RoMA: a Method for Neural Network Robustness Measurement and Assessment

Neural network models have become the leading solution for a large variety of tasks, such as classification, language processing, protein folding, and others. However, their reliability is heavily plagued by adversarial inputs: small input perturbations that cause the model to produce erroneous outputs. Adversarial inputs can occur naturally when the system's environment behaves randomly, even in the absence of a malicious adversary, and are a severe cause for concern when attempting to deploy neural networks within critical systems. In this paper, we present a new statistical method, called Robustness Measurement and Assessment (RoMA), which can measure the expected robustness of a neural network model. Specifically, RoMA determines the probability that a random input perturbation might cause misclassification. The method allows us to provide formal guarantees regarding the expected frequency of errors that a trained model will encounter after deployment. Our approach can be applied to large-scale, black-box neural networks, which is a significant advantage compared to recently proposed verification methods. We apply our approach in two ways: comparing the robustness of different models, and measuring how a model's robustness is affected by the magnitude of input perturbation. One interesting insight obtained through this work is that, in a classification network, different output labels can exhibit very different robustness levels. We term this phenomenon categorial robustness. Our ability to perform risk and robustness assessments on a categorial basis opens the door to risk mitigation, which may prove to be a significant step towards neural network certification in safety-critical applications

Avaliação da efetividade da realização de teleconsultorias na qualificação dos referenciamentos entre Atenção Primária e Atenção Especializada para pacientes portadores de condições crônicas em Endocrinologia

Introdução: O referenciamento de pacientes da Atenção Primária à Saúde (APS) para outros níveis de atenção é uma atividade necessária para garantir a integralidade do cuidado e um importante determinante na qualidade e custos em saúde. O mais importante desfecho deve ser o benefício para o paciente, provendo a consulta com a especialidade médica certa, no tempo certo e no lugar certo. Entretanto, nas últimas décadas, em todo o mundo, houve um aumento nas taxas de encaminhamento sem isso significar ganhos em saúde para as populações. Para estruturar um sistema de saúde com capacidade de prover serviços integrados e coordenados, é fundamental a conformação de redes de atenção à saúde. Nessas redes, a APS deve cumprir três papéis, os quais a legitimam como nível central de cuidado dos pacientes: resolução, se estima que entre 75 e 85% dos problemas de saúde de uma população devem ser resolvidos na APS; coordenação, capacidade de orientar os fluxos de pessoas e informações entre os nós da rede; responsabilização, necessidade de acolher e se responsabilizar pelas demandas da sua população. Ferramentas de Telessaúde tem potencial para auxiliar a APS no seu papel ordenador dos sistemas de saúde. Esta tese tem o objetivo de avaliar a efetividade da criação de protocolos e realização de teleconsultorias na qualificação da referência de pacientes da APS para serviços especializados em endocrinologia. Métodos: Na primeira etapa foi desenvolvido um passo-a-passo para desenvolvimento de protocolos de regulação ambulatorial. Realizou-se amostragem dos encaminhamentos de pacientes não residentes em Porto Alegre em lista de espera para consulta com serviços especializados em endocrinologia na capital do Rio Grande do Sul. Foram identificados os seis motivos mais comuns, os quais são responsáveis por mais de 80% da lista de espera para a especialidade: diabetes mellitus, hipotireoidismo, hipertireoidismo, nódulo de tireoide, bócio multinodular e obesidade. Para esses motivos, desenvolveu-se protocolos de regulação ambulatorial com dois objetivos principais: determinar condições clínicas que indubitavelmente justificavam o encaminhamento para serviço especializado; e descrever qual deveria ser o conteúdo descritivo mínimo informado pelos médicos da APS para justificar a necessidade do encaminhamento. Esses protocolos de regulação foram aprovados na Comissão Intergestores Bipartite do Estado do Rio Grande do Sul. Foi avaliado a implantação desse protocolo para todo os 303 municípios que encaminharam pacientes para Porto Alegre desde novembro de 2013. A segunda etapa foi realizar um ensaio clínico randomizado (ECR) em cluster para avaliar a efetividade da teleconsultoria associada a regulação do acesso na qualificação dos encaminhamentos para endocrinologia. Foram randomizados 56 de um total de 96 municípios elegíveis (cada município deveria ter entre 10 e 99 encaminhamentos para endocrinologia em lista de espera), com 3471 pacientes. Os encaminhamentos dos pacientes de ambos os grupos foram regulados com o uso dos protocolos de encaminhamento. Nos municípios do grupo intervenção, acrescido à regulação foram ofertadas consultorias estruturadas por meio telefônico (p-consultations) entre os Primary Care Phisicians (PCPs) e médicos consultores do serviço 0800 do Núcleo de Telessaúde do Rio Grande do Sul (TelessaúdeRS/UFRGS). Os objetivos da consultoria eram revisar o diagnóstico e/ou o manejo clínico do paciente, nos seus diferentes aspectos, e qualificar o cuidado, quando oportuno. Após discussão do caso, os médicos assistentes eram questionados se o encaminhamento deveria ser mantido ou poderia ser cancelado. 11 Resultados: a implantação do protocolo melhorou a adequabilidade dos referenciamentos. De um total de 9.746 pacientes avaliados de novembro de 2013 até maio de 2016, 2812 (28,9%) dos encaminhamentos foram aprovados em uma primeira análise. A proporção de encaminhamentos aprovados aumentou 5.1% por mês (OR 1.051 CI 95% 1.045-1.056, p<0,001), variando de 16.4% em novembro de 2013 até 44% em maio de 2016. A análise do ECR mostrou que o proporção de encaminhamentos aprovados no grupo intervenção foi 16,7% menor que no grupo controle (29,6% vs. 46,3%; OR=0,48, p< 0,001), com um encaminhamento evitado para cada seis regulados (NNT=6). A realização das teleconsultorias mostrou alta eficácia, com a resolução na Atenção Primária à Saúde de dois encaminhamentos para teleconsultoria a cada três referenciamentos discutidos. Conclusão: A utilização de ferramentas de telessaúde como as p-consultorias são uma estratégia possível para qualificar os referenciamentos de pacientes da APS, desde que sejam incorporadas como um nó obrigatório no sistema logístico das informações em saúde. Nesse cenário, a telessaúde torna-se um facilitador no transporte de informações entre os níveis de atenção (centro de comunicações), evitando o deslocamento físico de pacientes (custos e riscos), garantindo o uso das melhores práticas assistenciais (qualidade), aumentando a resolutividade da APS (integralidade) e fortalecendo a responsabilidade dos médicos com seus pacientes (coordenação). Nesse cenário as TICs podem ser o elo da informação entre serviços. Contudo, mais do que isso, é necessário um grupo de profissionais gerenciando as TICs para provocar a mudança. Esse conjunto de agentes reguladores armados com TICs potentes e centradas nas pessoas pode ser um dos pontos de inflexão para a mudança. Mas, para a mudança acontecer, temos que entender que a telemedicina não é uma medicina diferente, mas sim o único caminho para onde a medicina pode e deve avançar.Introduction: The referral of patients from Primary Health Care (PHC) to other levels of care is a necessary activity to ensure comprehensive care and an important determinant of the quality and health costs. The most important outcome should be the benefit to the patient, providing consultation in the right specialty at the right time and the right place. However, in recent decades, throughout the world, there was an increase in referral rates without reflecting an improvement in population’s health. To design a health system capable of providing integrated and coordinated services, shaping the health care networks is critical. In these networks, PHC must perform three roles that legitimize as the main level of patient care: resolution, it is estimated that between 75 to 85% of the health problems of a population must be resolved in PHC; coordination, ability to guide the flow of people and information between network nodes; accountability, need to accept and take responsibility for the demands of its population. Telehealth tools have the potential to assist PHC in its coordinating role of health systems. This thesis aims to evaluate the effectiveness of referral protocols and teleconsultation on improving the quality of PHC’s referral to endocrinology services. Methods: The first task was to develop referral protocols to ambulatory consultations at specialized services. Initially, we analyze a 5% sample of the waiting list of patients referred from the countryside of Rio Grande do Sul State to Endocrinology services in the State capital, Porto Alegre. We identified the six most common reasons that were responsible for more than 85% of the waiting list for Endocrinology: Diabetes mellitus, hypothyroidism, hyperthyroidism, thyroid nodules, multinodular goiter, and obesity. Then we developed referrals protocols for those diseases with two principal objectives: to determine clinical conditions that undoubtedly justified referral to specialized service, and define what should be the minimum descriptive content informed by the PHC to explain the need for this referral. Those referral protocols were approved by the Bipartite Commission of the State of Rio Grande do Sul. We evaluate the implementation of this protocol to all 303 cities that referred patients to Porto Alegre since November 2013. The second step was to conduct a randomized cluster clinical trial to evaluate the effectiveness of teleconsultations on qualifying referrals to Endocrinology. 56 clusters were randomized of a total of 93 eligible cities (each city should have between 10 and 99 referrals for Endocrinology waiting list). Referrals of control group and intervention group were regulated using our standard protocols. In the intervention group was also offered the use of a structured consultation by telephone (p-consultations) between the attending physician of the patient and the physician teleconsultor of the 0800 hotline from Telehealth Project of Rio Grande do Sul (TelessaúdeRS/UFRGS). The p-consultation objectives were to review the diagnosis and clinical management of the patient, in its different aspects, qualifying care when appropriate. After discussing the case, the attending physicians were asked whether the referral should be maintained or could be canceled. Results: Protocols implementation improved the suitability of the referrals. From 9746 patients evaluated from November 2013 to May 2016, 2812 (28%) of referrals were approved in a preliminary analysis. The proportion of approved referrals raised by 5,1% per month (OR 1.051 CI 95% 1.045-1.056, p<0,001), ranging from 16,4% in November 2013 to 44% in May 2016. The ECR analysis showed that the ratio of referrals approved in the intervention group was 16,7% lower than the ratio of referrals approved in the control group (29,6% vs. 46,3%; OR=0,48, p< 0,001), with one referral avoidance from six regulated referrals (NNT = 6). The teleconsultation showed high efficacy, providing resolution of two out of three discussed cases at the primary care level of assistance. Conclusion: The use of Telehealth tools such as p-consultations are a possible strategy to qualify referrals of patients from PHC, since incorporated as a mandatory node in the logistic system of health information. In this scenario, the telehealth bridge the information between levels of care (communications center), prevent the physical transport of patients (costs and risks), ensuring the use of a best care practices (quality), increasing resolubility of PHC (comprehensiveness) and strengthening the responsibility of physician with their patients (coordination). ICTs can be the link between information services. However, more than that, a group of professionals is necessary to promote this changes. This set of armed regulators with powerful and patient-centered ICT can be one of the turning points for change. But for change to happen, we have to understand that telemedicine is not a different medicine, but the only way to where medicine can and should move forward

Standoff Detection via Single-Beam Spectral Notch Filtered Pulses

We demonstrate single-beam coherent anti-Stokes Raman spectroscopy (CARS), for detecting and identifying traces of solids, including minute amounts of explosives, from a standoff distance (>50 m) using intense femtosecond pulses. Until now, single-beam CARS methods relied on pulse-shapers in order to obtain vibrational spectra. Here we present a simple and easy-to-implement detection scheme, using a commercially available notch filter, that does not require the use of a pulse-shaper.Comment: 3 pages, 3 figure

Referring quality assessment of primary health care for nephrology in Rio Grande do Sul, Brazil

Telemedicin

Standoff Detection of Solid Traces by Single-Beam Nonlinear Raman Spectroscopy Using Shaped Femtosecond Pulses

We demonstrate a single-beam, standoff (>10m) coherent anti-Stokes Raman scattering spectroscopy (CARS) of various materials, including trace amounts of explosives and nitrate samples, under ambient light conditions. The multiplex measurement of characteristic molecular vibrations with <20cm-1 spectral resolution is carried out using a single broadband (>550cm-1) phase-shaped femtosecond laser pulse. We exploit the strong nonresonant background signal for amplification of the weak backscattered resonant CARS signal by using a homodyne detection scheme. This facilitates a simple, highly sensitive single-beam spectroscopic technique, with a potential for hazardous materials standoff detection applications

Explainability Using Bayesian Networks for Bias Detection: FAIRness with FDO

In this paper we aim to provide an implementation of the FAIR Data Points (FDP) spec, that will apply our bias detection algorithm and automatically calculate a FAIRness score (FNS). FAIR metrics would be themselves represented as FDOs, and could be presented via a visual dashboard, and be machine accessible (Mons 2020, Wilkinson et al. 2016). This will enable dataset owners to monitor the level of FAIRness of their data. This is a step forward in making data FAIR, i.e., Findable, Accessible, Interoperable, and Reusable; or simply, Fully AI Ready data.First we may discuss the context of this topic with respect to Deep Learning (DL) problems. Why are Bayesian Networks (BN, explained below) beneficial for such issues?Explainability – Obtaining a directed acyclic graph (DAG) from a BN training provides coherent information about independence variables in the data base. In a generic DL problem, features are functions of these variables. Thus, one can derive which variables are dominant in our system. When customers or business units are interested in the cause of a neural net outcome, this DAG structure can be both a source to provide importance and clarify the model.Dimension Reduction — BN provides the joint distribution of our variables and their associations. The latter may play a role in reducing the features that we induce to the DL engine: If we know that for random variables X,Y the conditional entropy of X in Y are low, we may omit X since Y provides its nearly entire information. We have, therefore, a tool that can statistically exclude redundant variablesTagging Behavior – This section can be less evident for those who work in domains such as vision or voice. In some frameworks, labeling can be an obscure task (to illustrate, consider a sentiment problem with many categories that may overlap). When we tag the data, we may rely on some features within the datasets and generate conditional probability. Training BN, when we initialize an empty DAG, may provide outcomes in which the target is a parent of other nodes. Observing several tested examples, these outcomes reflect these “taggers’ manners”. We can therefore use DAGs not merely for the purpose of model development in machine learning but mainly learning taggers policy and improve it if needed.The conjunction of DL and Casual inference — Causal Inference is a highly developed domain in data analytics. It offers tools to resolve questions that on the one hand, DL models commonly do not and, on the other hand, the real-world raises. There is a need to find a framework in which these tools will work in conjunction. Indeed, such frameworks already exist (e.g., GNN). But a mechanism that merges typical DL problems causality is less common. We believe that the flow, as described in this paper, is a good step in the direction of achieving benefits from this conjunction.Fairness and Bias – Bayesian networks, in their essence, are not a tool for bias detection but they reveal which of the columns (or which of the data items) is dominant and modify other variables. When we discuss noise and bias, we address these faults to the column and not to the model or to the entire data base. However, assume we have a set of tools to measure bias (Purian et al. 2022). Bayesian networks can provide information about the prominence of these columns (as they are “cause” or “effect” in the data), thus allow us to assess the overall bias in the database.What are Bayesian Networks?The motivation for using Bayesian Networks (BN) is to learn the dependencies within a set of random variables. The networks themselves are directed acyclic graphs (DAG), which mimic the joint distribution of the random variables (e.g., Perrier et al. (2008)). The graph structure follows the probabilistic dependencies factorization of the joint distribution: a node V depends only on its parents (a r.v X independent of the other nodes will be presented as a parent free node).Real-World ExampleIn this paper we present a way of using the DL engine tabular data, with the python package bnlearn. Since this project is commercial, the variable names were masked; thus, they will have meaningless names.Constructing Our DAGWe begin by finding our optimal DAG.import bnlearn as bnDAG = bn.structure_learning.fit(dataframe) We now have a DAG. It has a set of nodes and an adjacency matrix that can be found as follow:print(DAG['adjmat']) The outcome has this form Fig. 1a.Where rows are sources (namely the direction of the arc is from the left column to the elements in the row) and columns are targets (i.e., the header of the column receives the arcs). When we begin drawing the obtained DAG, we get for one set of variables the following image: Fig. 1b.We can see that the target node in the rectangle is a source for many nodes. We can see that it still points arrows itself to two nodes. We will discuss this in the discussion (i.e., Rauber 2021). We have more variables, therefore I increased the number of nodes. Adding the information provided a new source for the target (i.e., its entire row is “False”). The obtained graph is the following: Fig. 1c.So, we know how to construct a DAG. Now we need to train its parameters. Code-wise we perform this as follows:model_mle = bn.parameter_learning.fit(DAG, dataframe, methodtype='maximumlikelihood')We can change ‘maximulikelihood’ with ‘bayes’ as described beyond. The outcome of this training is a set of factorized conditional distributions that reflect the DAG’s structure. It has this form for a given variable: Fig. 1d. The code to create DAG presentation is provided in Fig. 2. DiscussionIn this paper we have presented some of the theoretical concepts of Bayesian Networks and the usage they provide in constructing an approximated DAG for a set of variables. In addition, we presented a real-world example of end to end DAG learning: Constructing it using BN, training its parameters using maximum likelihood estimation (MLE) methods, and performing and inference.FAIR metrics, represented as FDOs, can also be visualised and monitored, taking care of data FAIRness

Synthesizing computer generated holograms with reduced number of perspective projections

Abstract: We present an improved method for recording a synthesized Fourier hologram under incoherent white-light illumination. The advantage of the method is that the number of real projections needed for generating the hologram is significantly reduced. The new method, designated as synthetic projection holography, is demonstrated experimentally. We show that the synthetic projection holography barely affects the reconstructed images. However, by increasing the number of observed projections one can improve the synthetic projection hologram quality

Importance of the teleregulation monitor in a telehealth core

Telemedicin

O serviço de telessaúde : uma ação estratégica no estado do Rio Grande do Sul

O projeto RegulaSUS do TelessaúdeRS-UFRGS visa qualificar o referenciamento de usuários da atenção primária à saúde (APS) para a atenção ambulatorial especializada no Estado do Rio Grande do Sul. Sua atuação consiste em: reduzir a fila de espera, priorizar o atendimento a situações potencialmente graves, e otimizar a resolutividade da APS. Para isso, foram desenvolvidos protocolos de encaminhamento (PE) para os motivos mais frequentes de encaminhamento em especialidades selecionadas conjuntamente entre TelessaúdeRS e Secretaria Estadual de Saúde. Os protocolos estabelecem os critérios clínicos que justificam consulta especializada e o conjunto mínimo de informações a serem fornecidas pelo médico assistente para que a solicitação seja autorizada e o paciente entre na fila de espera. Através da revisão dos encaminhamentos no sistema informatizado, aqueles que não preenchem os critérios são direcionados para realização de teleconsultoria. Até o momento, foram desenvolvidos pelo RegulaSUS 250 PE e realizadas 48.389 teleconsultorias, no período de novembro/2013 à março/2018. Dentre os principais resultados, reduziu-se a fila de espera de: Endocrinologia de 7269 para 3508 (52%); Nefrologia de 551 para 342 (38%); Pneumologia de 3650 para 1421 (59%); Neurologia de 5087 para 3818 (25%); e Estomatologia de 329 para 94 (71%). Em média, a cada 3 casos discutidos, 2 encaminhamentos podem ser evitados, com concordância do médico assistente. Dados históricos do sistema informatizado de regulação do estado indicam redução do número de usuários do sistema de saúde em espera pela consulta especializada, revertendo uma tendência prévia de aumento progressivo, aumentando a resolutividade da APSFil: Garcia, Miguel. Universidade Federal do Rio Grande do Sul (Brasil)Fil: Rados, Dimitris. Universidade Federal do Rio Grande do Sul (Brasil)Fil: Silva, Lucas . Universidade Federal do Rio Grande do Sul (Brasil)Fil: Szekut, Michelle. Universidade Federal do Rio Grande do Sul (Brasil)Fil: Katz, Natan . Universidade Federal do Rio Grande do Sul (Brasil)Fil: Roman, Rudi. Universidade Federal do Rio Grande do Sul (Brasil

Impacto da teleconsultoria no acesso para o serviço especializado em endocrinologia

Telemedicin