CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection

Robust face detection in the wild is one of the ultimate components to support various facial related problems, i.e. unconstrained face recognition, facial periocular recognition, facial landmarking and pose estimation, facial expression recognition, 3D facial model construction, etc. Although the face detection problem has been intensely studied for decades with various commercial applications, it still meets problems in some real-world scenarios due to numerous challenges, e.g. heavy facial occlusions, extremely low resolutions, strong illumination, exceptionally pose variations, image or video compression artifacts, etc. In this paper, we present a face detection approach named Contextual Multi-Scale Region-based Convolution Neural Network (CMS-RCNN) to robustly solve the problems mentioned above. Similar to the region-based CNNs, our proposed network consists of the region proposal component and the region-of-interest (RoI) detection component. However, far apart of that network, there are two main contributions in our proposed network that play a significant role to achieve the state-of-the-art performance in face detection. Firstly, the multi-scale information is grouped both in region proposal and RoI detection to deal with tiny face regions. Secondly, our proposed network allows explicit body contextual reasoning in the network inspired from the intuition of human vision system. The proposed approach is benchmarked on two recent challenging face detection databases, i.e. the WIDER FACE Dataset which contains high degree of variability, as well as the Face Detection Dataset and Benchmark (FDDB). The experimental results show that our proposed approach trained on WIDER FACE Dataset outperforms strong baselines on WIDER FACE Dataset by a large margin, and consistently achieves competitive results on FDDB against the recent state-of-the-art face detection methods

ICP versus Laser Doppler Cerebrovascular Reactivity Indices to Assess Brain Autoregulatory Capacity

Objective: To explore the relationship between various autoregulatory indices in order to determine which approximate small-vessel/microvascular autoregulatory capacity most accurately. Methods: Utilizing a retrospective cohort of traumatic brain injury (TBI) patients (N=41) with: transcranial Doppler (TCD), intracranial pressure (ICP) and cortical laser Doppler flowmetry (LDF), we calculated various continuous indices of autoregulation and cerebrovascular responsiveness: A. ICP derived (pressure reactivity index (PRx) – correlation between ICP and mean arterial pressure (MAP), PAx – correlation between pulse amplitude of ICP (AMP) and MAP, RAC – correlation between AMP and cerebral perfusion pressure (CPP)), B. TCD derived (Mx – correlation between mean flow velocity (FVm) and CPP, Mx_a – correlation betrween FVm and MAP, Sx – correlation between systolic flow velocity (FVs) and CPP, Sx_a – correlation between FVs and MAP, Dx – correlation between diastolic flow index (FVd) and CPP, Dx_a – correlation between FVd and MAP), and LDF derived (Lx – correlation between LDF cerebral blood flow (CBF) and CPP, Lx_a – correlation between LDF-CBF and MAP). We assessed the relationship between these indices via Pearson correlation, Friedman test, principal component analysis (PCA), agglomerative hierarchal clustering (AHC) and k-means cluster analysis (KMCA). Results: LDF based autoregulatory index (Lx) was most associated with TCD based Mx/Mx_a and Dx/Dx_a across Pearson correlation, PCA, AHC and KMCA. Lx was only remotely associated with ICP based indices (PRx, PAx, RAC). TCD based Sx/Sx_a were more closely associated with ICP derived PRx, PAx and RAC. This indicates that vascular derived indices of autoregulatory capacity (ie. TCD and LDF based) co-vary, with Sx/Sx_a being the exception. Whereas, indices of cerebrovascular reactivity derived from pulsatile CBV (ie. ICP indices) appear to not be closely related to those of vascular origin. Conclusions: Transcranial Doppler Mx is the most closely associated with LDF based Lx/Lx_a. Both Sx/Sx-a and the ICP derived indices appear to be dissociated with LDF based cerebrovascular reactivity, leaving Mx/Mx-a as a better surrogate for the assessment of cortical small vessel/microvascular cerebrovascular reactivity. Sx/Sx_a co-cluster/co-vary with ICP derived indices, as seen in our previous work.This work was made possible through salary support through the Cambridge Commonwealth Trust Scholarship, the Royal College of Surgeons of Canada – Harry S. Morton Travelling Fellowship in Surgery, the University of Manitoba Clinician Investigator Program, R. Samuel McLaughlin Research and Education Award, the Manitoba Medical Service Foundation, and the University of Manitoba Faculty of Medicine Dean’s Fellowship Fund. These studies were supported by National Institute for Healthcare Research (NIHR, UK) through the Acute Brain Injury and Repair theme of the Cambridge NIHR Biomedical Research Centre, an NIHR Senior Investigator Award to DKM. Authors were also supported by a European Union Framework Program 7 grant (CENTER-TBI; Grant Agreement No. 602150) MC is supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI17C1790). JD is supported by a Woolf Fisher Scholarship (NZ)

'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

Comparison of high versus low frequency cerebral physiology for cerebrovascular reactivity assessment in traumatic brain injury: a multi-center pilot study

Current accepted cerebrovascular reactivity indices suffer from the need of high frequency data capture and export for post-acquisition processing. The role for minute-by-minute data in cerebrovascular reactivity monitoring remains uncertain. The goal was to explore the statistical time-series relationships between intra-cranial pressure (ICP), mean arterial pressure (MAP) and pressure reactivity index (PRx) using both 10-s and minute data update frequency in TBI. Prospective data from 31 patients from 3 centers with moderate/severe TBI and high-frequency archived physiology were reviewed. Both 10-s by 10-s and minute-by-minute mean values were derived for ICP and MAP for each patient. Similarly, PRx was derived using 30 consecutive 10-s data points, updated every minute. While long-PRx (L-PRx) was derived via similar methodology using minute-by-minute data, with L-PRx derived using various window lengths (5, 10, 20, 30, 40, and 60 min; denoted L-PRx_5, etc.). Time-series autoregressive integrative moving average (ARIMA) and vector autoregressive integrative moving average (VARIMA) models were created to analyze the relationship of these parameters over time. ARIMA modelling, Granger causality testing and VARIMA impulse response function (IRF) plotting demonstrated that similar information is carried in minute mean ICP and MAP data, compared to 10-s mean slow-wave ICP and MAP data. Shorter window L-PRx variants, such as L-PRx_5, appear to have a similar ARIMA structure, have a linear association with PRx and display moderate-to-strong correlations (r ~ 0.700, p Peer reviewe

Transport control by coherent zonal flows in the core/edge transitional regime

3D Braginskii turbulence simulations show that the energy flux in the core/edge transition region of a tokamak is strongly modulated - locally and on average - by radially propagating, nearly coherent sinusoidal or solitary zonal flows. The flows are geodesic acoustic modes (GAM), which are primarily driven by the Stringer-Winsor term. The flow amplitude together with the average anomalous transport sensitively depend on the GAM frequency and on the magnetic curvature acting on the flows, which could be influenced in a real tokamak, e.g., by shaping the plasma cross section. The local modulation of the turbulence by the flows and the excitation of the flows are due to wave-kinetic effects, which have been studied for the first time in a turbulence simulation.Comment: 5 pages, 5 figures, submitted to PR

An Exploratory Analysis of the Latent Structure of Process Data via Action Sequence Autoencoder

Computer simulations have become a popular tool of assessing complex skills such as problem-solving skills. Log files of computer-based items record the entire human-computer interactive processes for each respondent. The response processes are very diverse, noisy, and of nonstandard formats. Few generic methods have been developed for exploiting the information contained in process data. In this article, we propose a method to extract latent variables from process data. The method utilizes a sequence-to-sequence autoencoder to compress response processes into standard numerical vectors. It does not require prior knowledge of the specific items and human-computers interaction patterns. The proposed method is applied to both simulated and real process data to demonstrate that the resulting latent variables extract useful information from the response processes.Comment: 28 pages, 13 figure

Transition from ion-coupled to electron-only reconnection: Basic physics and implications for plasma turbulence

Using kinetic particle-in-cell (PIC) simulations, we simulate reconnection conditions appropriate for the magnetosheath and solar wind, i.e., plasma beta (ratio of gas pressure to magnetic pressure) greater than 1 and low magnetic shear (strong guide field). Changing the simulation domain size, we find that the ion response varies greatly. For reconnecting regions with scales comparable to the ion Larmor radius, the ions do not respond to the reconnection dynamics leading to ''electron-only'' reconnection with very large quasi-steady reconnection rates. The transition to more traditional ''ion-coupled'' reconnection is gradual as the reconnection domain size increases, with the ions becoming frozen-in in the exhaust when the magnetic island width in the normal direction reaches many ion inertial lengths. During this transition, the quasi-steady reconnection rate decreases until the ions are fully coupled, ultimately reaching an asymptotic value. The scaling of the ion outflow velocity with exhaust width during this electron-only to ion-coupled transition is found to be consistent with a theoretical model of a newly reconnected field line. In order to have a fully frozen-in ion exhaust with ion flows comparable to the reconnection Alfv\'en speed, an exhaust width of at least several ion inertial lengths is needed. In turbulent systems with reconnection occurring between magnetic bubbles associated with fluctuations, using geometric arguments we estimate that fully ion-coupled reconnection requires magnetic bubble length scales of at least several tens of ion inertial lengths

“Am I my genes?”: Questions of identity among individuals confronting genetic disease

Purpose: To explore many questions raised by genetics concerning personal identities that have not been fully investigated. Methods: We interviewed in depth, for 2 hours each, 64 individuals who had or were at risk for Huntington disease, breast cancer, or alpha-1 antitrypsin deficiency. Results: These individuals struggled with several difficult issues of identity. They drew on a range of genotypes and phenotypes (e.g., family history alone; mutations, but no symptoms; or symptoms). They often felt that their predicament did not fit preexisting categories well (e.g., “sick,” “healthy,” “disabled,” “predisposed”), due in part to uncertainties involved (e.g., unclear prognoses, since mutations may not produce symptoms). Hence, individuals varied in how much genetics affected their identity, in what ways, and how negatively. Factors emerged related to disease, family history, and other sources of identity. These identities may, in turn, shape disclosure, coping, and other health decisions. Conclusions: Individuals struggle to construct a genetic identity. They view genetic information in highly subjective ways, varying widely in what aspects of genetic information they focus on and how. These data have important implications for education of providers (to assist patients with these issues), patients, and family members; and for research, to understand these issues more fully

Automatic Classification of Roof Shapes for Multicopter Emergency Landing Site Selection

Geographic information systems (GIS) now provide accurate maps of terrain, roads, waterways, and building footprints and heights. Aircraft, particularly small unmanned aircraft systems, can exploit additional information such as building roof structure to improve navigation accuracy and safety particularly in urban regions. This paper proposes a method to automatically label building roof shape types. Satellite imagery and LIDAR data from Witten, Germany are fed to convolutional neural networks (CNN) to extract salient feature vectors. Supervised training sets are automatically generated from pre-labeled buildings contained in the OpenStreetMap database. Multiple CNN architectures are trained and tested, with the best performing networks providing a condensed feature set for support vector machine and decision tree classifiers. Satellite and LIDAR data fusion is shown to provide greater classification accuracy than through use of either data type individually