Deep learning for real-time single-pixel video

Single-pixel cameras capture images without the requirement for a multi-pixel sensor, enabling the use of state-of-the-art detector technologies and providing a potentially low-cost solution for sensing beyond the visible spectrum. One limitation of single-pixel cameras is the inherent trade-off between image resolution and frame rate, with current compressive (compressed) sensing techniques being unable to support real-time video. In this work we demonstrate the application of deep learning with convolutional auto-encoder networks to recover real-time 128 × 128 pixel video at 30 frames-per-second from a single-pixel camera sampling at a compression ratio of 2%. In addition, by training the network on a large database of images we are able to optimise the first layer of the convolutional network, equivalent to optimising the basis used for scanning the image intensities. This work develops and implements a novel approach to solving the inverse problem for single-pixel cameras efficiently and represents a significant step towards real-time operation of computational imagers. By learning from examples in a particular context, our approach opens up the possibility of high resolution for task-specific adaptation, with importance for applications in gas sensing, 3D imaging and metrology

The impact of numerical viscosity in SPH simulations of galaxy clusters

A SPH code employing a time-dependent artificial viscosity scheme is used to construct a large set of N-body/SPH cluster simulations for studying the impact of artificial viscosity on the thermodynamics of the ICM and its velocity field statistical properties. Spectral properties of the gas velocity field are investigated by measuring for the simulated clusters the velocity power spectrum E(k). The longitudinal component E_c(k) exhibits over a limited range a Kolgomorov-like scaling k^{-5/3}, whilst the solenoidal power spectrum component E_s(k) is strongly influenced by numerical resolution effects. The dependence of the spectra E(k) on dissipative effects is found to be significant at length scales 100-300Kpc, with viscous damping of the velocities being less pronounced in those runs with the lowest artificial viscosity. The turbulent energy density radial profile E_{turb}(r) is strongly affected by the numerical viscosity scheme adopted in the simulations, with the turbulent-to-total energy density ratios being higher in the runs with the lowest artificial viscosity settings and lying in the range between a few percent and ~10%. These values are in accord with the corresponding ratios extracted from previous cluster simulations realized using mesh-based codes. At large cluster radii, the mass correction terms to the hydrostatic equilibrium equation are little affected by the numerical viscosity of the simulations, showing that the X-ray mass bias is already estimated well in standard SPH simulations. Finally, simulations in which the gas can cool radiatively are characterized by the presence in the cluster inner regions of high levels of turbulence, generated by the interaction of the compact cool gas core with the ambient medium.Comment: 32 pages, 22 figures, accepted for publication in A&

More is Less: Inducing Sparsity via Overparameterization

In deep learning it is common to overparameterize neural networks, that is, to use more parameters than training samples. Quite surprisingly training the neural network via (stochastic) gradient descent leads to models that generalize very well, while classical statistics would suggest overfitting. In order to gain understanding of this implicit bias phenomenon we study the special case of sparse recovery (compressed sensing) which is of interest on its own. More precisely, in order to reconstruct a vector from underdetermined linear measurements, we introduce a corresponding overparameterized square loss functional, where the vector to be reconstructed is deeply factorized into several vectors. We show that, if there exists an exact solution, vanilla gradient flow for the overparameterized loss functional converges to a good approximation of the solution of minimal $\ell_1$-norm. The latter is well-known to promote sparse solutions. As a by-product, our results significantly improve the sample complexity for compressed sensing via gradient flow/descent on overparameterized models derived in previous works. The theory accurately predicts the recovery rate in numerical experiments. Our proof relies on analyzing a certain Bregman divergence of the flow. This bypasses the obstacles caused by non-convexity and should be of independent interest

Recovery practices in Division 1 collegiate athletes in North America

© 2018 Elsevier Ltd Objectives: Establish current practice and attitudes towards recovery in a group of Division-1 Collegiate athletes from North America. Design: A 16-item questionnaire was administered via custom software in an electronic format. Participants: 152 student athletes from a Division-1 Collegiate school across 3 sports (Basketball, American Football, Soccer). Main outcome measures: The approaches and attitudes to recovery in both training and competition. Results: Sleep, cold water immersion (CWI) and nutrition were perceived to be the most effective modalities (88, 84 and 80% of the sample believed them to have a benefit respectively). Over half the sample did not believe in using compression for recovery. With regard to actual usage, CWI was the most used recovery modality and matched by athletes believing in, and using, the approach (65%). Only 24% of student athletes believed in, and used, sleep as a recovery modality despite it being rated and perceived as the most effective. Conclusions: Collectively, there is a discrepancy between perception and use of recovery modalities in Collegiate athletes

A neurobiological and computational analysis of target discrimination in visual clutter by the insect visual system.

Some insects have the capability to detect and track small moving objects, often against cluttered moving backgrounds. Determining how this task is performed is an intriguing challenge, both from a physiological and computational perspective. Previous research has characterized higher-order neurons within the fly brain known as 'small target motion detectors‘ (STMD) that respond selectively to targets, even within complex moving surrounds. Interestingly, these cells still respond robustly when the velocity of the target is matched to the velocity of the background (i.e. with no relative motion cues). We performed intracellular recordings from intermediate-order neurons in the fly visual system (the medulla). These full-wave rectifying, transient cells (RTC) reveal independent adaptation to luminance changes of opposite signs (suggesting separate 'on‘ and 'off‘ channels) and fast adaptive temporal mechanisms (as seen in some previously described cell types). We show, via electrophysiological experiments, that the RTC is temporally responsive to rapidly changing stimuli and is well suited to serving an important function in a proposed target-detecting pathway. To model this target discrimination, we use high dynamic range (HDR) natural images to represent 'real-world‘ luminance values that serve as inputs to a biomimetic representation of photoreceptor processing. Adaptive spatiotemporal high-pass filtering (1st-order interneurons) shapes the transient 'edge-like‘ responses, useful for feature discrimination. Following this, a model for the RTC implements a nonlinear facilitation between the rapidly adapting, and independent polarity contrast channels, each with centre-surround antagonism. The recombination of the channels results in increased discrimination of small targets, of approximately the size of a single pixel, without the need for relative motion cues. This method of feature discrimination contrasts with traditional target and background motion-field computations. We show that our RTC-based target detection model is well matched to properties described for the higher-order STMD neurons, such as contrast sensitivity, height tuning and velocity tuning. The model output shows that the spatiotemporal profile of small targets is sufficiently rare within natural scene imagery to allow our highly nonlinear 'matched filter‘ to successfully detect many targets from the background. The model produces robust target discrimination across a biologically plausible range of target sizes and a range of velocities. We show that the model for small target motion detection is highly correlated to the velocity of the stimulus but not other background statistics, such as local brightness or local contrast, which normally influence target detection tasks. From an engineering perspective, we examine model elaborations for improved target discrimination via inhibitory interactions from correlation-type motion detectors, using a form of antagonism between our feature correlator and the more typical motion correlator. We also observe that a changing optimal threshold is highly correlated to the value of observer ego-motion. We present an elaborated target detection model that allows for implementation of a static optimal threshold, by scaling the target discrimination mechanism with a model-derived velocity estimation of ego-motion. Finally, we investigate the physiological relevance of this target discrimination model. We show that via very subtle image manipulation of the visual stimulus, our model accurately predicts dramatic changes in observed electrophysiological responses from STMD neurons.Thesis (Ph.D.) - University of Adelaide, School of Molecular and Biomedical Science, 200

Biophysical modeling of a cochlear implant system: progress on closed-loop design using a novel patient-specific evaluation platform

The modern cochlear implant is one of the most successful neural stimulation devices, which partially mimics the workings of the auditory periphery. In the last few decades it has created a paradigm shift in hearing restoration of the deaf population, which has led to more than 324,000 cochlear implant users today. Despite its great success there is great disparity in patient outcomes without clear understanding of the aetiology of this variance in implant performance. Furthermore speech recognition in adverse conditions or music appreciation is still not attainable with today's commercial technology. This motivates the research for the next generation of cochlear implants that takes advantage of recent developments in electronics, neuroscience, nanotechnology, micro-mechanics, polymer chemistry and molecular biology to deliver high fidelity sound. The main difficulties in determining the root of the problem in the cases where the cochlear implant does not perform well are two fold: first there is not a clear paradigm on how the electrical stimulation is perceived as sound by the brain, and second there is limited understanding on the plasticity effects, or learning, of the brain in response to electrical stimulation. These significant knowledge limitations impede the design of novel cochlear implant technologies, as the technical specifications that can lead to better performing implants remain undefined. The motivation of the work presented in this thesis is to compare and contrast the cochlear implant neural stimulation with the operation of the physiological healthy auditory periphery up to the level of the auditory nerve. As such design of novel cochlear implant systems can become feasible by gaining insight on the question `how well does a specific cochlear implant system approximate the healthy auditory periphery?' circumventing the necessity of complete understanding of the brain's comprehension of patterned electrical stimulation delivered from a generic cochlear implant device. A computational model, termed Digital Cochlea Stimulation and Evaluation Tool (‘DiCoStET’) has been developed to provide an objective estimate of cochlear implant performance based on neuronal activation measures, such as vector strength and average activation. A patient-specific cochlea 3D geometry is generated using a model derived by a single anatomical measurement from a patient, using non-invasive high resolution computed tomography (HRCT), and anatomically invariant human metrics and relations. Human measurements of the neuron route within the inner ear enable an innervation pattern to be modelled which joins the space from the organ of Corti to the spiral ganglion subsequently descending into the auditory nerve bundle. An electrode is inserted in the cochlea at a depth that is determined by the user of the tool. The geometric relation between the stimulation sites on the electrode and the spiral ganglion are used to estimate an activating function that will be unique for the specific patient's cochlear shape and electrode placement. This `transfer function', so to speak, between electrode and spiral ganglion serves as a `digital patient' for validating novel cochlear implant systems. The novel computational tool is intended for use by bioengineers, surgeons, audiologists and neuroscientists alike. In addition to ‘DiCoStET’ a second computational model is presented in this thesis aiming at enhancing the understanding of the physiological mechanisms of hearing, specifically the workings of the auditory synapse. The purpose of this model is to provide insight on the sound encoding mechanisms of the synapse. A hypothetical mechanism is suggested in the release of neurotransmitter vesicles that permits the auditory synapse to encode temporal patterns of sound separately from sound intensity. DiCoStET was used to examine the performance of two different types of filters used for spectral analysis in the cochlear implant system, the Gammatone type filter and the Butterworth type filter. The model outputs suggest that the Gammatone type filter performs better than the Butterworth type filter. Furthermore two stimulation strategies, the Continuous Interleaved Stimulation (CIS) and Asynchronous Interleaved Stimulation (AIS) have been compared. The estimated neuronal stimulation spatiotemporal patterns for each strategy suggest that the overall stimulation pattern is not greatly affected by the temporal sequence change. However the finer detail of neuronal activation is different between the two strategies, and when compared to healthy neuronal activation patterns the conjecture is made that the sequential stimulation of CIS hinders the transmission of sound fine structure information to the brain. The effect of the two models developed is the feasibility of collaborative work emanating from various disciplines; especially electrical engineering, auditory physiology and neuroscience for the development of novel cochlear implant systems. This is achieved by using the concept of a `digital patient' whose artificial neuronal activation is compared to a healthy scenario in a computationally efficient manner to allow practical simulation times.Open Acces

Sparse and Redundant Representations for Inverse Problems and Recognition

Sparse and redundant representation of data enables the description of signals as linear combinations of a few atoms from a dictionary. In this dissertation, we study applications of sparse and redundant representations in inverse problems and object recognition. Furthermore, we propose two novel imaging modalities based on the recently introduced theory of Compressed Sensing (CS). This dissertation consists of four major parts. In the first part of the dissertation, we study a new type of deconvolution algorithm that is based on estimating the image from a shearlet decomposition. Shearlets provide a multi-directional and multi-scale decomposition that has been mathematically shown to represent distributed discontinuities such as edges better than traditional wavelets. We develop a deconvolution algorithm that allows for the approximation inversion operator to be controlled on a multi-scale and multi-directional basis. Furthermore, we develop a method for the automatic determination of the threshold values for the noise shrinkage for each scale and direction without explicit knowledge of the noise variance using a generalized cross validation method. In the second part of the dissertation, we study a reconstruction method that recovers highly undersampled images assumed to have a sparse representation in a gradient domain by using partial measurement samples that are collected in the Fourier domain. Our method makes use of a robust generalized Poisson solver that greatly aids in achieving a significantly improved performance over similar proposed methods. We will demonstrate by experiments that this new technique is more flexible to work with either random or restricted sampling scenarios better than its competitors. In the third part of the dissertation, we introduce a novel Synthetic Aperture Radar (SAR) imaging modality which can provide a high resolution map of the spatial distribution of targets and terrain using a significantly reduced number of needed transmitted and/or received electromagnetic waveforms. We demonstrate that this new imaging scheme, requires no new hardware components and allows the aperture to be compressed. Also, it presents many new applications and advantages which include strong resistance to countermesasures and interception, imaging much wider swaths and reduced on-board storage requirements. The last part of the dissertation deals with object recognition based on learning dictionaries for simultaneous sparse signal approximations and feature extraction. A dictionary is learned for each object class based on given training examples which minimize the representation error with a sparseness constraint. A novel test image is then projected onto the span of the atoms in each learned dictionary. The residual vectors along with the coefficients are then used for recognition. Applications to illumination robust face recognition and automatic target recognition are presented

Genome-wide gene expression profiling of stress response in a spinal cord clip compression injury model.

BackgroundThe aneurysm clip impact-compression model of spinal cord injury (SCI) is a standard injury model in animals that closely mimics the primary mechanism of most human injuries: acute impact and persisting compression. Its histo-pathological and behavioural outcomes are extensively similar to human SCI. To understand the distinct molecular events underlying this injury model we analyzed global mRNA abundance changes during the acute, subacute and chronic stages of a moderate to severe injury to the rat spinal cord.ResultsTime-series expression analyses resulted in clustering of the majority of deregulated transcripts into eight statistically significant expression profiles. Systematic application of Gene Ontology (GO) enrichment pathway analysis allowed inference of biological processes participating in SCI pathology. Temporal analysis identified events specific to and common between acute, subacute and chronic time-points. Processes common to all phases of injury include blood coagulation, cellular extravasation, leukocyte cell-cell adhesion, the integrin-mediated signaling pathway, cytokine production and secretion, neutrophil chemotaxis, phagocytosis, response to hypoxia and reactive oxygen species, angiogenesis, apoptosis, inflammatory processes and ossification. Importantly, various elements of adaptive and induced innate immune responses span, not only the acute and subacute phases, but also persist throughout the chronic phase of SCI. Induced innate responses, such as Toll-like receptor signaling, are more active during the acute phase but persist throughout the chronic phase. However, adaptive immune response processes such as B and T cell activation, proliferation, and migration, T cell differentiation, B and T cell receptor-mediated signaling, and B cell- and immunoglobulin-mediated immune response become more significant during the chronic phase.ConclusionsThis analysis showed that, surprisingly, the diverse series of molecular events that occur in the acute and subacute stages persist into the chronic stage of SCI. The strong agreement between our results and previous findings suggest that our analytical approach will be useful in revealing other biological processes and genes contributing to SCI pathology

Modeling Complex Biological and Mechanical Movements: Applications to Animal Locomotion and Gesture Classification in Robotic Surgery

Mutual interaction between biology and robots can significantly benefit both fields. The richness and diversity in animal locomotion and movement provides an extensive resource for inspiration in engineering design of robots. On the other hand, bio-mimetic and bio-inspired robots play a critical role in testing hypotheses in biology and neuromechanics. Modeling complex biological and mechanical movements is at the core of this mutual interaction. Models and analytical tools are required for decoding and analysis of behavior in biological and mechanical systems, both at low level (sensory systems and control) and high level (activity recognition). This dissertation is focused on modeling approaches for biological and mechanical movements. We first primarily focus on physics-based template modeling to answer a long-standing question in animal locomotion: why do animals often produce substantial forces in directions that do not directly contribute to movement? We examine the weakly electric knifefish, a well-suited model system to investigate the relationship between mutually opposing forces and locomotor control. We use slow-motion videography to study the ribbon-fin motion and develop a physics-based template model at the task-level for tracking behavior. Using the developed physics-based model integrated with experiments with a biomimetic robot, we demonstrate that the production and differential control of mutually opposing forces is a strategy that generates passive stabilization while simultaneously enhancing maneuverability, thereby simplifies neural control. The second part of this work aims to propose a more general data-driven system-theoretic framework for decoding complex behaviors. Specifically we introduce a new class of linear time-invariant dynamical systems with sparse inputs (LDS-SI). In the proposed framework, at each time instant, the input to the system is sparse with respect to a dictionary of inputs. In the context of complex behaviors, the dictionary may represent the dictionary of inputs for all possible simple behaviors. We propose a convex optimization formulation for the state estimation with unknown inputs in LDS-SI. We derive sufficient conditions for the perfect joint recovery and explore the results with simulation. We demonstrate the power of the proposed framework in the analysis of complex gestures in robotic surgery. Results are better than state-of-the-art methods in joint segmentation and classification of surgical gestures in a dataset of suturing task trials performed by different surgeons

Osteochondral Tissue Engineering: The Potential of Electrospinning and Additive Manufacturing

The socioeconomic impact of osteochondral (OC) damage has been increasing steadily over time in the global population, and the promise of tissue engineering in generating biomimetic tissues replicating the physiological OC environment and architecture has been falling short of its projected potential. The most recent advances in OC tissue engineering are summarised in this work, with a focus on electrospun and 3D printed biomaterials combined with stem cells and biochemical stimuli, to identify what is causing this pitfall between the bench and the patients' bedside. Even though significant progress has been achieved in electrospinning, 3D-(bio)printing, and induced pluripotent stem cell (iPSC) technologies, it is still challenging to artificially emulate the OC interface and achieve complete regeneration of bone and cartilage tissues. Their intricate architecture and the need for tight spatiotemporal control of cellular and biochemical cues hinder the attainment of long-term functional integration of tissue-engineered constructs. Moreover, this complexity and the high variability in experimental conditions used in different studies undermine the scalability and reproducibility of prospective regenerative medicine solutions. It is clear that further development of standardised, integrative, and economically viable methods regarding scaffold production, cell selection, and additional biochemical and biomechanical stimulation is likely to be the key to accelerate the clinical translation and fill the gap in OC treatment