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Kawasaki disease, multisystem inflammatory syndrome in children: antibody-induced mast cell activation hypothesis

Published in:
J Pediatrics & Pediatr Med. 2020; 4(2): 1-7

Summary

Multisystem Inflammatory Syndrome in Children (MIS-C) is appearing in infants, children, and young adults in association with COVID-19 (coronavirus disease 2019) infections of SARS-CoV-2. Kawasaki Disease (KD) is one of the most common vasculitides of childhood. KD presents with similar symptoms to MIS-C especially in severe forms such as Kawasaki Disease Shock Syndrome (KDSS). The observed symptoms for MIS-C and KD are consistent with Mast Cell Activation Syndrome (MCAS) characterized by inflammatory molecules released from activated mast cells. Based on the associations of KD with multiple viral and bacterial pathogens, we put forward the hypothesis that KD and MIS-C result from antibody activation of mast cells by Fc receptor-bound pathogen antibodies causing a hyperinflammatory response upon second pathogen exposure. Within this hypothesis, MIS-C may be atypical KD or a KD-like disease associated with SARS-CoV-2. We extend the mast cell hypothesis that increased histamine levels are inducing contraction of effector cells with impeded blood flow through cardiac capillaries. In some patients, pressure from impeded blood flow, within cardiac capillaries, may result in increased coronary artery blood pressure leading to aneurysms, a well-known complication in KD.
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Summary

Multisystem Inflammatory Syndrome in Children (MIS-C) is appearing in infants, children, and young adults in association with COVID-19 (coronavirus disease 2019) infections of SARS-CoV-2. Kawasaki Disease (KD) is one of the most common vasculitides of childhood. KD presents with similar symptoms to MIS-C especially in severe forms such as Kawasaki...

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Bayesian estimation of PLDA with noisy training labels, with applications to speaker verification

Published in:
2020 IEEE Intl. Conf. on Acoustics, Speech and Signal Processing, ICASSP, 4-8 May 2020.

Summary

This paper proposes a method for Bayesian estimation of probabilistic linear discriminant analysis (PLDA) when training labels are noisy. Label errors can be expected during e.g. large or distributed data collections, or for crowd-sourced data labeling. By interpreting true labels as latent random variables, the observed labels are modeled as outputs of a discrete memoryless channel, and the maximum a posteriori (MAP) estimate of the PLDA model is derived via Variational Bayes. The proposed framework can be used for PLDA estimation, PLDA domain adaptation, or to infer the reliability of a PLDA training list. Although presented as a general method, the paper discusses specific applications for speaker verification. When applied to the Speakers in the Wild (SITW) Task, the proposed method achieves graceful performance degradation when label errors are introduced into the training or domain adaptation lists. When applied to the NIST 2018 Speaker Recognition Evaluation (SRE18) Task, which includes adaptation data with noisy speaker labels, the proposed technique provides performance improvements relative to unsupervised domain adaptation.
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Summary

This paper proposes a method for Bayesian estimation of probabilistic linear discriminant analysis (PLDA) when training labels are noisy. Label errors can be expected during e.g. large or distributed data collections, or for crowd-sourced data labeling. By interpreting true labels as latent random variables, the observed labels are modeled as...

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One giant leap for computer security

Summary

Today's computer systems trace their roots to an era of trusted users and highly constrained hardware; thus, their designs fundamentally emphasize performance and discount security. This article presents a vision for how small steps using existing technologies can be combined into one giant leap for computer security.
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Summary

Today's computer systems trace their roots to an era of trusted users and highly constrained hardware; thus, their designs fundamentally emphasize performance and discount security. This article presents a vision for how small steps using existing technologies can be combined into one giant leap for computer security.

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Discriminative PLDA for speaker verification with X-vectors

Published in:
IEEE Signal Processing Letters [submitted]

Summary

This paper proposes a novel approach to discrimina-tive training of probabilistic linear discriminant analysis (PLDA) for speaker verification with x-vectors. Model over-fitting is a well-known issue with discriminative PLDA (D-PLDA) forspeaker verification. As opposed to prior approaches which address this by limiting the number of trainable parameters, the proposed method parameterizes the discriminative PLDA (D-PLDA) model in a manner which allows for intuitive regularization, permitting the entire model to be optimized. Specifically, the within-class and across-class covariance matrices which comprise the PLDA model are expressed as products of orthonormal and diagonal matrices, and the structure of these matrices is enforced during model training. The proposed approach provides consistent performance improvements relative to previous D-PLDA methods when applied to a variety of speaker recognition evaluations, including the Speakers in the Wild Core-Core, SRE16, SRE18 CMN2, SRE19 CMN2, and VoxCeleb1 Tasks. Additionally, when implemented in Tensorflow using a modernGPU, D-PLDA optimization is highly efficient, requiring less than 20 minutes.
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Summary

This paper proposes a novel approach to discrimina-tive training of probabilistic linear discriminant analysis (PLDA) for speaker verification with x-vectors. Model over-fitting is a well-known issue with discriminative PLDA (D-PLDA) forspeaker verification. As opposed to prior approaches which address this by limiting the number of trainable parameters, the proposed method...

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Weather radar network benefit model for flash flood casualty reduction

Author:
Published in:
J. Appl. Meteor. Climatol., Vol. 59, No. 4, April 2020, pp. 589-604.

Summary

A monetized flash flood casualty reduction benefit model is constructed for application to meteorological radar networks. Geospatial regression analyses show that better radar coverage of the causative rainfall improves flash flood warning performance. Enhanced flash flood warning performance is shown to decrease casualty rates. Consequently, these two effects in combination allow a model to be formed that links radar coverage to flash flood casualty rates. When this model is applied to the present-day contiguous U.S. weather radar network, results yield a flash-flood-based benefit of $316 million (M) yr-1. The remaining benefit pools are more modest ($13M yr-1 for coverage improvement and $69M yr-1 maximum for all areas of radar quantitative precipitation estimation improvements), indicative of the existing weather radar network's effectiveness in supporting the flash flood warning decision process.
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Summary

A monetized flash flood casualty reduction benefit model is constructed for application to meteorological radar networks. Geospatial regression analyses show that better radar coverage of the causative rainfall improves flash flood warning performance. Enhanced flash flood warning performance is shown to decrease casualty rates. Consequently, these two effects in combination...

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A hands-on middle-school robotics software program at MIT

Summary

Robotics competitions at the high school level attract a large number of students across the world. However, there is little emphasis on leveraging robotics to get middle school students excited about pursuing STEM education. In this paper, we describe a new program that targets middle school students in a local, four-week setting at the Massachusetts Institute of Technology (MIT). It aims to excite students by teaching the very basics of computer vision and robotics. The students program mini car-like robots, equipped with state-of-the-art computers, to navigate autonomously in a mock race track. We describe the hardware and software infrastructure that enables the program, the details of our curriculum, and the results of a short assessment. In addition, we describe four short programs, as well as a session where we teach high school teachers how to teach similar courses at their schools to their own students. The self-assessment indicates that the students feel more confident in programming and robotics after leaving the program, which we hope will enable them to pursue STEM education and robotics initiatives at school.
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Summary

Robotics competitions at the high school level attract a large number of students across the world. However, there is little emphasis on leveraging robotics to get middle school students excited about pursuing STEM education. In this paper, we describe a new program that targets middle school students in a local...

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A workflow for non-linear load parameter estimation using a power-hardware-in-the-loop experimental testbed

Published in:
2020 IEEE Applied Power Electronics Conf. and Expo., APEC, 15-19 March 2020.

Summary

Low-inertia microgrids may easily have a single load which can make up most of the total load, thereby greatly affecting stability and power quality. Instead of static load models, dynamic load models are presented here for constant current loads (CILs) and constant power loads (CPLs). Next, a flexible Power-Hardware-in-the-Loop (PHiL) testbed is employed for the experiments in this work. The PHiL testbed consists of a real-time computer working with a power amplifier in order to perturb its voltage and frequency. A connected load serves as the device under test (DUT). Using the captured experimental data as a reference, a parameter estimation algorithm is then implemented. The resulting parameter estimates are used to define simulation models. Both the CIL and CPL dynamic models are simulated to produce waveforms that closely resemble experimental waveforms. The algorithm, referred to as an enhanced monte carlo algorithm (EMCA), is explained in this work. Finally, the EMCA's resulting parameter estimates are presented.
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Summary

Low-inertia microgrids may easily have a single load which can make up most of the total load, thereby greatly affecting stability and power quality. Instead of static load models, dynamic load models are presented here for constant current loads (CILs) and constant power loads (CPLs). Next, a flexible Power-Hardware-in-the-Loop (PHiL)...

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Topological effects on attacks against vertex classification

Summary

Vertex classification is vulnerable to perturbations of both graph topology and vertex attributes, as shown in recent research. As in other machine learning domains, concerns about robustness to adversarial manipulation can prevent potential users from adopting proposed methods when the consequence of action is very high. This paper considers two topological characteristics of graphs and explores the way these features affect the amount the adversary must perturb the graph in order to be successful. We show that, if certain vertices are included in the training set, it is possible to substantially an adversary's required perturbation budget. On four citation datasets, we demonstrate that if the training set includes high degree vertices or vertices that ensure all unlabeled nodes have neighbors in the training set, we show that the adversary's budget often increases by a substantial factor---often a factor of 2 or more---over random training for the Nettack poisoning attack. Even for especially easy targets (those that are misclassified after just one or two perturbations), the degradation of performance is much slower, assigning much lower probabilities to the incorrect classes. In addition, we demonstrate that this robustness either persists when recently proposed defenses are applied, or is competitive with the resulting performance improvement for the defender.
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Summary

Vertex classification is vulnerable to perturbations of both graph topology and vertex attributes, as shown in recent research. As in other machine learning domains, concerns about robustness to adversarial manipulation can prevent potential users from adopting proposed methods when the consequence of action is very high. This paper considers two...

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Augmented Annotation Phase 3

Author:
Published in:
MIT Lincoln Laboratory Report TR-1248

Summary

Automated visual object detection is an important capability in reducing the burden on human operators in many DoD applications. To train modern deep learning algorithms to recognize desired objects, the algorithms must be "fed" more than 1000 labeled images (for 55%–85% accuracy according to project Maven - Oct 2017 O6, Working Group slide 27) of each particular object. The task of labeling training data for use in machine learning algorithms is human intensive, requires special software, and takes a great deal of time. Estimates from ImageNet, a widely used and publicly available visual object detection dataset, indicate that humans generated four annotations per minute in the overall production of ImageNet annotations. DoD's need is to reduce direct object-by-object human labeling particularly in the video domain where data quantity can be significant. The Augmented Annotations System addresses this need by leveraging a small amount of human annotation effort to propagate human initiated annotations through video to build an initial labeled dataset for training an object detector, and utilizing an automated object detector in an iterative loop to assist humans in pre-annotating new datasets.
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Summary

Automated visual object detection is an important capability in reducing the burden on human operators in many DoD applications. To train modern deep learning algorithms to recognize desired objects, the algorithms must be "fed" more than 1000 labeled images (for 55%–85% accuracy according to project Maven - Oct 2017 O6...

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Medical countermeasures analysis of 2019-nCoV and vaccine risks for antibody-dependent enhancement (ADE)

Published in:
https://www.preprints.org/manuscript/202003.0138/v1

Summary

Background: In 80% of patients, COVID-19 presents as mild disease. 20% of cases develop severe (13%) or critical (6%) illness. More severe forms of COVID-19 present as clinical severe acute respiratory syndrome, but include a T-predominant lymphopenia, high circulating levels of proinflammatory cytokines and chemokines, accumulation of neutrophils and macrophages in lungs, and immune dysregulation including immunosuppression. Methods: All major SARS-CoV-2 proteins were characterized using an amino acid residue variation analysis method. Results predict that most SARS-CoV-2 proteins are evolutionary constrained, with the exception of the spike (S) protein extended outer surface. Results were interpreted based on known SARS-like coronavirus virology and pathophysiology, with a focus on medical countermeasure development implications. Findings: Non-neutralizing antibodies to variable S domains may enable an alternative infection pathway via Fc receptor-mediated uptake. This may be a gating event for the immune response dysregulation observed in more severe COVID-19 disease. Prior studies involving vaccine candidates for FCoV SARS-CoV-1 and Middle East Respiratory Syndrome coronavirus (MERS-CoV) demonstrate vaccination-induced antibody-dependent enhancement of disease (ADE), including infection of phagocytic antigen presenting cells (APC). T effector cells are believed to play an important role in controlling coronavirus infection; pan-T depletion is present in severe COVID-19 disease and may be accelerated by APC infection. Sequence and structural conservation of S motifs suggests that SARS and MERS vaccine ADE risks may foreshadow SARS-CoV-2 S-based vaccine risks. Autophagy inhibitors may reduce APC infection and T-cell depletion. Amino acid residue variation analysis identifies multiple constrained domains suitable as T cell vaccine targets. Evolutionary constraints on proven antiviral drug targets present in SARS-CoV-1 and SARS-CoV-2 may reduce risk of developing antiviral drug escape mutants. Interpretation: Safety testing of COVID-19 S protein-based B cell vaccines in animal models is strongly encouraged prior to clinical trials to reduce risk of ADE upon virus exposure.
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Summary

Background: In 80% of patients, COVID-19 presents as mild disease. 20% of cases develop severe (13%) or critical (6%) illness. More severe forms of COVID-19 present as clinical severe acute respiratory syndrome, but include a T-predominant lymphopenia, high circulating levels of proinflammatory cytokines and chemokines, accumulation of neutrophils and macrophages...

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