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Neural networks are powerful tools. Applying them in computer systems—operating systems, databases, and networked systems—attracts much attention. However, neural networks are complicated black boxes that may produce unexpected results. To train networks with well-defined behaviors, we introduce ouroboros, a system that constructs verified neural networks. Verified neural networks are those that satisfy user-defined safety properties, known as specifications. Ouroboros builds verified networks by a training-verification loop that combines deep learning training and neural network verification. The system employs multiple techniques to fill the gap between today’s verification and the properties required for systems. Ouroboros also accelerates the training-verification loop by spec-aware learning. Our experiments show that ouroboros can train verified networks for five applications that we study and has a 2.8× speedup on average compared with the vanilla training-verification loop. 

We present Magellan/IMACS and Magellan/MIKE spectroscopy of the ultra-faint dwarf (UFD) galaxy Pictor~II (Pic~II) that is located only 12 kpc from the Large Magellanic Cloud (LMC). From the IMACS spectroscopy, we identify 13 member stars and measure a mean heliocentric velocity of , a velocity dispersion of , a mean metallicity of , and an upper limit on the metallicity dispersion of . We measure detailed elemental abundances for the brightest star, finding $\text{[Fe/H]}=-3.3$, high [ $\alpha$/Fe] ratios, and no detectable neutron capture elements, similar to stars in other UFDs. However, this star has an unusually high [Sc/Fe] ratio. The dynamical mass-to-light ratio ( $M/L={760}_{-420}^{+910}{M}_{\odot }{L}_{\odot }^{-1}$), size, and chemical abundances confirms that Pic~II is a dark matter-dominated dwarf galaxy. We perform detailed orbit modeling of Pic~II in a combined Milky Way (MW) and LMC potential and find that Pic~II is highly likely to be a long-term LMC satellite. Furthermore, we find that Pic II is likely still bound to the LMC today. Pic~II is the seventh LMC-associated UFD and among the most metal-poor UFDs known. We further update the morphological parameters with deeper Dark Energy Camera (DECam) photometry, compute the dark matter properties for dark matter indirect detection searches, verify the extremely low metallicity with narrowband CaHK imaging, and briefly discuss tidal influences of the LMC and MW. 

Abstract The Green Bank 820 MHz pulsar survey covers ≃173 deg²in the Cygnus X region of the Galaxy, centered onl= 84.°5 andb= 1.°5. Significant star formation is present in this region, and lines of sight pass through three arms of the Galaxy (Orion–Cygnus, Perseus, and an outer arm). Using the Green Bank Telescope, we recorded 200 MHz of bandwidth for 4.5 minutes at 81.92μs resolution for each of 3457 observed survey pointings during 2016 and 2017, covering about two-thirds of the total area. We searched these data for pulsars and report the discovery of six new pulsars—PSRs J2016+3820, J2016+4231, J2019+3810, J2035+3538, J2035+3655, and J2041+4551—and the codiscovery of PSR J2057+4701. PSR J2035+3655 is in a short (4.5 hr) binary orbit; we report the full binary solution and weakly constrain the mass of the pulsar via a marginal (2σ) detection of the Shapiro delay. We also searched the survey data for known pulsars to estimate the survey’s sensitivity and measured 820 MHz pulse widths and flux density for 20 detected sources. For sources that were also detected in the Green Bank North Celestial Cap survey at 350 MHz, we measure scattering parameters and compare to expectations for the region. With these results, we revisit the population estimates that motivated this survey and consider the impact of the survey’s yield on their underlying models. We note an apparent underestimate in dispersion measure predictions from typical Galactic electron density models in the survey region, and discuss future observation strategies. 

Inclusion in mathematics education is strongly tied to discourse rich classrooms, where students ideas play a central role. Talk moves are specific discursive practices that promote inclusive and equitable participation in classroom discussions. This paper describes the development of the TalkMoves application, which provides teachers with detailed feedback on their usage of talk moves based on accountable talk theory. Building on our recent work to automate the classification of teacher talk moves, we have expanded the application to also include feedback on a set of student talk moves. We present results from several deep learning models trained to classify student sentences into student talk moves with performance up to 73% F1. The classroom data used for training these models were collected from multiple sources that were pre-processed and annotated by highly reliable experts. We validated the performance of the model on an out-of-sample dataset which included 166 classroom transcripts collected from teachers piloting the application. 

As the COVID-19 pandemic is disrupting life worldwide, related online communities are popping up. In particular, two “new” communities, /r/China flu and /r/Coronavirus, emerged on Reddit and have been dedicated to COVID- related discussions from the very beginning of this pandemic. With /r/Coronavirus promoted as the official community on Reddit, it remains an open question how users choose between these two highly-related communities. In this paper, we characterize user trajectories in these two communities from the beginning of COVID-19 to the end of September 2020. We show that new users of /r/China flu and /r/Coronavirus were similar from January to March. After that, their differences steadily increase, both in language distance and membership prediction, as the pandemic continues to unfold. Furthermore, users who started at /r/China flu from January to March were more likely to leave, while those who started in later months tend to remain highly “loyal”. To understand this difference, we develop a movement analysis framework to understand membership changes in these two communities and identify a significant proportion of /r/China flu members (around 50%) that moved to /r/Coronavirus in February. This movement turns out to be highly predictable based on other subreddits that users were previously active in. Our work demonstrates how two highly-related communities emerge and develop their own identity in a crisis, and highlights the important role of existing communities in understanding such an emergence. 

TalkMoves is an innovative application designed to support K-12 mathematics teachers to reflect on, and continuously improve their instructional practices. This application combines state-of-the-art natural language processing capabilities with automated speech recognition to automatically analyze classroom recordings and provide teachers with personalized feedback on their use of specific types of discourse aimed at broadening and deepening classroom conversations about mathematics. These specific discourse strategies are referred to as “talk moves” within the mathematics education community and prior research has documented the ways in which systematic use of these discourse strategies can positively impact student engagement and learning. In this article, we describe the TalkMoves application’s cloud-based infrastructure for managing and processing classroom recordings, and its interface for providing teachers with feedback on their use of talk moves during individual teaching episodes. We present the series of model architectures we developed, and the studies we conducted, to develop our best-performing, transformer-based model (F1 = 79.3%). We also discuss several technical challenges that need to be addressed when working with real-world speech and language data from noisy K-12 classrooms. 

TalkMoves is an innovative application designed to support K-12 mathematics teachers to reflect on, and continuously improve their instructional practices. This application com- bines state-of-the-art natural language processing capabilities with automated speech recognition to automatically analyze classroom recordings and provide teachers with personalized feedback on their use of specific types of discourse aimed at broadening and deepening classroom conversations about mathematics. These specific discourse strategies are referred to as “talk moves” within the mathematics education com- munity and prior research has documented the ways in which systematic use of these discourse strategies can positively impact student engagement and learning. In this article, we describe the TalkMoves application’s cloud-based infrastruc- ture for managing and processing classroom recordings, and its interface for providing teachers with feedback on their use of talk moves during individual teaching episodes. We present the series of model architectures we developed, and the studies we conducted, to develop our best-performing, transformer-based model (F1 = 79.3%). We also discuss sev- eral technical challenges that need to be addressed when working with real-world speech and language data from noisy K-12 classrooms. 

1. Plants face many environmental stresses that can impact their survival, development and fitness. Insects are the most diverse, abundant and threatening herbivores in nature. As a consequence, plants produce direct chemical and physical defences to reduce herbivory. They also release volatiles to recruit natural enemies that indirectly protect them from herbivory. The recruitment of parasitic wasps can benefit plant fitness because they ultimately kill their insect hosts. 2. Recently, studies showed that parasitoids can indirectly mediate plant defences by modulating herbivore oral secretions. In addition to the direct benefits of parasitoids in terms of reducing herbivore survival, we tested if the reduction in induced defences by parasitized caterpillars compared to non-parasitized caterpillars may reduce the costs associated with defence expression. 3. We provide evidence that tomato plants treated with saliva from parasitized caterpillars have significantly higher fitness parameters including increased flower numbers (16.3%) and heavier fruit weight (13.5%), compared to plants treated with saliva from non-parasitized caterpillars. Since plants were grown without actual herbivores, the higher values for these fitness parameters were due to lower costs of induced defences and not due to reduced herbivory by parasitized caterpillars. Furthermore, the resulting seed germination time was shorter and the germination rate was higher when the maternal plants were previously exposed to parasitized herbivore treatment compared to control (non-treated) plants. 4. Overall, application of saliva did not result in transgenerational priming of offspring defence responses. However, offspring of parents exposed to caterpillar saliva had lower constitutive levels and higher induced levels of trypsin inhibitor than offspring from unexposed parents. 5. This study shows that the saliva of parasitized caterpillars can modulate plant defences and further demonstrates that the lower induction of plant defences is associated with elevated plant fitness in the absence of herbivore feeding, suggesting that induced plant defences are costly.