Miracle-dz/UniFAR-FTUs huggingface dataset:
Cannabidiol reverses attentional bias to cigarette cues in a human experimental model of tobacco withdrawal

Abstract Background and Aims Cannabidiol (CBD), a non‐intoxicating cannabinoid found in cannabis, may be a promising novel smoking cessation treatment due to its anxiolytic properties, minimal side effects and research showing that it may modify drug cue salience. We used an experimental medicine approach with dependent cigarette smokers to investigate if (1) overnight nicotine abstinence, compared with satiety, will produce greater attentional bias (AB), higher pleasantness ratings of cigarette‐related stimuli and increased craving and withdrawal; and (2) CBD in comparison to placebo, would attenuate AB, pleasantness of cigarette‐related stimuli, craving and withdrawal and not produce any side effects. Design Randomized, double‐blind cross‐over study with a fixed satiated session followed by two overnight abstinent sessions. Setting UK laboratory. Participants Thirty non‐treatment‐seeking, dependent cigarette smokers recruited from the community. Intervention and comparator 800 mg oral CBD, or matched placebo (PBO) in a counterbalanced order Measurements AB to pictorial tobacco cues was recorded using a visual probe task and an explicit rating task. Withdrawal, craving, side effects, heart rate and blood pressure were assessed repeatedly. Findings When participants received PBO, tobacco abstinence increased AB (P = 0.001, d = 0.789) compared with satiety. However, CBD reversed this effect, such that automatic AB was directed away from cigarette cues (P = 0.007, d = 0.704) and no longer differed from satiety (P = 0.82). Compared with PBO, CBD also reduced explicit pleasantness of cigarette images (P = 0.011; d = 0.514). Craving (Bayes factor = 7.08) and withdrawal (Bayes factor = 6.95) were unaffected by CBD, but greater in abstinence compared with satiety. Systolic blood pressure decreased under CBD during abstinence. Conclusions A single 800‐mg oral dose of cannabidiol reduced the salience and pleasantness of cigarette cues, compared with placebo, after overnight cigarette abstinence in dependent smokers. Cannabidiol did not influence tobacco craving or withdrawal or any subjectively rated side effects.
Inclusión educativa y diversidad cultural en América Latina

El articulo centra su atencion en los desafios que enfrentan las politicas de equidad educativa en America Latina, region signada por una gran diversidad cultural. Tras detenerse en aportar algunas precisiones en torno a las nociones de equidad y diversidad, el texto pasa a una breve caracterizacion de la heterogeneidad social y cultural en los paises latinoamericanos para centrarse luego en identificar un conjunto de factores y dinamicas propias del funcionamiento del sistema educativo –en particular en el nivel medio de ensenanza- que hacen obstaculo a la meta de una educacion para todos.
An Approach to Prioritize Classes in a Multi-objective Software Maintenance Framework

: Genetic algorithms have become popular in automating software refactoring and an increasing level of attention is being given to the use of multi-objective approaches. This paper investigated the use of a mul-ti-objective genetic algorithm to automate software refactoring using a purpose built tool, MultiRefactor. The tool used a metric function to measure quality in a software system and tested a second objective to measure the importance of the classes being refactored. This priority objective takes as input a set of classes to favor and, optionally, a set of classes to disfavor as well. The multi-objective setup refactors the input program to improve its quality using the quality objective, while also focusing on the classes specified by the user. An experiment was constructed to measure the multi-objective approach against the alternative mono-objective approach that does not use an objective to measure priority of classes. The two approaches were tested on six different open source Java programs. The multi-objective approach was found to give significantly better priority scores across all inputs in a similar time, while also generating improvements in the quality scores.
Lateralized Theta Wave Connectivity and Language Performance in 2- to 5-Year-Old Children

Recent neuroimaging studies support the view that a left-lateralized brain network is crucial for language development in children. However, no previous studies have demonstrated a clear link between lateralized brain functional network and language performance in preschool children. Magnetoencephalography (MEG) is a noninvasive brain imaging technique and is a practical neuroimaging method for use in young children. MEG produces a reference-free signal, and is therefore an ideal tool to compute coherence between two distant cortical rhythms. In the present study, using a custom child-sized MEG system, we investigated brain networks while 78 right-handed preschool human children (32–64 months; 96% were 3-4 years old) listened to stories with moving images. The results indicated that left dominance of parietotemporal coherence in theta band activity (6-8 Hz) was specifically correlated with higher performance of language-related tasks, whereas this laterality was not correlated with nonverbal cognitive performance, chronological age, or head circumference. Power analyses did not reveal any specific frequencies that contributed to higher language performance. Our results suggest that it is not the left dominance in theta oscillation per se, but the left-dominant phase-locked connectivity via theta oscillation that contributes to the development of language ability in young children.
Unsupervised Clustering and Automatic Language Model Generation for ASR

The goal of an automatic speech recognition system is to enable the computer in understanding human speech and act accordingly. In order to realize this goal, language modeling plays an important role. It works as a knowledge source through mimicking human comprehension mechanism in understanding the language. Among many other approaches, statistical language modeling technique is widely used in automatic speech recognition systems. However, the generation of reliable and robust statistical model is very difficult task, especially for a large vocabulary system. For a large vocabulary system, the performance of such a language model degrades as the vocabulary size increases. Hence, the performance of the speech recognition system also degrades due to the increased complexity and mutual confusion among the candidate words in the language model. In order to solve these problems, reduction of language model size as well as minimization of mutual confusion between words are required. In our work, we have employed clustering techniques, using self-organizing map, to build topical language models. Moreover, in order to capture the inherent semantics of sentences, a lexical dictionary, WordNet has been used in the clustering process. This thesis work focuses on various aspects of clustering, language model generation, extraction of task dependent acoustic parameters, and their implementations under the framework of the CMU Sphinx3 speech engine decoder. The preliminary results, presented in this thesis show the effectiveness of the topical language models.
Building a Japanese parsed corpus while improving the parsing system

In January 1996, we started a project to construct a Japanese parsed corpus and to simultaneously improve a morphological analyzer and a dependency structure analyzer. In this project, human annotators not only correct the erroneous analyses produced by the parsing system, but also improve the parsing system/grammar: ﬁnding problematic ﬁxed expressions, picking out phrases which have exceptional functions, classifying unseen types of clauses, and so on. The project ended in July 2000 with the release of an enhanced version of the Japanese parsing system and 40,000 sentences of parsed corpus.
The XML web: a first study

Although originally designed for large-scale electronic publishing, XML plays an increasingly important role in the exchange of data on the Web. In fact, it is expected that XML will become the lingua franca of the Web, eventually replacing HTML. Not surprisingly, there has been a great deal of interest on XML both in industry and in academia. Nevertheless, to date no comprehensive study on the XML Web (i.e., the subset of the Web made of XML documents only) nor on its contents has been made. This paper is the first attempt at describing the XML Web and the documents contained in it. Our results are drawn from a sample of a repository of the publicly available XML documents on the Web, consisting of about 200,000 documents. Our results show that, despite its short history, XML already permeates the Web, both in terms of generic domains and geographically. Also, our results about the contents of the XML Web provide valuable input for the design of algorithms, tools and systems that use XML in one form or another.
HMGraph OLAP: a novel framework for multi-dimensional heterogeneous network analysis

As information continues to grow at an explosive rate, more and more heterogeneous network data sources are coming into being. While OLAP (On-Line Analytical Processing) techniques have been proven effective for analyzing and mining structured data, unfortunately, to our best knowledge, there are no OLAP tools available that are able to analyze multi-dimensional heterogeneous networks from different perspectives and with multiple granularities. Therefore, we have developed a novel HMGraph OLAP (Heterogeneous and Multi-dimensional Graph OLAP) framework for the purpose of providing more dimensions and operations to mine multi-dimensional heterogeneous information network. After information dimensions and topological dimensions, we have been the first to propose entity dimensions, which represent an important dimension for heterogeneous network analysis. On the basis of this notion, we designed HMGraph OLAP operations named (Rotate and Stretch for entity dimensions, which are able to mine relationships between different entities. We then proposed the HMGraph Cube, which is an efficient data warehousing model for HMGraph OLAP. In addition, through comparison with common strategies, we have shown that the optimizations we have proposed deliver better performance. Finally, we have implemented a HMGraph OLAP prototype, LiterMiner, which has proven effective for the analysis of multi-dimensional heterogeneous networks.
Ja n 20 11 Fuzzy h-ideals of a Γ-hemiring and its operator hemirings

Hemirings [3] which provide a common generalization of rings and distributive lattices arise naturally in such diverse areas of mathematics as combinatorics, functional analysis, graph theory, automata theory, mathematical modelling and parallel computation systems etc.(for example, see [3], [4]). Hemirings have also been proved to be an important algebraic tool in theoretical computer science, see for instance [4], for some detail and example. Ideals of
Research on Textile Stain Detection Technique Based on Machine Vision

The automatic detection of medical textile stains has always been a hot spot. An automatic detection method is proposed based on machine vision, which can solve the technical difficult problem of automatic detection technique efficiently. The procedure of the textile image processing has been researched, and the emphasis should be put on the image pre- processing and edge extraction. The experiments show that this detecting method of textile stains has a high detection rate relatively.
Robust Bayesian Precoding for Mitigation of TDD Hardware Calibration Errors

The predominantly used channel reciprocity condition in time-division-duplex mode only holds in practical systems if the transmit and receive hardware chains at the base station (BS) and at the user equipments are perfectly calibrated. Otherwise, there is a mismatch between the uplink (UL) and downlink channel state information (CSI) that leads to performance degradations. In this paper, we consider a multi-user MIMO system with hardware calibration errors (CaEs) at the BS and derive robust Bayesian precoders that minimize inter-user interference using the UL CSI and CaEs statistics as side information. The proposed robust precoding outperforms conventional non-robust precoding, and its gains compared to non-robust precoding increase with increasing numbers of users and CaEs. Furthermore, robust precoding can be used to relax calibration requirements, which is a result of practical interest. Our approach is applicable to any number of BS antennas but is especially important for massive MIMO systems where larger CaEs might be expected.
Dependence of lattice strain relaxation, absorbance, and sheet resistance on thickness in textured ZnO@B transparent conductive oxide for thin-film solar cell applications

The interplay of surface texture, strain relaxation, absorbance, grain size, and sheet resistance in textured, boron-doped ZnO (ZnO@B), transparent conductive oxide (TCO) materials of different thicknesses used for thin film, solar cell applications is investigated. The residual strain induced by the lattice mismatch and the difference in the thermal expansion coefficient for thicker ZnO@B is relaxed, leading to an increased surface texture, stronger absorbance, larger grain size, and lower sheet resistance. These experimental results reveal the optical and material characteristics of the TCO layer, which could be useful for enhancing the performance of solar cells through an optimized TCO layer.
Projection Data Upsampling for Sparse and Low-Dose CT Scout Scans

In Computed Tomography (CT) scout scans are a first initial acquisition that determine scan parameters and final imaging regions. Current state-of-the art scout scans are 2D X-ray images acquired with the stationary CT scanner gantry and the moving table. A 3D scout volume based on a low-dose, sparse-view helical acquisition would be better for the scan planing, but image volumes usually suffer from high noise and especially angular under-sampling artifacts. We therefore evaluate the usage of a convolutional neuronal network to perform a non-linear upsampling and denoising of the input projection data. We compare our approach to a least-squares optimal linear interpolation and evaluate both algorithms with scout scan data synthesized from real patient datasets.
Contextualizing Data on a Content Management System

Content Management Systems (CMSs) are known for their ability for storing data, both structured and non-structured data. However they are not able to associate meaning and context to the stored information. Furthermore, these systems do not meet the needs and expectations of their users, because as the size of data increases, the system loses its capacity of retrieving meaningful results. In order to overcome this issue, we propose a method to implement data contextualization on a CMS. The proposed method consists of enriching the data with semantic information, allowing a more accurate retrieval of results. The implementation of this approach was validated by applying this contextualization method to a currently used CMS with real information. With this improved CMS, it is expected that the users will be able to retrieve data related to their initial search.
A Fast Rendering Method for Refractive and Reflective Caustics Due to Water Surfaces

In order to synthesize realistic images of scenes that include water surfaces, the rendering of optical effectscaused by waves on the water surface, such as caustics and reflection, is necessary. However, rendering causticsis quite complex and time‐consuming. In recent years, the performance of graphics hardware has made significantprogress. This fact encourages researchers to study the acceleration of realistic image synthesis. We present herea method for the fast rendering of refractive and reflective caustics due to water surfaces. In the proposed method,an object is expressed by a set of texture mapped slices. We calculate the intensities of the caustics on the objectby using the slices and store the intensities as textures. This makes it possible to render caustics at interactive rateby using graphics hardware. Moreover, we render objects that are reflected and refracted due to the water surfaceby using reflection/refraction mapping of these slices.
Towards Methodical Calibration: A Case Study of Enterprise Switch Measurements

In this work we discuss the general problem of how to undertake the thorough calibration of empirical data, by which we mean identifying (and ideally remedying) shortcomings and biases present in the data due to the process by which we collected it. We illustrate a methodology for proceeding with such calibration in the context of network trace measurements; in particular, traces captured from switches within an enterprise. We argue that such calibration fundamentally requires proceeding in a progressive fashion, building up an understanding of the data's quality first regarding basic properties and then onward to more complex properties. In addition, the procedure often has an iterative nature, where the investigation of these more complex properties can lead to revisiting earlier calibration steps in order to further refine them. While the methodology often proves labor ‐ intensive, it arguably plays a vital role in establishing the ultimate soundness of any subsequent analysis based on the data.
Pseudoredundant vacuum energy

We discuss models that can account for today's dark energy. The underlying cosmological constant may be Planck scale but starts as a redundant coupling which can be eliminated by a field redefinition. The observed vacuum energy arises when the redundancy is explicitly broken, say by a nonminimal coupling to curvature. We give a recipe for constructing models, including R+1/R-type models, that realize this mechanism and satisfy all solar system constraints on gravity. A similar model, based on Gauss-Bonnet gravity, provides a technically natural explanation for dark energy and exhibits an interesting seesaw behavior: a large underlying cosmological constant gives rise to both low- and high-curvature solutions. Such models could be statistically favored in the string landscape.
Optimizing Event Pattern Matching Using Business Process Models

A growing number of enterprises use complex event processing for monitoring and controlling their operations, while business process models are used to document working procedures. In this work, we propose a comprehensive method for complex event processing optimization using business process models. Our proposed method is based on the extraction of behavioral constraints that are used, in turn, to rewrite patterns for event detection, and select and transform execution plans. We offer a set of rewriting rules that is shown to be complete with respect to the all, seq, and any patterns. The effectiveness of our method is demonstrated in an experimental evaluation with a large number of processes from an insurance company. We illustrate that the proposed optimization leads to significant savings in query processing. By integrating the optimization in state-of-the-art systems for event pattern matching, we demonstrate that these savings materialize in different technical infrastructures and can be combined with existing optimization techniques.
The Interaction of IGP Weight Optimization with BGP

Link weight optimization is shown to be a key issue in engineering of IGPs using shortest path first routing. The IGP weight optimization problem seeks a weight array resulting an optimal load distribution in the network based on the topology information and a traffic demand matrix. Several solution methods for various kinds of this problem have been proposed in the literature. However, the interaction of IGP with BGP is generally neglected in these studies. In reality, the optimized weights may not perform as well as expected, since updated link weights can cause shifts in the traffic demand matrix by hot-potato routing in the decision process of BGP. Hot-potato routing occurs when BGP decides the egress router for a destination prefix according to the IGP lengths. This paper mainly investigates the possible degradation of an IGP weight optimization tool due to hot-potato routing under a worst-case example and some experiments which are carried out by using an open source traffic engineering toolbox. Furthermore, it proposes an approach based on robust optimization to overcome the negative effect of hot-potato routing and analyzes its performance
Broadband Robust Adaptive Beamforming in the Presence of Correlated interferences and DOA uncertainties

A novel method of broadband robust adaptive beamforming is proposed in this paper to deal with the signal cancellation caused by correlated interferences and DOA uncertainties. To achieve this goal, the classic techniques of spectral averaging and diagonal loading are integrated into the beamforming, with the former aiming at decorrelation and the latter at robustness against DOA uncertainties. Moreover, the optimal loading is selected based on worst-case performance optimization and a simple closed-form solution to it is suggested after some approximations. Besides its simplicity and low computational cost, this analytical solution reveals how different factors affect the optimal loading. Compared with many relevant methods, the proposed one achieves better robustness against DOA uncertainties, while suppressing both correlated and uncorrelated interferences effectively. Its excellent performance is confirmed via a number of numerical examples.
Analyzing the Performance of the Bundler Algorithm

The Bundler algorithm is a structure from motion algorithm that produces point clouds from lists of unordered images. While Agarwal et al have demonstrated that the algorithm can produce accurate point clouds efficiently for very large datasets, they also list several ”research challenges,” including scale, variability of images, and accuracy. In this paper, we analyze the performance of the algorithm in these areas, using a variety of metrics.
Human Complex Trait Genetics: Lifting the Lid of the Genomics Toolbox - from Pathways to Prediction

During the initial stages of the genome revolution human genetics was hugely successful in discovering the underlying genes for monogenic diseases. Over 3,000 monogenic diseases have been discovered with simple patterns of inheritance. The unravelling and identification of the genetic variants underlying complex or multifactorial traits, however, is proving much more elusive. There have been over 1,000 significant variants found for many quantitative and binary traits yet they explain very little of the estimated genetic variance or heritability evident from family analysis. There are many hypotheses as to why this might be the case. This apparent lack of information is holding back the clinical application of genetics and shedding doubt on whether more of the same will reveal where the remainder of the variation lies. Here we explore the current state of play, the types of variants we can detect and how they are currently exploited. Finally we look at the future challenges we must face to persuade the human genome to yield its secrets.