Temporal contexts: Effective text classification in evolving document collections

The management of a huge and growing amount of information available nowadays makes Automatic Document Classification (ADC), besides crucial, a very challenging task. Furthermore, the dynamics inherent to classification problems, mainly on the Web, make this task even more challenging. Despite this fact, the actual impact of such temporal evolution on ADC is still poorly understood in the literature. In this context, this work concerns to evaluate, characterize and exploit the temporal evolution to improve ADC techniques. As first contribution we highlight the proposal of a pragmatical methodology for evaluating the temporal evolution in ADC domains. Through this methodology, we can identify measurable factors associated to ADC models degradation over time. Going a step further, based on such analyzes, we propose effective and efficient strategies to make current techniques more robust to natural shifts over time. We present a strategy, named temporal context selection, for selecting portions of the training set that minimize those factors. Our second contribution consists of proposing a general algorithm, called Chronos, for determining such contexts. By instantiating Chronos, we are able to reduce uncertainty and improve the overall classification accuracy. Empirical evaluations of heuristic instantiations of the algorithm, named WindowsChronos and FilterChronos, on two real document collections demonstrate the usefulness of our proposal. Comparing them against state-of-the-art ADC algorithms shows that selecting temporal contexts allows improvements on the classification accuracy up to 10%. Finally, we highlight the applicability and the generality of our proposal in practice, pointing out this study as a promising research direction.     

Molds and Resists Studies for Nanoimprint Lithography of Electrodes in Low-Voltage Polymer Thin-Film Transistors

A low-cost patterning of electrodes was investigated looking forward to replacing conventional photolithography for the processing of low-operating voltage polymeric thin-film transistors. Hard silicon, etched by sulfur hexafluoride and oxygen gas mixture, and flexible polydimethylsiloxane imprinting molds were studied through atomic force microscopy (AFM) and field emission gun scanning electron microscopy. The higher the concentration of oxygen in reactive ion etching, the lower the etch rate, sidewall angle, and surface roughness. A concentration around 30 % at 100 mTorr, 65 W and 70 sccm was demonstrated as adequate for submicrometric channels, presenting a reduced etch rate of 176 nm/min. Imprinting with positive photoresist AZ1518 was compared to negative SU-8 2002 by optical microscopy and AFM. Conformal results were obtained only with the last resist by hot embossing at 120 °C and 1 kgf/cm² for 2 min, followed by a 10 min post-baking at 100 °C. The patterning procedure was applied to define gold source and drain electrodes on oxide-covered substrates to produce bottom-gate bottom-contact transistors. Poly(3-hexylthiophene) (P3HT) devices were processed on high-κ titanium oxynitride (TiO _x N _y ) deposited by radiofrequency magnetron sputtering over indium tin oxide-covered glass to achieve low-voltage operation. Hole mobility on micrometric imprinted channels may approach amorphous silicon (～0.01 cm²/V s) and, since these devices operated at less than 5 V, they are not only suitable for electronic applications but also as sensors in aqueous media.     

Generalized Hermitian Radial Basis Functions Implicits from polygonal mesh constraints

In this work we investigate a generalized interpolation approach using radial basis functions to reconstruct implicit surfaces from polygonal meshes. With this method, the user can define with great flexibility three sets of constraint interpolants: points, normals, and tangents; allowing to balance computational complexity, precision, and feature modeling. Furthermore, this flexibility makes possible to avoid untrustworthy information, such as normals estimated on triangles with bad aspect ratio. We present results of the method for applications related to the problem of modeling 2D curves from polygons and 3D surfaces from polygonal meshes. We also apply the method to problems involving subdivision surfaces and front-tracking of moving boundaries. Finally, as our technique generalizes the recently proposed HRBF Implicits technique, comparisons with this approach are also conducted.     

Parallelization of the FMM on distributed-memory GPGPU systems for acoustic-scattering prediction

In this work, we carry out the parallelization of the single level Fast Multipole Method (FMM) for solving acoustic-scattering problems (using the Helmholtz equation) on distributed-memory GPGPU systems. With the aim of enlarging the scope of feasible simulations, the presented solution combines the techniques developed for our distributed-memory CPU solver with our shared-memory GPGPU solver. The performance of the developed solution is proved using two different GPGPU clusters: the first one consists of two workstations with NVIDIA GTX 480 GPUs linked by a Gigabit Ethernet network, and the second one comprises four nodes with NVIDIA Tesla M2090 GPUs linked by an Infiniband network.     

Dynamic Epistemic Logic for Implicit and Explicit Beliefs

Epistemic logic with its possible worlds semantic model is a powerful framework that allows us to represent an agent’s information not only about propositional facts, but also about her own information. Nevertheless, agents represented in this framework are logically omniscient: their information is closed under logical consequence. This property, useful in some applications, is an unrealistic idealisation in some others. Many proposals to solve this problem focus on weakening the properties of the agent’s information, but some authors have argued that solutions of this kind are not completely adequate because they do not look at the heart of the matter: the actions that allow the agent to reach such omniscient state. Recent works have explored how acts of observation, inference, consideration and forgetting affect an agent’s implicit and explicit knowledge; the present work focuses on acts that affect an agent’s implicit and explicit beliefs. It starts by proposing a framework in which these two notions can be represented, and then it looks into their dynamics, first by reviewing the existing notion of belief revision, and then by introducing a rich framework for representing diverse forms of inference that involve both knowledge and beliefs.     

Forecasting Financial Failure of Firms via Genetic Algorithms

Given a wide amount of possible ratios available for constructing a LOGIT model for forecasting bankruptcy, this paper provides a computational search methodology, only guided by data, for selecting the financial ratios employed in the model. This procedure is based on genetic algorithms which are used to explore the universe of models made available by all possible existing financial ratios (with very redundant information). This search process of the correct model is guided by the Schwarz information criterion. As an empirical illustration, the methodology is applied to forecasting the failure of firms in the Spanish building industry using annual public accounting information.     

An efficient algorithm for optimal routing applied to convoy merging manoeuvres in urban environments

This paper describes an innovative routing strategy for intelligent transportation units willing to perform merging manoeuvres with a moving convoy. In particular, we consider a transportation unit located inside a city (pursuer unit), and which wishes to join a convoy that is constantly moving around the city. We first describe a solution that considers idealistic conditions, i.e., the traveling time along each street is constant. We then go on to improve our first approach to deal with the realistic random nature of the traveling times experienced by the pursuer and by the convoy leader. Our search strategy applies Dynamic Programming to achieve a meeting point that is optimal in two ways: on one hand, the optimal destination is the one closest to the current pursuer??s position; on the other hand, the optimal meeting point must minimize the time that elapses until the pursuer meets the convoy (considering that the pursuer must always arrive first). Calculating the optimal path to every possible destination is highly inefficient, error prone and time consuming. On the contrary, we propose an efficient search strategy that will achieve the meeting point and the path to it, both optimally and in a very short time. This enables the real-time application of our approach either for finding new solutions or for re-planning old ones owing to unexpected real-conditions.     

Optimizing location and size of rural schools in Chile

The Chilean Ministry of Education oversees preschool, primary, and secondary education in both urban and rural areas. Many parts of Chile are sparsely populated and there are currently over 4,000 rural schools (almost 38% of all schools in Chile) educating 9.5% of the students in the country. Many of the rural schools are small with only one teacher responsible for instruction of all local students (multigrade schools). The geographical distribution of the rural schools has not been coordinated and this has resulted in unequal utilization of existing schools and some unreasonably long travel distances by students. Good management of the rural schools is fundamental to meeting Chile's goal of providing quality education to its citizens. Seeking to improve the situation, the Ministry of Education ordered a study of the optimal location and size of rural schools with the general goals of reducing the number of lesser quality multigrade schools and reducing student travel distances while maintaining reasonable costs. This paper presents results of this study obtained using an integer linear program that has been embedded in a geographical information system. We present computational results for the entire country. Recommendations include where to open new rural schools as well as where to expand, reduce, close, or leave unchanged existing schools. We show how recommendations are sensitive to key parameters such as the cost of transportation.     

Cyclic staff scheduling: optimization models for some real-life problems

In this work, we propose a general integer programming model to address the staff scheduling problem, flexible enough to be easily adapted to a wide-range of real-world problems. The model is applied with slight changes to two case studies: a glass plant and a continuous care unit, and also to a collection of benchmark instances available in the literature. The emphasis of our approach is on a novel formulation of sequence constraints and also on workload balance, which is tackled through cyclic scheduling. Models are solved using the CPLEX solver. Computational results indicate that optimal solutions can be achieved within a reasonable amount of time.     

Do Intelligent Leaders Make a Difference? The Effect of a Leader's Emotional Intelligence on Followers' Creativity

This research investigates the connection between emotional intelligence (EI) and creativity. This was studied by exploring: (i) an association between leaders' EI and their followers' creative output; (ii) an association between six sub‐dimensions of EI and creativity; and (iii) a mediating role of climate in the link between EI and creativity. Two questionnaires (one for leaders and one for employees) were used to collect data in a hospital. Sixty‐six usable leader‐employee dyads were collected. The findings confirmed a positive relationship between leaders' EI and employees' creativity. At an EI's sub‐dimensions level, the current research showed an association between creativity, on one hand, and self‐encouragement and understanding of own emotions, on the other. Finally, no mediating effect of climate was observed. The absence of a mediating effect is interesting, since it suggests a direct link between leaders' EI and employees' creativity, regardless of the climate. This is important, since it calls attention to the paramount role of leaders in shaping individual and organizational behaviours as far as creativity is concerned. The paper also discusses implications for management and practice.