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We present a convolutional autoencoder to denoise pulses from a p-type point contact high-purity germanium detector similar to those used in several rare event searches. While we focus on training procedures that rely on detailed detector physics simulations, we also present implementations requiring only noisy detector pulses to train the model. We validate our autoencoder on both simulated data and calibration data from an 241Am source, the latter of which is used to show that the denoised pulses are statistically compatible with data pulses. We demonstrate that our denoising method is able to preserve the underlying shapes of the pulses well, offering improvement over traditional denoising methods. We also show that the shaping time used to calculate energy with a trapezoidal filter can be significantly reduced while maintaining a comparable energy resolution. Under certain circumstances, our denoising method can improve the overall energy resolution. The methods we developed to remove electronic noise are straightforward to extend to other detector technologies. Furthermore, the latent representation from the encoder is also of use in quantifying shape-based characteristics of the signals. Our work has great potential to be used in particle physics experiments and beyond.

We used 18 oculomotor performance metrics (oculometrics) to capture largely independent features of human ocular tracking. Our primary goal was to examine tracking eye movements in a healthy population under monocular and binocular viewing, as well as to examine the potential effects of line-of-sight eye dominance and spatial/directional tuning. We compared the ocular responses of 17 healthy well-rested participants using a radial step-ramp paradigm under three viewing conditions: both-eyes viewing, left-eye viewing, and right-eye viewing. Our findings revealed that binocular viewing enhanced performance over that during monocular viewing for 11 oculometrics, with eye dominance associated with the selective enhancement of 3 oculometrics of visual motion processing. A comparison of binocular and dominant-eye viewing allowed us to segregate the direct enhancements of binocularity from those due simply to the inclusion of the dominant eye in binocular viewing and showed that viewing with two eyes is only directly responsible for the enhancement of 9 oculometrics. Our examination of spatial/directional tuning revealed largely isotropic enhancement due to binocularity, as well as several anisotropies in retinal functional processing: (1) a Nasal-Temporal asymmetry for pursuit latency and direction noise, and a Superior-Inferior asymmetry for latency, and (2) anisotropic enhancement in initial acceleration and direction noise (primarily for nasal retina) and speed noise (primarily for superior retina) when viewing through the dominant eye. We also documented Horizontal-Vertical anisotropies in initial acceleration, steady-state gain, proportion smooth, and speed responsiveness for both monocular and binocular viewing. Our findings demonstrate that there is isotropic enhancement from binocular viewing across a wide range of visuomotor features and that important normative characteristics of visual motion processing are shaped by retinal processing non-uniformly across visual space, modulated by eye dominance and perhaps related to previously found normative structural anisotropies in retinal thickness. This constellation of findings characterizes the subtle natural non-linear variations in visuomotor performance to provide insight into the relative roles of the retina and other brain areas in shaping visuomotor performance and to enable the detection of neurological and ophthalmological impairment through comparison with properly matched baselines in support of future research and clinical applications.

In this dramatic retelling of one of history's great \"what-ifs,\" Mark R. Anderson examines the American colonies' campaign to bring Quebec into the Continental confederation and free the Canadians from British \"tyranny.\" This significant reassessment of a little-studied campaign examines developments on both sides of the border that rapidly proceeded from peaceful diplomatic overtures to a sizable armed intervention. The military narrative encompasses Richard Montgomery's plodding initial operations, Canadian partisan cooperation with officers like Ethan Allen, and the harrowing experiences of Benedict Arnold's Kennebec expedition, as well as the sudden collapse of British defenses that secured the bulk of the province for the rebel cause. The book provides new insight into both Montgomery's tragic Québec City defeat and a small but highly significant loyalist uprising in the rural northern parishes that was suppressed by Arnold and his Canadian patriot allies. Anderson closely examines the evolving relationships between occupiers and occupied, showing how rapidly changing circumstances variously fostered cooperation and encouraged resistance among different Canadian elements. The book homes in on the key political and military factors that ultimately doomed America's first foreign war of liberation and resulted in the Continental Army's decisive expulsion from Canada on the eve of the Declaration of Independence. The first full treatment of this fascinating chapter in Revolutionary War history in over a century, Anderson's account is especially revealing in its presentation of contentious British rule in Quebec, and of Continental beliefs that Canadiens would greet the soldiers as liberators and allies in a common fight against the British yoke. This thoroughly researched and action-packed history will appeal to American and Canadian history buffs and military experts alike.

Variability in snow melt onset (MO) on Arctic sea ice since 1979 is examined by determining the area of sea ice experiencing the onset of melting during the melt season on a daily basis. The daily MO area of the snow and ice surface is determined from passive microwave satellite-derived MO dates for the Arctic Ocean and sub-regions. Annual accumulations of MO area are determined by summing the time series of daily MO area through the melt season. Daily areas and annual accumulations of MO area highlight inter-annual and regional variability in the timing of MO area, which is sensitive to day-to-day variations in spring weather conditions. Two distinct spatial patterns in MO area accumulations including an intense, fast accumulating melt area pattern and a slow accumulating melt pattern are examined for two melting events in the Kara Sea. In comparing the 34 years of MO dates for the Arctic Ocean and sub-regions, melt accumulations have changed during the period. In the earlier years, 1979–1987, the MO generally was later in the year than the mean, while in more recent years, the MO accumulations have been occurring earlier in the melt season. The sub-regions of the Arctic Ocean also exhibit greater annual variability than the Arctic Ocean.

Adverse weather conditions are responsible for millions of vehicular crashes, thousands of deaths, and billions of dollars per year in economic and congestion costs. Many transportation agencies utilize a performance or mobility metric to assess how well they maintain road access; however, there is only limited consideration of meteorological impacts to the success of their operations. This research develops the Nebraska winter severity index (NEWINS), which is a daily event-driven index derived for the Nebraska Department of Transportation (NDOT). The NEWINS includes a categorical storm classification framework to capture atmospheric conditions and possible road impacts across diverse spatial regions of Nebraska. A 10-yr (2006–16) winter season database of meteorological variables for Nebraska was obtained from the National Centers for Environmental Information. The NEWINS is based on a weighted linear combination applied to the collected storm classification database to measure severity. The NEWINS results were compared to other meteorological variables, many used in other agencies’ winter severity indices. This comparison verified the NEWINS robustness for the observed events for the 10-yr period. An assessment of the difference between days with observed snow versus days with accumulated snow revealed 39% fewer snow-accumulated days than snow-observed days. Furthermore, the NEWINS results highlighted the greater number of events during the 2009/10 winter season and the lack of events during the 2011/12 winter season. It is expected that the NEWINS could help transportation personnel allocate efficiently resources during adverse weather events. Moreover, the NEWINS framework can be used by other agencies to assess their weather sensitivity.

Forecast systems provide decision support for end users ranging from the solar energy industry to municipalities concerned with road safety. Pavement temperature is an important variable when considering vehicle response to various weather conditions. A complex relationship exists between tire and pavement temperatures that affects vehicle performance. Many forecast systems suffer from inaccurate radiation forecasts resulting in part from the inability to model different types of clouds and their influence on radiation. This research focuses on forecast improvement by determining how cloud type impacts pavement temperature and the amount of shortwave radiation reaching the surface. The study region is the Great Plains where surface radiation data were obtained from the High Plains Regional Climate Center's Automated Weather Data Network stations. Pavement temperature data were obtained from the Meteorological Assimilation Data Ingest System. Cloud-type identification was possible via the Naval Research Laboratory Cloud Classification algorithm, and clouds were subsequently sorted into five distinct groups: clear conditions, low clouds, middle clouds, high clouds, and cumuliform clouds. Statistical analyses during the daytime in June 2011 revealed that cloud cover lowered pavement temperatures by up to approximately 10°C and dampened downwelling shortwave radiation by up to 400 W m−2. These pavement temperatures and surface radiation observations were strongly correlated, with a maximum correlation coefficient of 0.83. A comparison between cloud-type group identified and cloud cover observed from satellite images provided a measure of confidence in the results and identified cautions with using satellite-based cloud detection.