DCR: Double Component Ranking for Building Reliable Cloud Applications

Since cloud applications are usually large-scale, it is too expensive to enhance the reliability of all components for building highly reliable cloud applications. Therefore, we need to identify significant components which have great impact on the system reliability. FTCloud, an existing approach, ranks the components only considering the impact of component internal failures and ignoring error propagation. However, error propagation is also an important factor on the system reliability. To attack the problem, we propose an improved component ranking framework, named DCR, to identify significant components in cloud applications. DCR employs two individual algorithms to rank the components twice and determines a set of the most significant components based on the two ranking results. In addition, DCR does not require information of component invocation frequencies. Extensive experiments are provided to evaluate DCR and compare it with FTCloud. The experimental results show that DCR outperforms FTCloud in almost all cases

Loss of Scribble confers cisplatin resistance during NSCLC chemotherapy via Nox2/ROS and Nrf2/PD-L1 signaling

Background: Cisplatin resistance remains a major clinical obstacle to the successful treatment of non-small cell lung cancer (NSCLC). Scribble contributes to ROS-induced inflammation and cisplatin-elevated toxic reactive oxygen species (ROS) promotes cell death. However, it is unknown whether and how Scribble is involved in the cisplatin-related cell death and the underlying mechanism of Scribble in response to chemotherapies and in the process of oxidative stress in NSCLC. Methods: We used two independent cohorts of NSCLC samples derived from patients treated with platinumcontaining chemotherapy and xenograft modeling in vivo. We analyzed the correlation between Scribble and Nox2 or Nrf2/PD-L1 both in vivo and in vitro, and explored the role of Scribble in cisplatin-induced ROS and apoptosis. Findings: Clinical analysis revealed that Scribble expression positively correlatedwith clinical outcomes and chemotherapeutic sensitivity in NSCLC patients. Scribble protected Nox2 protein from proteasomal degradation. Scribble knockdown induced cisplatin resistance by blocking Nox2/ROS and apoptosis in LRR domaindependent manner. In addition, low levels of Scribble correlated with high levels of PD-L1 via activation of Nrf2 transcription in vivo and in vitro. Interpretations: Our study revealed that polarity protein Scribble increased cisplatin-induced ROS generation and is beneficial to chemotherapeutic outcomes in NSCLC. Although Scribble deficiency tends to lead to cisplatin resistance by Nox2/ROS and Nrf2

Secrets of RLHF in Large Language Models Part II: Reward Modeling

Reinforcement Learning from Human Feedback (RLHF) has become a crucial technology for aligning language models with human values and intentions, enabling models to produce more helpful and harmless responses. Reward models are trained as proxies for human preferences to drive reinforcement learning optimization. While reward models are often considered central to achieving high performance, they face the following challenges in practical applications: (1) Incorrect and ambiguous preference pairs in the dataset may hinder the reward model from accurately capturing human intent. (2) Reward models trained on data from a specific distribution often struggle to generalize to examples outside that distribution and are not suitable for iterative RLHF training. In this report, we attempt to address these two issues. (1) From a data perspective, we propose a method to measure the strength of preferences within the data, based on a voting mechanism of multiple reward models. Experimental results confirm that data with varying preference strengths have different impacts on reward model performance. We introduce a series of novel methods to mitigate the influence of incorrect and ambiguous preferences in the dataset and fully leverage high-quality preference data. (2) From an algorithmic standpoint, we introduce contrastive learning to enhance the ability of reward models to distinguish between chosen and rejected responses, thereby improving model generalization. Furthermore, we employ meta-learning to enable the reward model to maintain the ability to differentiate subtle differences in out-of-distribution samples, and this approach can be utilized for iterative RLHF optimization

Inhibition of Epithelial–Mesenchymal Transition and Tissue Regeneration by Waterborne Titanium Dioxide Nanoparticles

Titanium dioxide nanoparticles (TiO₂NPs) are among the most widely manufactured nanomaterials with broad applications in food industry, cosmetics, and medicine. Although the toxicity of TiO₂NPs at high doses has been extensively explored, the potential health risks of TiO₂NPs exposure at nontoxic concentrations remain poorly understood. Epithelial–mesenchymal transition (EMT) plays pivotal roles in a diversity of physiological and pathological processes, including tissue regeneration and cancer metastasis. In this study, we find that the cellular uptake of TiO₂NPs inhibits EMT-mediated cell remodeling and cell migration without exhibiting cytotoxicity. Further investigation reveals that TiO₂NPs suppress the process of EMT through the blockade of transforming growth factor-β (TGFβ) signaling. Particularly, TiO₂NPs interact with the TGFβ receptor TβRI/II complex, induce its lysosomal degradation, and thereby downregulate expression of TGFβ target genes. Moreover, we show that waterborne TiO₂NPs do not elicit toxicity in healthy tissues but hamper EMT-mediated wound healing in two animal models. Long-term exposure of TiO₂NPs in environmental water and drinking water impede the regeneration of amputated fin in zebrafish and the recovery of intestinal mucosal damage in colitic mice. Our results reveal the previously unknown effects of TiO₂NPs during tissue remodeling and repair, which have significant implications in their risk assessment and management