Self-Adaptive resource allocation for event monitoring with uncertainty in Sensor Networks

Event monitoring is an important application of sensor networks. Multiple parties, with different surveillance targets, can share the same network, with limited sensing resources, to monitor their events of interest simultaneously. Such a system achieves profit by allocating sensing resources to missions to collect event related information (e.g., videos, photos, electromagnetic signals). We address the problem of dynamically assigning resources to missions so as to achieve maximum profit with uncertainty in event occurrence. We consider timevarying resource demands and profits, and multiple concurrent surveillance missions. We model each mission as a sequence of monitoring attempts, each being allocated with a certain amount of resources, on a specific set of events that occurs as a Markov process. We propose a Self-Adaptive Resource Allocation algorithm (SARA) to adaptively and efficiently allocate resources according to the results of previous observations. By means of simulations we compare SARA to previous solutions and show SARA’s potential in finding higher profit in both static and dynamic scenarios

Spontaneous Isotropy Breaking: A Mechanism for CMB Multipole Alignments

We introduce a class of models in which statistical isotropy is broken spontaneously in the CMB by a non-linear response to long-wavelength fluctuations in a mediating field. These fluctuations appear as a gradient locally and pick out a single preferred direction. The non-linear response imprints this direction in a range of multipole moments. We consider two manifestations of isotropy breaking: additive contributions and multiplicative modulation of the intrinsic anisotropy. Since WMAP exhibits an alignment of power deficits, an additive contribution is less likely to produce the observed alignments than the usual isotropic fluctuations, a fact which we illustrate with an explicit cosmological model of long-wavelength quintessence fluctuations. This problem applies to other models involving foregrounds or background anisotropy that seek to restore power to the CMB. Additive models that account directly for the observed power exacerbate the low power of the intrinsic fluctuations. Multiplicative models can overcome these difficulties. We construct a proof of principle model that significantly improves the likelihood and generates stronger alignments than WMAP in 30-45% of realizations.Comment: 13 pages, 10 figure

A Simple Method to Estimate the Time-dependent ROC Curve Under Right Censoring

The time-dependent Receiver Operating Characteristic (ROC) curve is often used to study the diagnostic accuracy of a single continuous biomarker, measured at baseline, on the onset of a disease condition when the disease onset may occur at different times during the follow-up and hence may be right censored. Due to censoring, the true disease onset status prior to the pre-specified time horizon may be unknown on some patients, which causes difficulty in calculating the time-dependent sensitivity and specificity. We study a simple method that adjusts for censoring by weighting the censored data by the conditional probability of disease onset prior to the time horizon given the biomarker and the observed censoring time. Our numerical study shows that the proposed method produces unbiased and efficient estimators of time-dependent sensitivity and specificity as well as area under the ROC curve, and outperforms several other published methods currently implemented in R packages

Compensation of Beer-Lambert attenuation using non-diffracting Bessel beams

We report on a versatile method to compensate the linear attenuation in a medium, independently of its microscopic origin. The method exploits diffraction-limited Bessel beams and tailored on-axis intensity profiles which are generated using a phase-only spatial light modulator. This technique for compensating one of the most fundamental limiting processes in linear optics is shown to be efficient for a wide range of experimental conditions (modifying the refractive index and the attenuation coefficient). Finally, we explain how this method can be advantageously exploited in applications ranging from bio-imaging light sheet microscopy to quantum memories for future quantum communication networks

An Unsupervised Approach to Ultrasound Elastography with End-to-end Strain Regularisation

Quasi-static ultrasound elastography (USE) is an imaging modality that consists of determining a measure of deformation (i.e.strain) of soft tissue in response to an applied mechanical force. The strain is generally determined by estimating the displacement between successive ultrasound frames acquired before and after applying manual compression. The computational efficiency and accuracy of the displacement prediction, also known as time-delay estimation, are key challenges for real-time USE applications. In this paper, we present a novel deep-learning method for efficient time-delay estimation between ultrasound radio-frequency (RF) data. The proposed method consists of a convolutional neural network (CNN) that predicts a displacement field between a pair of pre- and post-compression ultrasound RF frames. The network is trained in an unsupervised way, by optimizing a similarity metric be-tween the reference and compressed image. We also introduce a new regularization term that preserves displacement continuity by directly optimizing the strain smoothness. We validated the performance of our method by using both ultrasound simulation and in vivo data on healthy volunteers. We also compared the performance of our method with a state-of-the-art method called OVERWIND [17]. Average contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of our method in 30 simulation and 3 in vivo image pairs are 7.70 and 6.95, 7 and 0.31, respectively. Our results suggest that our approach can effectively predict accurate strain images. The unsupervised aspect of our approach represents a great potential for the use of deep learning application for the analysis of clinical ultrasound data.Comment: Accepted at MICCAI 202

NiftyNet: a deep-learning platform for medical imaging

Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this application requires substantial implementation effort. Thus, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D and 3D images and computational graphs by default. We present 3 illustrative medical image analysis applications built using NiftyNet: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. NiftyNet enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.Comment: Wenqi Li and Eli Gibson contributed equally to this work. M. Jorge Cardoso and Tom Vercauteren contributed equally to this work. 26 pages, 6 figures; Update includes additional applications, updated author list and formatting for journal submissio

Intraoperative Organ Motion Models with an Ensemble of Conditional Generative Adversarial Networks

In this paper, we describe how a patient-specific, ultrasound-probe-induced prostate motion model can be directly generated from a single preoperative MR image. Our motion model allows for sampling from the conditional distribution of dense displacement fields, is encoded by a generative neural network conditioned on a medical image, and accepts random noise as additional input. The generative network is trained by a minimax optimisation with a second discriminative neural network, tasked to distinguish generated samples from training motion data. In this work, we propose that 1) jointly optimising a third conditioning neural network that pre-processes the input image, can effectively extract patient-specific features for conditioning; and 2) combining multiple generative models trained separately with heuristically pre-disjointed training data sets can adequately mitigate the problem of mode collapse. Trained with diagnostic T2-weighted MR images from 143 real patients and 73,216 3D dense displacement fields from finite element simulations of intraoperative prostate motion due to transrectal ultrasound probe pressure, the proposed models produced physically-plausible patient-specific motion of prostate glands. The ability to capture biomechanically simulated motion was evaluated using two errors representing generalisability and specificity of the model. The median values, calculated from a 10-fold cross-validation, were 2.8+/-0.3 mm and 1.7+/-0.1 mm, respectively. We conclude that the introduced approach demonstrates the feasibility of applying state-of-the-art machine learning algorithms to generate organ motion models from patient images, and shows significant promise for future research.Comment: Accepted to MICCAI 201

Reflex syncope in the setting of deglutition: A case report

Background: Deglutition syncope was first identified by Thomas Spens in 1793 as a rare form of neurally-mediated transient loss of consciousness secondary to an atypical vasovagal reflex during swallow-induced esophageal dilation. Due to the lack of validated diagnosing criteria, detailed history is imperative to guide timely evaluation and management.Case: A 58-year-old male with a past medical history of bipolar disorder, PTSD, anxiety, cocaine and methamphetamine use presented after a syncopal episode. He reported multiple syncopal episodes over the past five years associated with swallowing. During the most recent incident, the patient reported consuming food when he started feeling fullness in his throat, associated with lightheadedness and diaphoresis prior to a syncope episode. Patient's ED course was unremarkable with stable vitals without orthostasis, negative troponin, chest radiography and ECG. Patient was admitted for further syncope work up.Decision-making: Modified barium swallow study and CT chest were performed to evaluate dysphagia and anatomical abnormality, which revealed mild esophageal reflux and normal anatomy respectively. The patient was allowed to eat. He was placed on continuous telemetry. Patient had several witnessed syncopal events while eating. Corresponding telemetry strips demonstrated bradycardia with a low of 10 bpm along with a 3 second pause.Echocardiogram revealed EF of 35-40% with no significant structural abnormalities. Left heart catheterization revealed normal coronaries. Given his symptomatic bradycardia associated with swallowing, a permanent pacemaker device was suggested as the definitive treatment. Patient requested time to consider the decision, but ultimately decided to leave the hospital against medical advice.Conclusion: This case demonstrates the broad differential for syncope and lack of validated diagnostic criteria for situational syncope. We have excluded esophageal and structural pathology. Patient's symptoms were corresponding to telemetry findings. His psychiatric illness and substance dependence made management challenging, but detailed history and high clinical suspicion guided our diagnosis