Knowledge graph-based retrieval-augmented generation seeks to mitigate hallucinations in Large Language Models (LLMs) caused by insufficient or outdated knowledge. However, existing methods often fail to fully exploit the prior knowledge embedded in knowledge graphs (KGs), particularly their structural information and explicit or implicit constraints. The former can enhance the faithfulness of LLMs' reasoning, while the latter can improve the reliability of response generations. Motivated by these, we propose a trustworthy reasoning framework, termed Deliberation over Priors (\texttt{DP}), which sufficiently utilizes the priors contained in KGs. Specifically, \texttt{DP} adopts a progressive knowledge distillation strategy that integrates structural priors into LLMs through a combination of supervised fine-tuning and Kahneman-Tversky Optimization, thereby improving the faithfulness of relation path generation. Furthermore, our framework employs a reasoning-introspection strategy, which guides LLMs to perform refined reasoning verification based on extracted constraint priors, ensuring the reliability of response generation. Extensive experiments on three benchmark datasets demonstrate that \texttt{DP} achieves new state-of-the-art performance, especially a H@1 improvement of 13% on the ComplexWebQuestions dataset, and generates highly trustworthy responses. We also conduct various analyses to verify its flexibility and practicality. Code is available at [https://github.com/mira-ai-lab/Deliberation-on-Priors](https://github.com/mira-ai-lab/Deliberation-on-Priors).

Kinase inhibitors are crucial in cancer treatment, but drug resistance and side effects hinder the development of effective drugs. To address these challenges, it is essential to analyze the polypharmacology of kinase inhibitor and identify compound with high selectivity profile. This study presents KinomeMETA, a framework for profiling the activity of small molecule kinase inhibitors across a panel of 661 kinases. By training a meta-learner based on a graph neural network and fine-tuning it to create kinase-specific learners, KinomeMETA outperforms benchmark multi-task models and other kinase profiling models. It provides higher accuracy for understudied kinases with limited known data and broader coverage of kinase types, including important mutant kinases. Case studies on the discovery of new scaffold inhibitors for membrane-associated tyrosine- and threonine-specific cdc2-inhibitory kinase and selective inhibitors for fibroblast growth factor receptors demonstrate the role of KinomeMETA in virtual screening and kinome-wide activity profiling. Overall, KinomeMETA has the potential to accelerate kinase drug discovery by more effectively exploring the kinase polypharmacology landscape.

Enhancing cancer treatment efficacy remains a significant challenge in human health. Immunotherapy has witnessed considerable success in recent years as a treatment for tumors. However, due to the heterogeneity of diseases, only a fraction of patients exhibit a positive response to immune checkpoint inhibitor (ICI) therapy. Various single-gene-based biomarkers and tumor mutational burden (TMB) have been proposed for predicting clinical responses to ICI; however, their predictive ability is limited. We propose the utilization of the Text Graph Convolutional Network (GCN) method to comprehensively assess the impact of multiple genes, aiming to improve the predictive capability for ICI response. We developed TG468, a Text GCN model framing drug response prediction as a text classification task. By combining natural language processing (NLP) and graph neural network techniques, TG468 effectively handles sparse and high-dimensional exome sequencing data. As a result, TG468 can distinguish survival time for patients who received ICI therapy and outperforms single gene biomarkers, TMB and some classical machine learning models. Additionally, TG468's prediction results facilitate the identification of immune status differences among specific patient types in the Cancer Genome Atlas dataset, providing a rationale for the model's predictions. Our approach represents a pioneering use of a GCN model to analyze exome data in patients undergoing ICI therapy and offers inspiration for future research using NLP technology to analyze exome sequencing data.

Monitoring a process and its file I/O behaviors is important for security inspection for a data center server against intrusions, malware infection and information leakage. In the case of the Linux kernel 2.6, a set of hook functions called the Linux Security Module (LSM) has been implemented in order to monitor and control the system calls. By using the LSM we can inspect the activity of unknown malicious processes. However, a sophisticated attacker could breach the kernel configurations using the rootkits. Furthermore since the monitoring results of the malicious process activity are stored as a file on Hard Disk Drive (HDD), it will be easily manipulated by the attacker. In this paper, we propose a secure monitoring scheme that addresses the attacks against the monitoring module and its result for security inspection of the data center server. The monitoring module is implemented as a LSM-based function and protected by the kernel protection technique. The integrity of the monitoring result is guaranteed by using a Mandatory Access Control (MAC) of the Linux kernel and a mechanism of the trusted process invocation. This mechanism can serve as an infrastrucuture of secure inspection platform for data center server because the integrity of the monitoring module and its result is guaranteed.

A remote attestation that measures files on a hard disk drive (HDD) is important for intrusion detection on a data center server. When the server is infected by a rootkit or when a file measurement application is manipulated, the response of the kernel or the measurement application is not reliable. A trusted platform module (TPM) that achieves a chain of trust from BIOS to kernel upon booting is proposed to provide the remote attestation. However, as the data center server is rarely rebooted, the TPM is ill suited for file measurements of the running server. In this paper, we propose an on-demand remote attestation scheme for HDD files of the server. We designed and implemented a trust chain from the BIOS via the kernel and the file measurement application to the HDD files on a running server for secure integrity measurement. A memory virtualization technique is applied to guarantee the integrity of the running kernel, and the file measurement application is verified using a code signature. Also, we implement a mechanism that attaches the server's signature to a measurement result in a trusted kernel. Finally, our proposed scheme achieves a result whereby the remote verifier can measure the integrity of the server files securely at any time.

An important security challenge is to protect the execution of security-sensitive code on legacy systems from malware that may infect the OS, applications, or system devices. Prior work experienced a tradeoff between the level of security achieved and efficiency. In this work, we leverage the features of modern processors from AMD and Intel to overcome the tradeoff to simultaneously achieve a high level of security and high performance. We present TrustVisor, a special-purpose hypervisor that provides code integrity as well as data integrity and secrecy for selected portions of an application. TrustVisor achieves a high level of security, first because it can protect sensitive code at a very fine granularity, and second because it has a very small code base (only around 6K lines of code) that makes verification feasible. TrustVisor can also attest the existence of isolated execution to an external entity. We have implemented TrustVisor to protect security-sensitive code blocks while imposing less than 7% overhead on the legacy OS and its applications in the common case.

Though all of the current main-stream OSs have supported superpage to some extent, most of them need runtime information provided by applications, simulator or other tools. Transparent superpage support is a further step, while so far there are only very few primitive attempts for Linux. In this paper, we propose the design of GISP (global information based super page support), a transparent superpage support framework in Linux kernel. GISP adopts the basic idea of the reservation-based policy, and uses LMO (lightweight memory object) and POPMAP (population map) to manage the page allocation for applications. GISP could provide the core functions of superpage support while keeping the memory continuity by dynamic pages recycling. We implement it in Linux 2.4.17 on PKUnity SoC, and evaluate it for real workloads and benchmarks. We obtain substantial performance benefits from 8.1% to 24.0%. Compared with the best transparent superpage support in Linux up to now, we achieve better performance from 0.4% to 4.6% in most cases; even the worst case has comparable performance improvements within 2.6%. Otherwise, it keeps low management cost during system running which is suitable for not only scientific applications but also commodity applications.

We propose SecVisor, a tiny hypervisor that ensures code integrity for commodity OS kernels. In particular, SecVisor ensures that only user-approved code can execute in kernel mode over the entire system lifetime. This protects the kernel against code injection attacks, such as kernel rootkits. SecVisor can achieve this propertyeven against an attacker who controls everything but the CPU, the memory controller, and system memory chips. Further, SecVisor can even defend against attackers with knowledge of zero-day kernel exploits.