I hear you eat and speak: automatic recognition of eating condition and food type, use-cases, and impact on ASR performance

We propose a new recognition task in the area of computational paralinguistics: automatic recognition of eating conditions in speech, i. e., whether people are eating while speaking, and what they are eating. To this end, we introduce the audio-visual iHEARu-EAT database featuring 1.6 k utterances of 30 subjects (mean age: 26.1 years, standard deviation: 2.66 years, gender balanced, German speakers), six types of food (Apple, Nectarine, Banana, Haribo Smurfs, Biscuit, and Crisps), and read as well as spontaneous speech, which is made publicly available for research purposes. We start with demonstrating that for automatic speech recognition (ASR), it pays off to know whether speakers are eating or not. We also propose automatic classification both by brute-forcing of low-level acoustic features as well as higher-level features related to intelligibility, obtained from an Automatic Speech Recogniser. Prediction of the eating condition was performed with a Support Vector Machine (SVM) classifier employed in a leave-one-speaker-out evaluation framework. Results show that the binary prediction of eating condition (i. e., eating or not eating) can be easily solved independently of the speaking condition; the obtained average recalls are all above 90%. Low-level acoustic features provide the best performance on spontaneous speech, which reaches up to 62.3% average recall for multi-way classification of the eating condition, i. e., discriminating the six types of food, as well as not eating. The early fusion of features related to intelligibility with the brute-forced acoustic feature set improves the performance on read speech, reaching a 66.4% average recall for the multi-way classification task. Analysing features and classifier errors leads to a suitable ordinal scale for eating conditions, on which automatic regression can be performed with up to 56.2% determination coefficient

"Hook"-calibration of GeneChip-microarrays: Chip characteristics and expression measures

<p>Abstract</p> <p>Background</p> <p>Microarray experiments rely on several critical steps that may introduce biases and uncertainty in downstream analyses. These steps include mRNA sample extraction, amplification and labelling, hybridization, and scanning causing chip-specific systematic variations on the raw intensity level. Also the chosen array-type and the up-to-dateness of the genomic information probed on the chip affect the quality of the expression measures. In the accompanying publication we presented theory and algorithm of the so-called hook method which aims at correcting expression data for systematic biases using a series of new chip characteristics.</p> <p>Results</p> <p>In this publication we summarize the essential chip characteristics provided by this method, analyze special benchmark experiments to estimate transcript related expression measures and illustrate the potency of the method to detect and to quantify the quality of a particular hybridization. It is shown that our single-chip approach provides expression measures responding linearly on changes of the transcript concentration over three orders of magnitude. In addition, the method calculates a detection call judging the relation between the signal and the detection limit of the particular measurement. The performance of the method in the context of different chip generations and probe set assignments is illustrated. The hook method characterizes the RNA-quality in terms of the 3'/5'-amplification bias and the sample-specific calling rate. We show that the proper judgement of these effects requires the disentanglement of non-specific and specific hybridization which, otherwise, can lead to misinterpretations of expression changes. The consequences of modifying probe/target interactions by either changing the labelling protocol or by substituting RNA by DNA targets are demonstrated.</p> <p>Conclusion</p> <p>The single-chip based hook-method provides accurate expression estimates and chip-summary characteristics using the natural metrics given by the hybridization reaction with the potency to develop new standards for microarray quality control and calibration.</p

The Portland Spectator, February 2014

Editor: Jake Stein Articles in this issue include: Free Labor; Great Hot Drinks; The Proof is in The Meat; and Cookie Madnesshttps://pdxscholar.library.pdx.edu/spectator/1054/thumbnail.jp

Investigating Microinsurance Issues by Using Laboratory Experiments to Evaluate the Welfare of Insurance

This thesis uses laboratory experiments to develop a methodology to estimate the expected welfare benefits of insurance for individuals, conditional on their risk preferences. This methodology is then applied to study the welfare effects of issues that impact microinsurance, or insurance for the poor. The first result is that insurance take-up not a good proxy for the expected welfare gain of an individual’s choice to purchase or not to purchase insurance. The second result is that basis risk reduces the welfare obtained from index insurance. This welfare is significantly improved by having greater behavioral consistency with the Reduction of Compound Lotteries axiom. Finally, the risk of contract non-performance from the insurer significantly reduces the welfare obtained from insurance purchase decisions

Statistical Inference for Simultaneous Clustering of Gene Expression Data

Current methods for analysis of gene expression data are mostly based on clustering and classification of either genes or samples. We offer support for the idea that more complex patterns can be identified in the data if genes and samples are considered simultaneously. We formalize the approach and propose a statistical framework for two-way clustering. A simultaneous clustering parameter is defined as a function of the true data generating distribution, and an estimate is obtained by applying this function to the empirical distribution. We illustrate that a wide range of clustering procedures, including generalized hierarchical methods, can be defined as parameters which are compositions of individual mappings for clustering patients and genes. This framework allows one to assess classical properties of clustering methods, such as consistency, and to formally study statistical inference regarding the clustering parameter. We present results of simulations designed to assess the asymptotic validity of different bootstrap methods for estimating the distributions of estimated simultaneous clustering parameters. The method is illustrated on a publicly available data set

The Andromeda Project. I. Deep HST-WFPC2 V,I photometry of 16 fields toward the disk and the halo of the M31 galaxy. Probing the stellar content and metallicity distribution

HST-WFPC2 F555W and F814W photometry were obtained for 16 fields of the luminous nearby spiral galaxy M31, sampling the stellar content of the disk and the halo at different distances from the center, from ~ 20 to ~ 150 arcmin (i.e. ~ 4.5 to 35 kpc), down to limiting V and I magnitudes of ~ 27. The Color-Magnitude diagrams (CMD) show the presence of complex stellar populations, including an intermediate age/young population and older populations with a wide range of metallicity. Those fields superposed on the disk of M31 generally show a blue plume of stars which we identify with main sequence members. Accordingly, the star formation rate over the last 0.5 Gyr appears to have varied dramatically with location in the disk. All the CMDs show a prominent Red Giant Branch (RGB) with a descending tip in the V band, characteristic of metallicity higher than 1/10 Solar. A red clump is detected in all of the fields, and a weak blue horizontal branch is frequently present. The metallicity distributions (MDs), obtained by comparison of the RGB stars with globular cluster templates, are basically similar in all the sampled fields: they all show a long, albeit scantly populated metal-poor tail and a main component at [Fe/H] ~ -0.6. However, some differences also exist, e.g. in some fields a very metal-rich ([Fe/H] >= -0.2) component is present. Whereas the fraction of metal-poor stars seems to be approximately constant in all fields, the fraction of very-metal-rich stars varies with position and seems to be more prominent in those fields superposed on the disk and/or with the presence of streams or substructures. This might indicate and possibly trace interaction effects with some companion, e.g. M32.Comment: 23 pages (including 5 tables), 22 figures, submitted to A&

A hierarchal framework for recognising activities of daily life

PhDIn today’s working world the elderly who are dependent can sometimes be neglected by society. Statistically, after toddlers it is the elderly who are observed to have higher accident rates while performing everyday activities. Alzheimer’s disease is one of the major impairments that elderly people suffer from, and leads to the elderly person not being able to live an independent life due to forgetfulness. One way to support elderly people who aspire to live an independent life and remain safe in their home is to find out what activities the elderly person is carrying out at a given time and provide appropriate assistance or institute safeguards. The aim of this research is to create improved methods to identify tasks related to activities of daily life and determine a person’s current intentions and so reason about that person’s future intentions. A novel hierarchal framework has been developed, which recognises sensor events and maps them to significant activities and intentions. As privacy is becoming a growing concern, the monitoring of an individual’s behaviour can be seen as intrusive. Hence, the monitoring is based around using simple non intrusive sensors and tags on everyday objects that are used to perform daily activities around the home. Specifically there is no use of any cameras or visual surveillance equipment, though the techniques developed are still relevant in such a situation. Models for task recognition and plan recognition have been developed and tested on scenarios where the plans can be interwoven. Potential targets are people in the first stages of Alzheimer’s disease and in the structuring of the library of kernel plan sequences, typical routines used to sustain meaningful activity have been used. Evaluations have been carried out using volunteers conducting activities of daily life in an experimental home environment. The results generated from the sensors have been interpreted and analysis of developed algorithms has been made. The outcomes and findings of these experiments demonstrate that the developed hierarchal framework is capable of carrying activity recognition as well as being able to carry out intention analysis, e.g. predicting what activity they are most likely to carry out next