A Worst-Case Approximate Analysis of Peak Age-of-Information Via Robust Queueing Approach

A new timeliness metric, called Age-of-Information (AoI), has recently attracted a lot of research interests for real-time applications with information updates. It has been extensively studied for various queueing models based on the probabilistic approaches, where the analyses heavily depend on the properties of specific distributions (e.g., the memoryless property of the exponential distribution or the i.i.d. assumption). In this work, we take an alternative new approach, the robust queueing approach, to analyze the Peak Age-of-Information (PAoI). Specifically, we first model the uncertainty in the stochastic arrival and service processes using uncertainty sets. This enables us to approximate the expected PAoI performance for very general arrival and service processes, including those exhibiting heavy-tailed behaviors or correlations, where traditional probabilistic approaches cannot be applied. We then derive a new bound on the PAoI in the single-source single-server setting. Furthermore, we generalize our analysis to two-source single-server systems with symmetric arrivals, which involves new challenges (e.g., the service times of the updates from two sources are coupled in one single uncertainty set). Finally, through numerical experiments, we show that our new bounds provide a good approximation for the expected PAoI. Compared to some well-known bounds in the literature (e.g., one based on Kingman's bound under the i.i.d. assumption) that tends to be inaccurate under light load, our new approximation is accurate under both light and high loads, both of which are critical scenarios for the AoI performance.Comment: Published in IEEE INFOCOM 202

Waiting but not Aging: Optimizing Information Freshness Under the Pull Model

The Age-of-Information is an important metric for investigating the timeliness performance in information-update systems. In this paper, we study the AoI minimization problem under a new Pull model with replication schemes, where a user proactively sends a replicated request to multiple servers to "pull" the information of interest. Interestingly, we find that under this new Pull model, replication schemes capture a novel tradeoff between different values of the AoI across the servers (due to the random updating processes) and different response times across the servers, which can be exploited to minimize the expected AoI at the user's side. Specifically, assuming Poisson updating process for the servers and exponentially distributed response time, we derive a closed-form formula for computing the expected AoI and obtain the optimal number of responses to wait for to minimize the expected AoI. Then, we extend our analysis to the setting where the user aims to maximize the AoI-based utility, which represents the user's satisfaction level with respect to freshness of the received information. Furthermore, we consider a more realistic scenario where the user has no prior knowledge of the system. In this case, we reformulate the utility maximization problem as a stochastic Multi-Armed Bandit problem with side observations and leverage a special linear structure of side observations to design learning algorithms with improved performance guarantees. Finally, we conduct extensive simulations to elucidate our theoretical results and compare the performance of different algorithms. Our findings reveal that under the Pull model, waiting does not necessarily lead to aging; waiting for more than one response can often significantly reduce the AoI and improve the AoI-based utility in most scenarios.Comment: 15 pages. arXiv admin note: substantial text overlap with arXiv:1704.0484

Impact of vancomycin-induced changes in the intestinal microbiota on the pharmacokinetics of simvastatin

The pharmacokinetic (PK) properties of drugs are affected in several ways by interactions with microbiota. The aim of this study was to investigate the effects of oral vancomycin on the gut microbiota and, consequently, on the PKs of simvastatin. An open-label, single arm, sequential crossover study was conducted in six healthy Korean male subjects. After 6 days on a control diet, simvastatin 40 mg was orally administered to the subjects before and after 1 week of oral vancomycin treatment. Blood samples for PK analysis and fecal samples for metagenomic and metabolomic analyses were collected. After vancomycin treatment, the richness of microbiota considerably decreased, and the composition was altered. In particular, the relative abundance of Bacteroidetes decreased, whereas that of proteobacteria increased. In addition, changes in fecal metabolites, including D-glucuronic acid, were observed. However, systemic exposure of simvastatin was not changed whereas that of hydroxysimvastatin showed a tendency to increase. The relationship between the change of PKs of simvastatin and the change of gut microbiota and fecal metabolites were not clearly observed

Spatiotemporal-Enhanced Network for Click-Through Rate Prediction in Location-based Services

In Location-Based Services(LBS), user behavior naturally has a strong dependence on the spatiotemporal information, i.e., in different geographical locations and at different times, user click behavior will change significantly. Appropriate spatiotemporal enhancement modeling of user click behavior and large-scale sparse attributes is key to building an LBS model. Although most of existing methods have been proved to be effective, they are difficult to apply to takeaway scenarios due to insufficient modeling of spatiotemporal information. In this paper, we address this challenge by seeking to explicitly model the timing and locations of interactions and proposing a Spatiotemporal-Enhanced Network, namely StEN. In particular, StEN applies a Spatiotemporal Profile Activation module to capture common spatiotemporal preference through attribute features. A Spatiotemporal Preference Activation is further applied to model the personalized spatiotemporal preference embodied by behaviors in detail. Moreover, a Spatiotemporal-aware Target Attention mechanism is adopted to generate different parameters for target attention at different locations and times, thereby improving the personalized spatiotemporal awareness of the model.Comprehensive experiments are conducted on three large-scale industrial datasets, and the results demonstrate the state-of-the-art performance of our methods. In addition, we have also released an industrial dataset for takeaway industry to make up for the lack of public datasets in this community.Comment: accepted by CIKM workshop 202

Titanium dioxide nanoparticles oral exposure to pregnant rats and its distribution

Background: Titanium dioxide (TiO2) nanoparticles are among the most manufactured nanomaterials in the industry, and are used in food products, toothpastes, cosmetics and paints. Pregnant women as well as their conceptuses may be exposed to TiO2 nanoparticles; however, the potential effects of these nanoparticles during pregnancy are controversial, and their internal distribution has not been investigated. Therefore, in this study, we investigated the potential effects of oral exposure to TiO2 nanoparticles and their distribution during pregnancy. TiO2 nanoparticles were orally administered to pregnant Sprague-Dawley rats (12 females per group) from gestation days (GDs) 6 to 19 at dosage levels of 0, 100, 300 and 1000 mg/kg/day, and then cesarean sections were conducted on GD 20. Results: In the maternal and embryo-fetal examinations, there were no marked toxicities in terms of general clinical signs, body weight, food consumption, organ weights, macroscopic findings, cesarean section parameters and fetal morphological examinations. In the distribution analysis, titanium contents were increased in the maternal liver, maternal brain and placenta after exposure to high doses of TiO2 nanoparticles. Conclusion: Oral exposure to TiO2 during pregnancy increased the titanium concentrations in the maternal liver, maternal brain and placenta, but these levels did not induce marked toxicities in maternal animals or affect embryo-fetal development. These results could be used to evaluate the human risk assessment of TiO2 nanoparticle oral exposure during pregnancy, and additional comprehensive toxicity studies are deemed necessary considering the possibility of complex exposure scenarios and the various sizes of TiO2 nanoparticles

Salinomycin enhances doxorubicin-induced cytotoxicity in multidrug resistant MCF-7/MDR human breast cancer cells via decreased efflux of doxorubicin

Salinomycin is a monocarboxylic polyether antibiotic, which is widely used as an anticoccidial agent. The anticancer property of salinomycin has been recognized and is based on its ability to induce apoptosis in human multidrug resistance (MDR). The present study investigated whether salinomycin reverses MDR towards chemotherapeutic agents in doxorubicin-resistant MCF-7/MDR human breast cancer cells. The results demonstrated that doxorubicin-mediated cytotoxicity was significantly enhanced by salinomycin in the MCF-7/MDR cells, and this occurred in a dose-dependent manner. This finding was consistent with subsequent observations made under a confocal microscope, in which the doxorubicin fluorescence signals of the salinomycin-treated cells were higher compared with the cells treated with doxorubicin alone. In addition, flow cytometric analysis revealed that salinomycin significantly increased the net cellular uptake and decreased the efflux of doxorubicin. The expression levels of MDR-1 and MRP-1 were not altered at either the mRNA or protein levels in the cells treated with salinomycin. These results indicated that salinomycin was mediated by its ability to increase the uptake and decrease the efflux of doxorubicin in MCF-7/MDR cells. Salinomycin reversed the resistance of doxorubicin, suggesting that chemotherapy in combination with salinomycin may benefit MDR cancer therapyopen