No Arabic abstract
We present an open source toolkit of flight-proven electronic devices which can be used to track, terminate and recover high altitude balloon flights and payloads. Comprising a beacon, pyrotechnic and non-pyrotechnic cut-down devices plus associated software, the toolkit can be used to: (i) track the location of a flight via Iridium satellite communication; (ii) release lift and/or float balloons manually or at pre-defined altitudes; (iii) locate the payload after descent. The size and mass of the toolkit make it suitable for use on weather or sounding balloon flights. We describe the technology readiness level of the toolkit, based on over 20 successful flights to altitudes of typically 32,000 m.
We present nbodykit, an open-source, massively parallel Python toolkit for analyzing large-scale structure (LSS) data. Using Python bindings of the Message Passing Interface (MPI), we provide parallel implementations of many commonly used algorithms in LSS. nbodykit is both an interactive and scalable piece of scientific software, performing well in a supercomputing environment while still taking advantage of the interactive tools provided by the Python ecosystem. Existing functionality includes estimators of the power spectrum, 2 and 3-point correlation functions, a Friends-of-Friends grouping algorithm, mock catalog creation via the halo occupation distribution technique, and approximate N-body simulations via the FastPM scheme. The package also provides a set of distributed data containers, insulated from the algorithms themselves, that enable nbodykit to provide a unified treatment of both simulation and observational data sets. nbodykit can be easily deployed in a high performance computing environment, overcoming some of the traditional difficulties of using Python on supercomputers. We provide performance benchmarks illustrating the scalability of the software. The modular, component-based approach of nbodykit allows researchers to easily build complex applications using its tools. The package is extensively documented at http://nbodykit.readthedocs.io, which also includes an interactive set of example recipes for new users to explore. As open-source software, we hope nbodykit provides a common framework for the community to use and develop in confronting the analysis challenges of future LSS surveys.
Although there exists a large sample of known exoplanets, little spectroscopic data exists that can be used to study their global atmospheric properties. This deficiency can be addressed by performing phase-resolved spectroscopy -- continuous spectroscopic observations of a planets entire orbit about its host star -- of transiting exoplanets. Planets with characteristics suitable for atmospheric characterization have orbits of several days, thus phase curve observations are highly resource intensive, especially for shared use facilities. In this work, we show that an infrared spectrograph operating from a high altitude balloon platform can perform phase-resolved spectroscopy of hot Jupiter-type exoplanets with performance comparable to a space-based telescope. Using the EXoplanet Climate Infrared TElescope (EXCITE) experiment as an example, we quantify the impact of the most important systematic effects that we expect to encounter from a balloon platform. We show an instrument like EXCITE will have the stability and sensitivity to significantly advance our understanding of exoplanet atmospheres. Such an instrument will both complement and serve as a critical bridge between current and future space-based near infrared spectroscopic instruments.
We present a method for attaining sub-arcsecond pointing stability during sub- orbital balloon flights, as designed for in the High Altitude Lensing Observatory (HALO) concept. The pointing method presented here has the potential to perform near-space quality optical astronomical imaging at 1-2% of the cost of space-based missions. We also discuss an architecture that can achieve sufficient thermomechanical stability to match the pointing stability. This concept is motivated by advances in the development and testing of Ultra Long Duration Balloon (ULDB) flights which promise to allow observation campaigns lasting more than three months. The design incorporates a multi-stage pointing architecture comprising: a gondola coarse azimuth control system, a multi-axis nested gimbal frame structure with arcsecond stability, a telescope de-rotator to eliminate field rotation, and a fine guidance stage consisting of both a telescope mounted angular rate sensor and guide CCDs in the focal plane to drive a fast-steering mirror. We discuss the results of pointing tests together with a preliminary thermo-mechanical analysis required for sub-arcsecond pointing at high altitude. Possible future applications in the areas of wide-field surveys and exoplanet searches are also discussed.
Existing works, including ELMO and BERT, have revealed the importance of pre-training for NLP tasks. While there does not exist a single pre-training model that works best in all cases, it is of necessity to develop a framework that is able to deploy various pre-training models efficiently. For this purpose, we propose an assemble-on-demand pre-training toolkit, namely Universal Encoder Representations (UER). UER is loosely coupled, and encapsulated with rich modules. By assembling modules on demand, users can either reproduce a state-of-the-art pre-training model or develop a pre-training model that remains unexplored. With UER, we have built a model zoo, which contains pre-trained models based on different corpora, encoders, and targets (objectives). With proper pre-trained models, we could achieve new state-of-the-art results on a range of downstream datasets.
Familia is an open-source toolkit for pragmatic topic modeling in industry. Familia abstracts the utilities of topic modeling in industry as two paradigms: semantic representation and semantic matching. Efficient implementations of the two paradigms are made publicly available for the first time. Furthermore, we provide off-the-shelf topic models trained on large-scale industrial corpora, including Latent Dirichlet Allocation (LDA), SentenceLDA and Topical Word Embedding (TWE). We further describe typical applications which are successfully powered by topic modeling, in order to ease the confusions and difficulties of software engineers during topic model selection and utilization.