Using a novel source-localized phase regressor technique for evaluation of the vascular contribution to semantic category area localization in BOLD fMRI.

Numerous studies have shown that gradient-echo blood oxygen level dependent (BOLD) fMRI is biased toward large draining veins. However, the impact of this large vein bias on the localization and characterization of semantic category areas has not been examined. Here we address this issue by comparing standard magnitude measures of BOLD activity in the Fusiform Face Area (FFA) and Parahippocampal Place Area (PPA) to those obtained using a novel method that suppresses the contribution of large draining veins: source-localized phase regressor (sPR). Unlike previous suppression methods that utilize the phase component of the BOLD signal, sPR yields robust and unbiased suppression of large draining veins even in voxels with no task-related phase changes. This is confirmed in ideal simulated data as well as in FFA/PPA localization data from four subjects. It was found that approximately 38% of right PPA, 14% of left PPA, 16% of right FFA, and 6% of left FFA voxels predominantly reflect signal from large draining veins. Surprisingly, with the contributions from large veins suppressed, semantic category representation in PPA actually tends to be lateralized to the left rather than the right hemisphere. Furthermore, semantic category areas larger in volume and higher in fSNR were found to have more contributions from large veins. These results suggest that previous studies using gradient-echo BOLD fMRI were biased toward semantic category areas that receive relatively greater contributions from large veins

Tracking Articulator Movements Using Orientation Measurements

This paper introduces a new method to track articulator movements, specifically jaw position and angle, using 5 degree of freedom (5 DOF) orientation data. The approach uses a quaternion rotation method to accomplish this jaw tracking during speech using a single senor on the mandibular incisor. Data were collected using the NDI Wave Speech Research System for one pilot subject with various speech tasks. The degree of jaw rotation from the proposed approach is compared with traditional geometric calculation. Results show that the quaternion based method is able to describe jaw angle trajectory and gives more accurate and smooth estimation of jaw kinematics

The Electromagnetic Articulography Mandarin Accented English (EMA-MAE) Corpus of Acoustic and 3D Articulatory Kinematic Data

There is a significant need for more comprehensive electromagnetic articulography (EMA) datasets that can provide matched acoustics and articulatory kinematic data with good spatial and temporal resolution. The Marquette University Electromagnetic Articulography Mandarin Accented English (EMA-MAE) corpus provides kinematic and acoustic data from 40 gender and dialect balanced speakers representing 20 Midwestern standard American English L1 speakers and 20 Mandarin Accented English (MAE) L2 speakers, half Beijing region dialect and half are Shanghai region dialect. Three dimensional EMA data were collected at a 400 Hz sampling rate using the NDI Wave system, with articulatory sensors on the midsagittal lips, lower incisors, tongue blade and dorsum, plus lateral lip corner and tongue body. Sensors provide three-dimensional position data as well as two-dimensional orientation data representing the orientation of the sensor plane. Data have been corrected for head movement relative to a fixed reference sensor and also adjusted using a biteplate calibration system to place the data in an articulatory working space relative to each subject\u27s individual midsagittal and maxillary occlusal planes. Speech materials include isolated words chosen to focus on specific contrasts between the English and Mandarin languages, as well as sentences and paragraphs for continuous speech, totaling approximately 45 minutes of data per subject. A beta version of the EMA-MAE corpus is now available, and the full corpus is in preparation for public release to help advance research in areas such as pronunciation modeling, acoustic-articulatory inversion, L1-L2 comparisons, pronunciation error detection, and accent modification training

Vowel Production in Mandarin Accented English and American English: Kinematic and Acoustic Data from the Marquette University Mandarin Accented English Corpus

Few electromagnetic articulography (EMA) datasets are publicly available, and none have focused systematically on non-native accented speech. We introduce a kinematic-acoustic database of speech from 40 (gender and dialect balanced) participants producing upper-Midwestern American English (AE) L1 or Mandarin Accented English (MAE) L2 (Beijing or Shanghai dialect base). The Marquette University EMA-MAE corpus will be released publicly to help advance research in areas such as pronunciation modeling, acoustic-articulatory inversion, L1-L2 comparisons, pronunciation error detection, and accent modification training. EMA data were collected at a 400 Hz sampling rate with synchronous audio using the NDI Wave System. Articulatory sensors were placed on the midsagittal lips, lower incisors, and tongue blade and dorsum, as well as on the lip corner and lateral tongue body. Sensors provide five degree-of-freedom measurements including three-dimensional sensor position and two-dimensional orientation (pitch and roll). In the current work we analyze kinematic and acoustic variability between L1 and L2 vowels. We address the hypothesis that MAE is characterized by larger differences in the articulation of back vowels than front vowels and smaller vowel spaces compared to AE. The current results provide a seminal comparison of the kinematics and acoustics of vowel production between MAE and AE speakers

Parallel Reference Speaker Weighting for Kinematic-Independent Acoustic-to-Articulatory Inversion

Acoustic-to-articulatory inversion, the estimation of articulatory kinematics from an acoustic waveform, is a challenging but important problem. Accurate estimation of articulatory movements has the potential for significant impact on our understanding of speech production, on our capacity to assess and treat pathologies in a clinical setting, and on speech technologies such as computer aided pronunciation assessment and audio-video synthesis. However, because of the complex and speaker-specific relationship between articulation and acoustics, existing approaches for inversion do not generalize well across speakers. As acquiring speaker-specific kinematic data for training is not feasible in many practical applications, this remains an important and open problem. This paper proposes a novel approach to acoustic-to-articulatory inversion, Parallel Reference Speaker Weighting (PRSW), which requires no kinematic data for the target speaker and a small amount of acoustic adaptation data. PRSW hypothesizes that acoustic and kinematic similarities are correlated and uses speaker-adapted articulatory models derived from acoustically derived weights. The system was assessed using a 20-speaker data set of synchronous acoustic and Electromagnetic Articulography (EMA) kinematic data. Results demonstrate that by restricting the reference group to a subset consisting of speakers with strong individual speaker-dependent inversion performance, the PRSW method is able to attain kinematic-independent acoustic-to-articulatory inversion performance nearly matching that of the speaker-dependent model, with an average correlation of 0.62 versus 0.63. This indicates that given a sufficiently complete and appropriately selected reference speaker set for adaptation, it is possible to create effective articulatory models without kinematic training data

Palate-referenced Articulatory Features for Acoustic-to-Articulator Inversion

The selection of effective articulatory features is an important component of tasks such as acoustic-to-articulator inversion and articulatory synthesis. Although it is common to use direct articulatory sensor measurements as feature variables, this approach fails to incorporate important physiological information such as palate height and shape and thus is not as representative of vocal tract cross section as desired. We introduce a set of articulator feature variables that are palate referenced and normalized with respect to the articulatory working space in order to improve the quality of the vocal tract representation. These features include normalized horizontal positions plus the normalized palatal height of two midsagittal and one lateral tongue sensor, as well as normalized lip separation and lip protrusion. The quality of the feature representation is evaluated subjectively by comparing the variances and vowel separation in the working space and quantitatively through measurement of acoustic-to-articulator inversion error. Results indicate that the palate-referenced features have reduced variance and increased separation between vowels spaces and substantially lower inversion error than direct sensor measures

Four hot DOGs eaten up with the EVN

Hot dust-obscured galaxies (hot DOGs) are a rare class of hyperluminous infrared galaxies recently identified with the Wide-field Infrared Survey Explorer (WISE) satellite. The majority of the ~1000-member all-sky population should be at high redshifts (z~2-3), at the peak of star formation in the history of the Universe. This class most likely represents a short phase during galaxy merging and evolution, a transition from starburst- to AGN-dominated phases. For the first time, we observed four hot DOGs with known mJy-level radio emission using the European VLBI Network (EVN) at 1.7 GHz, in a hope to find compact radio features characteristic to AGN activity. All four target sources are detected at ~15-30 mas angular resolution, confirming the presence of an active nucleus. The sources are spatially resolved, i.e. the flux density of the VLBI-detected components is smaller than the total flux density, suggesting that a fraction of the radio emission originates from larger-scale (partly starburst-related) activity. Here we show the preliminary results of our e-EVN observations made in 2014 February, and discuss WISE J1814+3412, an object with kpc-scale symmetric radio structure, in more detail.Comment: 6 pages, 1 figure; appears in the proceedings of the 12th European VLBI Network Symposium and Users Meeting (7-10 October 2014, Cagliari, Italy), eds. A. Tarchi, M. Giroletti & L. Feretti. JREF Proceedings of Science, PoS(EVN 2014)003, http://pos.sissa.it/archive/conferences/230/003/EVN%202014_003.pd

Towards Analyzing Semantic Robustness of Deep Neural Networks

Despite the impressive performance of Deep Neural Networks (DNNs) on various vision tasks, they still exhibit erroneous high sensitivity toward semantic primitives (e.g. object pose). We propose a theoretically grounded analysis for DNN robustness in the semantic space. We qualitatively analyze different DNNs' semantic robustness by visualizing the DNN global behavior as semantic maps and observe interesting behavior of some DNNs. Since generating these semantic maps does not scale well with the dimensionality of the semantic space, we develop a bottom-up approach to detect robust regions of DNNs. To achieve this, we formalize the problem of finding robust semantic regions of the network as optimizing integral bounds and we develop expressions for update directions of the region bounds. We use our developed formulations to quantitatively evaluate the semantic robustness of different popular network architectures. We show through extensive experimentation that several networks, while trained on the same dataset and enjoying comparable accuracy, do not necessarily perform similarly in semantic robustness. For example, InceptionV3 is more accurate despite being less semantically robust than ResNet50. We hope that this tool will serve as a milestone towards understanding the semantic robustness of DNNs.Comment: Presented at European conference on computer vision (ECCV 2020) Workshop on Adversarial Robustness in the Real World ( https://eccv20-adv-workshop.github.io/ ) [best paper award]. The code is available at https://github.com/ajhamdi/semantic-robustnes

Four hot DOGs in the microwave

Hot dust-obscured galaxies (hot DOGs) are a rare class of hyperluminous infrared galaxies identified with the Wide-field Infrared Survey Explorer (WISE) satellite. The majority of them is at high redshifts (z~2-3), at the peak epoch of star formation in the Universe. Infrared, optical, radio, and X-ray data suggest that hot DOGs contain heavily obscured, extremely luminous active galactic nuclei (AGN). This class may represent a short phase in the life of the galaxies, signifying the transition from starburst- to AGN-dominated phases. Hot DOGs are typically radio-quiet, but some of them show mJy-level emission in the radio (microwave) band. We observed four hot DOGs using the technique of very long baseline interferometry (VLBI). The 1.7-GHz observations with the European VLBI Network (EVN) revealed weak radio features in all sources. The radio is free from dust obscuration and, at such high redshifts, VLBI is sensitive only to compact structures that are characteristic of AGN activity. In two cases (WISE J0757+5113, WISE J1603+2745), the flux density of the VLBI-detected components is much smaller than the total flux density, suggesting that ~70-90 per cent of the radio emission, while still dominated by AGN, originates from angular scales larger than probed by the EVN. The source WISE J1146+4129 appears a candidate compact symmetric object, and WISE J1814+3412 shows a 5.1-kpc double structure, reminiscent of hot spots in a medium-sized symmetric object. Our observations support that AGN residing in hot DOGs may be genuine young radio sources where starburst and AGN activities coexist.Comment: 8 pages, 4 tables, 1 figure; accepted for publication in the Monthly Notices of the Royal Astronomical Societ

Two in one? A possible dual radio-emitting nucleus in the quasar SDSS J1425+3231

The radio-emitting quasar SDSS J1425+3231 (z=0.478) was recently found to have double-peaked narrow [O III] optical emission lines. Based on the analysis of the optical spectrum, Peng et al. (2011) suggested that this object harbours a dual active galactic nucleus (AGN) system, with two supermassive black holes (SMBHs) separated on the kpc scale. SMBH pairs should be ubiquitous according to hierarchical galaxy formation scenarios in which the host galaxies and their central black holes grow together via interactions and eventual mergers. Yet the number of presently-confirmed dual SMBHs on kpc or smaller scales remains small. A possible way to obtain direct observational evidence for duality is to conduct high-resolution radio interferometric measurements, provided that both AGN are in an evolutionary phase when some activity is going on in the radio. We used the technique of Very Long Baseline Interferometry (VLBI) to image SDSS J1425+3231. Observations made with the European VLBI Network (EVN) at 1.7 GHz and 5 GHz frequencies in 2011 revealed compact radio emission at sub-mJy flux density levels from two components with a projected linear separation of \sim2.6 kpc. These two components support the possibility of a dual AGN system. The weaker component remained undetected at 5 GHz, due to its steep radio spectrum. Further study will be necessary to securely rule out a jet--shock interpretation of the less dominant compact radio source. Assuming the dual AGN interpretation, we discuss black hole masses, luminosities, and accretion rates of the two components, using available X-ray, optical, and radio data. While high-resolution radio interferometric imaging is not an efficient technique to search blindly for dual AGN, it is an invaluable tool to confirm the existence of selected candidates.Comment: 7 pages, 2 figures. Accepted for publication in Monthly Notices of the Royal Astronomical Societ