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We present integral field spectroscopy toward and around J1044+0353, a rapidly growing, low-metallicity galaxy which produces extreme [O III] line emission. A new map of the O32 flux ratio reveals a density-bounded ionization cone emerging from the starburst. The interaction of the hydrogen ionizing radiation, produced by the very young starburst, with a cavity previously carved out by a galactic outflow, whose apex lies well outside the starburst region, determines the pathway for global Lyman continuum (LyC) escape. In the region within a few hundred parsecs of the young starburst, we demonstrate that superbubble breakthrough and blowout contribute distinct components to the [O III] line profile, broad and very-broad emission line wings, respectively. We draw attention to the large [O III] luminosity of the broad component and argue that this emission comes from photoionized, superbubble shells rather than a galactic wind as is often assumed. The spatially resolved H eII 4686 nebula appears to be photoionized by young star clusters. Stellar wind emission from these stars is likely the source of line wings detected on the He II line profile. This broader He II component indicates slow stellar winds, consistent with an increase in stellar rotation (and a decrease in effective escape speed) at the metallicity of J1044+0353. At least in J1044+0353, the recent star formation history plays a critical role in generating a global pathway for LyC escape, and the anisotropic escape would likely be missed by direct observations of the LyC., Comment: Accepted for publication in the Astrophysical Journal. 34 pages, 12 figures, and 3 tables

J1044+0353 is considered a local analog of the young galaxies that ionized the intergalactic medium at high-redshift due to its low mass, low metallicity, high specific star formation rate, and strong high-ionization emission lines. We use integral field spectroscopy to trace the propagation of the starburst across this small galaxy using Balmer emission- and absorption-line equivalent widths and find a post-starburst population (~ 15 - 20 Myr) roughly one kpc east of the much younger, compact starburst (~ 3 - 4 Myr). Using the direct electron temperature method to map the O/H abundance ratio, we find similar metallicity (1 to 3 sigma) between the starburst and post-starburst regions but with a significant dispersion of about 0.3 dex within the latter. We also map the Doppler shift and width of the strong emission lines. Over scales several times the size of the galaxy, we discover a velocity gradient parallel to the galaxy's minor axis. The steepest gradients (~ 30 $\mathrm{km \ s^{-1} \ kpc^{-1}}$) appear to emanate from the oldest stellar association. We identify the velocity gradient as an outflow viewed edge-on based on the increased line width and skew in a biconical region. We discuss how this outflow and the gas inflow necessary to trigger the starburst affect the chemical evolution of J1044+0353. We conclude that the stellar associations driving the galactic outflow are spatially offset from the youngest association, and a chemical evolution model with a metal-enriched wind requires a more realistic inflow rate than a homogeneous chemical evolution model., Comment: 23 pages, 10 figures

Emotion recognition from speech is a challenging task. Re-cent advances in deep learning have led bi-directional recur-rent neural network (Bi-RNN) and attention mechanism as astandard method for speech emotion recognition, extractingand attending multi-modal features - audio and text, and thenfusing them for downstream emotion classification tasks. Inthis paper, we propose a simple yet efficient neural networkarchitecture to exploit both acoustic and lexical informationfrom speech. The proposed framework using multi-scale con-volutional layers (MSCNN) to obtain both audio and text hid-den representations. Then, a statistical pooling unit (SPU)is used to further extract the features in each modality. Be-sides, an attention module can be built on top of the MSCNN-SPU (audio) and MSCNN (text) to further improve the perfor-mance. Extensive experiments show that the proposed modeloutperforms previous state-of-the-art methods on IEMOCAPdataset with four emotion categories (i.e., angry, happy, sadand neutral) in both weighted accuracy (WA) and unweightedaccuracy (UA), with an improvement of 5.0% and 5.2% respectively under the ASR setting., Comment: First two authors contributed equally.Accepted by ICASSP 2021

Segmentation of anatomical structures is a fundamental image analysis task for many applications in the medical field. Deep learning methods have been shown to perform well, but for this purpose large numbers of manual annotations are needed in the first place, which necessitate prohibitive levels of resources that are often unavailable. In an active learning framework of selecting informed samples for manual labeling, expert clinician time for manual annotation can be optimally utilized, enabling the establishment of large labeled datasets for machine learning. In this paper, we propose a novel method that combines representativeness with uncertainty in order to estimate ideal samples to be annotated, iteratively from a given dataset. Our novel representativeness metric is based on Bayesian sampling, by using information-maximizing autoencoders. We conduct experiments on a shoulder magnetic resonance imaging (MRI) dataset for the segmentation of four musculoskeletal tissue classes. Quantitative results show that the annotation of representative samples selected by our proposed querying method yields an improved segmentation performance at each active learning iteration, compared to a baseline method that also employs uncertainty and representativeness metrics. For instance, with only 10% of the dataset annotated, our method reaches within 5% of Dice score expected from the upper bound scenario of all the dataset given as annotated (an impractical scenario due to resource constraints), and this gap drops down to a mere 2% when less than a fifth of the dataset samples are annotated. Such active learning approach to selecting samples to annotate enables an optimal use of the expert clinician time, being often the bottleneck in realizing machine learning solutions in medicine., Comment: 10 pages, 7 figures

Segmentation is essential for medical image analysis tasks such as intervention planning, therapy guidance, diagnosis, treatment decisions. Deep learning is becoming increasingly prominent for segmentation, where the lack of annotations, however, often becomes the main limitation. Due to privacy concerns and ethical considerations, most medical datasets are created, curated, and allow access only locally. Furthermore, current deep learning methods are often suboptimal in translating anatomical knowledge between different medical imaging modalities. Active learning can be used to select an informed set of image samples to request for manual annotation, in order to best utilize the limited annotation time of clinical experts for optimal outcomes, which we focus on in this work. Our contributions herein are two fold: (1) we enforce domain-representativeness of selected samples using a proposed penalization scheme to maximize information at the network abstraction layer, and (2) we propose a Borda-count based sample querying scheme for selecting samples for segmentation. Comparative experiments with baseline approaches show that the samples queried with our proposed method, where both above contributions are combined, result in significantly improved segmentation performance for this active learning task., Comment: 8 pages, 4 figures, Accepted to MICCAI 2018 Workshop: Deep Learning in Medical Image Analysis (DLMIA)