Recovery of spatially varying acoustical properties via automated partial differential equation identification.

Observable dynamics, such as waves propagating on a surface, are generally governed by partial differential equations (PDEs), which are determined by the physical properties of the propagation media. The spatial variations of these properties lead to spatially dependent PDEs. It is useful in many fields to recover the variations from the observations of dynamical behaviors on the material. A method is proposed to form a map of the physical properties' spatial variations for a material via data-driven spatially dependent PDE identification and applied to recover acoustical properties (viscosity, attenuation, and phase speeds) for propagating waves. The proposed data-driven PDE identification scheme is based on ℓ1-norm minimization. It does not require any PDE term that is assumed active from the prior knowledge and is the first approach that is capable of identifying spatially dependent PDEs from measurements of phenomena. In addition, the method is efficient as a result of its non-iterative nature and can be robust against noise if used with an integration transformation technique. It is demonstrated in multiple experimental settings, including real laser measurements of a vibrating aluminum plate. Codes and data are available online at https://tinyurl.com/4wza8vxs.

Blind source separation by long-term monitoring: A variational autoencoder to validate the clustering analysis.

Noise exposure influences the comfort and well-being of people in several contexts, such as work or learning environments. For instance, in offices, different kind of noises can increase or drop the employees' productivity. Thus, the ability of separating sound sources in real contexts plays a key role in assessing sound environments. Long-term monitoring provide large amounts of data that can be analyzed through machine and deep learning algorithms. Based on previous works, an entire working day was recorded through a sound level meter. Both sound pressure levels and the digital audio recording were collected. Then, a dual clustering analysis was carried out to separate the two main sound sources experienced by workers: traffic and speech noises. The first method exploited the occurrences of sound pressure levels via Gaussian mixture model and K-means clustering. The second analysis performed a semi-supervised deep clustering analyzing the latent space of a variational autoencoder. Results show that both approaches were able to separate the sound sources. Spectral matching and the latent space of the variational autoencoder validated the assumptions underlying the proposed clustering methods.

Sound source localization based on multi-task learning and image translation network.

Supervised learning-based sound source localization (SSL) methods have been shown to achieve a promising localization accuracy in the past. In this paper, MTIT, SSL for indoors using Multi-Task learning and Image Translation network, an image translation-based deep neural networks (DNNs) framework for SSL is presented to predict the locations of sound sources with random positions in a continuous space. We extract and represent the spatial features of the sound signals as beam response at each direction which can indicate the chance of the source in each point of the room. We utilize the multi-task learning (MTL) based training framework. There are one encoder and two decoders in our DNN. The encoder aims to obtain a compressed representation of the input beamspectrum surfaces while the two decoders focus on two tasks in parallel. One decoder focuses on resolving the multipath caused by reverberation and the other decoder predicts the source location. Since these two decoders share the same encoder, by training these two decoders in parallel, the shared representations are refined. We comprehensively evaluate the localization performance of our method in the simulated data, measured impulse response and real recordings datasets and compare it with multiple signal classification, steered response power with phase transform, and a competing convolutional neural network approach. It turns out that MTIT can outperform all of the baseline methods in a dynamic environment and also can achieve a good generalization performance.

Automated partial differential equation identification.

Inspired by recent developments in data-driven methods for partial differential equation (PDE) estimation, we use sparse modeling techniques to automatically estimate PDEs from data. A dictionary consisting of hypothetical PDE terms is constructed using numerical differentiation. Given data, PDE terms are selected assuming a parsimonious representation, which is enforced using a sparsity constraint. Unlike previous PDE identification schemes, we make no assumptions about which PDE terms are responsible for a given field. The approach is demonstrated on synthetic and real video data, with physical phenomena governed by wave, Burgers, and Helmholtz equations. Codes are available at https://github.com/NoiseLab-RLiu/Automate-PDE-identification.

Ipsilateral Protection and Bailout for Large-Bore Access.

This manuscript describes the refinements of ipsilateral wire protection and bailout strategy for large-bore femoral access and especially transcatheter aortic valve replacement. This ipsilateral wire protection requires no additional expenses and can provide effective arteriotomy site protection without the need for contralateral femoral access, especially in cases when contralateral wiring and crossover are not feasible. Ipsilateral wiring can be done both as prophylactic protection and as bailout strategy. The exact steps required for ipsilateral protection and bailout are described. A comparison between ipsilateral wiring with conventional contralateral femoral and transradial wire protection is delineated.

"I think I could have designed it better, but I didn't think that it was my place": a critical review of home modification practices from the perspectives of health and of design.

Purpose: The research presented demonstrates the disharmony between end user goals and their consideration in service outcomes within ageing-in-place and asks "what can design offer health" within this domain.Methods: Data was collected using semi-structured interviews with various stakeholders within the context of ageing in place. All data are thematically analysed through a theoretical lens of control theory.Results: The results demonstrate a contrast between purported patient-centred care models, and a human-centred design model. This contrast in cultures causes a disconnect between the health practitioners and the end users, with a lack of clarity about the end user's intended engagement within the modification of their environment. Consequently, the goals of older adults are inadequately represented as typical home modification design processes often fail to support the reflection of goals in practice, in turn, restricting client engagement and control. Reviewing occupational therapy practices through the critical lens of control has highlighted opportunities for service improvements.Conclusion: The consideration of co-design methodologies within home modification design is a way to reinforce client engagement and provide better pathways for older adults to remain in control and raise acceptability of modification through a better-informed decision-making process.Implications for RehabilitationThe following points detail the implications of this research upon the rehabilitation practice and theory: Compliance with recommendations is deeply connected to a person's intrinsic sense of control within the clinical decision-making process.Co-design practices between practitioners and clients provide and novel pathway to achieve truly person-centred care and create better service experiences and clinical outcomes.The human-centred design methodology is highly applicable within clinical practice and provides an opportunity for clinicians to see and learn about their patients through a holistic lens centred around goals and motivations rather than physical impairments.The scoping of health literacy should be inclusive of all service artefacts and touchpoints that a client may encounter throughout the entire duration of experience, this includes design artefacts such as architectural drawings and other home modification designs.

Angioplasty Balloon Entrapped Fully Inflated and Detached Within the Left Main Coronary Artery.

Described is a complication of rescue left main coronary stenting due to threatened left main closure following a "valve in valve" TAVR procedure. Left main stent post dilatation with a NC Trek balloon was complicated by a failure to deflate the balloon. The patient spiraled into cardiogenic shock and was stabilized by an intra-aortic balloon pump. Attempts to withdraw the balloon resulted in shearing of the balloon off the shaft. Parallel wiring and sequential balloon dilatations allowed left main bifurcation stenting while crushing the inflated balloon underneath the stent's struts. Follow up of 13 months was uneventful. The management of entrapped inflated coronary balloon is reviewed.

How Should I Manage a Challenging Aortic Prosthesis Angle During Valve-in-Valve Implantation?

Valve-in-valve TAVR procedure stalled due to challenging aortic to prosthesis angle not allowing the advancement of the Evolut R into the failing 25 mm Magna prosthesis. Efforts to better align the TAVR device with the valve prosthesis by using conventional methods failed. Eventually, a buddy wire allowed advancement and successful deployment of the Evolut R 26 mm within the stenosed surgical valve. The role and methodology of buddy wire use in TAVR, valve-in-valve procedures (first report) and structural heart procedures is reviewed.

Machine learning in acoustics: Theory and applications.

Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science. We survey the recent advances and transformative potential of machine learning (ML), including deep learning, in the field of acoustics. ML is a broad family of techniques, which are often based in statistics, for automatically detecting and utilizing patterns in data. Relative to conventional acoustics and signal processing, ML is data-driven. Given sufficient training data, ML can discover complex relationships between features and desired labels or actions, or between features themselves. With large volumes of training data, ML can discover models describing complex acoustic phenomena such as human speech and reverberation. ML in acoustics is rapidly developing with compelling results and significant future promise. We first introduce ML, then highlight ML developments in four acoustics research areas: source localization in speech processing, source localization in ocean acoustics, bioacoustics, and environmental sounds in everyday scenes.

High-resolution seismic tomography of Long Beach, CA using machine learning.

We use a machine learning-based tomography method to obtain high-resolution subsurface geophysical structure in Long Beach, CA, from seismic noise recorded on a "large-N" array with 5204 geophones (~13.5 million travel times). This method, called locally sparse travel time tomography (LST) uses unsupervised machine learning to exploit the dense sampling obtained by ambient noise processing on large arrays. Dense sampling permits the LST method to learn directly from the data a dictionary of local, or small-scale, geophysical features. The features are the small scale patterns of Earth structure most relevant to the given tomographic imaging scenario. Using LST, we obtain a high-resolution 1 Hz Rayleigh wave phase speed map of Long Beach. Among the geophysical features shown in the map, the important Silverado aquifer is well isolated relative to previous surface wave tomography studies. Our results show promise for LST in obtaining detailed geophysical structure in travel time tomography studies.

Introduction to special issue on compressive sensing in acoustics.

Compressive sensing (CS) in acoustics has received significant attention in the last decade, and thus motivates this special issue. CS emerged from the signal processing and applied math community and has since generated compelling results in acoustics. This special issue primarily addresses the acoustics CS topics of compressive beamforming and holography. For a sound field observed on a sensor array, CS reconstructs the direction of arrival of multiple sources using a sparsity constraint. Similarly, in holography a sparsity constraint gives improved sound field reconstruction over conventional ℓ2-regularization. Other topics in this issue include sparse array configurations (as co-arrays) and sparse sensing in acoustic communication.

Dictionary learning of sound speed profiles.

To provide constraints on the inversion of ocean sound speed profiles (SSPs), SSPs are often modeled using empirical orthogonal functions (EOFs). However, this regularization, which uses the leading order EOFs with a minimum-energy constraint on the coefficients, often yields low resolution SSP estimates. In this paper, it is shown that dictionary learning, a form of unsupervised machine learning, can improve SSP resolution by generating a dictionary of shape functions for sparse processing (e.g., compressive sensing) that optimally compress SSPs; both minimizing the reconstruction error and the number of coefficients. These learned dictionaries (LDs) are not constrained to be orthogonal and thus, fit the given signals such that each signal example is approximated using few LD entries. Here, LDs describing SSP observations from the HF-97 experiment and the South China Sea are generated using the K-SVD algorithm. These LDs better explain SSP variability and require fewer coefficients than EOFs, describing much of the variability with one coefficient. Thus, LDs improve the resolution of SSP estimates with negligible computational burden.

Compressive acoustic sound speed profile estimation.

Ocean acoustic sound speed profile (SSP) estimation requires the inversion of acoustic fields using limited observations. Compressive sensing (CS) asserts that certain underdetermined problems can be solved in high resolution, provided their solutions are sparse. Here, CS is used to estimate SSPs in a range-independent shallow ocean by inverting a non-linear acoustic propagation model. It is shown that SSPs can be estimated using CS to resolve fine-scale structure.

Two autoimmune diabetes loci influencing T cell apoptosis control susceptibility to experimental autoimmune myocarditis.

The pathogenesis of immune-mediated myocarditis depends on genetic and environmental factors. To study the genetic mechanisms, we have developed a model of experimental autoimmune myocarditis in the A.SW mouse. Here we provide evidence that loci on murine chromosome 6, and possibly chromosome 1, are involved in regulating susceptibility. Moreover, these loci overlap with loci implicated in other autoimmune diseases including diabetes in the NOD mouse. These two loci also regulate apoptosis in thymocytes as well as peripheral T cells in the NOD mouse, and we report further that A.SW mice demonstrate the same characteristics in apoptosis. These results suggest that common pathogenetic mechanisms involving apoptosis of both thymic and peripheral T cells are shared by multiple autoimmune diseases.