Miracle-dz/UniFAR-FTUs huggingface dataset:
Managing stochastic algorithms CV variability using an interval valued multiple comparison procedure

The usual procedure to compare metaheuristics or evolutionary algorithms using cross validation is repeating the training stage several times for each train/test pair. In general, the results of the different repetitions are not independent and this practice is questionable. In this work, it is suggested to represent the results of each train/test set pair by an interval or by a fuzzy set and use the proper statistical tests to these kind of data. In this way, the paired comparison of these sets leads to an interval valued p-value or to a fuzzy p-value that holds information about the mean differences but also about the differences in dispersion between the compared algorithms. Keywords-Experimental design; cross validation; stochastic algorithms; fuzzy statistics
An Analysis of Global Timber Markets

Abstract This paper presents a model of global timber markets that captures the evolution of abroad array of forest resources and timber market margins over time. These margins includethe inaccessible northern and tropical margins, plantation establishment, and timberlandmanagement. A baseline case is presented and discussed. Five alternative scenarios are thenpresented. These scenarios allow us to consider several important questions about timbermarket behavior and the future supply of industrial fiber: (1) What happens along the northernand the tropical inaccessible margins? (2) What role do timber plantations play? and (3) Howdo shifts in management intensity interact with market forces? The baseline case suggests thatboth prices and harvests rise over the next 150 years, with most of the increased harvestcoming from existing and newly established plantations. Future gains in harvests result mainlyfrom intensification of management, through additional plantation establishment and higherlevels of management on selected forests rather than higher harvests in currently inaccessibleforests. Prices and harvests are most sensitive to alternative demand and plantationestablishment scenarios, and less sensitive to costs of accessing extensive forests.Keywords : global timber markets, forest plantations, model, forecastJEL Classification Numbers : Q10, Q21, Q23, Q24
Biomechanical modeling of abdominal muscle system considering tendinous intersection and abdominal cavity's compressibility

In this paper, we demonstrate that a musculoskeletal model with an anatomically-inspired abdomen model can estimate human-like abdominal muscle tension, in contrast to previous musculoskeletal models or models based on joint elasticity. We first model the abdominal structure based on anatomical knowledge. The anatomically-inspired abdomen model consists of a Tendinous Intersection model that connects the muscle bellies of Rectus Abdominis and represents its pathway, a Rectus Abdominis model that represents the muscle contraction part, and a balloon-type abdominal cavity model that represents the volumetric abdominal cavity. These models compute the intra-abdominal pressure and its effect on the lumbar vertebrae joint torques. We use these models with our musculoskeletal model to estimate the abdominal muscle tension during sitting up motion with optical motion capture, inverse kinematics, and inverse dynamics computation. Our computational results show that our abdomen model estimates the muscle tension with a waveform similar to the muscle activity measured by electromyography, while the previous musculoskeletal model and the joint elasticity model estimate obviously different muscle tension patterns. These results imply that the intra-abdominal pressure is critical in estimating the abdominal muscle tension rather than the inertial properties and the joint elasticity.
Tuning Out Security Warnings: A Longitudinal Examination of Habituation Through fMRI, Eye Tracking, and Field Experiments

Research in the fields of information systems and human-computer interaction has shown that habituation— decreased response to repeated stimulation—is a serious threat to the effectiveness of security warnings. Although habituation is a neurobiological phenomenon that develops over time, past studies have only examined this problem cross-sectionally. Further, past studies have not examined how habituation influences actual security warning adherence in the field. For these reasons, the full extent of the problem of habituation is unknown. We address these gaps by conducting two complementary longitudinal experiments. First, we performed an experiment collecting fMRI and eye-tracking data simultaneously to directly measure habituation to security warnings as it develops in the brain over a five-day workweek. Our results show not only a general decline of participants’ attention to warnings over time but also that attention recovers at least partially between workdays without exposure to the warnings. Further, we found that updating the appearance of a warning— that is, a polymorphic design—substantially reduced habituation of attention. Second, we performed a three-week field experiment in which users were naturally exposed to privacy permission warnings as they installed apps on their mobile devices. Consistent with our fMRI results, users’ warning adherence substantially decreased over the three weeks. However, for users who received polymorphic permission warnings, adherence dropped at a substantially lower rate and remained high after three weeks, compared to users who received standard warnings. Together, these findings provide the most complete view yet of the problem of habituation to security warnings and demonstrate that polymorphic warnings can substantially improve adherence.
Visual Data Combination for Object Detection and Localization for Autonomous Robot Manipulation Tasks

For mobile robot manipulation, autonomous object detection and localization is at the present still an open issue. In this paper is presented a method for detection and localization of simple colored geometric objects like cubes, prisms and cylinders, located over a table. The method proposed uses a passive stereovision system and consists on two steps. The first is colored object detection, where it is used a combination of a color segmentation procedure with an edge detection method, to restrict colored regions. Second step consists on pose recovery; where the merge of the colored objects detection mask is combined with the disparity map coming from stereo camera. Later step is very important to avoid noise inherent to the stereo correlation process. Filtered 3D data is then used to determine the main plane where the objects are posed, the table, and then the footprint is used to localize them in the stereo camera reference frame and then to the world reference frame.
THE IASA 10 MeV CW-LINAC

The installation of a 10 MeV electron injector as a Project for the RaceTrack Microtron at the Institute of Accelerating Systems & Applications (IASA) 1 is presented. This Maquette line is composed by a 5 MeV injector linac with RF structures of the side-coupled type, followed by a 4m-booster section of the same type. This system will provide a realistic test facility for all subsystems (power station, klystron, wave-guiding, chiller, control, interlock and safety system), with exception of recirculations of the IASA cascade RTM facility. It is envisioned that its beams will be used for applied physics research.
An Accurate Closed-Form Approximation of the Energy Efficiency-Spectral Efficiency Trade-Off over the MIMO Rayleigh Fading Channel

Energy efficiency (EE) is gradually becoming one of the key criteria, along with the spectral efficiency (SE), for evaluating communication system performances. However, minimizing the EE while maximizing the SE are conflicting objectives and, thus, the main criterion for designing efficient communication systems will become the trade-off between SE and EE. The EE-SE trade-off for the multi-input multi-output (MIMO) Rayleigh fading channel has been accurately approximated in the past but only in the low-SE regime. In this paper, we propose a novel and more generic closed-form approximation of this EE-SE trade-off which exhibits a greater accuracy for a wider range of SE values and antenna configurations. Our expression, which can easily be used for evaluating and comparing the EE-SE trade-off of MIMO communication system, has been utilized in this paper for analyzing the impact of using multiple antennas on the EE and the EE gain of MIMO in comparison with single-input single-output (SISO) system. Our results indicate that EE can be improved predominantly through receive diversity in the low-SE regime and that MIMO is far more energy efficient than SISO at high SE over the Rayleigh fading channel.
Resilience as a New System Engineering for Cloud Computing

It has become increasingly evident that large scale systems such as clouds can be brittle and may exhibit unpredictable behavior when faced with unexpected disturbances. Even weak and innocuous disturbances can bring down the system inoperative and may introduce catastrophic disasters to the society. The goal of this research is to explore the fundamental principles and theories that govern cloud system resilience and to provide novel and effective mechanisms to model and enhance the resilience of cloud. A food web like process interaction model is developed and system resilience enhancement mechanisms are proposed based on the control of the strength of interactions. Also, the effectiveness and limitations of modularization on resilience enhancement is illustrated by using a replica consistency control protocol. The research has shown that weakening key process interactions and modularizing complex systems are very effective on resilience enhancement. 
Mannose-Binding Lectin Deficiency Is Associated With Smaller Infarction Size and Favorable Outcome in Ischemic Stroke Patients

Background The Mannose-binding lectin (MBL) pathway of complement plays a pivotal role in the pathogenesis of ischemia/reperfusion (I/R) injury after experimental ischemic stroke. As comparable data in human ischemic stroke are limited, we investigated in more detail the association of MBL deficiency with infarction volume and functional outcome in a large cohort of patients receiving intravenous thrombolysis or conservative treatment. Methodology/Principal Findings In a post hoc analysis of a prospective cohort study, admission MBL concentrations were determined in 353 consecutive patients with an acute ischemic stroke of whom 287 and 66 patients received conservative and thrombolytic treatment, respectively. Stroke severity, infarction volume, and functional outcome were studied in relation to MBL concentrations at presentation to the emergency department. MBL levels on admission were not influenced by the time from symptom onset to presentation (p = 0.53). In the conservative treatment group patients with mild strokes at presentation, small infarction volumes or favorable outcomes after three months demonstrated 1.5 to 2.6-fold lower median MBL levels (p = 0.025, p = 0.0027 and p = 0.046, respectively) compared to patients with more severe strokes. Moreover, MBL deficient patients (<100 ng/ml) were subject to a considerably decreased risk of an unfavorable outcome three months after ischemic stroke (adjusted odds ratio 0.38, p<0.05) and showed smaller lesion volumes (mean size 0.6 vs. 18.4 ml, p = 0.0025). In contrast, no association of MBL concentration with infarction volume or functional outcome was found in the thrombolysis group. However, the small sample size limits the significance of this observation. Conclusions MBL deficiency is associated with smaller cerebral infarcts and favorable outcome in patients receiving conservative treatment. Our data suggest an important role of the lectin pathway in the pathophysiology of cerebral I/R injury and might pave the way for new therapeutic interventions.
Cultural order and parents’ motivations for practising home education in China

ABSTRACT There is little literature which provides any analysis regarding the relationship between cultural order and home education. Research reported here found that home education has grown steadily in recent decades and that parents’ motivation for home education was closely associated with cultural order. It was noticeable that Christian and Confucian values seemed to provide philosophical support for Chinese middle-class families who have chosen to educate their children at home. The parents reported that they preferred to cultivate in their children their favourite culture and beliefs, rather than to merely facilitate their achievement of excellent academic outcomes.
Intuitive Decision Making

Should executives make decisions based on what their ? gut? tells them? Lately that idea has lost some favor, as technology? s ability to accumulate and analyze data has rapidly increased ? supplanting, according to some accounts, the high-level manager? s need to draw heavily on intuition. But intuition needs some rescuing from its detractors, and the place to start is by clarifying what it really is, and how it should be developed. Intuition is not a magical sixth sense or a paranormal process; nor does it signify the opposite of reason or random and whimsical decision making. Rather, intuition is a highly complex and highly developed form of reasoning that is based on years of experience and learning, and on facts, patterns, concepts, procedures and abstractions stored in one? s head. In this article, the authors draw on examples from the worlds of chess, neuroscience and business ? especially Austria? s KTM Sportmotorcycle AG ? to show that intuitive decision making should not be prematurely buried. They point out that although the study of intuition has not been extensively explored as a part of management science, studies reveal that several ingredients are critical to intuition? s development: years of domain-specific experience; the cultivation of personal and professional networks; the development of emotional intelligence; a tolerance for mistakes; a healthy sense of curiosity; and a sense of intuition? s limits. Companies should, of course, continue to exploit their abilities to mine data as a means of obtaining a competitive edge. But they shouldn? t overlook the continuing value of experienced executives who can draw on their intuition to make decisions when the numbers yield a question rather than an answer: Now what do we do?
Writing Reusable Digital Geometry Algorithms in a Generic Image Processing Framework

Digital Topology software should reﬂect the generality of the underlying mathematics: mapping the latter to the former requires gener-icity. By designing generic solutions, one can eﬀectively reuse digital topology data structures and algorithms. We propose an image processing framework focused on the Generic Programming paradigm in which an algorithm on the paper can be turned into a single code, written once and usable with various input types. This approach enables users to design and implement new methods at a lower cost, try cross-domain experiments and help generalize results.
Automatic Optimization of Thread Mapping for a GPGPU Programming Framework

Although General Purpose computation on GPU (GPGPU) is widely used for high-performance computing, standard programming frameworks such as CUDA and Open CL are still difficult to use. They require low-level specifications and hand-optimization is a large burden. Therefore we are developing an easier framework named MESI-CUDA. Based on a virtual shared memory model, MESI-CUDA hides low level memory management and data transfer from the user. The compiler generates low-level code and also optimizes memory accesses applying conventional hand-optimizing techniques. However, creating GPU threads is same as CUDA, thread mapping, i.e. Thread indexing and the size of thread blocks run on each streaming multiprocessors (SM), are specified by the user. The mapping largely affects the execution performance and may obstruct automatic optimization of MESI-CUDA compiler. Therefore, the user must find optimal specification considering physical parameters. In this paper, we propose a new thread mapping scheme. We introduce new thread creation syntax specifying hardware-independent logical mapping, which is converted into optimized physical mapping at compile time. Making static analysis of array index expressions, we obtain groups of threads accessing the same or neighboring array elements. Mapping such threads into the same thread block and assigning consecutive thread indices, the physical mapping is determined to maximize the effect of memory access optimization. As the result of evaluation, our scheme could find optimal mapping strategy for three benchmark programs.
Interactive Volume Navigation

Volume navigation is the interactive exploration of volume data sets by "flying" the viewpoint through the data, producing a volume rendered view at each frame. We present an inexpensive perspective volume navigation method designed to be run on a PC platform with accelerated 3D graphics hardware. The heart of the method is a two-phase perspective ray casting algorithm that takes advantage of the coherence inherent in adjacent frames during navigation. The algorithm generates a sequence of approximate volume-rendered views in a fraction of the time that would be required to compute them individually. The algorithm handles arbitrarily large volumes by dynamically swapping data within the current view frustum into main memory as the viewpoint moves through the volume. We also describe an interactive volume navigation application based on this algorithm. The application renders gray-scale, RGB, and labeled RGB volumes by volumetric compositing, allows trilinear interpolation of sample points, and implements progressive refinement during pauses in user input.
Towards Unsupervised Ontology Learning from Data

Data-driven elicitation of ontologies from structured data is a well-recognized knowledge acquisition bottleneck. The development of efficient techniques for (semi-)automating this task is therefore practically vital -- yet, hindered by the lack of robust theoretical foundations. In this paper, we study the problem of learning Description Logic TBoxes from interpretations, which naturally translates to the task of ontology learning from data. In the presented framework, the learner is provided with a set of positive interpretations (i.e., logical models) of the TBox adopted by the teacher. The goal is to correctly identify the TBox given this input. We characterize the key constraints on the models that warrant finite learnability of TBoxes expressed in selected fragments of the Description Logic EL and define corresponding learning algorithms.
Early experiences in application level I/O tracing on blue gene systems

On todays massively parallel processing (MPP) supercomputers, it is increasingly important to understand I/O performance of an application both to guide scalable application development and to tune its performance. These two critical steps are often enabled by performance analysis tools to obtain performance data on thousands of processors in an MPP system. To this end, we present the design, implementation, and early experiences of an application level I/O tracing library and the corresponding tool for analyzing and optimizing I/O performance on Blue Gene (BG) MPP systems. This effort was a part of IBM UPC Toolkit for BG systems. To our knowledge, this is the first comprehensive application-level I/O monitoring, playback, and optimizing tool available on BG systems. The preliminary experiments on popular NPB BTIO benchmark show that the tool is much useful on facilitating detailed I/O performance analysis.
Mediterranean Conference on Information Systems ( MCIS ) 9-1-2010 SIMILARITY ENHACEMENT IN TIME-AWARE RECOMMENDER SYSTEMS

Time-aware recommender systems (TARS) are systems that take into account a time factor the age of the user data. There are three approaches for using a time factor: (1) the user data may be given different weights by their age, (2) it may be treated as a step in a biological process and (3) it may be compared in different time frames to find a significant pattern. This research deals with the latter approach. When dividing the data into several time frames, matching users becomes more difficult similarity between users that was once identified in the total time frame may disappear when trying to match between them in smaller time frames. The user matching problem is largely affected by the sparsity problem, which is well known in the recommender system literature. Sparsity occurs where the actual interactions between users and data items is much smaller in comparison to the entire collection of possible interactions. The sparsity grows as the data is split into several time frames for comparison. As sparsity grows, matching similar users in different time frames becomes harder, increasing the need for finding relevant neighboring users. Our research suggests a flexible solution for dealing with the similarity limitation of current methods. To overcome the similarity problem, we suggest dividing items into multiple features. Using these features we extract several user interests, which can be compared among users. This comparison results in more user matches than in current TARS.
Artificial Neural Networks and Ranking Approach for Probe Selection and Classification of Microarray Data

Acute leukemia classification into its Myeloid and Lymphoblastic subtypes is usually accomplished according to the morphological appearance of the tumor. Nevertheless, cells from the two subtypes can have similar histopathological appearance, which makes screening procedures very difficult. Correct classification of patients in the initial phases of the disease would allow doctors to properly prescribe cancer treatment. Therefore, the development of alternative methods, to the usual morphological classification, is needed in order to improve classification rates and treatment. This paper is based on the principle that DNA microarray data extracted from tumors contain sufficient information to differentiate leukemia subtypes. The classification task is described as a general pattern recognition problem, requiring initial representation by causal quantitative features, followed by the construction of a classifier. In order to show the validity of our methods, a publicly available dataset of acute leukemia comprising 72 samples with 7,129 features was used. The dataset was split into two subsets: the training dataset with 38 samples and the test dataset with 34 samples. Feature selection methods were applied to the training dataset. The 50 most predictive genes, according to each method, were selected. Artificial Neural Network (ANN) classifiers were developed to compare the feature selection methods. Among the 50 genes selected using the best classifier, 21 are consistent with previous work and 4 additional ones are clearly related to tumor molecular processes. The remaining 25 selected genes were able to classify the test dataset, correctly, using the ANN.