Fengbo Ma, Zixin Rao, Xiaoting Li, Zhetao Chen, Hongyue Sun, Yiping Zhao, Xianyan Chen, Zhen Xiang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Recently, anomaly change detection has attracted considerable interest in hyperspectral image (HSI) analysis. Hyperspectral anomaly change detection (HACD) focuses more on detecting anomalies between multi-temporal hyperspectral images. This paper presents a novel anomaly change detection algorithm that has the following innovations. First, using the variational autoencoder to reconstruct the background of input, forcing its latent features to obey the Gaussian distribution, which can help to reconstruct the background better. Second, in order to obtain a better quality reconstruction of the background map, the spectral angular distance is introduced as a model constraint. Third, impose constraints on the encoder to better learn the background information of the input and improve the reconstruction effect of the network. The proposed algorithm has superior performance over some other algorithms on "Viareggio 2013" datasets.

Deep learning and convolution neural networks(CNNs) have recently made advances in polarimetric synthetic aperture radar(PolSAR) image classification. Deep learning methods rely on large amounts of data, but acquiring and labeling PolSAR data is challenging. This paper proposes a complex domain fully convolution network(FCN) based on a new transfer learning method(TF-CVFCN) for PolSAR image classification. First, a large amount of unlabeled data is used to train on Unet to obtain the parameters of the pre-trained model. Then the parameters are migrated to the new FCN network to classify a PolSAR image. In this paper, a new transfer learning method is used, which can be performed on the new model after migrating the parameters of the pre-trained network. Experimental results verify the effectiveness of our proposed method.

Although Convolutional Neural Network (CNN) and Vision Transformer (ViT) have excellent performance in hyperspectral image (HSI) classification. However, due to the inherent network limitations, CNN cannot fully mine the spectral feature information well in HSI, and ViT could not effective extract the local spatial feature of HSI. In order to solve the above problems, we propose a new network which is 3D-CNN Multi-Head Self-Attention Fusion Transformer (3DMFT), which is combined with Transformer and CNN. 3DMFT fuses 3D-CNN and QKV to learn the deep and shallow features in HSI. Moreover, the local spatial-spectral position encoding obtains spatial-spectral position information between elements , and then induct pyramid model to transfer image feature from shallow layer to deep layer. Experimental results show that 3DMFT can obtain global context dependencies and local delicate feature well. Compared with some state-of-the-art methods, the proposed 3DMFT network is more efficient.

Smart manufacturing and Industry 4.0 are bringing disruptive changes to the manufacturing sector. Smart manufacturing increases productivity, creates safer conditions for workers, and simplifies product customization, all while decreasing business expenses [2]. To this end, we have created a flexible three-component architecture for remote machine management, using it to build a digital twin prototype of a Creality Ender-3 Pro 3D printer located on the University at Buffalo campus. This twin provides users with the ability to monitor and control the machine from anywhere in the world through a web interface. Our system improves upon existing technologies, such as Octoprint [1], through the addition of twin views. It also relies upon cheaper components, using the Arduino and ESP32 rather than the Raspberry Pi. Finally, existing technologies tend to focus on one specific type of machine. In contrast, our framework is flexible, capable of supporting many different machines.

Additive manufacturing (AM) enables flexible part geometry and functionality, and reduces product development life cycle by direct layer-wise fabrication from CAD files. In the last decade, great achievements are made on AM materials, machines, processes, etc. However, the quality of the AM parts is still questionable for industrial specifications. On the one hand, AM part quality variables can be either quantitative, such as dimensional accuracy, or qualitative, such as binary indicators for voids, missing features, or surface roughness. On the other hand, both offline process setting variables and functional in situ process variables can be measured and modeled with both quantitative and qualitative (QQ) quality response variables. In this paper, the QQ quality response variables are modeled by offline process setting variables and in situ process variables via functional QQ models. The modeling of these in situ process variables provides the basis for real-time monitoring and control for AM processes. Simulation studies and experimental data from a fused deposition modeling process are performed to demonstrate the effectiveness of the proposed method.

Graphical models are widely used to model the statistical relationships among variables in a system. Existing graphical models can be used to model the relationships among scalar variables, but cannot be directly applied to model a system with functional variables. In this paper, a novel functional graphical model is proposed to model complex systems where functional variables are measured. To cope with the small sample size problem, we further develop a special sparsity penalization approach to robustly learn the graphical model from limited sample size, and develop a difference from the mean penalization for functional variables. Simulation studies and a case study in a plasma spray manufacturing process are used to demonstrate the effectiveness of the proposed method.