Matrix factorizations for quantum complete intersections

We introduce twisted matrix factorizations for quantum complete intersections of codimension two. For such an algebra, we show that in a given dimension, almost all the indecomposable modules with bounded minimal projective resolutions correspond to such matrix factorizations.Comment: 13 page

Deep sea spy: a collaborative annotation tool

Since 2010, remote hydrothermal ecosystems are continuously being monitored using video cameras deployed on instrumented platforms. The acquisition of high-frequency video data from deep-sea observatories like EMSOAzores or Ocean Networks Canada provide information on species behaviour, feeding habits, growth, reproduction and organisms’ response to changes in environmental conditions. Video cameras acquire hourly data representing thousands of hours and Tera Bytes of footage but their manual processing is time-consuming and highly labour-intensive, and cannot be comprehensively undertaken by individual researchers. In order to help preliminary manual assessment of this huge imagery archive, a free online annotation tool was developed to gather contributions from a wider community. The Deep Sea Spy system offers a fun and engaging web interface to members of the public to help perform initial footage annotations. The platform now hosts 623 active annotators who contributed 179,663 annotations to 19,541 images. Preliminary analyses highlight a high variability among participants but show promising results to detect trends in species abundance variation over time. Ultimately, the information gathered via this approach can help improving the algorithms necessary to produce accurate automated detection in imagery using a machine learning approach

Monitoring ecological dynamics on complex hydrothermal structures: A novel photogrammetry approach reveals fine‐scale variability of vent assemblages

We set out to characterize the fine-scale processes acting on interannual dynamics of deep-sea vent fauna by using a novel approach involving a 5-yr time series of 3D photogrammetry models acquired at the Eiffel Tower sulfide edifice (Lucky Strike vent field, Mid-Atlantic Ridge). Consistently, with the overall stability of the vent edifice, total mussel cover did not undergo drastic changes, suggesting that they have been at a climax stage for at least 25 yr based on previous data. Successional patterns showed consistency over time, illustrating the dynamic equilibrium of the ecological system. In contrast, microbial mats significantly declined, possibly due to magmatic events. The remaining environmental variability consisted of decimeter-scale displacement of vent outflows, resulting from their opening or closure or from the progressive accretion of sulfide material. As a result, vent mussels showed submeter variability in the immediate vicinity of vent exits, possibly by repositioning in response to that fine-scale regime of change. As former studies were not able to quantify processes at submeter scales in complex settings, this pioneering work demonstrates the potential of 3D photogrammetry models for conducting long-term monitoring in the deep sea. We observed that the ability of mussels to displace may enable them to cope with changing local conditions in a stable system. However, the long-term stability of mussel assemblages questions their capacity to withstand large-scale disturbances and may imply a low resilience of these “climax” communities. This suggests that they may be particularly vulnerable to the negative effects of mining activities in hydrothermal ecosystems

Automated Image Analysis for the Detection of Benthic Crustaceans and Bacterial Mat Coverage Using the VENUS Undersea Cabled Network

The development and deployment of sensors for undersea cabled observatories is presently biased toward the measurement of habitat variables, while sensor technologies for biological community characterization through species identification and individual counting are less common. The VENUS cabled multisensory network (Vancouver Island, Canada) deploys seafloor camera systems at several sites. Our objective in this study was to implement new automated image analysis protocols for the recognition and counting of benthic decapods (i.e., the galatheid squat lobster, Munida quadrispina), as well as for the evaluation of changes in bacterial mat coverage (i.e., Beggiatoa spp.), using a camera deployed in Saanich Inlet (103 m depth). For the counting of Munida we remotely acquired 100 digital photos at hourly intervals from 2 to 6 December 2009. In the case of bacterial mat coverage estimation, images were taken from 2 to 8 December 2009 at the same time frequency. The automated image analysis protocols for both study cases were created in MatLab 7.1. Automation for Munida counting incorporated the combination of both filtering and background correction (Median- and Top-Hat Filters) with Euclidean Distances (ED) on Red-Green-Blue (RGB) channels. The Scale-Invariant Feature Transform (SIFT) features and Fourier Descriptors (FD) of tracked objects were then extracted. Animal classifications were carried out with the tools of morphometric multivariate statistic (i.e., Partial Least Square Discriminant Analysis; PLSDA) on Mean RGB (RGBv) value for each object and Fourier Descriptors (RGBv+FD) matrices plus SIFT and ED. The SIFT approach returned the better results. Higher percentages of images were correctly classified and lower misclassification errors (an animal is present but not detected) occurred. In contrast, RGBv+FD and ED resulted in a high incidence of records being generated for non-present animals. Bacterial mat coverage was estimated in terms of Percent Coverage and Fractal Dimension. A constant Region of Interest (ROI) was defined and background extraction by a Gaussian Blurring Filter was performed. Image subtraction within ROI was followed by the sum of the RGB channels matrices. Percent Coverage was calculated on the resulting image. Fractal Dimension was estimated using the box-counting method. The images were then resized to a dimension in pixels equal to a power of 2, allowing subdivision into sub-multiple quadrants. In comparisons of manual and automated Percent Coverage and Fractal Dimension estimates, the former showed an overestimation tendency for both parameters. The primary limitations on the automatic analysis of benthic images were habitat variations in sediment texture and water column turbidity. The application of filters for background corrections is a required preliminary step for the efficient recognition of animals and bacterial mat patches

Expanding dispersal studies at hydrothermal vents through species identification of cryptic larval forms

Author Posting. © The Author(s), 2010. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Marine Biology 157 (2010): 1049-1062, doi:10.1007/s00227-009-1386-8.The rapid identification of hydrothermal vent-endemic larvae to the species level is a key limitation to understanding the dynamic processes that control the abundance and distribution of fauna in such a patchy and ephemeral environment. Many larval forms collected near vents, even those in groups such as gastropods that often form a morphologically distinct larval shell, have not been identified to species. We present a staged approach that combines morphological and molecular identification to optimize the capability, efficiency, and economy of identifying vent gastropod larvae from the northern East Pacific Rise (NEPR). With this approach, 15 new larval forms can be identified to species. A total of 33 of the 41 gastropod species inhabiting the NEPR, and 26 of the 27 gastropod species known to occur specifically in the 9° 50’ N region, can be identified to species. Morphological identification efforts are improved by new protoconch descriptions for Gorgoleptis spiralis, Lepetodrilus pustulosus, Nodopelta subnoda, and Echinopelta fistulosa. Even with these new morphological descriptions, the majority of lepetodrilids and peltospirids require molecular identification. Restriction fragment length polymorphism digests are presented as an economical method for identification of five species of Lepetodrilus and six species of peltospirids. The remaining unidentifiable specimens can be assigned to species by comparison to an expanded database of 18S ribosomal DNA. The broad utility of the staged approach was exemplified by the revelation of species-level variation in daily planktonic samples and the identification and characterization of egg capsules belonging to a conid gastropod Gymnobela sp. A. The improved molecular and morphological capabilities nearly double the number of species amenable to field studies of dispersal and population connectivity.Funding was provided by as Woods Hole Oceanographic Institution Deep Ocean Exploration Institute grant to L.M and S. Beaulieu, National Science Foundation grants OCE-0424953, OCE-9712233, and OCE-9619605 to L.M, OCE-0327261 to T.S., and OCE-0002458 to K. Von Damm, and a National Defense Science and Engineering Graduate fellowship to D.A

Convolutional neural networks for hydrothermal vents substratum classification: An introspective study

The increasing availability of seabed images has created new opportunities and challenges for monitoring and better understanding the spatial distribution of fauna and substrata. To date, however, deep-sea substratum classification relies mostly on visual interpretation, which is costly, time-consuming, and prone to human bias or error. Motivated by the success of convolutional neural networks in learning semantically rich representations directly from images, this work investigates the application of state-of-the-art network architectures, originally employed in the classification of non-seabed images, for the task of hydrothermal vent substrata image classification. In assessing their potential, we conduct a study on the generalization, complementarity and human interpretability aspects of those architectures. Specifically, we independently trained deep learning models with the selected architectures using images obtained from three distinct sites within the Lucky-Strike vent field and assessed the models' performances on-site as well as off-site. To investigate complementarity, we evaluated a classification decision committee (CDC) built as an ensemble of networks in which individual predictions were fused through a majority voting scheme. The experimental results demonstrated the suitability of the deep learning models for deep-sea substratum classification, attaining accuracies reaching up to 80% in terms of F1-score. Finally, by further investigating the classification uncertainty computed from the set of individual predictions of the CDC, we describe a semiautomatic framework for human annotation, which prescribes visual inspection of only the images with high uncertainty. Overall, the results demonstrated that high accuracy values of over 90% F1-score can be obtained with the framework, with a small amount of human intervention

The EMSO Generic Instrument Module (EGIM): standardized and interoperable instrumentation for ocean observation

The oceans are a fundamental source for climate balance, sustainability of resources and life on Earth, therefore society has a strong and pressing interest in maintaining and, where possible, restoring the health of the marine ecosystems. Effective, integrated ocean observation is key to suggesting actions to reduce anthropogenic impact from coastal to deep-sea environments and address the main challenges of the 21st century, which are summarized in the UN Sustainable Development Goals and Blue Growth strategies. The European Multidisciplinary Seafloor and water column Observatory (EMSO), is a European Research Infrastructure Consortium (ERIC), with the aim of providing long-term observations via fixed-point ocean observatories in key environmental locations across European seas from the Arctic to the Black Sea. These may be supported by ship-based observations and autonomous systems such as gliders. In this paper, we present the EMSO Generic Instrument Module (EGIM), a deployment ready multi-sensor instrumentation module, designed to measure physical, biogeochemical, biological and ecosystem variables consistently, in a range of marine environments, over long periods of time. Here, we describe the system, features, configuration, operation and data management. We demonstrate, through a series of coastal and oceanic pilot experiments that the EGIM is a valuable standard ocean observation module, which can significantly improve the capacity of existing ocean observatories and provides the basis for new observatories. The diverse examples of use included the monitoring of fish activity response upon oceanographic variability, hydrothermal vent fluids and particle dispersion, passive acoustic monitoring of marine mammals and time series of environmental variation in the water column. With the EGIM available to all the EMSO Regional Facilities, EMSO will be reaching a milestone in standardization and interoperability, marking a key capability advancement in addressing issues of sustainability in resource and habitat management of the oceans.This work was funded by the project EMSODEV (Grant agreement No 676555) supported by DG Research and Innovation of the European Commission under the Research Infrastructures Programme of the H2020. EMSO-link EC project (Grant agreement No 731036) provided additional funding. Other projects which supported the work include Plan Estatal de Investigación Científica y Técnica y de Innovación 2017–2020, project BITER-LANDER PID2020- 114732RB-C32, iFADO (Innovation in the Framework of the Atlantic Deep Ocean, 2017–2021) EAPA_165/2016. The Spanish Government contributed through the “Severo Ochoa Centre Excellence” accreditation to ICM-CSIC (CEX2019-000928-S) and the Research Unit Tecnoterra (ICM-CSIC/UPC). UK colleagues were supported by Climate Linked Atlantic Sector Science (CLASS) project supported by NERC National Capability funding (NE/R015953/1).Peer ReviewedArticle signat per 33 autors/es: Nadine Lantéri; Henry A. Ruh; Andrew Gates; Enoc Martínez; Joaquin del Rio Fernandez; Jacopo Aguzzi; Mathilde Cannat; Eric Delory; Davide Embriaco; Robert Huber; Marjolaine Matabos;George Petihakis; Kieran Reilly; Jean-François Rolin; Mike van der Schaar; Michel André; Jérôme Blandin; Andrés Cianca; Marco Francescangeli; Oscar Garcia; Susan Hartman; Jean-Romain Lagadec; Julien Legrand; Paris Pagonis; Jaume Piera; Xabier Remirez; Daniel M. Toma; Giuditta Marinaro; Bertrand Moreau; Raul Santana; Hannah Wright; Juan José Dañobeitia; Paolo FavaliPostprint (published version

The New Seafloor Observatory (OBSEA) for Remote and Long-Term Coastal Ecosystem Monitoring

A suitable sampling technology to identify species and to estimate population dynamics based on individual counts at different temporal levels in relation to habitat variations is increasingly important for fishery management and biodiversity studies. In the past two decades, as interest in exploring the oceans for valuable resources and in protecting these resources from overexploitation have grown, the number of cabled (permanent) submarine multiparametric platforms with video stations has increased. Prior to the development of seafloor observatories, the majority of autonomous stations were battery powered and stored data locally. The recently installed low-cost, multiparametric, expandable, cabled coastal Seafloor Observatory (OBSEA), located 4 km off of Vilanova i la Gertrú, Barcelona, at a depth of 20 m, is directly connected to a ground station by a telecommunication cable; thus, it is not affected by the limitations associated with previous observation technologies. OBSEA is part of the European Multidisciplinary Seafloor Observatory (EMSO) infrastructure, and its activities are included among the Network of Excellence of the European Seas Observatory NETwork (ESONET). OBSEA enables remote, long-term, and continuous surveys of the local ecosystem by acquiring synchronous multiparametric habitat data and bio-data with the following sensors: Conductivity-Temperature-Depth (CTD) sensors for salinity, temperature, and pressure; Acoustic Doppler Current Profilers (ADCP) for current speed and direction, including a turbidity meter and a fluorometer (for the determination of chlorophyll concentration); a hydrophone; a seismometer; and finally, a video camera for automated image analysis in relation to species classification and tracking. Images can be monitored in real time, and all data can be stored for future studies. In this article, the various components of OBSEA are described, including its hardware (the sensors and the network of marine and land nodes), software (data acquisition, transmission, processing, and storage), and multiparametric measurement (habitat and bio-data time series) capabilities. A one-month multiparametric survey of habitat parameters was conducted during 2009 and 2010 to demonstrate these functions. An automated video image analysis protocol was also developed for fish counting in the water column, a method that can be used with cabled coastal observatories working with still images. Finally, bio-data time series were coupled with data from other oceanographic sensors to demonstrate the utility of OBSEA in studies of ecosystem dynamics

Deep-Sea Fauna Segmentation: A Comparative Analysis of Convolutional and Vision Transformer Architectures at Lucky Strike Vent Field

Due to recent technological developments, the acquisition and availability of deep-sea imagery has increased exponentially in the last years, leading to an increasing backlog in image annotation and processing, attributable to limited specialized human resources. In this work, we investigate the performance of well-established convolutional neural networks and Vision Transformer (ViT) based architectures, namely, DeepLabv3+ and UNETR, for the segmentation of fauna in deep-sea images. The dataset consists of images captured at the Lucky Strike Vent field, located on the mid-Atlantic ridge, of three edifices named Montsegur, White Castle, and Eiffel Tower. Our experimental investigation reveals that the Vision Transformer consistently outperforms the fully convolutional deep learning architecture, by approximately 14% in terms of F1-Score, demonstrating the effectiveness of ViTs in capturing intricate patterns and long-range dependencies present in deep-sea imagery. Our findings highlight the potential of ViTs as a promising approach for accurate semantic segmentation in challenging environmental contexts, paving the way for improved understanding and analysis of deep-sea ecosystems

sFDvent: A global trait database for deep‐sea hydrothermal‐vent fauna

Motivation: Traits are increasingly being used to quantify global biodiversity patterns, with trait databases growing in size and number, across diverse taxa. Despite grow‐ ing interest in a trait‐based approach to the biodiversity of the deep sea, where the impacts of human activities (including seabed mining) accelerate, there is no single re‐ pository for species traits for deep‐sea chemosynthesis‐based ecosystems, including hydrothermal vents. Using an international, collaborative approach, we have compiled the first global‐scale trait database for deep‐sea hydrothermal‐vent fauna – sFD‐ vent (sDiv‐funded trait database for the Functional Diversity of vents). We formed a funded working group to select traits appropriate to: (a) capture the performance of vent species and their influence on ecosystem processes, and (b) compare trait‐based diversity in different ecosystems. Forty contributors, representing expertise across most known hydrothermal‐vent systems and taxa, scored species traits using online collaborative tools and shared workspaces. Here, we characterise the sFDvent da‐ tabase, describe our approach, and evaluate its scope. Finally, we compare the sFD‐ vent database to similar databases from shallow‐marine and terrestrial ecosystems to highlight how the sFDvent database can inform cross‐ecosystem comparisons. We also make the sFDvent database publicly available online by assigning a persistent, unique DOI. Main types of variable contained: Six hundred and forty‐six vent species names, associated location information (33 regions), and scores for 13 traits (in categories: community structure, generalist/specialist, geographic distribution, habitat use, life history, mobility, species associations, symbiont, and trophic structure). Contributor IDs, certainty scores, and references are also provided. Spatial location and grain: Global coverage (grain size: ocean basin), spanning eight ocean basins, including vents on 12 mid‐ocean ridges and 6 back‐arc spreading centres. Time period and grain: sFDvent includes information on deep‐sea vent species, and associated taxonomic updates, since they were first discovered in 1977. Time is not recorded. The database will be updated every 5 years. Major taxa and level of measurement: Deep‐sea hydrothermal‐vent fauna with spe‐ cies‐level identification present or in progress. Software format: .csv and MS Excel (.xlsx).This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited