Recent advancements in computational pathology and artificial intelligence have significantly improved whole slide image (WSI) classification. However, the gigapixel resolution of WSIs and the scarcity of manual annotations present substantial challenges. Multiple instance learning (MIL) is a promising weakly supervised learning approach for WSI classification. Recently research revealed employing pseudo bag augmentation can encourage models to learn various data, thus bolstering models' performance. While directly inheriting the parents' labels can introduce more noise by mislabeling in training. To address this issue, we translate the WSI classification task from weakly supervised learning to semi-weakly supervised learning, termed SWS-MIL, where adaptive pseudo bag augmentation (AdaPse) is employed to assign labeled and unlabeled data based on a threshold strategy. Using the “student-teacher” pattern, we introduce a feature augmentation technique, MergeUp, which merges bags with low-priority bags to enhance inter-category information, increasing training data diversity. Experimental results on the CAMELYON-16, BRACS, and TCGA-LUNG datasets demonstrate the superiority of our method over existing state-of-the-art approaches, affirming its efficacy in WSI classification.

Hyperspectral target detection (HTD) is an approach to identify targets of interest in a scene by utilizing prior target spectra. Existing deep learning-based HTD methods usually need to generate a large number of samples for network training, and these generated samples often suffer from distortion. In addition, most of the methods can only be applied to a single scene. To address these issues, this study proposes a self-supervised meta-transfer learning (SelfMTL) method to improve the generalization ability and adaptability of the model through contrastive representation. First, labeled source data, which contains rich feature information, is utilized to train the global-local spectral contrastive learning (GLSL) module by randomly constructing positive and negative pairs from different land covers for the classification task, aiming to effectively discriminate the similarities and differences between spectra. Then, a small sample (only one target-background pair) fine-tuning is utilized to transfer the pretrained GLSL to different target detection (TD) tasks. Finally, a novel adaptive spatial-spectral enhancement (ASSE) module is proposed, which takes into account the joint learning constraints of spatial and spectral information to obtain the final detection result map. The experimental results on four real hyperspectral images (HSIs) datasets verify the superiority of SelfMTL in comparison to many classical and SOTA HTD methods. The codes are available athttps://github.com/ShissHAN/SelfMTL.

Accurate diagnosis of cerebral tumors is crucial for effective clinical therapeutics and prognosis. However, limitations in brain biopsy tissues and the scarcity of pathologists specializing in cerebral tumors hinder comprehensive clinical tests for precise diagnosis. To address these challenges, we first established a brain tumor dataset of 3,520 cases collected from multiple centers. We then proposed a novel Hierarchically Optimized Multiple Instance Learning (HOMIL) method for classifying six common brain tumor types, glioma grading, and predicting the origin of brain metastatic cancers. The feature encoder and aggregator in HOMIL were trained alternately based on specific datasets and tasks. Compared to other multiple instance learning (MIL) methods, HOMIL achieved state-of-the-art performance with impressive accuracies: 93.29% / 85.60% for brain tumor classification, 91.21% / 96.93% for glioma grading, and 86.36% / 79.28% for origin determination on internal/external datasets. Additionally, HOMIL effectively located multi-scale regions of interest, enabling an in-depth analysis through features and heatmaps. Extensive visualization demonstrated HOMIL's ability to cluster features within the same type while establishing distinct boundaries between tumor types. It also identified critical areas on pathological slides, regardless of tumor size.

During the drug discovery and design process, the acid-base dissociation constant (pKa) of a molecule is critically emphasized due to its crucial role in influencing the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties and biological activity. However, the experimental determination of pKa values is often laborious and complex. Moreover, existing prediction methods exhibit limitations in both the quantity and quality of the training data, as well as in their capacity to handle the complex structural and physicochemical properties of compounds, consequently impeding accuracy and generalization. Therefore, developing a method that can quickly and accurately predict molecular pKa values will to some extent help the structural modification of molecules, and thus assist the development process of new drugs. In this study, we developed a cutting-edge pKa prediction model named GR-pKa (Graph Retention pKa), leveraging a message-passing neural network and employing a multi-fidelity learning strategy to accurately predict molecular pKa values. The GR-pKa model incorporates five quantum mechanical properties related to molecular thermodynamics and dynamics as key features to characterize molecules. Notably, we originally introduced the novel retention mechanism into the message-passing phase, which significantly improves the model's ability to capture and update molecular information. Our GR-pKa model outperforms several state-of-the-art models in predicting macro-pKa values, achieving impressive results with a low mean absolute error of 0.490 and root mean square error of 0.588, and a high R2 of 0.937 on the SAMPL7 dataset.

Hyperspectral target detection (HTD) has been a critical issue in the field of Earth observation, with widespread applications in both military and civilian domains. However, existing deep learning-based HTD methods are hindered due to insufficient and low-quality prior training samples, as well as inadequate background suppression capabilities. To address these issues, this article proposes an adversarial sample generation-based multiscale Siamese network (AGMS) for HTD. First, the AGMS utilizes the idea of generative adversarial learning based on the prior few targets and diverse backgrounds to generate adversarial target-background sample pairs, thereby producing high-quality training samples, which enhances the distinctiveness between the target and background samples by adversarially training the generator to produce the target/background samples. In addition, to further highlight the targets and suppress the backgrounds, a difference amplification loss and an adaptive weighted binary cross-entropy loss are proposed. Finally, a multiscale convolutional Siamese network model is designed to explore the generated spectral information at multiple levels and achieve target detection through contrastive learning. Numerous experimental results on four real HSI datasets verify the superiority of the AGMS in comparison to many classical and recently proposed HTD methods. The codes are available athttps://github.com/ShissHAN/AGMS.

OBJECTIVE: Acute kidney injury (AKI) is a common complication after pediatric cardiac surgery, and the early detection of AKI may allow for timely preventive or therapeutic measures. However, current AKI prediction researches pay less attention to time information among time-series clinical data and model building strategies that meet complex clinical application scenario. This study aims to develop and validate a model for predicting postoperative AKI that operates sequentially over individual time-series clinical data. MATERIALS AND METHODS: A retrospective cohort of 3386 pediatric patients extracted from PIC database was used for training, calibrating, and testing purposes. A time-aware deep learning model was developed and evaluated from 3 clinical perspectives that use different data collection windows and prediction windows to answer different AKI prediction questions encountered in clinical practice. We compared our model with existing state-of-the-art models from 3 clinical perspectives using the area under the receiver operating characteristic curve (ROC AUC) and the area under the precision-recall curve (PR AUC). RESULTS: Our proposed model significantly outperformed the existing state-of-the-art models with an improved average performance for any AKI prediction from the 3 evaluation perspectives. This model predicted 91% of all AKI episodes using data collected at 24 h after surgery, resulting in a ROC AUC of 0.908 and a PR AUC of 0.898. On average, our model predicted 83% of all AKI episodes that occurred within the different time windows in the 3 evaluation perspectives. The calibration performance of the proposed model was substantially higher than the existing state-of-the-art models. CONCLUSIONS: This study showed that a deep learning model can accurately predict postoperative AKI using perioperative time-series data. It has the potential to be integrated into real-time clinical decision support systems to support postoperative care planning.

In the robust optimization of Electrical Multiple Units (EMU) brake module, a multi-objective robust optimization mathematical model of the brake module based on Radial Basis Function Neural Network (RBFNN) that is improved by Genetic Particle Swarm Optimization (GPSO-RBFNN) is proposed, considering the complex mathematical mapping relationship between the design variables and the target responses. For improving the calculation efficiency, an Improved Non-Dominated Sorted Genetic Algorithm-III (INSGA-III) is proposed. Firstly, the key design variables to total mass and natural frequency are selected by the sensitivity analysis for the brake module. Secondly, the sample value of each design variable of the brake module and its corresponding quality characteristic value are obtained through the orthogonal test, and then the Signal-to-Noise Ratio (SNR) of each quality characteristic can be calculated. The sample value obtained from the orthogonal test design is used as the input and the corresponding signal-to-noise ratio is used as the output for the training and testing of GPSO-RBFNN. Finally, establish the multi-objective robust optimization mathematical model based on GPSO-RBFNN, and the INSGA-III is used to solve the optimization model. Then compare the robust optimization result with the traditional design scheme. The result shows that the prediction accuracy of GPSO-RBFNN is much higher than that of RBFNN; the computational efficiency of INSGA-III is higher than that of NSGA-III. After optimization, the SNR of each quality characteristic of the brake module is improved, and the multi-objective robust optimization of the EMU brake module is realized.

Strong PUF represented by arbiter PUF is suitable for the authentication of resource-constrained devices. However, conventional arbiter PUF is vulnerable to modeling attacks due to its linear structure. In this paper, we propose an arbiter PUF with time-variant obfuscation scheme (TVO-APUF), which feeds the external random challenges into the linear feedback shift register (LFSR) module to determine the real challenge of underlying arbiter PUF, thus obfuscating the linear mapping relationship between challenge and response, leading to significant resistance to modeling attacks. In addition, LFSR module with low hardware overhead can be updated at any time to prevent reply attack. We implement a 48-stage TVO-APUF on Xilinx Spartan-6 FPGA board. The experimental results show that the proposed TVO-APUF can effectively resist modeling attacks such as logistic regression (LR), support vector machine (SVM) and evolutionary strategy (ES) with a maximum prediction rate of 53 % and slight effects on uniformity, stability and uniqueness.

In this paper, a novel high dynamic range (HDR) image zero-watermarking algorithm against the tone mapping attack is proposed. In order to extract stable and invariant features for robust zero-watermarking, the shift-invariant shearlet transform (SIST) is used to transform the HDR image. Firstly, the HDR image is converted to CIELAB color space, and the L component is selected to perform SIST for obtaining the low-frequency subband containing the robust structure information of the image. Secondly, the low-frequency subband is divided into nonoverlapping blocks, which are transformed by using discrete cosine transform (DCT) and singular value decomposition (SVD) to obtain the maximum singular values for constructing a binary feature image. To increase the watermarking security, a hybrid chaotic mapping (HCM) is employed to get the scrambled watermark. Finally, an exclusive-or operation is performed between the binary feature image and the scrambled watermark to compute robust zero-watermark. Experimental results show that the proposed algorithm has a good capability of resisting tone mapping and other image processing attacks.

Ethernet plays an important role in the layer 2 network. Unfortunately, the tremendous Address Resolution Protocol (ARP) broadcast traffic among massive hosts limits the scale of Ethernet. Recently, Software-Defined Network (SDN) has been proposed to suppress broadcast traffic by centralized control. However, existing approaches based on SDN suffer from an adaptability limitation as they cannot independently obtain ARP table entries. In this paper, we propose SDARP, a Software-Defined Address Resolution Proxy, to suppress broadcast traffic by centrally processing all ARP packets. To overcome the adaptability limitation, SDARP centrally obtains and maintains ARP table entries by independently resolving the header of ARP messages. SDARP is a SDN application. We prototype SDARP based on the open-source SDN controller RYU, and conduct experiments on the Mininet-based virtual testbed. The emulation results demonstrate that SDARP is transparent to hosts, effectively reduces ARP traffic to 7.1%, eliminates the broadcast storm and reduces the response time of the Internet Control Message Protocol to 35.9%.

Named Data Networking (NDN) is currently a hot research topic in the field of network architecture, and its transport control mechanism is one of the key technologies needed to be studied. Since the transport in NDN network has the characteristic of multi-source, the implicit congestion detection mechanism of the traditional TCP protocol is no longer suitable for the NDN network. In this paper, we propose a novel congestion control protocol for NDN network based on explicit feedback - ECP (Explicit Control Protocol), which detects the condition of network congestion proactively, and sends explicit feedback to the receiver. According to the feedback, the receiver can adjust the sending rate of Interests in order to control the sending rate of Datas from the sender, thus to realize the congestion control of the network. The simulation results based on NdnSIM show that the ECP protocol performs higher transfer efficiency and stability compared to the current NDN transport protocol using TCP implicit detection mechanism.