Beyond Tree Planting in Urban Forest Climate Adaptation Actions

Forests in cities, and the communities that steward and benefit from them, face significant disruption due to climate change. It is now time to build the capacity in our institutions and in forested natural areas to help navigate multiple overlapping crises and systems change. This case study from Seattle, Washington provides perspective on how to mitigate climate change beyond tree planting

Impurity Operators in RSOS Models

We give a construction of impurity operators in the `algebraic analysis' picture of RSOS models. Physically, these operators are half-infinite insertions of certain fusion-RSOS Boltzmann weights. They are the face analogue of insertions of higher spin lines in vertex models. Mathematically, they are given in terms of intertwiners of $U(\hat{sl}_2)_q$ modules. We present a detailed perturbation theory check of the conjectural correspondence between the physical and mathematical constructions in a particular simple example.Comment: Latex, 24 pages, uses amsmath, amsthm, amssymb, epic, eepic and texdraw style files (Minor typos corrected) (minor changes

Differential negative reinforcement of other behavior to increase compliance with wearing an anti-strip suit

Using a changing-criterion design, we replicated and extended a study (Cook, Rapp, & Schulze, 2015) on differential negative reinforcement of other behavior (DNRO). More specifically, educational assistants implemented DNRO to teach a 12-year-old boy with autism spectrum disorder to comply with wearing an anti-strip suit to prevent inappropriate fecal behavior in a school setting. The duration for which the participant wore the suit systematically increased from 2 s at the start of treatment to the entire duration of the school day at the termination of the study. Moreover, these effects were generalized to a new school with novel staff and persisted for more than a year. These findings replicate prior research on DNRO and further support the use of the intervention to increase compliance with wearing protective items, or medical devices, in practical settings

The Distance to Nova V959 Mon from VLA Imaging

Determining reliable distances to classical novae is a challenging but crucial step in deriving their ejected masses and explosion energetics. Here we combine radio expansion measurements from the Karl G. Jansky Very Large Array with velocities derived from optical spectra to estimate an expansion parallax for nova V959 Mon, the first nova discovered through its gamma-ray emission. We spatially resolve the nova at frequencies of 4.5-36.5 GHz in nine different imaging epochs. The first five epochs cover the expansion of the ejecta from 2012 October to 2013 January, while the final four epochs span 2014 February to 2014 May. These observations correspond to days 126 through 199 and days 615 through 703 after the first detection of the nova. The images clearly show a non-spherical ejecta geometry. Utilizing ejecta velocities derived from 3D modelling of optical spectroscopy, the radio expansion implies a distance between 0.9 +/- 0.2 and 2.2 +/- 0.4 kpc, with a most probable distance of 1.4 +/- 0.4 kpc. This distance implies a gamma-ray luminosity much less than the prototype gamma-ray-detected nova, V407 Cyg, possibly due to the lack of a red giant companion in the V959 Mon system. V959 Mon also has a much lower gamma-ray luminosity than other classical novae detected in gamma-rays to date, indicating a range of at least a factor of 10 in the gamma-ray luminosities for these explosions.Comment: 11 pages, 8 figures, 3 tables, submitted to ApJ 2015-01-21, under revie

The Peculiar Multi-Wavelength Evolution Of V1535 Sco

We present multi-wavelength observations of the unusual nova V1535 Sco throughout its outburst in 2015. Early radio observations were consistent with synchrotron emission, and early X-ray observations revealed the presence of high-energy (>1 keV) photons. These indicated that strong shocks were present during the first ~2 weeks of the nova's evolution. The radio spectral energy distribution was consistent with thermal emission from week 2 to week 6. Starting in week 7, the radio emission again showed evidence of synchrotron emission and there was an increase in X-ray emission, indicating a second shock event. The optical spectra show evidence for at least two separate outflows, with the faster outflow possibly having a bipolar morphology. The optical and near infrared light curves and the X-ray measurements of the hydrogen column density indicated that the companion star is likely a K giant.Comment: 20 pages, 13 figures, under review at ApJ, updated to match the most recent version submitted to the refere

Dynamic Key-Value Memory Networks for Knowledge Tracing

Knowledge Tracing (KT) is a task of tracing evolving knowledge state of students with respect to one or more concepts as they engage in a sequence of learning activities. One important purpose of KT is to personalize the practice sequence to help students learn knowledge concepts efficiently. However, existing methods such as Bayesian Knowledge Tracing and Deep Knowledge Tracing either model knowledge state for each predefined concept separately or fail to pinpoint exactly which concepts a student is good at or unfamiliar with. To solve these problems, this work introduces a new model called Dynamic Key-Value Memory Networks (DKVMN) that can exploit the relationships between underlying concepts and directly output a student's mastery level of each concept. Unlike standard memory-augmented neural networks that facilitate a single memory matrix or two static memory matrices, our model has one static matrix called key, which stores the knowledge concepts and the other dynamic matrix called value, which stores and updates the mastery levels of corresponding concepts. Experiments show that our model consistently outperforms the state-of-the-art model in a range of KT datasets. Moreover, the DKVMN model can automatically discover underlying concepts of exercises typically performed by human annotations and depict the changing knowledge state of a student.Comment: To appear in 26th International Conference on World Wide Web (WWW), 201

Performance Testing of Window Installation and Flashing Details

Protection of interface at windows and other penetrations from rainwater intrusion is a primary need of building structures. This is especially true when the building is in a high weather exposure location or in a climate in which the ability for walls to dry may be limited. Two areas of specific concern are: 1) the bottom corners of windows where damage is most commonly seen, and 2) the area around curved, arched or round-top windows where it is difficult to install the standard flashing materials. This paper reviews performance testing of window flashing installation methods commonly used in the trade, as well as improved methods made possible by recent advancements in flashing products. A series of laboratory tests were designed to determine water resistance, air leakage resistance and durability of several installation methods with different flashing materials. Windows were installed in test wall sections using several methods. The installations were monitored and evaluated for ease of installation and then tested for air leakage and water resistance using ASTM E283 and ASTM E331. The durability of the installations was then evaluated by subjecting the walls to thermal cycling (0 to 160oF) and retesting for water resistance using ASTM E331. Recommendations for best practice installation based on the testing results and key material selection issues are presented

How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds.

We seek to create agents that both act and communicate with other agents in pursuit of a goal. Towards this end, we extend LIGHT (Urbanek et al. 2019)—a large-scale crowd-sourced fantasy text-game—with a dataset of quests. These contain natural language motivations paired with in-game goals and human demonstrations; completing a quest might require dialogue or actions (or both). We introduce a reinforcement learning system that (1) incorporates large-scale language modeling-based and commonsense reasoning-based pre-training to imbue the agent with relevant priors; and (2) leverages a factorized action space of action commands and dialogue, balancing between the two. We conduct zero-shot evaluations using held-out human expert demonstrations, showing that our agents are able to act consistently and talk naturally with respect to their motivations