Learning Deep Features in Instrumental Variable Regression

Instrumental variable (IV) regression is a standard strategy for learning causal relationships between confounded treatment and outcome variables from observational data by using an instrumental variable, which affects the outcome only through the treatment. In classical IV regression, learning proceeds in two stages: stage 1 performs linear regression from the instrument to the treatment; and stage 2 performs linear regression from the treatment to the outcome, conditioned on the instrument. We propose a novel method, deep feature instrumental variable regression (DFIV), to address the case where relations between instruments, treatments, and outcomes may be nonlinear. In this case, deep neural nets are trained to define informative nonlinear features on the instruments and treatments. We propose an alternating training regime for these features to ensure good end-to-end performance when composing stages 1 and 2, thus obtaining highly flexible feature maps in a computationally efficient manner. DFIV outperforms recent state-of-the-art methods on challenging IV benchmarks, including settings involving high dimensional image data. DFIV also exhibits competitive performance in off-policy policy evaluation for reinforcement learning, which can be understood as an IV regression task

Intelligent chilled mirror humidity sensor

A new, intelligent, chilled mirror humidity instrument has been designed for use on buoys and ships. The design goal is to make high quality dew point temperature measurements for a period of up to one year from an unattended platform, while consuming as little power as possible. Nominal system accuracy is 0.3°C, and a measure of data quality is provided to indicate possible drift in calibration. Energy consumption is typically 800 Joules per measurement; standby power consumption is 0.05 watts. Control of the instrument is managed by an onboard central processing unit which is programmable in BASIC, and communication to an external data logger is provided through an RS232 compatible interface. This report describes the preliminary sensor tests that led to this new design and provides the complete technical description required for fabrication.Funding was provided by the Office of Naval Research under contract Number N00014-84-C-0134, and the National Science Foundation through grant Number OCE87- 09614

Measuring memetic algorithm performance on image fingerprints dataset

Personal identification has become one of the most important terms in our society regarding access control, crime and forensic identification, banking and also computer system. The fingerprint is the most used biometric feature caused by its unique, universality and stability. The fingerprint is widely used as a security feature for forensic recognition, building access, automatic teller machine (ATM) authentication or payment. Fingerprint recognition could be grouped in two various forms, verification and identification. Verification compares one on one fingerprint data. Identification is matching input fingerprint with data that saved in the database. In this paper, we measure the performance of the memetic algorithm to process the image fingerprints dataset. Before we run this algorithm, we divide our fingerprints into four groups according to its characteristics and make 15 specimens of data, do four partial tests and at the last of work we measure all computation time

Effectiveness of general practitioner-delivered nutrition care interventions on dietary and health outcomes in adults with diet-related chronic conditions: a systematic review protocol.

OBJECTIVE: This systematic review will evaluate the effectiveness of nutrition care interventions delivered by general practitioners versus usual care or no care on dietary and health outcomes in adults with diet-related chronic conditions or risk states. INTRODUCTION: General practitioners are usually the first contacts in the health care system for patients with diet-related chronic conditions. While there is some evidence that general practitioners can be effective in delivering nutrition care for a number of outcomes, to inform future care, an update of the evidence is required as well as an examination of which components are associated with positive outcomes. INCLUSION CRITERIA: Published studies will be included if they report on adults with or at risk of diet-related chronic conditions; one-on-one nutrition care interventions individually delivered by general practitioners during primary care consultations; usual or no care as comparators; dietary and/or health outcomes with a minimum three-month follow-up; and randomized controlled trials. Included studies will be available in, or able to be translated into, English and will have no date restrictions. METHODS: The databases to be searched will include CINAHL, Embase, MEDLINE, and ProQuest Nursing and Allied Health. Following deduplication, two reviewers will independently screen the titles and abstracts in Covidence, followed by the full texts of potentially relevant studies. Disagreements will be resolved through discussion or with a third reviewer. Included studies will be critically appraised and data will be extracted using a modified JBI tool. Findings will be reported in tables and narrative synthesis, and pooled with statistical meta-analysis, where possible. SYSTEMATIC REVIEW REGISTRATION NUMBER: PROSPERO CRD42021289011

Moving from atheoretical to theoretical approaches to interprofessional client-centred collaborative practice

In this chapter we revisit the importance of theory in the development of interprofessional client centred education and practice (IPCEP). We focus specifically on the theoretical underpinnings and development of a workshop model aimed at moving practitioners from atheoretical to theoretical collaborative practice

Development of integrated ecological standards of sustainable forest management at an operational scale

Within Canada, and internationally, an increasing demand that forests be managed to maintain all resources has led to the development of criteria and indicators of sustainable forest management. There is, however, a lack of understanding, at an operational scale, how to evaluate and compare forest management activities to ensure the sustainability of all resources. For example, nationally, many of the existing indicators are too broad to be used directly at a local scale of forest management; provincially, regulations are often too prescriptive and rigid to allow for adaptive management; and forest certification programs, often based largely on public or stake-holder opinion instead of scientific understanding, may be too local in nature to permit a comparison of operations across a biome. At an operational scale indicators must be relevant to forest activities and ecologically integrated. In order to aid decision-makers in the adaptive management necessary for sustainable forest management, two types of indicators are identified: those that are prescriptive to aid in planning forest management and those that are evaluative to be used in monitoring and suggesting improvements. An integrated approach to developing standards based on an ecosystem management paradigm is outlined for the boreal forest where the variability inherent in natural systems is used to define the limits within which forest management is ecologically sustainable. Sustainability thresholds are thus defined by ecosystem response after natural disturbances. For this exercise, standards are proposed for biodiversity, forest productivity via regeneration, soil conservation and aquatic resources. For each of these standards, planning indicators are developed for managing forest conditions while forest values are evaluated by environmental indicators, thus leading to a continuous cycle of improvement. Approaches to developing critical thresholds and corresponding prescriptions are also outlined. In all cases, the scale of evaluation is clearly related to the landscape (or FMU) level while the stand level is used for measurement purposes. In this view the forest should be managed as a whole even though forest interventions are usually undertaken at the stand level

Pediatric training and practice of Canadian chiropractic and naturopathic doctors: A 2004-2014 comparative study

Backgound: To assess chiropractic (DC) and naturopathic doctors' (ND) knowledge, attitudes, and behaviour with respect to the pediatric patients in their practice. Methods: Cross-sectional surveys were developed in collaboration with DC and ND educators. Surveys were sent to randomly selected DCs and NDs in Ontario, Canada in 2004, and a national online survey was conducted in 2014. Data were analyzed using descriptive statistics, t-tests, non-parametric tests, and linear regression. Results: Response rates for DCs were n=172 (34%) in 2004, n=553 (15.5%) in 2014, and for NDs, n=171 (36%) in 2004, n=162 (7%) in 2014. In 2014, 366 (78.4%) of DCs and 83 (61%) of NDs saw one or more pediatric patients per week. Pediatric training was rated as inadequate by most respondents in both 2004 and 2014, with most respondents (n=643, 89.9%) seeking post-graduate training by 2014. Respondents' comfort in treating children and youth is based on experience and post-graduate training. Both DCs and NDs that see children and youth in their practices address a broad array of pediatric health concerns, from well child care and preventative health, to mild and serious illness. Conclusions: Although the response rate in 2014 is low, the concerns identified a decade earlier remain. The majority of responding DCs and NDs see infants, children, and youth for a variety of health conditions and issues, but self-assess their undergraduate pediatric training as inadequate. We encourage augmented pediatric educational content be included as core curriculum for DCs and NDs and suggest collaboration with institutions/organizations with expertise in pediatric education to facilitate curriculum development, especially in areas that affect patient safety

EphA4 signaling regulates phospholipase Cgamma1 activation, cofilin membrane association, and dendritic spine morphology

Specialized postsynaptic structures known as dendritic spines are the primary sites of glutamatergic innervation at synapses of the CNS. Previous studies have shown that spines rapidly remodel their actin cytoskeleton to modify their shape and this has been associated with changes in synaptic physiology. However, the receptors and signaling intermediates that restructure the actin network in spines are only beginning to be identified. We reported previously that the EphA4 receptor tyrosine kinase regulates spine morphology. However, the signaling pathways downstream of EphA4 that induce spine retraction on ephrin ligand binding remain poorly understood. Here, we demonstrate that ephrin stimulation of EphA4 leads to the recruitment and activation of phospholipase Cgamma1 (PLCgamma1) in heterologous cells and in hippocampal slices. This interaction occurs through an Src homology 2 domain of PLCgamma1 and requires the EphA4 juxtamembrane tyrosines. In the brain, PLCgamma1 is found in multiple compartments of synaptosomes and is readily found in postsynaptic density fractions. Consistent with this, PLC activity is required for the maintenance of spine morphology and ephrin-induced spine retraction. Remarkably, EphA4 and PLC activity modulate the association of the actin depolymerizing/severing factor cofilin with the plasma membrane. Because cofilin has been implicated previously in the structural plasticity of spines, this signaling may enable cofilin to depolymerize actin filaments and restructure spines at sites of ephrin-EphA4 contact

Robust Online Hamiltonian Learning

In this work we combine two distinct machine learning methodologies, sequential Monte Carlo and Bayesian experimental design, and apply them to the problem of inferring the dynamical parameters of a quantum system. We design the algorithm with practicality in mind by including parameters that control trade-offs between the requirements on computational and experimental resources. The algorithm can be implemented online (during experimental data collection), avoiding the need for storage and post-processing. Most importantly, our algorithm is capable of learning Hamiltonian parameters even when the parameters change from experiment-to-experiment, and also when additional noise processes are present and unknown. The algorithm also numerically estimates the Cramer-Rao lower bound, certifying its own performance.Comment: 24 pages, 12 figures; to appear in New Journal of Physic