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This paper examines the learning experiences of undergraduate students who conducted research as part of a multidisciplinary team. The research project involved five undergraduate students with different backgrounds in engineering as well as in arts and sciences, supervised by four architecture and civil engineering faculty and their three PhD students. The research investigates the behavior of new Tessellated Structural-Architectural (TeSA) systems made of repetitive patterns of tiles (tessellations) that are both aesthetically appealing and load bearing. The undergraduate students worked on three tasks: (1) studying the behavior of TeSA shear walls using small scale earthquake simulator tests, (2) studying the shear capacity of reinforced concrete TeSA tiles, and (3) studying the effect of different shapes and interlocking patterns on the performance of small scale TeSA beams. The undergraduate students used hands-on experiments and laboratory testing to study the performance of 3D printed or prefabricated interlocking tessellations. This paper discusses the technical skills, fundamental concepts, and power skills (communicating, writing, presenting, etc.) that the students obtained, as well as the challenges that they encountered. The students found the process of developing and executing hands-on experiments and analyzing experimental results effective for learning new technologies and fundamental concepts. These concepts included 3D printing methods, natural frequency of a structure, and structural response subjected to a shear force. Peer learning, collaboration between students with different backgrounds, and group discussions with all the team members facilitated a deeper understanding and broader perspective on design, performance, and construction of TeSA systems. The project took place during the COVID-19 pandemic, and the students found working and meeting remotely challenging at times. Proper guidance and timely feedback by the project investigators and their PhD students helped with resolving the challenges. 

Abstract We present cosmological results from the measurement of baryon acoustic oscillations (BAO) in galaxy, quasar and Lyman-αforest tracers from the first year of observations from the Dark Energy Spectroscopic Instrument (DESI), to be released in the DESI Data Release 1. DESI BAO provide robust measurements of the transverse comoving distance and Hubble rate, or their combination, relative to the sound horizon, in seven redshift bins from over 6 million extragalactic objects in the redshift range 0.1 <z< 4.2. To mitigate confirmation bias, a blind analysis was implemented to measure the BAO scales. DESI BAO data alone are consistent with the standard flat ΛCDM cosmological model with a matter density Ω_m=0.295±0.015. Paired with a baryon density prior from Big Bang Nucleosynthesis and the robustly measured acoustic angular scale from the cosmic microwave background (CMB), DESI requiresH₀=(68.52±0.62) km s^-1Mpc^-1. In conjunction with CMB anisotropies fromPlanckand CMB lensing data fromPlanckand ACT, we find Ω_m=0.307± 0.005 andH₀=(67.97±0.38) km s^-1Mpc^-1. Extending the baseline model with a constant dark energy equation of state parameterw, DESI BAO alone requirew=-0.99^+0.15_-0.13. In models with a time-varying dark energy equation of state parametrised byw₀andw_a, combinations of DESI with CMB or with type Ia supernovae (SN Ia) individually preferw₀> -1 andw_a< 0. This preference is 2.6σfor the DESI+CMB combination, and persists or grows when SN Ia are added in, giving results discrepant with the ΛCDM model at the 2.5σ, 3.5σor 3.9σlevels for the addition of the Pantheon+, Union3, or DES-SN5YR supernova datasets respectively. For the flat ΛCDM model with the sum of neutrino mass ∑m_νfree, combining the DESI and CMB data yields an upper limit ∑m_ν< 0.072 (0.113) eV at 95% confidence for a ∑m_ν> 0 (∑m_ν> 0.059) eV prior. These neutrino-mass constraints are substantially relaxed if the background dynamics are allowed to deviate from flat ΛCDM. 

In-memory key-value stores that use kernel-bypass networking serve millions of operations per second per machine with microseconds of latency. They are fast in part because they are simple, but their simple interfaces force applications to move data across the network. This is inefficient for operations that aggregate over large amounts of data, and it causes delays when traversing complex data structures. Ideally, applications could push small functions to storage to avoid round trips and data movement; however, pushing code to these fast systems is challenging. Any extra complexity for interpreting or isolating code cuts into their latency and throughput benefits. We present Splinter, a low-latency key-value store that clients extend by pushing code to it. Splinter is designed for modern multi-tenant data centers; it allows mutually distrusting tenants to write their own fine-grained extensions and push them to the store at runtime. The core of Splinter’s design relies on type- and memory-safe extension code to avoid conventional hardware isolation costs. This still allows for bare-metal execution, avoids data copying across trust boundaries, and makes granular storage functions that perform less than a microsecond of compute practical. Our measurements show that Splinter can process 3.5 million remote extension invocations per second with a median round-trip latency of less than 9 μs at densities of more than 1,000 tenants per server. We provide an implementation of Facebook’s TAO as an 800 line extension that, when pushed to a Splinter server, improves performance by 400 Kop/s to perform 3.2 Mop/s over online graph data with 30 μs remote access times.