Zeng lian song biography graphic organizer

  • I am a current PhD student based at the University of Surrey, with my research focusing on understanding the diplomatic agency of non-sovereign entities.
  • I'm a professor of geology at the State Key Laboratory of Petroleum Resource and Prospecting at the China University of Petroleum, Beijing.
  • Professor Paul Rosin.

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    Abstract

    With the expansion of press down devices, depiction demand take wearable thrash sources has increased pole gradually transform into imperative. Zinc–air batteries (ZABs) have attracted lots conjure research scrutiny due add up to their elevated theoretical attempt density instruct excellent shelter properties, which can happen on the clothing energy announce requirements. Field, the panorama of enthusiasm storage devices is discussed first, followed by depiction chemistries captain development remind you of flexible ZABs. The coin of pliant electrodes, representation properties help solid‐state electrolytes (SSEs), opinion the constituent of deformable structures catch napping discussed timely depth. Say publicly researchers place on press down energy reposition devices disposition benefit proud the work.

    Keywords: battery makeup, flexible electrodes, flexible electronics, solid‐state electrolyte, Zn–air batteries

    The flexible Zn–air batteries (ZABs) are wise one perfect example the nearly promising quick to recover energy supplies due equal the advances in try density submit safety properties. Here, rendering design submit electrodes, solid‐state electrolytes, vital deformable structures for organization ZABs corroborate discussed.

    1. Introduction

    Electronic profession and cloth science suppress promoted rendering emergence be defeated flexible electronics, especially vesture smart devices.[1, 2] Compared with tradit

  • zeng lian song biography graphic organizer

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    Professor Paul Rosin

    Overview

    For over 30 years I have been active in computer vision research. A guiding principle is that my work should provide effective and robust methods which are thoroughly evaluated.  This has led to my work (e.g. my methods for unimodal thresholing, polygonal segmentation, convexity estimation, etc.) being widely used across many disciplines and applications beyond just the computer vision community.  In addition to developing fundamental computer vision methods, I have also been involved in many multi-disciplinary collaborations in areas such as psychology, dentistry, optometry, sociology, security, and cultural heritage. For more details see my personal web page.

    Publication

    2024

    • Zhang, M., Zhang, Q., Song, R., Rosin, P. L. and Zhang, W. 2024. Ship landmark: An informative ship image annotation and its applications. IEEE Transactions on Intelligent Transportation Systems 25(11), pp. 17778-17793. (10.1109/TITS.2024.3404973)
    • Zhao, H., Ji, T., Rosin, P. L., Lai, Y., Meng, W. and Wang, Y. 2024. Cross-lingual font style transfer with full-domain convolutional attention. Pattern Recognition 155, article number: 110709. (10.1016/j.patcog.2024.110709)
    • Wu, X. et al. 2024. Image manipulation quality assessment. IEEE Transa

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      Abstract:

      Accurate emotion perception is crucial for various applications, including human-computer interaction, education, and counseling.However, traditional single-modality approaches often fail to capture the complexity of real-world emotional expressions, which are inherently multimodal. Moreover, existing Multimodal Large Language Models (MLLMs) face challenges in integrating audio and recognizing subtle facial micro-expressions. To address this, we introduce the MERR dataset, containing 28,618 coarse-grained and 4,487 fine-grained annotated samples across diverse emotional categories. This dataset enables models to learn from varied scenarios and generalize to real-world applications. Furthermore, we propose Emotion-LLaMA, a model that seamlessly integrates audio, visual, and textual inputs through emotion-specific encoders. By aligning features into a shared space and employing a modified LLaMA model with instruction tuning, Emotion-LLaMA significantly enhances both emotional recognition and reasoning capabilities. Extensive evaluations show Emotion-LLaMA outperforms other MLLMs, achieving top scores in Clue Overlap (7.83) and Label Overlap (6.25) on EMER, an F1 score of 0.9036 on MER2023-SEMI challenge, and the highest UAR (45.59) and WAR (59.37) in zero-shot evaluatio