Dairy calves' preference for rearing substrate

Rearing substrate is an important component of the pre-weaning environment of dairy calves. Traditional substrate types, such as sawdust, are becoming difficult and/or expensive for farmers to obtain in New Zealand. Therefore, there is a need to evaluate alternative rearing substrates for dairy calves that that are economically viable for farmers, readily available and provide an acceptable level of animal welfare. The preference of dairy calves for four different rearing substrates and the effects on behaviour and physiology were evaluated. At 1 wk of age, 24 calves were housed in groups of four, in pens which were evenly divided into four rearing substrates: sawdust, rubber, sand and stones. During the first 3 d calves were given free access to all four substrates. Calves were then restricted to each substrate type for 48 h. In order to rank preference, calves were subsequently exposed to two surfaces simultaneously for 48 h until calves experienced all six treatment combinations. Finally, calves were given free access to all four substrates simultaneously for 48 h. Lying behaviour and location in the pen was recorded for 24 h at the end of each experimental period using handycams and accelerometers. Preference was determined based on lying times on each substrate. The insulating properties of each substrate were assessed using iButtons®. During the initial free choice period, the proportion of time spent standing (p < .001) and lying (p < .001) was influenced by substrate. Calves spent a higher proportion of time on sawdust (88%) than all other substrates (rubber: 6%, sand: 4% and stones: 3%). When restricted to each substrate, calves spent more (p .05) of rearing substrate on the frequency of jumps, buck/kicks, head to object and mount/frontal pushing. Calves spent more (p .05) of rearing substrate on the number and duration of lying bouts. We detected no effect (p > .05) of rearing substrate on concentrations of cortisol, lactate, glucose, or white blood cell, neutrophil and lymphocyte count or the neutrophil:lymphocyte ratio. The insulating properties were greatest for sawdust and lowest for sand. During the pairwise choice period, calves had a strong preference for one substrate over another, spending on average, 89% of their time on the preferred surface. Calves preference ranking was for sawdust, rubber, sand then stones as determined by the proportion of time spent on each surface. At the end of the study, when given free access to all rearing substrates again, calves spent a higher proportion of time on sawdust (85%) than all other substrates (rubber: 5%, sand: 7% and stones: 3%). In conclusion, dairy calves showed a clear preference for sawdust over rubber, sand and stones. This preference remained consistent over the course of the study. The calves’ preference for sawdust may be associated with the physical and thermal properties in comparison to the alternative substrates. However, factors such as cost to the farmer, availability and practicality of alternative substrates need to be considered along with animal preferences before any recommendations can be made

Role of Physical Therapists in the Management of Individuals at Risk for or Diagnosed With Venous Thromboembolism: Evidence-Based Clinical Practice Guideline

The American Physical Therapy Association (APTA), in conjunction with the Cardiovascular & Pulmonary and Acute Care sections of APTA, have developed this clinical practice guideline to assist physical therapists in their decision-making process when treating patients at risk for venous thromboembolism (VTE) or diagnosed with a lower extremity deep vein thrombosis (LE DVT). No matter the practice setting, physical therapists work with patients who are at risk for or have a history of VTE. This document will guide physical therapist practice in the prevention of, screening for, and treatment of patients at risk for or diagnosed with LE DVT. Through a systematic review of published studies and a structured appraisal process, key action statements were written to guide the physical therapist. The evidence supporting each action was rated, and the strength of statement was determined. Clinical practice algorithms, based on the key action statements, were developed that can assist with clinical decision making. Physical therapists, along with other members of the health care team, should work to implement these key action statements to decrease the incidence of VTE, improve the diagnosis and acute management of LE DVT, and reduce the long-term complications of LE DVT

Shrinkage of Decision Lists and DNF Formulas

We establish nearly tight bounds on the expected shrinkage of decision lists and DNF formulas under the p-random restriction R_p for all values of p ? [0,1]. For a function f with domain {0,1}?, let DL(f) denote the minimum size of a decision list that computes f. We show that E[DL(f ? R_p)] ? DL(f)^log_{2/(1-p)}((1+p)/(1-p)). For example, this bound is ?{DL(f)} when p = ?5-2 ? 0.24. For Boolean functions f, we obtain the same shrinkage bound with respect to DNF formula size plus 1 (i.e., replacing DL(?) with DNF(?)+1 on both sides of the inequality)

Applying MDL to Learning Best Model Granularity

The Minimum Description Length (MDL) principle is solidly based on a provably ideal method of inference using Kolmogorov complexity. We test how the theory behaves in practice on a general problem in model selection: that of learning the best model granularity. The performance of a model depends critically on the granularity, for example the choice of precision of the parameters. Too high precision generally involves modeling of accidental noise and too low precision may lead to confusion of models that should be distinguished. This precision is often determined ad hoc. In MDL the best model is the one that most compresses a two-part code of the data set: this embodies ``Occam's Razor.'' In two quite different experimental settings the theoretical value determined using MDL coincides with the best value found experimentally. In the first experiment the task is to recognize isolated handwritten characters in one subject's handwriting, irrespective of size and orientation. Based on a new modification of elastic matching, using multiple prototypes per character, the optimal prediction rate is predicted for the learned parameter (length of sampling interval) considered most likely by MDL, which is shown to coincide with the best value found experimentally. In the second experiment the task is to model a robot arm with two degrees of freedom using a three layer feed-forward neural network where we need to determine the number of nodes in the hidden layer giving best modeling performance. The optimal model (the one that extrapolizes best on unseen examples) is predicted for the number of nodes in the hidden layer considered most likely by MDL, which again is found to coincide with the best value found experimentally.Comment: LaTeX, 32 pages, 5 figures. Artificial Intelligence journal, To appea

Conformal Prediction: a Unified Review of Theory and New Challenges

In this work we provide a review of basic ideas and novel developments about Conformal Prediction -- an innovative distribution-free, non-parametric forecasting method, based on minimal assumptions -- that is able to yield in a very straightforward way predictions sets that are valid in a statistical sense also in in the finite sample case. The in-depth discussion provided in the paper covers the theoretical underpinnings of Conformal Prediction, and then proceeds to list the more advanced developments and adaptations of the original idea.Comment: arXiv admin note: text overlap with arXiv:0706.3188, arXiv:1604.04173, arXiv:1709.06233, arXiv:1203.5422 by other author

Agnostic Membership Query Learning with Nontrivial Savings: New Results, Techniques

(Abridged) Designing computationally efficient algorithms in the agnostic learning model (Haussler, 1992; Kearns et al., 1994) is notoriously difficult. In this work, we consider agnostic learning with membership queries for touchstone classes at the frontier of agnostic learning, with a focus on how much computation can be saved over the trivial runtime of 2^n$. This approach is inspired by and continues the study of ``learning with nontrivial savings'' (Servedio and Tan, 2017). To this end, we establish multiple agnostic learning algorithms, highlighted by: 1. An agnostic learning algorithm for circuits consisting of a sublinear number of gates, which can each be any function computable by a sublogarithmic degree k polynomial threshold function (the depth of the circuit is bounded only by size). This algorithm runs in time 2^{n -s(n)} for s(n) \approx n/(k+1), and learns over the uniform distribution over unlabelled examples on \{0,1\}^n. 2. An agnostic learning algorithm for circuits consisting of a sublinear number of gates, where each can be any function computable by a \sym^+ circuit of subexponential size and sublogarithmic degree k. This algorithm runs in time 2^{n-s(n)} for s(n) \approx n/(k+1), and learns over distributions of unlabelled examples that are products of k+1 arbitrary and unknown distributions, each over \{0,1\}^{n/(k+1)} (assume without loss of generality that k+1 divides n)

CREATE: Clinical Record Analysis Technology Ensemble

In this thesis, we describe an approach that won a psychiatric symptom severity prediction challenge. The challenge was to correctly predict the severity of psychiatric symptoms on a 4-point scale. Our winning submission uses a novel stacked machine learning architecture in which (i) a base data ingestion/cleaning step was followed by the (ii) derivation of a base set of features defined using text analytics, after which (iii) association rule learning was used in a novel way to generate new features, followed by a (iv) feature selection step to eliminate irrelevant features, followed by a (v) classifier training algorithm in which a total of 22 classifiers including new classifier variants of AdaBoost and RandomForest were trained on seven different data views, and (vi) finally an ensemble learning step, in which ensembles of best learners were used to improve on the accuracy of individual learners. All of this was tested via standard 10-fold cross-validation on training data provided by the N-GRID challenge organizers, of which the three best ensembles were selected for submission to N-GRID\u27s blind testing. The best of our submitted solutions garnered an overall final score of 0.863 according to the organizer\u27s measure. All 3 of our submissions placed within the top 10 out of the 65 total submissions. The challenge constituted Track 2 of the 2016 Centers of Excellence in Genomic Science (CEGS) Neuropsychiatric Genome-Scale and RDOC Individualized Domains (N-GRID) Shared Task in Clinical Natural Language Processing