Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data.

In many applications, one works with neural network models trained by someone else. For such pretrained models, one may not have access to training data or test data. Moreover, one may not know details about the model, e.

Shallow neural networks for fluid flow reconstruction with limited sensors.

In many applications, it is important to reconstruct a fluid flow field, or some other high-dimensional state, from limited measurements and limited data. In this work, we propose a shallow neural network-based learning methodology for such fluid flow reconstruction. Our approach learns an end-to-end mapping between the sensor measurements and the high-dimensional fluid flow field, without any heavy preprocessing on the raw data.

Maturation of Cerebellar Purkinje Cell Population Activity during Postnatal Refinement of Climbing Fiber Network.

Neural circuits undergo massive refinements during postnatal development. In the developing cerebellum, the climbing fiber (CF) to Purkinje cell (PC) network is drastically reshaped by eliminating early-formed redundant CF to PC synapses. To investigate the impact of CF network refinement on PC population activity during postnatal development, we monitored spontaneous CF responses in neighboring PCs and the activity of populations of nearby CF terminals using in vivo two-photon calcium imaging.

Factors impacting rural Pacific Island veterans' access to care: A qualitative examination.

Pacific Island veterans suffer from greater severity of posttraumatic stress disorder (PTSD) compared with Caucasian veterans but face substantial barriers to mental health care. However, the factors that may dissuade or facilitate veterans in the Pacific Islands from seeking mental health care are not known. The main aim of this study was to identify how internal and external factors interact to impact wounded warriors' access to and use of mental health services.

Weighted SGD for ℓ Regression with Randomized Preconditioning.

In recent years, stochastic gradient descent (SGD) methods and randomized linear algebra (RLA) algorithms have been applied to many large-scale problems in machine learning and data analysis. SGD methods are easy to implement and applicable to a wide range of convex optimization problems. In contrast, RLA algorithms provide much stronger performance guarantees but are applicable to a narrower class of problems.

Identifying important ions and positions in mass spectrometry imaging data using CUR matrix decompositions.

Mass spectrometry imaging enables label-free, high-resolution spatial mapping of the chemical composition of complex, biological samples. Typical experiments require selecting ions and/or positions from the images: ions for fragmentation studies to identify keystone compounds and positions for follow up validation measurements using microdissection or other orthogonal techniques. Unfortunately, with modern imaging machines, these must be selected from an overwhelming amount of raw data.

Think locally, act locally: detection of small, medium-sized, and large communities in large networks.

It is common in the study of networks to investigate intermediate-sized (or "meso-scale") features to try to gain an understanding of network structure and function. For example, numerous algorithms have been developed to try to identify "communities," which are typically construed as sets of nodes with denser connections internally than with the remainder of a network. In this paper, we adopt a complementary perspective that communities are associated with bottlenecks of locally biased dynamical processes that begin at seed sets of nodes, and we employ several different community-identification procedures (using diffusion-based and geodesic-based dynamics) to investigate community quality as a function of community size.

LSRN: A PARALLEL ITERATIVE SOLVER FOR STRONGLY OVER- OR UNDERDETERMINED SYSTEMS.

We describe a parallel iterative least squares solver named LSRN that is based on random normal projection. LSRN computes the min-length solution to min ‖ - ‖, where ∈ ℝ with ≫ or ≪ , and where may be rank-deficient. Tikhonov regularization may also be included.

rCUR: an R package for CUR matrix decomposition.

Background: Many methods for dimensionality reduction of large data sets such as those generated in microarray studies boil down to the Singular Value Decomposition (SVD). Although singular vectors associated with the largest singular values have strong optimality properties and can often be quite useful as a tool to summarize the data, they are linear combinations of up to all of the data points, and thus it is typically quite hard to interpret those vectors in terms of the application domain from which the data are drawn. Recently, an alternative dimensionality reduction paradigm, CUR matrix decompositions, has been proposed to address this problem and has been applied to genetic and internet data.

Efficient genomewide selection of PCA-correlated tSNPs for genotype imputation.

The linkage disequilibrium structure of the human genome allows identification of small sets of single nucleotide polymorphisms (SNPs) (tSNPs) that efficiently represent dense sets of markers. This structure can be translated into linear algebraic terms as evidenced by the well documented principal components analysis (PCA)-based methods. Here we apply, for the first time, PCA-based methodology for efficient genomewide tSNP selection; and explore the linear algebraic structure of the human genome.

Impact on hospital functions following the 2010 Chilean earthquake.

Objective: The objective of this study was to assess the impact of the 2010 Chilean earthquake on hospital functions and services. Hospitals functioning in a post-disaster environment must provide emergency medical care related to the event, in addition to providing standard community health services. This study focused on damage to both structural and nonstructural components, as well as to utility services.

Dental identification system based on unwrapped CT images.

Dental comparison of postmortem (PM) and ante-mortem (AM) radiographs provides one of the best avenues for the forensic identification of human remains. Nevertheless conventional dental comparison is labor-intensive, subjective, and has several inherent drawbacks. This paper presents a semi-automated image analysis system designed to assist the forensic dentist with the task of identifying human remains.

CUR matrix decompositions for improved data analysis.

Principal components analysis and, more generally, the Singular Value Decomposition are fundamental data analysis tools that express a data matrix in terms of a sequence of orthogonal or uncorrelated vectors of decreasing importance. Unfortunately, being linear combinations of up to all the data points, these vectors are notoriously difficult to interpret in terms of the data and processes generating the data. In this article, we develop CUR matrix decompositions for improved data analysis.

Synthesizing panoramic radiographs by unwrapping dental CT data.

A method for synthesizing panoramic radiographs from dental CT data is presented. The method is based on the principles of panoramic radiography with a continuously-moving rotation center. The method computes discrete pixel sums through the CT data along normals to the medial axis of the dental arch.

Training retention of Level C personal protective equipment use by emergency medical services personnel.

Objectives: To assess the six-month training retention for out-of-hospital providers donning and doffing Level C personal protective equipment (PPE).

Methods: In this prospective observational study, 36 out-of-hospital providers enrolled in a paramedic program were trained in Level C (chemical-resistant coverall, butyl gloves, and boots and an air-purifying respirator) PPE use. A standardized training module and checklist of critical actions developed by a hazardous materials (hazmat) technician were used to evaluate donning and doffing.

PCA-correlated SNPs for structure identification in worldwide human populations.

Existing methods to ascertain small sets of markers for the identification of human population structure require prior knowledge of individual ancestry. Based on Principal Components Analysis (PCA), and recent results in theoretical computer science, we present a novel algorithm that, applied on genomewide data, selects small subsets of SNPs (PCA-correlated SNPs) to reproduce the structure found by PCA on the complete dataset, without use of ancestry information. Evaluating our method on a previously described dataset (10,805 SNPs, 11 populations), we demonstrate that a very small set of PCA-correlated SNPs can be effectively employed to assign individuals to particular continents or populations, using a simple clustering algorithm.

Intra- and interpopulation genotype reconstruction from tagging SNPs.

The optimal method to be used for tSNP selection, the applicability of a reference LD map to unassayed populations, and the scalability of these methods to genome-wide analysis, all remain subjects of debate. We propose novel, scalable matrix algorithms that address these issues and we evaluate them on genotypic data from 38 populations and four genomic regions (248 SNPs typed for approximately 2000 individuals). We also evaluate these algorithms on a second data set consisting of genotypes available from the HapMap database (1336 SNPs for four populations) over the same genomic regions.

Relationship between maternal and fetal plasma glucose and insulin concentrations during graded maternal hyperglycemic states in primates.

Our goal was to conduct a controlled study using an established timed-pregnant baboon model to describe the maternal and fetal plasma glucose and insulin concentrations during graded increases in maternal circulating glucose levels. Timed-pregnant baboons were operated on during the second half of pregnancy, and after recovery from surgery, maternal glucose infusions were started. To determine changes in plasma glucose and insulin concentrations, maternal and fetal blood samples were obtained before glucose infusion and at 30-minute intervals to include 30 minutes postinfusion.

Constructivism and psychotherapy.

Constructivism is a metatheoretical perspective that embraces diverse traditions in medicine, philosophy, psychology, and spiritual wisdom. Constructive psychotherapy emphasizes complex cycles in the natural ordering and reorganizing processes that characterize all development in living systems. Individuals are encouraged to view themselves as active participants in their lives.

Constructive suggestions for the practical education of professional life counselors.

Professional life counselors will serve an increasingly important role in the life quality of global citizens in the 21st century. The optimal preparation of service providers will reflect basic principles of human development, professional helping, and educational processes. The dynamic systems appreciations of constructivism offer valuable scaffoldings for mentoring and apprenticeship in human helping.