris County, Texas, during the 2017 Hurricane Harvey. To deal with data imbalance, FloodDamageCast incorporates a generative adversarial networks-based data augmentation coupled with an efficient machine learning model. The results demonstrate the framework's ability to identify high-damage spatial areas that would be overlooked by baseline models. Insights gleaned from flood damage nowcasting can assist emergency responders to more efficiently identify repair needs, allocate resources, and streamline on-the-ground inspections, thereby saving both time and effort. Keywords: Flood damage nowcasting Data augmentation Generative adversarial network Light gradient-boosting machine Imbalance learning 1 Introduction Flood hazards wreak havoc on urban areas, resulting in both physical destruction and loss of life in densely populated regions. In the United States alone, annual insurance claims have hovered around $1 billion per year over the past four decades [1]. This financial burden is expected to persist and potentially worsen due to the escalating frequency and intensity of flood events resulting from climate change [2, 3]. Rapid damage assessment of flooded areas is essential for swift response and recovery of affected communities. Emergency responders and public officials rely primarily on visual inspection to evaluate flood damage, incurring significantly delaying the recovery process. Expediting the flood damage assessment process is instrumental to accelerating post-disaster recovery efforts and bolstering community resilience against flood hazards, Currently, the main approach for estimating flood damage is based on specifying inundation depths then utilizing historical flood depth damage curves [4, 5]. The applicability of this approach for flood damage nowcasting, however, would be limited due to significant computation effort needed to model inundation depths using hydrological models based on the principles of hydrodynamics [6, 7, 8, 9].

Air-bearing platforms for simulating the rotational dynamics of satellites require highly precise ground truth systems. Unfortunately, commercial motion capture systems used for this scope are complex and expensive. This paper shows a novel and versatile method to compute the attitude of rotational air-bearing platforms using a monocular camera and sets of fiducial markers. The work proposes a geometry-based iterative algorithm that is significantly more accurate than other literature methods that involve the solution of the Perspective-n-Point problem. Additionally, auto-calibration procedures to perform a preliminary estimation of the system parameters are shown. The developed methodology is deployed onto a Raspberry Pi 4 micro-computer and tested with a set of LED markers. Data obtained with this setup are compared against computer simulations of the same system to understand and validate the attitude estimation performances. Simulation results show expected 1-sigma accuracies in the order of $\sim$ 12 arcsec and $\sim$ 37 arcsec for about- and cross-boresight rotations of the platform, and average latency times of 6 ms.

Artemis was the twin sister of Apollo and goddess of the moon in Greek mythology. NASA has chosen her to personify its path back to the moon, which will see astronauts return to the lunar surface by 2025 - including the first woman and the next man. Artemis 1, formerly Exploration Mission-1, is the first in a series of increasingly complex missions that will enable human exploration to the moon and Mars. Artemis 1 will be the first integrated flight test of NASA's deep space exploration system: the Orion spacecraft, Space Launch System (SLS) rocket and the ground systems at Kennedy Space Center in Cape Canaveral, Florida. Artemis 1 will be an uncrewed flight that will provide a foundation for human deep space exploration, and demonstrate our commitment and capability to extend human existence to the moon and beyond.

Visual and linguistic concepts naturally organize themselves in a hierarchy, where a textual concept "dog" entails all images that contain dogs. Despite being intuitive, current large-scale vision and language models such as CLIP do not explicitly capture such hierarchy. We propose MERU, a contrastive model that yields hyperbolic representations of images and text. Hyperbolic spaces have suitable geometric properties to embed tree-like data, so MERU can better capture the underlying hierarchy in image-text datasets. Our results show that MERU learns a highly interpretable and structured representation space while being competitive with CLIP's performance on standard multi-modal tasks like image classification and image-text retrieval.

A plethora of approaches have been proposed for joint entity-relation (ER) extraction. Most of these methods largely depend on a large amount of manually annotated training data. However, manual data annotation is time consuming, labor intensive, and error prone. Human beings learn using both data (through induction) and knowledge (through deduction). Answer Set Programming (ASP) has been a widely utilized approach for knowledge representation and reasoning that is elaboration tolerant and adept at reasoning with incomplete information. This paper proposes a new approach, ASP-enhanced Entity-Relation extraction (ASPER), to jointly recognize entities and relations by learning from both data and domain knowledge. In particular, ASPER takes advantage of the factual knowledge (represented as facts in ASP) and derived knowledge (represented as rules in ASP) in the learning process of neural network models. We have conducted experiments on two real datasets and compare our method with three baselines. The results show that our ASPER model consistently outperforms the baselines.

Scale-space filtering provides a powerful framework for the structural feature extraction, and classification and recognition of waveforms. It is based on convolving a signal with a one-parametric family of kernels and the convolutions can be used to construct certain trees to correspond to the original signal ([5,17,23,26,28]). In this article we solve the following important problems: (I) We construct a maximal set of kernels that allows us to construct trees and have the property that the signals with the same shape result in equivalent trees. It turns out that this maximal set of kernels is a set of pth frac tional derivatives of a Gaussian.

Transformer networks have revolutionized NLP representation learning since they were introduced. Though a great effort has been made to explain the representation in transformers, it is widely recognized that our understanding is not sufficient. One important reason is that there lack enough visualization tools for detailed analysis. In this paper, we propose to use dictionary learning to open up these "black boxes" as linear superpositions of transformer factors. Through visualization, we demonstrate the hierarchical semantic structures captured by the transformer factors, e.g., word-level polysemy disambiguation, sentence-level pattern formation, and long-range dependency. While some of these patterns confirm the conventional prior linguistic knowledge, the rest are relatively unexpected, which may provide new insights. We hope this visualization tool can bring further knowledge and a better understanding of how transformer networks work. The code is available at https://github.com/zeyuyun1/TransformerVis

The aggravating effects of climate change and the growing population in hurricane-prone areas escalate the challenges in large-scale hurricane evacuations. While hurricane preparedness and response strategies vastly rely on the accuracy and timeliness of the predicted households' evacuation decisions, current studies featuring psychological-driven linear models leave some significant limitations in practice. Hence, the present study proposes a new methodology for predicting households' evacuation decisions constructed by easily accessible demographic and resource-related predictors compared to current models with a high reliance on psychological factors. Meanwhile, an enhanced logistic regression (ELR) model that could automatically account for nonlinearities (i.e., univariate and bivariate threshold effects) by an interpretable machine learning approach is developed to secure the accuracy of the results. Specifically, low-depth decision trees are selected for nonlinearity detection to identify the critical thresholds, build a transparent model structure, and solidify the robustness. Then, an empirical dataset collected after Hurricanes Katrina and Rita is hired to examine the practicability of the new methodology. The results indicate that the enhanced logistic regression (ELR) model has the most convincing performance in explaining the variation of the households' evacuation decision in model fit and prediction capability compared to previous linear models. It suggests that the proposed methodology could provide a new tool and framework for the emergency management authorities to improve the estimation of evacuation traffic demands in a timely and accurate manner.