The ocean is warming and acidifying, increasing the risk of mass mortality events for temperature-sensitive shellfish such as oysters. This motivates the development of long-term monitoring systems. However, human labor is costly and long-duration underwater work is highly hazardous, thus favoring robotic solutions as a safer and more efficient option. To enable underwater robots to make real-time, environment-aware decisions without human intervention, we must equip them with an intelligent "brain." This highlights the need for persistent,wide-area, and low-cost benthic monitoring. To this end, we present DREAM, a Vision Language Model (VLM)-guided autonomy framework for long-term underwater exploration and habitat monitoring. The results show that our framework is highly efficient in finding and exploring target objects (e.g., oysters, shipwrecks) without prior location information. In the oyster-monitoring task, our framework takes 31.5% less time than the previous baseline with the same amount of oysters. Compared to the vanilla VLM, it uses 23% fewer steps while covering 8.88% more oysters. In shipwreck scenes, our framework successfully explores and maps the wreck without collisions, requiring 27.5% fewer steps than the vanilla model and achieving 100% coverage, while the vanilla model achieves 60.23% average coverage in our shipwreck environments.

Oysters are ecologically and commercially important species that require frequent monitoring to track population demographics (e.g. abundance, growth, mortality). Current methods of monitoring oyster reefs often require destructive sampling methods and extensive manual effort. Therefore, they are suboptimal for small-scale or sensitive environments. A recent alternative, the ODYSSEE model, was developed to use deep learning techniques to identify live oysters using video or images taken in the field of oyster reefs to assess abundance. The validity of this model in identifying live oysters on a reef was compared to expert and non-expert annotators. In addition, we identified potential sources of prediction error. Although the model can make inferences significantly faster than expert and non-expert annotators (39.6 s, $2.34 \pm 0.61$ h, $4.50 \pm 1.46$ h, respectively), the model overpredicted the number of live oysters, achieving lower accuracy (63\%) in identifying live oysters compared to experts (74\%) and non-experts (75\%) alike. Image quality was an important factor in determining the accuracy of the model and the annotators. Better quality images improved human accuracy and worsened model accuracy. Although ODYSSEE was not sufficiently accurate, we anticipate that future training on higher-quality images, utilizing additional live imagery, and incorporating additional annotation training classes will greatly improve the model's predictive power based on the results of this analysis. Future research should address methods that improve the detection of living vs. dead oysters.

Oysters are a vital keystone species in coastal ecosystems, providing significant economic, environmental, and cultural benefits. As the importance of oysters grows, so does the relevance of autonomous systems for their detection and monitoring. However, current monitoring strategies often rely on destructive methods. While manual identification of oysters from video footage is non-destructive, it is time-consuming, requires expert input, and is further complicated by the challenges of the underwater environment. To address these challenges, we propose a novel pipeline using stable diffusion to augment a collected real dataset with realistic synthetic data. This method enhances the dataset used to train a YOLOv10-based vision model. The model is then deployed and tested on an edge platform in underwater robotics, achieving a state-of-the-art 0.657 mAP@50 for oyster detection on the Aqua2 platform.

Abstract-Breast cancer (BC) significantly contributes to cancer-related mortality in women, underscoring the criticality of early detection for optimal patient outcomes. A mammography is a key tool for identifying and diagnosing breast abnormalities; however, accurately distinguishing malignant mass lesions remains challenging. To address this issue, we propose a novel deep learning approach for BC screening utilizing mammography images. Our proposed model comprises three distinct stages: data collection from established benchmark sources, image segmentation employing an Atrous Convolution-based Attentive and Adaptive Trans-Res-UNet (ACA-ATRUNet) architecture, and BC identification via an Atrous Convolution-based Attentive and Adaptive Multiscale DenseNet (ACA-AMDN) model. The hyperparameters within the ACA-ATRUNet and ACA-AMDN models are optimised using the Modified Mussel Length-based Eurasian Oystercatcher Optimization (MML-EOO) algorithm. Performance evaluation, leveraging multiple metrics, is conducted, and a comparative analysis against conventional methods is presented. Our experimental findings reveal that the proposed BC detection framework attains superior precision rates in early disease detection, demonstrating its potential to enhance mammography-based screening methodologies. Keywords: Breast Cancer; Mammograms; Atrous Convolution-based Attentive and Adaptive Trans-Res-UNet; Modified Mussel Length-based Eurasian Oystercatcher Optimization; Atrous Convolution based Attentive and Adaptive Multi-scale DenseNet 1. Introduction The most prevalent type of malignancy in women is BC. Next to cancer, it is the second leading reason of mortality in women [1]. One in every 36 female deaths is related to BC, or around 3% of all female deaths are caused by BC. In order to improve the survival rate of the patient, early BC identification is crucial [2]. Researchers are introducing increasingly accurate models for BC diagnosis into practice because of the tremendous fatality and high expense of cancer-related treatment [3, 4]. Radiotherapists use mammography as an efficient imaging method to detect and screen the presence of BC. Mammography is the primary clinical test for BC and is quite accurate in predicting BC. Breast lumps and calcifications are considered the early signs of BC, respectively.

Autonomous navigation in the underwater environment is challenging due to limited visibility, dynamic changes, and the lack of a cost-efficient accurate localization system. We introduce UIVNav, a novel end-to-end underwater navigation solution designed to drive robots over Objects of Interest (OOI) while avoiding obstacles, without relying on localization. UIVNav uses imitation learning and is inspired by the navigation strategies used by human divers who do not rely on localization. UIVNav consists of the following phases: (1) generating an intermediate representation (IR), and (2) training the navigation policy based on human-labeled IR. By training the navigation policy on IR instead of raw data, the second phase is domain-invariant -- the navigation policy does not need to be retrained if the domain or the OOI changes. We show this by deploying the same navigation policy for surveying two different OOIs, oyster and rock reefs, in two different domains, simulation, and a real pool. We compared our method with complete coverage and random walk methods which showed that our method is more efficient in gathering information for OOIs while also avoiding obstacles. The results show that UIVNav chooses to visit the areas with larger area sizes of oysters or rocks with no prior information about the environment or localization. Moreover, a robot using UIVNav compared to complete coverage method surveys on average 36% more oysters when traveling the same distances. We also demonstrate the feasibility of real-time deployment of UIVNavin pool experiments with BlueROV underwater robot for surveying a bed of oyster shells.

Robots are becoming an essential part of many operations including marine exploration or environmental monitoring. However, the underwater environment presents many challenges, including high pressure, limited visibility, and harsh conditions that can damage equipment. Real-world experimentation can be expensive and difficult to execute. Therefore, it is essential to simulate the performance of underwater robots in comparable environments to ensure their optimal functionality within practical real-world contexts.OysterSim generates photo-realistic images and segmentation masks of objects in marine environments, providing valuable training data for underwater computer vision applications. By integrating ChatGPT into underwater simulations, users can convey their thoughts effortlessly and intuitively create desired underwater environments without intricate coding. \invis{Moreover, researchers can realize substantial time and cost savings by evaluating their algorithms across diverse underwater conditions in the simulation.} The objective of ChatSim is to integrate Large Language Models (LLM) with a simulation environment~(OysterSim), enabling direct control of the simulated environment via natural language input. This advancement can greatly enhance the capabilities of underwater simulation, with far-reaching benefits for marine exploration and broader scientific research endeavors.

Imagine a situation: you are the scientist who wants to create a robot using unsupervised learning. At first, you want him to be smart, so you spend two weeks putting in all the data. Then, you ask him to write articles about global warming in physical copy. It understands everything now so what it needs to do is to write and write and write. You keep it working and go to eat your dinner.

One of the most valuable bivalve molluscs in Sweden is the flat oyster, Ostrea edulis. However, domestic production of oysters fails to meet the demand on the local market at the same time as the Swedish aquaculture industry has difficulties expanding due to limited availability of oyster spat. Traditionally in Sweden, spat for aquaculture have been collected with the help of sea-based collectors, but since the introduction and establishment of the Pacific oyster (Magallana gigas) in 2006, the possibility of collecting spat with this field-based technology has drastically decreased. Both flat oysters and Pacific oysters attach to the collectors. Since aquaculture of Pacific oysters is not allowed in Sweden due to that the species is classified as an invasive species, the collected oyster spat must be sorted by species and all Pacific oysters must be destroyed, which is neither practical nor economically feasible for the industry today.

Even as global businesses continue to embrace big data and data analytics, the challenge many face is: how to derive the most value from the big data. The latter refers to very large sets of data that cannot be handled with traditional methods. With artificial intelligence (AI) & its subset machine learning (ML) becoming mainstream, i.e. moving from the laboratory to the commercial market, one option companies today have to handle their voluminous data is machine learning. Analytics has moved on from the traditional methods to automated solutions for better business intelligence. In fact, it's gone beyond the scope of a standalone human analyst.

The fishing industry has been experimenting with artificial intelligence, to pass on the skills of experienced fishers amid a serious shortage of newcomers entering the industry. AI is being tapped to revolutionize the sector and boost profits by analyzing past fishing data, weather conditions and ocean currents, to forecast the locations of fertile fishing grounds or propose efficient methods for oyster culture farming. "AI shows this is the area for a good catch today," says Taizo Takasu, the executive director of Takasui, a fisheries company in Nobeoka, Miyazaki Prefecture, holding a tablet in his hand. Takasu, 52, is a chief fisherman of a fleet that catches sardines and mackerel with fishing nets offshore in southwestern Kyushu. Since last year, he has been taking part in an experiment that uses AI to select fishing areas.