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Excitation of coherent phonons has the potential to dramatically alter the electronic structure of Dirac and Weyl semimetals, enabling sub-picosecond control of their optical and electronic properties. The Dirac semimetal SrMnSb2 is a candidate for such control, with a coherent-phonon mode that is predicted to close and reopen a gap at the Dirac node. Here, through a series of ultrafast pump-probe experiments, we establish suitable samples and conditions for driving the coherent phonon to high amplitude and attempting to observe the gap’s closure. Films of SrMnSb2 grown by molecular-beam epitaxy are shown to have phononic properties matching those of bulk crystals. We find that the phonon can be strongly excited by pump pulses with wavelength near 1500 nm, which will excite a 30-nm film almost uniformly and will penetrate the arsenic capping layers that protect the films. We find that samples withstand pump pulses of fluence up to 20 mJ/cm2, and we demonstrate the potential for sequences of pulses to amplify the oscillation while suppressing other phonon modes. Armed with our new knowledge of the conditions for exciting the desired coherent phonon, future experiments will be well prepared to measure its motion and to observe phononic control of the Dirac-point gap. 

Supported by the National Science Foundation's Improving Undergraduate STEM Education: Hispanic-Serving Institutions (IUSE-HSI) Program, a collaborative summer research internship initiative united a public four-year institution with two local community colleges to offer community college students significant engineering research opportunities and hands-on experiences. In summer 2023, four students from the community college in computer science and engineering participated in a eight-week research internship project in a research lab at the four-year university. This internship project aimed to develop and implement of real-time computer vision on energy-efficient cortex-m microprocessor. This projet explores a unique approach to engage community college students in the realm of artificial intelligence research. By focusing on the development and implementation of real-time computer vision on energy-efficient Cortex-M microprocessors, we offer a practical and educational avenue for students to delve into the burgeoning field of AI. Through a combination of theoretical understanding and practical application, students are empowered to explore AI concepts, gain proficiency in low-power computing, and contribute to real-world AI projects. Furthermore, the project offered student interns a valuable opportunity to refine their research capabilities, particularly in the realms of scientific writing and presentation, while simultaneously boosting their self-assurance and enthusiasm for pursuing STEM careers in the field of AI. 

This paper summarizes the main results and contributions of the MagNet Challenge 2023, an open-source research initiative for data-driven modeling of power magnetic materials. The MagNet Challenge has (1) advanced the stateof-the-art in power magnetics modeling; (2) set up examples for fostering an open-source and transparent research community; (3) developed useful guidelines and practical rules for conducting data-driven research in power electronics; and (4) provided a fair performance benchmark leading to insights on the most promising future research directions. The competition yielded a collection of publicly disclosed software algorithms and tools designed to capture the distinct loss characteristics of power magnetic materials, which are mostly open-sourced. We have attempted to bridge power electronics domain knowledge with state-of-the-art advancements in artificial intelligence, machine learning, pattern recognition, and signal processing. The MagNet Challenge has greatly improved the accuracy and reduced the size of data-driven power magnetic material models. The models and tools created for various materials were meticulously documented and shared within the broader power electronics community.