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O presente trabalho tem por objetivo apresentar e contextualizar a teoria da norma jurídica a partir da filosofia da linguagem formulada por Tercio Sampaio Ferraz Junior. O autor propõe uma abordagem pragmática da norma jurídica, para determinação de um sistema explicativo do comportamento humano enquanto regulado por normas. A decidibilidade de conflitos norteia todo o modelo jurídico proposto por Ferraz Jr. A metodologia utilizada neste trabalho está na base logica dedutiva, com base em analise bibliográfica. Palavras-chave: norma jurídica; discurso normativo; pragmática jurídica; validade; dogmática jurídica

The growing interest in the development of cost-effective, straightforward, and rapid analytical systems has found cellulose-based materials, including cellulose derivatives, cellulose-based gels, nanocellulosic materials, and the corresponding (nano)cellulose-based composites, to be valuable platforms for sensor development. The present work presents recent advances in the development of cellulose-based sensors for the determination of volatile analytes and derivatives of analytical relevance. In particular, strategies described in the literature for the fabrication and modification of cellulose-based substrates with responsive materials are summarized. In addition, selected contributions reported in the field of paper-based volatile sensors are discussed, with a particular emphasis on quick response (QR) code paper-based platforms, intelligent films for food freshness monitoring, and sensor arrays for volatile discrimination purposes. Furthermore, analytical strategies devised for the determination of ionic species by in situ generation of volatile derivatives in both paper-based analytical devices (PADs) and microfluidic PADs will also be described. Graphical abstract

Refletimos sobre as construções de sentido presentes na obra Black Is King, de Beyoncé, que ativam a ideia de uma África reimaginada e fabulada. Ao apresentar elementos positivos relacionados aos conceitos de negro e de negritude, o filme rompe com imagens de controle ocidentais sobre pessoas racializadas, mostrando-as de outras formas, como nas performances da gravidez e da maternidade presentes na produção. Em narrativa não-linear e pouco comum na chamada cultura mainstream, Black Is King resgata o passado em uma atitude como a do pássaro sankofa, que volta para buscar o que é seu. O filme se desenrola em imagens, canções e histórias que evocam uma visão do tempo em espiral, que volta e vem, reinventando um passado roubado e propondo, no presente, outros lugares e imaginários para corpos negros.

The “Mental Health Impact of COVID-19 Pandemic on NIMH Patients and Volunteers” study was a longitudinal study launched in spring 2020 by researchers at NIMH, to investigate the effect of the emerging COVID-19 pandemic on mental health. For each participant, the study collected personal characteristics, such as demographics, psychological traits, and clinical history, together with personal circumstances at regular intervals during their enrollment in the study. In November of 2022, we submitted a preprint (https://doi.org/10.31234/osf.io/7enqw, attached as 10.31234psyarxiv_preprint.pdf), in which we examined the degree to which a variety of mental health outcomes over time for an individual can be predicted from personal characteristics and their changing circumstances, using regression models trained on other study participants. We found that it is possible to predict the variation of a participant’s mental health outcomes from time point to time point, for most of the outcomes we consider. This capability is dominated by information about outcome at the point of enrollment in the study, but can be improved by considering personal characteristics and circumstances. In this pre-registration we formalize our hypotheses before applying the model we developed to the final held out dataset. All numbered references below ( § sections, figures, tables, and reference articles) refer to the preprint.

Autonomous Mobility-on-Demand (AMoD) systems are a rapidly evolving mode of transportation in which a centrally coordinated fleet of self-driving vehicles dynamically serves travel requests. The control of these systems is typically formulated as a large network optimization problem, and reinforcement learning (RL) has recently emerged as a promising approach to solve the open challenges in this space. However, current RL-based approaches exclusively focus on learning from online data, fundamentally ignoring the per-sample-cost of interactions within real-world transportation systems. To address these limitations, we propose to formalize the control of AMoD systems through the lens of offline reinforcement learning and learn effective control strategies via solely offline data, thus readily available to current mobility operators. We further investigate design decisions and provide experiments on real-world mobility systems showing how offline learning allows to recover AMoD control policies that (i) exhibit performance on par with online methods, (ii) drastically improve data efficiency, and (iii) completely eliminate the need for complex simulated environments. Crucially, this paper demonstrates that offline reinforcement learning is a promising paradigm for the application of RL-based solutions within economically-critical systems, such as mobility systems.

Pectin methylesterase (PME) is the target-enzyme in orange juice processing, and its inactivation preserves the original juice’s cloud, a valuable quality attribute for consumers. This study was primarily undertaken to evaluate the combination of supercritical carbon dioxide (SC-CO2) and mild temperatures on PME inactivation in the juice. Physicochemical and color parameters were also evaluated. Pressures (P) in the range of 74 to 351 bar, temperatures (T) between 33°C and 67°C, and holding times (t) varying from 20 to 70 min were tested using a central composite rotational design. Determination of PME activity, pH, titratable acidity, soluble solids, ratio, lightness, °hue and chroma in freshly extracted/raw and treated juice samples, and total color difference (TCD) between raw and treated juice were carried out. The percentage of PME reduction widely ranged from 13.7% to 91.8%. The variable P had a significant effect (p ≤ 0.05) on TCD and acidity variation. T exhibited a significant effect on the PME reduction, TCD, and soluble solids variation; however, t impacted on the soluble solids variation only. The interaction between P and t (P.t) affected the soluble solids and acidity variation, and T.t affected TCD. The combination of SC-CO2 and mild temperatures is potentially capable of inactivating PME in orange juice.

O estado de Minas Gerais possui uma rica fauna de anfíbios anuros, contudo, algumas regiões ainda são pouco subamostradas, como o sul do estado. Portanto, o objetivo do presente estudo é realizar um inventário da anurofauna em florestas estacionais semideciduais no sul do estado de Minas Gerais. O trabalho foi conduzido no período de novembro de 2013 a abril de 2018, totalizando 157 dias de amostragens em 12 municípios. Foram registradas 32 espécies de anuros distribuídas em seis famílias, ampliando o número de espécies de anuros para o sul do estado de Minas Gerais incluindo espécies raras e em declínio populacional como Vitreorana uranoscopa e Proceratophrys boiei. Nessa perspectiva, ações como a criação de Unidades de Conservação na região são urgentes.

Tendo em vista as fortes pressões por parte dos centros propagadores de ciência, e que, por isso, escolas confessionais tem mantido um relacionamento eclético com as práticas pedagógicas seculares, este artigo objetiva analisar o problema do ecletismo na educação escolar cristã, bem como destacar a necessidade de escolas distintamente cristãs, de maneira que não apenas a fundação filosófica, mas toda a superestrutura do sistema educacional seja formada por elementos e processos que estão em harmonia com a cosmovisão bíblica. Para tanto, realizou-se como procedimento metodológico uma pesquisa pura e dedutiva, por meio de revisão bibliográfica sistemática de trabalhos publicados em revistas científicas e livros que tratam sobre o tema em questão. Diante disso, verificou-se que o ecletismo é inconsistente e incoerente consigo mesmo e, por tanto, de natureza insatisfatória para a educação cristã, a qual é dissemelhante e consagrada por se estruturar completamente na existência de um Deus Trino, que está em processo de restauração de todas as coisas, e que, a partir disso, os educadores cristãos devem desenvolver práticas que unam os princípios eternos do cristianismo com as necessidades e peculiaridades do seu contexto.

A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding object concepts in a vector space using data collected from humans in a triplet odd-one-out task. VICE uses variational inference to obtain sparse, non-negative representations of object concepts with uncertainty estimates for the embedding values. These estimates are used to automatically select the dimensions that best explain the data. We derive a PAC learning bound for VICE that can be used to estimate generalization performance or determine a sufficient sample size for experimental design. VICE rivals or outperforms its predecessor, SPoSE, at predicting human behavior in the triplet odd-one-out task. Furthermore, VICE's object representations are more reproducible and consistent across random initializations, highlighting the unique advantage of using VICE for deriving interpretable embeddings from human behavior., Comment: Accepted at NeurIPS 2022

Intelligent Transportation Systems (ITS) underpin the concept of Mobility as a Service (MaaS), which requires universal and seamless users' access across multiple public and private transportation systems while allowing operators' proportional revenue sharing. Current user sensing technologies such as Walk-in/Walk-out (WIWO) and Check-in/Check-out (CICO) have limited scalability for large-scale deployments. These limitations prevent ITS from supporting analysis, optimization, calculation of revenue sharing, and control of MaaS comfort, safety, and efficiency. We focus on the concept of implicit Be-in/Be-out (BIBO) smartphone-sensing and classification. To close the gap and enhance smartphones towards MaaS, we developed a proprietary smartphone-sensing platform collecting contemporary Bluetooth Low Energy (BLE) signals from BLE devices installed on buses and Global Positioning System (GPS) locations of both buses and smartphones. To enable the training of a model based on GPS features against the BLE pseudo-label, we propose the Cause-Effect Multitask Wasserstein Autoencoder (CEMWA). CEMWA combines and extends several frameworks around Wasserstein autoencoders and neural networks. As a dimensionality reduction tool, CEMWA obtains an auto-validated representation of a latent space describing users' smartphones within the transport system. This representation allows BIBO clustering via DBSCAN. We perform an ablation study of CEMWA's alternative architectures and benchmark against the best available supervised methods. We analyze performance's sensitivity to label quality. Under the na\"ive assumption of accurate ground truth, XGBoost outperforms CEMWA. Although XGBoost and Random Forest prove to be tolerant to label noise, CEMWA is agnostic to label noise by design and provides the best performance with an 88\% F1 score., Comment: 20 pages, 13 figures, 3 tables

Introducción: las acciones de sprint constituyen el mecanismo de lesión de la musculatura isquiotibial más común en el fútbol y la incidencia de lesión parece lejos de reducirse. Objetivos: analizar los factores biomecánicos del sprint asociados a la lesión de la musculatura isquiotibial en el fútbol. Metodología: revisión en las bases de datos Pubmed, Cochrane y Scopus. Resultados: La actividad de los músculos del tronco y la anteversión pélvica durante la fase posterior del swing; la actividad de la musculatura glútea y la posición del tronco durante la fase anterior del swing; y la aplicación de fuerza horizontal durante la fase de apoyo del sprint son elementos clave en la prevención y rehabilitación de lesiones en los isquiotibiales. Conclusiones: existe una importante brecha de conocimiento en relación a las variables biomecánicas del sprint asociadas a la lesión isquiotibial en el fútbol. Introduction: sprinting actions are the most common hamstring injury mechanism in football and the incidence of injury seems far from being reduced. Objectives: to analyze sprinting biomechanical factors associated with hamstring injury in football. Methodology: literature review using Pubmed, Cochrane and Scopus databases. Results: the activity of the trunk muscles and anterior pelvic tilt during posterior phase of the swing, the activity of gluteal muscles and trunk position during anterior phase of the swing; and the application of horizontal force during stance phase of sprinting are key elements in the prevention and rehabilitation of hamstring injury. Conclusions: there is an important knowledge gap in relation to the biomechanical variables of the sprint associated with the hamstring injury in the football environment. Graduado o Graduada en Fisioterapia por la Universidad Pública de Navarra Fisioterapian Graduatua Nafarroako Unibertsitate Publikoan

The travel demand modeling field is ripe for re-invention as we are on the cusp of the next generation of models. We find ourselves in arguably the most dynamic transport environment in the history of the field. Massive data are becoming available and new developments in data analysis methods are being developed. Researchers from disciplines such as computer science and physics are entering the domain, along with high-tech, entrepreneurial firms. There is also a change in the nature of data. Whereas historically the most important data sets in transportation���Census data, household travel surveys, origin-destination surveys���were collected by public agencies, the new generation of ���big data��� is most often held by private entities (Shuldiner and Shuldiner 2013). There are important questions around the continued availability of such data to modelers, as well as what model developments need to take place to fully exploit the potential of such data. Finally, not only are new data and methods changing the modeling, but transport technology and patterns are themselves rapidly changing as new modes such as app-based ride sharing of cars, bikes, and scooters are making their presence felt in large US cities. Looking to the future, partly or fully autonomous passenger, freight, and aerial vehicles are rapidly advancing.

Predicting the supply and demand of transport systems is vital for efficient traffic management, control, optimization, and planning. For example, predicting where from/to and when people intend to travel by taxi can support fleet managers to distribute resources; better predicting traffic speeds/congestion allows for pro-active control measures or for users to better choose their paths. Making spatio-temporal predictions is known to be a hard task, but recently Graph Neural Networks (GNNs) have been widely applied on non-euclidean spatial data. However, most GNN models require a predefined graph, and so far, researchers rely on heuristics to generate this graph for the model to use. In this paper, we use Neural Relational Inference to learn the optimal graph for the model. Our approach has several advantages: 1) a Variational Auto Encoder structure allows for the graph to be dynamically determined by the data, potentially changing through time; 2) the encoder structure allows the use of external data in the generation of the graph; 3) it is possible to place Bayesian priors on the generated graphs to encode domain knowledge. We conduct experiments on two datasets, namely the NYC Yellow Taxi and the PEMS road traffic datasets. In both datasets, we outperform benchmarks and show performance comparable to state-of-the-art. Furthermore, we do an in-depth analysis of the learned graphs, providing insights on what kinds of connections GNNs use for spatio-temporal predictions in the transport domain.

We propose a decoding-based approach to detect context effects on neural codes in longitudinal neural recording data. The approach is agnostic to how information is encoded in neural activity, and can control for a variety of possible confounding factors present in the data. We demonstrate our approach by determining whether it is possible to decode location encoding from prefrontal cortex in the mouse and, further, testing whether the encoding changes due to task engagement., Comment: 39 pages, 13 figures

Financiado para publicación en acceso aberto: Universidade de Vigo/CISUG The present work reports on the development of a 3D origami microfluidic paper-based analytical device (3D µPAD) for the determination of iodide and iodate in edible seaweeds by smartphone-based colorimetric detection. In addition, a methacrylate holder was designed and fabricated to enhance interlayer contact in 3D µPADs, obtaining excellent sensitivity and precision, also allowing real-time monitoring in a straightforward and expeditious way. The reported assay, based on the formation of a blue colored triiodide-starch complex at the detection areas of the 3D µPAD, represents an affordable, fast and greener alternative for the simultaneous determination of inorganic iodine species. Under optimal conditions, the proposed method showed limits of detection and quantification of 9.8 and 32.7 µM for I- and 0.6 and 1.8 µM for IO3-, respectively. The repeatability, expressed as relative standard deviation, was 1.7% and 3.3% for I- and IO3-, respectively. The proposed 3D µPAD was applied to the determination of iodine species in extracts of edible seaweeds and related food additives, showing satisfactory recoveries (90–109%). Agencia Estatal de Investigación | Ref. RTI2018-093697-B-I00

Portugues Esse trabalho teve o objetivo de desenvolver um aplicativo para dispositivos moveis, com o intuito de contribuir para as campanhas educativas de saude bucal no municipio de Remigio-PB. O estudo foi desenvolvido com alunos das turmas do 9° ano em duas escolas municipais da zona urbana. Inicialmente, foi apresentado o aplicativo de cunho informativo para o total de 100 alunos e em seguida, foram aplicados dois questionarios. O primeiro questionario tratava sobre o conhecimento que os alunos tinham em relacao a saude bucal e o segundo tratava sobre a aceitacao do aplicativo para dispositivos moveis. Para a elaboracao do aplicativo foi utilizada a plataforma Android Studio (versao 27.1.1) e para a criacao de aplicativos nessa plataforma, utilizou-se a programacao em JAVA. Observou-se que cerca de 31% dos alunos ja tiveram alguma doenca bucal, e apenas duas enfermidades ja haviam sido contraidas pelos estudantes, destacando-se a carie, com 66% e a afta com 34%. 32% dos alunos afirmaram que sabem os fatores causadores das doencas bucais, e 89% afirmaram que sabem como prevenir essas doencas bucais. 34% dos alunos acharam as informacoes do aplicativo relevantes e 66,1% acham que as chances de contrair alguma doenca causada pela ma higiene bucal iria diminuir caso esse aplicativo fosse disponibilizado para a populacao. 48% dos alunos avaliaram o aplicativo como “Bom”. Conclui-se que os resultados alcancados foram bastante positivos em relacao a aceitacao do aplicativo, que tem o intuito de divulgar informacoes e tem o objetivo de ajudar nas campanhas educativas sobre a saude bucal.Palavras-chave: Doencas bucais. Aplicativo. Android studio. Java. Ingles This paper had the objective of developing an app for mobile devices, aiming to contribute to educational campaigns of oral health in the municipality of Remigio-PB. The study was developed with students of the 9th grade class in two municipal schools in the urban zone. Initially, the informative application was presented for the total of 100 students and then two questionnaires were applied. The first questionnaire dealt with students' knowledge of oral health and the second was about accepting the mobile application. For the elaboration of the application was used the platform Android Studio (version 27.1.1) and for the creation of applications in this platform, was used the programming in JAVA. It was observed that about 31% of the students already had some oral disease, and only two diseases had been contracted by the students, especially caries, with 66% and the cold sore with 34%. 32% of the students said they know the factors that cause oral diseases, and 89% said they know how to prevent these oral diseases. 34% of the students found the application information relevant and 66.1% felt that the chances of contracting a disease caused by poor oral hygiene would decrease if this application was made available to the population. 48% of students rated the application as "Good". It is concluded that the results achieved were very positive in relation to the acceptance of the application, which is intended to disseminate information and aims to assist in educational campaigns on oral health.Keywords: Oral diseases. Application. Android studio. Java.

Orientador : João Mauricio Rosario Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecanica Resumo: Este trabalho apresenta a integração de empresa e focaliza a modelagem empresarial como uma abordagem dessa integração, tendo como contexto a realidade das empresas de manufatura atuais e como referencial teórico, as arquiteturas de referência CIMOS A (Open System Architecture for CIM), GRAI-GIM (GRAI Integrated Methodology), a arquitetura PERA (Purdue Enterprise Reference Architecture) e a metodologia CimOsa/rg. Descreve o ambiente experimental PIPEF A (Plataforma Industrial para Pesquisa, Ensino e Formação em Automação), redefine seus elementos principais e procede a sua modelagem empresarial, desenvolvendo um modelo conceitual segundo a sistemática da metodologia CimOsa/rg e a arquitetura CIMOS A, contando com o auxílio da ferramenta computacional Cimtool. O modelo descreve a plataforma de maneira semi-formal e não-ambígüa, segundo dois domínios: Planejamento e Controle da Produção e Chão de Fábrica, a partir dos quais explicita seus requisitos funcionais, em termos de Processos de Negócio e Atividades de Empresa; determina as necessidades de informação em termos de Vistas de Objetos e Objetos de Empresa e esses em Segmentos de um Modelo Semântico de Informação. Exprime também os conhecimentos utilizados em um subconjunto dessas atividades e os recursos necessários Abstract: This work describes enterprise integration focusing enterprise modeling as an integration approach. It considers manufacturing enterprise reality as the current context and CIMOS A (Open System Architecture for CIM), GRAI-GIM (GRAI Integrated Methodology), PERA (Purdue Enterprise Reference Architecture) and CimOsalrg methodology as its theoretical reference. It presents PIPEFA (Industrial Platform for Research, Teaching, and Training in Automation) experimental environment, redefines its main elements and develops a conceptual model according to CimOsalrg methodology and CIMOS A architecture. The development process is supported by Cimtool, a toei CASE (Computed-aided Software Engineering). The developed model expresses the plataform on a semi-formal and unambiguous manner, in terms of two domains: Production Control and Shop FIoor; the model defines PIFEFA's functional requirements in terms of Business Processes and Enterprise Activities, and also shows its information requirements in terms of enterprise Object View and Enterprise Objects; these latter are organized into three Segments of a Semantic Information Mode! Mestrado Mecânica dos Sólidos Mestre em Engenharia Mecânica

Cyclomaltodextrin glucanotransferase (CGTase) is used mainly for the industrial production of cyclodextrins (CDs). Their potential use and availability have played a decisive role in increasing demand for CDs. The aim of the present study was to immobilize the commercial CGTase (Toruzyme® 3.0L) into controlled pore silica by surface anchoring (SCGT-A) and covalent bonding (SCGT-CB) and to apply the immobilized enzymes in the production of CDs. The SCGT-A method proved to be efficient due to its short immobilization time and, it was possible to immobilize 89.63% of CGTase, in terms of activity. Commercial CGTase immobilized by SCGT-CB provided an immobilization yield of 48.44%, also in terms of activity. There was an increase in total CD production of 60.97% and 34.90% for SCGT-A and SCGT-CB, respectively, in a reaction medium with 10% ethanol, compared to the same media without ethanol. Higher CD production occured at 24 and 48 h for SCGT-A and SCGT-CB methods, respectively. The use of SCGT-A was more efficient than SCGT-CB and free enzyme (CGT-F) after 24 h of reaction. The increase production of CDs obtained by the SCGT-A method and the demonstrated continuous production of CDs with the SCGT-CB method make the use of immobilized CGTase viable for industrial applications.

This study proposes a novel approach that combines theory and data-driven choice models using Artificial Neural Networks (ANNs). In particular, we use continuous vector representations, called embeddings, for encoding categorical or discrete explanatory variables with a special focus on interpretability and model transparency. Although embedding representations within the logit framework have been conceptualized by Pereira (2019), their dimensions do not have an absolute definitive meaning, hence offering limited behavioral insights in this earlier work. The novelty of our work lies in enforcing interpretability to the embedding vectors by formally associating each of their dimensions to a choice alternative. Thus, our approach brings benefits much beyond a simple parsimonious representation improvement over dummy encoding, as it provides behaviorally meaningful outputs that can be used in travel demand analysis and policy decisions. Additionally, in contrast to previously suggested ANN-based Discrete Choice Models (DCMs) that either sacrifice interpretability for performance or are only partially interpretable, our models preserve interpretability of the utility coefficients for all the input variables despite being based on ANN principles. The proposed models were tested on two real world datasets and evaluated against benchmark and baseline models that use dummy-encoding. The results of the experiments indicate that our models deliver state-of-the-art predictive performance, outperforming existing ANN-based models while drastically reducing the number of required network parameters.

ACTAS DO 17º ENCONTRO PORTUGUÊS DE COMPUTAÇÃO GRÁFICA, Computação Gráfica em Tempo Real e Paralelização de Algoritmos, 221, 229, Francisco Pereira, João Paulo Moura, José Afonso Bulas Cruz Luís Magalhães, and Alan Chamers

We study entanglement-assisted quantum error-correcting codes (EAQECCs) arising from classical one-point algebraic geometry codes from the Hermitian curve with respect to the Hermitian inner product. Their only unknown parameter is $c$, the number of required maximally entangled quantum states since the Hermitian dual of an AG code is unknown. In this article, we present an efficient algorithmic approach for computing $c$ for this family of EAQECCs. As a result, this algorithm allows us to provide EAQECCs with excellent parameters over any field size., Comment: 23 pages

Nos últimos anos, vários investigadores têm proposto métodos semiautomáticos para a geração expedita de modelos virtuais de edifícios e até cidades, em alguns casos direcionados à reconstrução do património cultural. No entanto, a maioria das abordagens propostas foca a geração de exteriores. Apesar da investigação realizada em torno da geração de interiores não ser inexistente, denota-se ainda uma falha de regras reais de suporte à geração dos referidos interiores. Para além disso, a especificação das regras de geração envolve extensas análises a documentos como livros, documentos históricos, plantas e outras representações gráficas, bem como o conhecimento de linguagens de gramática. Pretende-se seguir estas linhas de investigação a fim de obter uma ferramenta de modelação expedita que produza modelos de alta-fidelidade, empregando modelação procedimental. Este artigo tem como objetivo a apresentação da arquitetura geral de um sistema para a reconstrução virtual de sítios arqueológicos, incluindo exteriores e interiores de edifícios, aplicando um novo método de extração de informação a partir de dados geográficos e descrições textuais. Também se pretende apresentar a ontologia abstrata que regula a extração de dados e que define o esquema semântico de relação entre os diferentes elementos que integram uma cidade genérica, de forma a permitir a derivação por parte de outros estilos arquitetónicos., 20o EPCG: Encontro Portugues de Computacao Grafica, Artigos longos, 89, 95, Telmo Adão, Luís Magalhães, Maximino Bessa, António Coelho, Augusto Sousa, Nuno Rodrigues, Alexandrino Gonçalves, Roberto Rodrigues, Francisco Pereira, João Moura, and Luís Paulo Reis, Grammars and Other Rewriting Systems, Computational Geometry and Object Modeling, Ontology, Rule-based generation model, Procedural Modelling, Three-Dimensional Graphics and Realism

Shared mobility services require accurate demand models for effective service planning. On the one hand, modeling the full probability distribution of demand is advantageous because the entire uncertainty structure preserves valuable information for decision-making. On the other hand, demand is often observed through the usage of the service itself, so that the observations are censored, as they are inherently limited by available supply. Since the 1980s, various works on Censored Quantile Regression models have performed well under such conditions. Further, in the last two decades, several papers have proposed to implement these models flexibly through Neural Networks. However, the models in current works estimate the quantiles individually, thus incurring a computational overhead and ignoring valuable relationships between the quantiles. We address this gap by extending current Censored Quantile Regression models to learn multiple quantiles at once and apply these to synthetic baseline datasets and datasets from two shared mobility providers in the Copenhagen metropolitan area in Denmark. The results show that our extended models yield fewer quantile crossings and less computational overhead without compromising model performance., Comment: 13 pages, 9 figures, 5 tables

Poster presented at the 2020 conference for the Organization for Human Brain Mapping

This short article describes a deep neural network trained to perform automatic segmentation of human body parts in natural scenes. More specifically, we trained a Bayesian SegNet with concrete dropout on the Pascal-Parts dataset to predict whether each pixel in a given frame was part of a person's hair, head, ear, eyebrows, legs, arms, mouth, neck, nose, or torso.

Discrete choice models (DCMs) require a priori knowledge of the utility functions, especially how tastes vary across individuals. Utility misspecification may lead to biased estimates, inaccurate interpretations and limited predictability. In this paper, we utilize a neural network to learn taste representation. Our formulation consists of two modules: a neural network (TasteNet) that learns taste parameters (e.g., time coefficient) as flexible functions of individual characteristics; and a multinomial logit (MNL) model with utility functions defined with expert knowledge. Taste parameters learned by the neural network are fed into the choice model and link the two modules. Our approach extends the L-MNL model (Sifringer et al., 2020) by allowing the neural network to learn the interactions between individual characteristics and alternative attributes. Moreover, we formalize and strengthen the interpretability condition - requiring realistic estimates of behavior indicators (e.g., value-of-time, elasticity) at the disaggregated level, which is crucial for a model to be suitable for scenario analysis and policy decisions. Through a unique network architecture and parameter transformation, we incorporate prior knowledge and guide the neural network to output realistic behavior indicators at the disaggregated level. We show that TasteNet-MNL reaches the ground-truth model's predictability and recovers the nonlinear taste functions on synthetic data. Its estimated value-of-time and choice elasticities at the individual level are close to the ground truth. On a publicly available Swissmetro dataset, TasteNet-MNL outperforms benchmarking MNLs and Mixed Logit model's predictability. It learns a broader spectrum of taste variations within the population and suggests a higher average value-of-time.

This study presents a semi-nonparametric Latent Class Choice Model (LCCM) with a flexible class membership component. The proposed model formulates the latent classes using mixture models as an alternative approach to the traditional random utility specification with the aim of comparing the two approaches on various measures including prediction accuracy and representation of heterogeneity in the choice process. Mixture models are parametric model-based clustering techniques that have been widely used in areas such as machine learning, data mining and patter recognition for clustering and classification problems. An Expectation-Maximization (EM) algorithm is derived for the estimation of the proposed model. Using two different case studies on travel mode choice behavior, the proposed model is compared to traditional discrete choice models on the basis of parameter estimates' signs, value of time, statistical goodness-of-fit measures, and cross-validation tests. Results show that mixture models improve the overall performance of latent class choice models by providing better out-of-sample prediction accuracy in addition to better representations of heterogeneity without weakening the behavioral and economic interpretability of the choice models.

In order to interact with objects in our environment, humans rely on an understanding of the actions that can be performed on them, as well as their properties. When considering concrete motor actions, this knowledge has been called the object affordance. Can this notion be generalized to any type of interaction that one can have with an object? In this paper we introduce a method to represent objects in a space where each dimension corresponds to a broad mode of interaction, based on verb selectional preferences in text corpora. This object embedding makes it possible to predict human judgments of verb applicability to objects better than a variety of alternative approaches. Furthermore, we show that the dimensions in this space can be used to predict categorical and functional dimensions in a state-of-the-art mental representation of objects, derived solely from human judgements of object similarity. These results suggest that interaction knowledge accounts for a large part of mental representations of objects., Comment: 17 pages, 13 figures, 6 tables

With an increased focus on minimizing traffic externalities in metropolitan areas, a growing interest in environmentally friendly and shared mobility systems has emerged, such as electric car-sharing systems. However, increasing demand and larger area coverage often make it difficult to keep cars available where and when customers need them. This problem can be alleviated by predicting for how long cars stay vacant at given pick-up/drop-off locations. To maximize their usage, it can be more beneficial to relocate the cars at certain periods to more desired locations. In this paper, we tackle the problem of predicting time-to-pickup for shared cars in a probabilistic way as a function of time by applying time-to-event modelling through survival analysis. Both statistical and deep neural network approaches to survival regression were investigated. The Cox proportional hazards model (CPH) is compared to the deep neural network model DeepSurv. To predict survival times, a two-step approach was formulated, where in the upper level a classification is used to classify cars into two groups based on idle time duration, whereas in the lower level for each given group time-to-event modelling is applied. DeepSurv method demonstrated a stronger fit compared to CPH. The two-step approach resulted in over 15% improvement in performance, comparing to the one-step approach, where no classification is used., Proceedings from the Annual Transport Conference at Aalborg University, Vol. 27 No. 1 (2020): Proceedings from the Annual Transport Conference at Aalborg University

Deep neural networks (DNNs) are being increasingly used to make predictions from functional magnetic resonance imaging (fMRI) data. However, they are widely seen as uninterpretable "black boxes", as it can be difficult to discover what input information is used by the DNN in the process, something important in both cognitive neuroscience and clinical applications. A saliency map is a common approach for producing interpretable visualizations of the relative importance of input features for a prediction. However, methods for creating maps often fail due to DNNs being sensitive to input noise, or by focusing too much on the input and too little on the model. It is also challenging to evaluate how well saliency maps correspond to the truly relevant input information, as ground truth is not always available. In this paper, we review a variety of methods for producing gradient-based saliency maps, and present a new adversarial training method we developed to make DNNs robust to input noise, with the goal of improving interpretability. We introduce two quantitative evaluation procedures for saliency map methods in fMRI, applicable whenever a DNN or linear model is being trained to decode some information from imaging data. We evaluate the procedures using a synthetic dataset where the complex activation structure is known, and on saliency maps produced for DNN and linear models for task decoding in the Human Connectome Project (HCP) dataset. Our key finding is that saliency maps produced with different methods vary widely in interpretability, in both in synthetic and HCP fMRI data. Strikingly, even when DNN and linear models decode at comparable levels of performance, DNN saliency maps score higher on interpretability than linear model saliency maps (derived via weights or gradient). Finally, saliency maps produced with our adversarial training method outperform those from other methods.

Objetiva-se neste artigo, analisar a sentença da Corte Interamericana de Direitos Humanos (CtIDH) que se debruçou sobre o assunto ao tratar do caso Trabalhadores da Fazenda Brasil Verde vs. Brasil. Tem-se, como pergunta de pesquisa, a seguinte: o que a CtIDH entende como escravidão? Foram adotadas abordagens variadas de pesquisa qualitativa, a partir de revisão bibliográfica disponível sobre o tema. Conclui-se que a escravidão é forma de exploração que rouba a dignidade do trabalhador, muitas vezes mediante o cerceamento da liberdade.

Travel demand forecasting models play an important role in guiding policy, planning, and design of transportation systems. There is no shortage of literature critiquing the accuracy of model forecasts (see, for example, Pickrell, 1989; Wachs, 1990; Pickrell, 1992; Flyvbjerg, Skamris Holm, and Buhl 2005; Richmond, 2005; Flyvbjerg, 2007; Bain, 2009; Parthasarathi and Levinson, 2010; Welde and Odeck, 2011; Hartgen, 2013; Nicolaisen and Driscoll, 2014; Schmitt, 2016; Odeck and Welde, 2017, and Voulgaris, 2019), not to mention several high-profile lawsuits (Saulwick 2014, Stacey 2015, Rubin 2018). Many researchers and practitioners feel more can be done to advance rigorous travel analysis methods for the public good (see, e.g., zephyrtransport.org). Motivated by these critiques, a two-day, NSF-funded workshop was held at UC Berkeley in the Spring of 2017 to engage in a fundamental review of the state of the art in travel demand modeling, to discuss the future of the field, and to propose new directions and processes for advancing the science.Travel demand forecasting is an inherently practical enterprise. While academics drive the fundamental research, the users of travel demand models and forecasts are typically government agencies and transport operators that use the models to inform long-range investment, funding, and planning decisions. Private firms play a key role in assisting the agencies in both development and application of the models, and, more recently, high-tech firms have entered the development fray. While all of these actors have important roles in advancing the science of the field, in this report we focus our attention primarily on the academic side of the enterprise, consistent with the orientation of the funding agency (NSF), and in order to make the task manageable. That said, other sectors are represented in various parts of this report as they interface with academics or play particularly central roles in our proposals for advancing the science.

This study develops an online predictive optimization framework for dynamically operating a transit service in an area of crowd movements. The proposed framework integrates demand prediction and supply optimization to periodically redesign the service routes based on recently observed demand. To predict demand for the service, we use Quantile Regression to estimate the marginal distribution of movement counts between each pair of serviced locations. The framework then combines these marginals into a joint demand distribution by constructing a Gaussian copula, which captures the structure of correlation between the marginals. For supply optimization, we devise a linear programming model, which simultaneously determines the route structure and the service frequency according to the predicted demand. Importantly, our framework both preserves the uncertainty structure of future demand and leverages this for robust route optimization, while keeping both components decoupled. We evaluate our framework using a real-world case study of autonomous mobility in a university campus in Denmark. The results show that our framework often obtains the ground truth optimal solution, and can outperform conventional methods for route optimization, which do not leverage full predictive distributions., Comment: 34 pages, 12 figures, 5 tables

Entanglement-assisted quantum (QUENTA) codes are a subclass of quantum error-correcting codes which use entanglement as a resource. These codes can provide error correction capability higher than the codes derived from the traditional stabilizer formalism. In this paper, it is shown a general method to construct QUENTA codes from cyclic codes. Afterwards, the method is applied to Reed-Solomon codes, BCH codes, and general cyclic codes. We use the Euclidean and Hermitian construction of QUENTA codes. Two families of QUENTA codes are maximal distance separable (MDS), and one is almost MDS or almost near MDS. The comparison of the codes in this paper is mostly based on the quantum Singleton bound.

To study users' travel behaviour and travel time between origin and destination, researchers employ travel surveys. Although there is consensus in the field about the potential, after over ten years of research and field experimentation, Smartphone-based travel surveys still did not take off to a large scale. Here, computer intelligence algorithms take the role that operators have in Traditional Travel Surveys; since we train each algorithm on data, performances rest on the data quality, thus on the ground truth. Inaccurate validations affect negatively: labels, algorithms' training, travel diaries precision, and therefore data validation, within a very critical loop. Interestingly, boundaries are proven burdensome to push even for Machine Learning methods. To support optimal investment decisions for practitioners, we expose the drivers they should consider when assessing what they need against what they get. This paper highlights and examines the critical aspects of the underlying research and provides some recommendations: (i) from the device perspective, on the main physical limitations; (ii) from the application perspective, the methodological framework deployed for the automatic generation of travel diaries; (iii)from the ground truth perspective, the relationship between user interaction, methods, and data.

To study how mental object representations are related to behavior, we estimated sparse, non-negative representations of objects using human behavioral judgments on images representative of 1,854 object categories. These representations predicted a latent similarity structure between objects, which captured most of the explainable variance in human behavioral judgments. Individual dimensions in the low-dimensional embedding were found to be highly reproducible and interpretable as conveying degrees of taxonomic membership, functionality, and perceptual attributes. We further demonstrated the predictive power of the embeddings for explaining other forms of human behavior, including categorization, typicality judgments, and feature ratings, suggesting that the dimensions reflect human conceptual representations of objects beyond the specific task., Comment: Accepted in ICLR 2019

O objetivo foi avaliar os efeitos dos recipientes e substratos no crescimento e na qualidade de mudas de cafeeiro em diferentes cultivares. O experimento foi conduzido no setor de produção de mudas do Ifsuldeminas - Campus Inconfidentes, Inconfidentes/MG, em um delineamento de blocos casualizados com três repetições por tratamento, sendo as parcelas constituídas por 30 plantas. O esquema fatorial foi de 2 x 2 x 5 sendo constituídos por dois recipientes, dois tipos de substratos e cinco cultivares de cafeeiro. Foram avaliados os parâmetros de crescimento e qualidade das mudas. Os resultados obtidos permitiram verificar que para a produção de mudas, os recipientes e os substratos interfere no crescimento das mudas cafeeiras, apresentando melhor qualidade as mudas no recipiente tubete com a utilização do substrato comercial, sendo a cultivar Acauã e a cultivar Topázio 1190, produzida em sacolas com substrato comercial com melhores resultados. Pela análise multivariada verificou-se que o maior crescimento das mudas em altura e menor diâmetro de coleto foi responsável pela menor qualidade das mesmas. Diante disso, conclui-se que o saquinho preenchidos com substrato comercial, proporcionou mudas com maior crescimento. O tubete, preenchido com substrato comercial, proporcionou mudas de melhor qualidade; a cultivar que apresentou um maior desequilíbrio foi a Catuaí 144, no saquinho; as cultivares que apresentam melhores IQD são a Acauã com uso de tubete em substrato comercial e a Topázio 1190 na sacolinha; a variável que mais contribui para o IQD foi a MSSR; o substrato padrão limitou o crescimento das raízes.

Spatio-temporal problems are ubiquitous and of vital importance in many research fields. Despite the potential already demonstrated by deep learning methods in modeling spatio-temporal data, typical approaches tend to focus solely on conditional expectations of the output variables being modeled. In this paper, we propose a multi-output multi-quantile deep learning approach for jointly modeling several conditional quantiles together with the conditional expectation as a way to provide a more complete "picture" of the predictive density in spatio-temporal problems. Using two large-scale datasets from the transportation domain, we empirically demonstrate that, by approaching the quantile regression problem from a multi-task learning perspective, it is possible to solve the embarrassing quantile crossings problem, while simultaneously significantly outperforming state-of-the-art quantile regression methods. Moreover, we show that jointly modeling the mean and several conditional quantiles not only provides a rich description about the predictive density that can capture heteroscedastic properties at a neglectable computational overhead, but also leads to improved predictions of the conditional expectation due to the extra information and a regularization effect induced by the added quantiles., Comment: 12 pages, 9 figures

Este artigo realiza uma discussão acerca do conceito de pós-humanismo, da figuração ciborgue de Donna Haraway, assim como da noção de vulnerabilidade elaborada por Judith Butler. São sugeridas apropriações para se pensar condições e modos de vida na contemporaneidade a partir da análise de acontecimentos jornalísticos e a maneira com que repercutem na sociedade. Como método utiliza-se a revisão bibliográfica. Por fim, realiza-se a proposição de indicações metodológicas para se analisar coberturas jornalísticas com base nesses conceitos previamente discutidos.

This study aims to identify chaplains' understanding in relation to spirituality, and to analyze the chaplaincy's contribution in the care of terminal patients. It is exploratory, qualitative research undertaken with seven hospital chaplains in two public hospitals in João Pessoa in the Brazilian state of Paraíba. The data were collected through semi-structured interviews and were subjected to content analysis. The categories which emerged were: Spirituality in the view of chaplains, and Contributions of the chaplaincy in employing spirituality in the care of the terminal patient. These categories evidenced that the chaplains value the spiritual dimension in their practice, helping the patient to cope with the end-of-life process. It is hoped that this study will provide resources for further investigations, bearing in mind that studies on the chaplaincy and spirituality in the care for the terminal patient remain incipient. Este estudio tuvo como objetivo identificar la comprensión de los capellanes con relación a la espiritualidad y analizar la contribución de la capellanía al cuidado de enfermos terminales. Esta es una investigación exploratoria y cualitativa realizada con siete capellanes hospitalarios en 2 hospitales públicos en João Pessoa, Paraíba, Brasil. Los datos fueron recogidos por medio de entrevistas semiestructuradas y sometidos a análisis de contenido. Las categorías emergidas fueron: Espiritualidad bajo la visión de capellanes y Contribuciones de la capellanía al emplear espiritualidad en el cuidado al enfermo terminal. Estas categorías mostraron que los capellanes valoran la dimensión espiritual en su práctica y ayudan al paciente a hacer frente al proceso de terminalidad. Se espera que esta investigación proporcione recursos para nuevos abordajes, teniendo en cuenta que los estudios acerca de la capellanía y la espiritualidad en la asistencia al enfermo terminal aún son incipientes. Este estudo teve por objetivos identificar a compreensão dos capelães em relação à espiritualidade e analisar a contribuição da capelania no cuidado de pacientes terminais. Trata-se de pesquisa exploratória e qualitativa realizada com sete capelães hospitalares em dois hospitais públicos em João Pessoa-PB. Os dados foram coletados por meio de entrevistas semiestruturadas e submetidos à análise de conteúdo. As categorias emergidas foram: Espiritualidade na visão de capelães e Contribuições da capelania ao empregar a espiritualidade no cuidado ao paciente terminal. Tais categorias evidenciaram que os capelães valorizam a dimensão espiritual em sua prática, auxiliando o paciente a enfrentar o processo de terminalidade. Espera-se que esta pesquisa proporcione recursos para novas investigações, tendo em vista que os estudos sobre a capelania e a espiritualidade na assistência ao paciente terminal ainda são incipientes.

The calibration of dynamic traffic assignment (DTA) models involves the estimation of model parameters to best replicate real-world measurements. Good calibration is essential to estimate and predict accurately traffic states, which are crucial for traffic management applications to alleviate congestion. A widely used approach to calibrate simulation-based DTA models is the extended Kalman filter (EKF). The EKF assumes that the DTA model parameters are unconstrained, although they are in fact constrained; for instance, origin–destination (O-D) flows are nonnegative. This assumption is typically not problematic for small- and medium-scale networks in which the EKF has been successfully applied. However, in large-scale networks (which typically contain numbers of O-D pairs with small magnitudes of flow), the estimates may severely violate constraints. In consequence, simply truncating the infeasible estimates may result in the divergence of EKF, leading to extremely poor state estimations and predictions. To address this issue, a constrained EKF (CEKF) approach is presented; it imposes constraints on the posterior distribution of the state estimators to obtain the maximum a posteriori (MAP) estimates that are feasible. The MAP estimates are obtained with a heuristic followed by the coordinate descent method. The procedure determines the optimum and are computationally faster by 31.5% over coordinate descent and by 94.9% over the interior point method. Experiments on the Singapore expressway network indicated that the CEKF significantly improved model accuracy and outperformed the traditional EKF (up to 78.17%) and generalized least squares (up to 17.13%) approaches in state estimation and prediction.