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We explore the cosmic evolution of the fraction of dust-obscured star formation predicted by thesimbacosmological hydrodynamic simulations featuring an on-the-fly model for dust formation, evolution, and destruction. We find that up toz= 3, our results are broadly consistent with previous observational results of little to no evolution in obscured star formation. However, atz> 3 we find strong evolution at fixed galaxy stellar mass toward greater amounts of obscured star formation, in tension with high-redshift observations. We explain the trend of increasing obscuration at higher redshifts by evolving star-dust geometry, as the dust-to-stellar mass ratios remain relatively constant across cosmic time. We additionally see that at a fixed redshift, more massive galaxies have a higher fraction of their star formation obscured, which is explained by increased dust-to-stellar mass ratios at higher stellar masses. Finally, we estimate the contribution of dust-obscured star formation to the total star formation rate budget and find that the dust-obscured star formation history peaks aroundz∼ 2−3, and becomes subdominant atz≳ 5. The dominance of obscured star formation at redshiftsz≲ 4 is consistent with our results for the evolution of the obscured star formation fraction at fixed stellar mass to higher values at higher redshift because there exist fewer massive, heavily obscured galaxies at high redshift.

 

We report the experimental demonstration of an optical differentiation wavefront sensor (ODWS) based on binary pixelated linear and nonlinear amplitude filtering in the far-field. We trained and tested a convolutional neural network that reconstructs the spatial phase map from nonlinear-filter-based ODWS data for which an analytic reconstruction algorithm is not available. It shows accurate zonal retrieval over different magnitudes of wavefronts and on randomly shaped wavefronts. This work paves the way for the implementation of simultaneously sensitive, high dynamic range, and high-resolution wavefront sensing.

 

Simulations are widely used to teach science in grade schools. These Ralph Knipper rak0035@auburn.edu Auburn University Auburn, Alabama, USA Sadhana Puntambekar puntambekar@education.wisc.edu University of Wisconsin-Madison Madison, Wisconsin, USA Large Language Models, Conversational AI, Meta-Conversation, simulations are often augmented with a conversational artificial intelligence (AI) agent to provide real-time scaffolding support for students conducting experiments using the simulations. AI agents are highly tailored for each simulation, with a predesigned set of Instructional Goals (IGs). This makes it difficult for teachers to adjust IGs as the agent may no longer align with the revised IGs. Additionally, teachers are hesitant to adopt new third-party simulations for the same reasons. In this research, we introduce SimPal, a Large Language Model (LLM) based meta-conversational agent, to solve this misalignment issue between a pre-trained conversational AI agent and the constantly evolving pedagogy of instructors. Through natural conversation with SimPal, teachers first explain their desired IGs, based on which SimPal identifies a set of relevant physical variables and their relationships to create symbolic representations of the desired IGs. The symbolic representations can then be leveraged to design prompts for the original AI agent to yield better alignment with the desired IGs. We empirically evaluated SimPal using two LLMs, ChatGPT-3.5 and PaLM 2, on 63 Physics simulations from PhET and Golabz. Additionally, we examined the impact of different prompting techniques on LLM’s performance by utilizing the TELeR taxonomy to identify relevant physical variables for the IGs. Our findings showed that SimPal can do this task with a high degree of accuracy when provided with a well-defined prompt. 

Recent research has made significant progress in text-to-image editing, yet numerous areas remain under explored. In this work, we propose a novel application in the culinary arts, leveraging diffusion models to adjust a range of dishes into a variety of cuisines. Our approach infuses each dish with unique twists representative of diverse culinary traditions and ingredient profiles. We introduce the Cuisine Transfer task and a comprehensive framework for its execution, along with a curated dataset comprising over 1600 unique food samples at the ingredient level. Additionally, we propose three Cuisine Transfer task specific metrics to accurately evaluate our method and address common failure scenarios in existing image editing techniques. Our evaluations demonstrate that our method significantly outperforms baseline models on the Cuisine Transfer task 

Two-dimensional QCD with Nc colors and Nf flavors of massless fermions in the fundamental representation is expected to exhibit conformal behavior in the infrared governed by a u(Nf) Wess–Zumino–Witten model with level Nc. Using numerical analysis within the lattice formalism with exactly massless overlap fermions, we show the emergence of such behavior in the infrared limit. Both the continuum extrapolated low-lying eigenvalues of the massless Dirac operator and the propagator of scalar mesons exhibit a flow from the ultraviolet to the infrared. We find that the amplitude of the conserved current correlator remains invariant under the flow, while the amplitude of the scalar correlator approaches Nf-independent values in the infrared.