Interoperable Summary Description Model Using Dublin Core

This paper proposes an interoperable metadata model generating summary description for multimedia content using Dublin Core (DC). The motive is based on the fundamental concepts such as (1) Description information about the multimedia content is essential in multimedia content access, search and retrieval process (2) the existing metadata are too complicated to use in applications such as e-cataloguing and browsing of e-commerce. As an approach to solve the problem, summary description that may be optimally minimal descriptive elements set derived from existing metadata schemes (full descriptions) is described in this paper. The proposed summary description generator model is achieved using thesaurus approach built on the basis of DC and any existing various metadata schemes

Deep Discrete Hashing with Self-supervised Pairwise Labels

Hashing methods have been widely used for applications of large-scale image retrieval and classification. Non-deep hashing methods using handcrafted features have been significantly outperformed by deep hashing methods due to their better feature representation and end-to-end learning framework. However, the most striking successes in deep hashing have mostly involved discriminative models, which require labels. In this paper, we propose a novel unsupervised deep hashing method, named Deep Discrete Hashing (DDH), for large-scale image retrieval and classification. In the proposed framework, we address two main problems: 1) how to directly learn discrete binary codes? 2) how to equip the binary representation with the ability of accurate image retrieval and classification in an unsupervised way? We resolve these problems by introducing an intermediate variable and a loss function steering the learning process, which is based on the neighborhood structure in the original space. Experimental results on standard datasets (CIFAR-10, NUS-WIDE, and Oxford-17) demonstrate that our DDH significantly outperforms existing hashing methods by large margin in terms of~mAP for image retrieval and object recognition. Code is available at \url{https://github.com/htconquer/ddh}

Promotional Effect on Selective Catalytic Reduction of NO x

W and Ce are known to be a good promoters to improve selective catalytic reduction (SCR) activity for V2O5/TiO2 catalysts. This work aimed at finding the optimum ratio and loading of promoters (W and Ce) on V2O5/TiO2 catalyst in order to improve SCR reactivity in low temperature region and to minimize N2O formation in high temperature region. In addition, we changed the order of impregnation between W and Ce precursors on V2O5/TiO2 catalyst during the preparation and observed its effect on SCR activity and N2 selectivity. We utilized various analytical techniques, such as N2 adsorption-desorption, X-ray diffraction (XRD), and temperature-programmed reduction with hydrogen (H2 TPR) to investigate the physicochemical properties of catalysts. It was found that W- and Ce-overloaded V2O5/TiO2 catalyst such as W/Ce/V/TiO2 (15 : 15 : 1 wt%) showed the most remarkable DeNOx properties over the wide temperature region. Additionally, this catalyst significantly suppressed N2O formation during SCR reaction, especially in high temperature region (350–400°C). Based on the characterization results, it was found that such superior activity originated from the improved reducibility and morphology of W and Ce species on V2O5/TiO2 catalyst when they are incorporated together at high loading

Blending-NeRF: Text-Driven Localized Editing in Neural Radiance Fields

Text-driven localized editing of 3D objects is particularly difficult as locally mixing the original 3D object with the intended new object and style effects without distorting the object's form is not a straightforward process. To address this issue, we propose a novel NeRF-based model, Blending-NeRF, which consists of two NeRF networks: pretrained NeRF and editable NeRF. Additionally, we introduce new blending operations that allow Blending-NeRF to properly edit target regions which are localized by text. By using a pretrained vision-language aligned model, CLIP, we guide Blending-NeRF to add new objects with varying colors and densities, modify textures, and remove parts of the original object. Our extensive experiments demonstrate that Blending-NeRF produces naturally and locally edited 3D objects from various text prompts. Our project page is available at https://seokhunchoi.github.io/Blending-NeRF/Comment: Accepted to ICCV 2023. The first two authors contributed equally to this wor

Robust Distributed Clustering Algorithm Over Multitask Networks

We propose a new adaptive clustering algorithm that is robust to various multitask environments. Positional relationships among optimal vectors and a reference signal are determined by using the mean-square deviation relation derived from a one-step least-mean-square update. Clustering is performed by combining determinations on the positional relationships at several iterations. From this geometrical basis, unlike the conventional clustering algorithms using simple thresholding method, the proposed algorithm can perform clustering accurately in various multitask environments. Simulation results show that the proposed algorithm has more accurate estimation accuracy than the conventional algorithms and is insensitive to parameter selection.11Ysciescopu

Both Basic and Acidic Amino Acid Residues of IpTxa Are Involved in Triggering Substate of RyR1

Imperatoxin A (IpTxa) is known to modify the gating of skeletal ryanodine receptor (RyR1). In this paper, the ability of charged aa residues of IpTxa to induce substate of native RyR1 in HSR was examined. Our results show that the basic residues (e.g., Lys19, Lys20, Lys22, Arg23, and Arg24) are important for producing substate of RyR1. In addition, other basic residues (e.g., Lys30, Arg31, and Arg33) near the C-terminus and some acidic residues (e.g., Glu29, Asp13, and Asp2) are also involved in the generation of substate. Residues such as Lys8 and Thr26 may be involved in the self-regulation of substate of RyR1, since alanine substitution of the aa residues led to a drastic conversion to the substate. The modifications of the channel gating by the wild-type and mutant toxins were similar in purified RyR1. Taken together, the specific charge distributions on the surface of IpTxa are essential for regulation of the channel gating of RyR1

A Multiwell-Based Detection Platform with Integrated PDMS Concentrators for Rapid Multiplexed Enzymatic Assays

We report an integrated system for accelerating assays with concentrators in a standard 12-well plate (ISAAC-12) and demonstrate its versatility for rapid detection of matrix metalloproteinase (MMP)-9 expression in the cell culture supernatant of breast cancer cell line MDA-MB-231 by accelerating the enzymatic reaction and end-point signal intensity via electrokinetic preconcentration. Using direct printing of a conductive ion-permselective polymer on a polydimethylsiloxane (PDMS) channel, the new microfluidic concentrator chip can be built without modifying the underlying substrate. Through this decoupling fabrication strategy, our microfluidic concentrator chip can easily be integrated with a standard multiwell plate, the de facto laboratory standard platform for high-throughput assays, simply by reversible bonding on the bottom of each well. It increases the reaction rate of enzymatic assays by concentrating the enzyme and the reaction product inside each well simultaneously for rapid multiplexed detection.publishersversionpublishe