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  1. It has been recognized that jobs across different domains is becoming more data driven, and many aspects of the economy, society, and daily life depend more and more on data. Undergraduate education offers a critical link in providing more data science and engineering (DSE) exposure to students and expanding the supply of DSE talent. The National Academies have identified that effective DSE education requires both appropriate classwork and hands-on experience with real data and real applications. Currently significant progress has been made in classwork, while progress in hands-on research experience has been lacking. To fill this gap, we have proposedmore »to create data-enabled engineering project (DEEP) modules based on real data and applications, which is currently funded by the National Science Foundation (NSF) under the Improving Undergraduate STEM Education (IUSE) program. To achieve project goal, we have developed two internet-of-things (IoT) enabled laboratory engineering testbeds (LETs) and generated real data under various application scenarios. In addition, we have designed and developed several sample DEEP modules in interactive Jupyter Notebook using the generated data. These sample DEEP modules will also be ported to other interactive DSE learning environments, including Matlab Live Script and R Markdown, for wide and easy adoption. Finally, we have conducted metacognitive awareness gain (MAG) assessments to establish a baseline for assessing the effectiveness of DEEP modules in enhancing students’ reflection and metacognition. The DEEP modules that are currently being developed target students in Chemical Engineering, Electrical Engineering, Computer Science, and MS program in Data Science at xxx University. The modules will be deployed in the Spring of 2021, and we expect to have immediate impact to the targeted classes and students. We also anticipate that the DEEP modules can be adopted without modification to other disciplines in Engineering such as Mechanical, Industrial and Aerospace Engineering. They can also be easily extended to other disciplines in other colleges such as Liberal Arts by incorporating real data and applications from the respective disciplines. In this work, we will share our ideas, the rationale behind the proposed approach, the planned tasks for the project, the demonstration of modules developed, and potential dissemination venues.« less
    Free, publicly-accessible full text available July 26, 2022
  2. Gaia16aye was a binary microlensing event discovered in the direction towards the northern Galactic disc and was one of the first microlensing events detected and alerted to by the Gaia space mission. Its light curve exhibited five distinct brightening episodes, reaching up to I  = 12 mag, and it was covered in great detail with almost 25 000 data points gathered by a network of telescopes. We present the photometric and spectroscopic follow-up covering 500 days of the event evolution. We employed a full Keplerian binary orbit microlensing model combined with the motion of Earth and Gaia around the Sun tomore »reproduce the complex light curve. The photometric data allowed us to solve the microlensing event entirely and to derive the complete and unique set of orbital parameters of the binary lensing system. We also report on the detection of the first-ever microlensing space-parallax between the Earth and Gaia located at L2. The properties of the binary system were derived from microlensing parameters, and we found that the system is composed of two main-sequence stars with masses 0.57 ± 0.05 M ⊙ and 0.36 ± 0.03 M ⊙ at 780 pc, with an orbital period of 2.88 years and an eccentricity of 0.30. We also predict the astrometric microlensing signal for this binary lens as it will be seen by Gaia as well as the radial velocity curve for the binary system. Events such as Gaia16aye indicate the potential for the microlensing method of probing the mass function of dark objects, including black holes, in directions other than that of the Galactic bulge. This case also emphasises the importance of long-term time-domain coordinated observations that can be made with a network of heterogeneous telescopes.« less
  3. ABSTRACT

    We present deep rest-frame UV spectroscopic observations using the Gran Telescopio Canarias of six gravitationally lensed Lyα emitters (LAEs) at 2.36 < z < 2.82 selected from the BELLS GALLERY survey. By taking the magnifications into account, we show that LAEs can be as luminous as LLyα ≃ 30 × 1042 erg s−1 and MUV ≃ −23 (AB) without invoking an AGN component, in contrast with previous findings. We measure Lyα rest-frame equivalent widths, $EW_{0}\,\rm (Ly\alpha)$, ranging from 16 to 50 Å and Lyα escape fractions, $f_{\rm esc}\, \rm (Ly\alpha)$, from 10 per cent to 40 per cent. Large $EW_{0}\, \rm (Ly\alpha)$ and $f_{\rm esc}\, \rm (Ly\alpha)$ are foundmore »predominantly in LAEs showing weak low-ionization ISM absorption (EW0 ≲ 1 Å) and narrow Lyα profiles (≲300 km s−1 FWHM) with their peak close (≲80 km s−1) to their systemic redshifts, suggestive of less scatter from low H i column densities that favours the escape of Lyα photons. We infer stellar metallicities of Z/Z⊙ ≃ 0.2 in almost all LAEs by comparing the P-Cygni profiles of the wind lines N v1240 Å and C iv1549 Å with those from stellar synthesis models. We also find a trend between MUV and the velocity offset of ISM absorption lines, such as the most luminous LAEs experience stronger outflows. The most luminous LAEs show star formation rates up to ≃180 M⊙ yr−1, yet they appear relatively blue (βUV ≃ −1.8 to −2.0) showing evidence of little dust attenuation [E(B − V) = 0.10–0.14]. These luminous LAEs may be particular cases of young starburst galaxies that have had no time to form large amounts of dust. If so, they are ideal laboratories to study the early phase of massive star formation, stellar and dust mass growth, and chemical enrichment histories of starburst galaxies at high-z.

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  4. The democratization of data is transforming our world. Together with the advances in computer and engineering technology, these advancements drive the rapid change in the landscape of jobs and work. There are many reports indicating that industry finds itself constrained by today’s relatively small supply of well-trained data science talent, and hiring demand for data scientists has begun to increase rapidly; some projections forecast that approximately 2.7 million new data science positions will be available by 2020. Unsurprisingly, the data science and engineering (DSE) programs across the nation have grown significantly in the past a few years. DSE education requiresmore »both appropriate classwork and hands-on experience with real data and real applications. While significant progress has been made in the former, one key aspect that yet to be addressed is hands-on experience incorporating real-world applications. In this work, we will review the efforts that explore real data and application based data science education.« less
  5. The democratization of data is transforming our world. Together with the advances in computer and engineering technology, these advancements drive the rapid change in the landscape of jobs and work. There are many reports indicating that industry finds itself constrained by today’s relatively small supply of well-trained data science talent, and hiring demand for data scientists has begun to increase rapidly; some projections forecast that approximately 2.7 million new data science positions will be available by 2020. Unsurprisingly, the data science and engineering (DSE) programs across the nation have grown significantly in the past a few years. DSE education requiresmore »both appropriate classwork and hands-on experience with real data and real applications. While significant progress has been made in the former, one key aspect that yet to be addressed is hands-on experience incorporating real-world applications. In this work, we will review the efforts that explore real data and application based data science education.« less