Knowledge Graph Question Answering (KGQA) addresses natural language questions by leveraging structured information stored in knowledge graphs. However, existing KGQA methods are overly concerned with improving the quality of responses by retrieving information, neglecting to identify which type of knowledge is truly useful to optimize the performance of the KGQA system, resulting in redundant retrieval. At the same time, these methods have limitations in aligning user intent and insufficient semantic richness in responses. In this work, we propose IP-KGQA, which introduce a intent-aware prompt learning scheme for KGQA framework. A Selection-Driven Efficient Retrieval (SER) module is incorporated in the framework, which classifies user questions to ensure that only long-tail questions are directed to the knowledge graph retrieval to enhance system efficiency. To filter and select the most relevant triplets, aligning retrieved information more closely with user intent, we introduce the User Intent-aware Filtering (UIF) module, where Monte Carlo sampling is applied to obtain the optimal triplets. The Domain-specific Context Prompt Extension (DCPE) module is utilized in collaboration with a fine-tuned large language model (LLM) to integrate domain-specific knowledge into the responses, ensuring that the answers are enriched in terms of semantic quality. Extensive experiments have been conducted on the CommonSenseQA and TriviaQA datasets, which demonstrate that IP-KGQA outperforms the existing methods in terms of retrieval efficiency, answer accuracy and user intent alignment.

Synthetic aperture radar (SAR) is vulnerable to interference, including intentional and unintentional-ones. Noise frequency modulated (FM) interference is a kind of intentional interference, which has the characteristics of broadband and randomness, which makes the noise FM signal become a kind of most commonly used interference signal. Noise FM interference will have a serious impact on the SAR image, but the current algorithms for interference suppression are not sufficiently studied. This paper extends a time-domain cancellation algorithm for suppressing the noise FM interference of SAR. This algorithm can reconstruct the noise FM interference signal from the contaminated SAR echo, and then suppress the interference component in the echo by time-domain cancellation. Finally, this paper validates the superior performance of the algorithm by point target simulation and Radarsat-1 data. The proposed method is valid even when the signal-to-interference ratio is lower than -40dB.

Software architecture security design is a key stage in developing business-oriented system, such as business-critical system, ICT system and AI system. Many typical accidents also remind us that the security of software architecture even plays a more important role than the code security in most software systems. However, there are very few researches which focus on the security of software architecture. Especially, we don’t find a systematic and feasible quantitative method to evaluate the security of software architecture. To fill this gap, we launched a research project focusing on software architecture security since 2019 and try to provide a series of quantitative methods for evaluating the security of software architecture. In this paper, a business-oriented quantitative method was proposed to evaluate the security of software architecture from the view of business-critical security insurance. In our method, both business dependency graph (BDG) and multi-hierarchical dependency graph (MHDG) are defined and constructed first for describing businesses and their relationships, and system components and their relationships; then, attack points and business key-points are identified and labeled on BDG and MHDG; and further, all potential attack paths and effective attack paths in the system based on MHDG are detected; and finally, a set of security indicators is defined and used to evaluate the security of software architecture quantitatively. For more effective experiments, we use software architectures with different styles and different evolution versions. Experimental results show that our method can reflect effectively the security characteristics of software architecture with different styles, can find the security changes of different architecture evolution versions for the same system, and can perform quantitative evaluation of software architecture security efficiently.

In radar detection, small and slow targets are easily overwhelmed by strong clutter. Traditional methods, such as singular value decomposition (SVD) and robust principal component analysis (RPCA), can suppress clutter and recover targets by using low-rank and sparse models. However, these methods rely on fixed prior information, which lacks adaptivity and suffers from unfavorable extensive manual hyperparameter tuning. To address these issues, a data-driven deep network model combined with an iterative algorithm called unfolding joint sparse recovery network (UFJSR-Net) is proposed to achieve the improved target detection performance. First, a joint sparse recovery (JSR) model is established and the fast iterative shrinkage/thresholding algorithm (FISTA) is derived to solve this model. Then, one iteration consisting of a linear operation and a nonlinear one can be recast into a single network layer and the stacking and combination of all layers will form the UFSJR-Net. Finally, the properties of the target and clutter can be learned by paired inputs and outputs training data to optimize the hyperparameters in the iterative algorithm to obtain the JSR model. Experiments on simulation data and measured radar data demonstrate that the proposed method exhibits advantages in detection performance over traditional decomposition methods under strong clutter with different intensities.

Advances in machine learning have enabled the prediction of immune system responses to prophylactic and therapeutic vaccines. However, the engineering task of designing vaccines remains a challenge. In particular, the genetic variability of the human immune system makes it difficult to design peptide vaccines that provide widespread immunity in vaccinated populations. We introduce a framework for evaluating and designing peptide vaccines that uses probabilistic machine learning models, and demonstrate its ability to produce designs for a SARS-CoV-2 vaccine that outperform previous designs. We provide a theoretical analysis of the approximability, scalability, and complexity of our framework.

Autofocus is one of the key problems in inverse synthetic aperture radar (ISAR) since the noncooperation of the target motion. For sparse aperture ISAR, classical autofocus algorithms are not suitable due to the discontinuity of the azimuth sampling. In this paper, a novel framework is proposed for ISAR autofocus with sparse aperture. The autofocus problem is transformed into an optimization problem with l0norm and nonlocal total variation (NLTV) regularization constraints. Therefore, both spatial sparsity and structural information of the target can be considered in the process. Dual iterative computation which combines regularization method and conjugate gradient (CG) algorithm is applied to reconstruct the image and correct the phase error. Results of real data experiments show the effectiveness of the proposed method.

SUMMARY: T cells play a critical role in cellular immune responses to pathogens and cancer and can be activated and expanded by Major Histocompatibility Complex (MHC)-presented antigens contained in peptide vaccines. We present a machine learning method to optimize the presentation of peptides by class II MHCs by modifying their anchor residues. Our method first learns a model of peptide affinity for a class II MHC using an ensemble of deep residual networks, and then uses the model to propose anchor residue changes to improve peptide affinity. We use a high throughput yeast display assay to show that anchor residue optimization improves peptide binding. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The avionics system is an important part of modern fighters, and the analysis and processing of large data generated by the avionics system can provide some guidance for the pilot's decision-making. After analyzing the existing distributed framework, a multi-platform avionics data system is designed and implemented to solve the problem that heterogeneous real-time data generated by a large number of sensors is difficult to be managed efficiently. With the help of the cloud platform, the system supports functions of data collection, data classification management, data storage and data analysis. It can establish the related model based on the historical data and it can make real-time prediction with real-time data and correlation model. The test results show that the system functional testing requirements coverage rate is 100%, and performance and stability are both in line with the requirements.