Robust Machine Learning Applied to Astronomical Datasets I: Star-Galaxy Classification of the SDSS DR3 Using Decision Trees

We provide classifications for all 143 million non-repeat photometric objects in the Third Data Release of the Sloan Digital Sky Survey (SDSS) using decision trees trained on 477,068 objects with SDSS spectroscopic data. We demonstrate that these star/galaxy classifications are expected to be reliable for approximately 22 million objects with r < ~20. The general machine learning environment Data-to-Knowledge and supercomputing resources enabled extensive investigation of the decision tree parameter space. This work presents the first public release of objects classified in this way for an entire SDSS data release. The objects are classified as either galaxy, star or nsng (neither star nor galaxy), with an associated probability for each class. To demonstrate how to effectively make use of these classifications, we perform several important tests. First, we detail selection criteria within the probability space defined by the three classes to extract samples of stars and galaxies to a given completeness and efficiency. Second, we investigate the efficacy of the classifications and the effect of extrapolating from the spectroscopic regime by performing blind tests on objects in the SDSS, 2dF Galaxy Redshift and 2dF QSO Redshift (2QZ) surveys. Given the photometric limits of our spectroscopic training data, we effectively begin to extrapolate past our star-galaxy training set at r ~ 18. By comparing the number counts of our training sample with the classified sources, however, we find that our efficiencies appear to remain robust to r ~ 20. As a result, we expect our classifications to be accurate for 900,000 galaxies and 6.7 million stars, and remain robust via extrapolation for a total of 8.0 million galaxies and 13.9 million stars. [Abridged]Comment: 27 pages, 12 figures, to be published in ApJ, uses emulateapj.cl

Sex and Gender Differences in Travel-Associated Disease

Background. No systematic studies exist on sex and gender differences across a broad range of travel-associated diseases. Methods. Travel and tropical medicine GeoSentinel clinics worldwide contributed prospective, standardized data on 58,908 patients with travel-associated illness to a central database from 1 March 1997 through 31 October 2007. We evaluated sex and gender differences in health outcomes and in demographic characteristics. Statistical significance for crude analysis of dichotomous variables was determined using hi; 2 tests with calculation of odds ratios (ORs) and 95% confidence intervals (CIs). The main outcome measure was proportionate morbidity of specific diagnoses in men and women. The analyses were adjusted for age, travel duration, pretravel encounter, reason for travel, and geographical region visited. Results. We found statistically significant (Pµ.001) differences in morbidity by sex. Women are proportionately more likely than men to present with acute diarrhea (OR, 1.13; 95% CI, 1.09-1.38), chronic diarrhea (OR, 1.28; 95% CI, 1.19-1.37), irritable bowel syndrome (OR, 1.39; 95% CI, 1.24-1.57), upper respiratory tract infection (OR, 1.23; 95% CI, 1.14-1.33); urinary tract infection (OR, 4.01; 95% CI, 3.34-4.71), psychological stressors (OR, 1.3; 95% CI, 1.14-1.48), oral and dental conditions, or adverse reactions to medication. Women are proportionately less likely to have febrile illnesses (OR, 0.15; 95% CI, 0.10-0.21); vector-borne diseases, such as malaria (OR, 0.46; 95% CI, 0.41-0.51), leishmaniasis, or rickettsioses (OR, 0.57; 95% CI, 0.43-0.74); sexually transmitted infections (OR, 0.68; 95% CI 0.58-0.81); viral hepatitis (OR, 0.34; 95% CI, 0.21-0.54); or noninfectious problems, including cardiovascular disease, acute mountain sickness, and frostbite. Women are statistically significantly more likely to obtain pretravel advice (OR, 1.28; 95% CI, 1.23-1.32), and ill female travelers are less likely than ill male travelers to be hospitalized (OR, 0.45; 95% CI, 0.42-0.49). Conclusions. Men and women present with different profiles of travel-related morbidity. Preventive travel medicine and future travel medicine research need to address gender-specific intervention strategies and differential susceptibility to diseas

Approaches for advancing scientific understanding of macrosystems

The emergence of macrosystems ecology (MSE), which focuses on regional- to continental-scale ecological patterns and processes, builds upon a history of long-term and broad-scale studies in ecology. Scientists face the difficulty of integrating the many elements that make up macrosystems, which consist of hierarchical processes at interacting spatial and temporal scales. Researchers must also identify the most relevant scales and variables to be considered, the required data resources, and the appropriate study design to provide the proper inferences. The large volumes of multi-thematic data often associated with macrosystem studies typically require validation, standardization, and assimilation. Finally, analytical approaches need to describe how cross-scale and hierarchical dynamics and interactions relate to macroscale phenomena. Here, we elaborate on some key methodological challenges of MSE research and discuss existing and novel approaches to meet them

High-precision buffer circuit for suppression of regenerative oscillation

Precision analog signal conditioning electronics have been developed for wind tunnel model attitude inertial sensors. This application requires low-noise, stable, microvolt-level DC performance and a high-precision buffered output. Capacitive loading of the operational amplifier output stages due to the wind tunnel analog signal distribution facilities caused regenerative oscillation and consequent rectification bias errors. Oscillation suppression techniques commonly used in audio applications were inadequate to maintain the performance requirements for the measurement of attitude for wind tunnel models. Feedback control theory is applied to develop a suppression technique based on a known compensation (snubber) circuit, which provides superior oscillation suppression with high output isolation and preserves the low-noise low-offset performance of the signal conditioning electronics. A practical design technique is developed to select the parameters for the compensation circuit to suppress regenerative oscillation occurring when typical shielded cable loads are driven

Results of Prevention of REStenosis with Tranilast and its Outcomes (PRESTO) trial

BACKGROUND: Restenosis after percutaneous coronary intervention (PCI) is a major problem affecting 15% to 30% of patients after stent placement. No oral agent has shown a beneficial effect on restenosis or on associated major adverse cardiovascular events. In limited trials, the oral agent tranilast has been shown to decrease the frequency of angiographic restenosis after PCI. METHODS AND RESULTS: In this double-blind, randomized, placebo-controlled trial of tranilast (300 and 450 mg BID for 1 or 3 months), 11 484 patients were enrolled. Enrollment and drug were initiated within 4 hours after successful PCI of at least 1 vessel. The primary end point was the first occurrence of death, myocardial infarction, or ischemia-driven target vessel revascularization within 9 months and was 15.8% in the placebo group and 15.5% to 16.1% in the tranilast groups (P=0.77 to 0.81). Myocardial infarction was the only component of major adverse cardiovascular events to show some evidence of a reduction with tranilast (450 mg BID for 3 months): 1.1% versus 1.8% with placebo (P=0.061 for intent-to-treat population). The primary reason for not completing treatment was > or =1 hepatic laboratory test abnormality (11.4% versus 0.2% with placebo, P<0.01). In the angiographic substudy composed of 2018 patients, minimal lumen diameter (MLD) was measured by quantitative coronary angiography. At follow-up, MLD was 1.76+/-0.77 mm in the placebo group, which was not different from MLD in the tranilast groups (1.72 to 1.78+/-0.76 to 80 mm, P=0.49 to 0.89). In a subset of these patients (n=1107), intravascular ultrasound was performed at follow-up. Plaque volume was not different between the placebo and tranilast groups (39.3 versus 37.5 to 46.1 mm(3), respectively; P=0.16 to 0.72). CONCLUSIONS: Tranilast does not improve the quantitative measures of restenosis (angiographic and intravascular ultrasound) or its clinical sequelae

The Big-O Problem for Max-Plus Automata is Decidable (PSPACE-Complete)

We show that the big-O problem for max-plus automata is decidable and PSPACE-complete. The big-O (or affine domination) problem asks whether, given two max-plus automata computing functions f and g, there exists a constant c such that f &lt; cg+ c. This is a relaxation of the containment problem asking whether f &lt; g, which is undecidable. Our decidability result uses Simon's forest factorisation theorem, and relies on detecting specific elements, that we call witnesses, in a finite semigroup closed under two special operations: stabilisation and flattening

Reconstructing signal during brain stimulation with Stim-BERT: a self-supervised learning model trained on millions of iEEG files

Brain stimulation has become a widely accepted treatment for neurological disorders such as epilepsy and Parkinson’s disease. These devices not only deliver therapeutic stimulation but also record brain activity, offering valuable insights into neural dynamics. However, brain recordings during stimulation are often blanked or contaminated by artifact, posing significant challenges for analyzing the acute effects of stimulation. To address these challenges, we propose a transformer-based model, Stim-BERT, trained on a large intracranial EEG (iEEG) dataset to reconstruct brain activity lost during stimulation blanking. To train the Stim-BERT model, 4,653,720 iEEG channels from 380 RNS system patients were tokenized into 3 (or 4) frequency band bins using 1 s non-overlapping windows resulting in a total vocabulary size of 1,000 (or 10,000). Stim-BERT leverages self-supervised learning with masked tokens, inspired by BERT’s success in natural language processing, and shows significant improvements over traditional interpolation methods, especially for longer blanking periods. These findings highlight the potential of transformer models for filling in missing time-series neural data, advancing neural signal processing and our efforts to understand the acute effects of brain stimulation

Robust Machine Learning Applied to Astronomical Datasets III: Probabilistic Photometric Redshifts for Galaxies and Quasars in the SDSS and GALEX

We apply machine learning in the form of a nearest neighbor instance-based algorithm (NN) to generate full photometric redshift probability density functions (PDFs) for objects in the Fifth Data Release of the Sloan Digital Sky Survey (SDSS DR5). We use a conceptually simple but novel application of NN to generate the PDFs - perturbing the object colors by their measurement error - and using the resulting instances of nearest neighbor distributions to generate numerous individual redshifts. When the redshifts are compared to existing SDSS spectroscopic data, we find that the mean value of each PDF has a dispersion between the photometric and spectroscopic redshift consistent with other machine learning techniques, being sigma = 0.0207 +/- 0.0001 for main sample galaxies to r < 17.77 mag, sigma = 0.0243 +/- 0.0002 for luminous red galaxies to r < ~19.2 mag, and sigma = 0.343 +/- 0.005 for quasars to i < 20.3 mag. The PDFs allow the selection of subsets with improved statistics. For quasars, the improvement is dramatic: for those with a single peak in their probability distribution, the dispersion is reduced from 0.343 to sigma = 0.117 +/- 0.010, and the photometric redshift is within 0.3 of the spectroscopic redshift for 99.3 +/- 0.1% of the objects. Thus, for this optical quasar sample, we can virtually eliminate 'catastrophic' photometric redshift estimates. In addition to the SDSS sample, we incorporate ultraviolet photometry from the Third Data Release of the Galaxy Evolution Explorer All-Sky Imaging Survey (GALEX AIS GR3) to create PDFs for objects seen in both surveys. For quasars, the increased coverage of the observed frame UV of the SED results in significant improvement over the full SDSS sample, with sigma = 0.234 +/- 0.010. We demonstrate that this improvement is genuine. [Abridged]Comment: Accepted to ApJ, 10 pages, 12 figures, uses emulateapj.cl

Approaches to advance scientific understanding of macrosystems ecology

The emergence of macrosystems ecology (MSE), which focuses on regional- to continental-scale ecological pat- terns and processes, builds upon a history of long-term and broad-scale studies in ecology. Scientists face the difficulty of integrating the many elements that make up macrosystems, which consist of hierarchical processes at interacting spatial and temporal scales. Researchers must also identify the most relevant scales and variables to be considered, the required data resources, and the appropriate study design to provide the proper inferences. The large volumes of multi-thematic data often associated with macrosystem studies typically require valida- tion, standardization, and assimilation. Finally, analytical approaches need to describe how cross-scale and hierarchical dynamics and interactions relate to macroscale phenomena. Here, we elaborate on some key methodological challenges of MSE research and discuss existing and novel approaches to meet them