Satisfiability of cross product terms is complete for real nondeterministic polytime Blum-Shub-Smale machines

Nondeterministic polynomial-time Blum-Shub-Smale Machines over the reals give rise to a discrete complexity class between NP and PSPACE. Several problems, mostly from real algebraic geometry / polynomial systems, have been shown complete (under many-one reduction by polynomial-time Turing machines) for this class. We exhibit a new one based on questions about expressions built from cross products only.Comment: In Proceedings MCU 2013, arXiv:1309.104

Eating Attitudes and Food Intakes of Elite Adolescent Female Figure Skaters: a Cross Sectional Study

Background: Elite adolescent female figure skaters compete in an aesthetic-based sport that values thin builds and lithe figures. To conform to the sport’s physical requirements, skaters may alter their eating patterns in unhealthful directions. This study assesses the eating attitudes and dietary intakes of elite adolescent female figure skaters to assess the potential nutritional risks among them. Methods: Thirty-six elite competitive adolescent female figure skaters (mean age 16 ± 2.5 SD years) completed self-administered three-day records of dietary intake and simultaneous physical activity records during training season. Two months later, they attended a national training camp during which they completed the Eating Attitudes Test (EAT-40), provided fasting blood samples, and had heights and weights measured. Results: Participants’ mean body mass index (BMI) was 19.8 ± 2.1 SD. Their BMIs were within the normal range, and the majority (70%) did not report a history of recent weight loss. The mean EAT-40 score was normal (19.5 ± 13.5 SD) and below the cut-off score of 30 that indicates clinically significant eating pathology. However, one-quarter of the skaters had EAT-40 scores above 30. The skaters reported a mean energy intake of 1491 ± 471 SD kcal/day (31 ± 10 SD kcal/kg), with 61.6% of calories from carbohydrate, 14.6% from protein, and 23.7% from fat. Their reported dietary intakes were high in carbohydrates but low in total energy, fat, and bone-building nutrients. Conclusions: Although these highly active young women compete in a sport that prizes leanness, they had appropriate weights. The athletes reported dietary intakes that were far below estimated energy needs and were at moderate risk of disordered eating. Anticipatory guidance is warranted to improve their dietary intakes, particularly of bone-building nutrients

A Hierarchical Diffusion Model Analysis of Age Effects on Visual Word Recognition

Reading is one of the most popular leisure activities and it is routinely performed by most individuals even in old age. Successful reading enables older people to master and actively participate in everyday life and maintain functional independence. Yet, reading comprises a multitude of subprocesses and it is undoubtedly one of the most complex accomplishments of the human brain. Not surprisingly, findings of age-related effects on word recognition and reading have been partly contradictory and are often confined to only one of four central reading subprocesses, i.e., sublexical, orthographic, phonological and lexico-semantic processing. The aim of the present study was therefore to systematically investigate the impact of age on each of these subprocesses. A total of 1,807 participants (young, N = 384; old, N = 1,423) performed four decision tasks specifically designed to tap one of the subprocesses. To account for the behavioral heterogeneity in older adults, this subsample was split into high and low performing readers. Data were analyzed using a hierarchical diffusion modeling approach, which provides more information than standard response time/accuracy analyses. Taking into account incorrect and correct response times, their distributions and accuracy data, hierarchical diffusion modeling allowed us to differentiate between age- related changes in decision threshold, non-decision time and the speed of information uptake. We observed longer non-decision times for older adults and a more conservative decision threshold. More importantly, high-performing older readers outperformed younger adults at the speed of information uptake in orthographic and lexico-semantic processing, whereas a general age- disadvantage was observed at the sublexical and phonological levels. Low- performing older readers were slowest in information uptake in all four subprocesses. Discussing these results in terms of computational models of word recognition, we propose age-related disadvantages for older readers to be caused by inefficiencies in temporal sampling and activation and/or inhibition processes

Structural gray matter features and behavioral preliterate skills predict future literacy – A machine learning approach

When children learn to read, their neural system undergoes major changes to become responsive to print. There seem to be nuanced interindividual differences in the neurostructural anatomy of regions that later become integral parts of the reading network. These differences might affect literacy acquisition and, in some cases, might result in developmental disorders like dyslexia. Consequently, the main objective of this longitudinal study was to investigate those interindividual differences in gray matter morphology that might facilitate or hamper future reading acquisition. We used a machine learning approach to examine to what extent gray matter macrostructural features and cognitive-linguistic skills measured before formal literacy teaching could predict literacy 2 years later. Forty-two native German-speaking children underwent T1-weighted magnetic resonance imaging and psychometric testing at the end of kindergarten. They were tested again 2 years later to assess their literacy skills. A leave-one-out cross-validated machine-learning regression approach was applied to identify the best predictors of future literacy based on cognitive-linguistic preliterate behavioral skills and cortical measures in a priori selected areas of the future reading network. With surprisingly high accuracy, future literacy was predicted, predominantly based on gray matter volume in the left occipito-temporal cortex and local gyrification in the left insular, inferior frontal, and supramarginal gyri. Furthermore, phonological awareness significantly predicted future literacy. In sum, the results indicate that the brain morphology of the large-scale reading network at a preliterate age can predict how well children learn to read

SmartAQnet 2020: A New Open Urban Air Quality Dataset from Heterogeneous PM Sensors

The increasing attention paid to urban air quality modeling places higher requirements on urban air quality datasets. This article introduces a new urban air quality dataset—the SmartAQnet2020 dataset—which has a large span and high resolution in both time and space dimensions. The dataset contains 248,572,003 observations recorded by over 180 individual measurement devices, including ceilometers, Radio Acoustic Sounding System (RASS), mid- and low-cost stationary measuring equipment equipped with meteorological sensors and particle counters, and low-weight portable measuring equipment mounted on different platforms such as trolley, bike, and UAV

KELT-25 b and KELT-26 b: A Hot Jupiter and a Substellar Companion Transiting Young A Stars Observed by TESS

We present the discoveries of KELT-25 b (TIC 65412605, TOI-626.01) and KELT-26 b (TIC 160708862, TOI-1337.01), two transiting companions orbiting relatively bright, early A stars. The transit signals were initially detected by the KELT survey and subsequently confirmed by Transiting Exoplanet Survey Satellite (TESS) photometry. KELT-25 b is on a 4.40 day orbit around the V = 9.66 star CD-24 5016 (Teff=8280-180+440 K, M ∗ = 2.18-0.11+0.12 M o˙), while KELT-26 b is on a 3.34 day orbit around the V = 9.95 star HD 134004 (Teff = 8640-240+500 K, M ∗ = 1.93-0.16+0.14 M o˙), which is likely an Am star. We have confirmed the substellar nature of both companions through detailed characterization of each system using ground-based and TESS photometry, radial velocity measurements, Doppler tomography, and high-resolution imaging. For KELT-25, we determine a companion radius of R P = 1.64-0.043+0.039 R J and a 3σ upper limit on the companion\u27s mass of ∼64 M J. For KELT-26 b, we infer a planetary mass and radius of M P = 1.41-0.51+0.43MJ and R P = 1.94-0.058+0.060 R J. From Doppler tomographic observations, we find KELT-26 b to reside in a highly misaligned orbit. This conclusion is weakly corroborated by a subtle asymmetry in the transit light curve from the TESS data. KELT-25 b appears to be in a well-aligned, prograde orbit, and the system is likely a member of the cluster Theia 449

SmartAQnet – neuer smarter Weg zur räumlichen Erfassung von Feinstaub

Mit dem Forschungsprojekt SmartAQnet wird ein smarter Weg zur räumlichen Bestimmung von Feinstaub untersucht und am Modellstandort Augsburg erprobt. Forschungsansatz ist die Erfassung und Zusammenführung unterschiedlicher Qualitäten von Feinstaubmesswerten mit Fernerkundungsdaten. Feinstaubmesswerte können hierbei von Jedermann (z. B. mit Ultra-Low-Cost-Sensoren) bis hin zu offiziellen Messnetzen (mit hochpräziser Messtechnik) in die Datenarchitektur eingespeist werden. Eine neuartige Internet-of-Things-Analyseplattform soll Daten zur Anwendung sowohl für Planer als auch für den Bürger bieten, welche der nachhaltigen Gesundheitsvorsorge dienen können (z. B. App für eine luftqualitätsbezogene Navigation)