Enhancing Large Language Models for Secure Code Generation: A Dataset-driven Study on Vulnerability Mitigation

Large language models (LLMs) have brought significant advancements to code generation, benefiting both novice and experienced developers. However, their training using unsanitized data from open-source repositories, like GitHub, introduces the risk of inadvertently propagating security vulnerabilities. To effectively mitigate this concern, this paper presents a comprehensive study focused on evaluating and enhancing code LLMs from a software security perspective. We introduce SecuCoGen\footnote{SecuCoGen has been uploaded as supplemental material and will be made publicly available after publication.}, a meticulously curated dataset targeting 21 critical vulnerability types. SecuCoGen comprises 180 samples and serves as the foundation for conducting experiments on three crucial code-related tasks: code generation, code repair and vulnerability classification, with a strong emphasis on security. Our experimental results reveal that existing models often overlook security concerns during code generation, leading to the generation of vulnerable code. To address this, we propose effective approaches to mitigate the security vulnerabilities and enhance the overall robustness of code generated by LLMs. Moreover, our study identifies weaknesses in existing models' ability to repair vulnerable code, even when provided with vulnerability information. Additionally, certain vulnerability types pose challenges for the models, hindering their performance in vulnerability classification. Based on these findings, we believe our study will have a positive impact on the software engineering community, inspiring the development of improved methods for training and utilizing LLMs, thereby leading to safer and more trustworthy model deployment

Antireflection self-reference method based on ultrathin metallic nanofilms for improving terahertz reflection spectroscopy

We present the potential of an antireflection self-reference method based on ultrathin tantalum nitride (TaN) nanofilms for improving terahertz (THz) reflection spectroscopy. The antireflection self-reference method is proposed to eliminate mutual interference caused by unwanted reflections, which significantly interferes with the important reflection from the actual sample in THz reflection measurement. The antireflection self-reference model was investigated using a wave-impedance matching approach, and the theoretical model was verified in experimental studies. We experimentally demonstrated this antireflection selfreference method can completely eliminate the effect of mutual interference, accurately recover the actual sample’s reflection and improve THz reflection spectroscopy. Our method paves the way to implement a straightforward, accurate and efficient approach to investigate THz properties of the liquids and biological samplesThe Fund from Hefei University of Technology (407-0371000019); Sichuan Province Science and Technology Support Program (No. 2016GZ0250); the Fundamental Research Funds for the Central Universities (Grant No. JD2017JGPY0006); National Natural Science Foundation of China (Grant No.51607050); MINECO (MAT2015–74381-JIN to B.P., RYC2014–16962 and CTQ2017-89588-R to P.dP.); Xunta de Galicia (Centro singular de investigación de Galicia accreditation 2016–2019, ED431G/09); European Union (European Regional Development Fund – ERDF)S

Bi-nuclear tensor Schatten-p norm minimization for multi-view subspace clustering

Multi-view subspace clustering aims to integrate the complementary information contained in different views to facilitate data representation. Currently, low-rank representation (LRR) serves as a benchmark method. However, we observe that these LRR-based methods would suffer from two issues: limited clustering performance and high computational cost since (1) they usually adopt the nuclear norm with biased estimation to explore the low-rank structures; (2) the singular value decomposition of large-scale matrices is inevitably involved. Moreover, LRR may not achieve low-rank properties in both intra-views and interviews simultaneously. To address the above issues, this paper proposes the Bi-nuclear tensor Schatten-p norm minimization for multi-view subspace clustering (BTMSC). Specifically, BTMSC constructs a third-order tensor from the view dimension to explore the high-order correlation and the subspace structures of multi-view features. The Bi-Nuclear Quasi-Norm (BiN) factorization form of the Schatten-p norm is utilized to factorize the third-order tensor as the product of two small-scale thirdorder tensors, which not only captures the low-rank property of the third-order tensor but also improves the computational efficiency. Finally, an efficient alternating optimization algorithm is designed to solve the BTMSC model. Extensive experiments with ten datasets of texts and images illustrate the performance superiority of the proposed BTMSC method over state-of-the-art methods

Robustness meets low-rankness: unified entropy and tensor learning for multi-view subspace clustering

In this paper, we develop the weighted error entropy-regularized tensor learning method for multi-view subspace clustering (WETMSC), which integrates the noise disturbance removal and subspace structure discovery into one unified framework. Unlike most existing methods which focus only on the affinity matrix learning for the subspace discovery by different optimization models and simply assume that the noise is independent and identically distributed (i.i.d.), our WETMSC method adopts the weighted error entropy to characterize the underlying noise by assuming that noise is independent and piecewise identically distributed (i.p.i.d.). Meanwhile, WETMSC constructs the self-representation tensor by storing all self-representation matrices from the view dimension, preserving high-order correlation of views based on the tensor nuclear norm. To solve the proposed nonconvex optimization method, we design a half-quadratic (HQ) additive optimization technology and iteratively solve all subproblems under the alternating direction method of multipliers framework. Extensive comparison studies with state-of-the-art clustering methods on real-world datasets and synthetic noisy datasets demonstrate the ascendancy of the proposed WETMSC method

Differential expression of six genes and correlation with fatness traits in a unique broiler population

AbstractPrevious results from genome wide association studies (GWASs) in chickens divergently selected for abdominal fat content of Northeast Agricultural University (NEAUHLF) showed that many single nucleotide polymorphism (SNP) variants were associated with abdominal fat content. Of them, six top significant SNPs at the genome level were located within SRD5A3, SGCZ, DLC1, GBE1, GALNT9 and DNAJB6 genes. Here, expression levels of these six candidate genes were investigated in abdominal fat and liver tissue between fat and lean broilers from the 14th generation population of NEAUHLF. The results showed that expression levels of SRD5A3, SGCZ and DNAJB6 in the abdominal fat and SRD5A3, DLC1, GALNT9, DNAJB6 and GBE1 in the liver tissue differed significantly between the fat and lean birds, and were correlated with abdominal fat traits. The findings will provide important references for further function investigation of the six candidate genes involved in abdominal fat deposition in chickens

Magnetic order and fluctuations in quasi-two-dimensional planar magnet Sr(Co1−x_{1-x}Nix_x)2_2As2_2

We use neutron scattering to investigate spin excitations in Sr(Co$_{1-x}$Ni$_{x})_2$As$_2$, which has a $c$-axis incommensurate helical structure of the two-dimensional (2D) in-plane ferromagnetic (FM) ordered layers for $0.013\leq x \leq 0.25$. By comparing the wave vector and energy dependent spin excitations in helical ordered Sr(Co$_{0.9}$Ni$_{0.1}$)$_2$As$_2$ and paramagnetic SrCo$_2$As$_2$, we find that Ni-doping, while increasing lattice disorder in Sr(Co$_{1-x}$Ni$_{x})_2$As$_2$, enhances quasi-2D FM spin fluctuations. However, our band structure calculations within the combined density functional theory and dynamic mean field theory (DFT+DMFT) failed to generate a correct incommensurate wave vector for the observed helical order from nested Fermi surfaces. Since transport measurements reveal increased in-plane and $c$-axis electrical resistivity with increasing Ni-doping and associated lattice disorder, we conclude that the helical magnetic order in Sr(Co$_{1-x}$Ni$_{x})_2$As$_2$ may arise from a quantum order-by-disorder mechanism through the itinerant electron mediated Ruderman-Kittel-Kasuya-Yosida (RKKY) interactions

A Novel Regulator of Preadipocyte Differentiation, Transcription Factor TCF21, Functions Partially Through Promoting LPL Expression

The transcription factor TCF21 has been previously shown to be specifically expressed in white preadipocytes in mice. However, the exact biological function of TCF21 in the context of adipogenesis remains unknown. In the current study, we used chicken lines selected based on their abdominal fat content, and observed a significant decrease in TCF21 mRNA and protein levels in the abdominal fat of lean broilers relative to fat broilers. Moreover, TCF21 expression increased throughout preadipocyte differentiation in vitro. We also found that TCF21 knockdown and over-expression attenuated and promoted preadipocyte differentiation, respectively, as evidenced by appropriate changes in lipid droplet accumulation and altered expressions of C/EBPa, LPL, and A-FABP. Additional chromatin immunoprecipitation analyses and luciferase assays demonstrated that TCF21 promotes the transcription of LPL by directly binding to the E-box motif in the LPL promoter. Together, these results show that TCF21 is a novel regulator of preadipocyte differentiation, in part by directly promoting LPL expression

Depletion of TRRAP induces p53-independent senescence in liver cancer by downregulating mitotic genes

Hepatocellular carcinoma (HCC) is an aggressive subtype of liver cancer with few effective treatments and the underlying mechanisms that drive HCC pathogenesis remain poorly characterized. Identifying genes and pathways essential for HCC cell growth will aid the development of new targeted therapies for HCC. Using a kinome CRISPR screen in three human HCC cell lines, we identified transformation/transcription domain-associated protein (TRRAP) as an essential gene for HCC cell proliferation. TRRAP has been implicated in oncogenic transformation, but how it functions in cancer cell proliferation is not established. Here, we show that depletion of TRRAP or its co-factor, histone acetyltransferase KAT5, inhibits HCC cell growth via induction of p53- and p21-independent senescence. Integrated cancer genomics analyses using patient data and RNA-sequencing identified mitotic genes as key TRRAP/KAT5 targets in HCC, and subsequent cell cycle analyses revealed that TRRAP- and KAT5-depleted cells are arrested at G2/M phase. Depletion of TOP2A, a mitotic gene and TRRAP/KAT5 target, was sufficient to recapitulate the senescent phenotype of TRRAP/KAT5 knockdown. CONCLUSION: Our results uncover a role for TRRAP/KAT5 in promoting HCC cell proliferation via activation of mitotic genes. Targeting the TRRAP/KAT5 complex is a potential therapeutic strategy for HCC