Optimal sampling strategies for multiscale stochastic processes

In this paper, we determine which non-random sampling of fixed size gives the best linear predictor of the sum of a finite spatial population. We employ different multiscale superpopulation models and use the minimum mean-squared error as our optimality criterion. In multiscale superpopulation tree models, the leaves represent the units of the population, interior nodes represent partial sums of the population, and the root node represents the total sum of the population. We prove that the optimal sampling pattern varies dramatically with the correlation structure of the tree nodes. While uniform sampling is optimal for trees with ``positive correlation progression'', it provides the worst possible sampling with ``negative correlation progression.'' As an analysis tool, we introduce and study a class of independent innovations trees that are of interest in their own right. We derive a fast water-filling algorithm to determine the optimal sampling of the leaves to estimate the root of an independent innovations tree.Comment: Published at http://dx.doi.org/10.1214/074921706000000509 in the IMS Lecture Notes--Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

Almost Budget Balanced Mechanisms with Scalar Bids For Allocation of a Divisible Good

This paper is about allocation of an infinitely divisible good to several rational and strategic agents. The allocation is done by a social planner who has limited information because the agents' valuation functions are taken to be private information known only to the respective agents. We allow only a scalar signal, called a bid, from each agent to the social planner. Yang and Hajek [Jour. on Selected Areas in Comm., 2007] as well as Johari and Tsitsiklis [Jour. of Oper. Res., 2009] proposed a scalar strategy Vickrey-Clarke-Groves (SSVCG) mechanism with efficient Nash equilibria. We consider a setting where the social planner desires minimal budget surplus. Example situations include fair sharing of Internet resources and auctioning of certain public goods where revenue maximization is not a consideration. Under the SSVCG framework, we propose a mechanism that is efficient and comes close to budget balance by returning much of the payments back to the agents in the form of rebates. We identify a design criterion for {\em almost budget balance}, impose feasibility and voluntary participation constraints, simplify the constraints, and arrive at a convex optimization problem to identify the parameters of the rebate functions. The convex optimization problem has a linear objective function and a continuum of linear constraints. We propose a solution method that involves a finite number of constraints, and identify the number of samples sufficient for a good approximation.Comment: Accepted for publication in the European Journal of Operational Research (EJOR

Plate wave resonance with air-coupled ultrasonics

Air‐coupled ultrasonic transducers can excite plate waves in metals and composites. The coincidence effect, i.e., the wave vector of plate wave coincides with projection of exciting airborne sound vector, leads to a resonance which strongly amplifies the sound transmission through the plate. The resonance depends on the angle of incidence and the frequency. In the present study, the incidence angle for maximum transmission (θmax)is measured in plates of steel, aluminum, carbon fiber reinforced composites and honeycomb sandwich panels. The variations of (θmax) with plate thickness are compared with theoretical values in steel, aluminum and quasi‐isotropic carbon fiber composites. The enhanced transmission of air‐coupled ultrasound at oblique incidence can substantially improve the probability of flaw detection in plates and especially in honeycomb structures. Experimental air‐coupled ultrasonic scan of subtle flaws in CFRP laminates showed definite improvement of signal‐to‐noise ratio with oblique incidence atθmax

Assessment of surgical patients’ knowledge about anaesthesia and anaesthesiologist in a tertiary care teaching institute-a survey

Background: Anaesthesia is a speciality, playing a crucial role in the perioperative care of patients. Complex surgeries are facilitated due to the anaesthesiologists catering to the ever-advancing surgical field requirements. Although an important speciality, patients are unaware of the speciality of anaesthesia and anaesthesiologist. Patients are subjecting themselves for surgical procedures without having adequate knowledge of anaesthesia and anaesthesiologist. Hence, the survey was designed with the aim to assess the patients’ knowledge about anaesthesia and anaesthesiologist in surgical patients.Methods: A cross sectional study conducted on four hundred surgical inpatients using predesigned questionnaire containing questions related to the knowledge of anaesthesia and anaesthesiologist. The results were expressed as percentages. Chi-square/ Fisher Exact test was used to find the significance of study parameters.Results: Anaesthesiologists were considered as “doctors” by 60.5% of patients; Thirty three percent of the survey population had “no idea” of Anaesthesia. The survey populations’ knowledge about complications was 32.25%. Awareness about separate consent for anaesthesia was 49%. Anaesthesiologists’ work place was not known to 77.5% of survey population. Anaesthesiologists’ role in operation theatre was known only in 59.5% and remaining 40.5% were unaware of Anaesthesiologists’ role. None of the patients preferred to meet the Anaesthesiologists before surgery.Conclusions: Surgical inpatients in tertiary care hospital have a poor understanding of anaesthesia and poor recognition of the role of anaesthesiologist. The knowledge about anaesthesia and anaesthesiologist is not known in general population. Hence, anaesthesiologists must work towards getting recognition for the speciality of anaesthesiology and the anaesthesiologists

Empirical Analysis of Electron Beam Lithography Optimization Models from a Pragmatic Perspective

Electron Beam (EB) lithography is a process of focussing electron beams on silicon wafers to design different integrated circuits (ICs). It uses an electron gun, a blanking electrode, multiple electron lenses, a deflection electrode, and control circuits for each of these components. But the lithography process causes critical dimension overshoots, which reduces quality of the underlying ICs. This is caused due to increase in beam currents, frequent electron flashes, and reducing re-exposure of chip areas. Thus, to overcome these issues, researchers have proposed a wide variety of optimization models, each of which vary in terms of their qualitative & quantitative performance. These models also vary in terms of their internal operating characteristics, which causes ambiguity in identification of optimum models for application-specific use cases. To reduce this ambiguity, a discussion about application-specific nuances, functional advantages, deployment-specific limitations, and contextual future research scopes is discussed in this text. Based on this discussion, it was observed that bioinspired models outperform linear modelling techniques, which makes them highly useful for real-time deployments. These models aim at stochastically evaluation of optimum electron beam configurations, which improves wafer’s quality & speed of imprinting when compared with other models. To further facilitate selection of these models, this text compares them in terms of their accuracy, throughput, critical dimensions, deployment cost & computational complexity metrics. Based on this discussion, researchers will be able to identify optimum models for their performance-specific use cases. This text also proposes evaluation of a novel EB Lithography Optimization Metric (EBLOM), which combines multiple performance parameters for estimation of true model performance under real-time scenarios. Based on this metric, researchers will be able to identify models that can perform optimally with higher performance under performance-specific constraints

SkipConvGAN: Monaural Speech Dereverberation using Generative Adversarial Networks via Complex Time-Frequency Masking

With the advancements in deep learning approaches, the performance of speech enhancing systems in the presence of background noise have shown significant improvements. However, improving the system's robustness against reverberation is still a work in progress, as reverberation tends to cause loss of formant structure due to smearing effects in time and frequency. A wide range of deep learning-based systems either enhance the magnitude response and reuse the distorted phase or enhance complex spectrogram using a complex time-frequency mask. Though these approaches have demonstrated satisfactory performance, they do not directly address the lost formant structure caused by reverberation. We believe that retrieving the formant structure can help improve the efficiency of existing systems. In this study, we propose SkipConvGAN - an extension of our prior work SkipConvNet. The proposed system's generator network tries to estimate an efficient complex time-frequency mask, while the discriminator network aids in driving the generator to restore the lost formant structure. We evaluate the performance of our proposed system on simulated and real recordings of reverberant speech from the single-channel task of the REVERB challenge corpus. The proposed system shows a consistent improvement across multiple room configurations over other deep learning-based generative adversarial frameworks.Comment: Published in: IEEE/ACM Transactions on Audio, Speech, and Language Processing ( Volume: 30

Complex-Valued Time-Frequency Self-Attention for Speech Dereverberation

Several speech processing systems have demonstrated considerable performance improvements when deep complex neural networks (DCNN) are coupled with self-attention (SA) networks. However, the majority of DCNN-based studies on speech dereverberation that employ self-attention do not explicitly account for the inter-dependencies between real and imaginary features when computing attention. In this study, we propose a complex-valued T-F attention (TFA) module that models spectral and temporal dependencies by computing two-dimensional attention maps across time and frequency dimensions. We validate the effectiveness of our proposed complex-valued TFA module with the deep complex convolutional recurrent network (DCCRN) using the REVERB challenge corpus. Experimental findings indicate that integrating our complex-TFA module with DCCRN improves overall speech quality and performance of back-end speech applications, such as automatic speech recognition, compared to earlier approaches for self-attention.Comment: Interspeech 2022: ISCA Best Student Paper Award Finalis

Association of Breakfast Intake with Obesity, Dietary and Physical Activity Behavior Among Urban School-Aged Adolescents in Delhi, India: Results of a Cross-Sectional Study

In developed countries, regular breakfast consumption is inversely associated with excess weight and directly associated with better dietary and improved physical activity behaviors. Our objective was to describe the frequency of breakfast consumption among school-going adolescents in Delhi and evaluate its association with overweight and obesity as well as other dietary, physical activity, and sedentary behaviors. Methods: Design: Cross-sectional study. Setting: Eight schools (Private and Government) of Delhi in the year 2006. Participants: 1814 students from 8th and 10th grades; response rate was 87.2%; 55% were 8th graders, 60% were boys and 52% attended Private schools. Main outcome measures: Body mass index, self-reported breakfast consumption, diet and physical activity related behaviors, and psychosocial factors. Data analysis: Mixed effects regression models were employed, adjusting for age, gender, grade level and school type (SES). Results: Significantly more Government school (lower SES) students consumed breakfast daily as compared to Private school (higher SES) students (73.8% vs. 66.3%; p<0.01). More 8th graders consumed breakfast daily vs. 10th graders (72.3% vs. 67.0%; p<0.05). A dose-response relationship was observed such that overall prevalence of overweight and obesity among adolescents who consumed breakfast daily (14.6%) was significantly lower vs. those who only sometimes (15.2%) or never (22.9%) consumed breakfast (p<0.05 for trend). This relationship was statistically significant for boys (15.4 % vs. 16.5% vs. 26.0; p<0.05 for trend) but not for girls. Intake of dairy products, fruits and vegetables was 5.5 (95% CI 2.4-12.5), 1.7 (95% CI 1.1-2.5) and 2.2 (95% CI 1.3-3.5) times higher among those who consumed breakfast daily vs. those who never consumed breakfast. Breakfast consumption was associated with greater physical activity vs. those who never consumed breakfast. Positive values and beliefs about healthy eating; body image satisfaction; and positive peer and parental influence were positively associated with daily breakfast consumption, while depression was negatively associated. Conclusion: Daily breakfast consumption is associated with less overweight and obesity and with healthier dietary-and physical activity-related behaviors among urban Indian students. Although prospective studies should confirm the present results, intervention programs to prevent or treat childhood obesity in India should consider emphasizing regular breakfast consumption.Obesity Prevention Center, University of MinnesotaPRIME program of the University of Texas, School of Public Health (Stigler, PI)Center for Health Promotion and Disease Prevention Research in Underserved Population