Tailored composite wings with elastically produced chordwise camber

Four structural concepts were created which produce chordwise camber deformation that results in enhanced lift. A wing box can be tailored to utilize each of these with composites. In attempting to optimize the aerodynamic benefits, researchers found that there are two optimum designs that are of interest. There is a weight optimum which corresponds to the maximum lift per unit structural weight. There is also a lift optimum that corresponds to maximum absolute lift. Experience indicates that a large weight penalty accompanies the transition from weight to lift optimum designs. New structural models, the basic deformation mechanisms that are utilized, and typical analytical results are presented. It appears that lift enhancements of sufficient magnitude can be produced to render this type of wing tailoring of practical interest

Human detection and tracking via Ultra-Wideband (UWB) radar

This paper presents an algorithm for human presence detection and tracking using an Ultra-Wideband (UWB) impulse-based mono-static radar. UWB radar can complement other human tracking technologies, as it works well in poor visibility conditions. UWB electromagnetic wave scattering from moving humans forms a complex returned signal structure which can be approximated to a specular multi-path scattering model (SMPM). The key technical challenge is to simultaneously track multiple humans (and non-humans) using the complex scattered waveform observations. We develop a multiple-hypothesis tracking (MHT) framework that solves the complicated data association and tracking problem for an SMPM of moving objects/targets. Human presence detection utilizes SMPM signal features, which are tested in a classical likelihood ratio (LR) detector framework. The process of human detection and tracking is a combination of the MHT method and the LR human detector. We present experimental results in which a mono-static UWB radar tracks human and non-human targets, and detects human presence by discerning human from moving non-human objects

Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning

As machine learning (ML) models gain traction in clinical applications, understanding the impact of clinician and societal biases on ML models is increasingly important. While biases can arise in the labels used for model training, the many sources from which these biases arise are not yet well-studied. In this paper, we highlight disparate censorship (i.e., differences in testing rates across patient groups) as a source of label bias that clinical ML models may amplify, potentially causing harm. Many patient risk-stratification models are trained using the results of clinician-ordered diagnostic and laboratory tests of labels. Patients without test results are often assigned a negative label, which assumes that untested patients do not experience the outcome. Since orders are affected by clinical and resource considerations, testing may not be uniform in patient populations, giving rise to disparate censorship. Disparate censorship in patients of equivalent risk leads to undertesting in certain groups, and in turn, more biased labels for such groups. Using such biased labels in standard ML pipelines could contribute to gaps in model performance across patient groups. Here, we theoretically and empirically characterize conditions in which disparate censorship or undertesting affect model performance across subgroups. Our findings call attention to disparate censorship as a source of label bias in clinical ML models.Comment: 48 pages, 18 figures. Machine Learning for Healthcare Conference (MLHC 2022

Ultra narrow AuPd and Al wires

In this letter we discuss a novel and versatile template technique aimed to the fabrication of sub-10 nm wide wires. Using this technique, we have successfully measured AuPd wires, 12 nm wide and as long as 20 $\mu$m. Even materials that form a strong superficial oxide, and thus not suited to be used in combination with other techniques, can be successfully employed. In particular we have measured Al wires, with lateral width smaller or comparable to 10 nm, and length exceeding 10 $\mu$m.Comment: 4 pages, 4 figures. Pubblished in APL 86, 172501 (2005). Added erratum and revised Fig.

Assessment of Vessel Density on Non-Contrast Computed Tomography to Detect Basilar Artery Occlusion

Introduction: Basilar artery occlusion (BAO) may be clinically occult due to variable and non-specific symptomatology. We evaluated the qualitative and quantitative determination of a hyperdense basilar artery (HDBA) on non-contrast computed tomography (NCCT) brain for the diagnosis of BAO.Methods: We conducted a case control study of patients with confirmed acute BAO vs a control group of suspected acute stroke patients without BAO. Two EM attending physicians, one third-year EM resident, and one medical student performed qualitative and quantitative assessments for the presence of a HDBA on axial NCCT images. Our primary outcome measures were sensitivity and specificity for BAO. Our secondary outcomes were inter-rater and intra-rater reliability of the qualitative and quantitative assessments.Results: We included 60 BAO and 65 control patients in our analysis. Qualitative assessment of the hyperdense basilar artery sign was poorly sensitive (54%–72%) and specific (55%–89%). Quantitative measurement improved the specificity of hyperdense basilar artery assessment for diagnosing BAO, with a threshold of 61.0–63.8 Hounsfield units demonstrating relatively high specificity of 85%–94%. There was moderate inter-rater agreement for the qualitative assessment of HDBA (Fleiss’ kappa statistic 0.508, 95% confidence interval: 0.435–0.581). Agreement improved for quantitative assessments, but still fell in the moderate range (Shrout-Fleiss intraclass correlation coefficient: 0.635). Intra-rater reliability for the quantitative assessments of the two attending physician reviewers demonstrated substantial consistency.Conclusion: Our results highlight the importance of carefully examining basilar artery density when interpreting the NCCT of patients with altered consciousness or other signs and symptoms concerning for an acute basilar artery occlusion. If the Hounsfield unit density of the basilar artery exceeds 61 Hounsfield units, BAO should be highly suspected

A Three-regime Model of Network Pruning

Recent work has highlighted the complex influence training hyperparameters, e.g., the number of training epochs, can have on the prunability of machine learning models. Perhaps surprisingly, a systematic approach to predict precisely how adjusting a specific hyperparameter will affect prunability remains elusive. To address this gap, we introduce a phenomenological model grounded in the statistical mechanics of learning. Our approach uses temperature-like and load-like parameters to model the impact of neural network (NN) training hyperparameters on pruning performance. A key empirical result we identify is a sharp transition phenomenon: depending on the value of a load-like parameter in the pruned model, increasing the value of a temperature-like parameter in the pre-pruned model may either enhance or impair subsequent pruning performance. Based on this transition, we build a three-regime model by taxonomizing the global structure of the pruned NN loss landscape. Our model reveals that the dichotomous effect of high temperature is associated with transitions between distinct types of global structures in the post-pruned model. Based on our results, we present three case-studies: 1) determining whether to increase or decrease a hyperparameter for improved pruning; 2) selecting the best model to prune from a family of models; and 3) tuning the hyperparameter of the Sharpness Aware Minimization method for better pruning performance.Comment: ICML 202

Vicarious Reinforcement in Rhesus Macaques (Macaca Mulatta)

What happens to others profoundly influences our own behavior. Such other-regarding outcomes can drive observational learning, as well as motivate cooperation, charity, empathy, and even spite. Vicarious reinforcement may serve as one of the critical mechanisms mediating the influence of other-regarding outcomes on behavior and decision-making in groups. Here we show that rhesus macaques spontaneously derive vicarious reinforcement from observing rewards given to another monkey, and that this reinforcement can motivate them to subsequently deliver or withhold rewards from the other animal. We exploited Pavlovian and instrumental conditioning to associate rewards to self (M1) and/or rewards to another monkey (M2) with visual cues. M1s made more errors in the instrumental trials when cues predicted reward to M2 compared to when cues predicted reward to M1, but made even more errors when cues predicted reward to no one. In subsequent preference tests between pairs of conditioned cues, M1s preferred cues paired with reward to M2 over cues paired with reward to no one. By contrast, M1s preferred cues paired with reward to self over cues paired with reward to both monkeys simultaneously. Rates of attention to M2 strongly predicted the strength and valence of vicarious reinforcement. These patterns of behavior, which were absent in non-social control trials, are consistent with vicarious reinforcement based upon sensitivity to observed, or counterfactual, outcomes with respect to another individual. Vicarious reward may play a critical role in shaping cooperation and competition, as well as motivating observational learning and group coordination in rhesus macaques, much as it does in humans. We propose that vicarious reinforcement signals mediate these behaviors via homologous neural circuits involved in reinforcement learning and decision-making

Tax evasion and exchange equity: a reference-dependent approach

The standard portfolio model of tax evasion with a public good produces the perverse conclusion that when taxpayers perceive the public good to be under-/overprovided, an increase in the tax rate increases/decreases evasion. The author treats taxpayers as thinking in terms of gains and losses relative to an endogenous reference level, which reflects perceived exchange equity between the value of taxes paid and the value of public goods supplied. With these alternative behavioral assumptions, the author overturns the aforementioned result in a direction consistent with the empirical evidence. The author also finds a role for relative income in determining individual responses to a change in the marginal rate of tax

UWB radar-based human target tracking

This paper presents a framework and algorithms for tracking the range of moving humans via a mono-static ultra-wideband (UWB) radar. The approach is based on a specular multi-path model for UWB radar scatters from walking humans. Empirical studies show that multipath time-of-arrival (TOA) can be modeled as a point process whose behavior is governed by a Gamma distribution. Based on this insight, we develop a tracking procedure that combines a Kalman Filter with a point process observation model whose measurements are processed with an Expectation-Maximization (EM) procedure. As a byproduct, the EM procedure solves the multi-target data segmentation and data association problems. We present experimental results in which a monostatic UWB radar tracks both individual and up to two human targets