Geometry Characteristics Research Articles

Explicit3D: Graph network with spatial inference for single image 3D object detection

Indoor 3D object detection is an essential task in single image scene understanding, impacting spatial cognition fundamentally in visual reasoning. Existing works on 3D object detection from a single image either pursue this goal through independent predictions of each object or implicitly reason over all possible objects, failing to harness relational geometric information between objects. To address this problem, we propose a sparse graph-based pipeline named Explicit3D based on object geometry and semantics features. Taking the efficiency into consideration, we further define a relatedness score and design a novel dynamic pruning method via group sampling for sparse scene graph generation and updating. Furthermore, our Explicit3D introduces homogeneous matrices and defines new relative loss and corner loss to model the spatial difference between target pairs explicitly. Instead of using ground-truth labels as direct supervision, our relative and corner loss are derived from homogeneous transforms, which renders the model to learn the geometric consistency between objects. The experimental results on the SUN RGB-D dataset demonstrate that our Explicit3D achieves better performance balance than the-state-of-the-art.

The engineering elastic constants of bio-inspired sandwich cores with wavy cylinders

Beetle Elytron Plates (BEPs) represent a new class of biomimetic sandwich cores with excellent mechanical properties and inspired by the internal structure of the beetle elytra. The cores have a hexagonal centre-symmetric configuration with through-thickness cylinders in the unit cell. The present work describes the behaviour of the engineering elastic constants of an evolution of these BEP topologies that feature cylinders with waviness. These BEP core configurations are simulated using Finite Element models, with periodic boundary conditions for volumetric homogenization. The models are then used to perform a parametric analysis against the geometry characteristics of the new cellular configurations. This work further explores the potential design space of BEP cellular metamaterials by altering the geometry of the trabeculae and facilitates further investigation into the mechanical properties of these new BEP topologies with wave-shaped units. Although – and as expected - the specific nondimensional out-of-plane tensile modulus has values here close to unity, BEP topologies with out-of-plane wave-shaped cylinders show enhanced in-plane moduli values than classical straight cylinder configurations, except for the Young's modulus of MBEP cores with only one semi-wave. The same trend is observed for the nondimensional Young's modulus of EBEP configurations, whereas the situation is opposite for the MBEP topologies. The nondimensional out-of-plane shear modulus decreases for both EBEP and MBEP configurations compared to the straight cylinder cores.

Classification of architectural and MEP BIM objects for building performance evaluation

Numerous factors in current workflows reduce the completeness, accuracy, and reliability of the information exchanged between Building Information Modelling (BIM) authoring tools and building performance simulation and analysis software. Automated classification of BIM objects could ameliorate most of the interoperability problems. However, architectural, structural, mechanical, electrical, and plumbing (MEP), and other building objects have a wide range of shapes, features, and properties, which makes identifying them reliably and accurately with any single AI tool difficult. This study explored methods and techniques for automated classification of architectural and MEP BIM objects that can significantly reduce or eliminate the manual effort required to correct data exchange and interoperability issues in pre-processing building models for performance evaluation. A series of ensemble models using a stacking mechanism of four classification tools (semantic keyword search, rule-based inferencing, machine learning using object geometry features, and deep learning using visual shape features) were implemented and tested. A BIM object dataset with 3,410 instances belonging to 40 classes was compiled for training and testing the models. The dataset contains diverse architecture and MEP objects with high-quality selected instances and is available for public use. We found that an ensemble learning approach that exploits the various types of information available in instance models may exhibit superior object classification performance than any individual tool. The ensemble model applying all four tools achieved 91.0 % accuracy (F1 score 0.88), suggesting that automated classification using ensemble learning is an effective strategy.

Upper Cretaceous contourites from northwestern Poland in the vicinity of the Szamotuły salt diapir

This paper presents the results of seismostratigraphic interpretation of the Upper Cretaceous sedimentary succession preserved within two synclines flanking the Szamotuły diapir in northwestern Poland. This succession is characterized by a complex Santonian–Campanian internal geometry characteristic of contourites – that is, deposits formed by contour (bottom) currents. The aim of the present paper is to document these contourites using 2D seismic reflection profiles calibrated by the Obrzycko 1 well. The contourite drifts in the immediate vicinity of the Szamotuły structure exhibit elongated mounded shapes, with adjacent concave moats. At greater distances from the diapir, gradual aggradational patterns are observed. The formation of these Santonian– Campanian contourites was associated with growth of the Szamotuły diapir during regional compression and Polish Basin inversion. These contour currents and associated contourites formed an integral part of a regional axial depositional system developed within the flanks of the Mid-Polish Anticlinorium. Furthermore, this paper discusses the potential role of contourites as palaeomorphological indicators of palaeoslopes in varied geodynamics settings, such as inverting sedimentary basins, as opposed to the passive margins upon which they have been most commonly documented.

Mono3DVG: 3D Visual Grounding in Monocular Images

We introduce a novel task of 3D visual grounding in monocular RGB images using language descriptions with both appearance and geometry information. Specifically, we build a large-scale dataset, Mono3DRefer, which contains 3D object targets with their corresponding geometric text descriptions, generated by ChatGPT and refined manually. To foster this task, we propose Mono3DVG-TR, an end-to-end transformer-based network, which takes advantage of both the appearance and geometry information in text embeddings for multi-modal learning and 3D object localization. Depth predictor is designed to explicitly learn geometry features. The dual text-guided adapter is proposed to refine multiscale visual and geometry features of the referred object. Based on depth-text-visual stacking attention, the decoder fuses object-level geometric cues and visual appearance into a learnable query. Comprehensive benchmarks and some insightful analyses are provided for Mono3DVG. Extensive comparisons and ablation studies show that our method significantly outperforms all baselines. The dataset and code will be released.

SphereDiffusion: Spherical Geometry-Aware Distortion Resilient Diffusion Model

Controllable spherical panoramic image generation holds substantial applicative potential across a variety of domains. However, it remains a challenging task due to the inherent spherical distortion and geometry characteristics, resulting in low-quality content generation. In this paper, we introduce a novel framework of SphereDiffusion to address these unique challenges, for better generating high-quality and precisely controllable spherical panoramic images. For the spherical distortion characteristic, we embed the semantics of the distorted object with text encoding, then explicitly construct the relationship with text-object correspondence to better use the pre-trained knowledge of the planar images. Meanwhile, we employ a deformable technique to mitigate the semantic deviation in latent space caused by spherical distortion. For the spherical geometry characteristic, in virtue of spherical rotation invariance, we improve the data diversity and optimization objectives in the training process, enabling the model to better learn the spherical geometry characteristic. Furthermore, we enhance the denoising process of the diffusion model, enabling it to effectively use the learned geometric characteristic to ensure the boundary continuity of the generated images. With these specific techniques, experiments on Structured3D dataset show that SphereDiffusion significantly improves the quality of controllable spherical image generation and relatively reduces around 35% FID on average.

Engineering the Neural Collapse Geometry of Supervised-Contrastive Loss (Student Abstract)

Supervised-contrastive loss (SCL) is an alternative to cross-entropy (CE) for classification tasks that makes use of similarities in the embedding space to allow for richer representations. Previous works have used trainable prototypes to help improve test accuracy of SCL when training under imbalance. In this work, we propose the use of fixed prototypes to help engineering the feature geometry when training with SCL. We gain further insights by considering a limiting scenario where the number of prototypes far outnumber the original batch size. Through this, we establish a connection to CE loss with a fixed classifier and normalized embeddings. We validate our findings by conducting a series of experiments with deep neural networks on benchmark vision datasets.

Abstract 6716: DLL3 TriTCE Co-Stim: A next generation trispecific T cell engager with integrated CD28 costimulation for the treatment of DLL3-expressing cancers

Abstract Small cell lung cancer (SCLC) is an aggressive neuroendocrine cancer with a poor prognosis and high unmet medical need. Current standards of care, including chemotherapy, targeted therapy and immunotherapy, are associated with limited duration of response. Furthermore, SCLC is characterized by an immunosuppressive microenvironment and poor T cell infiltration which present additional treatment challenges. Delta-like ligand 3 (DLL3) is an inhibitory Notch ligand that is aberrantly overexpressed in SCLC and other neuroendocrine tumors, but minimally expressed in normal tissues, and is a new therapeutic target. Bispecific T cell engagers (TCE) targeting DLL3 have entered the clinic and demonstrated promising anti-tumor activity in SCLC patients; however, we believe there is room to improve on the rate and depth of response. To address treatment challenges and enhance the durability and sustainability of T cell activation, we have engineered a trispecific co-stimulatory T cell engager (TriTCE Co-stim) molecule that optimally engages CD3 and CD28 and redirects and enhances cytotoxic T cell responses to DLL3-expressing tumor cells while maintaining a desired safety profile. A panel of DLL3xCD3xCD28 trispecific antibodies (Abs) was engineered with a conventional anti-CD28 agonist paratope in a variety of Ab formats, geometries, paratope affinities and specificities. Geometry and design features of the tri-specific Abs were informed by prior TriTCE Co-stim molecule optimization1. AzymetricTM and EFECTTM platforms were used to facilitate heterodimeric multi-chain antibody assembly with silenced Fc gamma function. Lead TriTCE Co-stim Abs were selected for enhanced DLL3-dependent antitumor cytotoxic activity and increased T cell function including T cell activation, proliferation and cytokine production, compared to competitor bispecific TCEs. DLL3-independent activity and cytokine release were assessed with human PBMCs in solid and solution phase assays to evaluate the potential for cytokine release induction. In summary, we have identified multiple TriTCE Co-Stim Ab formats with improved DLL3-dependent cytotoxicity and T cell activation over competitor bispecific TCEs across multiple DLL3-expressing tumors. By screening various Ab formats, geometries and paratope affinities, we have selected lead DLL3 TriTCE Co-stim Abs displaying target-dependent activation of T cells with potent anti-tumor activity which may translate to improved and more durable antitumor responses in the treatment DLL3-expressing cancers. Citation Format: Peter Repenning, Desmond Lau, Diana C. Hernaez, Alec Robinson, Diego P. Escanda, Mariana Rocha, Begonia S. Moreno, John Zhang, Polly Shao, Nichole Escalante, Lisa Newhook, Purva Bhojane, Chayne L. Piscitelli, Paul A. Moore, Thomas Spreter Von Kreudenstein, Nina E. Weisser. DLL3 TriTCE Co-Stim: A next generation trispecific T cell engager with integrated CD28 costimulation for the treatment of DLL3-expressing cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6716.

Multi-scale hash encoding based neural geometry representation

Recently, neural implicit function-based representation has attracted more and more attention, and has been widely used to represent surfaces using differentiable neural networks. However, surface reconstruction from point clouds or multi-view images using existing neural geometry representations still suffer from slow computation and poor accuracy. To alleviate these issues, we propose a multi-scale hash encoding-based neural geometry representation which effectively and efficiently represents the surface as a signed distance field. Our novel neural network structure carefully combines low-frequency Fourier position encoding with multi-scale hash encoding. The initialization of the geometry network and geometry features of the rendering module are accordingly redesigned. Our experiments demonstrate that the proposed representation is at least 10 times faster for reconstructing point clouds with millions of points. It also significantly improves speed and accuracy of multi-view reconstruction. Our code and models are available at https://github.com/Dengzhi-USTC/Neural-Geometry-Reconstruction.

An adaptive anisotropic bilateral filtering method for mesh data in scale space

Three-dimensional mesh data of parts, such as blades and engine bodies, have been widely used in industrial fields. Due to the different kinds of noise during mesh acquisition and the machining deficiency of parts, the mesh quality tends to be insufficient for subsequent operations. Therefore, mesh denoising is a necessary and critical procedure to improve mesh quality. Existing methods commonly apply geometry features smoothing, which may also create unexpected results, such as volume shrinkage and blurring of sharp edges. This paper proposed an adaptive anisotropic bilateral filtering method for mesh data in scale space. Firstly, the mesh is decomposed into a smooth base with low frequency and a height vector field with high frequency based on scale space theory. The denoising of the vertex spatial field is transformed into the denoising of the height vector field, aiming to only consider high-frequency information. Secondly, the bilateral filter scheme with the anisotropic Gaussian kernel is proposed to denoise the height vector field, removing noise mixed with features. The parameters in the bilateral filter scheme are chosen adaptively by maximizing the designed probability density function. The mean square angular error of the proposed method is less than 0.15 rad, which is superior to the general-purpose algorithms, for instance, Laplacian filtering, bilateral mesh filtering and bilateral normal filtering algorithm.

Additive In-Time Manufacturing of Customised Orthoses

Additive manufacturing of plastic components in medical technology enables greater freedom of design when designing patient-specific products, in particular, in production of customised medical products, such as orthoses. In the present contribution, the advantages of a digital process chain are combined, from the 3D scan of the patient to CAD-supported modelling of the corrective form and the orthosis design until the path planning of a printable geometry. The main disadvantages of current additive printing techniques, such as the fused filament fabrication (FFF) process, are high printing times (>12 h) for larger components as well as the low degree of freedom in the 2.5D printing technique that prevent the subsequent application of geometry features to the product. The fast SEAMHex (Screw Extrusion Additive Manufacturing) printing technology with a hexapod kinematic printing bed provides a solution to the mentioned difficulties. Consequently, the high-performance printer has been prepared for the individual requirements of medical technology in terms of materials and geometries. An effective additive manufacturing process has been realised and tested in combination with a digital process chain for orthosis modelling.

Cracks-suppression perceptual geometry coding for dynamic point clouds

Dynamic point clouds can effectively describe 3D objects and natural scenes, providing users with an immersive visual experience, but their huge amount of data requires efficient compression tools. To this end, the video-based point cloud compression (V-PCC) standard was developed, however, it may lead to cracks in the compressed point cloud at low bitrates. To solve the problem, this paper proposes a cracks-suppression perceptual geometry coding method for dynamic point cloud. Firstly, considering that the edge regions of 2D geometry patches in V-PCC are prone to cracks, a prior knowledge based crack region detection and preprocessing scheme is designed to reduce cracks. Secondly, considering the unique perceptual geometry characteristics of point cloud, a projected geometry curvature based structural similarity (PGC-SSIM) is proposed to evaluate geometry quality of point clouds, which contains the geometric perception information of point cloud, and is more consistent with the human visual perception. Finally, based on the PGC-SSIM, an adaptive quantization parameter adjustment strategy is designed for rate-distortion optimization in geometry coding of dynamic point clouds. The experimental results show that the proposed method can effectively reduce cracks in the compressed point cloud without reducing the coding performance compared to the V-PCC anchor. Moreover, the presented PGC-SSIM can be used to improve the visual quality of the compressed point cloud.

Benzazole-Based ESIPT Fluorophores: Proton Transfer from the Chalcogen Perspective. A Combined Theoretical and Experimental Study.

In this study, we present a comprehensive photophysical investigation of ESIPT-reactive benzazole derivatives in both solution and the solid state. These derivatives incorporate different chalcogen atoms (O, S, and Se) into their structures, and we explore how these variations impact their electronic properties in both ground and excited states. Changes in the UV-Vis absorption and fluorescence emission spectra were analyzed and correlated with the chalcogen atom and solvent polarity. In general, the spectral band of the benzazole derivative containing selenium was redshifted in both the ground and excited states compared to that of its oxygen and sulfur counterparts. Furthermore, we observed that the solvent played a distinctive role in influencing the ESIPT process within these compounds, underscoring once again the significant influence of the chalcogen atom on their photophysical behavior. Theoretical calculations provided a deeper understanding of the molecular dynamics, electronic structures, and photophysical properties of these compounds. These calculations highlighted the effect of chalcogen atoms on the molecular geometry, absorption and emission characteristics, and intramolecular hydrogen bonding, revealing intricate details of the ESIPT mechanism. The integration of experimental and computational data offers a detailed view of the structural and electronic factors governing the photophysical behavior of benzazole derivatives.

First Monte Carlo beam model for ultra-high dose rate radiotherapy with a compact electron LINAC.

FLASH radiotherapy based on ultra-high dose rate (UHDR) is actively being studied by the radiotherapy community. Dedicated UHDR electron devices are currently a mainstay for FLASH studies. To present the first Monte Carlo (MC) electron beam model for the UHDR capable Mobetron (FLASH-IQ) as a dose calculation and treatment planning platform for preclinical research and FLASH-radiotherapy (RT) clinical trials. The initial beamline geometry of the Mobetron was provided by the manufacturer, with the first-principal implementation realized in the Geant4-based GAMOS MC toolkit. The geometry and electron source characteristics, such as energy spectrum and beamline parameters, were tuned to match the central-axis percentage depth dose (PDD) and lateral profiles for the pristine beam measured during machine commissioning. The thickness of the small foil in secondary scatter affected the beam model dominantly and was fine tuned to achieve the best agreement with commissioning data. Validation of the MC beam modeling was performed by comparing the calculated PDDs and profiles with EBT-XD radiochromic film measurements for various combinations of applicators and inserts. The nominal 9 MeV electron FLASH beams were best represented by a Gaussian energy spectrum with mean energy of 9.9 MeV and variance (σ) of 0.2 MeV. Good agreement between the MC beam model and commissioning data were demonstrated with maximal discrepancy<3% for PDDs and profiles. Hundred percent gamma pass rate was achieved for all PDDs and profiles with the criteria of 2mm/3%. With the criteria of 2mm/2%, maximum, minimum and mean gamma pass rates were (100.0%, 93.8%, 98.7%) for PDDs and (100.0%, 96.7%, 99.4%) for profiles, respectively. A validated MC beam model for the UHDR capable Mobetron is presented for the first time. The MC model can be utilized for direct dose calculation or to generate beam modeling input required for treatment planning systems for FLASH-RT planning. The beam model presented in this work should facilitate translational and clinical FLASH-RT for trials conducted on the Mobetron FLASH-IQ platform.

Quantitative Assessment of Agitator Performance in an Anchor-Stirred Tank: Investigating the Impact of Geometry, Eccentricity, and Rheological Characteristics

Quantitative Assessment of Agitator Performance in an Anchor-Stirred Tank: Investigating the Impact of Geometry, Eccentricity, and Rheological Characteristics

A reaction–diffusion coupled kinetic model for hydrothermal acid pretreatment of wheat straw

Hydrothermal acid pretreatment is a promising method to overcome the recalcitrance of lignocellulosic biomass for subsequent efficient enzymatic hydrolysis and biofuel production. However, little is known about heterogeneous catalysis reactions involving the proton diffusion in the pore of lignocellulosic biomass coupled with the hydrolysis of hemicellulose and cellulose catalyzed by proton. In this study, a reaction–diffusion coupled kinetic model for hydrothermal acid pretreatment of wheat straw was developed based on the visualization of particle geometry and pore features. The increase rate of average proton concentration in the pores is higher at a higher temperature or a smaller particle size. A mathematical model was successfully developed to describe the time courses of average proton concentration dictated by initial acid concentration and an unsteady diffusion fitting parameter. The kinetic parameters of each reaction step for hydrothermal acid pretreatment of wheat straw were obtained by fitting experimental data. The activation energies for the hydrolysis of slow-hydrolyzing cellulose, fast-hydrolyzing cellulose, and hemicellulose were determined of 937.8 kJ·mol−1, 90.7 kJ·mol−1, and 39.8 kJ·mol−1, respectively, indicating the temperature dependence of slow-hydrolyzing cellulose with a high crystalline index is strongest. The prediction accuracy of this model can be further improved at higher stirring speeds and initial acid concentrations. Additionally, the model demonstrates high prediction accuracy across three different particle sizes. Generally, this model can gratifyingly correlate data of formation and degradation of monosaccharides and furfural in the hydrothermal acid pretreatment of wheat straw with fair accuracy.

Geophysical characterization of groundwater aquifers in the Western Debrecen area, Hungary: insights from gravity, magnetotelluric, and electrical resistivity tomography

The recent study followed a multi-methodological approach integrating gravity, magnetotelluric (MT), and electrical resistivity tomography (ERT) to investigate the geometry and hydrological characteristics of the main hydrostratigraphical units in the Western Debrecen area, Eastern Hungary. The integration of these methods aims to delineate potential zones for groundwater development and guide effective extraction strategies. In the gravity investigation, the Bouguer anomaly map undergoes spectral analysis for the separation of shallow and deep features, offering a preliminary indication of basement rock depth. Subsequently, gravity data inversion is employed to map variations in basement rock topography, revealing a basin structure, with sediment thicknesses extending up to 2 km. On the other hand, the MT data are modeled using the 1D Occam inversion algorithm to validate the results of the gravity data analysis. This inversion, constrained with lithological logs is further utilized to delineate the main hydrostratigraphical units in the study area. Accordingly, four units are identified, including the Nagyalföld Aquifer, Algyő and Endrődi Aquitards, the Badenian Aquifer, and the Pre-Neogene Aquitard. Consequently, Dar Zarrouk parameters based transmissivity and the hydraulic conductivity of the aquifer units are measured. The Nagyalföld aquifer showed a hydraulic conductivity that ranged between 7.9 and 11.9 m/day, while the Badenian aquifer showed an average hydraulic conductivity of 13.1 m/day. The ERT data are employed to map the spatial distribution of the depth to the water table. The shallow water table is observed in regions characterized by an elevated thickness of sedimentary rocks, attributed to their high specific capacity. Integrating these hydrogeophysical methods provided a comprehensive understanding of the subsurface hydrology and enabled better-informed decision-making for groundwater development.

Discovery and learning of feature geometry in underwater acoustic sensing: Case studies and challenges in active sonar applications

Interpretable artificial intelligence (AI) and related machine learning (ML) techniques are gaining popularity for underwater acoustic sensing applications. However, interpretability of machine learnt features poses a fundamental challenge to successful application of these powerful data science techniques to underwater acoustics. Autonomous sonar target recognition is especially interesting from the data science perspective as beyond target-specific features, a sonar ping response typically includes significant interference from the environment, e.g., clutter, multipath scattering, etc. Such environmental interference, resulting from complex, dynamic, unpredictable, and often unknown factors, manifest as identifiable structures in acoustic color or alternate multi-dimensional feature representations that can lead to machine learning and classification errors. In this talk, we will discuss these challenges commonly encountered in underwater acoustics using case studies from field experiments in active sonar target recognition. Some results will also include physics-driven simulations in this domain to provide robust ground truths. In particular, we will posit how braided feature geometry and its representation, as well geometry of overlap between features can render a sonar target feature informative, explainable, and discoverable. [Work funded by the ONR Grant Nos. N000142112420 and N000142312503 and DoD Navy (NEEC) Grant No. N001742010016.]

The Definite Article am- [ʔam-] of Jazani Arabic: An Autosegmental Analysis

A decent number of studies have discussed phonological or morphological aspects of the definite article in Standard or Classical Arabic. However, only a few have described the definite article in Southern Arabic dialects. Arabic consonants are divided into two categories based on how they affect the definite article al- [ʔal-]. Fourteen consonants with the [+coronal] feature cause assimilation, whereas the remaining consonants with [-coronal] do not. This process raises the question of whether this is also the case with the definite article [ʔam-] of the Southern dialect Jazani Arabic. Thus, one goal of this study was to examine whether assimilation occurs in the first place with [ʔam-]. If so, does it assimilate to consonants with specific features? Does it fully or partially assimilate to other consonants? Does directionality play a role in assimilation? Enlightened by autosegmental phonology and feature geometry, this study presents a novel dataset and a non-linear phonological analysis of Jazani [ʔam-] via linking or delinking features. Results showed that [ʔam-] completely assimilated and caused geminates when followed by [m] and partially assimilated when followed by [b] or [w] but never after [-labial] sounds. Assimilation occurred progressively or regressively based on the sonority hierarchy of the consonants. In addition, assimilation only occurred across morphological boundaries and never within one morpheme.

Automated detection of fin whale calls recorded with distributed acoustic sensing

As the global biodiversity crisis intensifies, wildlife monitoring has become a necessity. From an oceanographic perspective, the tracking of baleen whales allows studies of their habitat use and can support call density estimates, which are indicative of ocean health. Distributed Acoustic Sensing (DAS) is a promising observational technique that measures strain rates along a spare optical fiber with a spatial resolution of tens of meters to distances of ∼100 km. Over 4-days in November 2021, a public domain DAS dataset was collected on the submarine cables of the Ocean Observatories Initiative Regional Cabled Array off the coast of central Oregon. The experiment recorded the acoustic signals from tens of thousands of fin whale calls as well as blue whale calls, ship signals and T-phases. We will describe preliminary efforts to develop automated methods to detect and localize fin whale calls using small representative examples of the DAS data. Our longer-term goal is to extend this approach to the full data set to understand the sensitivity of DAS to fin whale calls as function of cable geometry and seafloor characteristics and to study the distribution of calling fin whales in coastal waters off central Oregon. [Work supported by ONR.]