A quantitative approach to ranking corporate law precedents in the Brazilian Superior Court of Justice

This paper aims to contribute to the goal of finding influential legal precedents by quantitative methods. A lot of work has been made in this direction worldwide, especially in the context of common law jurisdictions. However, this type of work is extremely scarce in the Brazilian literature. In addition, our work also contributes to the research of network analysis and the law by applying these methods to unprecedented amount of data and narrowing our inquiry to a single law area, corporate law. Furthermore, whereas most of the literature applying network analysis to judicial decisions had access to readily available data on the citations to precedent within each ruling, our raw data was nothing but the full text of decisions. We focus on data produced by the Superior Court of Justice (STJ), the highest court in Brazil for matters of federal law, including statutory interpretation of civil, criminal and corporate law. The Court issued an astonishing 282040 opinions tagged as related to corporate law between 2008 and 2018. This amount of cases is unparalleled internationally for superior courts and for studies in network analysis and law. In our results, we rank precedents quantitatively based on the citations they receive and make. We also qualitatively analyze some of the results, especially related to groups identified in the network with the Modularity algorithm. Our findings also reveal that corporate law jurisprudence in the STJ is quantitatively dominated by a few legal issues around one single theme that is only tangentially related to corporate law. That is, a type of contract used for the expansion of telephone landlines, which also allowed the consumer to become a shareholder of the telecommunication company. This comparison is especially pertinent because the utter lack of data on the quantitative weight of STJ precedents means the national literature has been operating in a void of objective measurements, one which has been filled with cherry-picked rulings and subjective ranking criteria.

     

Multiple evolutionary origins of the fungus causing Panama disease of banana: concordant evidence from nuclear and mitochondrial gene genealogies

Panama disease of banana, caused by the fungus Fusarium oxysporum f. sp. cubense, is a serious constraint both to the commercial production of banana and cultivation for subsistence agriculture. Previous work has indicated that F. oxysporum f. sp. cubense consists of several clonal lineages that may be genetically distant. In this study we tested whether lineages of the Panama disease pathogen have a monophyletic origin by comparing DNA sequences of nuclear and mitochondrial genes. DNA sequences were obtained for translation elongation factor 1alpha and the mitochondrial small subunit ribosomal RNA genes for F. oxysporum strains from banana, pathogenic strains from other hosts and putatively nonpathogenic isolates of F. oxysporum. Cladograms for the two genes were highly concordant and a partition-homogeneity test indicated the two datasets could be combined. The tree inferred from the combined dataset resolved five lineages corresponding to "F. oxysporum f. sp. cubense" with a large dichotomy between two taxa represented by strains most commonly isolated from bananas with Panama disease. The results also demonstrate that the latter two taxa have significantly different chromosome numbers. F. oxysporum isolates collected as nonpathogenic or pathogenic to other hosts that have very similar or identical elongation factor 1alpha and mitochondrial small subunit genotypes as banana pathogens were shown to cause little or no disease on banana. Taken together, these results indicate Panama disease of banana is caused by fungi with independent evolutionary origins.     

Transmission power and data rate aware routing on wireless networks

Wireless networks can vary both the transmission power and modulation of links. Existing routing protocols do not take transmission power control (TPC) and modulation adaptation (also known as rate adaptation – RA) into account at the same time, even though the performance of wireless networks can be significantly improved when routing algorithms use link characteristics to build their routes. This article proposes and evaluates extensions to routing protocols to cope with TPC and RA. The enhancements can be applied to any link state or distance vector routing protocols. An evaluation considering node density, node mobility and link error show that TPC- and RA-aware routing algorithms improve the average latency and the end-to-end throughput, while consuming less energy than traditional protocols.     

SUTIL – Network selection based on utility function and integer linear programming

This work presents SUTIL, a mechanism for network selection in the context of next generation networks (NGN). SUTIL selection mechanism prioritizes networks with higher relevance to the application and lower energy consumption and it enables full and seamless connectivity to mobile user devices and applications. Consequently, SUTIL contributes to realize the vision of ubiquitous computing, in which services, devices, and sensor-enriched environments interact anytime, anywhere to accomplish human designed tasks. The provided solution is based on utility function and integer linear programming and it aims at: (i) maximizing the user satisfaction while meeting application QoS and (ii) minimizing the energy consumption of devices when connecting to a target network. The solution is global since it considers for a given base station all devices that are simultaneously candidate for handoff. Simulation results showed the benefits of SUTIL usage in NGN environments.     

3D face computational photography using PCA spaces

In this paper, we present a 3D face photography system based on a facial expression training dataset, composed of both facial range images (3D geometry) and facial texture (2D photography). The proposed system allows one to obtain a 3D geometry representation of a given face provided as a 2D photography, which undergoes a series of transformations through the texture and geometry spaces estimated. In the training phase of the system, the facial landmarks are obtained by an active shape model (ASM) extracted from the 2D gray-level photography. Principal components analysis (PCA) is then used to represent the face dataset, thus defining an orthonormal basis of texture and another of geometry. In the reconstruction phase, an input is given by a face image to which the ASM is matched. The extracted facial landmarks and the face image are fed to the PCA basis transform, and a 3D version of the 2D input image is built. Experimental tests using a new dataset of 70 facial expressions belonging to ten subjects as training set show rapid reconstructed 3D faces which maintain spatial coherence similar to the human perception, thus corroborating the efficiency and the applicability of the proposed system.     

A DSS for integrated distribution of empty and full containers

A DSS integrating empty and full containers transshipment operations is presented, addressing the typically unbalanced export/import containers trading problem. The problem is modeled as a network, where nodes represent customers, leasing companies, harbors and warehouses, while arcs represent transportation routes. The underlying mathematical model operates in stages, first prioritizing and adjusting full containers demands considering available empty containers supplies, and then statically optimizing costs. Transportation routes are registered and dynamically controlled, cyclically, for a given time horizon. The DSS is flexible, allowing several parameters to be configured. Experimental examples using randomly generated parameters were conducted to evaluate the effectiveness of the system.     

Anodic stripping voltammetric determination of copper(II) using a functionalized carbon nanotubes paste electrode modified with crosslinked chitosan

The development and application of a functionalized carbon nanotubes paste electrode (CNPE) modified with crosslinked chitosan for determination of Cu(II) in industrial wastewater, natural water and human urine samples by linear scan anodic stripping voltammetry (LSASV) are described. Different electrodes were constructed using chitosan and chitosan crosslinked with glutaraldehyde (CTS-GA) and epichlorohydrin (CTS-ECH). The best voltammetric response for Cu(II) was obtained with a paste composition of 65% (m/m) of functionalized carbon nanotubes, 15% (m/m) of CTS-ECH, and 20% (m/m) of mineral oil using a solution of 0.05 mol L⁻¹ KNO₃ with pH adjusted to 2.25 with HNO₃, an accumulation potential of −0.3 V vs. Ag/AgCl (3.0 mol L⁻¹ KCl) for 300 s and a scan rate of 100 mV s⁻¹. Under these optimal experimental conditions, the voltammetric response was linearly dependent on the Cu(II) concentration in the range from 7.90 × 10⁻⁸ to 1.60 × 10⁻⁵ mol L⁻¹ with a detection limit of 1.00 × 10⁻⁸ mol L⁻¹. The samples analyses were evaluated using the proposed sensor and a good recovery of Cu(II) was obtained with results in the range from 98.0% to 104%. The analysis of industrial wastewater, natural water and human urine samples obtained using the proposed CNPE modified with CTS-ECH electrode and those obtained using a comparative method are in agreement at the 95% confidence level.     

Constructive learning neural network applied to identification and control of a fuel-ethanol fermentation process

In the present work, a constructive learning algorithm was employed to design a near-optimal one-hidden layer neural network structure that best approximates the dynamic behavior of a bioprocess. The method determines not only a proper number of hidden neurons but also the particular shape of the activation function for each node. Here, the projection pursuit technique was applied in association with the optimization of the solvability condition, giving rise to a more efficient and accurate computational learning algorithm. As each activation function of a hidden neuron is defined according to the peculiarities of each approximation problem, better rates of convergence are achieved, guiding to parsimonious neural network architectures. The proposed constructive learning algorithm was successfully applied to identify a MIMO bioprocess, providing a multivariable model that was able to describe the complex process dynamics, even in long-range horizon predictions. The resulting identification model was considered as part of a model-based predictive control strategy, producing high-quality performance in closed-loop experiments.     

Hand gesture recognition from depth and infrared Kinect data for CAVE applications interaction

This paper presents a real-time framework that combines depth data and infrared laser speckle pattern (ILSP) images, captured from a Kinect device, for static hand gesture recognition to interact with CAVE applications. At the startup of the system, background removal and hand position detection are performed using only the depth map. After that, tracking is started using the hand positions of the previous frames in order to seek for the hand centroid of the current one. The obtained point is used as a seed for a region growing algorithm to perform hand segmentation in the depth map. The result is a mask that will be used for hand segmentation in the ILSP frame sequence. Next, we apply motion restrictions for gesture spotting in order to mark each image as a ‘Gesture’ or ‘Non-Gesture’. The ILSP counterparts of the frames labeled as “Gesture” are enhanced by using mask subtraction, contrast stretching, median filter, and histogram equalization. The result is used as the input for the feature extraction using a scale invariant feature transform algorithm (SIFT), bag-of-visual-words construction and classification through a multi-class support vector machine (SVM) classifier. Finally, we build a grammar based on the hand gesture classes to convert the classification results in control commands for the CAVE application. The performed tests and comparisons show that the implemented plugin is an efficient solution. We achieve state-of-the-art recognition accuracy as well as efficient object manipulation in a virtual scene visualized in the CAVE.     

An adaptive learning approach for 3-D surface reconstruction from point clouds.

In this paper, we propose a multiresolution approach for surface reconstruction from clouds of unorganized points representing an object surface in 3-D space. The proposed method uses a set of mesh operators and simple rules for selective mesh refinement, with a strategy based on Kohonen's self-organizing map (SOM). Basically, a self-adaptive scheme is used for iteratively moving vertices of an initial simple mesh in the direction of the set of points, ideally the object boundary. Successive refinement and motion of vertices are applied leading to a more detailed surface, in a multiresolution, iterative scheme. Reconstruction was experimented on with several point sets, including different shapes and sizes. Results show generated meshes very close to object final shapes. We include measures of performance and discuss robustness.