A new algorithm for high-precision submarine topography imaging

730-738In this paper, a new algorithm for high-precision submarine topography imaging is proposed. The innovative idea comes from the most effective and widely used instruments: Multibeam echo sounder (MBES) and side scan sonar (SSS). The MBES can acquire bathymetric information with high precision, but its along-track resolution is related to the result of the beam angle multiplied by the slant range. The SSS combined with synthetic aperture sonar technology can achieve a high-precision along-track imaging resolution, but it cannot acquire bathymetric information directly below it. The proposed algorithm uses the beam footprints of the MBES in the along-track direction to perform the aperture synthesis and uses the time-domain and beam-domain imaging algorithms to acquire high-precision along-track imaging resolution and bathymetric information, to improve the along-track resolution and obtain the bathymetric information with high precision at the same time. Finally, an experiment is performed to evaluate the effectiveness of our method. Experimental results demonstrate that two targets, 13 cm in size, can be clearly observed from the obtained imaging. Moreover, their bathymetric information can be calculated by using the beamforming angle information

CluCDD:Contrastive Dialogue Disentanglement via Clustering

A huge number of multi-participant dialogues happen online every day, which leads to difficulty in understanding the nature of dialogue dynamics for both humans and machines. Dialogue disentanglement aims at separating an entangled dialogue into detached sessions, thus increasing the readability of long disordered dialogue. Previous studies mainly focus on message-pair classification and clustering in two-step methods, which cannot guarantee the whole clustering performance in a dialogue. To address this challenge, we propose a simple yet effective model named CluCDD, which aggregates utterances by contrastive learning. More specifically, our model pulls utterances in the same session together and pushes away utterances in different ones. Then a clustering method is adopted to generate predicted clustering labels. Comprehensive experiments conducted on the Movie Dialogue dataset and IRC dataset demonstrate that our model achieves a new state-of-the-art result.Comment: 5 page

Learning Robust Visual-Semantic Embedding for Generalizable Person Re-identification

Generalizable person re-identification (Re-ID) is a very hot research topic in machine learning and computer vision, which plays a significant role in realistic scenarios due to its various applications in public security and video surveillance. However, previous methods mainly focus on the visual representation learning, while neglect to explore the potential of semantic features during training, which easily leads to poor generalization capability when adapted to the new domain. In this paper, we propose a Multi-Modal Equivalent Transformer called MMET for more robust visual-semantic embedding learning on visual, textual and visual-textual tasks respectively. To further enhance the robust feature learning in the context of transformer, a dynamic masking mechanism called Masked Multimodal Modeling strategy (MMM) is introduced to mask both the image patches and the text tokens, which can jointly works on multimodal or unimodal data and significantly boost the performance of generalizable person Re-ID. Extensive experiments on benchmark datasets demonstrate the competitive performance of our method over previous approaches. We hope this method could advance the research towards visual-semantic representation learning. Our source code is also publicly available at https://github.com/JeremyXSC/MMET

Deep Multimodal Fusion for Generalizable Person Re-identification

Person re-identification plays a significant role in realistic scenarios due to its various applications in public security and video surveillance. Recently, leveraging the supervised or semi-unsupervised learning paradigms, which benefits from the large-scale datasets and strong computing performance, has achieved a competitive performance on a specific target domain. However, when Re-ID models are directly deployed in a new domain without target samples, they always suffer from considerable performance degradation and poor domain generalization. To address this challenge, in this paper, we propose DMF, a Deep Multimodal Fusion network for the general scenarios on person re-identification task, where rich semantic knowledge is introduced to assist in feature representation learning during the pre-training stage. On top of it, a multimodal fusion strategy is introduced to translate the data of different modalities into the same feature space, which can significantly boost generalization capability of Re-ID model. In the fine-tuning stage, a realistic dataset is adopted to fine-tine the pre-trained model for distribution alignment with real-world. Comprehensive experiments on benchmarks demonstrate that our proposed method can significantly outperform previous domain generalization or meta-learning methods. Our source code will also be publicly available at https://github.com/JeremyXSC/DMF

Single-shot measurement of wavelength-resolved state of polarization dynamics in ultrafast lasers using dispersed division-of-amplitude

Characterization of the state of polarization (SOP) of ultrafast laser emission is relevant in several application fields such as field manipulation, pulse shaping, testing of sample characteristics, and biomedical imaging. Nevertheless, since high-speed detection and wavelength-resolved measurements cannot be simultaneously achieved by commercial polarization analyzers, single-shot measurements of the wavelength-resolved SOP of ultrafast laser pulses have rarely been reported. Here, we propose a method for single-shot, wavelength-resolved SOP measurements that exploits the method of division-of-amplitude under far-field transformation. A large accumulated chromatic dispersion is utilized to time-stretch the laser pulses via dispersive Fourier transform, so that spectral information is mapped into a temporal waveform. By calibrating our test matrix with different wavelengths, wavelength-resolved SOP measurements are achieved, based on the division-of-amplitude approach, combined with high-speed opto-electronic processing. As a proof-of-concept demonstration, we reveal the complex wavelength-dependent SOP dynamics in the build-up of dissipative solitons. The experimental results show that the dissipative soliton exhibits far more complex wavelength-related polarization dynamics, which are not shown in single-shot spectrum measurement. Our method paves the way for single-shot measurement and intelligent control of ultrafast lasers with wavelength-resolved SOP structures, which could promote further investigations of polarization-related optical signal processing techniques, such as pulse shaping and hyperspectral polarization imaging

A Method for Estimating Dominant Acoustic Backscatter Mechanism of Water-Seabed Interface via Relative Entropy Estimation

It is important to distinguish the dominant mechanism of seabed acoustic scattering for the quantitative inversion of seabed parameters. An identification scheme is proposed based on Bayesian inversion with the relative entropy used to estimate dominant acoustic backscatter mechanism. DiffeRential Evolution Adaptive Metropolis is used to obtain samples from posterior probability density in Bayesian inversion. Three mechanisms for seabed scattering are considered: scattering from a rough water-seabed interface, scattering from volume heterogeneities, and mixed scattering from both interface roughness and volume heterogeneities. Roughness scattering and volume scattering are modelled based on Fluid Theories using Small-Slope Approximation and Small-Perturbation Fluid Approximation, respectively. The identification scheme is applied to three simulated observation data sets. The results indicate that the scheme is promising and appears capable of distinguishing sediment volume from interface roughness scattering and can correctly identify the dominant acoustic backscatter mechanism

Confning TiO2 Nanotubes in PECVD‑Enabled Graphene Capsules Toward Ultrafast K‑Ion Storage: In Situ TEM/XRD Study and DFT Analysis

© 2020, © 2020, The Author(s). Titanium dioxide (TiO2) has gained burgeoning attention for potassium-ion storage because of its large theoretical capacity, wide availability, and environmental benignity. Nevertheless, the inherently poor conductivity gives rise to its sluggish reaction kinetics and inferior rate capability. Here, we report the direct graphene growth over TiO2 nanotubes by virtue of chemical vapor deposition. Such conformal graphene coatings effectively enhance the conductive environment and well accommodate the volume change of TiO2 upon potassiation/depotassiation. When paired with an activated carbon cathode, the graphene-armored TiO2 nanotubes allow the potassium-ion hybrid capacitor full cells to harvest an energy/power density of 81.2 Wh kg−1/3746.6 W kg−1. We further employ in situ transmission electron microscopy and operando X-ray diffraction to probe the potassium-ion storage behavior. This work offers a viable and versatile solution to the anode design and in situ probing of potassium storage technologies that is readily promising for practical applications.[Figure not available: see fulltext.]