On the deduction of galaxy abundances with evolutionary neural networks

A growing number of indicators are now being used with some confidence to measure the metallicity(Z) of photoionisation regions in planetary nebulae, galactic HII regions(GHIIRs), extra-galactic HII regions(EGHIIRs) and HII galaxies(HIIGs). However, a universal indicator valid also at high metallicities has yet to be found. Here, we report on a new artificial intelligence-based approach to determine metallicity indicators that shows promise for the provision of improved empirical fits. The method hinges on the application of an evolutionary neural network to observational emission line data. The network's DNA, encoded in its architecture, weights and neuron transfer functions, is evolved using a genetic algorithm. Furthermore, selection, operating on a set of 10 distinct neuron transfer functions, means that the empirical relation encoded in the network solution architecture is in functional rather than numerical form. Thus the network solutions provide an equation for the metallicity in terms of line ratios without a priori assumptions. Tapping into the mathematical power offered by this approach, we applied the network to detailed observations of both nebula and auroral emission lines in the optical for a sample of 96 HII-type regions and we were able to obtain an empirical relation between Z and S23 with a dispersion of only 0.16 dex. We show how the method can be used to identify new diagnostics as well as the nonlinear relationship supposed to exist between the metallicity Z, ionisation parameter U and effective (or equivalent) temperature T*.Comment: Accepted for publication in PASP. 6 pages, 2 figure

Astrophysical S-factor for 16^{16}O+16^{16}O within the adiabatic molecular picture

The astrophysical S-factor for $^{16}$O + $^{16}$O is investigated within the adiabatic molecular picture. It very well explains the available experimental data. The collective radial mass causes a pronounced resonant structure in the S-factor excitation function, providing a motivation for measuring the $^{16}$O + $^{16}$O fusion cross section at deep sub-barrier energies.Comment: 5 pages, 2 figures, SOTANCP2008 Conference, Strasbourg, France, May 13-16, 2008, To appear in IJMP

Scaling Laws Do Not Scale

Recent work has proposed a power law relationship, referred to as ``scaling laws,'' between the performance of artificial intelligence (AI) models and aspects of those models' design (e.g., dataset size). In other words, as the size of a dataset (or model parameters, etc) increases, the performance of a given model trained on that dataset will correspondingly increase. However, while compelling in the aggregate, this scaling law relationship overlooks the ways that metrics used to measure performance may be precarious and contested, or may not correspond with how different groups of people may perceive the quality of models' output. In this paper, we argue that as the size of datasets used to train large AI models grows, the number of distinct communities (including demographic groups) whose data is included in a given dataset is likely to grow, each of whom may have different values. As a result, there is an increased risk that communities represented in a dataset may have values or preferences not captured by (or in the worst case, at odds with) the metrics used to evaluate model performance for scaling laws. We end the paper with implications for AI scaling laws -- that models may not, in fact, continue to improve as the datasets get larger -- at least not for all people or communities impacted by those models

Supersampling and network reconstruction of urban mobility

Understanding human mobility is of vital importance for urban planning, epidemiology, and many other fields that aim to draw policies from the activities of humans in space. Despite recent availability of large scale data sets related to human mobility such as GPS traces, mobile phone data, etc., it is still true that such data sets represent a subsample of the population of interest, and then might give an incomplete picture of the entire population in question. Notwithstanding the abundant usage of such inherently limited data sets, the impact of sampling biases on mobility patterns is unclear -- we do not have methods available to reliably infer mobility information from a limited data set. Here, we investigate the effects of sampling using a data set of millions of taxi movements in New York City. On the one hand, we show that mobility patterns are highly stable once an appropriate simple rescaling is applied to the data, implying negligible loss of information due to subsampling over long time scales. On the other hand, contrasting an appropriate null model on the weighted network of vehicle flows reveals distinctive features which need to be accounted for. Accordingly, we formulate a "supersampling" methodology which allows us to reliably extrapolate mobility data from a reduced sample and propose a number of network-based metrics to reliably assess its quality (and that of other human mobility models). Our approach provides a well founded way to exploit temporal patterns to save effort in recording mobility data, and opens the possibility to scale up data from limited records when information on the full system is needed.Comment: 14 pages, 4 figure

Rusty microglia: trainers of innate immunity in Alzheimer's disease

Alzheimer's disease, the most common form of dementia, is marked by progressive cognitive and functional impairment believed to reflect synaptic and neuronal loss. Recent preclinical data suggests that lipopolysaccharide (LPS)-activated microglia may contribute to the elimination of viable neurons and synapses by promoting a neurotoxic astrocytic phenotype, defined as A1. The innate immune cells, including microglia and astrocytes, can either facilitate or inhibit neuroinflammation in response to peripherally applied inflammatory stimuli, such as LPS. Depending on previous antigen encounters, these cells can assume activated (trained) or silenced (tolerized) phenotypes, augmenting or lowering inflammation. Iron, reactive oxygen species (ROS), and LPS, the cell wall component of gram-negative bacteria, are microglial activators, but only the latter can trigger immune tolerization. In Alzheimer's disease, tolerization may be impaired as elevated LPS levels, reported in this condition, fail to lower neuroinflammation. Iron is closely linked to immunity as it plays a key role in immune cells proliferation and maturation, but it is also indispensable to pathogens andmalignancies which compete for its capture. Danger signals, including LPS, induce intracellular iron sequestration in innate immune cells to withhold it from pathogens. However, excess cytosolic iron increases the risk of inflammasomes' activation, microglial training and neuroinflammation. Moreover, it was suggested that free iron can awaken the dormant central nervous system (CNS) LPS-shedding microbes, engendering prolonged neuroinflammation that may override immune tolerization, triggering autoimmunity. In this review, we focus on iron-related innate immune pathology in Alzheimer's disease and discuss potential immunotherapeutic agents for microglial de-escalation along with possible delivery vehicles for these compounds

Oxidative Stress Response to Short Duration Bout of Submaximal Aerobic Exercise in Healthy Young Adults

The purpose of this study was to investigate the oxidative stress response to a short duration bout of submaximal exercise in a cohort of healthy young adults. 15 apparently healthy college age males and females completed a modified Bruce-protocol treadmill test to 75–80% of their heart rate reserve. Blood samples collected immediately before (pre-exercise), immediately after, 30, 60 and 120 minutes post-exercise were assayed for total antioxidant capacity (TAC), superoxide disumutase (SOD), thiobarbituric acid-reactive substances (TBARS), and protein carbonyls (PC). SOD activity was significantly increased from pre-exercise levels at 30 minutes (77%), 60 minutes (33%), and 120 minutes (37%) post-exercise. TAC levels were also significantly increased from pre-exercise levels at 60 minutes (30%) and 120 minutes (33%) post-exercise. There were no significant changes in biomarkers for reactive oxygen/nitrogen species (RONS) mediated damage (TBARS and PC) across all post-exercise time points. In a cohort of healthy young adults, a short duration bout of submaximal aerobic exercise elicited increases in antioxidant activity/concentration, but did not evoke changes in oxidative stress-induced damage. These results may suggest that: (1) short duration bouts of submaximal aerobic exercise are sufficient to induce RONS generation; and (2) the antioxidant defense system is capable of protecting against enhanced RONS production induced by a short duration, submaximal exercise bout in healthy young adults