Mitigating non-Lambertian surfaces issues in Stereo Matching with Neural Radiance Fields

Depth estimation from images has long been regarded as a preferable alternative compared to expensive and intrusive active sensors, such as LiDAR and ToF. The topic has attracted the attention of an increasingly wide audience thanks to the great amount of application domains, such as autonomous driving, robotic navigation and 3D reconstruction. Among the various techniques employed for depth estimation, stereo matching is one of the most widespread, owing to its robustness, speed and simplicity in setup. Recent developments has been aided by the abundance of annotated stereo images, which granted to deep learning the opportunity to thrive in a research area where deep networks can reach state-of-the-art sub-pixel precision in most cases. Despite the recent findings, stereo matching still begets many open challenges, two among them being finding pixel correspondences in presence of objects that exhibits a non-Lambertian behaviour and processing high-resolution images. Recently, a novel dataset named Booster, which contains high-resolution stereo pairs featuring a large collection of labeled non-Lambertian objects, has been released. The work shown that training state-of-the-art deep neural network on such data improves the generalization capabilities of these networks also in presence of non-Lambertian surfaces. Regardless being a further step to tackle the aforementioned challenge, Booster includes a rather small number of annotated images, and thus cannot satisfy the intensive training requirements of deep learning. This thesis work aims to investigate novel view synthesis techniques to augment the Booster dataset, with ultimate goal of improving stereo matching reliability in presence of high-resolution images that displays non-Lambertian surfaces.

Introducing CHIMeRA: from the development of a comprehensive biomedical database to the analysis of its sub-components.

In this thesis we discuss the expansion of an existing project, called CHIMeRA, which is a comprehensive biomedical network, and the analysis of its sub-components by using graph theory. We describe how it is structured internally, what are the existing databases from which it retrieves information and what machine learning techniques are used in order to produce new knowledge. We also introduce a new technique for graph exploration that is aimed to speed-up the network cover time under the condition that the analyzed graph is stellar; if this condition is satisfied, the improvement in the performance compared to the conventional exploration technique is extremely appealing. We show that the stellar structure is highly recurrent for sub-networks in CHIMeRA generated by queries, which made this technique even more interesting. Finally, we describe the convenience in using the CHIMeRA network for research purposes and what it could become in a very near future.

Metodi di deep learning per la decodifica di segnali neurali registrati dalla corteccia posteriore parietale di macaco

Le interfacce cervello-macchina (BMIs) permettono di guidare devices esterni utilizzando segnali neurali. Le BMIs rappresentano un’importante tecnologia per tentare di ripristinare funzioni perse in patologie che interrompono il canale di comunicazione tra cervello e corpo, come malattie neurodegenerative o lesioni spinali. Di importanza chiave per il corretto funzionamento di una BCI è la decodifica dei segnali neurali per trasformarli in segnali idonei per guidare devices esterni. Negli anni sono stati implementati diversi tipi di algoritmi. Tra questi gli algoritmi di machine learning imparano a riconoscere i pattern neurali di attivazione mappando con grande efficienza l’input, possibilmente l’attività dei neuroni, con l’output, ad esempio i comandi motori per guidare una possibile protesi. Tra gli algoritmi di machine learning ci si è focalizzati sulle deep neural networks (DNN). Un problema delle DNN è l’elevato tempo di training. Questo infatti prevede il calcolo dei parametri ottimali della rete per minimizzare l’errore di predizione. Per ridurre questo problema si possono utilizzare le reti neurali convolutive (CNN), reti caratterizzate da minori parametri di addestramento rispetto ad altri tipi di DNN con maggiori parametri come le reti neurali ricorrenti (RNN). In questo elaborato è esposto uno studio esplorante l’utilizzo innovativo di CNN per la decodifica dell’attività di neuroni registrati da macaco sveglio mentre svolgeva compiti motori. La CNN risultante ha consentito di ottenere risultati comparabili allo stato dell’arte con un minor numero di parametri addestrabili. Questa caratteristica in futuro potrebbe essere chiave per l’utilizzo di questo tipo di reti all’interno di BMIs grazie ai tempi di calcolo ridotti, consentendo in tempo reale la traduzione di un segnale neurale in segnali per muovere neuroprotesi.

Cyber threat intelligence: identifying hardcoded secrets in GitHub

The usage of version control systems and the capabilities of storing the source code in public platforms such as GitHub increased the number of passwords, API Keys and tokens that can be found and used causing a massive security issue for people and companies. In this project, SAP's secret scanner Credential Digger is presented. How it can scan repositories to detect hardcoded secrets and how it manages to filter out the false positives between them. Moreover, how I have implemented the Credential Digger's pre-commit hook. A performance comparison between three different implementations of the hook based on how it interacts with the Machine Learning model is presented. This project also includes how it is possible to use already detected credentials to decrease the number false positive by leveraging the similarity between leaks by using the Bucket System.

O corpo e seus textos : o estetico, o politico e o pedagogico na dança

Universidade Estadual de Campinas. Faculdade de Educação Física

O desenvolvimento do comportamento do bebê prematuro no primeiro ano de vida

Este artigo apresenta uma investigação do comportamento de 21 bebês prematuros (de 33 a 36 semanas de gestação), de 1 a 12 meses, realizada no Hospital da Universidade de São Paulo, Brasil, utilizando a Escala de Desenvolvimento do Comportamento da Criança - EDCC. Os resultados mostraram que os prematuros, a partir do 3º mês apresentaram uma recuperação importante no rítmo de desenvolvimento do comportamento, reduzindo de modo estatisticamente significante a frequência de comportamentos com indicadores patológicos, mas mantendo um fator de risco de 23% de comportamentos não-normalizados aos 12 meses incompletos. Este estudo contribui com elementos que podem favorecer o acompanhamento do processo de desenvolvimento do comportamento destas crianças e na detecção precoce de atrasos ou possíveis distúrbios neste processo

E-Learning Used in a Training Course on Atraumatic Restorative Treatment (ART) for Brazilian Dentists

The purpose of this study was to assess the benefits of using e-learning resources in a dental training course on Atraumatic Restorative Treatment (ART). This e-course was given in a DVD format, which presented the ART technique and philosophy. The participants were twenty-four dentists from the Brazilian public health system. Prior to receiving the DVD, the dentists answered a questionnaire regarding their personal data, previous knowledge about ART, and general interest in training courses. The dentists also participated in an assessment process consisting of a test applied before and after the course. A single researcher corrected the tests, and intraexaminer reproducibility was calculated (kappa=0.89). Paired t-tests were carried out to compare the means between the assessments, showing a significant improvement in the performance of the subjects on the test taken after the course (p<0.05). A linear regression model was used with the difference between the means as the outcome. A greater improvement on the test results was observed among female dentists (p=0.034), dentists working for a shorter period of time in the public health system (p=0.042), and dentists who used the ART technique only for urgent and/or temporary treatment (p=0.010). In conclusion, e-learning has the potential of improving the knowledge that dentists working in the public health system have about ART, especially those with less clinical experience and less knowledge about the subject.

Non-exhaustive tests for critical power estimation

Objective. - The aim of this study was to propose a new method that allows for the estimation of critical power (CP) from non-exhaustive tests using ratings of perceived exertion (RPE). Methods. - Twenty-two subjects underwent two practice trials for ergometer and Borg 15-point scale familiarization, and adaptation to severe exhaustive exercise. After then, four exercise bouts were performed on different days for the estimation of CP and anaerobic work capacity (AWC) by linear work-time equation, and CP(15), CP(17), AWC(15) and AWC(17) were estimated using the work and time to attainment of RPE15 and RPE17 based on the Borg 15-point scale. Results. - The CP, CP(15) and CP(17) (170-177W) were not significantly different (P>0.05). However, AWC, AWC(15) and AWC(17) were all different from each other. The correlations between CP(15) and CP(17), with CP were strong (R=0.871 and 0.911, respectively), but the AWC(15) and AWC(17) were not significantly correlated with AWC. Conclusion. - Sub-maximal. RPE responses can be used for the estimation of CP from non-exhaustive exercise protocols. (C) 2009 Elsevier Masson SAS. All rights reserved.

Hyperplane navigation: A method to set individual scores in fMRI group datasets

Recent studies have demonstrated that spatial patterns of fMRI BOLD activity distribution over the brain may be used to classify different groups or mental states. These studies are based on the application of advanced pattern recognition approaches and multivariate statistical classifiers. Most published articles in this field are focused on improving the accuracy rates and many approaches have been proposed to accomplish this task. Nevertheless, a point inherent to most machine learning methods (and still relatively unexplored in neuroimaging) is how the discriminative information can be used to characterize groups and their differences. In this work, we introduce the Maximum Uncertainty Linear Discrimination Analysis (MLDA) and show how it can be applied to infer groups` patterns by discriminant hyperplane navigation. In addition, we show that it naturally defines a behavioral score, i.e., an index quantifying the distance between the states of a subject from predefined groups. We validate and illustrate this approach using a motor block design fMRI experiment data with 35 subjects. (C) 2008 Elsevier Inc. All rights reserved.

On learning context-free and context-sensitive languages

The long short-term memory (LSTM) is not the only neural network which learns a context sensitive language. Second-order sequential cascaded networks (SCNs) are able to induce means from a finite fragment of a context-sensitive language for processing strings outside the training set. The dynamical behavior of the SCN is qualitatively distinct from that observed in LSTM networks. Differences in performance and dynamics are discussed.

Computer-aided recognition of dental implants in X-ray images

Dental implant recognition in patients without available records is a time-consuming and not straightforward task. The traditional method is a complete user-dependent process, where the expert compares a 2D X-ray image of the dental implant with a generic database. Due to the high number of implants available and the similarity between them, automatic/semi-automatic frameworks to aide implant model detection are essential. In this study, a novel computer-aided framework for dental implant recognition is suggested. The proposed method relies on image processing concepts, namely: (i) a segmentation strategy for semi-automatic implant delineation; and (ii) a machine learning approach for implant model recognition. Although the segmentation technique is the main focus of the current study, preliminary details of the machine learning approach are also reported. Two different scenarios are used to validate the framework: (1) comparison of the semi-automatic contours against implant’s manual contours of 125 X-ray images; and (2) classification of 11 known implants using a large reference database of 601 implants. Regarding experiment 1, 0.97±0.01, 2.24±0.85 pixels and 11.12±6 pixels of dice metric, mean absolute distance and Hausdorff distance were obtained, respectively. In experiment 2, 91% of the implants were successfully recognized while reducing the reference database to 5% of its original size. Overall, the segmentation technique achieved accurate implant contours. Although the preliminary classification results prove the concept of the current work, more features and an extended database should be used in a future work.

Psicologia das motivações ajurídicas do sentenciar : o lado invisivel da decisão

RESUMO: A presente investigação erige como objective inicial predizer as potencialidades/qualidades avaliativas da Grelha de Observação (Louro, 2005), realçando os diferentes comportamentos, a nível verbal e não-verbal, presentes em tribunal, relativamente aos arguidos, vítimas, testemunhas e juízes, num conjunto 34 julgamentos presenciados, no tribunal da Boa-Hora, 4ª Vara Criminal. Nesta medida, foram preenchidas 249 grelhas em contexto judicial, 190 do sexo feminino e 59 do sexo masculino, das quais, 43 grelhas referiam-se a arguidos dispostos 34 julgamentos, devido ao facto de haver julgamentos com mais do que um arguido; 14 a vítimas, dado a maior parte dos julgamentos a vítima fazer-se representar pelo Ministério Público; 108 a testemunhas e 73 grelhas aplicadas a 4 juízes presidentes de cada colectivo. Verificaram-se diferenças estatisticamente significativas no que toca aos comportamentos verbal e não verbal apresentados pelas personagens judiciais. Os resultados foram apoiados e discutidos com base na literatura revista. ABSTRACT: The present investigation aims at predicting the evaluative potential/quality of the Grelha de Observação (Louro, 2005), highlighting the different behaviors (verbal and non-verbal) displayed in a court of law, regarding the arguidos, victims, witnesses and judges, in a set of 34 observed trials at the court of Boa-Hora, “4rd” Vara Criminal. Therefore, 249 grills were filled in judicial contxt, 190 females and 59 males, from which, 43 grills were arguidos from the 34 trials (in some trials, there were more than one arguido); 14 regarding victims, since most trials she is represented by the public prosecution service; 108 witnesses and 73 grills were applied to 4 judges presidents from each collective jury. Statistically significant differences were found for the comparison between judicial characters for verbal and non-verbal behavior. The results were supported and discussed from the revised literature.

Statistical classification of road pavements using near field vehicle rolling noise measurements

Low noise surfaces have been increasingly considered as a viable and cost-effective alternative to acoustical barriers. However, road planners and administrators frequently lack information on the correlation between the type of road surface and the resulting noise emission profile. To address this problem, a method to identify and classify different types of road pavements was developed, whereby near field road noise is analyzed using statistical learning methods. The vehicle rolling sound signal near the tires and close to the road surface was acquired by two microphones in a special arrangement which implements the Close-Proximity method. A set of features, characterizing the properties of the road pavement, was extracted from the corresponding sound profiles. A feature selection method was used to automatically select those that are most relevant in predicting the type of pavement, while reducing the computational cost. A set of different types of road pavement segments were tested and the performance of the classifier was evaluated. Results of pavement classification performed during a road journey are presented on a map, together with geographical data. This procedure leads to a considerable improvement in the quality of road pavement noise data, thereby increasing the accuracy of road traffic noise prediction models.

Symbolic Knowledge Extraction from Trained Neural Networks Governed by Lukasiewicz Logics

This work describes a methodology to extract symbolic rules from trained neural networks. In our approach, patterns on the network are codified using formulas on a Lukasiewicz logic. For this we take advantage of the fact that every connective in this multi-valued logic can be evaluated by a neuron in an artificial network having, by activation function the identity truncated to zero and one. This fact simplifies symbolic rule extraction and allows the easy injection of formulas into a network architecture. We trained this type of neural network using a back-propagation algorithm based on Levenderg-Marquardt algorithm, where in each learning iteration, we restricted the knowledge dissemination in the network structure. This makes the descriptive power of produced neural networks similar to the descriptive power of Lukasiewicz logic language, minimizing the information loss on the translation between connectionist and symbolic structures. To avoid redundance on the generated network, the method simplifies them in a pruning phase, using the "Optimal Brain Surgeon" algorithm. We tested this method on the task of finding the formula used on the generation of a given truth table. For real data tests, we selected the Mushrooms data set, available on the UCI Machine Learning Repository.

Metalearning in ALBidS: A Strategic Bidding System for electricity markets

Metalearning is a subfield of machine learning with special pro-pensity for dynamic and complex environments, from which it is difficult to extract predictable knowledge. The field of study of this work is the electricity market, which due to the restructuring that recently took place, became an especially complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotia-tion entities. The proposed metalearner takes advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that pro-vides decision support to electricity markets’ participating players. Using the outputs of each different strategy as inputs, the metalearner creates its own output, considering each strategy with a different weight, depending on its individual quality of performance. The results of the proposed meth-od are studied and analyzed using MASCEM - a multi-agent electricity market simulator that models market players and simulates their operation in the market. This simulator provides the chance to test the metalearner in scenarios based on real electricity market´s data.