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The internal velocity structure within dense gaseous cores plays a crucial role in providing the initial conditions for star formation in molecular clouds. However, the kinematic properties of dense gas at core scales (∼0.01−0.1 pc) has not been extensively characterized because of instrument limitations until the unique capabilities of GBT-Argus became available. The ongoing GBT-Argus Large Program, Dynamics in Star-forming Cores (DiSCo) thus aims to investigate the origin and distribution of angular momentum of star-forming cores. DiSCo will survey all starless cores and Class 0 protostellar cores in the Perseus molecular complex down to ∼0.01 pc scales with <0.05 km s−1 velocity resolution using the dense gas tracer N2H+. Here, we present the first data sets from DiSCo towards the B1 and NGC 1333 regions in Perseus. Our results suggest that a dense core’s internal velocity structure has little correlation with other core-scale properties, indicating these gas motions may be originated externally from cloud-scale turbulence. These first data sets also reaffirm the ability of GBT-Argus for studying dense core velocity structure and provided an empirical basis for future studies that address the angular momentum problem with a statistically broad sample.

 

Communication between human and mobile agents is getting increasingly important as such agents are widely deployed in our daily lives. Vision-and-Dialogue Navigation is one of the tasks that evaluate the agent’s ability to interact with humans for assistance and navigate based on natural language responses. In this paper, we explore the Navigation from Dialogue History (NDH) task, which is based on the Cooperative Vision-and-Dialogue Navigation (CVDN) dataset, and present a state-of-the-art model which is built upon Vision-Language transformers. However, despite achieving competitive performance, we find that the agent in the NDH task is not evaluated appropriately by the primary metric – Goal Progress. By analyzing the performance mismatch between Goal Progress and other metrics (e.g., normalized Dynamic Time Warping) from our state-of-the-art model, we show that NDH’s sub-path based task setup (i.e., navigating partial trajectory based on its correspondent subset of the full dialogue) does not provide the agent with enough supervision signal towards the goal region. Therefore, we propose a new task setup called NDH-Full which takes the full dialogue and the whole navigation path as one instance. We present a strong baseline model and show initial results on this new task. We further describe several approaches that we try, in order to improve the model performance (based on curriculum learning, pre-training, and data-augmentation), suggesting potential useful training methods on this new NDH-Full task. 

The evergrowing plastic production and the caused concerns of plastic waste accumulation have stimulated the need for waste plastic chemical recycling/valorization. Current methods suffer from harsh reaction conditions and long reaction time. Herein we demonstrate a non-thermal plasma-assisted method for rapid hydrogenolysis of polystyrene (PS) at ambient temperature and atmospheric pressure, generating high yield (>40 wt%) of C₁–C₃hydrocarbons and ethylene being the dominant gas product (Selectivity of ethylene,S_C2H4 > 70%) within ~10 min. The fast reaction kinetics is attributed to highly active hydrogen plasma, which can effectively break bonds in polymer and initiate hydrogenolysis under mild condition. Efficient hydrogenolysis of post-consumer PS materials using this method is also demonstrated, suggesting a promising approach for fast retrieval of small molecular hydrocarbon modules from plastic materials as well as a good capability to process waste plastics in complicated conditions.

 

Phones, the segmental units of the International Phonetic Al-phabet (IPA), are used for lexical distinctions in most human languages; Tones, the suprasegmental units of the IPA,are used in perhaps 70%. Many previous studies have explored cross-lingual adaptation of automatic speech recognition(ASR) phone models, but few have explored the multilingual and cross-lingual transfer of synchronization between phones and tones. In this paper, we test four Connectionist Temporal Classification (CTC)-based acoustic models, differing in the degree of synchrony they impose between phones and tones.Models are trained and tested multilingually in three languages,then adapted and tested cross-lingually in a fourth. Both synchronous and asynchronous models are effective in both multi-lingual and cross-lingual settings. Synchronous models achieve lower error rate in the joint phone+tone tier, but asynchronous training results in lower tone error rate.