Due to the broad-spectrum and high-efficiency antibacterial activity, antimicrobial peptides (AMPs) and their functions have been studied in the field of drug discovery. Using biological experiments to detect the AMPs and corresponding activities require a high cost, whereas computational technologies do so for much less. Currently, most computational methods solve the identification of AMPs and their activities as two independent tasks, which ignore the relationship between them. Therefore, the combination and sharing of patterns for two tasks is a crucial problem that needs to be addressed. In this study, we propose a deep learning model, called DMAMP, for detecting AMPs and activities simultaneously, which is benefited from multi-task learning. The first stage is to utilize convolutional neural network models and residual blocks to extract the sharing hidden features from two related tasks. The next stage is to use two fully connected layers to learn the distinct information of two tasks. Meanwhile, the original evolutionary features from the peptide sequence are also fed to the predictor of the second task to complement the forgotten information. The experiments on the independent test dataset demonstrate that our method performs better than the single-task model with 4.28% of Matthews Correlation Coefficient (MCC) on the first task, and achieves 0.2627 of an average MCC which is higher than the single-task model and two existing methods for five activities on the second task. To understand whether features derived from the convolutional layers of models capture the differences between target classes, we visualize these high-dimensional features by projecting into 3D space. In addition, we show that our predictor has the ability to identify peptides that achieve activity against Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). We hope that our proposed method can give new insights into the discovery of novel antiviral peptide drugs.

3-D human pose estimation or human tracking has always been the focus of research in the human–computer interaction community. As the calibration step of human pose estimation, subject-specific modeling is crucially important to the subsequent pose estimation process. It not only provides a priori knowledge but also clearly defines the tracking target. This article presents a fully automatic subject modeling framework to reconstruct human pose, shape, as well as the body texture in a challenging optimization scenario. By integrating powerful differentiable rendering into the subject-specific modeling pipeline, the proposed method transforms the texture reconstruction problem into analysis by synthesis minimization and solves it efficiently by a gradient-based method. Furthermore, a novel covariance matrix adaptation annealing algorithm is proposed to attack the high-dimensional multimodal optimization problem in an adaptive manner. The domain knowledge of hierarchical human anatomy is seamlessly injected to the annealing optimization process by using a soft covariance matrix mask. All together contributes to the novel algorithm robust to the temptation of local minima. Experiments on the Human3.6 M dataset and the People-Snapshot dataset demonstrate the competitive results to the state of the art both qualitatively and quantitatively.

Objective. Electrocardiogram (ECG) is an important diagnostic tool that has been the subject of much research in recent years. Owing to a lack of well-labeled ECG record databases, most of this work has focused on heartbeat arrhythmia detection based on ECG signal quality. Approach. A record quality filter was designed to judge ECG signal quality, and a random forest method, a multilayer perceptron, and a residual neural network (RESNET)-based convolutional neural network were implemented to provide baselines for ECG record classification according to three different principles. A new multimodel method was constructed by fusing the random forest and RESNET approaches. Main Results. Owing to its ability to combine discriminative human-crafted features with RESNET deep features, the proposed new method showed over 88 classification accuracy and yielded the best results in comparison with alternative methods. Significance. A new multimodel fusion method was presented for abnormal cardiovascular detection based on ECG data. The experimental results show that separable convolution and multiscale convolution are vital for ECG record classification and are effective for use with one-dimensional ECG sequences.

Knowledge graphs represent real-world facts as structured triplets and underpin a wide range of applications, including question answering, recommendation, and retrieval-augmented generation. Automatically extracting such triplets from unstructured text is essential for scalable knowledge base construction. Traditional extraction methods require task-specific training data and struggle to generalize across domains. Large language models (LLMs) offer an alternative through in-context learning, enabling flexible extraction without fine-tuning. However, LLMs frequently hallucinate—generating plausible triplets unsupported by the source text. The root cause is the lack of provenance: existing methods produce triplets without explicit links to their textual origins, making faithfulness unverifiable. This paper presents Anchor-Extraction-Verification-Supplement (AEVS), a framework that grounds every triplet element to the source text. AEVS operates in three stages: (1) anchor discovery identifies entities, relation phrases, and attribute values with precise positions, forming a constrained extraction vocabulary; (2) grounded extraction generates triplets linked to discovered anchors; and (3) restoration-based verification validates triplets through hierarchical matching, with a coverage-aware supplement ensuring comprehensive extraction. Experiments on WebNLG, REBEL, and Wiki-NRE demonstrate consistent improvements over both trained models and LLM-based baselines. Ablation studies confirm that anchor-based constraints are the primary mechanism for hallucination reduction. Dedicated analyses of anchor discovery quality, computational cost (2.83–4.28 LLM calls per sample), and hallucination rates (0.23–20.23% across model–dataset configurations) provide insights into the framework’s practical applicability and limitations.