The L - \sigma\ relation for HII galaxies in green

The correlation between emission-line luminosity (L) and profile width (sigma) for HII Galaxies provides a powerful method to measure the distances to galaxies over a wide range of redshifts. In this paper we use SDSS spectrophotometry to explore the systematics of the correlation using the [OIII]5007 lines instead of Halpha or Hbeta to measure luminosities and line widths. We also examine possible systematic effects involved in measuring the profile-widths and the luminosities through different apertures. We find that the green L-sigma relation defined using [OIII]5007 luminosities is significantly more sensitive than Hbeta to the effects of age and the physical conditions of the nebulae, which more than offsets the advantage of the higher strength of the [OIII]5007 lines. We then explore the possibility of mixing [OIII]5007 profile-widths with SDSS Hbeta luminosities using the Hubble constant H0 to quantify the possible systematic effects. We find the mixed L(Hbeta) sigma[OIII] relation to be at least as powerful as the canonical L-sigma relation as a distance estimator, and we show that the evolutionary corrections do not change the slope and the scatter of the correlation, and therefore, do not bias the L-sigma distance indicator at high redshifts. Locally, however, the luminosities of the Giant HII Regions that provide the zero-point calibrators are sensitive to evolutionary corrections and may bias the Hubble constant if their mean ages, as measured by the equivalent widths of Hbeta, are significantly different from the mean age of the HII Galaxies. Using a small sample of 16 ad-hoc zero point calibrators we obtain a value of H0 = 66.4\pm4.5 km s^-1 Mpc^-1 for the Hubble constant, which is fully consistent with the best modern determinations, and that is not biased by evolutionary corrections.Comment: 14 pages, 7 figures. Accepted for publication by A&

IRDI en la Evaluación de Bebés con Problemas de Desarrollo Asociados al Autismo

Esta pesquisa é um estudo exploratório com o objetivo de investigar contribuições que o protocolo Indicadores Clínicos de Risco para o Desenvolvimento Infantil (IRDI) pode trazer para avaliação de bebês que estejam se desenvolvendo em um percurso autístico. Para tanto, foram comparados resultados do IRDI e do Modified Checklist for Autism in Toddlers (M-CHAT) em 43 bebês de 18 meses de vida. O estudo demonstrou que dentre os riscos detectados pelo IRDI também está o autismo. A análise estatística aponta para a possibilidade de o eixo Suposição de Sujeito (SS) não diferenciar bebês com problemas de desenvolvimento associados ao autismo daqueles que apresentam um desenvolvimento típico. O eixo Alternância Presença/Ausência (PA) parece ser aquele que mais distingue os dois grupos de bebês. A vinheta clínica demonstra que o IRDI pode ser utilizado como um operador de leitura que auxilia a compreensão de dinâmicas familiares com potencial para orientar intervenções no contexto da saúde pública.Esta investigacion es un estudio exploratorio con objeto de investigar contribuciones que el protocolo Indicadores Clínicos de Riesgo para el Desarrollo Infantil (IRDI) puede traer para evaluación de bebés que estén desarrollándose en un camino autístico. Para tanto, fueron comparados resultados del IRDI y del Modified Checklist for Autism in Toddlers (M-CHAT) en 43 bebés de 18 meses de vida. El estudio demostró que los riesgos detectados por el IRDI también incluyen está el autismo. El análisis estadístico indica la posibilidad de que el eje Suposición de Sujeto (SS) no diferencie bebés con problemas de desarrollo asociados al autismo de aquellos con desarrollo típico. El eje Alternancia Presencia/Ausencia (PA) parece ser el que más distingue los dos grupos de bebés. La viñeta clínica demuestra que el IRDI puede ser utilizado como un operador de lectura que auxilia la comprensión de dinámicas familiares con potencial para orientar intervenciones en el contexto de la salud pública.This exploratory study investigates the contributions the Clinical Risk Indicators in Child Development (CDRI) may bring for the evaluation of infants who might be considered in autistic development. To do so, results of the evaluation – using CDRI and the Modified Checklist for Autism (M-CHAT) – of 43 babies who were 18 months old were compared. The present study showed that autism is amongst the risks the CDRI detects. The statistical analysis highlights that the axis Subject Assumption (SA) may not differentiate infants who present developmental problems associated with autism from typically developing ones. The Alternate Presence/Absence (PA) axis seems to be the one that most distinguishes these groups of infants. The clinical vignette demonstrates that CDRI can be used as a guide that helps to understand the family dynamics and can guide the interventions made in public health services

Energy harvesting from the vibrations of a passing train: effect of speed variability

This paper builds on a previous study which investigated the amount of energy that could be harvested from the vibration induced by a passing train using a trackside energy harvester. In that study, the optimum parameters of the device were determined for a train passing at a particular speed. However, the effect of the train speed variability on the amount of energy harvested was not explored. In this paper a study is thus undertaken to determine this effect using experimental data from train passages at a site in the UK. Furthermore, a model is developed to investigate the optimum design parameters of the energy harvester when trains pass by at slightly different speeds. This is then validated using the experimental data. It is found that, provided the variability in the train speed is less than about 1% from the nominal speed, then a harvester tuned so that its natural frequency matches one of the trainload dominant frequencies at the line speed is a reasonable design condition

DIP-RL: Demonstration-Inferred Preference Learning in Minecraft

In machine learning for sequential decision-making, an algorithmic agent learns to interact with an environment while receiving feedback in the form of a reward signal. However, in many unstructured real-world settings, such a reward signal is unknown and humans cannot reliably craft a reward signal that correctly captures desired behavior. To solve tasks in such unstructured and open-ended environments, we present Demonstration-Inferred Preference Reinforcement Learning (DIP-RL), an algorithm that leverages human demonstrations in three distinct ways, including training an autoencoder, seeding reinforcement learning (RL) training batches with demonstration data, and inferring preferences over behaviors to learn a reward function to guide RL. We evaluate DIP-RL in a tree-chopping task in Minecraft. Results suggest that the method can guide an RL agent to learn a reward function that reflects human preferences and that DIP-RL performs competitively relative to baselines. DIP-RL is inspired by our previous work on combining demonstrations and pairwise preferences in Minecraft, which was awarded a research prize at the 2022 NeurIPS MineRL BASALT competition, Learning from Human Feedback in Minecraft. Example trajectory rollouts of DIP-RL and baselines are located at https://sites.google.com/view/dip-rl.Comment: Paper accepted at The Many Facets of Preference Learning Workshop at the International Conference on Machine Learning (ICML), Honolulu, Hawaii, USA, 202

Value of central venous to arterial CO2 difference after early goal-directed therapy in septic shock patients

Background and aims: Venous to arterial difference of carbon dioxide (Pv–aCO2) tracks tissue blood flow. We aimed to evaluate if Pv–aCO2 measured from a superior central vein sample is a prognostic index (ICU length of stay, SOFA score, 28th mortality rate) just after early goal-directed therapy (EGDT)comparing its ICU admission values between patients with normal and abnormal (>6 mm Hg) Pv–aCO2. As secondary objectives, we evaluated the relationship of Pv–aCO2 with other variables of perfusion during the 24 hours that followed EGDT. Materials and methods: Prospective observational study conducted in an academic ICU adult septic shock patients after a 6-hour complete EGTD. Hemodynamic measurements, arterial/central venous blood gases, and arterial lactate were obtained on ICU admission and after 6, 18 and 24 hours. Results: Sixty patients were included. Admission Pv–aCO2 values showed no prognostic value. Admission Pv–aCO2 (ROC curve 0.527 [CI 95% 0.394 to 0.658]) values showed low specificity and sensitivity as predictors of mortality. There was a difference observed in the mean Pv–aCO2 between nonsurvivors (NS) and survivors (S) after 6 hours. Central venous oxygen saturation (ScvO2) and Pv–aCO2 showed significant correlation (R2 = –0.41, P 70%) and abnormal Pv–aCO2 (>6 mm Hg) showed higher SOFA scores. Normal Pv–aCO2 group cleared their lactate levels in comparison to the abnormal Pv–aCO2 group. Conclusion: In septic shock, admission Pv–aCO2 after EGDT is not related to worse outcomes. An abnormal Pv–aCO2 along with a normal ScvO2 is related to organ dysfunction

Contextuality with vanishing coherence and maximal robustness to dephasing

Generalized contextuality is a resource for a wide range of communication and information processing protocols. However, contextuality is not possible without coherence, and so can be destroyed by dephasing noise. Here, we explore the robustness of contextuality to partially dephasing noise in a scenario related to state discrimination (for which contextuality is a resource). We find that a vanishing amount of coherence is sufficient to demonstrate the failure of noncontextuality in this scenario, and we give a proof of contextuality that is robust to arbitrary amounts of partially dephasing noise. This is in stark contrast to partially depolarizing noise, which is always sufficient to destroy contextuality.Comment: 13 pages, 7 figures. Comments are welcome

Full phase space resonant anomaly detection

Physics beyond the Standard Model that is resonant in one or more dimensions has been a longstanding focus of countless searches at colliders and beyond. Recently, many new strategies for resonant anomaly detection have been developed, where sideband information can be used in conjunction with modern machine learning, in order to generate synthetic datasets representing the Standard Model background. Until now, this approach was only able to accommodate a relatively small number of dimensions, limiting the breadth of the search sensitivity. Using recent innovations in point cloud generative models, we show that this strategy can also be applied to the full phase space, using all relevant particles for the anomaly detection. As a proof of principle, we show that the signal from the R&D dataset from the LHC Olympics is findable with this method, opening up the door to future studies that explore the interplay between depth and breadth in the representation of the data for anomaly detection

The Temporal Requirements for Insulin Signaling During Development in Drosophila

Recent studies have indicated that the insulin-signaling pathway controls body and organ size in Drosophila, and most metazoans, by signaling nutritional conditions to the growing organs. The temporal requirements for insulin signaling during development are, however, unknown. Using a temperature-sensitive insulin receptor (Inr) mutation in Drosophila, we show that the developmental requirements for Inr activity are organ specific and vary in time. Early in development, before larvae reach the “critical size” (the size at which they commit to metamorphosis and can complete development without further feeding), Inr activity influences total development time but not final body and organ size. After critical size, Inr activity no longer affects total development time but does influence final body and organ size. Final body size is affected by Inr activity from critical size until pupariation, whereas final organ size is sensitive to Inr activity from critical size until early pupal development. In addition, different organs show different sensitivities to changes in Inr activity for different periods of development, implicating the insulin pathway in the control of organ allometry. The reduction in Inr activity is accompanied by a two-fold increase in free-sugar levels, similar to the effect of reduced insulin signaling in mammals. Finally, we find that varying the magnitude of Inr activity has different effects on cell size and cell number in the fly wing, providing a potential linkage between the mode of action of insulin signaling and the distinct downstream controls of cell size and number. We present a model that incorporates the effects of the insulin-signaling pathway into the Drosophila life cycle. We hypothesize that the insulin-signaling pathway controls such diverse effects as total developmental time, total body size and organ size through its effects on the rate of cell growth, and proliferation in different organs