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Highlights • A two-stream framework for multispectral pedestrian detection is presented. • A novel cross-modality interactive attention mechanism for fusion is proposed. • The approach utilizes the modal correlations and fuses features adaptively. • The approach outperforms the related works with better effectiveness. Abstract Multispectral pedestrian detection is an emerging solution with great promise in many around-the-clock applications, such as automotive driving and security surveillance. To exploit the complementary nature and remedy contradictory appearance between modalities, in this paper, we propose a novel cross-modality interactive attention network that takes full advantage of the interactive properties of multispectral input sources. Specifically, we first utilize the color (RGB) and thermal streams to build up two detached feature hierarchy for each modality, then by taking the global features, correlations between two modalities are encoded in the attention module. Next, the channel responses of halfway feature maps are recalibrated adaptively for subsequent fusion operation. Our architecture is constructed in the multi-scale format to better deal with different scales of pedestrians, and the whole network is trained in an end-to-end way. The proposed method is extensively evaluated on the challenging KAIST multispectral pedestrian dataset and achieves state-of-the-art performance with high efficiency. [ABSTRACT FROM AUTHOR]

Meningioma is the most common primary tumor of the central nervous system (CNS). Individualized treatment strategies should be formulated for the patients according to the WHO (World Health Organization) grade. Our aim was to investigate the effectiveness of various machine learning and traditional statistical models in predicting the WHO grade of preoperative patients with meningioma. Patients diagnosed with meningioma after surgery in West China Hospital and Shangjin Hospital of Sichuan University from 2009 to 2016 were included in the study cohort. As the training cohort (n = 1975), independent risk factors associated with high-grade meningioma were used to establish the Nomogram model. which was validated in a subsequent cohort (n = 1048) from 2017 to 2019 in our hospital. Logistic regression (LR), XGboost, Adaboost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) models were determined using F1 score, recall, accuracy, the area under the curve (ROC), calibration plot and decision curve analysis (DCA) were used to evaluate the different models. Logistic regression showed better predictive performance and interpretability than machine learning. Gender, recurrence history, T1 signal intensity, enhanced signal degree, peritumoral edema, tumor diameter, cystic, location, and NLR index were identified as independent risk factors and added to the nomogram. The AUC (Area Under Curve) value of RF was 0.812 in the training set, 0.807 in the internal validation set, and 0.842 in the external validation set. The calibration curve and DCA (Decision Curve Analysis) indicated that it had better prediction efficiency of LR than others. The Nomogram preoperative prediction model of meningioma of WHO II and III grades showed effective prediction ability. While machine learning exhibits strong fitting ability, it performs poorly in the validation set. [ABSTRACT FROM AUTHOR]

In the realm of automated industry and smart production, the deployment of fault warning systems is crucial for ensuring equipment reliability and enhancing operational efficiency. Although there are a multitude of existing methodologies for fault warning, the proficiency of these systems in processing and analysing data is increasingly challenged by the progression of industrial apparatus and the escalating magnitude and intricacy of the data involved. To address these challenges, this research outlines an innovative fault warning methodology that combines a bi-directional long short-term memory (Bi-LSTM) network with an enhanced hunter–prey optimisation (EHPO) algorithm. The Bi-LSTM network is strategically utilised to outline complex temporal patterns in machinery operational data, while the EHPO algorithm is employed to meticulously fine-tune the hyperparameters of the Bi-LSTM, aiming to enhance the accuracy and generalisability of fault warning. The EHPO algorithm, building upon the foundational hunter–prey optimisation (HPO) framework, introduces an advanced population initialisation process, integrates a range of strategic exploration methodologies, and strengthens its search paradigms through the incorporation of the differential evolution (DE) algorithm. This comprehensive enhancement aims to boost the global search efficiency and accelerate the convergence speed of the algorithm. Empirical analyses, conducted using datasets from real-world industrial scenarios, have validated the improved warning performance of this proposed methodology against some benchmark techniques, as evidenced by superior metrics such as root mean square error (RMSE) and mean absolute error (MAE), albeit with a slight increase in computational resource requirements. This study not only proposes a novel paradigm for fault warning within complex industrial frameworks but also contributes to the discourse on hyperparameter optimisation within the field of machine learning algorithms. [ABSTRACT FROM AUTHOR]

Hydration plays a crucial role in cement composites, but the traditional methods for measuring hydration heat face several limitations. In this study, we propose a machine learning-based approach to predict hydration heat at specific time points for three types of cement composites: ordinary Portland cement pastes, fly ash cement pastes, and fly ash–metakaolin cement composites. By adjusting the model architecture and analyzing the datasets, we demonstrate that the optimized artificial neural network model not only performs well during the learning process but also accurately predicts hydration heat for various cement composites from an extra dataset. This approach offers a more efficient way to measure hydration heat for cement composites, reducing the need for labor- and time-intensive sample preparation and testing. Furthermore, it opens up possibilities for applying similar machine learning approaches to predict other properties of cement composites, contributing to efficient cement research and production. [ABSTRACT FROM AUTHOR]

Developers often bundle unrelated changes (eg, bug fix and feature addition) in a single commit and then submit a 'poor cohesive' commit to version control system. Such a commit consists of multiple independent code changes and makes review of code changes harder. If the code changes before commit can be identified as related and unrelated ones, the 'cohesiveness' of a commit can be guaranteed. Inspired by the effectiveness of machine learning techniques in classification field, we model the relevance identification of code changes as a binary classification problem (ie, related and unrelated changes) and propose discriminative feature in software entities to characterize the relevance of code changes. In particular, to quantify the discriminative feature, 21 coupling rules and 4 cochanged type relationships are elaborately extracted from software entities to construct related changes vector ( RCV). Twenty-one coupling rules at granularities of class, attribute, and method can capture the relevance of code changes from structural coupling dimension, and 4 cochanged type relationships are defined to capture the change type combinations of software entities that may cause related changes. Based on RCV, machine learning algorithms are applied to identify the relevance of code changes. The experiment results show that probabilistic neural network and general regression neural network provide statistically significant improvements in accuracy of relevance identification of code changes over the other 4 machine learning algorithms. Related changes vector with 72 dimensions ( R C V72) outperforms other 2 RCVs with less dimensions. [ABSTRACT FROM AUTHOR]

• We focus on the challenging Incremental Few-Shot Object Detection (iFSD) problem and propose a feature transfer module to transfer the learned information from base features to the novel ones. • We propose a dual-stream network to transfer the knowledge learned from the base class to the novel class from the two levels of weight and network structure. • Extensive experiments are conducted on the challenging PASCAL VOC and MS COCO dataset under iFSD setting, demonstrating the effectiveness of our method. • The proposed method outperforms the existing methods. As a challenging problem in machine learning, incremental few-shot object detection (iFSD) [1] aims to incrementally detect novel classes with few examples, while keeping the previous knowledge without revisiting base classes. Here, we propose two models based on the observation that when new memories come, new connections will be created between memory cells in the brain [2]. The first one, which is called the multi-class head (MCH) model, simulates how humans add new memory-connections that every time novel classes come, a classification branch is added to predict novel classification. And the second model, called the bi-path multi-class head (BPMCH) model, adds a new backbone, which is initialized with the weight of the base class backbone, to transfer more knowledge of the base class to the novel class. Considering accuracy and speed, we choose the Fully Convolutional One-Stage Object Detection (FCOS) [3] + Adaptive Training Sample Selection (ATSS) [4] detector as our baseline. Our models are first trained on the base classes with abundant examples and then finetuned on novel classes with few examples, which not only maintain the knowledge learned from the base class but also transfer the knowledge to the novel class. Extensive experiments show that our models outperform the state-of-the-art model ONCE [1] on the COCO [5] and PASCAL VOC [6] by a large margin. [ABSTRACT FROM AUTHOR]