Large spatial datasets with non-Gaussian responses are increasingly common in environmental monitoring, ecology, and remote sensing, yet scalable Bayesian inference for such data remains challenging. Markov chain Monte Carlo (MCMC) methods are often prohibitive for large datasets, and existing variational Bayes methods rely on conjugacy or strong approximations that limit their applicability and can underestimate posterior variances. A scalable variational framework that incorporates semi-implicit variational inference (SIVI) with basis representations of spatial generalized linear mixed models (SGLMMs), which may not have conjugacy, is proposed. The proposed framework accommodates gamma, negative binomial, Poisson, Bernoulli, and Gaussian responses on continuous spatial domains. Across 20 simulation scenarios with 50,000 locations, SIVI achieves predictive accuracy and posterior distributions comparable to Metropolis–Hastings and Hamiltonian Monte Carlo while providing notable computational speedups. Applications to MODIS land surface temperature and Blue Jay abundance further demonstrate the utility of the approach for large non-Gaussian spatial datasets.
Large language models (LLMs) have significantly advanced the field of natural language processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of applications. However, directly applying LLMs to solve sophisticated problems in specific domains meets many hurdles, caused by the heterogeneity of domain data, the sophistication of domain knowledge, the uniqueness of domain objectives, and the diversity of the constraints (e.g., various social norms, cultural conformity, religious beliefs, and ethical standards in the domain applications). Domain specification techniques are key to make large language models disruptive in many applications. Specifically, to solve these hurdles, there has been a notable increase in research and practices conducted in recent years on the domain specialization of LLMs. This emerging field of study, with its substantial potential for impact, necessitates a comprehensive and systematic review to better summarize and guide ongoing work in this area. In this article, we present a comprehensive survey on domain specification techniques for large language models, an emerging direction critical for large language model applications. First, we propose a systematic taxonomy that categorizes the LLM domain-specialization techniques based on the accessibility to LLMs and summarizes the framework for all the subcategories as well as their relations and differences to each other. Second, we present an extensive taxonomy of critical application domains that can benefit dramatically from specialized LLMs, discussing their practical significance and open challenges. Last, we offer our insights into the current research status and future trends in this area.
To holistically understand the biology of animals, we must unravel the complexities and specificities of host-microbe interactions across animal taxa. Birds represent enigmatic and scientifically compelling hosts in which to understand these interactions. Here, we present a brief summary of a series of conversations among avian microbiome researchers regarding methodological challenges facing the avian microbiome field, where most research to date has focused on bacterial communities of the gut. Collectively, we acknowledged a commonly shared but underreported issue facing the avian microbiome field: that of difficulty in obtaining high-quality and high-yield microbial DNA from avian fecal samples. We discuss some of the potential reasons underlying low DNA yields, such as inhibitory compounds and rapid DNA degradation, and provide recommendations for how researchers in the avian microbiome field might cope with these methodological challenges. Collective and dedicated efforts to address these challenges will be required for a robust understanding of host-microbe interactions in avian systems.
The rapid development of the Internet of Things (IoT) has fueled the widespread adoption of Unmanned Aerial Vehicles (UAVs) or drones across various fields, including their use in applications such as surveillance and monitoring. UAVs flight capabilities allow it to effortlessly access previously inaccessible locations, providing real-time, high-resolution data-images and videos-of any desired area or target. Furthermore, the growth of Artificial Intelligence (AI), and edge computing technologies has empowered UAVs with high computational capabilities, making them suitable for diverse applications such as agriculture, transportation and border security. These technology advancements also equip UAVs with powerful on-board processing for sophisticated decision-making that enhances UAV activeness and intelligence. This survey explores the promising areas of UAVs for intelligent active surveillance and monitoring across diverse applications. First, the various levels of UAV activeness within applications are discussed; second, prior research is examined to identify the key technologies and architectures that power intelligent UAV systems; and third, several UAV applications in surveillance and monitoring, ranging from basic tasks to highly intelligent operations are explored. Finally, the survey concludes by discussing emerging research challenges and outlines a guiding road map for future research of highly interdisciplinary and emerging areas in UAV-based systems for surveillance and monitoring.
Baleen whales (mysticetes) underwent a profound transition from raptorial to filter feeding early in their history, allowing the exploitation of new food sources and environments. Their key structural innovation, baleen, is rarely fossilized, complicating reconstruction of the transition. Muscles used in food capture, locomotion, and respiration are anchored on developmentally discrete subunits of the sternum in living mysticetes, offering a novel approach for predicting muscle function and feeding style in extinct taxa. Elliptic Fourier Analysis was used to test the relationship between sternal shape and feeding style in extant balaenopteroid mysticetes. Results indicate that sternal shape changes during the ontogenetic transition from juvenile nursing to adult filter feeding and differs among adults of species with different styles of filter feeding, providing support for the use of sternal structure to predict the feeding habits of extinct taxa. The dense, block-shaped presterna and multiple sternebrae of early mysticetes suggest that they used static buoyancy control in nearshore environments and lacked the thoracic compliance necessary for deep diving. Variably enlarged sternohyoid muscle fields indicate that they all employed suction during prey capture. In sharp contrast, eomysticetes have osteoporotic, planar sterna without sternebrae and minimal sternohyoid fields, signaling deeper water habitats, diving facilitated by thoracic compliance, and little or no suction. Taxa crownward of eomysticetes display novel elaborations of the sternohyoid and sternocephalic muscle fields that characterize continuous and intermittent ram-feeding in living balaenopteroids.
