Limit theorems for functionals of long memory linear processes with infinite variance

Let $X=\{X_n: n\in\mathbb{N}\}$ be a long memory linear process in which the coefficients are regularly varying and innovations are independent and identically distributed and belong to the domain of attraction of an $\alpha$-stable law with $\alpha\in (0, 2)$. Then, for any integrable and square integrable function $K$ on $\mathbb{R}$, under certain mild conditions, we establish the asymptotic distributions of the partial sum $$\sum\limits_{n=1}^{N}\big[K(X_n)-\mathbb{E} K(X_n)\big]$$ as $N$ tends to infinity

DPR: An Algorithm Mitigate Bias Accumulation in Recommendation feedback loops

Recommendation models trained on the user feedback collected from deployed recommendation systems are commonly biased. User feedback is considerably affected by the exposure mechanism, as users only provide feedback on the items exposed to them and passively ignore the unexposed items, thus producing numerous false negative samples. Inevitably, biases caused by such user feedback are inherited by new models and amplified via feedback loops. Moreover, the presence of false negative samples makes negative sampling difficult and introduces spurious information in the user preference modeling process of the model. Recent work has investigated the negative impact of feedback loops and unknown exposure mechanisms on recommendation quality and user experience, essentially treating them as independent factors and ignoring their cross-effects. To address these issues, we deeply analyze the data exposure mechanism from the perspective of data iteration and feedback loops with the Missing Not At Random (\textbf{MNAR}) assumption, theoretically demonstrating the existence of an available stabilization factor in the transformation of the exposure mechanism under the feedback loops. We further propose Dynamic Personalized Ranking (\textbf{DPR}), an unbiased algorithm that uses dynamic re-weighting to mitigate the cross-effects of exposure mechanisms and feedback loops without additional information. Furthermore, we design a plugin named Universal Anti-False Negative (\textbf{UFN}) to mitigate the negative impact of the false negative problem. We demonstrate theoretically that our approach mitigates the negative effects of feedback loops and unknown exposure mechanisms. Experimental results on real-world datasets demonstrate that models using DPR can better handle bias accumulation and the universality of UFN in mainstream loss methods

Effect of combined sedation using multiple drugs on inflammatory cytokines in patients with acute respiratory distress syndrome

The innate immune response plays an important role in the pathophysiology of acute respiratory distress syndrome (ARDS); however, no drug has been proven to be beneficial in the management of ARDS. Therefore, the aim of this study was to investigate the effects of using combined sedatives on systemic inflammatory responses in patients with ARDS. A total of 90 patients with ARDS and an intubation time of > 120 h were randomly divided into the propofol group (group P), midazolam group (group M), and combined sedation group (group U). Patients in groups P and M were sedated with propofol and midazolam, respectively, whereas patients in group U were sedated with a combination of propofol, midazolam, and dexmedetomidine. The dosage of sedatives and vasoactive drugs, duration of mechanical ventilation, and incidence of sedative adverse reactions were documented. The dosage of sedatives and vasoactive drugs, as well as the incidence of sedative adverse reactions in group U, was significantly lower than those in groups P and M. Similarly, the duration of mechanical ventilation in group U was significantly shorter than that in groups P and M. Hence, inducing sedation through a combination of multiple drugs can significantly reduce their adverse effects, improve their sedative effect, inhibit systemic inflammatory responses, and improve oxygenation in patients with ARDS