Special Libraries, January 1925

Volume 16, Issue 1https://scholarworks.sjsu.edu/sla_sl_1925/1000/thumbnail.jp

A novel distance learning for elastic cross-modal audio-visual matching

In this work we propose a novel network formulation for joint representation of cross-modal audio and visual information base on metric learning. We employ a distance learning framework as a training procedure. For this purpose we introduce an elastic matching network (EmNet) and a novel loss function to learn the shared latent space representation of multi-modal information. The elastic matching network is capable of matching given face image (or audio voice clip) from diverse number of audio clips (or face images). We quantitatively and qualitatively evaluate the purposed approach on the standard audio-visual matching evaluation dataset, the overlap of VoxCeleb and VGGFace by both multi-way and binary audio-visual matching tasks. The promising performance comparing to the existing methods verifies the effectiveness of the proposed approach, which yields to a new state-of-the-art for cross-modal audio-visual matching

Interfacial and emulsifying properties of the electrostatic complex of β-lactoglobulin fibril and gum Arabic (Acacia Seyal)

Formation, interfacial and emulsifying properties of the electrostatic complex of β-lactoglobulin fibril (BLGF) and gum Arabic Acacia Seyal (AS) were investigated. Necklace-like soluble complex could be formed at pH 3.5, and its charge and interfacial properties depended on the BLGF content. With appropriate amount of BLGF (< 9.09 wt.%), the formed complex possessed a good dispersibility and surface activity. When excessive BLGF (9.09∼50 wt.%) existed, surface charge of the complex was gradually neutralized and aggregation occurred. Homogeneous oil-in-water emulsions could be stabilized by the complex and the droplet size decreased with increasing BLGF content. Higher content of BLGF (9.09∼50 wt.%) was detrimental for emulsification due to the aggregation of complex, and the formed emulsion tended to flocculate. Compared with AS, the complex formed emulsions were much more stable against heating (90 ℃, 30 min) and salting (200 mM NaCl) environments, and the emulsions were stable during long-term storage (46 days). Proposed mechanisms for the adsorption of BLGF/AS complex at the oil-water interface. Pure AS (i) could adsorb at the oil-water interface but formed a loose film due to its poor surface activity and insufficient adsorption amount. With addition of a small amount of fibrils (ii), soluble electrostatic complexes are formed and they can be adsorbed at the interface to formed a dense viscoelastic film due to the surface activity of the BLGF. With a higher content of fibrils (iii), surface charge of the complex tended to be neutralized, causing the aggregation. Because the presence of protein fibrils, they could also adsorb at the oil-water interface to produce a viscoelastic film. However, with a bigger size and irregular shape, the aggregates were difficult to array at the interface as densely as the soluble complex

Robust watermarking algorithm for medical volume data in internet of medical things

The advancement of 5G technology, big data and cloud storage has promoted the rapid development of the Internet of Medical Things (IoMT). Based on the strict security requirements and high level of accuracy required for disease diagnosis and pathological analysis, 3D medical volume data have been created in large numbers. The IoMT facilitates the rapid transfer of medical information and also makes the protection of pathological information and privacy information of patients increasingly prominent. To solve the security problem, a robust zero-watermarking algorithm based on 3D hyperchaos and 3D dual-tree complex wavelet transform is proposed according to the selected feature of medical volume data. The feature combines human visual features with improved perceptual hashing techniques. It is a robust and efficient binary sequence. When implementing the proposed algorithm, the watermark is first scrambled with 3D hyperchaos to enhance security. Then, 3D DTCWT-DCT transformation is applied to medical volume data, and the low-frequency coefficients that can represent the features are selected and binarized to generate the secret key to complete the watermark embedding and extraction. Zero embedding and blind extraction ensure that the original medical volume data is not altered in any form, which meets the special requirements for diagnosis. Simulation results show that the algorithm is robust and can effectively resist common attacks and geometric attacks. It used fewer robust features to effectively bound medical volume data and watermark information, saved bandwidth, and satisfied the security of transmission and storage of medical volume data in the Internet of medical things. In particular, compared with state-of-the-art technology, the proposed algorithm improves the average NC value by 46.67% under geometric attacks

A Novel Method of 3D Multipoint Geostatistical Inversion Using 2D Training Images

AbstractThe seismic inversion method combined with multipoint geostatistics theory has begun to receive attention, but the acquisition accuracy and calculation efficiency of 3D training image still need more optimization. This paper presents a novel method of 3D multipoint geostatistical inversion based on 2D training images directly. The 2D training image was scanned by the data template to acquire the multipoint statistical probability in 2D direction. The probability fusion method is used to fuse the 2D multipoint probability into 3D multipoint probability. The rock facies types and patterns of the simulated points are obtained by random sampling. On this basis, the elastic parameters are extracted from the statistical rock physics model, and the seismic records are convoluted. Then, the synthetic records and the actual records were compared under a given threshold. If the error exceeds the given threshold, the iterative adaptive spatial sampling method will be used to repeat the process above-mentioned, so as to ensure that the error is below the threshold. Because the 2D training image is easy to obtain and evaluate, the demand problem of 3D training image is solved. The 2D training image scanning, probability storage and access are more convenient, and the adaptive spatial sampling method is more efficient than the reject sampling, so as to ensure the operation efficiency. The model from the Stanford Center for Reservoir Forecasting is selected to test the effectiveness of this newly designed method

Robust and secure zero-watermarking algorithm for medical images based on Harris-SURF-DCT and chaotic map

To protect the patient information in medical images, this article proposes a robust watermarking algorithm for medical images based on Harris-SURF-DCT. First, the corners of the medical image are extracted using the Harris corner detection algorithm, and then, the previously extracted corners are described using the method of describing feature points in the SURF algorithm to generate the feature descriptor matrix. *en, the feature descriptor matrix is processed through the perceptual hash algorithm to obtain the feature vector of the medical image, which is a binary feature vector with a size of 32 bits. Secondly, to enhance the security of the watermark information, the logistic map algorithm is used to encrypt the watermark before embedding the watermark. Finally, with the help of cryptography knowledge, third party, and zero-watermarking technology, the algorithm can embed the watermark without modifying the medical image. When extracting the watermark, the algorithm can extract the watermark from the test image without the original image. In addition, the algorithm has strong robustness to conventional attacks and geometric attacks. Especially under geometric attacks, the algorithm performs better

Robust zero watermarking algorithm for medical images based on Zernike-DCT

Digital medical system not only facilitates the storage and transmission of medical information but also brings information security problems. Aiming at the security of medical images, a robust zero watermarking algorithm for medical images based on Zernike-DCT is proposed. *e algorithm first uses a chaotic logic sequence to preprocess and encrypt the watermark, then performs edge detection and Zernike moment processing on the original medical image to get the accurate edge points, and then performs discrete cosine transform (DCT) on them to get the feature vector. Finally, it combines perceptual Hash and zero watermark technology to generate the key to complete the watermark embedding and extraction. *e algorithm has good robustness to conventional and geometric attacks, strong antinoise ability, high positioning accuracy, and processing efficiency and is superior to the classical edge detection algorithm in extraction effect. It is a stable and reliable image edge detection algorithm

Quasi-antiphase diel patterns of abundance and cell size/biomass of picophytoplankton in the oligotrophic ocean

Picophytoplankton are the smallest, most abundant photosynthetic organisms in the ocean. Knowledge of the diel variability of these tiny microbes has important implications for the structure of microbial food webs and key biogeochemical processes. However, insight into the mechanisms that underlie picophytoplanktonic diel dynamics is limited. By combining a field survey with a published dataset, we found that cell numbers and cell sizes/biomasses of picophytoplankton were tightly synchronized to the day-night cycle, but they were in a quasi-antiphase relationship to each other. This pattern is a confirmation and extension of previous studies. Mortality rates showed that Prochlorococcus and Synechococcus were subject to considerable grazing pressure throughout the day and night. The quasi-antiphase diel cycles in abundance and cell size/biomass are likely determined by the light-dependent diel behavior of cell growth and division and continuous losses to grazing. This work significantly improves our understanding of autotrophic picoplankton in the oligotrophic ocean