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1. Chiral quantum phases and tricriticality in a Dicke triangle

   https://doi.org/10.1007/s44214-022-00019-5  

   Cheng, Guo-Jing; Fallas Padilla, Diego; Deng, Tao; Zhang, Yu-Yu; Pu, Han (November 2022, Quantum Frontiers)

   Abstract The existence of quantum tricriticality and exotic phases are found in a tricritical Dicke triangle (TDT) where three cavities, each one containing an ensemble of three-level atoms, are connected to each other through the action of an artificial magnetic field. The conventional superradiant phase (SR) is connected to the normal phase through first- and second-order boundaries, with tricritical points located at the intersection of such boundaries. Apart from the SR phase, a chiral superradiant (CSR) phase is found by tuning the artificial magnetic field. This phase is characterized by a nonzero photon current and its boundary presents chiral tricritical points (CTCPs). Through the study of different critical exponents, we are able to differentiate the universality class of the CTCP and TCP from that of second-order critical points, as well as find distinctive critical behavior among the two different superradiant phases. The TDT can be implemented in various systems, including atoms in optical cavities as well as the circuit QED system, allowing the exploration of a great variety of critical manifolds. 

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2. Online Neural Cell Tracking using Blob-Seed Segmentation and Optical Flow

   Yi, J.; Wu, P.; Huang, Q.; Qu, H.; Hoeppner, D.; Metaxas, D. (June 2019, CVPR 2019 Workshop)

   Existing neural cell tracking methods generally use the morphology cell features for data association. However, these features are limited to the quality of cell segmentation and are prone to errors for mitosis determination. To over- come these issues, in this work we propose an online multi- object tracking method that leverages both cell appearance and motion features for data association. In particular, we propose a supervised blob-seed network (BSNet) to predict the cell appearance features and an unsupervised optical flow network (UnFlowNet) for capturing the cell motions. The data association is then solved using the Hungarian al- gorithm. Experimental evaluation shows that our approach achieves better performance than existing neural cell track- ing methods. 

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3. JOINT SEGMENTATION AND FINE-GRAINED CLASSIFICATION OF NUCLEI IN HISTOPATHOLOGY IMAGES

   Qu, H.; Riedlinger, G.; Wu, P.; Huang, Q.; Yi, J.; De, S.; Metaxas, D. (April 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019))

   Nuclei segmentation and classification are two important tasks in the histopathology image analysis, because the mor- phological features of nuclei and spatial distributions of dif- ferent types of nuclei are highly related to cancer diagnosis and prognosis. Existing methods handle the two problems independently, which are not able to obtain the features and spatial heterogeneity of different types of nuclei at the same time. In this paper, we propose a novel deep learning based method which solves both tasks in a unified framework. It can segment individual nuclei and classify them into tumor, lymphocyte and stroma nuclei. Perceptual loss is utilized to enhance the segmentation of details. We also take advantages of transfer learning to promote the training of deep neural net- works on a relatively small lung cancer dataset. Experimental results prove the effectiveness of the proposed method. The code is publicly available 

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4. Machine‐Learning‐Based, Feature‐Rich Prediction of Alumina Microstructure from Hardness

   https://doi.org/10.1002/aidi.202400022  

   Geng, Xiao; Tang, Jianan; Wen, Ningxuan; Shaw, Jason; Sarkar, Siddhartha; Vargas‐Gonzalez, Lionel; Ku, Nicholas; Tong, Jianhua; Bordia, Rajendra_K; Li, Dongsheng; et al (May 2025, Advanced Intelligent Discovery)

   Predicting materials’ microstructure from the desired properties is critical for exploring new materials. Herein, a novel regression‐based prediction of scanning electron microscopy (SEM) images for the target hardness using generative adversarial networks (GANs) is demonstrated. This article aims at generating realistic SEM micrographs, which contain rich features (e.g., grain and neck shapes, tortuosity, spatial configurations of grain/pores). Together, these features affect material properties but are difficult to predict. A high‐performance GAN, named ‘Microstructure‐GAN’ (or M‐GAN), with residual blocks to significantly improve the details of synthesized micrographs is established . This algorithm was trained with experimentally obtained SEM micrographs of laser‐sintered alumina. After training, the high‐fidelity, feature‐rich micrographs can be predicted for an arbitrary target hardness. Microstructure details such as small pores and grain boundaries can be observed even at the nanometer scale (∼50 nm) in the predicted 1000× micrographs. A pretrained convolutional neural network (CNN) was used to evaluate the accuracy of the predicted micrographs with rich features for specific hardness. The relative bias of the CNN‐evaluated value of the generated micrographs was within 2.1%–2.7% from the values for experimental micrographs. This approach can potentially be applied to other microscopy data, such as atomic force, optical, and transmission electron microscopy. 

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5. Globally optimal OCT surface segmentation using a constrained IPM optimization

   https://doi.org/10.1364/OE.444369  

   Xie, Hui; Pan, Zhe; Zhou, Leixin; Zaman, Fahim_A; Chen, Danny_Z; Jonas, Jost_B; Xu, Weiyu; Wang, Ya_Xing; Wu, Xiaodong (January 2022, Optics Express)

   Segmentation of multiple surfaces in optical coherence tomography (OCT) images is a challenging problem, further complicated by the frequent presence of weak boundaries, varying layer thicknesses, and mutual influence between adjacent surfaces. The traditional graph-based optimal surface segmentation method has proven its effectiveness with its ability to capture various surface priors in a uniform graph model. However, its efficacy heavily relies on handcrafted features that are used to define the surface cost for the “goodness” of a surface. Recently, deep learning (DL) is emerging as a powerful tool for medical image segmentation thanks to its superior feature learning capability. Unfortunately, due to the scarcity of training data in medical imaging, it is nontrivial for DL networks toimplicitlylearn the global structure of the target surfaces, including surface interactions. This study proposes to parameterize the surface cost functions in the graph model and leverage DL to learn those parameters. The multiple optimal surfaces are then simultaneously detected by minimizing the total surface cost whileexplicitlyenforcing the mutual surface interaction constraints. The optimization problem is solved by the primal-dual interior-point method (IPM), which can be implemented by a layer of neural networks, enabling efficient end-to-end training of the whole network. Experiments on spectral-domain optical coherence tomography (SD-OCT) retinal layer segmentation demonstrated promising segmentation results with sub-pixel accuracy. 

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