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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. 

Abstract Of the more than 3000 radio pulsars currently known, only ∼300 are in binary systems, and only five of these consist of young pulsars with massive nondegenerate companions. We present the discovery and initial timing, accomplished using the Canadian Hydrogen Intensity Mapping Experiment (CHIME) telescope, of the sixth such binary pulsar, PSR J2108+4516, a 0.577 s radio pulsar in a 269 day orbit of eccentricity 0.09 with a companion of minimum mass 11 M ⊙ . Notably, the pulsar undergoes periods of substantial eclipse, disappearing from the CHIME 400–800 MHz observing band for a large fraction of its orbit, and displays significant dispersion measure and scattering variations throughout its orbit, pointing to the possibility of a circumstellar disk or very dense stellar wind associated with the companion star. Subarcsecond resolution imaging with the Karl G. Jansky Very Large Array unambiguously demonstrates that the companion is a bright, V ≃ 11 OBe star, EM* UHA 138, located at a distance of 3.26(14) kpc. Archival optical observations of EM* UHA 138 approximately suggest a companion mass ranging from 17.5 M ⊙ < M c < 23 M ⊙ , in turn constraining the orbital inclination angle to 50.°3 ≲ i ≲ 58.°3. With further multiwavelength follow-up, PSR J2108+4516 promises to serve as another rare laboratory for the exploration of companion winds, circumstellar disks, and short-term evolution through extended-body orbital dynamics. 

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. 

Herbivore-induced plant volatiles (HIPVs) are widely recognized as an ecologically important defensive response of plants against herbivory. Although the induction of this ‘cry for help’ has been well documented, only a few studies have investigated the inhibition of HIPVs by herbivores and little is known about whether herbivores have evolved mechanisms to inhibit the release of HIPVs. To examine the role of herbivore effectors in modulating HIPVs and stomatal dynamics, we conducted series of experiments combining pharmacological, surgical, genetic (CRISPR-Cas9) and chemical (GC-MS analysis) approaches. We show that the salivary enzyme, glucose oxidase (GOX), secreted by the caterpillar Helicoverpa zea on leaves, causes stomatal closure in tomato (Solanum lycopersicum) within 5 min, and in both tomato and soybean (Glycine max) for at least 48 h. GOX also inhibits the emission of several HIPVs during feeding by H. zea, including (Z)-3-hexenol, (Z)-jasmone and (Z)-3-hexenyl acetate, which are important airborne signals in plant defenses. Our findings highlight a potential adaptive strategy where an insect herbivore inhibits plant airborne defenses during feeding by exploiting the association between stomatal dynamics and HIPV emission.