The structured environments of embedded star-forming cores. PACS and SPIRE mapping of the enigmatic outflow source UYSO 1

The intermediate-mass star-forming core UYSO 1 has previously been found to exhibit intriguing features. While deeply embedded and previously only identified by means of its (sub-)millimeter emission, it drives two powerful, dynamically young, molecular outflows. Although the process of star formation has obviously started, the chemical composition is still pristine. We present Herschel PACS and SPIRE continuum data of this presumably very young region. The now complete coverage of the spectral energy peak allows us to precisely constrain the elevated temperature of 26 - 28 K for the main bulge of gas associated with UYSO1, which is located at the interface between the hot HII region Sh 2-297 and the cold dark nebula LDN 1657A. Furthermore, the data identify cooler compact far-infrared sources of just a few solar masses, hidden in this neighbouring dark cloud.Comment: accepted contribution for the forthcoming Herschel Special Issue of A&A, 5 pages (will appear as 4-page letter in the journal), 6 figure file

A knowledge-based expert system for scheduling of airborne astronomical observations

The Kuiper Airborne Observatory Scheduler (KAOS) is a knowledge-based expert system developed at NASA Ames Research Center to assist in route planning of a C-141 flying astronomical observatory. This program determines a sequence of flight legs that enables sequential observations of a set of heavenly bodies derived from a list of desirable objects. The possible flight legs are constrained by problems of observability, avoiding flyovers of warning and restricted military zones, and running out of fuel. A significant contribution of the KAOS program is that it couples computational capability with a reasoning system

Modeling chemistry in and above snow at Summit, Greenland – Part 1: Model description and results

Sun-lit snow is increasingly recognized as a chemical reactor that plays an active role in uptake, transformation, and release of atmospheric trace gases. Snow is known to influence boundary layer air on a local scale, and given the large global surface coverage of snow may also be significant on regional and global scales. We present a new detailed one-dimensional snow chemistry module that has been coupled to the 1-D atmospheric boundary layer model MISTRA. The new 1-D snow module, which is dynamically coupled to the overlaying atmospheric model, includes heat transport in the snowpack, molecular diffusion, and wind pumping of gases in the interstitial air. The model includes gas phase chemical reactions both in the interstitial air and the atmosphere. Heterogeneous and multiphase chemistry on atmospheric aerosol is considered explicitly. The chemical interaction of interstitial air with snow grains is simulated assuming chemistry in a liquid-like layer (LLL) on the grain surface. The coupled model, referred to as MISTRA-SNOW, was used to investigate snow as the source of nitrogen oxides (NOx) and gas phase reactive bromine in the atmospheric boundary layer in the remote snow covered Arctic (over the Greenland ice sheet) as well as to investigate the link between halogen cycling and ozone depletion that has been observed in interstitial air. The model is validated using data taken 10 June–13 June, 2008 as part of the Greenland Summit Halogen-HOx experiment (GSHOX). The model predicts that reactions involving bromide and nitrate impurities in the surface snow can sustain atmospheric NO and BrO mixing ratios measured at Summit, Greenland during this period

On Fragile Features and Batch Normalization in Adversarial Training

Modern deep learning architecture utilize batch normalization (BN) tostabilize training and improve accuracy. It has been shown that the BN layersalone are surprisingly expressive. In the context of robustness againstadversarial examples, however, BN is argued to increase vulnerability. That is,BN helps to learn fragile features. Nevertheless, BN is still used inadversarial training, which is the de-facto standard to learn robust features.In order to shed light on the role of BN in adversarial training, weinvestigate to what extent the expressiveness of BN can be used to robustifyfragile features in comparison to random features. On CIFAR10, we find thatadversarially fine-tuning just the BN layers can result in non-trivialadversarial robustness. Adversarially training only the BN layers from scratch,in contrast, is not able to convey meaningful adversarial robustness. Ourresults indicate that fragile features can be used to learn models withmoderate adversarial robustness, while random features cannot<br

The Ratio of Total to Selective Extinction Toward Baade's Window

We measure the ratio of total to selective extinction, R_{VI}=A_V/E(V-I), toward Baade's Window by comparing the VIK colors of 132 Baade's Window G and K giants from Tiede, Frogel, & Terndrup with the solar-neighborhood (V-I),(V-K) relation from Bessell & Brett. We find R_{VI}=2.283 +/- 0.016, and show that our measurement has no significant dependence on stellar type from G0 to K4. Adjusting the Paczynski et al. determination of the centroid of the dereddened Baade's Window clump for this revised value of $R_{VI}$, we find I_{0,RC}=14.43 and (V-I)_{0,RC}=1.058. This implies a distance to the Baade's Window clump of d_{BW} = 8.63 +/- 0.16 kpc, where the error bar takes account of statistical but not systematic uncertainties.Comment: 8 pages, 1 figure, submitted to Ap

Random and Adversarial Bit Error Robustness: {E}nergy-Efficient and Secure {DNN} Accelerators

Deep neural network (DNN) accelerators received considerable attention in recent years due to the potential to save energy compared to mainstream hardware. Low-voltage operation of DNN accelerators allows to further reduce energy consumption significantly, however, causes bit-level failures in the memory storing the quantized DNN weights. Furthermore, DNN accelerators have been shown to be vulnerable to adversarial attacks on voltage controllers or individual bits. In this paper, we show that a combination of robust fixed-point quantization, weight clipping, as well as random bit error training (RandBET) or adversarial bit error training (AdvBET) improves robustness against random or adversarial bit errors in quantized DNN weights significantly. This leads not only to high energy savings for low-voltage operation as well as low-precision quantization, but also improves security of DNN accelerators. Our approach generalizes across operating voltages and accelerators, as demonstrated on bit errors from profiled SRAM arrays, and achieves robustness against both targeted and untargeted bit-level attacks. Without losing more than 0.8%/2% in test accuracy, we can reduce energy consumption on CIFAR10 by 20%/30% for 8/4-bit quantization using RandBET. Allowing up to 320 adversarial bit errors, AdvBET reduces test error from above 90% (chance level) to 26.22% on CIFAR10