Binarised regression tasks: methods and evaluation metrics

Some supervised tasks are presented with a numerical output but decisions have to be made in a discrete, binarised, way, according to a particular cutoff. This binarised regression task is a very common situation that requires its own analysis, different from regression and classification—and ordinal regression. We first investigate the application cases in terms of the information about the distribution and range of the cutoffs and distinguish six possible scenarios, some of which are more common than others. Next, we study two basic approaches: the retraining approach, which discretises the training set whenever the cutoff is available and learns a new classifier from it, and the reframing approach, which learns a regression model and sets the cutoff when this is available during deployment. In order to assess the binarised regression task, we introduce context plots featuring error against cutoff. Two special cases are of interest, the \( UCE \) and \( OCE \) curves, showing that the area under the former is the mean absolute error and the latter is a new metric that is in between a ranking measure and a residual-based measure. A comprehensive evaluation of the retraining and reframing approaches is performed using a repository of binarised regression problems created on purpose, concluding that no method is clearly better than the other, except when the size of the training data is small.     

On the hardness of solving edge matching puzzles as SAT or CSP problems

Edge matching puzzles have been amongst us for a long time now and traditionally they have been considered, both, a children’s game and an interesting mathematical divertimento. Their main characteristics have already been studied, and their worst-case complexity has been properly classified as a NP-complete problem. It is in recent times, specially after being used as the problem behind a money-prized contest, with a prize of 2US$ million for the first solver, that edge matching puzzles have attracted mainstream attention from wider audiences, including, of course, computer science people working on solving hard problems. We consider these competitions as an interesting opportunity to showcase SAT/CSP solving techniques when confronted to a real world problem to a broad audience, a part of the intrinsic, i.e. monetary, interest of such a contest. This article studies the NP-complete problem known as edge matching puzzle using SAT and CSP approaches for solving it. We will focus on providing, first and foremost, a theoretical framework, including a generalized definition of the problem. We will design and show algorithms for easy and fast problem instances generation, generators with easily tunable hardness. Afterwards we will provide with SAT and CSP models for the problems and we will study problem complexity, both typical case and worst-case complexity. We will also provide some specially crafted heuristics that result in a boost in solving time and study which is the effect of such heuristics.     

Effect of thermal alkaline pretreatment on the anaerobic digestion of wasted activated sludge

The effect of alkaline pretreatment of waste-activated sludge, using two models to study the sequential hydrolysis rates of suspended (Sanders' surface model) and dissolved (Goel's saturation model) solids, on the mesophilic and thermophilic anaerobic digestion rate is evaluated. The pretreatment, which reduces the size of the solids, increases the reaction rate by increasing the surface area and the specific surface hydrolysis constant (K(SBK)); at thermophilic conditions from 0.45 x 10(-3) kg m(-2) d(-1) for the fresh sludge to 0.74 x 10(-3) kg m(-2) d(-1) for the pretreated sludge and at mesophilic conditions these values are 0.28 x 10(-3) kg m(-2) d(-1) and 0.47 x 10(-3) kg m(-2) d(-1) confirming the usefulness of a pretreatment for solids reduction. But for soluble solids, the thermoalkaline pretreatment decreases the reaction rates by inducing a competitive inhibition on the thermophilic anaerobic digestion rate while in the mesophilic range, a non-competitive inhibition is observed. A mathematical simulation of the consecutive reactions, suspended solids to dissolved solids and to methane in staged anaerobic thermophilic-mesophilic digestion, shows that with 4% suspended solids concentration it is better not to use a thermoalkaline pretreatment because overall solids reduction and total methane production are not as good as without pretreatment.     

A document is known by the company it keeps: neighborhood consensus for short text categorization

During the last decades the Web has become the greatest repository of digital information. In order to organize all this information, several text categorization methods have been developed, achieving accurate results in most cases and in very different domains. Due to the recent usage of Internet as communication media, short texts such as news, tweets, blogs, and product reviews are more common every day. In this context, there are two main challenges; on the one hand, the length of these documents is short, and therefore, the word frequencies are not informative enough, making text categorization even more difficult than usual. On the other hand, topics are changing constantly at a fast rate, causing the lack of adequate amounts of training data. In order to deal with these two problems we consider a text classification method that is supported on the idea that similar documents may belong to the same category. Mainly, we propose a neighborhood consensus classification method that classifies documents by considering their own information as well as information about the category assigned to other similar documents from the same target collection. In particular, the short texts we used in our evaluation are news titles with an average of 8 words. Experimental results are encouraging; they indicate that leveraging information from similar documents helped to improve classification accuracy and that the proposed method is especially useful when labeled training resources are limited.     

Evaluation of messaging middleware for high-performance cloud computing

Cloud computing is posing several challenges, such as security, fault tolerance, access interface singularity, and network constraints, both in terms of latency and bandwidth. In this scenario, the performance of communications depends both on the network fabric and its efficient support in virtualized environments, which ultimately determines the overall system performance. To solve the current network constraints in cloud services, their providers are deploying high-speed networks, such as 10 Gigabit Ethernet. This paper presents an evaluation of high-performance computing message-passing middleware on a cloud computing infrastructure, Amazon EC2 cluster compute instances, equipped with 10 Gigabit Ethernet. The analysis of the experimental results, confronted with a similar testbed, has shown the significant impact that virtualized environments still have on communication performance, which demands more efficient communication middleware support to get over the current cloud network limitations.     

Room temperature synthesis of γ-Fe2O3 by sonochemical route and its response towards butane

Nanocrystalline gamma iron oxide (γ-Fe₂O₃) has been synthesized at room temperature through sonication-assisted precipitation technique. The key in obtaining γ-Fe₂O₃ at room temperature lies in exploiting high-power ultrasound (600 W). The gas-sensing properties to n-butane of pure γ-Fe₂O₃ were investigated by studying the electrical properties of the sensor elements fabricated from the synthesized powder. The maximum response (90%) of the sensor to 1000 ppm n-butane at 300 °C can be explained on the basis of catalytic activity of the nanocrystallites. The response and recovery time of the sensor to 1000 ppm n-butane were less than 12 s and 120 s, respectively.     

Fuzzy Adaptive Control Law for Trajectory Tracking Based on a Fuzzy Adaptive Neural PID Controller of a Multi-rotor Unmanned Aerial Vehicle

International Journal of Control, Automation and Systems - This article presents a fuzzy adaptive control law (FACL) designed for tracking the trajectory of a low-scale unmanned aerial vehicle...     

Laser-treated austenitic steel and nickel alloy for human implants

The recent research in biocompatible materials has been useful in replacing and supporting the fractured natural human bones/joints. Under some condition, negative reaction like release of ions from the bare metal toward the human body fluid leads to corrosion. In this proposed research paper, the biocompatibility of the laser surface-modified austenitic stainless steel (SS316L) and nickel-based superalloy (Inconel 718) was studied. The investigation on laser-modified surfaces is evaluated through electrochemical polarization analysis using simulated body fluid (SBF). The samples subjected to electrochemical polarization analysis were characterized by optical image analysis, SEM, EDS, and XRD analysis. It was inferred that laser surface-modified materials provided enhanced corrosion resistance and bare nickel alloy is more susceptible to corrosion by SBF.