A novel graph convolutional neural network for predicting interaction sites on protein kinase inhibitors in phosphorylation

Protein kinase-inhibitor interactions are key to the phosphorylation of proteins involved in cell proliferation, differentiation, and apoptosis, which shows the importance of binding mechanism research and kinase inhibitor design. In this study, a novel machine learning module (i.e., the WL Box) was designed and assembled to the Prediction of Interaction Sites of Protein Kinase Inhibitors (PISPKI) model, which is a graph convolutional neural network (GCN) to predict the interaction sites of protein kinase inhibitors. The WL Box is a novel module based on the well-known Weisfeiler-Lehman algorithm, which assembles multiple switch weights to effectively compute graph features. The PISPKI model was evaluated by testing with shuffled datasets and ablation analysis using 11 kinase classes. The accuracy of the PISPKI model with the shuffled datasets varied from 83 to 86%, demonstrating superior performance compared to two baseline models. The effectiveness of the model was confirmed by testing with shuffled datasets. Furthermore, the performance of each component of the model was analyzed via the ablation study, which demonstrated that the WL Box module was critical. The code is available at https://github.com/feiqiwang/PISPKI.

Joint Classification and Regression for Visual Tracking with Fully Convolutional Siamese Networks

Visual tracking of generic objects is one of the fundamental but challenging problems in computer vision. Here, we propose a novel fully convolutional Siamese network to solve visual tracking by directly predicting the target bounding box in an end-to-end manner. We first reformulate the visual tracking task as two subproblems: a classification problem for pixel category prediction and a regression task for object status estimation at this pixel. With this decomposition, we design a simple yet effective Siamese architecture based classification and regression framework, termed SiamCAR, which consists of two subnetworks: a Siamese subnetwork for feature extraction and a classification-regression subnetwork for direct bounding box prediction. Since the proposed framework is both proposal- and anchor-free, SiamCAR can avoid the tedious hyper-parameter tuning of anchors, considerably simplifying the training. To demonstrate that a much simpler tracking framework can achieve superior tracking results, we conduct extensive experiments and comparisons with state-of-the-art trackers on a few challenging benchmarks. Without bells and whistles, SiamCAR achieves leading performance with a real-time speed. Furthermore, the ablation study validates that the proposed framework is effective with various backbone networks, and can benefit from deeper networks. Code is available at https://github.com/ohhhyeahhh/SiamCAR.

Human Activity Recognition via Hybrid Deep Learning Based Model

In recent years, Human Activity Recognition (HAR) has become one of the most important research topics in the domains of health and human-machine interaction. Many Artificial intelligence-based models are developed for activity recognition; however, these algorithms fail to extract spatial and temporal features due to which they show poor performance on real-world long-term HAR. Furthermore, in literature, a limited number of datasets are publicly available for physical activities recognition that contains less number of activities. Considering these limitations, we develop a hybrid model by incorporating Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for activity recognition where CNN is used for spatial features extraction and LSTM network is utilized for learning temporal information. Additionally, a new challenging dataset is generated that is collected from 20 participants using the Kinect V2 sensor and contains 12 different classes of human physical activities. An extensive ablation study is performed over different traditional machine learning and deep learning models to obtain the optimum solution for HAR. The accuracy of 90.89% is achieved via the CNN-LSTM technique, which shows that the proposed model is suitable for HAR applications.

Comprehensive ablation study of near-IR femtosecond laser action on the titanium-based alloy Ti6Al4V: morphological effects and surface structures at low and high fluences

The surface of a titanium-based alloy Ti6Al4V was subjected to modifications by a near-IR femtosecond Ti:Sapphire laser, emitting at 775 nm pulses of 200 fs duration, in single-pulse and multi-pulse regimes, with up to 400 accumulated pulses, and pulse energies ranging from 2.5 to 250 $$\upmu $$ μ J. The whole range of induced effects is presented, from gentle ablation and pattern occurrence to substantial crater formation. Very observable laser-induced parallel periodic surface structures are reported, appearing both within the damage spot area, with low fluences, and at the peripheries of the craters, with higher fluences—but also on crater walls, and inside the crater structures. Damage threshold fluences $$({F}_{\mathrm{th}})$$ ( F th ) and the incubation factor $$(\zeta )$$ ( ζ ) were also determined. Graphic abstract

Human pose estimation based on parallel atrous convolution and body structure constraints

Human pose estimation is still a challenging task in computer vision, especially in the case of camera view transformation, joints occlusions and overlapping, the task will be of ever-increasing difficulty to achieve success. Most existing methods pass the input through a network, which typically consists of high-to-low resolution sub-networks that are connected in series. Still, during the up-sampling process, the spatial relationships and details might be lost. This paper designs a parallel atrous convolutional network with body structure constraints (PAC-BCNet) to address the problem. Among the mentioned techniques, the parallel atrous convolution (PAC) is constructed to deal with scale changes by connecting multiple different atrous convolution sub-networks in parallel. And it is used to extract features from different scales without reducing the resolution. Besides, the body structure constraints (BC), which enhance the correlation between each keypoint, are constructed to obtain better spatial relationships of the body by designing keypoints constraints sets and improving the loss function. In this work, a comparative experiment of the serial atrous convolution, the parallel atrous convolution, the ablation study with and without body structure constraints are conducted, which reasonably proves the effectiveness of the approach. The model is evaluated on two widely used human pose estimation benchmarks (MPII and LSP). The method achieves better performance on both datasets.

Latent-Insensitive Autoencoders for Anomaly Detection

Reconstruction-based approaches to anomaly detection tend to fall short when applied to complex datasets with target classes that possess high inter-class variance. Similar to the idea of self-taught learning used in transfer learning, many domains are rich with similar unlabeled datasets that could be leveraged as a proxy for out-of-distribution samples. In this paper we introduce the latent-insensitive autoencoder (LIS-AE) where unlabeled data from a similar domain are utilized as negative examples to shape the latent layer (bottleneck) of a regular autoencoder such that it is only capable of reconstructing one task. We provide theoretical justification for the proposed training process and loss functions along with an extensive ablation study highlighting important aspects of our model. We test our model in multiple anomaly detection settings presenting quantitative and qualitative analysis showcasing the significant performance improvement of our model for anomaly detection tasks.

On Safety and Time Efficiency Enhancement of Robot Navigation in Crowded Environment utilizing Deep Reinforcement Learning

<div>The evolution of social robots has increased with the advent of recent artificial intelligence techniques. Alongside humans, social robots play active roles in various household and industrial applications. However, the safety of humans becomes a significant concern when robots navigate in a complex and crowded environment. In literature, the safety of humans in relation to social robots has been addressed by various methods; however, most of these methods compromise the time efficiency of the robot. For robots, safety and time-efficiency are two contrast elements where one dominates the other. To strike a balance between them, a multi-reward formulation in the reinforcement learning framework is proposed, which improves the safety together with time-efficiency of the robot. The multi-reward formulation includes both positive and negative rewards that encourage and punish the robot, respectively. The proposed reward formulation is tested on state-of-the-art methods of multi-agent navigation. In addition, an ablation study is performed to evaluate the importance of individual rewards. Experimental results signify that the proposed approach balances the safety and the time-efficiency of the robot while navigating in a crowded environment.</div>

On Safety and Time Efficiency Enhancement of Robot Navigation in Crowded Environment utilizing Deep Reinforcement Learning

<div>The evolution of social robots has increased with the advent of recent artificial intelligence techniques. Alongside humans, social robots play active roles in various household and industrial applications. However, the safety of humans becomes a significant concern when robots navigate in a complex and crowded environment. In literature, the safety of humans in relation to social robots has been addressed by various methods; however, most of these methods compromise the time efficiency of the robot. For robots, safety and time-efficiency are two contrast elements where one dominates the other. To strike a balance between them, a multi-reward formulation in the reinforcement learning framework is proposed, which improves the safety together with time-efficiency of the robot. The multi-reward formulation includes both positive and negative rewards that encourage and punish the robot, respectively. The proposed reward formulation is tested on state-of-the-art methods of multi-agent navigation. In addition, an ablation study is performed to evaluate the importance of individual rewards. Experimental results signify that the proposed approach balances the safety and the time-efficiency of the robot while navigating in a crowded environment.</div>

Attentively Learning Edge Distributions for Semantic Segmentation of Remote Sensing Imagery

Semantic segmentation has been a fundamental task in interpreting remote sensing imagery (RSI) for various downstream applications. Due to the high intra-class variants and inter-class similarities, inflexibly transferring natural image-specific networks to RSI is inadvisable. To enhance the distinguishability of learnt representations, attention modules were developed and applied to RSI, resulting in satisfactory improvements. However, these designs capture contextual information by equally handling all the pixels regardless of whether they around edges. Therefore, blurry boundaries are generated, rising high uncertainties in classifying vast adjacent pixels. Hereby, we propose an edge distribution attention module (EDA) to highlight the edge distributions of leant feature maps in a self-attentive fashion. In this module, we first formulate and model column-wise and row-wise edge attention maps based on covariance matrix analysis. Furthermore, a hybrid attention module (HAM) that emphasizes the edge distributions and position-wise dependencies is devised combing with non-local block. Consequently, a conceptually end-to-end neural network, termed as EDENet, is proposed to integrate HAM hierarchically for the detailed strengthening of multi-level representations. EDENet implicitly learns representative and discriminative features, providing available and reasonable cues for dense prediction. The experimental results evaluated on ISPRS Vaihingen, Potsdam and DeepGlobe datasets show the efficacy and superiority to the state-of-the-art methods on overall accuracy (OA) and mean intersection over union (mIoU). In addition, the ablation study further validates the effects of EDA.

Mobile-PolypNet : Light-weight Colon Polyp Segmentation Network for Low Resources Settings

Colon polyps, small clump of cells on the lining of the colon can lead to Colorectal cancer (CRC), one of the leading types of cancer globally. Hence, early detection of these polyps is crucial in the prevention of CRC. This paper proposes a lightweight deep learning model for colorectal polyp segmentation that achieved state-of-the-art accuracy while significantly reducing the model size and complexity. The proposed deep learning autoencoder model employs a set of state-of-the-art architectural blocks and optimization objective functions to achieve the desired efficiency. The model is trained and tested on five publicly available colorectal polyp segmentation datasets (CVC-ClinicDB, CVC-ColonDB, EndoScene, Kvasir, and ETIS). We also performed ablation testing on the model to test various aspects of the autoencoder architecture. We performed the model evaluation using most of the common image segmentation metrics. The backbone model achieved a dice score of 0.935 on the Kvasir dataset and 0.945 on the CVC-ClinicDB dataset improving the accuracy by 4.12% and 5.12% respectively over the current state-of-the-art network, while using 88 times fewer parameters, 40 times less storage space, and being computationally 17 times more efficient. Our ablation study showed that the addition of ConvSkip in the autoencoder slightly improves the model’s performance but it was not significant (p-value=0.815).