Physical Representation-based Predicate Optimization for a Visual Analytics Database

Querying the content of images, video, and other non-textual data sources requires expensive content extraction methods. Modern extraction techniques are based on deep convolutional neural networks (CNNs) and can classify objects within images with astounding accuracy. Unfortunately, these methods are slow: processing a single image can take about 10 milliseconds on modern GPU-based hardware. As massive video libraries become ubiquitous, running a content-based query over millions of video frames is prohibitive. One promising approach to reduce the runtime cost of queries of visual content is to use a hierarchical model, such as a cascade, where simple cases are handled by an inexpensive classifier. Prior work has sought to design cascades that optimize the computational cost of inference by, for example, using smaller CNNs. However, we observe that there are critical factors besides the inference time that dramatically impact the overall query time. Notably, by treating the physical representation of the input image as part of our query optimization---that is, by including image transforms, such as resolution scaling or color-depth reduction, within the cascade---we can optimize data handling costs and enable drastically more efficient classifier cascades. In this paper, we propose Tahoma, which generates and evaluates many potential classifier cascades that jointly optimize the CNN architecture and input data representation. Our experiments on a subset of ImageNet show that Tahoma's input transformations speed up cascades by up to 35 times. We also find up to a 98x speedup over the ResNet50 classifier with no loss in accuracy, and a 280x speedup if some accuracy is sacrificed.Comment: Camera-ready version of the paper submitted to ICDE 2019, In Proceedings of the 35th IEEE International Conference on Data Engineering (ICDE 2019

Generating Synthetic Data for Neural Keyword-to-Question Models

Search typically relies on keyword queries, but these are often semantically ambiguous. We propose to overcome this by offering users natural language questions, based on their keyword queries, to disambiguate their intent. This keyword-to-question task may be addressed using neural machine translation techniques. Neural translation models, however, require massive amounts of training data (keyword-question pairs), which is unavailable for this task. The main idea of this paper is to generate large amounts of synthetic training data from a small seed set of hand-labeled keyword-question pairs. Since natural language questions are available in large quantities, we develop models to automatically generate the corresponding keyword queries. Further, we introduce various filtering mechanisms to ensure that synthetic training data is of high quality. We demonstrate the feasibility of our approach using both automatic and manual evaluation. This is an extended version of the article published with the same title in the Proceedings of ICTIR'18.Comment: Extended version of ICTIR'18 full paper, 11 page

Joint coarse-and-fine reasoning for deep optical flow

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.We propose a novel representation for dense pixel-wise estimation tasks using CNNs that boosts accuracy and reduces training time, by explicitly exploiting joint coarse-and-fine reasoning. The coarse reasoning is performed over a discrete classification space to obtain a general rough solution, while the fine details of the solution are obtained over a continuous regression space. In our approach both components are jointly estimated, which proved to be beneficial for improving estimation accuracy. Additionally, we propose a new network architecture, which combines coarse and fine components by treating the fine estimation as a refinement built on top of the coarse solution, and therefore adding details to the general prediction. We apply our approach to the challenging problem of optical flow estimation and empirically validate it against state-of-the-art CNN-based solutions trained from scratch and tested on large optical flow datasets.Peer ReviewedPostprint (author's final draft

Joint Coarse-And-Fine Reasoning for Deep Optical Flow

We propose a novel representation for dense pixel-wise estimation tasks using CNNs that boosts accuracy and reduces training time, by explicitly exploiting joint coarse-and-fine reasoning. The coarse reasoning is performed over a discrete classification space to obtain a general rough solution, while the fine details of the solution are obtained over a continuous regression space. In our approach both components are jointly estimated, which proved to be beneficial for improving estimation accuracy. Additionally, we propose a new network architecture, which combines coarse and fine components by treating the fine estimation as a refinement built on top of the coarse solution, and therefore adding details to the general prediction. We apply our approach to the challenging problem of optical flow estimation and empirically validate it against state-of-the-art CNN-based solutions trained from scratch and tested on large optical flow datasets.Comment: Accepted in IEEE ICIP 2017. IEEE Copyrights: Personal use of this material is permitted. Permission from IEEE must be obtained for all other use

Tunable high-field magnetization in strongly exchange-coupled freestanding Co/CoO core/shell coaxial nanowires

The exchange bias properties of Co/CoO coaxial core/shell nanowires have been investigated with cooling and applied fields perpendicular to the wire axis. This configuration leads to unexpected exchange-bias effects. Firstly, the magnetization value at high fields is found to depend on the field-cooling conditions. This effect arises from the competition between the magnetic anisotropy and the Zeeman energies for cooling fields perpendicular to the wire axis. This allows imprinting pre-defined magnetization states to the AFM, as corroborated by micromagnetic simulations. Secondly, the system exhibits a high-field magnetic irreversibility, leading to open hysteresis loops, attributed to the AFM easy-axis reorientation during the reversal (effect similar to athermal training). A distinct way to manipulate the high-field magnetization in exchange-biased systems, beyond the archetypical effects, is thus experimentally and theoretically demonstrated

Transplacental infection by bovine alphaherpesvirus type 1 induces protein expression of COX-2, iNOS and inflammatory cytokines in fetal lungs and placentas

Bovine alphaherpesvirus type 1 (BoAHV-1) is associated with respiratory and reproductive syndromes. Until present the immunologic mechanisms involved in BoAHV-1 abortion are partially known. We studied key elements of the innate immune response in the placentas and fetal lungs from cattle experimentally-inoculated with BoAHV-1. These tissues were analyzed by histopathology. Furthermore, virus identification was performed by qPCR and the expression of the inflammatory cytokines such as tumor necrosis factor-alpha, interleukin 1-alpha and inflammatory mediators like inducible nitric oxide synthase and cyclooxeganse-2 was evaluated by immunohistochemistry. The viral transplacental infection was confirmed by the detection of BoAHV-1 by qPCR in the placenta and fetal organs, which revealed mild inflammatory lesions. Inducible nitric oxide synthase immunolabelling was high in the lungs of infected fetuses and placentas, as well as for tumor necrosis factor-alpha in the pulmonary parenchyma and cyclooxeganse-2 in fetal annexes. However, the expression of interleukin 1-alpha was weak in these organs. To our knowledge, this is the first study that provides strong evidence of an early immune response to BoAHV-1 infection in the conceptus. Advances in the knowledge of the complex immunological interactions at the feto-maternal unit during BoAHV-1 infection are needed to clarify the pathogenesis of abortion.Fil: Burucúa, Mercedes María. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Innovación para la Producción Agropecuaria y el Desarrollo Sostenible - Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce. Instituto de Innovación para la Producción Agropecuaria y el Desarrollo Sostenible; ArgentinaFil: Risalde, María A.. Universidad de Córdoba; EspañaFil: Cheuquepán Valenzuela, Felipe Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Innovación para la Producción Agropecuaria y el Desarrollo Sostenible - Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce. Instituto de Innovación para la Producción Agropecuaria y el Desarrollo Sostenible; ArgentinaFil: Quintana, Silvina. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones en Sanidad Producción y Ambiente. - Comisión de Investigaciones Científicas de la Provincia de Buenos Aires. Instituto de Investigaciones en Sanidad Producción y Ambiente; ArgentinaFil: Perez, Sandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigación Veterinaria de Tandil. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigación Veterinaria de Tandil. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Centro de Investigación Veterinaria de Tandil; ArgentinaFil: Canton, German. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Innovación para la Producción Agropecuaria y el Desarrollo Sostenible - Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce. Instituto de Innovación para la Producción Agropecuaria y el Desarrollo Sostenible; ArgentinaFil: Moore, Dadin Prando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Innovación para la Producción Agropecuaria y el Desarrollo Sostenible - Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce. Instituto de Innovación para la Producción Agropecuaria y el Desarrollo Sostenible; ArgentinaFil: Odeón, Anselmo C.. Universidad Nacional de Mar del Plata. Facultad de Cs.agrarias. Departamento de Producción Animal; ArgentinaFil: Agulló Ros, Irene. Universidad de Córdoba; EspañaFil: Scioli, Maria Valeria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Innovación para la Producción Agropecuaria y el Desarrollo Sostenible - Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce. Instituto de Innovación para la Producción Agropecuaria y el Desarrollo Sostenible; ArgentinaFil: Barbeito, Claudio Gustavo. Universidad Nacional de la Plata. Facultad de Ciencias Veterinarias. Laboratorio de Histología y Embriología Descriptiva, Experimental y Comparada; . Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Morrell, Eleonora Lidia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Innovación para la Producción Agropecuaria y el Desarrollo Sostenible - Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce. Instituto de Innovación para la Producción Agropecuaria y el Desarrollo Sostenible; ArgentinaFil: Marin, Maia Solange. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Innovación para la Producción Agropecuaria y el Desarrollo Sostenible - Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce. Instituto de Innovación para la Producción Agropecuaria y el Desarrollo Sostenible; Argentin