Changing depositional environments in an Upper Ordovician stratigraphic sequence, Ashlock Formation, Madison County, Kentucky

We investigate the sedimentology, stratigraphy, and depositional environments of a 7-meter, Upper Ordovician limestone sequence cropping out in Richmond, Madison County, Kentucky. The stratigraphic section lies within the Ashlock Formation with good lateral exposure stretching along 200 meters of a highway roadcut. We took approximately 20 samples from the measured section, focusing on representative samples and lithologic transitions. We use standard laboratory procedures in slabbing rock samples and making thin sections. The Ashlock Formation here consists of alternating layers of limey mudstone and limestone (field units A through F). Megafossils - brachiopods, bryozoans, trilobites, gastropods, ostracodes, coralline algae, and bivalves - are abundant in limestone units. These observations are consistent with depositional environments representing marine, shallow-water deposits. The contact between units F and G contains beads of weathered pyrite perhaps representing an omission surface. Field units G through J contain more terrigeneous mud. The observed transition from limestones to lithologies with more terrigenous mud suggests any combination of: (1) increase in water depth; (2) climatic change resulting in more runoff; or (3) tectonic activity delivering more mud to the basin. The stratigraphic section is capped by fossiliferous limestone (unit K), which again represents shallow, marine conditions without terrigenous input

Changing Depositional Environments in an Upper Ordovician Stratigraphic Sequence, Ashlock Formation, Madison County, Kentucky

We investigate the sedimentology, stratigraphy, and depositional environments of a 7-meter, Upper Ordovician limestone sequence cropping out in Richmond, Kentucky. The stratigraphic section lies within the Ashlock Formation with good lateral exposure stretching along 200 meters of a highway roadcut. We took approximately 20 samples from the measured section, focusing on representative samples and lithologic transitions. We use standard laboratory procedures in slabbing rock samples and making thin sections. The Ashlock Formation at this locality consists of alternating layers of limey mudstone and limestone. Megafossils - brachiopods, bryozoans, trilobites, gastropods, ostracodes, coralline algae, and bivalves - are abundant in various limestone units. The observed transitions from limestones and limy muds to lithologies with more terrigenous mud suggests any combination of: (1) migration of depositional environment with a slight increase in water depth; (2) climatic change resulting in more runoff; or (3) tectonic activity delivering more mud to the basin. These shallow water environments change to glauconitic mudstone and laminated shales, which we interpret as deeper shelf deposits. The measured section is capped by shaley limestones and mudstones that signal a return to shallow subtidal environments

Changing depositional environments in an Upper Ordovician stratigraphic sequence, Ashlock Formation, Madison County, Kentucky

We investigate the sedimentology, stratigraphy, and depositional environments of a 7-meter, Upper Ordovician limestone sequence cropping out in Richmond, Kentucky. The stratigraphic section lies within the Ashlock Formation with good lateral exposure stretching along 200 meters of a highway roadcut. We took approximately 20 samples from the measured section, focusing on representative samples and lithologic transitions. We use standard laboratory procedures in slabbing rock samples and making thin sections. The Ashlock Formation at this locality consists of alternating layers of limey mudstone and limestone. Megafossils - brachiopods, bryozoans, trilobites, gastropods, ostracodes, coralline algae, and bivalves - are abundant in various limestone units. The observed transitions from limestones and limy muds to lithologies with more terrigenous mud suggests any combination of: (1) migration of depositional environment with a slight increase in water depth; (2) climatic change resulting in more runoff; or (3) tectonic activity delivering more mud to the basin. These shallow water environments change to glauconitic mudstone and laminated shales, which we interpret as deeper shelf deposits. The measured section is capped by shaley limestones and mudstones that signal a return to shallow subtidal environments

Sensitivity of Slot-Based Object-Centric Models to their Number of Slots

Self-supervised methods for learning object-centric representations have recently been applied successfully to various datasets. This progress is largely fueled by slot-based methods, whose ability to cluster visual scenes into meaningful objects holds great promise for compositional generalization and downstream learning. In these methods, the number of slots (clusters) $K$ is typically chosen to match the number of ground-truth objects in the data, even though this quantity is unknown in real-world settings. Indeed, the sensitivity of slot-based methods to $K$, and how this affects their learned correspondence to objects in the data has largely been ignored in the literature. In this work, we address this issue through a systematic study of slot-based methods. We propose using analogs to precision and recall based on the Adjusted Rand Index to accurately quantify model behavior over a large range of $K$. We find that, especially during training, incorrect choices of $K$ do not yield the desired object decomposition and, in fact, cause substantial oversegmentation or merging of separate objects (undersegmentation). We demonstrate that the choice of the objective function and incorporating instance-level annotations can moderately mitigate this behavior while still falling short of fully resolving this issue. Indeed, we show how this issue persists across multiple methods and datasets and stress its importance for future slot-based models

DyST: Towards Dynamic Neural Scene Representations on Real-World Videos

Visual understanding of the world goes beyond the semantics and flat structure of individual images. In this work, we aim to capture both the 3D structure and dynamics of real-world scenes from monocular real-world videos. Our Dynamic Scene Transformer (DyST) model leverages recent work in neural scene representation to learn a latent decomposition of monocular real-world videos into scene content, per-view scene dynamics, and camera pose. This separation is achieved through a novel co-training scheme on monocular videos and our new synthetic dataset DySO. DyST learns tangible latent representations for dynamic scenes that enable view generation with separate control over the camera and the content of the scene.Comment: Project website: https://dyst-paper.github.io

RUST: Latent Neural Scene Representations from Unposed Imagery

Inferring the structure of 3D scenes from 2D observations is a fundamental challenge in computer vision. Recently popularized approaches based on neural scene representations have achieved tremendous impact and have been applied across a variety of applications. One of the major remaining challenges in this space is training a single model which can provide latent representations which effectively generalize beyond a single scene. Scene Representation Transformer (SRT) has shown promise in this direction, but scaling it to a larger set of diverse scenes is challenging and necessitates accurately posed ground truth data. To address this problem, we propose RUST (Really Unposed Scene representation Transformer), a pose-free approach to novel view synthesis trained on RGB images alone. Our main insight is that one can train a Pose Encoder that peeks at the target image and learns a latent pose embedding which is used by the decoder for view synthesis. We perform an empirical investigation into the learned latent pose structure and show that it allows meaningful test-time camera transformations and accurate explicit pose readouts. Perhaps surprisingly, RUST achieves similar quality as methods which have access to perfect camera pose, thereby unlocking the potential for large-scale training of amortized neural scene representations.Comment: CVPR 2023 Highlight. Project website: https://rust-paper.github.io

A relation between algebraic and transform-based reconstruction technique in computed tomography

In this contribution a coherent relation between the algebraic and the transform-based reconstruction technique for computed tomography is introduced using the mathematical means of two-dimensional signal processing. There are two advantages arising from that approach. First, the algebraic reconstruction technique can now be used efficiently regarding memory usage without considerations concerning the handling of large sparse matrices. Second, the relation grants a more intuitive understanding as to the convergence characteristics of the iterative method. Besides the gain in theoretical insight these advantages offer new possibilities for application-specific fine tuning of reconstruction techniques

Complex sequential data analysis: A systematic literature review of existing algorithms

This paper provides a review of past approaches to the use of deep-learning frameworks for the analysis of discrete irregularpatterned complex sequential datasets. A typical example of such a dataset is financial data where specific events trigger sudden irregular changes in the sequence of the data. Traditional deep-learning methods perform poorly or even fail when trying to analyse these datasets. The results of a systematic literature review reveal the dominance of frameworks based on recurrent neural networks

Biomassa e estoques de nutrientes em vegetação de pousio sob diferentes manejos em sistema agroflorestal seqüencial de corte-e-trituração na Amazônia Oriental.

O manejo da vegetação de pousio é importante para manutenção da produtividade em sistemas agroflorestais seqüenciais. Durante o período de pousio, o sistema acumula nutrientes para as culturas agrícolas subseqüentes. A introdução de espécies leguminosas associadas à adubação fosfatada de baixa solubilidade pode promover o acúmulo de biomassa e os estoques de nutrientes influenciando positivamente na produtividade das culturas agrícolas. O estudo da biomassa e dos estoques de nutrientes nesses agroecossistemas pode fornecer subsídios para o seu manejo. Este artigo compara estimativas da biomassa e estoques de nutrientes de três vegetações de pousio submetidos a diferentes tratamentos: (1) pousio espontâneo; (2) pousio enriquecido com leguminosas arbóreas (Sclerolobium paniculatum Vogel e Inga edulis Mart.), e (3) pousio enriquecido com leguminosas arbóreas submetidas à adubação fosfatada de baixa solubilidade. O experimento foi conduzido por 23 meses, em um sistema agroflorestal seqüencial de corte-e-trituração no município de Marapanim, Amazônia Oriental. Os resultados mostraram que o sistema de pousio enriquecido com leguminosas arbóreas, submetidas ou não à adubação fosfatada de baixa solubilidade, acumula maiores massas secas e estoques de nutrientes que o sistema com pousio espontâneoEditores técnicos: Roberto Porro, Milton Kanashiro, Maria do Socorro Gonçalves Ferreira, Leila Sobral Sampaio e Gladys Ferreira de Sousa