Unsupervised Learning of Semantic Audio Representations

Even in the absence of any explicit semantic annotation, vast collections of audio recordings provide valuable information for learning the categorical structure of sounds. We consider several class-agnostic semantic constraints that apply to unlabeled nonspeech audio: (i) noise and translations in time do not change the underlying sound category, (ii) a mixture of two sound events inherits the categories of the constituents, and (iii) the categories of events in close temporal proximity are likely to be the same or related. Without labels to ground them, these constraints are incompatible with classification loss functions. However, they may still be leveraged to identify geometric inequalities needed for triplet loss-based training of convolutional neural networks. The result is low-dimensional embeddings of the input spectrograms that recover 41% and 84% of the performance of their fully-supervised counterparts when applied to downstream query-by-example sound retrieval and sound event classification tasks, respectively. Moreover, in limited-supervision settings, our unsupervised embeddings double the state-of-the-art classification performance.Comment: Submitted to ICASSP 201

Solidifying Small Satellite Access to Orbit via the International Space Station (ISS): Cyclops' Deployment of the Lonestar SmallSat from the ISS

On January 29, 2016, the Space Station Integrated Kinetic Launcher for Orbital Payload Systems (SSIKLOPS), known as "Cyclops" to the International Space Station (ISS) community, deployed Lonestar from the ISS. The deployment of Lonestar, a collaboration between Texas A&M University and the University of Texas at Austin, continued to showcase the simplicity and reliability of the Cyclops deployment system. Cyclops, a NASA-developed, dedicated 10-100 kg class ISS SmallSat deployment system, utilizes the Japanese airlock and robotic systems to seamlessly insert SmallSats into orbit. This paper will illustrate Cyclops' successful deployment of Lonestar from the ISS as well as outline its concept of operations, interfaces, requirements, and processes

Development and Certification of a System for the Controlled Deployment of Micro-Satellite Payloads from the International Space Station

No abstract availabl

Paving the Way for Small Satellite Access to Orbit: Cyclops’ Deployment of SpinSat, the Largest Satellite ever Deployed from the International Space Station

The Space Station Integrated Kinetic Launcher for Orbital Payload Systems (SSIKLOPS), known as “Cyclops” to the International Space Station (ISS) community, successfully deployed the largest satellite ever (SpinSat) from the ISS on November 28, 2014. Cyclops, a collaboration between the NASA ISS Program, NASA Johnson Space Center Engineering, and Department of Defense Space Test Program (DoD STP) communities, is a dedicated 10-100 kg class ISS small satellite deployment system. This paper will showcase the successful deployment of SpinSat from the ISS. It will also outline the concept of operations, interfaces, requirements, and processes for satellites to utilize the Cyclops satellite deployment system

CNN Architectures for Large-Scale Audio Classification

Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio. We use various CNN architectures to classify the soundtracks of a dataset of 70M training videos (5.24 million hours) with 30,871 video-level labels. We examine fully connected Deep Neural Networks (DNNs), AlexNet [1], VGG [2], Inception [3], and ResNet [4]. We investigate varying the size of both training set and label vocabulary, finding that analogs of the CNNs used in image classification do well on our audio classification task, and larger training and label sets help up to a point. A model using embeddings from these classifiers does much better than raw features on the Audio Set [5] Acoustic Event Detection (AED) classification task.Comment: Accepted for publication at ICASSP 2017 Changes: Added definitions of mAP, AUC, and d-prime. Updated mAP/AUC/d-prime numbers for Audio Set based on changes of latest Audio Set revision. Changed wording to fit 4 page limit with new addition

NICMOS Imaging of a Damped Lyman-alpha Absorber at z=1.89 toward LBQS 1210+1731 : Constraints on Size and Star Formation Rate

We report results of a high-resolution imaging search (in rest frame H-$\alpha$ and optical continuum) for the galaxy associated with the damped Lyman-$\alpha$ (DLA) absorber at $z=1.892$ toward the $z_{em}=2.543$ quasar LBQS 1210+1731, using HST/NICMOS. After PSF subtraction, a feature is seen in both the broad-band and narrow-band images, at a projected separation of 0.25\arcsec from the quasar. If associated with the DLA, the object would be $\approx 2-3$ $h_{70}^{-1}$ kpc in size with a flux of $9.8 \pm 2.4$ $\mu$Jy in the F160W filter, implying a luminosity at $\lambda_{central}=5500$ {\AA} in the rest frame of $1.5 \times 10^{10}$ $h_{70}^{-2}$ L$_{\odot}$ at $z=1.89$, for $q_{0}=0.5$. However, no significant H-$\alpha$ emission is seen, suggesting a low star formation rate (SFR) (3 $\sigma$ upper limit of 4.0 $h_{70}^{-2}$ M$_{\odot}$ yr$^{-1}$), or very high dust obscuration. Alternatively, the object may be associated with the host galaxy of the quasar. H-band images obtained with the NICMOS camera 2 coronagraph show a much fainter structure $\approx 4-5$ $h_{70}^{-1}$ kpc in size and containing four knots of continuum emission, located 0.7\arcsec away from the quasar. We have probed regions far closer to the quasar sight-line than in most previous studies of high-redshift intervening DLAs. The two objects we report mark the closest detected high-redshift DLA candidates yet to any quasar sight line. If the features in our images are associated with the DLA, they suggest faint, compact, somewhat clumpy objects rather than large, well-formed proto-galactic disks or spheroids.Comment: 52 pages of text, 19 figures, To be published in Astrophysical Journal (accepted Dec. 8, 1999

Cognitive Function in Children With Type 1 Diabetes: A meta-analysis

OBJECTIVE—To quantify the magnitude and pattern of cognitive difficulties in pediatric type 1 diabetes as well as the effects associated with earlier disease onset and severe hypoglycemia