Cluster-Aided Mobility Predictions

Predicting the future location of users in wireless net- works has numerous applications, and can help service providers to improve the quality of service perceived by their clients. The location predictors proposed so far estimate the next location of a specific user by inspecting the past individual trajectories of this user. As a consequence, when the training data collected for a given user is limited, the resulting prediction is inaccurate. In this paper, we develop cluster-aided predictors that exploit past trajectories collected from all users to predict the next location of a given user. These predictors rely on clustering techniques and extract from the training data similarities among the mobility patterns of the various users to improve the prediction accuracy. Specifically, we present CAMP (Cluster-Aided Mobility Predictor), a cluster-aided predictor whose design is based on recent non-parametric bayesian statistical tools. CAMP is robust and adaptive in the sense that it exploits similarities in users' mobility only if such similarities are really present in the training data. We analytically prove the consistency of the predictions provided by CAMP, and investigate its performance using two large-scale datasets. CAMP significantly outperforms existing predictors, and in particular those that only exploit individual past trajectories

cc-differential uniformity, (almost) perfect cc-nonlinearity, and equivalences

In this article, we introduce new notions $cc$-differential uniformity, $cc$-differential spectrum, PccN functions and APccN functions, and investigate their properties. We also introduce $c$-CCZ equivalence, $c$-EA equivalence, and $c1$-equivalence. We show that $c$-differential uniformity is invariant under $c1$-equivalence, and $cc$-differential uniformity and $cc$-differential spectrum are preserved under $c$-CCZ equivalence. We characterize $cc$-differential uniformity of vectorial Boolean functions in terms of the Walsh transformation. We investigate $cc$-differential uniformity of power functions $F(x)=x^d$. We also illustrate examples to prove that $c$-CCZ equivalence is strictly more general than $c$-EA equivalence.Comment: 18 pages. Comments welcom

Conventional reversal of rocuronium-induced neuromuscular blockade by sugammadex in Korean children: pharmacokinetics, efficacy, and safety analyses

Background: Sugammadex is known to reverse neuromuscular blockade induced by non-depolarizing agents. In children, the recommended dose for reversal of moderate neuromuscular blockade is 2 mg/kg. We investigated the pharmacokinetics and pharmacodynamics of sugammadex in Korean children.Methods: Children (2–17 years of age) undergoing brain or spine surgery were enrolled and randomly assigned to control (neostigmine) and 2, 4, or 8 mg/kg sugammadex groups. Following induction of anesthesia and monitoring of the response to train-of-four stimulation, 1 mg/kg rocuronium was intravenously administered. Upon reappearance of the second twitch to train-of-four stimulation, the study drug was administered according to group allocation. The plasma concentrations of rocuronium and sugammadex were serially measured at nine predefined time points following study drug administration. To determine efficacy, we measured the time elapsed from drug administration to recovery of T4/T1 ≥ 0.9. For pharmacokinetics, non-compartmental analysis was performed and we monitored adverse event occurrence from the time of study drug administration until 24 h post-surgery.Results: Among the 29 enrolled participants, the sugammadex (2 mg/kg) and control groups showed recovery times [median (interquartile range)] of 1.3 (1.0–1.9) and 7.7 (5.3–21.0) min, respectively (p = 0.002). There were no significant differences in recovery time among the participants in sugammadex groups. The pharmacokinetics of sugammadex were comparable to those of literature findings. Although two hypotensive events related to sugammadex were observed, no intervention was necessary.Conclusion: The findings of this pharmacokinetic analysis and efficacy study of sugammadex in Korean children indicated that sugammadex (2 mg/kg) may be safely administered for reversing moderate neuromuscular blockade. Some differences in pharmacokinetics of sugammadex were observed according to age.Clinical Trial Registration:http://clinicaltrials.gov (NCT04347486

Use of adverse outcome pathways in chemical toxicity testing: potential advantages and limitations

Amid revolutionary changes in toxicity assessment brought about by increasing regulation of chemicals, adverse outcome pathways (AOPs) have emerged as a useful framework to assess adverse effect of chemicals using molecular level effect, which aid in setting environmental regulation policies. AOPs are biological maps that describe mechanisms linking molecular initiating event to adverse outcomes (AOs) at an individual level. Each AOP consists of a molecular initiating event, key events, and an AO. AOPs use molecular markers to predict endpoints currently used in risk assessment, promote alternatives to animal model-based test methods, and provide scientific explanations for the effects of chemical exposures. Moreover, AOPs enhance certainty in interpreting existing and new information. The application of AOPs in chemical toxicity testing will help shift the existing paradigm of chemical management based on apical endpoints toward active application of in silico and in vitro data

In Silico Molecular Docking and In Vivo Validation with Caenorhabditis elegans to Discover Molecular Initiating Events in Adverse Outcome Pathway Framework: Case Study on Endocrine-Disrupting Chemicals with Estrogen and Androgen Receptors

Molecular docking is used to analyze structural complexes of a target with its ligand for understanding the chemical and structural basis of target specificity. This method has the potential to be applied for discovering molecular initiating events (MIEs) in the Adverse Outcome Pathway framework. In this study, we aimed to develop in silico–in vivo combined approach as a tool for identifying potential MIEs. We used environmental chemicals from Tox21 database to identify potential endocrine-disrupting chemicals (EDCs) through molecular docking simulation, using estrogen receptor (ER), androgen receptor (AR) and their homology models in the nematode Caenorhabditis elegans (NHR-14 and NHR-69, respectively). In vivo validation was conducted on the selected EDCs with C. elegans reproductive toxicity assay using wildtype N2, nhr-14, and nhr-69 loss-of-function mutant strains. The chemicals showed high binding affinity to tested receptors and showed the high in vivo reproductive toxicity, and this was further confirmed using the mutant strains. The present study demonstrates that the binding affinity from the molecular docking potentially correlates with in vivo toxicity. These results prove that our in silico–in vivo combined approach has the potential to be applied for identifying MIEs. This study also suggests the potential of C. elegans as useful in the in vivo model for validating the in silico approach

Best Arm Identification in Multi-Agent Multi-Armed Bandits

We investigate the problem of best arm identification in Multi-Agent Multi-Armed Bandits (MAMABs) where the rewards are defined through afactor graph. The objective is to find an optimalglobal action with a prescribed level of confidenceand minimal sample complexity. We derive a tightinstance-specific lower bound of the sample complexity and characterize the corresponding optimal sampling strategy. Unfortunately, this boundis obtained by solving a combinatorial optimization problem with a number of variables and constraints exponentially growing with the number ofagents. We leverage Mean Field (MF) techniquesto obtain, in a computationally efficient manner,an approximation of the lower bound. The approximation scales at most as ρKd(where ρ, K,and d denote the number of factors in the graph,the number of possible actions per agent, and themaximal degree of the factor graph). We deviseMF-TaS (Mean-Field-Track-and-Stop), an algorithm whose sample complexity provably matchesour approximated lower bound. We illustratethe performance of MF-TaS numerically usingboth synthetic and real-world experiments (e.g.,to solve the antenna tilt optimization problem inradio communication networks).QC 20230915</p

Best Arm Identification in Multi-Agent Multi-Armed Bandits

We investigate the problem of best arm identification in Multi-Agent Multi-Armed Bandits (MAMABs) where the rewards are defined through afactor graph. The objective is to find an optimalglobal action with a prescribed level of confidenceand minimal sample complexity. We derive a tightinstance-specific lower bound of the sample complexity and characterize the corresponding optimal sampling strategy. Unfortunately, this boundis obtained by solving a combinatorial optimization problem with a number of variables and constraints exponentially growing with the number ofagents. We leverage Mean Field (MF) techniquesto obtain, in a computationally efficient manner,an approximation of the lower bound. The approximation scales at most as ρKd(where ρ, K,and d denote the number of factors in the graph,the number of possible actions per agent, and themaximal degree of the factor graph). We deviseMF-TaS (Mean-Field-Track-and-Stop), an algorithm whose sample complexity provably matchesour approximated lower bound. We illustratethe performance of MF-TaS numerically usingboth synthetic and real-world experiments (e.g.,to solve the antenna tilt optimization problem inradio communication networks).QC 20230915</p