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Background: Differential counting (DC) of different cell types in bone marrow (BM) aspiration smears is crucial for diagnosing hematological diseases. However, a clinically applicable method for automatic DC has yet to be developed., Objective: This study developed and validated an artificial intelligence (AI)-based algorithm for identifying and classifying nucleated cells in BM smears., Methods: In the development phase, a mask region-based convolutional neural network (Mask R-CNN)-based AI model was trained to detect and classify individual BM cells. We used a large data set of expert-annotated images representing a variety of disease categories. The BM slides were stained with Liu's stain or Wright-Giemsa stain. Consensus meetings were held to ensure experts from different institutes applied consistent criteria in classifying cells. Subsequently, the performance of the AI algorithm in identifying cell images and determining cell ratios was evaluated using a multinational clinical dataset., Results: The AI model was trained on 542 slides (85.1 % stained with Liu's stain and 14.9 % with Wright-Giemsa stain) containing 597,222 annotated cells. It achieved an accuracy of 0.94 for the testing dataset containing 26,170 cells. The performance of the AI model was further validated using another multinational real-world dataset (data obtained from three centers in Taiwan and one in the United States) comprising 200,639 cells. The AI model achieved an accuracy of 0.881 in classifying individual cells and demonstrated high precision in classifying blasts (0.927), bands and polymorphonuclear neutrophils (0.955), plasma cells (0.930), and lymphocytes (0.789). When the differential counting percentage of each cell type was assessed, a strong correlation (ρ > 0.8) between the AI and manual methods was observed for most cell categories., Conclusions: In this study, an AI algorithm was developed and clinically validated using large, multinational datasets. Our algorithm can locate and classify BM cells simultaneously and has potential clinical applicability for automating BM differential counting., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: NTUH and aetherAI Co., Ltd. own the intellectual property and possible commercial profit of the AI-based product derived from this study. Authors who are employees of the manufacturer of the AI-based product (aetherAI Co., Ltd.) contributed to study design, data annotation, software development, and website development for annotation, AI model development, clinical research, manuscript writing, manuscript review, and manuscript editing., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Multi-Modal Entity Alignment (MMEA), aiming to discover matching entity pairs on two multi-modal knowledge graphs (MMKGs), is an essential task in knowledge graph fusion. Through mining feature information of MMKGs, entities are aligned to tackle the issue that an MMKG is incapable of effective integration. The recent attempt at neighbors and attribute fusion mainly focuses on aggregating multi-modal attributes, neglecting the structure effect with multi-modal attributes for entity alignment. This paper proposes an innovative approach, namely TriFac, to exploit embedding refinement for factorizing the original multi-modal knowledge graphs through a two-stage MMKG factorization. Notably, we propose triplet-aware graph neural networks to aggregate multi-relational features. We propose multi-modal fusion for aggregating multiple features and design three novel metrics to measure knowledge graph factorization performance on the unified factorized latent space. Empirical results indicate the effectiveness of TriFac, surpassing previous state-of-the-art models on two MMEA datasets and a power system dataset., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Diagnosing diseases heavily relies on cell pathology images, but the extensive data in each manual identification of relevant cells labor-intensive, especially in regions with a scarcity of qualified healthcare professionals. This study aims to develop an intelligent system to enhance the diagnostic accuracy of cytopathology images by addressing image noise and segmentation issues, thereby improving the efficiency of medical professionals in disease diagnosis. We introduced an innovative system combining a self-supervised algorithm, SDN, for image denoising with data enhancement and image segmentation using the UPerMVit model. The UPerMVit model's novel attention mechanisms and modular architecture provide higher accuracy and lower computational complexity than traditional methods. The proposed system effectively reduces image noise and accurately segments annotated images, highlighting cellular structures relevant to medical staff. This enhances diagnostic accuracy and aids in the accurate identification of pathological cells. Our intelligent system offers a reliable tool for medical professionals, improving diagnostic efficiency and accuracy in cytopathologic image analysis. It provides significant technical support in regions lacking adequate medical expertise., (© 2024. The Author(s).)

As the segment of diseased tissue in PET images is time-consuming, laborious and low accuracy, this work proposes an automated framework for PET image screening, denoising and diseased tissue segmentation. First, taking into account the characteristics of PET images, the framework uses a differential activation filter to select whole-body images containing lesion tissue. Second, a new neural network containing residual connections which has powerful generalization performance compared with normal FCN network is proposed for PET image reconstruction and denoising. Finally, in the segmentation of lesion tissues, a custom clustering algorithm based on the density is used to distinguishe the lesion tissue part from the normal tissue. Tests on real medical PET images show that the whole automated framework has good performance and time cost in PET lesion image screening, image denoising and lesion tissue segmentation compared with other algorithms. The framework shows promising scientific study and application prospects.

Accurately predicting protein-ATP binding residues is critical for protein function annotation and drug discovery. Computational methods dedicated to the prediction of binding residues based on protein sequence information have exhibited notable advancements in predictive accuracy. Nevertheless, these methods continue to grapple with several formidable challenges, including limited means of extracting more discriminative features and inadequate algorithms for integrating protein and residue information. To address the problems, we propose ATP-Deep, a novel protein-ATP binding residues predictor. ATP-Deep harnesses the capabilities of unsupervised pre-trained language models and incorporates domain-specific evolutionary context information from homologous sequences. It further refines the embedding at the residue level through integration with corresponding protein-level information and employs a contextual-based co-attention mechanism to adeptly fuse multiple sources of features. The performance evaluation results on the benchmark datasets reveal that ATP-Deep achieves an AUC of 0.954 and 0.951, respectively, surpassing the performance of the state-of-the-art model. These findings underscore the effectiveness of assimilating protein-level information and deploying a contextual-based co-attention mechanism grounded in context to bolster the prediction performance of protein-ATP binding residues., Competing Interests: Declaration of competing interest The authors declare no conflict of interests., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Currently, signed network representation has been applied to many fields, e.g., recommendation platforms. A mainstream paradigm of network representation is to map nodes onto a low-dimensional space, such that the node proximity of interest can be preserved. Thus, a key aspect is the node proximity evaluation. Accordingly, three new node proximity metrics were proposed in this study, based on the rigorous theoretical investigation on a new distance metric - signed average first-passage time (SAFT). SAFT derives from a basic random-walk quantity for unsigned networks and can capture high-order network structure and edge signs. We conducted network representation using the proposed proximity metrics and empirically exhibited our advantage in solving two downstream tasks - sign prediction and link prediction. The code is publicly available., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: name@snu.ac.kr name@jbnu.ac.kr name@ugent.be, (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Osteosarcoma is the most common primary malignant bone tumor in children and adolescents. It has a high degree of malignancy and a poor prognosis in developing countries. The doctor manually explained that magnetic resonance imaging (MRI) suffers from subjectivity and fatigue limitations. In addition, the structure, shape, and position of osteosarcoma are complicated, and there is a lot of noise in MRI images. Directly inputting the original data set into the automatic segmentation system will bring noise and cause the model's segmentation accuracy to decrease. Therefore, this paper proposes an osteosarcoma MRI image segmentation system based on a deep convolution neural network, which solves the overfitting problem caused by noisy data and improves the generalization performance of the model. Firstly, we use Mean Teacher to optimize the data set. The noise data is put into the second round of training of the model to improve the robustness of the model. Then, we segment the image using a deep separable U-shaped network (SepUNet) and conditional random field (CRF). SepUnet can segment lesion regions of different sizes at multiple scales; CRF further optimizes the boundary. Finally, this article calculates the area of the tumor area, which provides a more intuitive reference for assisting doctors in diagnosis. More than 80000 MRI images of osteosarcoma from three hospitals in China were tested. The results show that the proposed method guarantees the balance of speed, accuracy, and cost under the premise of improving accuracy., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Jia Wu et al.)

Segmentation of liver in abdominal computed tomography (CT) is an important step for radiation therapy planning of hepatocellular carcinoma. Practically, a fully automatic segmentation of liver remains challenging because of low soft tissue contrast between liver and its surrounding organs, and its highly deformable shape. The purpose of this work is to develop a novel superpixel-based and boundary sensitive convolutional neural network (SBBS-CNN) pipeline for automated liver segmentation. The entire CT images were first partitioned into superpixel regions, where nearby pixels with similar CT number were aggregated. Secondly, we converted the conventional binary segmentation into a multinomial classification by labeling the superpixels into three classes: interior liver, liver boundary, and non-liver background. By doing this, the boundary region of the liver was explicitly identified and highlighted for the subsequent classification. Thirdly, we computed an entropy-based saliency map for each CT volume, and leveraged this map to guide the sampling of image patches over the superpixels. In this way, more patches were extracted from informative regions (e.g. the liver boundary with irregular changes) and fewer patches were extracted from homogeneous regions. Finally, deep CNN pipeline was built and trained to predict the probability map of the liver boundary. We tested the proposed algorithm in a cohort of 100 patients. With 10-fold cross validation, the SBBS-CNN achieved mean Dice similarity coefficients of 97.31  ±  0.36% and average symmetric surface distance of 1.77  ±  0.49 mm. Moreover, it showed superior performance in comparison with state-of-art methods, including U-Net, pixel-based CNN, active contour, level-sets and graph-cut algorithms. SBBS-CNN provides an accurate and effective tool for automated liver segmentation. It is also envisioned that the proposed framework is directly applicable in other medical image segmentation scenarios.

Using an immunohistochemistry (IHC) based method, diffuse large B-cell lymphoma (DLBCL) can be classified into germinal center B-cell (GCB) and non-GCB subtypes. However, the prognostic value of Hans algorithm was contradictory in the literature. Using IHC and fluorescence in situ hybridization, we analyzed the antibodies applied in Hans algorithm and other genetic factors in 601 DLBCL patients and prognostic value of Hans algorithm in 306 cases who were treated with chemoimmunotherapy. The results showed that patients with GCB subtype have better overall survival (OS) and progression-free survival (PFS) than non-GCB cases. However, to some extent, double positive (CD10(+)MUM1(+), DP) and triple negative (CD10(-)Bcl6(-)MUM(-), TN) showed different clinical characteristics and prognosis to others that were assigned to the same cell-of-origin group. The DP group showed similar OS (median OS: both not reached, P = 0.3650) and PFS (median PFS: 47.0 vs. 32.7 months, P = 0.0878) with the non-GCB group while the TN group showed similar OS (median OS: both not reached, P = 0.9278) and PFS (median PFS: both not reached, P = 0.9420) with the GCB group. In conclusion, Recognition of specific entities in Hans algorithm could help us to accurately predict outcome of the patients and choose the best clinical management for them.

Based on linguistic term sets and hesitant fuzzy sets, the concept of hesitant fuzzy linguistic sets was introduced. The focus of this paper is the multicriteria decision-making (MCDM) problems in which the criteria are in different priority levels and the criteria values take the form of hesitant fuzzy linguistic numbers (HFLNs). A new approach to solving these problems is proposed, which is based on the generalized prioritized aggregation operator of HFLNs. Firstly, the new operations and comparison method for HFLNs are provided and some linguistic scale functions are applied. Subsequently, two prioritized aggregation operators and a generalized prioritized aggregation operator of HFLNs are developed and applied to MCDM problems. Finally, an illustrative example is given to illustrate the effectiveness and feasibility of the proposed method, which are then compared to the existing approach.

Both transcription factors (TFs) and microRNAs (miRNAs) regulate gene expression. TFs activate or suppress the initiation of the transcription process and miRNAs regulate mRNAs post-transcriptionally, thus forming a temporally ordered regulatory event. Ectopic expression of key transcriptional regulators and/or miRNAs has been shown to be involved in various tumors. Therefore, uncovering the coregulation of TFs and miRNAs in human cancers may lead to the discovery of novel therapeutics. We introduced a two-stage learning fuzzy method to model TF-miRNA coregulation using both genomic data and verified regulatory relationships. In Stage 1, a learning (adaptive) fuzzy inference system (ANFIS) combines two sequence alignment features of TF and target by learning from verified TF-target pairs into a sequence matching score. Next, a non-learning FIS incorporates a sequence alignment score and a correlation score from paired TF-target gene expression to output a Stage 1 fuzzy score to infer whether a TF-target regulation exists. For significant TF-target pairs, in Stage 2, similar to Stage 1, we first infer whether a miRNA regulates each common target by an ANFIS, which incorporates their sequences and known miRNA-target relationships to output a sequence score. Next, an FIS incorporates the Stage 1 fuzzy score, Stage 2 sequence score and gene expression correlation score of a miRNA-target pair to determine whether TF-miRNA coregulation exists. We collected 54 (8) TF-miRNA-target triples validated in ER-positive (ER-negative) breast cancer cell lines in the same article, and they were used as positives. Negative examples were constructed for Stage 1 (Stage 2) by pairing TFs (miRNAs) with human housekeeping genes found in the literature; both positives and negatives were used to train ANFISs in the training step. This two-stage fizzy algorithm was applied to predict 54 (8) TF-miRNA coregulation triples in ER-positive (ER-negative) human breast cancer cell lines, and resulted in true-positive rates of 0.55 (0.74) and 0.57 (0.75) using 3-fold and n-fold cross validations (CVs), respectively. False-positive rate bound was 0.07 (0.13) for ER-positive (ER-negative) breast cancer using both 3-fold and n-fold CVs. Interestingly, among the 62 coregulatroy triples from ER-positive/negative breast cancer cells, about 72% have TF- and coregulatory miRNA expression simultaneously greater or less than the corresponding medians, while in the remaining 28% of TFs and their coregulatory miRNAs are conversely expressed. The proposed fuzzy algorithm performed well in identification of TF-miRNA coregulation triples in human breast cancer. After being trained by the corresponding verified coregulatory triples and genomic data, this algorithm can be applied to uncover novel coregulation in other cancers in the future., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Reconstruction of gene regulatory networks (GRNs) is of utmost interest and has become a challenge computational problem in system biology. However, every existing inference algorithm from gene expression profiles has its own advantages and disadvantages. In particular, the effectiveness and efficiency of every previous algorithm is not high enough. In this work, we proposed a novel inference algorithm from gene expression data based on differential equation model. In this algorithm, two methods were included for inferring GRNs. Before reconstructing GRNs, singular value decomposition method was used to decompose gene expression data, determine the algorithm solution space, and get all candidate solutions of GRNs. In these generated family of candidate solutions, gravitation field algorithm was modified to infer GRNs, used to optimize the criteria of differential equation model, and search the best network structure result. The proposed algorithm is validated on both the simulated scale-free network and real benchmark gene regulatory network in networks database. Both the Bayesian method and the traditional differential equation model were also used to infer GRNs, and the results were used to compare with the proposed algorithm in our work. And genetic algorithm and simulated annealing were also used to evaluate gravitation field algorithm. The cross-validation results confirmed the effectiveness of our algorithm, which outperforms significantly other previous algorithms.

A novel medical image quality index using grey relational coefficient calculation is proposed in this study. Three medical modalities, DR, CT and MRI, using 30 or 60 images with a total of 120 images used for experimentation. These images were first compressed at ten different compression ratios (10 ∼ 100) using a medical image compression algorithm named JJ2000. Following that, the quality of the reconstructed images was evaluated using the grey relational coefficient calculation. The results were shown consistent with popular objective quality metrics. The impact of different image aspects on four grey relational coefficient methods were further tested. The results showed that these grey relational coefficients have different slopes but very high consistency for various image areas. Nagai's grey relational coefficient was chosen in this study because of higher calculation speed and sensitivity. A comparison was also made between this method and other windows-based objective metrics for various window sizes. Studies found that the grey relational coefficient results are less sensitive to window size changes. The performance of this index is better than some windows-based objective metrics and can be used as an image quality index.

Simple Summary: This study presented an artificial intelligence-based classification emphasizing error identification and quantification of cellularity and nuclear morphological features in digital pathological images of common astrocytic tumors. The identification of incorrect predictions was essential for the subsequent development of better techniques. Quantifying cellularity and nuclear morphological features brought deeper insights into neoplastic morphology and paved the way for further development of a scoring system for objective classification and precision diagnosis to improve interobserver variations. Interobserver variations in the pathology of common astrocytic tumors impact diagnosis and subsequent treatment decisions. This study leveraged a residual neural network-50 (ResNet-50) in digital pathological images of diffuse astrocytoma, anaplastic astrocytoma, and glioblastoma to recognize characteristic pathological features and perform classification at the patch and case levels with identification of incorrect predictions. In addition, cellularity and nuclear morphological features, including axis ratio, circularity, entropy, area, irregularity, and perimeter, were quantified via a hybrid task cascade (HTC) framework and compared between different characteristic pathological features with importance weighting. A total of 95 cases, including 15 cases of diffuse astrocytoma, 11 cases of anaplastic astrocytoma, and 69 cases of glioblastoma, were collected in Taiwan Hualien Tzu Chi Hospital from January 2000 to December 2021. The results revealed that an optimized ResNet-50 model could recognize characteristic pathological features at the patch level and assist in diagnosis at the case level with accuracies of 0.916 and 0.846, respectively. Incorrect predictions were mainly due to indistinguishable morphologic overlap between anaplastic astrocytoma and glioblastoma tumor cell area, zones of scant vascular lumen with compact endothelial cells in the glioblastoma microvascular proliferation area mimicking the glioblastoma tumor cell area, and certain regions in diffuse astrocytoma with too low cellularity being misrecognized as the glioblastoma necrosis area. Significant differences were observed in cellularity and each nuclear morphological feature among different characteristic pathological features. Furthermore, using the extreme gradient boosting (XGBoost) algorithm, we found that entropy was the most important feature for classification, followed by cellularity, area, circularity, axis ratio, perimeter, and irregularity. Identifying incorrect predictions provided valuable feedback to machine learning design to further enhance accuracy and reduce errors in classification. Moreover, quantifying cellularity and nuclear morphological features with importance weighting provided the basis for developing an innovative scoring system to achieve objective classification and precision diagnosis among common astrocytic tumors. [ABSTRACT FROM AUTHOR]

The FRC algorithm is a commonly employed technique in LCD displays, aimed at enhancing the data bit depth through pixel dithering. However, it can also give rise to certain display issues such as noise, scrolling stripes, and flickering [1]. To address these problems comprehensively, this paper conducts an analysis from both temporal and spatial dimensions. It reveals that screen noise is closely associated with the uniformity of pixel data distribution throughout the entire FRC cycle time, while rolling stripes are linked to the spatial distribution of pixel data in both row and column directions. Furthermore, flickering arises due to variations in the number of FRC pattern pixels actually displayed per frame. The validity of these theories has been verified on an FPGA platform. [ABSTRACT FROM AUTHOR]

We derive an explicit expression for the Lellouch-Lüscher (LL) factor in the K → 3π decays at leading order (without derivative couplings). Several important technical details are addressed, like a proper decomposition into the isospin amplitudes, the choice of a minimal set of effective couplings and the renormalization, as well as the algorithm for the solution of the pertinent Faddeev equations in the infinite volume which is based on the contour deformation method. Most importantly, our numerical results demonstrate that the three-body force contributes very little to the LL factor. This result paves the way for the study of the K → 3π decays on the lattice. [ABSTRACT FROM AUTHOR]

The FRC algorithm is a commonly employed technique in LCD displays, aimed at enhancing the data bit depth through pixel dithering. However, it can also give rise to certain display issues such as noise, scrolling stripes, and flickering [1]. To address these problems comprehensively, this paper conducts an analysis from both temporal and spatial dimensions. It reveals that screen noise is closely associated with the uniformity of pixel data distribution throughout the entire FRC cycle time, while rolling stripes are linked to the spatial distribution of pixel data in both row and column directions. Furthermore, flickering arises due to variations in the number of FRC pattern pixels actually displayed per frame. The validity of these theories has been verified on an FPGA platform. [ABSTRACT FROM AUTHOR]

In order to improve the long-term reliability of lithium battery and ensure the safe operation of the system, a forecasting method combining whale optimization algorithm (WOA), variational mode decomposition (VMD) and the Gated Recurrent Unit model with Attention mechanism (GRU-Attention) was proposed. WOA – VMD was used to decompose the battery capacity data into the intrinsic mode component and residual component, which were, respectively, predicted by GRU-Attention, and finally effectively integrated to obtain accurate capacity prediction results. Through the experiments of the Li-ion battery data set of the Advanced Life Cycle Engineering Center of the University of Maryland, the proposed Li-ion battery combination prediction model has high prediction accuracy and stability. This method is a novel combination of advanced technologies and algorithms, and its effectiveness in improving the long-term reliability of lithium batteries has been verified by experiments. This research is of great significance for promoting the development and application of lithium battery technology. [ABSTRACT FROM AUTHOR]

In this paper, we study numerical solution of ruin probability of continuous time model. We develop an effective optimal adaptive particle swarm optimization-triangular neural network (PSO-TNN), which consists of three parts: particle swarm optimization algorithm (PSO) improved trigonometric function, extreme learning machine algorithm with initial conditions (IELM) and improved reduction algorithm. The results obtained that PSO-TNN is superior to triangular neural network (TNN) and physics-informed neural networks (PINNs), and PSO is superior to Aquila Optimizer (AO), Smell Agent Optimization (SAO), African vultures optimization algorithm (AVOA), Arithmetic optimization algorithm (AOA) in the optimization of neural network. Because the relationship between the number of neural networks and the mean square error is uncertain, we propose the adaptive reduction algorithm (AR). Through the comparison of numerical solutions with the analytical solutions and traditional numerical solutions, the PSO-TNN algorithm clearly reduced the mean square error and relative error. The PSO-TNN algorithm shows a clear improvement in terms of accuracy and overall efficiency. [ABSTRACT FROM AUTHOR]

Hyperparameter optimization plays a significant role in the overall performance of machine learning algorithms. However, the computational cost of algorithm evaluation can be extremely high for complex algorithm or large dataset. In this paper, we propose a model-based reinforcement learning with experience variable and meta-learning optimization method to speed up the training process of hyperparameter optimization. Specifically, an RL agent is employed to select hyperparameters and treat the k-fold cross-validation result as a reward signal to update the agent. To guide the agent's policy update, we design an embedding representation called "experience variable" and dynamically update it during the training process. Besides, we employ a predictive model to predict the performance of machine learning algorithm with the selected hyperparameters and limit the model rollout in short horizon to reduce the impact of the inaccuracy of the model. Finally, we use the meta-learning technique to pre-train the model for fast adapting to a new task. To prove the advantages of our method, we conduct experiments on 25 real HPO tasks and the experimental results show that with the limited computational resources, the proposed method outperforms the state-of-the-art Bayesian methods and evolution method. [ABSTRACT FROM AUTHOR]

An advanced high dynamic range (HDR) liquid crystal display (LCD) with the excellent performance was developed. The HDR Display technology can enhance the contrast ratio of pictures and reduce power consumption by utilizing adaptive controlled dynamic backlight. Improved methods about simplification of lookup tables (LUT), improvement of accuracy, and maintenance of details and chroma were studied, which make it a promising technique for displays. [ABSTRACT FROM AUTHOR]

With the popularization of 5G, the number of mobile devices has gone through explosive growth, the information and contents in the network have become richer, and the scale of the Social Internet of Things (SIoT) has been expanding. However, massive content transmission has also increased the burden of SIoT. In order to break through the limitations of IP architecture in addressing scheme, user interest mining and content caching, this article introduces Information-Centric Networking (ICN) into SIoT and proposes an ICN-SIoT solution. To reduce network latency, lower energy consumption, and avoid node death in large-scale data transmission, this paper designs a cloud-edge collaboration-based ICN-SIoT framework and constructs a content transmission algorithm based on multi-community and edge-caching (CTME). CTME dynamically detects two kinds of communities for mobility-supported routing of two types of packets defined in ICN, which effectively reduces packet forwarding times and node energy consumption, and avoid node death due to energy exhaustion. It also uses edge caching to manage node cache space to contribute to effective data transmission. The experimental results show that CTME has an outstanding performance in reducing energy consumption and improving data transmission efficiency. This study will be of great relevance to the development of SIoT. [ABSTRACT FROM AUTHOR]

This paper considers the problem of minimizing a convex expectation function with a set of inequality convex expectation constraints. We propose a stochastic augmented Lagrangian-type algorithm—namely, the stochastic linearized proximal method of multipliers—to solve this convex stochastic optimization problem. This algorithm can be roughly viewed as a hybrid of stochastic approximation and the traditional proximal method of multipliers. Under mild conditions, we show that this algorithm exhibits O (K − 1 / 2) expected convergence rates for both objective reduction and constraint violation if parameters in the algorithm are properly chosen, where K denotes the number of iterations. Moreover, we show that, with high probability, the algorithm has a O (log (K) K − 1 / 2) constraint violation bound and O (log 3 / 2 (K) K − 1 / 2) objective bound. Numerical results demonstrate that the proposed algorithm is efficient. History: Accepted byAntonio Frangioni, Area Editor for Design & Analysis ofAlgorithms—Continuous. Funding: This work was supported by the National Natural Science Foundation of China [Grants 11971089, 11731013, 12071055, and 11871135]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplementary Information [https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.1228] or is available from the IJOC GitHub software repository (https://github.com/INFORMSJoC) at http://dx.doi.org/10.5281/zenodo.6818229. [ABSTRACT FROM AUTHOR]

Background: The existing torque ripple suppression algorithms of permanent magnet synchronous motor (PMSM) do not consider the vehicle transmission system model, and cannot satisfy the torque ripple suppression in different working modes of the PMSM. Purpose: To deal with the resonance problem of hybrid electric vehicle (HEV), which caused by the torque ripple of PMSM under different working modes. Methods: An adaptive torque ripple suppression (ATRS) algorithm of the PMSM considering the influence of the transmission system is proposed in this study, the torque ripple characteristics of the motor control system, which influenced by the transmission system in the vehicle environment, can be well revealed. The ATRS algorithm adopts fuzzy quasi proportional resonant (FQPR) controller in parallel with slide Fourier feedback (SDFF) control, which reduces the number of parameters needs to be determined in practical application. The ATRS algorithm designs the FQPR controller to realise the adaptive adjustment of the parameters and track the specific frequency resonant signal, and uses the SDFF control to simplify the computation of calculating the feedforward harmonic compensation voltage, which ensures the accuracy, robustness and efficiency of the algorithm. Results: The control effect of the ATRS algorithm is analysed in the time domain and the frequency domain through simulation, and the improvement of the torsional vibration response of the transmission system before and after applying the algorithm at the pure electric mode resonant speed is analysed. The simulation results show that the torque ripple amplitude decreases 47.37% after applying the algorithm, and the proposed ATRS algorithm has a good control effect on the improvement of the steady torsional vibration response of the transmission system. Conclusions: The algorithm proposed in this paper can effectively reduce the harmonic current and suppress the torque ripple of the PMSM. At the same time, the transmission torque amplitude of the transmission system at the resonant harmonic frequency can be greatly reduced, which verifies the effectiveness of the torque fluctuation suppression algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

Normal aging causes profound changes of structural degeneration and glucose hypometabolism in the human brain, even in the absence of disease. In recent years, with the extensive exploration of the topological characteristics of the human brain, related studies in rats have begun to investigate. However, age-related alterations of topological properties in individual brain metabolic network of rats remain unknown. In this study, a total of 48 healthy female Sprague–Dawley (SD) rats were used, including 24 young rats and 24 aged rats. We used Jensen-Shannon Divergence Similarity Estimation (JSSE) method for constructing individual metabolic networks to explore age-related topological properties and rich-club organization changes. Compared with the young rats, the aged rats showed significantly decreased clustering coefficient (Cp) and local efficiency (E loc ) across the whole-brain metabolic network. In terms of changes in local network measures, degree (D) and nodal efficiency (E nod ) of left posterior dorsal hippocampus, and E nod of left olfactory tubercle were higher in the aged rats than in the young rats. About the rich-club analysis, the existence of rich-club organization in individual brain metabolic networks of rats was demonstrated. In addition, our findings further confirmed that rich-club connections were susceptible to aging. Relative to the young rats, the overall strength of rich-club connections was significantly reduced in the aged rats, while the overall strength of feeder and local connections was significantly increased. These findings demonstrated the age-related reorganization principle of the brain structure and improved our understanding of brain alternations during aging. [ABSTRACT FROM AUTHOR]

This article introduces a special issue on Advanced Graph Mining on the Web, focusing on the theory, algorithms, and applications of graph mining. The authors highlight the importance of graph mining in utilizing big data resources on the web, such as social media and online social networks, for various applications like event detection, social bot detection, and intelligent recommendation systems. The special issue consists of two parts, with Part 2 featuring 10 contributions covering topics such as graph similarity learning, semi-supervised learning on graphs, session-based recommender systems, and knowledge graph completion. The guest editors believe that the articles in this issue represent the current frontiers of graph mining and hope they will inspire further development in the field. [Extracted from the article]

As the Internet of Things (IoT) smart mobile devices explode in complex opportunistic social networks, the amount of data in complex networks is increasing. Large amounts of data cause high latency, high energy consumption, and low-reliability issues when dealing with computationally intensive and latency-sensitive emerging mobile applications. Therefore, we propose a task-sharing strategy that comprehensively considers delay, energy consumption, and terminal reputation value (DERV) for this context. The model consists of a task-sharing decision model that integrates latency and energy consumption, and a reputation value-based model for the allocation of the computational resource game. The two submodels apply an improved particle swarm algorithm and a Lagrange multiplier, respectively. Mobile nodes in the complex social network are given the opportunity to make decisions so that they can choose to share computationally intensive, latency-sensitive computing tasks to base stations with greater computing power in the same network. At the same time, to prevent malicious competition from end nodes, the base station decides the allocation of computing resources based on a database of reputation values provided by a trusted authority. The simulation results show that the proposed strategy can meet the service requirements of low delay, low power consumption, and high reliability for emerging intelligent applications. It effectively realizes the overall optimized allocation of computation sharing resources and promotes the stable transmission of massive data in complex networks. [ABSTRACT FROM AUTHOR]

With the rapid development of 5G era, the number of messages on the network has increased sharply. The traditional opportunistic networks algorithm has some shortcomings in processing data. Most traditional algorithms divide the nodes into communities and then perform data transmission according to the divided communities. However, these algorithms do not consider enough nodes' characteristics in the communities' division, and two positively related nodes may divide into different communities. Therefore, how to accurately divide the community is still a challenging issue. We propose an efficient data transmission strategy for community detection (EDCD) algorithm. When dividing communities, we use mobile edge computing to combine network topology attributes with social attributes. When forwarding the message, we select optimal relay node as transmission according to the coefficients of channels. In the simulation experiment, we analyze the efficiency of the algorithm in four different real datasets. The results show that the algorithm has good performance in terms of delivery ratio and routing overhead. [ABSTRACT FROM AUTHOR]

Hyperparameter tuning is critical for the performance of machine learning algorithms. However, a noticeable limitation is the high computational cost of algorithm evaluation for complex models or for large datasets, which makes the tuning process highly inefficient. In this paper, we propose a novel model-based method for efficient hyperparameter optimization. Firstly, we frame this optimization process as a reinforcement learning problem and then employ an agent to tune hyperparameters sequentially. In addition, a model that learns how to evaluate an algorithm is used to speed up the training. However, model inaccuracy is further exacerbated by long-term use, resulting in collapse performance. We propose a novel method for controlling the model use by measuring the impact of the model on the policy and limiting it to a proper range. Thus, the horizon of the model use can be dynamically adjusted. We apply the proposed method to tune the hyperparameters of the extreme gradient boosting and convolutional neural networks on 101 tasks. The experimental results verify that the proposed method achieves the highest accuracy on 86.1% of the tasks, compared with other state-of-the-art methods and the average ranking of runtime is significant lower than all methods by using the predictive model. [ABSTRACT FROM AUTHOR]

People all around the world have become increasingly dependent on online social networks (OSNs), meanwhile, the number of malicious accounts in OSNs is also rapidly growing. Traditional content-based data mining techniques and user graph-based methods are asking for more and more computing resources from networks providers, especially for the networks with huge and complicated network topologies. Moreover, traditional content-based analysis methods need to keep up with the times, which need to be retrained when the structure of users' data changes or when the malicious contents come along with some pop cultures. With the purpose of reducing the dependence on network providers' computing resources and improving the precision of detection, a novel detection method of malicious account, which bases on dynamic users' reputation information propagation, is proposed in this paper. According to the comparison result of requesting user's comprehensive reputation and malicious threshold, user can mark requesting user's reputation so as to achieve the purpose of malicious account detection and providing indirect recommended reputation information about requesting user for other users. Through experiments with two real-world datasets and comparison with two typical efficient detection algorithms, this algorithm can effectively detect malicious accounts without the central detection system as well as improve the detection precision. [ABSTRACT FROM AUTHOR]

In social communication, mobile devices can be regarded as socialization nodes in opportunistic social networks. Furthermore, they can carry and store useful information. Mobile devices can select destination nodes and deliver messages through opportunistic social networks because messages can be securely and conveniently stored, carried, and transmitted with nodes. However, many communities in traditional methods, may deliver messages often depending on one or two nodes, such as messages exchange by two nodes in Epidemic algorithm, or messages are sprayed with node in Spray and Wait algorithm. If those nodes are not enough cache and over-flooding, data transmission in communities may wait for a long time. In this study, communities are recombined and then duplication nodes are filtered in our method. According to evaluate contribution by node and restructure community, deliver ratio and overhead can be improved in opportunistic networks. With satisfactory results from simulation and comparison with some existing algorithms, the average delivery ratio of new research method is 0.91, which is 30% higher than that of Epidemic algorithm; the end-to-end delay reduces 75% with Spray and wait algorithm. [ABSTRACT FROM AUTHOR]

The proximal method of multipliers was proposed by Rockafellar [Augmented Lagrangians and applications of the proximal point algorithm in convex programming. Math Oper Res. 1976;1:97–116] for solving convex programming and it is a kind of proximal point method applied to convex programming. In this paper, we apply this method for solving nonlinear semidefinite programming problems, in which subproblems have better properties than those from the augmented Lagrange method. We prove that, under the linear independence constraint qualification and the strong second-order sufficiency optimality condition, the rate of convergence of the proximal method of multipliers, for a nonlinear semidefinite programming problem, is linear and the ratio constant is proportional to 1/c, where c is the penalty parameter that exceeds a threshold c ∗ > 0. Moreover, the rate of convergence of the proximal method of multipliers is superlinear when the parameter c increases to + ∞. [ABSTRACT FROM AUTHOR]

Predictor-based Neural Architecture Search is an important topic since it can efficiently reduce the computational cost of evaluating candidate architectures. Most existing predictor-based NAS algorithms aim to design different predictors to improve prediction performance. Unfortunately, even a promising performance predictor may suffer from the accuracy decline due to long-term and continuous usage, thus leading to the degraded performance of the search strategy. That naturally gives rise to the following problems: how do predictors affect search strategies and how to efficiently use the predictor? In this paper, we take Reinforcement Learning (RL) based search strategy to study theoretically and empirically the impact of predictors on search strategies. We first formulate an RL-Predictor-based NAS algorithm as model-based RL and analyze it with a guarantee of monotonic improvement. Then, based on this analysis, we propose a simple procedure of predictor usage, named m i x e d b a t c h , which contains ground-truth data and prediction data in a batch. The proposed procedure can efficiently reduce the impact of predictor errors on the RL-based search strategy with maintaining performance growth. Our algorithm, RL-Predictor-based Neural Architecture Search with Mixed batch (RPNASM), outperforms traditional NAS algorithms and prior state-of-the-art predictor-based NAS algorithms on three NAS-Bench-201 tasks and one NAS-Bench-ASR task. Our code is available at https://github.com/tjdeng/RPNASM. • Theoretically analyze predictor's impact on RL search strategy for the first time. • Perform comprehensive experiments to investigate RL-Predictor based NAS algorithms. • Propose RL-Predictor-based NAS framework to enhance search performance. [ABSTRACT FROM AUTHOR]

In online social network, many data are created by human activities. Data communication in social networks can be recorded and analyzed by evaluation. The identification of nodes with the same types of attribute among numerous users has become a research problem in the field of social opportunistic network. Many mobile devices by users have to save and deliver information moment by moment. However, cache space with devices in social network is limited; a large amount of data must be saved and transmitted on time in online social networks. Delay and low delivery ratio may affect communication in opportunistic network. Thus, we propose an algorithm based on user nodes and neighbors in opportunistic networks to improve the transmission environment. Such networks can identify surrounding neighbors to evaluate nodes with probability, which will evaluate neighbors, ensure the high probability of node preferential access to information, and achieve the objectives of cache adjustment. Cache by node can be distributed reasonably. Experiments and a comparison of social opportunistic network algorithms show that the proposed method improves the cache utilization rate of nodes, reduces data transmission delay, and improves the overall network efficiency. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc network has become an important component of the intelligent transportation system, what's more, the vehicle trajectory prediction has gradually become one of the hotter issues in this research. Vehicle trajectory prediction cannot only provide accurate location services, but also can monitor traffic conditions in advance, and then recommend the best route for the vehicle. For this purpose, this research established a new method for vehicle trajectory prediction (TPVN), which is mainly applied to predict the vehicle trajectory in the short term. Based on the regularity of vehicle movement, the algorithm is helpful to predict the vehicle trajectory so as to estimate the position of the vehicle motion probability. To improve the prediction accuracy, the motion patterns are divided into two types: simple pattern and complex pattern. The advantage of the TPVN algorithm is that the calculation result not only predicts the movement behavior of vehicles in different motion patterns but also the probability distribution of all possible trajectories of the vehicle in the future. Simulation on a large number of true trajectory datasets shows that the performance of TPVN outperforms than other classical algorithms. [ABSTRACT FROM AUTHOR]

We considers a distributionally robust optimization problem when the ambiguity set specifies the support as well as the mean and the covariance matrix of the uncertain parameters. After deriving a general deterministic reformulation for the distributionally robust optimization problem, we obtain tractable optimization reformulations when the support set is the whole space and when it is a convex polyhedral set. A hybrid method of Gurobi and a smoothing Newton conjugate gradient method is suggested to solve the tractable optimization problems and numerical results of the hybrid method for solving an illustrative example are reported. [ABSTRACT FROM AUTHOR]

In opportunistic networks, sensor communication areas are established by node movements. Information can be transmitted among communication areas. Relevance nodes in communication areas are important because they carry information and build bridges between areas and data packets so that information can be delivered to the destination node. This study proposes a routing algorithm called sensor communication area node extend (SCANE), which aims to select relevance nodes and to recombine communication areas. This algorithm also enables information to be transmitted from the start node to the destination node easily. The simulation indicates that compared with Epidemic, PRoPHET, and Spray and wait algorithms in opportunistic networks, the proposed algorithm improves delivery ratio, overhead, and delay time. [ABSTRACT FROM AUTHOR]

Multi-instance learning (MIL) is commonly used to classify a set of instances, also known as a bag, where labels for the training set are only available for each bag. Many MIL methods exist, but they often suffer from high computation complexity and the key information from MIL being ignored, which deteriorates the classification performance. Recently, locality-sensitive hashing (LSH), with its high scalability, has shown the ability in enhancing MIL performance. However, for these LSH-based methods, the fixed number of bits is used to represent each projected dimension, resulting in subtle information loss and the algorithm performance reduction. In this paper, we propose a self-adaptive LSH encoding method for MIL, termed as SALE. SALE uses LSH to generate the primary batches, followed by a self-adaptive process for reconstruction. Reconstructed bags are transformed into random super histograms (RSH) using an incomplete coding method, and then weighted through a scheme that takes advantage of key instances. These weighted RSHs are used to train the learning model. SALE efficiently deals with large MIL problems, due to its low complexity and RSH’s ability to exploit key information of MIL. Experiments demonstrate SALE’s good performance compared to state-of-the-art MIL methods. [ABSTRACT FROM AUTHOR]

Extreme Learning Machine (ELM) is a popular machine learning method which can flexibly simulate the relationships of real-world classification applications. When facing problems ( i.e. , data sets) with a smaller number of samples ( i.e. , instances), ELM may often result in the overfitting trouble. In this paper, we propose a new Instance Cloned Extreme Learning Machine (IC-ELM for short) which can handle numerous different classification problems. IC-ELM uses an instance cloning method to balance the input data’s distribution and extend the training data set, which alleviates the overfitting issue and enhances the testing classification accuracy. Experiments and comparisons on 20 UCI data sets, and validations on image and text classification applications, demonstrate that IC-ELM is able to achieve superior results compared to the original ELM algorithm and its variants, as well as several other classical machine learning algorithms. [ABSTRACT FROM AUTHOR]

Identifying the morphology of rock blocks is vital to accurate modelling of rock mass structures. This paper applies the concepts of directed edges and vertex chain operations which are typical for block tracing approach to block assembling approach to construct the structure of three-dimensional fractured rock masses. Polygon subtraction and union algorithms that rely merely on vertex chain operation are proposed, which allow a fast and convenient construction of complex faces/loops. Apart from its robustness in dealing with finite discontinuities and complex geometries, the advantages of the current methodology in tackling some challenging issues associated with the morphological analysis of rock blocks are addressed. In particular, the identification of complex blocks with interior voids such as cavity, pit and torus can be readily achieved based on the number and the type of loops. The improved morphology visualization approach can benefit the pre-processing stage when analyzing the stability of rock masses subject to various engineering impacts using the block theory and the discrete element method. [ABSTRACT FROM AUTHOR]

In distance metric learning, recent work has shown that value difference metric (VDM) with a strong attribute independence assumption outperforms other existing distance metrics. However, an open question is whether VDM with a less restrictive assumption can perform even better. Many approaches have been proposed to improve VDM by weakening the assumption. In this paper, we make a comprehensive survey on the existing improved approaches and then propose a new approach to improve VDM by attribute weighting. We name the proposed new distance function as attribute-weighted value difference metric (AWVDM). Moreover, we propose a modified attribute-weighted value difference metric (MAWVDM) by incorporating the learned attribute weights into the conditional probability estimates of AWVDM. AWVDM and MAWVDM significantly outperform VDM and inherit the computational simplicity of VDM simultaneously. Experimental results on a large number of UCI data sets validate the performance of AWVDM and MAWVDM. [ABSTRACT FROM AUTHOR]

Recent years have witnessed extensive studies of graph classification due to the rapid increase in applications involving structural data and complex relationships. To support graph classification, all existing methods require that training graphs should be relevant (or belong) to the target class, but cannot integrate graphs irrelevant to the class of interest into the learning process. In this paper, we study a new universum graph classification framework which leverages additional 'non-example' graphs to help improve the graph classification accuracy. We argue that although universum graphs do not belong to the target class, they may contain meaningful structure patterns to help enrich the feature space for graph representation and classification. To support universum graph classification, we propose a mathematical programming algorithm, ugBoost, which integrates discriminative subgraph selection and margin maximization into a unified framework to fully exploit the universum. Because informative subgraph exploration in a universum setting requires the search of a large space, we derive an upper bound discriminative score for each subgraph and employ a branch-and-bound scheme to prune the search space. By using the explored subgraphs, our graph classification model intends to maximize the margin between positive and negative graphs and minimize the loss on the universum graph examples simultaneously. The subgraph exploration and the learning are integrated and performed iteratively so that each can be beneficial to the other. Experimental results and comparisons on real-world dataset demonstrate the performance of our algorithm. [ABSTRACT FROM AUTHOR]

Recent years have seen an increasing number of scientists employing data parallel computing frameworks, such as Hadoop, in order to run data-intensive applications. Research on data-grouping-aware data placement for Hadoop has become increasingly popular. However, we observe that many data-grouping-aware data placement schemes are static, without taking MapReduce job execution frequency into consideration. Such data placements scheme will lead to severe performance degradation that is way below the potential efficiency of optimal data distribution when executing MapReduce jobs that are executed frequency. In this paper, we propose a new data-grouping-aware dynamic (DGAD) data placement method based on the job execution frequency. Firstly, we build a job access correlation relation model among the data blocks according to the relationships provided by the records about historical data block access. Then we use a clustering algorithm to divide data blocks into clusters according to the job access correlation relation model among the data blocks and propose a data placement algorithm based on data block clusters in order to put correlated data blocks within a cluster on the different nodes. Finally, a series of experiments are carried out in order to verify the method proposed in this paper. Experimental results show that the proposed method can effectively deal with the mass data and can obviously improve the execution efficiency of MapReduce. [ABSTRACT FROM AUTHOR]

Purpose: To predict pathological response of breast cancer to neoadjuvant chemotherapy (NAC) based on quantitative, multiregion analysis of dynamic contrast enhancement magnetic resonance imaging (DCE-MRI).Materials and Methods: In this Institutional Review Board-approved study, 35 patients diagnosed with stage II/III breast cancer were retrospectively investigated using 3T DCE-MR images acquired before and after the first cycle of NAC. First, principal component analysis (PCA) was used to reduce the dimensionality of the DCE-MRI data with high temporal resolution. We then partitioned the whole tumor into multiple subregions using k-means clustering based on the PCA-defined eigenmaps. Within each tumor subregion, we extracted four quantitative Haralick texture features based on the gray-level co-occurrence matrix (GLCM). The change in texture features in each tumor subregion between pre- and during-NAC was used to predict pathological complete response after NAC.Results: Three tumor subregions were identified through clustering, each with distinct enhancement characteristics. In univariate analysis, all imaging predictors except one extracted from the tumor subregion associated with fast washout were statistically significant (P < 0.05) after correcting for multiple testing, with area under the receiver operating characteristic (ROC) curve (AUC) or AUCs between 0.75 and 0.80. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.79 (P = 0.002) in leave-one-out cross-validation. This improved upon conventional imaging predictors such as tumor volume (AUC = 0.53) and texture features based on whole-tumor analysis (AUC = 0.65).Conclusion: The heterogeneity of the tumor subregion associated with fast washout on DCE-MRI predicted pathological response to NAC in breast cancer. J. Magn. Reson. Imaging 2016;44:1107-1115. [ABSTRACT FROM AUTHOR]

In this paper, we formulate a new multi-graph-view learning task, where each object to be classified contains graphs from multiple graph-views. This problem setting is essentially different from traditional single-graph-view graph classification, where graphs are collected from one single-feature view. To solve the problem, we propose a cross graph-view subgraph feature-based learning algorithm that explores an optimal set of subgraphs, across multiple graph-views, as features to represent graphs. Specifically, we derive an evaluation criterion to estimate the discriminative power and redundancy of subgraph features across all views, with a branch-and-bound algorithm being proposed to prune subgraph search space. Because graph-views may complement each other and play different roles in a learning task, we assign each view with a weight value indicating its importance to the learning task and further use an optimization process to find optimal weight values for each graph-view. The iteration between cross graph-view subgraph scoring and graph-view weight updating forms a closed loop to find optimal subgraphs to represent graphs for multi-graph-view learning. Experiments and comparisons on real-world tasks demonstrate the algorithm's superior performance. [ABSTRACT FROM AUTHOR]

Graph classification aims to learn models to classify structure data. To date, all existing graph classification methods are designed to target one single learning task and require a large number of labeled samples for learning good classification models. In reality, each real-world task may only have a limited number of labeled samples, yet multiple similar learning tasks can provide useful knowledge to benefit all tasks as a whole. In this paper, we formulate a new multi-task graph classification (MTG) problem, where multiple graph classification tasks are jointly regularized to find discriminative subgraphs shared by all tasks for learning. The niche of MTG stems from the fact that with a limited number of training samples, subgraph features selected for one single graph classification task tend to overfit the training data. By using additional tasks as evaluation sets, MTG can jointly regularize multiple tasks to explore high quality subgraph features for graph classification. To achieve this goal, we formulate an objective function which combines multiple graph classification tasks to evaluate the informativeness score of a subgraph feature. An iterative subgraph feature exploration and multi-task learning process is further proposed to incrementally select subgraph features for graph classification. Experiments on real-world multi-task graph classification datasets demonstrate significant performance gain. [ABSTRACT FROM PUBLISHER]