Unsupervised methods like deep image prior (DIP) leverage network priors for denoising without labeled data but suffer from slow convergence and overfitting, while deep random projector (DRP) improves efficiency via fixed weights and a random seed yet remains limited by its fully random initialization. In this work, we propose deep fixed projector (DFP), an enhanced DRP-based framework featuring three synergistic improvements: (1) initializing the seed with the noisy image to align optimization with the clean image manifold, (2) using pre-trained clean-to-clean encoder-decoder weights to embed structural priors and accelerate convergence, and (3) introducing inter-inference consistency (IIC), a self-supervised regularization that enforces output stability under input perturbations to suppress noise and reduce overfitting. Experiments show DFP consistently surpasses DIP, DRP, and recent variants in PSNR, SSIM, and LPIPS, while achieving faster convergence and robust denoising quality. Code is available athttps://github.com/Hu-China/Deep-Fixed-Projector.

Deep Reinforcement Learning (RL) has shown remarkable success in robotics with complex and heterogeneous dynamics. However, its vulnerability to unknown disturbances and adversarial attacks remains a significant challenge. In this paper, we propose a robust policy training framework that integrates model-based control principles with adversarial RL training to improve robustness without the need for external black-box adversaries. Our approach introduces a novel Hamilton-Jacobi reachability-guided disturbance for adversarial RL training, where we use interpretable worst-case or near-worst-case disturbances as adversaries against the robust policy. We evaluated its effectiveness across three distinct tasks: a reach-avoid game in both simulation and real-world settings, and a highly dynamic quadrotor stabilization task in simulation. We validate that our learned critic network is consistent with the ground-truth HJ value function, while the policy network shows comparable performance with other learning-based methods.

Despite being beneficial for rapid delivery of software, Kubernetes deployments can be susceptible to security attacks, which can cause serious consequences. A systematic characterization of how community-prescribed security configurations, i.e., security configurations that are recommended by security experts, can aid practitioners to secure their Kubernetes deployments. To that end, we conduct an empirical study with 53 security configurations recommended by the Center for Internet Security (CIS), 20 survey respondents, and 544 configuration files obtained from the open source software (OSS) and proprietary domains. From our empirical study, we observe: (i) practitioners can be unaware of prescribed security configurations as$5 \% \sim 40 {\%}$of the survey respondents are unfamiliar with 16 prescribed configurations; and (ii) for Company-A and OSS respectively, 18.0% and 17.9% of the configuration files include at least one violation of prescribed configurations. From our evaluation with 5 static application security testing (SAST) tools we find (i) only Kubescape to support all of the prescribed security configuration categories; (ii) the highest observed precision to be 0.41 and 0.43 respectively, for the Company-A and OSS datasets; and (iii) the highest observed recall to be respectively, 0.53 and 0.65 for the Company-A and OSS datasets. Our findings show a disconnect between what CIS experts recommend for Kubernetes-related configurations and what happens in practice. We conclude the paper by providing recommendations for practitioners and researchers. Dataset used for the paper is publicly available online.

Long noncoding RNAs (lncRNAs) are a large family of noncoding RNAs that play a critical role in various normal bioprocesses as well as tumorigenesis. However, the expression patterns and biological functions of lncRNAs in acute leukemia have not been well studied. Here, we performed transcriptome-wide lncRNA expression profiling of acute myeloid leukemia (AML) patient samples, along with non-leukemia control hematopoietic samples. We found that lncRNAs were differentially expressed in AML samples relative to control samples. Notably, we identified that lncRNAs upregulated in AML (relative to the control samples) are associated with a lower degree of DNA methylation and a higher ratio of being bound by transcription factors such as SP1, STAT4, ATF-2 and ELK-1 compared with those downregulated in AML. Moreover, an enrichment of H3K4me3 and a depletion of H3K27me3 were observed in upregulated lncRNAs in AML. Expression patterns of three types of lncRNAs (antisense, enhancer and intergenic lncRNAs) have previously been characterized. Of the identified lncRNAs, we found that high expression level lncRNA LOC285758 is associated with the poor prognosis in AML patients. Furthermore, we found that LOC285758 regulates proliferation of AML cell lines by enhancing the expression of HDAC2, a key factor in carcinogenesis. Collectively, our study depicts a landscape of important lncRNAs in AML and provides novel potential therapeutic targets and prognostic markers for AML treatment.