Center Calls Research Articles

Predicting noise-induced hearing loss with machine learning: the influence of tinnitus as a predictive factor.

This study aimed to determine which machine learning model is most suitable for predicting noise-induced hearing loss and the effect of tinnitus on the models' accuracy. Two hundred workers employed in a metal industry were selected for this study and tested using pure tone audiometry. Their occupational exposure histories were collected, analysed and used to create a dataset. Eighty per cent of the data collected was used to train six machine learning models and the remaining 20 per cent was used to test the models. Eight workers (40.5 per cent) had bilaterally normal hearing and 119 (59.5 per cent) had hearing loss. Tinnitus was the second most important indicator after age for noise-induced hearing loss. The support vector machine was the best-performing algorithm, with 90 per cent accuracy, 91 per cent F1 score, 95 per cent precision and 88 per cent recall. The use of tinnitus as a risk factor in the support vector machine model may increase the success of occupational health and safety programmes.

Galaxy morphology classification using multiscale convolution capsule network

ABSTRACT Classification of galaxy morphology is a hot issue in astronomical research. Although significant progress has been made in the last decade in classifying galaxy morphology using deep learning technology, there are still some deficiencies in spatial feature representation and classification accuracy. In this study, we present a multiscale convolutional capsule network (MSCCN) model for the classification of galaxy morphology. First, this model improves the convolutional layers using a multibranch structure to extract the multiscale hidden features of galaxy images. In order to further explore the hidden information in the features, the multiscale features are encapsulated and fed into the capsule layer. Second, we use a sigmoid function to replace the softmax function in dynamic routing, which can enhance the robustness of MSCCN. Finally, the classification model achieves 97 per cent accuracy, 96 per cent precision, 98 per cent recall, and 97 per cent F1-score under macroscopic averaging. In addition, a more comprehensive model evaluation was accomplished in this study. We visualized the morphological features for the part of sample set that used the t-distributed stochastic neighbour embedding (t-SNE) algorithm. The results show that the model has a better generalization ability and robustness, and it can be effectively used in the galaxy morphological classification.

Automated classification ofChandraX-ray point sources using machine learning methods

ABSTRACTA large number of unidentified sources found by astronomical surveys and other observations necessitate the use of an automated classification technique based on machine learning (ML) methods. The aim of this paper is to find a suitable automated classifier to identify the point X-ray sources in the Chandra Source Catalogue (CSC) 2.0 in the categories of active galactic nuclei (AGN), X-ray emitting stars, young stellar objects (YSOs), high-mass X-ray binaries (HMXBs), low-mass X-ray binaries (LMXBs), ultra luminous X-ray sources (ULXs), cataclysmic variables (CVs), and pulsars. The catalogue consists of ≈317 000 sources, out of which we select 277 069 point sources based on the quality flags available in CSC 2.0. In order to identify unknown sources of CSC 2.0, we use MW features, such as magnitudes in optical/ultraviolet bands from Gaia-EDR3, Sloan Digital Sky Survey, and GALEX, and magnitudes in infrared bands from 2MASS, WISE, and MIPS-Spitzer, in addition to X-ray features (flux and variability) from CSC 2.0. We find the Light Gradient Boosted Machine, an advanced decision tree-based ML classification algorithm, suitable for our purpose and achieve 93 per cent precision, 93 per cent recall score, and 0.91 Mathew’s Correlation coefficient score. With the trained classifier, we identified 54 770 (14 066) sources with more than 3σ (4σ) confidence, out of which there are 32 600 (8574) AGNs, 16,148 (5,166) stars, 5,184 (208) YSOs, 439 (46) HMXBs, 197 (71) LMXBs, 50 (0) ULXs, 89 (1) CVs, and 63 (0) pulsars. This method can also be useful for identifying sources of other catalogues reliably.

Deblending and classifying astronomical sources with Mask R-CNN deep learning

ABSTRACT We apply a new deep learning technique to detect, classify, and deblend sources in multiband astronomical images. We train and evaluate the performance of an artificial neural network built on the Mask Region-based Convolutional Neural Network image processing framework, a general code for efficient object detection, classification, and instance segmentation. After evaluating the performance of our network against simulated ground truth images for star and galaxy classes, we find a precision of 92 per cent at 80 per cent recall for stars and a precision of 98 per cent at 80 per cent recall for galaxies in a typical field with ∼30 galaxies arcmin−2. We investigate the deblending capability of our code, and find that clean deblends are handled robustly during object masking, even for significantly blended sources. This technique, or extensions using similar network architectures, may be applied to current and future deep imaging surveys such as Large Synoptic Survey Telescope and Wide-Field Infrared Survey Telescope. Our code, astro r-cnn, is publicly available at https://github.com/burke86/astro_rcnn.

Deep Learning Algorithms Applied to the Classification of Video Meteor Detections

Abstract The application of a class of advanced machine learning techniques, namely deep learning, has been applied to automating the confirmation/classification of potential meteor tracks in video imagery. Deep learning is shown to perform remarkably well, even surpassing human performance, and will likely supplant the need for human visual inspection and review of collected meteor imagery. When applied to time series measurements of meteor track centroid positions and integrated intensities obtained from each video frame, a recurrent neural network (RNN) has achieved 98.1 per cent recall, which is defined as the number of true meteors properly classified as meteors. The RNN allowed only 2.1 per cent leakage, defined herein as the number of false positives that were incorrectly identified as meteors. The desire is to maximize recall to avoid missed orbit estimations, while also minimizing false alarms leaking through to the next processing stage of multi-site trajectory and orbit estimation. When two-dimensional spatial imagery is available or the temporal image sequence can be reconstructed, these results climb to 99.94 per cent recall and only 0.4 per cent leakage when employing a convolutional neural network (CNN). This has been further generalized from a baseline of interleaved analog video to modern progressive scan digital imagery with equivalent results. The trained CNN, nicknamed MeteorNet, will be used for post-detection automated screening of potential meteor tracks and explored in the future as a potential upstream meteor detector.

Negation detection and word sense disambiguation in digital archaeology reports for the purposes of semantic annotation

Purpose – The purpose of this paper is to present the role and contribution of natural language processing techniques, in particular negation detection and word sense disambiguation in the process of Semantic Annotation of Archaeological Grey Literature. Archaeological reports contain a great deal of information that conveys facts and findings in different ways. This kind of information is highly relevant to the research and analysis of archaeological evidence but at the same time can be a hindrance for the accurate indexing of documents with respect to positive assertions. Design/methodology/approach – The paper presents a method for adapting the biomedicine oriented negation algorithm NegEx to the context of archaeology and discusses the evaluation results of the new modified negation detection module. A particular form of polysemy, which is inflicted by the definition of ontology classes and concerning the semantics of small finds in archaeology, is addressed by a domain specific word-sense disambiguation module. Findings – The performance of the negation dection module is compared against a “Gold Standard” that consists of 300 manually annotated pages of archaeological excavation and evaluation reports. The evaluation results are encouraging, delivering overall 89 per cent precision, 80 per cent recall and 83 per cent F-measure scores. The paper addresses limitations and future improvements of the current work and highlights the need for ontological modelling to accommodate negative assertions. Originality/value – The discussed NLP modules contribute to the aims of the OPTIMA pipeline delivering an innovative application of such methods in the context of archaeological reports for the semantic annotation of archaeological grey literature with respect to the CIDOC-CRM ontology.

John Anthony Henry

John Anthony Henry

Prenatal screening in the first trimester of pregnancy

Prenatal screening for fetal abnormalities in an accepted part of modern obstetric management. Improvements on current screening procedures need to address increased diagnostic efficacy and earlier diagnosis. This study evaluates diagnostic efficacy of PAPP-A and F beta-hCG in the detection of first trimester pregnancy abnormalities, including Down syndrome (DS). Of 731 pregnant volunteers, obtained from a mature age population undergoing chorionic villus sampling (CVS), 17 DS and 11 compromised (six numerical (excluding sex chromosome) aneuploidies, five spontaneously failed) pregnancies were detected. Application of an algorithm, which combines PAPP-A and F beta-hCG levels with material age, detected 66.6 per cent of DS pregnancies for a five per cent false positive rate. Similarly, for a 1-2 per cent recall rate, 72.2 per cent of compromised pregnancies were detected. This report supports the notion that prenatal screening at 9-12 weeks of pregnancy is achievable with PAPP-A and F beta hCG quantitation. Whereas mid-gestational screening targetted the detection of fetal abnormalities, screening earlier in pregnancy will detect other pregnancy-related abnormalities, in addition to aneuploidy.

Dreams and Dementia: A Laboratory Exploration of Dream Recall and Dream Content in Chronic Brain Syndrome Patients

Seventeen (seven mild and ten severe) chronic brain syndrome patients were testes in the sleep laboratory for dream recall and content. The mildly organic patients had 57 per cent recall while the severely organic patients had 35 per cent recall. We also studied four aged severely organic patients. These patients showed a recall rate of 8 per cent. The difference between the recall rates in mildly and severely organic middle-aged patients was not statistically significant. The difference between the recall rates of the aged severely organic patients and either or both of the middle-aged groups was statistically significant (p smaller than .001). In addition, we found dream content differences between the mildly and severely organic middle-aged groups and between the combined group and normal young men.

Document retrieval by means of an automatic classification algorithm for citations

The applicability of an automatic classification technique to information retrieval was investigated. A modified version of the Schiminovich algorithm was used to classify articles in the data base, utilizing citations found in their bibliographies and a “triggering file” of bibliographically related papers. Classes of articles were formed by comparing the citations in the articles with members of the triggering file. Those papers with similar citing patterns formed a group; citations occurring sufficiently often within the papers of a group formed a bibliography. Bibliographies in turn became new triggering files in an iterative procedure. Results of this nontraditional method were compared with those of retrieval by standard American Institute of Physics (AIP) subject analysis of the same material. A combination of the two methods was also investigated to test the hypothesis that one could be used to augment or refine the other. Nine cooperating physicists defined queries in terms of (1) the AIP classification scheme and (2) a list of articles likely to be cited in current relevant literature. The data base was 18 months of 1971–1972 physics journal articles (AIP SPIN tapes). The chief means of evaluating the results of the three retrieval approaches was a comparison of recall and precision values obtained from relevance judgments by the physicists. Average precision for the AIP subject analysis was 17 per cent and for the citation processing 62 per cent. Based on an assumption of 100 per cent recall for the AIP analysis, average recall for the citation processing was 45 per cent. All but one physicist would prefer to have both the citation and subject approaches available for information retrieval. Sometimes, only a few relevant articles are desired; at other times comprehensiveness is necessary. However, if only one method were available, all except one participant would choose the subject approach. They felt that one must be able to retrieve all the relevant articles, even if that would mean examining 25–30 irrelevant notices for every relevant one. Several expressed the notion that looking at a file with a very high percentage of relevant articles made one wonder what was missing.

RETRIEVAL CHARACTERISTICS OF THE INDEX TO METALS ABSTRACTS

A small‐scale test of Metals abstracts index, with ten profiles and five sections of the abstracts as data base, was carried out with a methodology which endeavoured to simulate real life use of this Index by an information scientist for current awareness searching. The performance results were 67 per cent recall, 40 per cent precision and 2 per cent fallout. An error analysis showed that 65 per cent of the errors were attibutable to human (searcher) factors. Some of the factors that must inevitably affect the assessment of printed indexes are discussed, together with the observation of two unexpected phenomena, namely an apparent relationship between precision and searching effort, and relationships between generality and both recall and fallout.

Coarticulation effects of post-consonantal vowels on the short-term recall of pre-consonantal vowels.

The purpose of this study was to see whether, in a vowel-consonant-vowel (VCV) nonsense word, the pre-consonantal acoustic effects of the post-consonantal vowel could influence the short-term recall of the pre-consonantal vowel. Recordings were made by a male speaker of VC and VCV utterances in which the six lax vowels of American English were the initial vowels, /t/ was the consonant, and /I/ and /α/ were the final vowels. By editing out the final vowels of some of the utterances, five conditions were produced: (1) control-Vt; (2) final /I/ deleted—Vt/I, (3) final /α/ deleted—Vt/α, (4) final /I/ retained—VtI; and (5) final /α/ retained—Vtα. These utterances were presented to listeners in lists of six for short-term recall of the initial vowels. Frequency of errors and distribution of errors based on feature analysis of the vowels were calculated. Statistical analysis showed that coarticulation effects did influence per cent recall scores but not error distribution. It was concluded that coarticulation effects probably influence the certainty with which vowel identifications are made but not the specification of features which determine the identification.

DREAM RECALL IN THE NORMAL AGED*

AbstractRecall from REM‐period awakenings was studied in the elderly. Twenty‐seven subjects (age range, 66 to 87 years) each slept one or two nights at the laboratory. From 166 REM awakenings there were 92 (55.4 per cent) instances of recall of dream content. This 55.4 per cent recall was significantly lower than the 87 per cent we previously found for the young adult. There was a tendency for women to have more frequent recall than men. REM dreams in the elderly were not manifestly concerned with loss or frustration. A small, nonsignificant positive correlation was found between the amount of recall and verbal intelligence.