The Magdalena Ridge Observatory Interferometer is an ambitious project to build a 10 telescope long-baseline optical/near-infrared in the mountains about a one-hour drive outside of Socorro, NM. The project is being led by New Mexico Institute of Mining and Technology and being built in cooperation with our primary collaborators at the University of Cambridge. We are currently funded via a cooperative agreement with the Air Force Research Lab in Albuquerque, NM to demonstrate imaging capabilities on geosynchronous objects. We have recently installed the second full beamline for the interferometer system and are working our way towards first fringes on an similar to 8m baseline later this year. In this manuscript, we report on the status of each of the subsystems, the installation progress and challenges to date, and on the ramp-up to measurements of first fringes. We also report on plans for early science and offer public shared-risk access with the facility in the near future.

The Beam Relay System at the Magdalena Ridge Observatory Interferometer, exposed to outdoor environmental conditions, includes 6-inch mirrors mounted on aluminum frames and steel platforms, equipped with piezoelectric motors and a laser/camera alignment system. This subsystem faces challenges with misalignments that disrupt observations, addressed by a proposed correction strategy. The system uses temperature sensor data around mirrors to predict and correct misalignments as a feedforward control system through calibrated motors, and incorporates a periodic closed-loop control system using light source and camera. Advanced predictive models refined over time using temperature, shear, and tilt data, aim to maintain beam stability within interferometric tolerances, ensuring optimal performance.

The Magdalena Ridge Observatory Interferometer (MROI) and its first science beam combiner the Free-space Optical multi-apertUre combineR for IntERferometry (FOURIER) are undergoing the first phase of construction near Magdalena, New Mexico. MROI and FOURIER are designed for unprecedented sensitivity to enable imaging of faint astronomical targets. FOURIER provides highly sensitive simultaneous interferometric observations in the J, H, and K bands. In preparation for first fringes with FOURIER, we are developing a data reduction pipeline to produce high quality science ready data products adhering to the OIFITS2 data standard.

Sub-word level alternations during inflection (apophonies) are an common linguistic phenomenon present in morphologically-rich languages, like Romanian. Inflection learning, or predicting the inflection class of a partially regular or fully irregular verb or noun in such a language has been a widely studied task in NLP, but generative models are limited to capturing the most common ending patterns and apophonies. In this paper, we show how to train a character-level Recurrent Neural Network language model to be able to accurately generate the full inflection of verbs in Romanian, Finish, and Spanish and model stem-level phonological alternations triggered by inflection in an unsupervised way. We also introduce a method to evaluate the accuracy of the generated inflections.

The Magdalena Ridge Observatory Interferometer has been conceived to be the most ambitious optical/near-infrared long-baseline imaging interferometer in the world today. We anticipate receiving the second telescope mount and enclosure and associated beamline infrastructure to enable us to attempt first fringes measurements early in 2023. Having reached this important milestone, we anticipate receiving the third copy of all beamline components about one year later and attempting closure phase measurements thereafter. We will present a status update and plans under the new Cooperative Agreement with AFRL for the next phases of the project.

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Most previous work on trainable language generation has focused on two paradigms: (a) using a generation decisions of an existing generator. Both approaches rely on the existence of a handcrafted generation component, which is likely to limit their scalability to new domains. The first contribution of this article is to present Bagel, a fully data-driven generation method that treats the language generation task as a search for the most likely sequence of semantic concepts and realization phrases, according to Factored Language Models (FLMs). As domain utterances are not readily available for most natural language generation tasks, a large creative effort is required to produce the data necessary to represent human linguistic variation for nontrivial domains. This article is based on the assumption that learning to produce paraphrases can be facilitated by collecting data from a large sample of untrained annotators using crowdsourcing—rather than a few domain experts—by relying on a coarse meaning representation. A second contribution of this article is to use crowdsourced data to show how dialogue naturalness can be improved by learning to vary the output utterances generated for a given semantic input. Two data-driven methods for generating paraphrases in dialogue are presented: (a) by sampling from the n-best list of realizations produced by Bagel's FLM reranker; and (b) by learning a structured perceptron predicting whether candidate realizations are valid paraphrases. We train Bagel on a set of 1,956 utterances produced by 137 annotators, which covers 10 types of dialogue acts and 128 semantic concepts in a tourist information system for Cambridge. An automated evaluation shows that Bagel outperforms utterance class LM baselines on this domain. A human evaluation of 600 resynthesized dialogue extracts shows that Bagel's FLM output produces utterances comparable to a handcrafted baseline, whereas the perceptron classifier performs worse. Interestingly, human judges find the system sampling from the n-best list to be more natural than a system always returning the first-best utterance. The judges are also more willing to interact with the n-best system in the future. These results suggest that capturing the large variation found in human language using data-driven methods is beneficial for dialogue interaction.

Recently, many BERT based approaches have 001 been proposed for task-oriented dialogue 002 (TOD) task. Despite their impressive perfor-003 mance, the insufficient utilization of deep se-004 mantic information and long-distance context 005 understanding makes it difficult for these meth-006 ods to digest complex dialogue scenarios for 007 they cannot obtain sufficient evidence from dia-008 logue data to support dialogue decision-making. 009 In this work, we propose a novel structured se-010 mantics reinforcement (SSR) method to handle 011 these issues. SSR reorganized the end-to-end 012 TOD structure, which mainly includes two key 013 components: 1. The dialogue symbolic mem-014 ory, which cache the objects mentioned in the 015 dialogue and the structure under the seman-016 tic relationship. 2. semantic projection mod-017 ule, understanding module, based on the pre-018 vious structured results, determines the source 019 of the slot extraction required for the current 020 task. And our approach achieves state-of-the-021 art results on dataset MultiWOZ 2.1, where 022 we acquire a joint goal accuracy beyond 60% 023 and also gains a significant effect on dataset 024 DSTC8. 025

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