Time-symmetry breaking in turbulence

In three-dimensional turbulent flows, the flux of energy from large to small scales breaks time symmetry. We show here that this irreversibility can be quantified by following the relative motion of several Lagrangian tracers. We find by analytical calculation, numerical analysis and experimental observation that the existence of the energy flux implies that, at short times, two particles separate temporally slower forwards than backwards, and the difference between forward and backward dispersion grows as $t^3$. We also find the geometric deformation of material volumes, surrogated by four points spanning an initially regular tetrahedron, to show sensitivity to the time-reversal with an effect growing linearly in $t$. We associate this with the structure of the strain rate in the flow.Comment: 5 pages, 4 figure

“I Want to Tea”: An Entering Medical Student's Perspective on Geriatrics

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/99642/1/jgs12376.pd

Effects of Flywheel Resistance Training on Muscle Function and Sport-Specific Performance in Collegiate Club Water Polo Players

Flywheel training has been shown to be beneficial for improving a multitude of muscle function and performance parameters, but its short-term training effects on athletic performance have yet to be established. PURPOSE: To investigate the effects of four weeks of flywheel squat training on lower body muscle function adaptations and sport-specific performance in collegiate club water polo players. METHODS: Thirteen men and women who participated in collegiate club water polo performed flywheel squat training twice a week for four weeks. Isokinetic knee extension peak power (PP) and peak torque (PT), flywheel squat peak power (FPP) and mean power (FMP), countermovement jump (CMJ), in-water jump height (WJH) and foot speed were assessed at Pre1 (0 weeks), Pre2 (4 weeks), and Post (8 weeks). Throughout the training period, muscle soreness was assessed with a visual analog scale every session, and FPP and FMP were assessed during sessions 2, 4, 6, and 8. RESULTS: Isokinetic PP (ES = .65) and PT (ES = .67) increased significantly from Pre1 to Post, and FPP and FMP increased between Pre2 and Post (ES = 1.1, 1.0 respectively), and Pre1 and Post (ES = .79, .82). CMJ and foot speed were not changed, and WJH displayed a significant change between Pre1 and Post (ES = 0.4). FPP increased 19% from session 2 to 4 and FMP increased 27% from session 2 to 6, and each remained elevated through session 8. Muscle soreness peaked at session 2 but tapered off by session 3. CONCLUSIONS: Four weeks of flywheel squat training in collegiate club water polo players elicited large gains (47-52%, Effect Size = ~1.0) in flywheel specific squat power, but did not influence sport-specific performance measures including CMJ, WJH, and foot speed. Water-based exercises and stretch-shortening cycle movements (plyometrics) in combination with effective resistance training programs, including flywheel-based training, are likely needed for marked sport skill improvements, along with longer-term training studies

Identifying Cohesive Local Community Structures in Networks

Identifying community structure in networks is an important topic in data mining research. One of the challenges is to find local communities without requiring the global knowledge of the entire network. Exiting techniques have several limitations. First, there is no widely accepted definition for community. Second, these algorithms either lack good stopping criteria or depend on predefined threshold parameters. In this research I propose a local cohesion based algorithm to identify local communities in networks. This algorithm is grounded on the widely accepted group cohesion definition in social network analysis research. The algorithm is self-contained and does not depend on predefined threshold parameter to terminate the identification process. The evaluation results show that the proposed algorithm is more effective than the benchmark algorithm and can identify meaningful local communities in very large networks such as the Amazon co-purchasing network

Multiply Robust Causal Inference with Double Negative Control Adjustment for Categorical Unmeasured Confounding

Unmeasured confounding is a threat to causal inference in observational studies. In recent years, use of negative controls to mitigate unmeasured confounding has gained increasing recognition and popularity. Negative controls have a longstanding tradition in laboratory sciences and epidemiology to rule out non-causal explanations, although they have been used primarily for bias detection. Recently, Miao et al. (2018) have described sufficient conditions under which a pair of negative control exposure and outcome variables can be used to nonparametrically identify the average treatment effect (ATE) from observational data subject to uncontrolled confounding. In this paper, we establish nonparametric identification of the ATE under weaker conditions in the case of categorical unmeasured confounding and negative control variables. We also provide a general semiparametric framework for obtaining inferences about the ATE while leveraging information about a possibly large number of measured covariates. In particular, we derive the semiparametric efficiency bound in the nonparametric model, and we propose multiply robust and locally efficient estimators when nonparametric estimation may not be feasible. We assess the finite sample performance of our methods in extensive simulation studies. Finally, we illustrate our methods with an application to the postlicensure surveillance of vaccine safety among children

CleanML: A Study for Evaluating the Impact of Data Cleaning on ML Classification Tasks

Data quality affects machine learning (ML) model performances, and data scientists spend considerable amount of time on data cleaning before model training. However, to date, there does not exist a rigorous study on how exactly cleaning affects ML -- ML community usually focuses on developing ML algorithms that are robust to some particular noise types of certain distributions, while database (DB) community has been mostly studying the problem of data cleaning alone without considering how data is consumed by downstream ML analytics. We propose a CleanML study that systematically investigates the impact of data cleaning on ML classification tasks. The open-source and extensible CleanML study currently includes 14 real-world datasets with real errors, five common error types, seven different ML models, and multiple cleaning algorithms for each error type (including both commonly used algorithms in practice as well as state-of-the-art solutions in academic literature). We control the randomness in ML experiments using statistical hypothesis testing, and we also control false discovery rate in our experiments using the Benjamini-Yekutieli (BY) procedure. We analyze the results in a systematic way to derive many interesting and nontrivial observations. We also put forward multiple research directions for researchers.Comment: published in ICDE 202