Machine Learning Theory and Practice as a Source of Insight into Universal Grammar

Articl

Perceiving environmental structure from optical motion

Generally speaking, one of the most important sources of optical information about environmental structure is known to be the deforming optical patterns produced by the movements of the observer (pilot) or environmental objects. As an observer moves through a rigid environment, the projected optical patterns of environmental objects are systematically transformed according to their orientations and positions in 3D space relative to those of the observer. The detailed characteristics of these deforming optical patterns carry information about the 3D structure of the objects and about their locations and orientations relative to those of the observer. The specific geometrical properties of moving images that may constitute visually detected information about the shapes and locations of environmental objects is examined

What is binocular disparity?

What are the geometric primitives of binocular disparity? The Venetian blind effect and other converging lines of evidence indicate that stereo-scopic depth perception derives from disparities of higher-order structure in images of surfaces. Image structure entails spatial variations of in-tensity, texture, and motion, jointly structured by observed surfaces. The spatial structure of bin-ocular disparity corresponds to the spatial struc-ture of surfaces. Independent spatial coordinates are not necessary for stereoscopic vision. Stere-opsis is highly sensitive to structural disparities associated with local surface shape. Disparate positions on retinal anatomy are neither neces-sary nor sufficient for stereopsis

Failure of non-vacuum steam sterilization processes for dental handpieces

Background: Dental handpieces are used in critical and semi-critical operative interventions. Although a number of dental professional bodies recommend that dental handpieces are sterilized between patient use there is a lack of clarity and understanding of the effectiveness of different steam sterilization processes. The internal mechanisms of dental handpieces contain narrow lumens (0·8-2·3mm) which can impede the removal of air and ingress of saturated steam required to achieve sterilization conditions. Aim: To identify the extent of sterilization failure in dental handpieces using a non-vacuum process. Methods: In-vitro and in-vivo investigations were conducted on commonly used UK benchtop steam sterilizers and three different types of dental handpieces. The sterilization process was monitored inside the lumens of dental handpieces using thermometric (TM) methods (dataloggers), chemical indicators (CI) and biological indicators (BI). Findings: All three methods of assessing achievement of sterility within dental handpieces that had been exposed to non-vacuum sterilization conditions demonstrated a significant number of failures (CI=8/3,024(fails/n tests); BI=15/3,024; TM=56/56) compared to vacuum sterilization conditions (CI=2/1,944; BI=0/1,944; TM=0/36). The dental handpiece most likely to fail sterilization in the non-vacuum process was the surgical handpiece. Non-vacuum sterilizers located in general dental practice had a higher rate of sterilization failure (CI=25/1,620; BI=32/1,620; TM=56/56) with no failures in vacuum process. Conclusion: Non-vacuum downward/gravity displacement, type-N steam sterilizers are an unreliable method for sterilization of dental handpieces in general dental practice. The handpiece most likely to fail sterilization is the type most frequently used for surgical interventions

Federal estate taxation and planning

Thesis (M.B.A.)--Boston Universit

Investigating steam penetration using thermometric methods in dental handpieces with narrow internal lumens during sterilizing processes with non-vacuum or vacuum processes

Background: Dental handpieces are required to be sterilized between patient use. Vacuum steam sterilization processes with fractionated pre/post-vacuum phases or unique cycles for specified medical devices, are required for hollow instruments with internal lumens to assure successful air removal. Entrapped air will compromise achievement of required sterilization conditions. Many countries and professional organisations still advocate non-vacuum sterilization processes for these devices. Aim: To investigate non-vacuum downward/gravity displacement, type-N steam sterilization of dental handpieces, using thermometric methods to measure time to achieve sterilization temperature at different handpiece locations. Methods: Measurements at different positions within air turbines were undertaken with thermocouples and dataloggers. Two examples of commonly used UK benchtop steam sterilizers were tested; a non-vacuum benchtop sterilizer (Little Sister 3, Eschmann, UK) and a vacuum benchtop sterilizer (Lisa, W&H, Austria). Each sterilizer cycle was completed with three handpieces and each cycle in triplicate. Findings: A total of 140 measurements inside dental handpiece lumens were recorded. We demonstrate that the non-vacuum process fails (time range 0-150 seconds) to reliably achieve sterilization temperatures within the time limit specified by the International standard (15 seconds equilibration time). The measurement point at the base of the handpiece failed in all test runs (n=9) to meet the standard. No failures were detected with the vacuum steam sterilization type B process with fractionated pre-vacuum and post-vacuum phases. Conclusion: Non-vacuum downward/gravity displacement, type-N steam sterilization processes are unreliable in achieving sterilization conditions inside dental handpieces and the base of the handpiece is the site most likely to fail

Predicting Human Metaphor Paraphrase Judgments with Deep Neural Networks

We propose a new annotated corpus for metaphor interpretation by paraphrase, and a novel DNN model for performing this task. Our corpus consists of 200 sets of 5 sen- tences, with each set containing one reference metaphorical sentence, and four ranked candi- date paraphrases. Our model is trained for a binary classification of paraphrase candidates, and then used to predict graded paraphrase ac- ceptability. It reaches an encouraging 75% ac- curacy on the binary classification task, and high Pearson (.75) and Spearman (.68) correla- tions on the gradient judgment prediction task

Using Deep Neural Networks to Learn Syntactic Agreement

We consider the extent to which different deep neural network (DNN) configurations can learn syntactic relations, by taking up Linzen et al.’s (2016) work on subject-verb agreement with LSTM RNNs. We test their methods on a much larger corpus than they used (a ⇠24 million example part of the WaCky corpus, instead of their ⇠1.35 million example corpus, both drawn from Wikipedia). We experiment with several different DNN architectures (LSTM RNNs, GRUs, and CNNs), and alternative parameter settings for these systems (vocabulary size, training to test ratio, number of layers, memory size, drop out rate, and lexical embedding dimension size). We also try out our own unsupervised DNN language model. Our results are broadly compatible with those that Linzen et al. report. However, we discovered some interesting, and in some cases, surprising features of DNNs and language models in their performance of the agreement learning task. In particular, we found that DNNs require large vocabularies to form substantive lexical embeddings in order to learn structural patterns. This finding has interesting consequences for our understanding of the way in which DNNs represent syntactic information. It suggests that DNNs learn syntactic patterns more efficiently through rich lexical embeddings, with semantic as well as syntactic cues, than from training on lexically impoverished strings that highlight structural patterns

Grammaticality, Acceptability, and Probability: A Probabilistic View of Linguistic Knowledge

The question of whether humans represent grammatical knowledge as a binary condition on membership in a set of well‐formed sentences, or as a probabilistic property has been the subject of debate among linguists, psychologists, and cognitive scientists for many decades. Acceptability judgments present a serious problem for both classical binary and probabilistic theories of grammaticality. These judgements are gradient in nature, and so cannot be directly accommodated in a binary formal grammar. However, it is also not possible to simply reduce acceptability to probability. The acceptability of a sentence is not the same as the likelihood of its occurrence, which is, in part, determined by factors like sentence length and lexical frequency. In this paper, we present the results of a set of large‐scale experiments using crowd‐sourced acceptability judgments that demonstrate gradience to be a pervasive feature in acceptability judgments. We then show how one can predict acceptability judgments on the basis of probability by augmenting probabilistic language models with an acceptability measure. This is a function that normalizes probability values to eliminate the confounding factors of length and lexical frequency. We describe a sequence of modeling experiments with unsupervised language models drawn from state‐of‐the‐art machine learning methods in natural language processing. Several of these models achieve very encouraging levels of accuracy in the acceptability prediction task, as measured by the correlation between the acceptability measure scores and mean human acceptability values. We consider the relevance of these results to the debate on the nature of grammatical competence, and we argue that they support the view that linguistic knowledge can be intrinsically probabilistic

Neurofeedback and biofeedback with 37 migraineurs: a clinical outcome study

<p>Abstract</p> <p>Background</p> <p>Traditional peripheral biofeedback has grade A evidence for effectively treating migraines. Two newer forms of neurobiofeedback, EEG biofeedback and hemoencephalography biofeedback were combined with thermal handwarming biofeedback to treat 37 migraineurs in a clinical outpatient setting.</p> <p>Methods</p> <p>37 migraine patients underwent an average of 40 neurofeedback sessions combined with thermal biofeedback in an outpatient biofeedback clinic. All patients were on at least one type of medication for migraine; preventive, abortive or rescue. Patients kept daily headache diaries a minimum of two weeks prior to treatment and throughout treatment showing symptom frequency, severity, duration and medications used. Treatments were conducted an average of three times weekly over an average span of 6 months. Headache diaries were examined after treatment and a formal interview was conducted. After an average of 14.5 months following treatment, a formal interview was conducted in order to ascertain duration of treatment effects.</p> <p>Results</p> <p>Of the 37 migraine patients treated, 26 patients or 70% experienced at least a 50% reduction in the frequency of their headaches which was sustained on average 14.5 months after treatments were discontinued.</p> <p>Conclusions</p> <p>All combined neuro and biofeedback interventions were effective in reducing the frequency of migraines with clients using medication resulting in a more favorable outcome (70% experiencing at least a 50% reduction in headaches) than just medications alone (50% experience a 50% reduction) and that the effect size of our study involving three different types of biofeedback for migraine (1.09) was more robust than effect size of combined studies on thermal biofeedback alone for migraine (.5). These non-invasive interventions may show promise for treating treatment-refractory migraine and for preventing the progression from episodic to chronic migraine.</p