Endoscopic laser-ablation for the treatment of orthotopic and ectopic ureteroceles in dogs: 13 cases (2008-2017).

BACKGROUND: Ureteroceles are a rare condition in dogs in which conventional treatments can result in substantial morbidity. Cystoscopic and fluoroscopic-guided laser ablation (CLA) of ureteroceles can successfully relieve obstruction. OBJECTIVES: To describe the technique and outcomes of attempting CLA for treatment of ureteroceles in dogs. ANIMALS: Thirteen client-owned dogs that underwent CLA for treatment of ureteroceles. METHODS: Retrospective multicentered study. Medical records were reviewed in all dogs that underwent CLA for ureterocele(s). A laser was used to extend the opening of the ureteral orifice (UO) unless surgical conversion was necessary. Data collected included signalment, clinicopathologic data, imaging, procedural findings, complications, and short- and long-term outcome. RESULTS: Thirteen dogs with 13 ureteroceles associated with 14 UOs resulting in ureteral obstruction were included. One ureterocele extended bilaterally. Treatment was initiated via retrograde cystoscopy (7 females), percutaneous perineal urethrocystoscopy (4 males), or percutaneous antegrade cystoscopy (2 males). Surgical conversion was necessary in 2 males. Ten of 14 (71%) UOs associated with the ureteroceles were ectopic. Thirteen of 14 had stenotic or imperforate UOs. No postoperative complications were noted. Preoperative incontinence or pollakiuria was present in 9 of 13 and 3 of 13 dogs and resolved in 8 of 9 and 3 of 3 dogs, respectively. Follow-up imaging showed resolution of all ureteroceles and improved ureteral/renal pelvic dilatation. Median follow-up time was 27 months (range, 3-96 months). CONCLUSIONS AND CLINICAL IMPORTANCE: Cystoscopic-guided laser ablation was effective for the treatment of ureteroceles(s) in 11 of 13 dogs

Deep Neural Network for Robust Speech Recognition With Auxiliary Features From Laser-Doppler Vibrometer Sensor

Recently, the signal captured from a laser Doppler vibrometer (LDV) sensor been used to improve the noise robustness automatic speech recognition (ASR) systems by enhancing the acoustic signal prior to feature extraction. This study proposes another approach in which auxiliary features extracted from the LDV signal are used alongside conventional acoustic features to further improve ASR performance based on the use of a deep neural network (DNN) as the acoustic model. While this approach is promising, the best training data sets for ASR do not include LDV data in parallel with the acoustic signal. Thus, to leverage such existing large-scale speech databases, a regres- sion DNN is designed to map acoustic features to LDV features. This regression DNN is well trained from a limited size parallel signal data set, then used to form pseudo-LDV features from a massive speech data set for parallel training of an ASR system. Our experiments show that both the features from the limited scale LDV data set as well as the massive scale pseudo-LDV features are able to train an ASR system that significantly outperforms one using acoustic features alone, in both quiet and noisy environments

LID-senone Extraction via Deep Neural Networks for End-to-End Language Identification

A key problem in spoken language identification (LID) is how to effectively model features from a given speech utterance. Recent techniques such as end-to-end schemes and deep neural networks (DNNs) utilising transfer learning such as bottleneck (BN) features, have demonstrated good overall performance, but have not addressed the extraction of LID-specific features. We thus propose a novel end-to-end neural network which aims to obtain effective LID-senone representations, which we define as being analogous to senones in speech recognition. We show that LID-senones combine a compact representation of the original acoustic feature space with a powerful descriptive and discriminative capability. Furthermore, a novel incremental training method is proposed to extract the weak language information buried in the acoustic features of insufficient language resources. Results on the six most confused languages in NIST LRE 2009 show good performance compared to state-of-the-art BN-GMM/i-vector and BN-DNN/i-vector systems. The proposed end-to-end network, coupled with an incremental training method which mitigates against over-fitting, has potential not just for LID, but also for other resource constrained tasks

End-to-end DNN-CNN Classification for Language Identification

A defining problem in spoken language identification (LID) is how to design effective representations which allow features to be extracted that are specific to language information. Recent advances in deep neural networks for feature extraction have led to significant improvements in results, with deep end-to-end methods proving effective. In this paper, a novel network is proposed and explored that models an effective representation using first and second-order statistics of features extracted from a well-trained phoneme-related DNN bottleneck network followed by a stack of CNN convolutional layers. The high-order statistics extracted through second order pooling at the output of the CNN are robust to speaker and channel variability, and background noise. Evaluation with NIST LRE 2009 shows improved performance compared to current state-of-the-art systems, achieving over 33% and 20% relative equal error rate (EER) improvement for 3s and 10s utterances

LID-senones and their statistics for language identification

Recent research on end-to-end training structures for language identification has raised the possibility that intermediate language-sensitive feature units exist which are analogous to phonetically-sensitive senones in automatic speech recognition systems. Termed LID (language identification)-senones, the statistics derived from these feature units have been shown to be beneficial in discriminating between languages, particularly for short utterances. This paper examines the evidence for the existence of LID-senones before designing and evaluating LID systems based on low and high level statistics of LID-senones with both generative and discriminative models. For the standard NIST LRE 2009 task on 23 languages, LID-senone based systems are shown to outperform state-of-the art DNN/i-vector methods both when LID-senones are used directly for classification and when LID-senone statistics are used for i-vector formation

End-to-End Language Identification Using High-Order Utterance Representation with Bilinear Pooling

A key problem in spoken language identification (LID) is how to design effective representations which are specific to language information. Recent advances in deep neural networks have led to significant improvements in results, with deep end-to-end methods proving effective. This paper proposes a novel network which aims to model an effective representation for high (first and second)-order statistics of LID-senones, defined as being LID analogues of senones in speech recognition. The high-order information extracted through bilinear pooling is robust to speakers, channels and background noise. Evaluation with NIST LRE 2009 shows improved performance compared to current state-of-the-art DBF/i-vector systems, achieving over 33% and 20% relative equal error rate (EER) improvement for 3s and 10s utterances and over 40% relative Cavg improvement for all durations

Quantum finite-size effects for dyonic magnons in the AdS_4 x CP^3

We compute quantum corrections to finite-size effects for various dyonic giant magnons in the AdS_4 x CP^3 in two different approaches. The off-shell algebraic curve method is used to quantize the classical string configurations in semi-classical way and to compute the corrections to the string energies. These results are compared with the F-term L\"uscher formula based on the S-matrix of the AdS_4 / CFT_3. The fact that the two results match exactly provides another stringent test for the all-loop integrability conjecture and the exact S-matrix based on it.Comment: 21 pages, No figures, corrected typos, added some reference

Absence of long-range ordered reconstruction on the GaAs(311)A surface

We have investigated the decapped GaAs(311)A surface using both scanning tunneling microscopy and synchrotron-radiation photoemission. While our data are in broad agreement with the structural model of GaAs(311)A proposed in a recent study [Wassermeier et al., Phys. Rev. B 51, 14 721 (1995)], we find considerable differences in the surface order. In particular, the As dimer rows are unbroken over much shorter length scales and are highly kinked. We observe a correspondingly lower degree of anisotropy in the surface roughness than that previously reported. An (8×1) reconstruction was not observed. An analysis of As 3d and Ga 3d core-level photoemission spectra suggests that surface As atoms are in only one bonding configuration while surface Ga adopts two different bonding states. We discuss possible origins for the core-level spectra surface components

On the worldsheet theory of the type IIA AdS(4) x CP(3) superstring

We perform a detailed study of the type IIA superstring in AdS(4) x CP(3). After introducing suitable bosonic light-cone and fermionic kappa worldsheet gauges we derive the pure boson and fermion SU(2|2) x U(1) covariant light-cone Hamiltonian up to quartic order in fields. As a first application of our derivation we calculate energy shifts for string configurations in a closed fermionic subsector and successfully match these with a set of light-cone Bethe equations. We then turn to investigate the mismatch between the degrees of freedom of scattering states and oscillatory string modes. Since only light string modes appear as fundamental Bethe roots in the scattering theory, the physical role of the remaining $4_F+4_B$ massive oscillators is rather unclear. By continuing a line of research initiated by Zarembo, we shed light on this question by calculating quantum corrections for the propagators of the bosonic massive fields. We show that, once loop corrections are incorporated, the massive coordinates dissolve in a continuum state of two light particles.Comment: 40 pages, 2 figures. v3: Minor clarifications made and reference list updated. Published version

Inspiratory muscle training reduces blood lactate concentration during volitional hyperpnoea

Although reduced blood lactate concentrations ([lac−]B) have been observed during whole-body exercise following inspiratory muscle training (IMT), it remains unknown whether the inspiratory muscles are the source of at least part of this reduction. To investigate this, we tested the hypothesis that IMT would attenuate the increase in [lac−]B caused by mimicking, at rest, the breathing pattern observed during high-intensity exercise. Twenty-two physically active males were matched for 85% maximal exercise minute ventilation (V˙Emax) and divided equally into an IMT or a control group. Prior to and following a 6 week intervention, participants performed 10 min of volitional hyperpnoea at the breathing pattern commensurate with 85% V˙Emax