Dispersal, facilitation, and burrow architecture in banner-tailed kangaroo rats

The largest and most dominant kangaroo rat species in the Chihuahuan Desert is the banner-tailed kangaroo rat (Dipodomys spectabilis). This keystone species constructs mounds containing a complex burrow system around which their ecosystem engineering activities are centered. I studied a population of banner-tailed kangaroo rats at the Sevilleta National Wildlife Refuge, New Mexico from 2005-2009. Specifically, I examined how banner-tailed kangaroo rats: 1) modify their mounds in response to seasonal conditions; 2) spatially affect harvester ants (Pogonomyrmex rugosus) through ecosystem engineering activities; and 3) differ in timing of natal dispersal between sexes. I used mark-recapture, genetic, experimental, and spatially-explicit methods to address these areas of interest. I observed that kangaroo rats remodeled their mounds seasonally in relation to changes in predation risk, seed spoilage risk, and metabolic costs. My results documented an additional keystone effect of banner-tailed kangaroo rats in the Chihuahuan Desert, a facilitatory impact on the spatial structure and dynamics of harvester ant colonies. I also experimentally determined that physiological cues influence timing of natal dispersal in males and females differently

The singular values of the GUE (less is more)

Some properties that nominally involve the eigenvalues of Gaussian Unitary Ensemble (GUE) can instead be phrased in terms of singular values. By discarding the signs of the eigenvalues, we gain access to a surprising decomposition: the singular values of the GUE are distributed as the union of the singular values of two independent ensembles of Laguerre type. This independence is remarkable given the well known phenomenon of eigenvalue repulsion. The structure of this decomposition reveals that several existing observations about large n limits of the GUE are in fact manifestations of phenomena that are already present for finite random matrices. We relate the semicircle law to the quarter-circle law by connecting Hermite polynomials to generalized Laguerre polynomials with parameter ± 1/2. Similarly, we write the absolute value of the determinant of the n x n GUE as a product n independent random variables to gain new insight into its asymptotic log-normality. The decomposition also provides a description of the distribution of the smallest singular value of the GUE, which in turn permits the study of the leading order behavior of the condition number of GUE matrices. The study is motivated by questions involving the enumeration of orientable maps, and is related to questions involving powers of complex Ginibre matrices. The inescapable conclusion of this work is that the singular values of the GUE play an unpredictably important role that had gone unnoticed for decades even though, in hindsight, so many clues had been around.National Science Foundation (U.S.) (Grant DMS–1035400)National Science Foundation (U.S.) (Grant DMS–1016125

The Dirichlet Markov Ensemble

We equip the polytope of $n\times n$ Markov matrices with the normalized trace of the Lebesgue measure of $\mathbb{R}^{n^2}$. This probability space provides random Markov matrices, with i.i.d. rows following the Dirichlet distribution of mean $(1/n,...,1/n)$. We show that if \bM is such a random matrix, then the empirical distribution built from the singular values of\sqrt{n} \bM tends as $n\to\infty$ to a Wigner quarter--circle distribution. Some computer simulations reveal striking asymptotic spectral properties of such random matrices, still waiting for a rigorous mathematical analysis. In particular, we believe that with probability one, the empirical distribution of the complex spectrum of \sqrt{n} \bM tends as $n\to\infty$ to the uniform distribution on the unit disc of the complex plane, and that moreover, the spectral gap of \bM is of order $1-1/\sqrt{n}$ when $n$ is large.Comment: Improved version. Accepted for publication in JMV

Autotuning multigrid with PetaBricks

Algorithmic choice is essential in any problem domain to realizing optimal computational performance. Multigrid is a prime example: not only is it possible to make choices at the highest grid resolution, but a program can switch techniques as the problem is recursively attacked on coarser grid levels to take advantage of algorithms with different scaling behaviors. Additionally, users with different convergence criteria must experiment with parameters to yield a tuned algorithm that meets their accuracy requirements. Even after a tuned algorithm has been found, users often have to start all over when migrating from one machine to another. We present an algorithm and autotuning methodology that address these issues in a near-optimal and efficient manner. The freedom of independently tuning both the algorithm and the number of iterations at each recursion level results in an exponential search space of tuned algorithms that have different accuracies and performances. To search this space efficiently, our autotuner utilizes a novel dynamic programming method to build efficient tuned algorithms from the bottom up. The results are customized multigrid algorithms that invest targeted computational power to yield the accuracy required by the user. The techniques we describe allow the user to automatically generate tuned multigrid cycles of different shapes targeted to the user's specific combination of problem, hardware, and accuracy requirements. These cycle shapes dictate the order in which grid coarsening and grid refinement are interleaved with both iterative methods, such as Jacobi or Successive Over-Relaxation, as well as direct methods, which tend to have superior performance for small problem sizes. The need to make choices between all of these methods brings the issue of variable accuracy to the forefront. Not only must the autotuning framework compare different possible multigrid cycle shapes against each other, but it also needs the ability to compare tuned cycles against both direct and (non-multigrid) iterative methods. We address this problem by using an accuracy metric for measuring the effectiveness of tuned cycle shapes and making comparisons over all algorithmic types based on this common yardstick. In our results, we find that the flexibility to trade performance versus accuracy at all levels of recursive computation enables us to achieve excellent performance on a variety of platforms compared to algorithmically static implementations of multigrid. Our implementation uses PetaBricks, an implicitly parallel programming language where algorithmic choices are exposed in the language. The PetaBricks compiler uses these choices to analyze, autotune, and verify the PetaBricks program. These language features, most notably the autotuner, were key in enabling our implementation to be clear, correct, and fast.National Science Foundation (U.S.) (Award CCF-0832997)GigaScale Systems Research Cente

Trainable, vision-based automated home cage behavioral phenotyping

We describe a fully trainable computer vision system enabling the automated analysis of complex mouse behaviors. Our system computes a sequence of feature descriptors for each video sequence and a classifier is used to learn a mapping from these features to behaviors of interest. We collected a very large manually annotated video database of mouse behaviors for training and testing the system. Our system performs on par with human scoring, as measured from the ground-truth manual annotations of thousands of clips of freely behaving mice. As a validation of the system, we characterized the home cage behaviors of two standard inbred and two nonstandard mouse strains. From this data, we were able to predict the strain identity of individual mice with high accuracy.California Institute of Technology. Broad Fellows Program in Brain CircuitryNational Science Council of Taiwan (TMS-094-1-A032

Language and Compiler Support for Auto-Tuning Variable-Accuracy Algorithms

Approximating ideal program outputs is a common technique for solving computationally difficult problems, for adhering to processing or timing constraints, and for performance optimization in situations where perfect precision is not necessary. To this end, programmers often use approximation algorithms, iterative methods, data resampling, and other heuristics. However, programming such variable accuracy algorithms presents difficult challenges since the optimal algorithms and parameters may change with different accuracy requirements and usage environments. This problem is further compounded when multiple variable accuracy algorithms are nested together due to the complex way that accuracy requirements can propagate across algorithms and because of the size of the set of allowable compositions. As a result, programmers often deal with this issue in an ad-hoc manner that can sometimes violate sound programming practices such as maintaining library abstractions. In this paper, we propose language extensions that expose trade-offs between time and accuracy to the compiler. The compiler performs fully automatic compile-time and installtime autotuning and analyses in order to construct optimized algorithms to achieve any given target accuracy. We present novel compiler techniques and a structured genetic tuning algorithm to search the space of candidate algorithms and accuracies in the presence of recursion and sub-calls to other variable accuracy code. These techniques benefit both the library writer, by providing an easy way to describe and search the parameter and algorithmic choice space, and the library user, by allowing high level specification of accuracy requirements which are then met automatically without the need for the user to understand any algorithm-specific parameters. Additionally, we present a new suite of benchmarks, written in our language, to examine the efficacy of our techniques. Our experimental results show that by relaxing accuracy requirements , we can easily obtain performance improvements ranging from 1.1× to orders of magnitude of speedup

Can initial vaginal bleeding patterns in etonogestrel implant users predict subsequent bleeding in the first 2 years of use?

ObjectivesTo evaluate if a simple method for characterizing vaginal bleeding patterns in etonogestrel contraceptive implant users can predict subsequent patterns and bleeding-related discontinuation over the first 2 years of use.Study designWe reanalyzed phase 3 study bleeding data for non-breastfeeding participants from the United States, Europe, Russia and Chile during the first 2 years of implant use to characterize and correlate bleeding patterns. We used 90-day reference periods with period 1.1 starting at Day 29 and ending at Day 118. We dichotomized bleeding patterns as "favorable" (amenorrhea, infrequent bleeding and normal frequency bleeding without prolonged bleeding) or "unfavorable' (prolonged and/or frequent bleeding) and tracked user groups based on these bleeding patterns in reference period 1.1 through Year 1 and from Year 1 through Year 2, respectively.ResultsWe evaluated data from 537 and 428 women with up to 1 and 2 years use, respectively. Of the 325 (60.5%) women with favorable bleeding in reference period 1.1, 275 (84.6%) reported favorable bleeding also in reference period 2, 197 (60.6%) reported favorable bleeding throughout Year 1, and favorable bleeding in 75-85% of reference periods in Year 2. Among 212 (39.5%) women with unfavorable bleeding in reference period 1.1, 118 (55.7%) continued with unfavorable bleeding in reference period 2, while about 40%-50% reported favorable patterns in RP 2, 3 and/or 4. Initial favorable bleeding resulted in lower discontinuation rates than initial unfavorable bleeding in years 1 (3.7% vs 12.7%, p≪.0001) and 2 (2.5% vs 16.5%, p≪.0001).ConclusionImplant users with favorable bleeding in the first reference period are likely to continue with favorable bleeding over the next 2 years. Initial bleeding patterns predict overall continuation rates in years 1 and 2. Implications Statement When evaluating vaginal bleeding in any 90-day reference period over 2 years of etonogestrel implant use, approximately 80% of women with favorable and 40% with unfavorable bleeding patterns will have favorable bleeding in the next reference periods. These findings can facilitate counseling regarding bleeding for women using the etonogestrel implant

The dynamics of animal social networks: Analytical, conceptual, and theoretical advances

Social network analysis provides a broad and complex perspective on animal sociality that is widely applicable to almost any species. Recent applications demonstrate the utility of network analysis for advancing our understanding of the dynamics, selection pressures, development, and evolution of complex social systems. However, most studies of animal social networks rely primarily on a descriptive approach. To propel the field of animal social networks beyond exploratory analyses and to facilitate the integration of quantitative methods that allow for the testing of ecologically and evolutionarily relevant hypotheses, we review methodological and conceptual advances in network science, which are underutilized in studies of animal sociality. First, we highlight how the use of statistical model- ing and triadic motifs analysis can advance our understanding of the processes that structure networks. Second, we discuss how the consideration of temporal changes and spatial constraints can shed light on the dynamics of social networks. Third, we consider how the study of variation at multiple scales can potentially transform our understanding of the structure and function of animal networks. We direct readers to analytical tools that facilitate the adoption of these new concepts and methods. Our goal is to provide behavioral ecologists with a toolbox of current methods that can stimulate novel insights into the ecological influences and evolutionary pressures structuring networks and advance our understanding of the proximate and ultimate processes that drive animal sociality

The dynamics of animal social networks: Analytical, conceptual, and theoretical advances

Social network analysis provides a broad and complex perspective on animal sociality that is widely applicable to almost any species. Recent applications demonstrate the utility of network analysis for advancing our understanding of the dynamics, selection pressures, development, and evolution of complex social systems. However, most studies of animal social networks rely primarily on a descriptive approach. To propel the field of animal social networks beyond exploratory analyses and to facilitate the integration of quantitative methods that allow for the testing of ecologically and evolutionarily relevant hypotheses, we review methodological and conceptual advances in network science, which are underutilized in studies of animal sociality. First, we highlight how the use of statistical model- ing and triadic motifs analysis can advance our understanding of the processes that structure networks. Second, we discuss how the consideration of temporal changes and spatial constraints can shed light on the dynamics of social networks. Third, we consider how the study of variation at multiple scales can potentially transform our understanding of the structure and function of animal networks. We direct readers to analytical tools that facilitate the adoption of these new concepts and methods. Our goal is to provide behavioral ecologists with a toolbox of current methods that can stimulate novel insights into the ecological influences and evolutionary pressures structuring networks and advance our understanding of the proximate and ultimate processes that drive animal sociality

Impact of an Interactive On-line Tool on Therapeutic Decision-Making for Patients with Advanced Non-Small-Cell Lung Cancer

Background:Treatment guidelines provide recommendations but cannot account for the wide variability in patient-tumor characteristics in individual patients. We developed an on-line interactive decision tool to provide expert recommendations for specific patient scenarios in the first-line and maintenance settings for advanced non–small-cell lung cancer. We sought to determine how providing expert feedback would influence clinical decision-making.Method:Five lung cancer experts selected treatment for 96 different patient cases based on patient and/or tumor-specific features. These data were used to develop an on-line decision tool. Participant physicians entered variables for their patient scenario with treatment choices, and then received expert treatment recommendations for that scenario. To determine the impact on decision-making, users were asked whether the expert feedback impacted their original plan.Results:A total of 442 individual physicians, of which 88% were from outside the United States, entered 653 cases, with report on impact in 389 cases. Expert feedback affected treatment choice in 73% of cases (23% changed and 50% confirmed decisions). For cases with epidermal growth factor receptor (EGFR) mutation or anaplastic lymphoma kinase (ALK) fusion, all experts selected targeted therapy whereas 51% and 58% of participants did not. Greater variability was seen between experts and participants for cases involving EGFR or ALK wild-type tumors. Participants were 2.5-fold more likely to change to expert recommended therapy for ALK fusions than for EGFR mutations (p = 0.017).Conclusion:This online tool for treatment decision-making resulted in a positive influence on clinician's decisions. This approach offers opportunities for improving quality of care and meets an educational need in application of new therapeutic paradigms