Resumen de HOMO-MEX en Iberlef 2023: Detección de discursos de odio en mensajes online dirigidos hacia la población LGBTQ+ hablante de español mexicano

The detection of hate speech and stereotypes in online platforms has gained significant attention in the field of Natural Language Processing (NLP). Among various forms of discrimination, LGBTQ+ phobia is prevalent on social media, particularly on platforms like Twitter. The objective of the HOMO-MEX task is to encourage the development of NLP systems that can detect and classify LGBTQ+ phobic content in Spanish tweets, regardless of whether it is expressed aggressively or subtly. The task aims to address the lack of dedicated resources for LGBTQ+ phobia detection in Spanish Twitter and encourages participants to employ multi-label classification approaches.La detección de discursos de odio y estereotipos en plataformas en línea ha suscitado gran atención en el campo del Procesamiento del Lenguaje Natural (PLN). Entre las diversas formas de discriminación, la LGBTQ+fobia es frecuente en las redes sociales, especialmente en plataformas como Twitter. El objetivo de la tarea HOMO-MEX es fomentar el desarrollo de sistemas de PLN que puedan detectar y clasificar contenido LGBTQ+fóbico en tuits en español, independientemente de si se expresa de forma agresiva o sutil. La tarea pretende abordar la falta de recursos dedicados a la detección de la fobia LGBTQ+ en Twitter en español y anima a los participantes a emplear enfoques de clasificación multietiqueta.This paper has been supported by PAPIIT projects IT100822, TA101722, and CONAHCYT CF-2023-G-64. Also, we thank Alejandro Ojeda Trueba for the creation of the HOMO-MEX presentation image. GBE is supported by a grant from the Ministry of Universities of the Government of Spain, financed by the European Union, NextGeneration EU (María Zambrano program)

Can involving clients in simulation studies help them solve their future problems? A transfer of learning experiment

It is often stated that involving the client in operational research studies increases conceptual learning about a system which can then be applied repeatedly to other, similar, systems. Our study provides a novel measurement approach for behavioural OR studies that aim to analyse the impact of modelling in long term problem solving and decision making. In particular, our approach is the first to operationalise the measurement of transfer of learning from modelling using the concepts of close and far transfer, and overconfidence. We investigate learning in discrete-event simulation (DES) projects through an experimental study. Participants were trained to manage queuing problems by varying the degree to which they were involved in building and using a DES model of a hospital emergency department. They were then asked to transfer learning to a set of analogous problems. Findings demonstrate that transfer of learning from a simulation study is difficult, but possible. However, this learning is only accessible when sufficient time is provided for clients to process the structural behaviour of the model. Overconfidence is also an issue when the clients who were involved in model building attempt to transfer their learning without the aid of a new model. Behavioural OR studies that aim to understand learning from modelling can ultimately improve our modelling interactions with clients; helping to ensure the benefits for a longer term; and enabling modelling efforts to become more sustainable

Analysis of matched case–control data with multiple ordered disease states: possible choices and comparisons

In an individually matched case–control study, effects of potential risk factors are ascertained through conditional logistic regression (CLR). Extension of CLR to situations with multiple disease or reference categories has been made through polychotomous CLR and is shown to be more efficient than carrying out separate CLRs for each subgroup. In this paper, we consider matched case–control studies where there is one control group, but there are multiple disease states with a natural ordering among themselves. This scenario can be observed when the cases can be further classified in terms of the seriousness or progression of the disease, for example, according to different stages of cancer. We explore several popular models for ordered categorical data in this context. We first adopt a cumulative logit or equivalently, a proportional-odds model to account for the ordinal nature of the data. The important distinction of this model from a stratified dichotomous and polychotomous logistic regression model is that the stratum-specific nuisance parameters cannot be eliminated in this model via the conditional-likelihood approach. We discuss a Mantel–Haenszel approach for analysing such data. We point out possible difficulties with standard likelihood-based approaches with the cumulative logit model when applied to case–control data. We then consider an alternative conditional adjacent-category logit model. We illustrate the methods by analysing data from a matched case–control study on low birthweight in newborns where infants are classified according to low and very low birthweight and a child with normal birthweight serves as a control. A simulation study compares the different ordinal methods with methods ignoring sub-classification of the ordered disease states. Copyright © 2007 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/56068/1/2790_ftp.pd

NFIRAOS: TMT's facility adaptive optics system

NFIRAOS, the TMT Observatory's initial facility AO system is a multi-conjugate AO system feeding science light from 0.8 to 2.5 microns wavelength to several near-IR client instruments. NFIRAOS has two deformable mirrors optically conjugated to 0 and 11.2 km, and will correct atmospheric turbulence with 50 per cent sky coverage at the galactic pole. An important requirement is to have very low background: the plan is to cool the optics; and one DM is on a tip/tilt stage to reduce surface count. NFIRAOS' real time control uses multiple sodium laser wavefront sensors and up to three IR natural guide star tip/tilt and/or tip/tilt/focus sensors located within each client instrument. Extremely large telescopes are sensitive to errors due to the variability of the sodium layer. To reduce this sensitivity, NFIRAOS uses innovative algorithms coupled with Truth wavefront sensors to monitor a natural star at low bandwidth. It also includes an IR acquisition camera, and a high speed NGS WFS for operation without lasers. For calibration, NFIRAOS includes simulators of both natural stars at infinity and laser guide stars at varying range distance. Because astrometry is an important science programme for NFIRAOS, there is a precision pinhole mask deployable at the input focal plane. This mask is illuminated by a science wavelength and flat-field calibrator that shines light into NFIRAOS' entrance window. We report on recent effort especially including trade studies to reduce field distortion in the science path and to reduce cost and complexity

Uranium isotope variation within vein type uranium ore deposits

Isotopic composition of uranium has previously been used to infer the depositional redox environment of uranium ore concentrates and also provide a potential signature to inform nuclear forensic investigations. This study evaluates the diagnostic power of the U isotope signature by investigating (1) the heterogeneity of U isotope compositions in samples collected from the same mine and/or vein, and (2) the influence of U ore processing on 238U/235U and 234U/238U ratios. These characteristics are explored via high precision mass spectrometric measurement of vein type uranium ore samples collected predominantly from mines located in central Portugal and Southwest England. Samples collected from the same vein and mine exhibit δ238U values from −0.16 to +0.03 (±0.04) ‰ and −1.6 to −64.7 (±0.4) ‰ for δ234U (±2SD). These variations can be attributed to redox-driven isotope fractionation processes and/or U redistribution during localised leaching and re-precipitation. Analyses of residues and leachates from small-scale batch experiments designed to simulate industrial U ore leaching procedures reveal significant positive and negative changes in isotope composition in the leachate relative to the bulk material (up to 0.21 ± 0.06‰ for δ238U and 62.0 ± 0.6‰ for δ234U). These findings highlight the possibility of significantly different δ238U and δ234U of uranium ore concentrate from the same mine even if manufacturing processes remain unchanged