'Part'ly first among equals: Semantic part-based benchmarking for state-of-the-art object recognition systems

An examination of object recognition challenge leaderboards (ILSVRC, PASCAL-VOC) reveals that the top-performing classifiers typically exhibit small differences amongst themselves in terms of error rate/mAP. To better differentiate the top performers, additional criteria are required. Moreover, the (test) images, on which the performance scores are based, predominantly contain fully visible objects. Therefore, `harder' test images, mimicking the challenging conditions (e.g. occlusion) in which humans routinely recognize objects, need to be utilized for benchmarking. To address the concerns mentioned above, we make two contributions. First, we systematically vary the level of local object-part content, global detail and spatial context in images from PASCAL VOC 2010 to create a new benchmarking dataset dubbed PPSS-12. Second, we propose an object-part based benchmarking procedure which quantifies classifiers' robustness to a range of visibility and contextual settings. The benchmarking procedure relies on a semantic similarity measure that naturally addresses potential semantic granularity differences between the category labels in training and test datasets, thus eliminating manual mapping. We use our procedure on the PPSS-12 dataset to benchmark top-performing classifiers trained on the ILSVRC-2012 dataset. Our results show that the proposed benchmarking procedure enables additional differentiation among state-of-the-art object classifiers in terms of their ability to handle missing content and insufficient object detail. Given this capability for additional differentiation, our approach can potentially supplement existing benchmarking procedures used in object recognition challenge leaderboards.Comment: Extended version of our ACCV-2016 paper. Author formatting modifie

Antitumor Activity of Pembrolizumab in Biomarker-Unselected Patients With Recurrent and/or Metastatic Head and Neck Squamous Cell Carcinoma: Results From the Phase Ib KEYNOTE-012 Expansion Cohort.

Purpose Treatment with pembrolizumab, an anti-programmed death-1 antibody, at 10 mg/kg administered once every 2 weeks, displayed durable antitumor activity in programmed death-ligand 1 (PD-L1) -positive recurrent and/or metastatic (R/M) head and neck squamous cell carcinoma (HNSCC) in the KEYNOTE-012 trial. Results from the expansion cohort, in which patients with HNSCC, irrespective of biomarker status, received a fixed dose of pembrolizumab at a less frequent dosing schedule, are reported. Patients and Methods Patients with R/M HNSCC, irrespective of PD-L1 or human papillomavirus status, received pembrolizumab 200 mg intravenously once every 3 weeks. Imaging was performed every 8 weeks. Primary end points were overall response rate (ORR) per central imaging vendor (Response Evaluation Criteria in Solid Tumors v1.1) and safety. Secondary end points included progression-free survival, overall survival, and association of response and PD-L1 expression. Patients who received one or more doses of pembrolizumab were included in analyses. Results Of 132 patients enrolled, median age was 60 years (range, 25 to 84 years), 83% were male, and 57% received two or more lines of therapy for R/M disease. ORR was 18% (95% CI, 12 to 26) by central imaging vendor and 20% (95% CI, 13 to 28) by investigator review. Median duration of response was not reached (range, ≥ 2 to ≥ 11 months). Six-month progression-free survival and overall survival rates were 23% and 59%, respectively. By using tumor and immune cells, a statistically significant increase in ORR was observed for PD-L1-positive versus -negative patients (22% v 4%; P = .021). Treatment-related adverse events of any grade and grade ≥ 3 events occurred in 62% and 9% of patients, respectively. Conclusion Fixed-dose pembrolizumab 200 mg administered once every 3 weeks was well tolerated and yielded a clinically meaningful ORR with evidence of durable responses, which supports further development of this regimen in patients with advanced HNSCC

Multilevel models of age-related changes in facial shape in adolescents

Here we study the effects of age on facial shape in adolescents by using a method called multilevel principal components analysis (mPCA). An associated multilevel multivariate probability distribution is derived and expressions for the (conditional) probability of age-group membership are presented. This formalism is explored via Monte Carlo (MC) simulated data in the first dataset; where age is taken to increase the overall scale of a three-dimensional facial shape represented by 21 landmark points and all other “subjective” variations are related to the width of the face. Eigenvalue plots make sense and modes of variation correctly identify these two main factors at appropriate levels of the mPCA model. Component scores for both single-level PCA and mPCA show a strong trend with age. Conditional probabilities are shown to predict membership by age group and the Pearson correlation coefficient between actual and predicted group membership is r = 0.99. The effects of outliers added to the MC training data are reduced by the use of robust covariance matrix estimation and robust averaging of matrices. These methods are applied to another dataset containing 12 GPA-scaled (3D) landmark points for 195 shapes from 27 white, male schoolchildren aged 11 to 16 years old. 21% of variation in the shapes for this dataset was accounted for by age. Mode 1 at level 1 (age) via mPCA appears to capture an increase in face height with age, which is consistent with reported pubertal changes in children. Component scores for both single-level PCA and mPCA again show a distinct trend with age. Conditional probabilities are again shown to reflect membership by age group and the Pearson correlation coefficient is given by r = 0.63 in this case. These analyses are an excellent first test of the ability of multilevel statistical methods to model age-related changes in facial shape in adolescents

Impact damage characteristics of carbon fibre metal laminates : experiments and simulation

In this work, the impact response of carbon fibre metal laminates (FMLs) was experimentally and numerically studied with an improved design of the fibre composite lay-up for optimal mechanical properties and damage resistance. Two different stacking sequences (Carall 3–3/2–0.5 and Carall 5–3/2–0.5) were designed and characterised. Damage at relatively low energy impact energies (≤30 J) was investigated using Ultrasonic C-scanning and X–ray Computed Tomography (X-RCT). A 3D finite element model was developed to simulate the impact induced damage in both metal and composite layers using Abaqus/Explicit. Cohesive zone elements were introduced to capture delamination occurring between carbon fibre/epoxy plies and debonding at the interfaces between aluminium and the composite layers. Carall 5–3/2–0.5 was found to absorb more energy elastically, which indicates better resistance to damage. A good agreement is obtained between the numerically predicted results and experimental measurements in terms of force and absorbed energy during impact where the damage modes such as delamination was well simulated when compared to non-destructive techniques (NDT)

Cognitive appraisal of environmental stimuli induces emotion-like states in fish

The occurrence of emotions in non-human animals has been the focus of debate over the years. Recently, an interest in expanding this debate to non-tetrapod vertebrates and to invertebrates has emerged. Within vertebrates, the study of emotion in teleosts is particularly interesting since they represent a divergent evolutionary radiation from that of tetrapods, and thus they provide an insight into the evolution of the biological mechanisms of emotion. We report that Sea Bream exposed to stimuli that vary according to valence (positive, negative) and salience (predictable, unpredictable) exhibit different behavioural, physiological and neuromolecular states. Since according to the dimensional theory of emotion valence and salience define a two-dimensional affective space, our data can be interpreted as evidence for the occurrence of distinctive affective states in fish corresponding to each the four quadrants of the core affective space. Moreover, the fact that the same stimuli presented in a predictable vs. unpredictable way elicited different behavioural, physiological and neuromolecular states, suggests that stimulus appraisal by the individual, rather than an intrinsic characteristic of the stimulus, has triggered the observed responses. Therefore, our data supports the occurrence of emotion-like states in fish that are regulated by the individual's perception of environmental stimuli.European Commission [265957 Copewell]; Fundacao para a Ciencia e Tecnologia [SFRH/BD/80029/2011, SFRH/BPD/72952/2010]info:eu-repo/semantics/publishedVersio

Sleep quality in middle-aged and elderly Chinese: distribution, associated factors and associations with cardio-metabolic risk factors

Background Poor sleep quality has been associated with increased risk of heart disease, diabetes and mortality. However, limited information exists on the distribution and determinants of sleep quality and its associations with cardio-metabolic risk factors in Chinese populations. We aimed to evaluate this in the current study. Methods A cross-sectional survey conducted in 2005 of 1,458 men and 1,831 women aged 50–70 years from urban and rural areas of Beijing and Shanghai. Using a questionnaire, sleep quality was measured in levels of well, common and poor. Comprehensive measures of socio-demographical and health factors and biomarkers of cardio-metabolic disease were recorded. These were evaluated in association with sleep quality using logistic regression models. Results Half of the population reported good sleep quality. After adjusting for potential confounders, women and Beijing residents had almost half the probability to report good sleep quality. Good physical and mental health (good levels of self-rated health (OR 2.48; 95%CI 2.08 to 2.96) and no depression (OR 4.05; 95%CI 3.12 to 5.26)) related to an increased chance of reporting good sleep quality, whereas short sleep duration (<7 hrs OR 0.10; 95%CI 0.07 to 0.14)) decreased it substantially. There were significant associations between levels of sleep quality and concentrations of plasma insulin, total and LDL cholesterol, and index of insulin resistance. Conclusion Levels of good sleep quality in middle-age and elderly Chinese were low. Gender, geographical location, self-rated health, depression and sleep quantity were major factors associated with sleep quality. Prospective studies are required to distil the factors that determine sleep quality and the effects that sleep patterns exert on cardio-metabolic health

Assessing the Impact of Blood Pressure on Cardiac Function Using Interpretable Biomarkers and Variational Autoencoders

Maintaining good cardiac function for as long as possible is a major concern for healthcare systems worldwide and there is much interest in learning more about the impact of different risk factors on cardiac health. The aim of this study is to analyze the impact of systolic blood pressure (SBP) on cardiac function while preserving the interpretability of the model using known clinical biomarkers in a large cohort of the UK Biobank population. We propose a novel framework that combines deep learning based estimation of interpretable clinical biomarkers from cardiac cine MR data with a variational autoencoder (VAE). The VAE architecture integrates a regression loss in the latent space, which enables the progression of cardiac health with SBP to be learnt. Results on 3,600 subjects from the UK Biobank show that the proposed model allows us to gain important insight into the deterioration of cardiac function with increasing SBP, identify key interpretable factors involved in this process, and lastly exploit the model to understand patterns of positive and adverse adaptation of cardiac function

A powerful method for detecting differentially expressed genes from GeneChip arrays that does not require replicates

BACKGROUND: Studies of differential expression that use Affymetrix GeneChip arrays are often carried out with a limited number of replicates. Reasons for this include financial considerations and limits on the available amount of RNA for sample preparation. In addition, failed hybridizations are not uncommon leading to a further reduction in the number of replicates available for analysis. Most existing methods for studying differential expression rely on the availability of replicates and the demand for alternative methods that require few or no replicates is high. RESULTS: We describe a statistical procedure for performing differential expression analysis without replicates. The procedure relies on a Bayesian integrated approach (BGX) to the analysis of Affymetrix GeneChips. The BGX method estimates a posterior distribution of expression for each gene and condition, from a simultaneous consideration of the available probe intensities representing the gene in a condition. Importantly, posterior distributions of expression are obtained regardless of the number of replicates available. We exploit these posterior distributions to create ranked gene lists that take into account the estimated expression difference as well as its associated uncertainty. We estimate the proportion of non-differentially expressed genes empirically, allowing an informed choice of cut-off for the ranked gene list, adapting an approach proposed by Efron. We assess the performance of the method, and compare it to those of other methods, on publicly available spike-in data sets, as well as in a proper biological setting. CONCLUSION: The method presented is a powerful tool for extracting information on differential expression from GeneChip expression studies with limited or no replicates

SCUBA-2 Ultra Deep Imaging EAO Survey (STUDIES). IV. Spatial Clustering and Halo Masses of Submillimeter Galaxies

We analyze an extremely deep 450 μm image (1σ = 0.56 mJy beam−1) of a sime300 arcmin2 area in the CANDELS/COSMOS field as part of the Sub-millimeter Common User Bolometric Array-2 Ultra Deep Imaging EAO Survey. We select a robust (signal-to-noise ratio ≥4) and flux-limited (≥4 mJy) sample of 164 submillimeter galaxies (SMGs) at 450 μm that have K-band counterparts in the COSMOS2015 catalog identified from radio or mid-infrared imaging. Utilizing this SMG sample and the 4705 K-band-selected non-SMGs that reside within the noise level ≤1 mJy beam−1 region of the 450 μm image as a training set, we develop a machine-learning classifier using K-band magnitude and color–color pairs based on the 13-band photometry available in this field. We apply the trained machine-learning classifier to the wider COSMOS field (1.6 deg2) using the same COSMOS2015 catalog and identify a sample of 6182 SMG candidates with similar colors. The number density, radio and/or mid-infrared detection rates, redshift and stellar-mass distributions, and the stacked 450 μm fluxes of these SMG candidates, from the S2COSMOS observations of the wide field, agree with the measurements made in the much smaller CANDELS field, supporting the effectiveness of the classifier. Using this SMG candidate sample, we measure the two-point autocorrelation functions from z = 3 down to z = 0.5. We find that the SMG candidates reside in halos with masses of sime(2.0 ± 0.5) × 1013 h −1 M ☉ across this redshift range. We do not find evidence of downsizing that has been suggested by other recent observational studies