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Deep learning methods hold strong promise for identifying biomarkers for clinical application. However, current approaches for psychiatric classification or prediction do not allow direct interpretation of original features. In the present study, we introduce a sparse deep neural network (DNN) approach to identify sparse and interpretable features for schizophrenia (SZ) case-control classification. An L0 -norm regularization is implemented on the input layer of the network for sparse feature selection, which can later be interpreted based on importance weights. We applied the proposed approach on a large multi-study cohort with gray matter volume (GMV) and single nucleotide polymorphism (SNP) data for SZ classification. A total of 634 individuals served as training samples, and the classification model was evaluated for generalizability on three independent datasets of different scanning protocols (N = 394, 255, and 160, respectively). We examined the classification power of pure GMV features, as well as combined GMV and SNP features. Empirical experiments demonstrated that sparse DNN slightly outperformed independent component analysis + support vector machine (ICA + SVM) framework, and more effectively fused GMV and SNP features for SZ discrimination, with an average error rate of 28.98% on external data. The importance weights suggested that the DNN model prioritized to select frontal and superior temporal gyrus for SZ classification with high sparsity, with parietal regions further included with lower sparsity, echoing previous literature. The results validate the application of the proposed approach to SZ classification, and promise extended utility on other data modalities and traits which ultimately may result in clinically useful tools.

In the past years, data-driven approaches such as deep learning have been widely applied on machinery signal processing to develop intelligent fault diagnosis systems. In real-world applications, domain shift problem usually occurs where the distribution of the labeled training data, denoted as source domain, is different from that of the unlabeled testing data, known as target domain. That results in serious diagnosis performance degradation. This paper proposes a novel domain adaptation method for rolling bearing fault diagnosis based on deep learning techniques. A deep convolutional neural network is used as the main architecture. The multi-kernel maximum mean discrepancies (MMD) between the two domains in multiple layers are minimized to adapt the learned representations from supervised learning in the source domain to be applied in the target domain. The domain-invariant features can be efficiently extracted in this way, and the cross-domain testing performance can be significantly improved. Experiments on two rolling bearing datasets are carried out to validate the effectiveness of the domain adaptation approach. Comparisons with other approaches and related works demonstrate the superiority of the proposed method. The experimental results of this study suggest the proposed domain adaptation method offers a new and promising tool for intelligent fault diagnosis.

Multiplexing information in light's fundamental attributes to create supplementary orthogonal data channels has been well heralded as an effective means for optical data storage with greatly enhanced capacities. However, robust decoding methods against inevitable crosstalks associated with experimental noise and writing imperfections as the increase of multiplexing dimensions represent a major hurdle preventing the effective practice of multi-dimensional optical recording. Here, we propose a deep-learning-based retrieval approach for robust decoding multiplexed information. An artificial neural network is trained to learn the crosstalks from multiplexed recording in disordered gold nanorod aggregates with loosened orthogonality constraints. The acquired raw readout images are analyzed by the trained neural network, which allows quick, high-fidelity, and reliable information retrieval from polarization-, wavelength-, and 3D spatially multiplexed data. The smart decoding protocol paves the way toward the mass-production ready and wide-spread application of high-capacity multi-dimensional optical storage. [ABSTRACT FROM AUTHOR]

During the ignition process of a solid rocket motor, the pressure changes dramatically and the ignition process is very complex as it includes multiple reactions. Successful completion of the ignition process is essential for the proper operation of solid rocket motors. However, the measurement of pressure becomes extremely challenging due to several issues such as the enormity and high cost of conducting tests on solid rocket motors. Therefore, it needs to be investigated using numerical calculations and other methods. Currently, the fundamental theories concerning the ignition process have not been fully developed. In addition, numerical simulations require significant simplifications. To address these issues, this study proposes a solid rocket motor pressure prediction method based on bidirectional long short-term memory (CBiLSTM) combined with adaptive Gaussian noise (AGN). The method utilizes experimental pressure data and simulated pressure data as inputs for co-training to predict pressure data under new operating conditions. By comparison, the AGN-CBiLSTM method has a higher prediction accuracy with a percentage error of 3.27% between the predicted and actual data. This method provides an effective way to evaluate the performance of solid rocket motors and has a wide range of applications in the aerospace field. [ABSTRACT FROM AUTHOR]

Urban emergency shelters are a special type of public service facility, and their planning and construction are directly related to the safety of urban residents' lives and property, as well as to the sustainable development of the city. Existing research on the site selection of shelters is not ideal when dealing with large‐scale scenarios and fails to accurately reflect the actual situation. To address this issue, this study proposes an emergency shelter site selection model based on deep reinforcement learning, IAM‐PPO. This model constructs the site selection problem as a Markov Decision Process and uses deep learning to extract information from the site selection scenario. It finds the final solution for shelter locations through continuous exploration and learning. To improve the training efficiency of the model, the action masking process is innovatively applied to the model. The research results and ablation experiments using Shanghai as a case study prove that, owing to the diversity of shelter service ranges and action masking mechanism, the model proposed in this study can provide efficient and accurate shelter location services. Moreover, the customizability of this model provides meaningful reference value for other public facility location problems. [ABSTRACT FROM AUTHOR]

Background: Accurate pancreas and pancreatic tumor segmentation from abdominal scans is crucial for diagnosing and treating pancreatic diseases. Automated and reliable segmentation algorithms are highly desirable in both clinical practice and research. Purpose: Segmenting the pancreas and tumors is challenging due to their low contrast, irregular morphologies, and variable anatomical locations. Additionally, the substantial difference in size between the pancreas and small tumors makes this task difficult. This paper proposes an attention‐enhanced multiscale feature fusion network (AMFF‐Net) to address these issues via 3D attention and multiscale context fusion methods. Methods: First, to prevent missed segmentation of tumors, we design the residual depthwise attention modules (RDAMs) to extract global features by expanding receptive fields of shallow layers in the encoder. Second, hybrid transformer modules (HTMs) are proposed to model deep semantic features and suppress irrelevant regions while highlighting critical anatomical characteristics. Additionally, the multiscale feature fusion module (MFFM) fuses adjacent top and bottom scale semantic features to address the size imbalance issue. Results: The proposed AMFF‐Net was evaluated on the public MSD dataset, achieving 82.12% DSC for pancreas and 57.00% for tumors. It also demonstrated effective segmentation performance on the NIH and private datasets, outperforming previous State‐Of‐The‐Art (SOTA) methods. Ablation studies verify the effectiveness of RDAMs, HTMs, and MFFM. Conclusions: We propose an effective deep learning network for pancreas and tumor segmentation from abdominal CT scans. The proposed modules can better leverage global dependencies and semantic information and achieve significantly higher accuracy than the previous SOTA methods. [ABSTRACT FROM AUTHOR]

Individual Tree Detection-and-Counting (ITDC) is among the important tasks in town areas, and numerous methods are proposed in this direction. Despite their many advantages, still, the proposed methods are inadequate to provide robust results because they mostly rely on the direct field investigations. This paper presents a novel approach involving high-resolution imagery and the Canopy-Height-Model (CHM) data to solve the ITDC problem. The new approach is studied in six urban scenes: farmland, woodland, park, industrial land, road and residential areas. First, it identifies tree canopy regions using a deep learning network from high-resolution imagery. It then deploys the CHM-data to detect treetops of the canopy regions using a local maximum algorithm and individual tree canopies using the region growing. Finally, it calculates and describes the number of individual trees and tree canopies. The proposed approach is experimented with the data from Shanghai, China. Our results show that the individual tree detection method had an average overall accuracy of 0.953, with a precision of 0.987 for woodland scene. Meanwhile, the R2 value for canopy segmentation in different urban scenes is greater than 0.780 and 0.779 for canopy area and diameter size, respectively. These results confirm that the proposed method is robust enough for urban tree planning and management. [ABSTRACT FROM AUTHOR]

Introduction: Immune dysregulation plays a major role in cancer progression. The quantification of lymphocytic spatial inflammation may enable spatial system biology, improve understanding of therapeutic resistance, and contribute to prognostic imaging biomarkers. Methods: In this paper, we propose a knowledge-guided deep learning framework to measure the lymphocytic spatial architecture on human H&E tissue, where the fidelity of training labels is maximized through single-cell resolution image registration of H&E to IHC. We demonstrate that such an approach enables pixel-perfect ground-truth labeling of lymphocytes on H&E as measured by IHC. We then experimentally validate our technique in a genetically engineered, immune-compromised Rag2 mouse model, where Rag2 knockout mice lacking mature lymphocytes are used as a negative experimental control. Such experimental validation moves beyond the classical statistical testing of deep learning models and demonstrates feasibility of more rigorous validation strategies that integrate computational science and basic science. Results: Using our developed approach, we automatically annotated more than 111,000 human nuclei (45,611 CD3/CD20 positive lymphocytes) on H&E images to develop our model, which achieved an AUC of 0.78 and 0.71 on internal hold-out testing data and external testing on an independent dataset, respectively. As a measure of the global spatial architecture of the lymphocytic microenvironment, the average structural similarity between predicted lymphocytic density maps and ground truth lymphocytic density maps was 0.86 ± 0.06 on testing data. On experimental mouse model validation, we measured a lymphocytic density of 96.5 ± %1% in a Rag2+/- control mouse, compared to an average of 16.2 ± %5% in Rag2-/- immune knockout mice (p<0.0001, ANOVA-test). Discussion: These results demonstrate that CD3/CD20 positive lymphocytes can be accurately detected and characterized on H&E by deep learning and generalized across species. Collectively, these data suggest that our understanding of complex biological systems may benefit from computationally-derived spatial analysis, as well as integration of computational science and basic science. [ABSTRACT FROM AUTHOR]

For the intelligent vulnerability detection, the system extracts the graph structured source code slices according to the vulnerability characteristics from the program dependency graph of source code, and then presents the graph structured slice information to carry out vulnerability detection by using the graph neural network model. Slice level vulnerability detection was realized and the vulnerability line was located at the code line level. In order to verify the effectiveness of the system, compared with the static vulnerability detection systems, the vulnerability detection system based on serialized text information, and the vulnerability detection system based on graph structured information, the experimental results show that the proposed system has a high accuracy in the vulnerability detection capability and a good performance in the vulnerability code line prediction.

Product configurators are recognised as critical toolkits enabling customers to co-create products with companies. Most available product configurators require customers to select suitable product attributes from predefined options. However, customers usually find the selection processes frustrating due to their lack of product knowledge. In view of the fact that customers often express their needs in imprecise and vague natural language, we define a new needs-based configuration mechanism and propose an implementation approach based on text embeddings and multilayer perceptron. Specifically, we leverage the massive amount of product reviews by encoding them into text embeddings. A multilayer perceptron is trained to map text embeddings to product attribute options. Experiment results indicate that the mapping has good generalisation capability to map customer needs into product configurations. The performance of our approach is comparable to that of deep learning-based approaches but with much higher efficiency in terms of computational complexity. Our needs-based configuration thus provides a quick and effective means of facilitating product customisation. It also demonstrates an innovative way of utilising customer resources in unstructured text to co-create products with companies. [ABSTRACT FROM AUTHOR]

In recent years, deep learning technology has developed significantly in the judicial field. More and more scholars are introducing deep learning to solve problems in the judicial field and improve the efficiency of judicial agencies in handling cases. An important task of judicial work is to identify the key elements of complex cases, establish the relationship between entities in the document, accurately grasp the development of the case, provide a basis for the understanding, analysis, and ruling of the case, and increase the interpretability of the case results. However, judging from the actual application situation, the application of deep learning technology in the judicial field is not yet mature and still faces huge challenges. On the one hand, the existing legal text element identification methods only consider the identification of core elements and ignore the basic elements. However, the basic elements contain necessary legal-related information, which is basic case information unique to the legal field and has important reference value for judicial staff to analyze cases. On the other hand, the traditional method of identifying key elements does not consider the contextual semantic relationship and ignores the element semantic information and global semantic information is lost, resulting in poor recognition of key elements of legal text. Therefore, in the identification of key elements of legal texts, not only the core elements and basic elements must be considered, but also the semantic features of sentences and the semantic features of elements must be comprehensively considered, thereby improving the effect of identifying key elements of legal texts and promoting the efficiency of judicial organs in handling cases. Aiming at the problem of identifying the basic elements of legal texts, this paper proposes a model for identifying the basic elements of legal texts based on dynamic representation. This algorithm uses the dynamic representation capability of the BERT model to vectorize text and considers global domain semantic information during the pre-training process to achieve a more comprehensive vector representation. Secondly, the memory component of the bidirectional long short-term memory network is used to integrate all the feature information between long-distance words in the legal text, effectively express the meaning of the word in the context, and realize the recognition of the basic elements of the legal text. In addition, the conditional random field model is used to learn the transfer rules of labels between entities and output the label sequence that best conforms to the actual rules. Finally, the basic element identification model proposed in this article has been significantly improved by comparing it with the current better methods. [ABSTRACT FROM AUTHOR]

The article introduces a new model called APF-GAN that improves upon existing generative adversarial network (GAN)-based models for image semantic segmentation. The model incorporates various strategies such as asymmetric pre-training and fine-tuning, progressive growth training, and semantic spatially-guided noise to enhance image quality and training efficiency. Experimental results demonstrate that the APF-GAN outperforms other models in terms of segmentation accuracy and aesthetics. The article also provides examples of images generated by the APF-GAN and discusses potential areas for future research. [Extracted from the article]