Counter-Narratives of La Raza Voices: An Exploration of the Personal and Professional Lived Experiences of Mexican-American/Chicana/O Faculty at California Catholic Institutions of Higher Education

Faculty members of color time and again encounter the greatest number of challenges and barriers (e.g., discrimination, isolation, marginalization, tokenism, inundated with workloads and service commitments, devalued research, and delayed promotion and tenure) in both entering academia and succeeding within academia. The purpose of this study was to explore the personal and professional lived experiences of eight self-identified native-born Mexican-American and Chicana/o tenured and tenure-track faculty members employed at four California Catholic institutions of higher education. This study utilized a qualitative narrative methodology employing the critical race tenets of counter-storytelling and the permanence of racism. Through use of this methodology, La Raza counter-story narratives shed light on various degrees of racism pertaining to their social and cultural climate, tenure and promotion process, and level of job satisfaction as ethnic minority faculty members in Catholic higher education. Themes elicited from La Raza faculty narratives were compared against the associated master narratives. Although La Raza participants\u27 lived experiences marginally substantiated previous findings related to faculty of color, their narratives enhanced limited findings with more depth and detail specific to Mexican-American and Chicana/o faculty in Catholic higher education. In addition, La Raza faculty provided numerous recommendations to assist Mexican-American and Chicana/o scholars in their pursuit of academic careers in Catholic higher education; current Mexican-American and Chicana/o faculty toward tenure and promotion; and academic administrators in their recruitment, promotion, and retention of Mexican-American and Chicana/o faculty in Catholic higher education. Apparent in their counter-story narratives, each La Raza participant has made personal and professional commitments and contributions to sustain the cultures of both their self-identified ethnicity and of their university

A Glimpse of Hopjs

International audienceHop.js is a multitier programming environment for JavaScript. Itallows a single JavaScript program to describe the client-side and theserver-side components of a web application. Its runtime environmentensures consistent executions of the application on the server and onthe client.This paper overviews the Hop.js design. It shows the JavaScriptextensions that makes it possible to conceive web applicationsglobally. It presents how Hop.js interacts with the outside world. Italso briefly presents the Hop.js implementation. It presents theHop.js web server implementation, the handling of server-sideparallelism, and the JavaScript and HTML compilers

Deep temperatures in the Paris Basin using tectonic-heat flow modelling

International audienceThe determination of deep temperatures in a basin is one of the key parameters in the exploration of geothermal energy. This study, carried out as part of the CLASTIQ-2 project, presents a 3 temperatures in the Paris Basin derived through a thermal-tectonic forward modelling method, calibrated using subsurface temperature values. The temperature dataset required for the calibration was compiled in 2007 as part of the CLASTIQ-1 project. The temperature measurement dataset is largely composed of BHT (some 2443 values). These BHT measurements required correction due to the thermal disturbance created during drilling. After correction, which was carried out using the Instantaneous Cylinder Source (ICS) method, 494 corrected BHT (BHTx) values were available for the modelling of the Paris Basin. In addition to these BHTx, some 15 DST measurements that are considered as close to the thermal equilibrium (i.e., ±5°C) were added to the temperature calibration values. According to this dataset of BHTx and DST, the average gradient in the Paris Basin was calculated as 34.9°C/km when the surface temperature is fixed at 10°C. The temperature values collected were then used to calibrate the tectonic-heat flow modelling. The model was computed at the lithospheric scale but focused on the temperature field in the sedimentary basin fill. The model takes into account the geodynamic evolution of the last 20 My, the heat production, and the specific heat conduction of each defined sedimentary layer. The result is a 3D thermal block that is presented in the form of isodepth maps. The results are strongly influenced by thermal conductivity variations such as those due to differences in sediment composition while faults create some more localised influences. The presence of anomalously radiogenic bodies beneath the basin, and/or by variations in lithosphere thickness resulting in possible heat production anomalies strongly influence the thermal variations the Paris Basin. The Alpine Orogeny created a slight temperature increase in the south-eastern part of the basin and inhomogeneities in the lithology of the basement generating additional sources of variation in the sedimentary pil

Movie Editing and Cognitive Event Segmentation in Virtual Reality Video

Traditional cinematography has relied for over a century on a well-established set of editing rules, called continuity editing, to create a sense of situational continuity. Despite massive changes in visual content across cuts, viewers in general experience no trouble perceiving the discontinuous flow of information as a coherent set of events. However, Virtual Reality (VR) movies are intrinsically different from traditional movies in that the viewer controls the camera orientation at all times. As a consequence, common editing techniques that rely on camera orientations, zooms, etc., cannot be used. In this paper we investigate key relevant questions to understand how well traditional movie editing carries over to VR. To do so, we rely on recent cognition studies and the event segmentation theory, which states that our brains segment continuous actions into a series of discrete, meaningful events. We first replicate one of these studies to assess whether the predictions of such theory can be applied to VR. We next gather gaze data from viewers watching VR videos containing different edits with varying parameters, and provide the first systematic analysis of viewers' behavior and the perception of continuity in VR. From this analysis we make a series of relevant findings; for instance, our data suggests that predictions from the cognitive event segmentation theory are useful guides for VR editing; that different types of edits are equally well understood in terms of continuity; and that spatial misalignments between regions of interest at the edit boundaries favor a more exploratory behavior even after viewers have fixated on a new region of interest. In addition, we propose a number of metrics to describe viewers' attentional behavior in VR. We believe the insights derived from our work can be useful as guidelines for VR content creation

Theoretical characterization of the lowest-energy absorption band of pyrrole

The lowest-energy band of the electronic spectrum of pyrrole has been studied with vibrational resolution by using multiconfigurational second-order perturbation theory (CASPT2) and its multistate extension (MS–CASPT2) in conjunction with large atomic natural orbital-type basis sets including Rydberg functions. The obtained results provide a consistent picture of the recorded spectrum in the energy region 5.5–6.5 eV and confirm that the bulk of the intensity of the band arises from a ππ∗ intravalence transition, in contradiction to recent theoretical claims. Computed band origins for the 3s,3p Rydberg electronic transitions are in agreement with the available experimental data, although new assignments are suggested. As illustrated in the paper, the proper treatment of the valence–Rydberg mixing is particularly challenging for ab initio methodologies and can be seen as the main source of deviation among the recent theoretical results as regards the position of the low-lying valence excited states of [email protected] ; [email protected]

Precipitación extrema en la Puna del Desierto de Atacama: ¿Cómo gestionar la incertidumbre actual y futura?

Chile is one of the Latin American countries most affected by Climate Change. There is a high level of uncertainty regarding the variability of precipitation and its projections in many regions of this country. This poses challenges for climate characterization and for defining strategies to reduce its risks. The study area is the Puna of Atacama Desert, Andean highlands located to the eastern side of the extreme arid lands, a region that concentrates the main copper and lithium mining at word scale, and where meteorological observations are scarce, with missing data and unreliable projections. Considering this data limitations, a daily precipitation database of 35 weather stations was constructed in order to evaluate some extreme precipitation indices that allow establishing changes between 1981-2017, in addition to spatial interpolations based on topography. It is concluded that most of the meteorological stations do not present significant trends of change, e.g. Extremely wet days (R99p), Wet days (RR) and Consecutive wet days (CWD). The index with the highest number of stations with a trend is CDD, which shows an increase in consecutive dry days. One of the main contributions of this research was to expand the number of observations and to generate maps of the spatial distribution of the indices of extremes. We are facing open questions regarding living with uncertainty, and meeting the challenges of maintaining records to increase the levels of certainty of climatic changes.Chile es uno de los países de América Latina más afectados por el cambio climático. Existe un elevado nivel de incertidumbre respecto a la variabilidad de las precipitaciones y sus proyecciones en muchas de sus regiones. Ello plantea desafíos para su caracterización climática y para definir estrategias para reducir los riesgos asociados. Se estudia la Puna del Desierto de Atacama, paisaje andino de altura que bordea las tierras áridas por el lado este, y que concentran las principales minas de cobre y litio a escala mundial, y donde existen escasas observaciones meteorológicas, con datos perdidos y proyecciones de poca fiabilidad. Es por ello que se construyó una base diaria de precipitación de 35 estaciones con el fin de evaluar algunos índices extremos que permitan establecer cambios entre 1981-2017, además de interpolaciones espaciales basadas en la topografía. Se concluye que la mayoría de las estaciones meteorológicas no presenta tendencias significativas de cambio, destacando días extremadamente húmedos (R99p), días húmedos (RR) y días húmedos consecutivos (CWD). El índice con mayor cantidad de estaciones con tendencia es CDD, que muestra un incremento de los días consecutivos secos. Uno de los principales aportes de esta investigación fue ampliar el número de observaciones y generar mapas de la distribución espacial de los índices de extremos. Nos quedan preguntas abiertas respecto a convivir con la incertidumbre, y alcanzar desafíos de mantener los registros para aumentar los niveles de certeza de los cambios climáticos.The authors acknowledge support by the Program CLIMAT AmSud Project PRELASA (21-CLIMAT-12)

Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations

We introduce a novel modeling approach for time series imputation and forecasting, tailored to address the challenges often encountered in real-world data, such as irregular samples, missing data, or unaligned measurements from multiple sensors. Our method relies on a continuous-time-dependent model of the series' evolution dynamics. It leverages adaptations of conditional, implicit neural representations for sequential data. A modulation mechanism, driven by a meta-learning algorithm, allows adaptation to unseen samples and extrapolation beyond observed time-windows for long-term predictions. The model provides a highly flexible and unified framework for imputation and forecasting tasks across a wide range of challenging scenarios. It achieves state-of-the-art performance on classical benchmarks and outperforms alternative time-continuous models