Reviewerly/exHarmony huggingface dataset:
Dual-stream pyramid and attention network for stereo matching

Authors:
raw_author_name: Taiping Xiong
raw_affiliation_string: Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin, China; School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China
raw_author_name: Minghua Pan
raw_affiliation_string: School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China
raw_author_name: Zhiguo Zhu
raw_affiliation_string: Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin, China; School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China
raw_author_name: Wei Cheng
raw_affiliation_string: Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin, China; School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China

Abstract: In order to accurately estimate the disparity of ill-posed regions, such as weak texture and occlusion regions, we propose DSPANet, a stereo matching network that incorporates a dual-stream pyramid module and a channel and spatial attention module. The dual-stream pyramid module captures numerous complementary features from different layers by utilizing multi-resolution inputs and feature extraction blocks. This approach enables the learning of local detailed features at various scales. These features at various scales are then combined to calculate the stereo matching cost. By incorporating channel and spatial attention module into the feature extraction process to obtain richer and more concise contextual information, the matching cost can be constructed more accurately, which provides powerful conditions for subsequent cost aggregation. In the cost aggregation stage, we utilize the stacked hourglass module for both encoding and decoding. Additionally, we incorporate 3D global attention upsampling during the decoding stage, which enables high-level features to provide guidance information to low-level features in a simple way. We evaluate our proposed method on the Scene Flow dataset, as well as the KITTI2012 and KITTI2015 datasets. The experimental results demonstrate that our DSPANet achieves superior performance and effectively enhances the matching results in ill-posed regions. Our code has been implemented using PyTorch and will be released after paper publication at https://github.com/Shi-LiQing/DSPANet.
MTT-27 Autonomous and Connected Systems: Report for 2021–2022 [MTT-S Society News]

Authors:
raw_author_name: Alessandro Cidronali
raw_affiliation_string: Department of Information Engineering, University of Florence, Firenze, Italy

Abstract: Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
A hierarchical probabilistic underwater image enhancement model with reinforcement tuning

Authors:
raw_author_name: Wei Song
raw_affiliation_string: Shanghai Ocean University, No. 999 Huchenghuan Rd, Pudong New District, Shanghai, 201306, China
raw_author_name: Zhihao Shen
raw_affiliation_string: Shanghai Ocean University, No. 999 Huchenghuan Rd, Pudong New District, Shanghai, 201306, China
raw_author_name: Minghua Zhang
raw_affiliation_string: Shanghai Ocean University, No. 999 Huchenghuan Rd, Pudong New District, Shanghai, 201306, China
raw_author_name: Yan Wang
raw_affiliation_string: Fudan University, Shanghai, 200433, China
raw_author_name: Antonio Liotta
raw_affiliation_string: Faculty of Engineering, Free University of Bozen-Bolzano, Bozen-Bolzano, 39100, Italy

Abstract: Underwater Image Enhancement (UIE) is a challenging problem due to the complex underwater environment. Traditional UIE methods can hardly adapt to various underwater environments. Deep learning-based UIE methods are more powerful but often rely on a large deal of real-world underwater images with distortion-free reference images. This gives rise to two issues: First, the reference images are highly uncertain because the ground-truth images cannot be are captured directly in underwater environment. Second, learning-based methods may lack generalization ability for diverse underwater environments. To tackle these issues, we propose HPUIE-RL, a hierarchical probabilistic UIE model facilitated by reinforcement learning. This model integrates UNet with hierarchical probabilistic modules to produce various enhanced candidate images that reflect the uncertainty of the enhancement. Then, a reinforcement learning fine-tuning framework is designed to fine-tune the pretrained model in an unsupervised manner, which responds to the dynamic underwater environment. Experiments on real-world datasets from diverse underwater environments demonstrate that our HPUIE-RL model outperforms state-of-the-art UIE methods regarding visual and quantitative performance and generalizability.
A low-noise, high-SNR and large-dynamic-range balanced homodyne detector for broadband squeezed light measurement

Authors:
raw_author_name: Liying Hou
raw_affiliation_string: Department of Physics, Lvliang University, Lishi, Shanxi 033001, China

Abstract: Squeezing-based quantum metrology has been extensively applied in the field of precision measurement. Performance characteristics of squeezed light are obtained by a balanced homodyne detector. Here, we report a large dynamic range balanced homodyne detector with low-noise and high-signal-to-noise-ratio (SNR) for broadband squeezed light measurement. We perform a comprehensive theoretical analysis of the SNR in a wide bandwidth and experimentally analyze the important printed circuit board (PCB) layout and electronic components. Consequently, in the 100 MHz frequency band, the SNR and common mode rejection ratio (CMRR) rise over 12.6 dB and 55 dB, respectively, and the electronic noise is suppressed below −90 dB. The directly observed squeezing level at 1550 nm with the specified detector is 11.8 dB at 2 MHz and 3.1 dB at 100 MHz.
Efficient number theoretic transform accelerator for CRYSTALS-Kyber

Authors:
raw_author_name: Trang Hoang
raw_affiliation_string: Ho Chi Minh City University of Technology, Vietnam National University Ho Chi Minh City
raw_author_name: Linh Tran
raw_affiliation_string: Ho Chi Minh City University of Technology, Vietnam National University Ho Chi Minh City

Abstract: &lt;p&gt;The national institute of standards and technology (NIST) has presented its draft of the module-lattice-based key-encapsulation mechanism standard (MLBKEMS), choosing cryptographic suite for algebraic lattices (CRYSTALS)- Kyber as the base encryption. Existing hardware implementations of modern cryptography will need to process the new standard efficiently. The primary process in CRYSTALS-Kyber key-encapsulation mechanism (KEM) is the number theoretic transform (NTT), which requires heavy computing power. This paper contributes an efficient hardware accelerator for NTT and inverse NTT (INTT) by CRYSTAL-Kyber parameters. The proposed design utilizes the K-RED algorithm for reducing polynomial multiplication. It also incorporates the BrentKung method for efficient modular addition and subtraction operation with an address generator to control the sequences of computation. On the Xilinx Artix 7 field programmable gate array (FPGA), our design achieves 262 MHz clock speed, utilizing only 1405 LUTs.&lt;/p&gt;
Navigation of mobile robots using neural networks and genetic algorithms

Authors:
raw_author_name: David Abad Perez
raw_affiliation_string: Alstom, Pinto, Spain.
raw_author_name: Basil Mohammed Al-Hadithi
raw_affiliation_string: Universidad Politecnica de Madrid, Madrid, Spain

Abstract: The navigation of robots has been a subject of widespread interest over the last few decades. In the previous years, traditional methods based on mathematical equations were used, and there has been an evolution towards the use of methods based on artificial intelligence. Two of which have been used in this work: neural networks and genetic algorithms. Neural networks are used as a machine learning model to teach the robot to move from any starting point to a goal, avoiding obstacles along the way. However, this model needs an algorithm to learn how to carry out this activity, which is what the genetic algorithm will be used for. Furthermore, this method of navigation will be compared with the traditional method based on potential fields, where it can be observed how this new method based on artificial intelligence improves and solves some typical problems of the old methods, such as the tendency to get stuck in local minima.
Table 1 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Authors:
raw_author_name: Yeman Brhane Hagos
raw_author_name: Dominic Patel
raw_author_name: Anna Mikolajczak
raw_author_name: Simon P. Castillo
raw_author_name: Thien-An Tran
raw_author_name: Lydia S.H. Lee
raw_author_name: Manuel Rodriguez-Justo
raw_author_name: Kwee L. Yong
raw_author_name: Yinyin Yuan

Abstract: &lt;p&gt;Patient characteristics: MGUS.&lt;/p&gt;
Figure 3 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Authors:
raw_author_name: Yeman Brhane Hagos
raw_author_name: Dominic Patel
raw_author_name: Anna Mikolajczak
raw_author_name: Simon P. Castillo
raw_author_name: Thien-An Tran
raw_author_name: Lydia S.H. Lee
raw_author_name: Manuel Rodriguez-Justo
raw_author_name: Kwee L. Yong
raw_author_name: Yinyin Yuan

Abstract: &lt;p&gt;Performance evaluation of MoSaicNet and AwareNet deep learning models: &lt;b&gt;A&lt;/b&gt;, The ROC curves and AUC values of the MoSaicNet superpixel classifier. The values in brackets indicate the 95% CI. &lt;b&gt;B,&lt;/b&gt; Two-dimensional mapping of superpixels using MoSaicNet learned 200-dimensional features after dimensionality reduction by UMAP. &lt;b&gt;C,&lt;/b&gt; The ROC curves and AUC values of single-cell classifier model on separately held test data. The values in brackets indicate the 95% CI. &lt;b&gt;D,&lt;/b&gt; UMAP features visualization of deep learned features by AwareNet single-cell classifier CNN. &lt;b&gt;E&lt;/b&gt; and &lt;b&gt;F,&lt;/b&gt; Validation of AwareNet model using correlation of density of CD8&lt;sup&gt;+&lt;/sup&gt; (&lt;b&gt;E&lt;/b&gt;) and CD4&lt;sup&gt;+&lt;/sup&gt; (&lt;b&gt;F&lt;/b&gt;) cells in panel 1 and panel 2.&lt;/p&gt;
Table 2 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Authors:
raw_author_name: Yeman Brhane Hagos
raw_author_name: Dominic Patel
raw_author_name: Anna Mikolajczak
raw_author_name: Simon P. Castillo
raw_author_name: Thien-An Tran
raw_author_name: Lydia S.H. Lee
raw_author_name: Manuel Rodriguez-Justo
raw_author_name: Kwee L. Yong
raw_author_name: Yinyin Yuan

Abstract: &lt;p&gt;Patient characteristics: paired diagnostic and posttreatment samples.&lt;/p&gt;
Data from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Authors:
raw_author_name: Yeman Brhane Hagos
raw_author_name: Dominic Patel
raw_author_name: Anna Mikolajczak
raw_author_name: Simon P. Castillo
raw_author_name: Thien-An Tran
raw_author_name: Lydia S.H. Lee
raw_author_name: Manuel Rodriguez-Justo
raw_author_name: Kwee L. Yong
raw_author_name: Yinyin Yuan

Abstract: &lt;div&gt;Abstract&lt;p&gt;Bone marrow trephine biopsy is crucial for the diagnosis of multiple myeloma. However, the complexity of bone marrow cellular, morphologic, and spatial architecture preserved in trephine samples hinders comprehensive evaluation. To dissect the diverse cellular communities and mosaic tissue habitats, we developed a superpixel-inspired deep learning method (MoSaicNet) that adapts to complex tissue architectures and a cell imbalance aware deep learning pipeline (AwareNet) to enable accurate detection and classification of rare cell types in multiplex immunohistochemistry images. MoSaicNet and AwareNet achieved an AUC of &gt;0.98 for tissue and cellular classification on separate test datasets. Application of MoSaicNet and AwareNet enabled investigation of bone heterogeneity and thickness as well as spatial histology analysis of bone marrow trephine samples from monoclonal gammopathies of undetermined significance (MGUS) and from paired newly diagnosed and posttreatment multiple myeloma. The most significant difference between MGUS and newly diagnosed multiple myeloma (NDMM) samples was not related to cell density but to spatial heterogeneity, with reduced spatial proximity of BLIMP1&lt;sup&gt;+&lt;/sup&gt; tumor cells to CD8&lt;sup&gt;+&lt;/sup&gt; cells in MGUS compared with NDMM samples. Following treatment of patients with multiple myeloma, there was a reduction in the density of BLIMP1&lt;sup&gt;+&lt;/sup&gt; tumor cells, effector CD8&lt;sup&gt;+&lt;/sup&gt; T cells, and regulatory T cells, indicative of an altered immune microenvironment. Finally, bone heterogeneity decreased following treatment of patients with multiple myeloma. In summary, deep learning–based spatial mapping of bone marrow trephine biopsies can provide insights into the cellular topography of the myeloma marrow microenvironment and complement aspirate-based techniques.&lt;/p&gt;Significance:&lt;p&gt;Spatial analysis of bone marrow trephine biopsies using histology, deep learning, and tailored algorithms reveals the bone marrow architectural heterogeneity and evolution during myeloma progression and treatment.&lt;/p&gt;&lt;/div&gt;
Figure 4 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Authors:
raw_author_name: Yeman Brhane Hagos
raw_author_name: Dominic Patel
raw_author_name: Anna Mikolajczak
raw_author_name: Simon P. Castillo
raw_author_name: Thien-An Tran
raw_author_name: Lydia S.H. Lee
raw_author_name: Manuel Rodriguez-Justo
raw_author_name: Kwee L. Yong
raw_author_name: Yinyin Yuan

Abstract: &lt;p&gt;Studying bone physiology using MoSaicNet. &lt;b&gt;A&lt;/b&gt;, Proportion of different compartments of BM trephine digital images. &lt;b&gt;B–E,&lt;/b&gt; One stacked bar represents a sample. Box plots showing the difference in %bone between samples from NDMM and posttreatment (&lt;b&gt;B&lt;/b&gt;), MGUS and NDMM (&lt;b&gt;C&lt;/b&gt;), different age groups (median age = 58.0 years; &lt;b&gt;D&lt;/b&gt;), and gender groups (&lt;b&gt;E&lt;/b&gt;). &lt;b&gt;F,&lt;/b&gt; Scatter plot showing the number of bone superpixels in 17 clusters from MGUS, NDMM, and posttreatment samples. The size of the dots represents the percentage of superpixels. The color represents the number of slides in each cluster. &lt;b&gt;G,&lt;/b&gt; Correlation of percentage of superpixels in each cluster between different patient groups. A point represents a cluster. &lt;b&gt;H,&lt;/b&gt; Scatter plot of slide-level heterogeneity of bone features measured by features variance (Supplementary Materials and Methods). A point represents a patient/slide. &lt;b&gt;I&lt;/b&gt; and &lt;b&gt;J,&lt;/b&gt; Box plots showing differences in bone density heterogeneity between NDMM and posttreatment (&lt;b&gt;I&lt;/b&gt;), and between MGUS and NDMM (&lt;b&gt;J&lt;/b&gt;). &lt;b&gt;K–N,&lt;/b&gt; Box plots showing the difference in bone thickness between samples from NDMM and posttreatment (&lt;b&gt;K&lt;/b&gt;), MGUS and NDMM (&lt;b&gt;L&lt;/b&gt;), and different age groups (median age = 58.0 years; &lt;b&gt;M&lt;/b&gt;) and gender (&lt;b&gt;N&lt;/b&gt;).&lt;/p&gt;
Supplementary Data from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Authors:
raw_author_name: Yeman Brhane Hagos
raw_author_name: Dominic Patel
raw_author_name: Anna Mikolajczak
raw_author_name: Simon P. Castillo
raw_author_name: Thien-An Tran
raw_author_name: Lydia S.H. Lee
raw_author_name: Manuel Rodriguez-Justo
raw_author_name: Kwee L. Yong
raw_author_name: Yinyin Yuan

Abstract: &lt;p&gt;Supplementary methods, tables and figures.&lt;/p&gt;
Figure 1 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Authors:
raw_author_name: Yeman Brhane Hagos
raw_author_name: Dominic Patel
raw_author_name: Anna Mikolajczak
raw_author_name: Simon P. Castillo
raw_author_name: Thien-An Tran
raw_author_name: Lydia S.H. Lee
raw_author_name: Manuel Rodriguez-Justo
raw_author_name: Kwee L. Yong
raw_author_name: Yinyin Yuan

Abstract: &lt;p&gt;Overview of computational deep learning and image processing pipelines for BM MIHC images: &lt;b&gt;A,&lt;/b&gt; MoSaicNet pipeline. The polygons (black) indicate superpixels. MoSaicNet dissects a tissue section into bone, blood, fat, and cellular tissue regions (Supplementary Materials and Methods). &lt;b&gt;B,&lt;/b&gt; AwareNet for attention-based cell detection and classification (Supplementary Materials and Methods). The attention image pixel values were generated from the abundance of cell types. An attention image was applied to the objective function during model parameter optimization to regularize the algorithm by assigning high attention to rare cell types. The cell detection algorithm generates a cell probability map. A postprocessing algorithm was developed to find the cell nucleus center, (&lt;i&gt;x&lt;/i&gt;, &lt;i&gt;y&lt;/i&gt;) location, from the probability map (Supplementary Materials and Methods). A patch centered on each cell was extracted and fed to deep learning (DL)-based classifier to infer its class. &lt;b&gt;C,&lt;/b&gt; Spatial and morphologic analysis of BM trephine samples. Bone texture and structural heterogeneity were investigated using an autoencoder-based machine learning method (Supplementary Materials and Methods). We used spatial proximity analysis to study the spatial relations of cells. &lt;i&gt;r&lt;/i&gt;, radius. Cell density refers to the number of cells per unit of tissue area.&lt;/p&gt;
Figure 2 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Authors:
raw_author_name: Yeman Brhane Hagos
raw_author_name: Dominic Patel
raw_author_name: Anna Mikolajczak
raw_author_name: Simon P. Castillo
raw_author_name: Thien-An Tran
raw_author_name: Lydia S.H. Lee
raw_author_name: Manuel Rodriguez-Justo
raw_author_name: Kwee L. Yong
raw_author_name: Yinyin Yuan

Abstract: &lt;p&gt;Computational methods for bone thickness analysis and cell infiltration patterns: &lt;b&gt;A,&lt;/b&gt; Image analysis to estimate bone thickness (Supplementary Materials and Methods). Using the same BM sample image as &lt;a href="#fig1" target="_blank"&gt;Fig. 1A&lt;/a&gt;, the bone segmentation (ii) is an output of MoSaicNet (Supplementary Materials and Methods), and each bone is displayed in a different color. The color bar shows the pixel intensity of the image in iii and iv. The pixel intensity on the skeleton indicates half of the bone thickness (Supplementary Materials and Methods). &lt;b&gt;B,&lt;/b&gt; Cell infiltration pattern analysis using NND and the null hypothesis of CSR (Supplementary Materials and Methods). Z &lt; −1.96, Z &gt; 1.96, and −1.96 ≤ Z ≤ 1.96 indicate a clustered, dispersed, and random distribution of observed cells, respectively. std, standard deviation; μ, mean NND of CSR.&lt;/p&gt;
Figure 6 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Authors:
raw_author_name: Yeman Brhane Hagos
raw_author_name: Dominic Patel
raw_author_name: Anna Mikolajczak
raw_author_name: Simon P. Castillo
raw_author_name: Thien-An Tran
raw_author_name: Lydia S.H. Lee
raw_author_name: Manuel Rodriguez-Justo
raw_author_name: Kwee L. Yong
raw_author_name: Yinyin Yuan

Abstract: &lt;p&gt;&lt;b&gt;A&lt;/b&gt; and &lt;b&gt;B,&lt;/b&gt; Spatial neighborhood of immune and tumor cells (&lt;b&gt;A&lt;/b&gt; and &lt;b&gt;B&lt;/b&gt;) and between MGUS and NDMM (&lt;b&gt;B&lt;/b&gt;). The &lt;i&gt;P&lt;/i&gt;* indicate &lt;i&gt;P&lt;/i&gt; values after multiple testing correction using the BH method. The points represent the mean and the bars are 95% CIs, indicating uncertainty. &lt;b&gt;C,&lt;/b&gt; Sample images showing an increased number of BLIMP1&lt;sup&gt;+&lt;/sup&gt; cells in the neighborhood with CD8&lt;sup&gt;+&lt;/sup&gt; on NDMM samples (NDMM example shown here is the same image as &lt;a href="#fig5" target="_blank"&gt;Fig. 5L&lt;/a&gt;) compared with MGUS samples. &lt;b&gt;D–I,&lt;/b&gt; Clustered or dispersed pattern of immune and tumor cells in BM trephine sample. Box plots showing the difference in NND and Z score between NDMM and posttreatment for CD8&lt;sup&gt;+&lt;/sup&gt; cells (&lt;b&gt;D&lt;/b&gt;), BLIMP1&lt;sup&gt;+&lt;/sup&gt; cells (&lt;b&gt;E&lt;/b&gt;), and FOXP3&lt;sup&gt;−&lt;/sup&gt;CD4&lt;sup&gt;+&lt;/sup&gt; cells (&lt;b&gt;F&lt;/b&gt;). Box plots showing the difference in NND and Z score between NDMM and MGUS for CD8&lt;sup&gt;+&lt;/sup&gt; cells (&lt;b&gt;G&lt;/b&gt;) and BLIMP1&lt;sup&gt;+&lt;/sup&gt; cells (&lt;b&gt;H&lt;/b&gt;), and between male and female for BLIMP1&lt;sup&gt;+&lt;/sup&gt; cells (&lt;b&gt;I&lt;/b&gt;). The unit of NND is μmol/L. The Z score shows the significance of the difference between the NND distribution for a given cell type from a complete spatial random distribution and the observed NND (Supplementary Materials and Methods).&lt;/p&gt;
Advances in Medical Imaging: Using Convolutional Neural Networks for White Blood Cell Identification

Authors:
raw_author_name: Ishwari Singh Rajput
raw_author_name: Sonam Tyagi
raw_author_name: Aditya Gupta
raw_author_name: Vibha Jain

Abstract: White blood cells (WBC) perform a vital function within the immune system by actively protecting the body from a wide range of diseases and foreign substances. Diverse types of WBCs exist, including neutrophils, lymphocytes, eosinophils, and monocytes, each possessing distinct roles within the immune response. Neutrophils are typically the initial immune cells to mobilize in response to infections and inflammation, exhibiting a rapid and robust reaction. Conversely, lymphocytes play a pivotal role in the recognition and targeted elimination of pathogens. Nevertheless, identifying and classifying WBCs poses significant challenges and demands considerable time, even for seasoned medical practitioners. The process of manual classification is frequently characterized by subjectivity and is susceptible to errors, thereby potentially compromising the precision of both diagnosis and treatment. In response to this challenge, scholars have devised deep learning methodologies that can automate the process of WBC classification, thereby enhancing its precision. This study employs a convolutional neural network (CNN) to classify WBCs based on imaging data. The CNN underwent training using a substantial dataset comprising body cell images. This training facilitated the acquisition of discerning characteristics specific to various WBC types, thereby enabling accurate classification. The methodology was evaluated within a simulated environment, yielding encouraging outcomes. The approach that was proposed successfully achieved an average accuracy rate of 98.33% in the classification of WBCs. This outcome serves as evidence of deep learning techniques enhancing the speed and accuracy of WBC classification.