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Symmetry
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Symmetry in Artificial Intelligence and Applications
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Symmetry in Artificial Intelligence and Applications

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 2131

Special Issue Editors

Dr. Chao Lian

E-Mail Website
Guest Editor
School of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Interests: computer science; artificial intelligence; deep learning; pattern recognition; signal processing; measurement
Dr. Xiaopeng Sha

E-Mail Website
Guest Editor
School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066000, China
Interests: signal processing; deep learning; artificial intelligence; intelligent detection

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) continues to transform science, industry, and society, and many recent advances reveal underlying regularities and balanced structures that can be viewed through the lens of symmetry. Recognizing and exploiting these patterns helps researchers to design models with higher efficiency, stronger generalization, and more interpretable features. This Special Issue invites original research and comprehensive reviews that examine the role of symmetry and related principles in AI theory, algorithm design, and practical deployment. Topics of interest include—but are not limited to—model simplification, pattern discovery, feature prioritization, and performance optimization across applications such as computer vision, speech processing, natural language understanding, robotics, and intelligent sensing. We particularly welcome interdisciplinary submissions that connect mathematical insights with real-world implementations.

Dr. Chao Lian
Dr. Xiaopeng Sha
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence (AI)
  • deep learning
  • machine learning (ML)
  • information processing
  • image processing
  • neural networks
  • symmetric networks/asymmetric networks
  • pattern recognition
  • signal processing
  • speech processing
  • natural language processing
  • robotics
  • object detection

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

34 pages, 6522 KB  
Article
Strictly Chronological CNN Embeddings with Gradient-Boosted Trees for Next-Day Log-Return Forecasting
by Zezhi Bao, Xiaofei Li, Menghuan Shi, Yueen Huang and Junjie Du
Symmetry 2026, 18(3), 416; https://doi.org/10.3390/sym18030416 - 27 Feb 2026
Viewed by 543
Abstract
Daily equity return forecasting is challenging due to low signal-to-noise ratios, heavy-tailed innovations, and persistent distribution drift. We study one-step-ahead log-return prediction using daily market variables and return-based transformations. We propose a CNN–LightGBM hybrid that transfers a last-step CNN embedding to a gradient-boosted [...] Read more.
Daily equity return forecasting is challenging due to low signal-to-noise ratios, heavy-tailed innovations, and persistent distribution drift. We study one-step-ahead log-return prediction using daily market variables and return-based transformations. We propose a CNN–LightGBM hybrid that transfers a last-step CNN embedding to a gradient-boosted tree regressor through explicit embedding standardization, which stabilizes the representation interface for tree learning. To reduce train-to-evaluation mismatch under drift, we adopt split-wise, training-only standardization with a recency-aware fit-latest-W rule. Return-related predictors are anchored on a one-sided wavelet-denoised close series, while other market channels are retained in their original form to preserve episodic extremes. Experiments on NIFTY50 with walk-forward model selection show statistically reliable accuracy gains over Naive0 and competitive performance against representative deep sequence baselines, and the supplementary evaluations on HDFC and INDA provide additional out-of-sample evidence on these two assets under the same strictly chronological protocol. A long-or-cash decision rule based on the return forecasts yields positive risk-adjusted performance under realistic transaction-cost assumptions, supporting the practical relevance of the predictive signal. Full article
(This article belongs to the Special Issue Symmetry in Artificial Intelligence and Applications)
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22 pages, 14012 KB  
Article
Video Frame Interpolation for Extreme Motion Scenes Based on Dual Alignment and Region-Adaptive Interaction
by Xin Ning, Jiantao Qu, Junyi Duan, Kun Yang and Youdong Ding
Symmetry 2025, 17(12), 2097; https://doi.org/10.3390/sym17122097 - 6 Dec 2025
Viewed by 1162
Abstract
Video frame interpolation in ultra-high-definition extreme motion scenes remains highly challenging due to large displacements, nonlinear motion, and occlusions that disrupt spatio-temporal symmetry. To address this issue, this study proposes a frame interpolation method for extreme motion scenes based on dual alignment and [...] Read more.
Video frame interpolation in ultra-high-definition extreme motion scenes remains highly challenging due to large displacements, nonlinear motion, and occlusions that disrupt spatio-temporal symmetry. To address this issue, this study proposes a frame interpolation method for extreme motion scenes based on dual alignment and region-adaptive interaction from the perspectives of cross-frame localization and adaptive reconstruction. Specifically, we design a two-stage motion information alignment strategy that obtains two types of motion information via optical flow estimation and offset estimation, and it progressively guides reference pixels for accurate long-range cross-frame localization, mitigating structural misalignment caused by limited receptive fields while simultaneously alleviating spatiotemporal asymmetry caused by inconsistent inter-frame motion speed and direction. Based on this, we introduce a region-adaptive interaction module that automatically adapts motion representations for different regions through cross-frame interaction and leverages distinct attention pathways to accurately capture both the global context and local high-frequency motion details. This achieves a dynamic feature fusion tailored to regional characteristics, significantly enhancing the model’s ability to perceive the overall structure and texture details in extreme motion scenarios. In addition, the introduction of a motion compensation module explicitly captures pixel motion relationships by constructing a global correlation matrix that compensates for the positioning errors of the dual alignment module in extreme motion or occlusion areas. The experimental results demonstrate that the proposed method achieves excellent overall performance in ultra-high-definition extreme motion scenes, with a PSNR improvement of 0.05 dB over state-of-the-art methods. In multi-frame interpolation tasks, it achieves an average PSNR gain of 0.31 dB, demonstrating strong cross-scene interpolation capability. Full article
(This article belongs to the Special Issue Symmetry in Artificial Intelligence and Applications)
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