A Greedy Algorithm for Building Compact Binary Activated Neural Networks

We study binary activated neural networks in the context of regression tasks, provide guarantees on the expressiveness of these particular networks and propose a greedy algorithm for building such networks. Aiming for predictors having small resources needs, the greedy approach does not need to fix in advance an architecture for the network: this one is built one layer at a time, one neuron at a time, leading to predictors that aren't needlessly wide and deep for a given task. Similarly to boosting algorithms, our approach guarantees a training loss reduction every time a neuron is added to a layer. This greatly differs from most binary activated neural networks training schemes that rely on stochastic gradient descent (circumventing the 0-almost-everywhere derivative problem of the binary activation function by surrogates such as the straight through estimator or continuous binarization). We show that our method provides compact and sparse predictors while obtaining similar performances to state-of-the-art methods for training binary activated networks

A State Estimation Framework for Fatigue Monitoring and Prognosis of Minimally Instrumented Structural and Biomechanical Systems

Fatigue damage is the continuous degradation of a material, primarily due to the formation of microcracks and resulting from the repeated application of stress cycles. Traditionally a fatigue analysis was performed during the structural design stage of a machine or structure; however, more recently there has been increased interest in the monitoring and prognosis of fatigue damage in existing and operating structures. In monitoring, the structure already exists and its mechanical properties can be estimated by processing sensor measurements and non-destructive testing. The traditional approach to fatigue monitoring is to carry out a visual inspection, find macroscopic cracks and then predict their growth. This was often carried out by finding changes in dynamic properties of the system, i.e. changes in modal frequencies, mode shapes, and modal damping. Yet in many cases, by the time the cracks grow to a point where they are detectable, the load bearing capacity of the structure has been greatly reduced. Therefore, a preferable approach is to track fatigue damage on the whole structure prior to the appearance of macroscopic cracks. This would allow for higher levels of reliability, larger lead times and reduced risk. Although no exact figures are available, it is estimated that upwards of 50% of mechanical failures in metallic structures can be attributed to fatigue. Structural health monitoring has been extensively studied for structural systems but hasn\u27t been applied to biomechanical systems where biomechanical failure is consistent with the process of mechanical fatigue. The objective of this dissertation is to show that state estimation algorithms, i.e. the Kalman filter, can be successfully formulated to estimate fatigue damage in near-real time for structural and biomechanical systems. The Kalman filter combines dynamic response measurements at minimal spatial locations with a structural model to estimate the response of the dynamical system at all model degrees-of-freedom. The estimates of the dynamic response of the instrumented structural systems are subsequently used for fatigue damage diagnosis and prognosis in combination with an empirical S-N curve. By quantifying the uncertainty in both the state estimate and S-N curve, the fatigue damage index becomes bounded based on a user-defined allowable probability of failure. The main contributions of this dissertation are summarized as follows: i) Development of a fatigue monitoring framework for structural and biomechanical systems; ii) Experimental validation of service life fatigue monitoring in near-real time for statically determinant structures; iii) Uncertainty quantification and propagation of system response and fatigue damage estimates using Kalman filters

Microwave-to-optical conversion in a room-temperature 87^{87}Rb vapor with frequency-division multiplexing control

Coherent microwave-to-optical conversion is crucial for transferring quantum information generated in the microwave domain to optical frequencies, where propagation losses can be minimised. Among the various physical platforms that have realized coherent microwave-to-optical transduction, those that use atoms as transducers have shown rapid progress in recent years. In this paper we report an experimental demonstration of coherent microwave-to-optical conversion that maps a microwave signal to a large, tunable 550(30) MHz range of optical frequencies using room-temperature $^{87}$Rb atoms. The inhomogeneous Doppler broadening of the atomic vapor advantageously supports the tunability of an input microwave channel to any optical frequency channel within the Doppler width, along with simultaneous conversion of a multi-channel input microwave field to corresponding optical channels. In addition, we demonstrate phase-correlated amplitude control of select channels, resulting in complete extinction of one of the channels, providing an analog to a frequency domain beam splitter across five orders of magnitude in frequency. With frequency-division multiplexing capability, multi-channel conversion, and amplitude control of frequency channels, neutral atomic systems may be effective quantum processors for quantum information encoded in frequency-bin qubits

PAC-Bayesian Learning of Aggregated Binary Activated Neural Networks with Probabilities over Representations

Considering a probability distribution over parameters is known as an efficient strategy to learn a neural network with non-differentiable activation functions. We study the expectation of a probabilistic neural network as a predictor by itself, focusing on the aggregation of binary activated neural networks with normal distributions over real-valued weights. Our work leverages a recent analysis derived from the PAC-Bayesian framework that derives tight generalization bounds and learning procedures for the expected output value of such an aggregation, which is given by an analytical expression. While the combinatorial nature of the latter has been circumvented by approximations in previous works, we show that the exact computation remains tractable for deep but narrow neural networks, thanks to a dynamic programming approach. This leads us to a peculiar bound minimization learning algorithm for binary activated neural networks, where the forward pass propagates probabilities over representations instead of activation values. A stochastic counterpart that scales to wide architectures is proposed

Approximate rogue wave solutions of the forced and damped Nonlinear Schr\"odinger equation for water waves

We consider the effect of the wind and the dissipation on the nonlinear stages of the modulational instability. By applying a suitable transformation, we map the forced/damped Nonlinear Schr\"odinger (NLS) equation into the standard NLS with constant coefficients. The transformation is valid as long as |{\Gamma}t| \ll 1, with {\Gamma} the growth/damping rate of the waves due to the wind/dissipation. Approximate rogue wave solutions of the equation are presented and discussed. The results shed some lights on the effects of wind and dissipation on the formation of rogue waves.Comment: 10 pages, 3 figure

Determination of the standard deviation for proficiency assessment from past participant's performances

The "uncertainty function" introduced by Thompson et al. estimates the reproducibility standard deviation as a function of concentration or mass fraction. This model was successfully applied to data derived from three proficiency testing schemes aiming at the quantification of cadmium, lead and mercury in blood and urine. This model allows the estimation of standard deviation for the performance assessment for proficiency testing rounds

Three-Dimensional Folding and Functional Organization Principles of the Drosophila Genome

SummaryChromosomes are the physical realization of genetic information and thus form the basis for its readout and propagation. Here we present a high-resolution chromosomal contact map derived from a modified genome-wide chromosome conformation capture approach applied to Drosophila embryonic nuclei. The data show that the entire genome is linearly partitioned into well-demarcated physical domains that overlap extensively with active and repressive epigenetic marks. Chromosomal contacts are hierarchically organized between domains. Global modeling of contact density and clustering of domains show that inactive domains are condensed and confined to their chromosomal territories, whereas active domains reach out of the territory to form remote intra- and interchromosomal contacts. Moreover, we systematically identify specific long-range intrachromosomal contacts between Polycomb-repressed domains. Together, these observations allow for quantitative prediction of the Drosophila chromosomal contact map, laying the foundation for detailed studies of chromosome structure and function in a genetically tractable system

Evaluating the effectiveness of lockdowns and restrictions during SARS-CoV-2 variant waves in the Canadian province of Nova Scotia

IntroductionAfter the initial onset of the SARS-CoV-2 pandemic, the government of Canada and provincial health authorities imposed restrictive policies to limit virus transmission and mitigate disease burden. In this study, the pandemic implications in the Canadian province of Nova Scotia (NS) were evaluated as a function of the movement of people and governmental restrictions during successive SARS-CoV-2 variant waves (i.e., Alpha through Omicron).MethodsPublicly available data obtained from community mobility reports (Google), the Bank of Canada Stringency Index, the “COVID-19 Tracker” service, including cases, hospitalizations, deaths, and vaccines, population mobility trends, and governmental response data were used to relate the effectiveness of policies in controlling movement and containing multiple waves of SARS-CoV-2.ResultsOur results indicate that the SARS-CoV-2 pandemic inflicted low burden in NS in the initial 2 years of the pandemic. In this period, we identified reduced mobility patterns in the population. We also observed a negative correlation between public transport (−0.78), workplace (−0.69), retail and recreation (−0.68) and governmental restrictions, indicating a tight governmental control of these movement patterns. During the initial 2 years, governmental restrictions were high and the movement of people low, characterizing a ‘seek-and-destroy’ approach. Following this phase, the highly transmissible Omicron (B.1.1.529) variant began circulating in NS at the end of the second year, leading to increased cases, hospitalizations, and deaths. During this Omicron period, unsustainable governmental restrictions and waning public adherence led to increased population mobility, despite increased transmissibility (26.41-fold increase) and lethality (9.62-fold increase) of the novel variant.DiscussionThese findings suggest that the low initial burden caused by the SARS-CoV-2 pandemic was likely a result of enhanced restrictions to contain the movement of people and consequently, the spread of the disease. Easing public health restrictions (as measured by a decline in the BOC index) during periods of high transmissibility of circulating COVID-19 variants contributed to community spread, despite high levels of immunization in NS

The impact of a physician-directed health information technology system on diabetes outcomes in primary care: a pre- and post-implementation study

Purpose To determine the impact of a physiciandirected, multifaceted health information technology (HIT) system on diabetes outcomes. Methods A pre/post-interventional study. Setting and participants The setting was Providence Primary Care Research Network in Oregon, with approximately 71 physicians caring for 117 369 patients in 13 clinic locations. The study covered Network patients with diabetes age 18 years and older. Intervention The study intervention included implementation of the CareManagerTM HIT system which augments an electronic medical record (EMR) by automating physician driven quality improvement interventions, including point-of-care decision support and care reminders, diabetes registry with care prompts, performance feedback with benchmarking and access to published evidence and patient educational materials. Measures The primary clinical measures included the change in mean value for low density lipoprotein (LDL) target <100 mg/dL or 2.6 mmol/l, blood pressure (BP) target <130/80 mmHg and glycated haemoglobin (HbA1c) target <7%, and the proportion of patients meeting guideline-recommended targets for those measures. All measures were analysed using closed and open cohort approaches. Results A total of 6072 patients were identified at baseline, 70% of whom were continuously enrolled during the 24-month study. Significant improvements were observed in all diabetes related outcomes except mean HbA1c. LDL goal attainment improved from 32% to 56% (P=0.002), while mean LDL decreased by 13 mg/dL (0.33 mmol/l, P=0.002). BP goal attainment increased significantly from 30% to 52%, with significant decreases in both mean systolic and diastolic BP. The proportion of patients with an HbA1c below 7% was higher at the end of the study (P=0.008). Mean patient satisfaction remained high, with no significant difference between baseline and follow-up. Total Relative Value Units per patient per year significantly increased as a result of an increase in the number of visits in year one and the coding complexity throughout. Conclusion Implementation of a physician-directed, multifaceted HIT system in primary care was associated with significantly improved diabetes process and outcome measures