Too Good to be Liked? When and How Prosocial Others are Disliked

Outstandingly prosocial individuals may not always be valued and admired, but sometimes depreciated and rejected. While prior research has mainly focused on devaluation of highly competent or successful individuals, comparable research in the domain of prosociality is scarce. The present research suggests two mechanisms why devaluation of extreme prosocial individuals may occur: they may (a) constitute very high comparison standards for observers, and may (b) be perceived as communal narcissists. Two experiments test these assumptions. We confronted participants with an extreme prosocial or an ordinary control target and manipulated comparative aspects of the situation (salient vs. non-salient comparison, Experiment 1), and narcissistic aspects of the target (showing off vs. being modest, Experiment 2). Consistent with our assumptions, the extreme prosocial target was liked less than the control target, and even more so when the comparison situation was salient (Experiment 1), and when the target showed off with her good deeds (Experiment 2). Implications that prosociality does not always breed more liking are discussed.

Nearly continuous Ca^+ optical clocks with stability at the 10^-18 level

Optical clocks are important for precise measurements in the field of physics. As reported, both the instability and uncertainty of optical lattice clocks are more than two orders of magnitude smaller than those of the best microwave clocks. Therefore, in the near future, optical clocks could be used to redefine the second. Nevertheless, an optical clock with reliability comparable to microwave clocks has not been achieved thus far. In this paper, we compared the frequencies of two Ca^+ optical clocks that were nearly continuously operated for 31 days. Through the comparison experiment, the frequency stability of a single clocks was found to be 6.3×10^-18 at an averaging time of 520 000 s and 7.9×10^-18 at averaging time of 262000 s, while the operating uptime rate reached more than 90% in the period of around 5 days. Thus, our experiment demonstrated that it is possible to increase the stability of single-ion optical clocks to the 10^-18 level, while still maintaining quasi-continuous operation with a high operating rate. This result further confirms that optical clocks can potentially be used to redefine the second.

The De-Icing Comparison Experiment (D-ICE): a study of broadband radiometric measurements under icing conditions in the Arctic

Surface-based measurements of broadband shortwave (solar) and longwave (infrared) radiative fluxes using thermopile radiometers are made regularly around the globe for scientific and operational environmental monitoring. The occurrence of ice on sensor windows in cold environments – whether snow, rime, or frost – is a common problem that is difficult to prevent as well as difficult to correct in post-processing. The Baseline Surface Radiation Network (BSRN) community recognizes radiometer icing as a major outstanding measurement uncertainty. Towards constraining this uncertainty, the De-Icing Comparison Experiment (D-ICE) was carried out at the NOAA Atmospheric Baseline Observatory in Utqiaġvik (formerly Barrow), Alaska, from August 2017 to July 2018. The purpose of D-ICE was to evaluate existing ventilation and heating technologies developed to mitigate radiometer icing. D-ICE consisted of 20 pyranometers and 5 pyrgeometers operating in various ventilator housings alongside operational systems that are part of NOAA's Barrow BSRN station and the US Department of Energy Atmospheric Radiation Measurement (ARM) program North Slope of Alaska and Oliktok Point observatories. To detect icing, radiometers were monitored continuously using cameras, with a total of more than 1 million images of radiometer domes archived. Ventilator and ventilator–heater performance overall was skillful with the average of the systems mitigating ice formation 77 % (many >90 %) of the time during which icing conditions were present. Ventilators without heating elements were also effective and capable of providing heat through roughly equal contributions of waste energy from the ventilator fan and adiabatic heating downstream of the fan. This provided ∼0.6 ∘C of warming, enough to subsaturate the air up to a relative humidity (with respect to ice) of ∼105 %. Because the mitigation technologies performed well, a near complete record of verified ice-free radiometric fluxes was assembled for the duration of the campaign. This well-characterized data set is suitable for model evaluation, in particular for the Year of Polar Prediction (YOPP) first Special Observing Period (SOP1). We used the data set to calculate short- and long-term biases in iced sensors, finding that biases can be up to +60 W m−2 (longwave) and −211 to +188 W m−2 (shortwave). However, because of the frequency of icing, mitigation of ice by ventilators, cloud conditions, and the timing of icing relative to available sunlight, the biases in the monthly means were generally less than the aggregate uncertainty attributed to other conventional sources in both the shortwave and longwave.

Rapid Airfield Damage Recovery Next Generation Backfill Technologies Comparison Experiment : Technology Comparison Experiment

The Rapid Airfield Damage Recovery (RADR) Next Generation Backfill Technology Comparison Experiment was conducted in July 2017 at the East Campus of the U.S. Army Engineer Research and Development Center (ERDC), located in Vicksburg, MS. The experiment evaluated three different crater backfill technologies to compare their performance and develop a technology trade-off a nalysis. The RADR next generation backfill technologies were compared to the current RADR standard backfill method of flowable fill. Results from this experiment provided useful information on technology rankings and trade-offs. This effort resulted in successful crater backfill solutions that were recommended for further end user evaluation.

A Data Augmentation Method for Deep Learning Based on Multi-Degree of Freedom (DOF) Automatic Image Acquisition

Deep learning technology is outstanding in visual inspection. However, in actual industrial production, the use of deep learning technology for visual inspection requires a large number of training data with different acquisition scenarios. At present, the acquisition of such datasets is very time-consuming and labor-intensive, which limits the further development of deep learning in industrial production. To solve the problem of image data acquisition difficulty in industrial production with deep learning, this paper proposes a data augmentation method for deep learning based on multi-degree of freedom (DOF) automatic image acquisition and designs a multi-DOF automatic image acquisition system for deep learning. By designing random acquisition angles and random illumination conditions, different acquisition scenes in actual production are simulated. By optimizing the image acquisition path, a large number of accurate data can be obtained in a short time. In order to verify the performance of the dataset collected by the system, the fabric is selected as the research object after the system is built, and the dataset comparison experiment is carried out. The dataset comparison experiment confirms that the dataset obtained by the system is rich and close to the real application environment, which solves the problem of dataset insufficient in the application process of deep learning to a certain extent.

The De-Icing Comparison Experiment (D-ICE): A study of broadband radiometric measurements under icing conditions in the Arctic

Surface-based measurements of broadband shortwave (solar) and longwave (infrared) radiative fluxes using thermopile radiometers are made regularly around the globe for scientific and operational environmental monitoring. The occurrence of ice on sensor windows in cold environments – whether snow, rime, or frost – is a common problem that is difficult to prevent as well as difficult to correct in post-processing. The Baseline Surface Radiation Network (BSRN) community recognizes radiometer icing as a major outstanding measurement uncertainty. Towards constraining this uncertainty, the De-Icing Comparison Experiment (D-ICE) was carried out at the NOAA Atmospheric Baseline Observatory in Utqiaġvik (formerly Barrow), Alaska, from August 2017 to July 2018. The purpose of D-ICE was to evaluate existing ventilation and heating technologies developed to mitigate radiometer icing. D-ICE consisted of 20 pyranometers and 5 pyrgeometers operating in various ventilator housings alongside operational systems that are part of NOAA's Barrow BSRN station and the U.S. Dept. of Energy Atmospheric Radiation Measurement (ARM) Program North Slope of Alaska and Oliktok Point observatories. To detect icing, radiometers were monitored continuously using cameras, with a total of more than 1 million images of radiometer domes archived. Ventilator and ventilator/heater performance overall was skilful with the average of the systems mitigating 77 % of icing and many being 90+ % effective. Ventilators without heating elements were also effective and capable of providing heat through roughly equal contributions of waste energy from the ventilator fan and adiabatic heating downstream of the fan. This provided ~ 0.6 C of warming, enough to subsaturate the air up to a relative humidity (w.r.t. ice) of ~ 105 %. Because the mitigation technologies performed well, a near complete record of verified ice-free radiometric fluxes were assembled for the duration of the campaign. This well-characterized data set is suitable for model evaluation, in particular for the Year of Polar Prediction (YOPP) first Special Observing Period (SOP1). We used the data set to calculate short- and long-term biases in iced sensors, finding that biases can be up to +60 W m−2 (longwave) and −211 to +188 W m−2 (shortwave). However, because of the frequency of icing, mitigation of ice by ventilators, cloud conditions, and the timing of icing relative to available sunlight, the biases in the monthly means were generally less than the aggregate uncertainty attributed to other conventional sources.