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Abstract Feedback from active galactic nuclei is a key process in the evolution of massive halos in the Universe. New observational information on feedback is crucial for improving the implementation of the physics in numerical models. In this work, we apply a novel image-manipulation technique, termed “X-arithmetic,” to a sample of 15 galaxy clusters and groups deeply observed with Chandra. This technique decomposes perturbations in feedback-dominated regions into images excluding either (1) weak shocks and sound waves, (2) bubbles inflated by jets, or (3) cooling and slow gas motions (isobaric perturbations), enabling efficient spatial identification of these features without involving spectroscopic analysis. We confirm the nature of previously (spectroscopically) identified features and newly establish the origin of other structures. We find that feedback produces multiple shocks in groups and massive galaxies, but only one to two shocks in clusters. Prominent isobaric structures are abundant around inner cavities in clusters, compared to almost no such structures in groups. These differences suggest that feedback effects are stronger in smaller-mass systems, possibly due to the shallower gravitational potential of groups or more violent feedback. Follow-up spectroscopy, guided by the X-arithmetic results, suggests that earlier-identified “isothermal shocks” could be a mix of isobaric and adiabatic structures. We applied X-arithmetic to galaxy cluster simulations, demonstrating its straightforward application and future potential for testing the feedback physics details in simulations. Our feasibility study shows that imaging data from future X-ray observatories like AXIS will be ideal for expanding X-arithmetic application to a larger sample of objects. 

Abstract The dynamics of star-forming gas can be affected by many physical processes, such as turbulence, gravity, supernova explosions, and magnetic fields. In this paper, we investigate several nearby star-forming regions (Orion, Upper Sco, Taurus, and Perseus) for kinematic imprints of these influences on the newly formed stars. Using Gaia DR3 astrometry and APOGEE DR17 radial velocities, we compute first-order velocity structure functions (VSFs) of young stars in galactic Cartesian coordinates in both 6D (3D positions and 3D velocities) and 4D (3D positions and each 1D velocity) to identify signatures of turbulence and anisotropic motion. We also construct 3D and 1D radial velocity profiles to identify coherent expansion trends, and compare stellar proper motions to plane-of-sky magnetic field orientations in Taurus and Perseus. We find that the VSFs are mildly anisotropic, with slightly different amplitudes, slopes, or features in different directions in several groups, but in general, they are all consistent with Larson’s Relation at intermediate length scales, especially in less compact groups. In several cases, the VSFs exhibit features suggestive of local energy injection from supernovae. Radial velocity profiles reveal clear anisotropic expansion in multiple groups, with the most extreme cases corresponding to those with the most anisotropic VSFs. In Perseus, we find that the motions of young stars are preferentially perpendicular to the local magnetic field. We find multiple, overlapping causes in each group for the observed kinematics. Our findings support that young stars remember more than just the turbulent state of their natal clouds. 

Significant advancements have occurred in the application of Large Language Models (LLMs) for social simulations. Despite this, their abilities to perform teaming in task-oriented social events are underexplored. Such capabilities are crucial if LLMs are to effectively mimic human-like social behaviors and form efficient teams to solve tasks. To bridge this gap, we introduce MetaAgents, a social simulation framework populated with LLM-based agents. MetaAgents facilitates agent engagement in conversations and a series of decision making within social contexts, serving as an appropriate platform for investigating interactions and interpersonal decision-making of agents. In particular, we construct a job fair environment as a case study to scrutinize the team assembly and skill-matching behaviors of LLM-based agents. We take advantage of both quantitative metrics evaluation and qualitative text analysis to assess their teaming abilities at the job fair. Our evaluation demonstrates that LLM-based agents perform competently in making rational decisions to develop efficient teams. However, we also identify limitations that hinder their effectiveness in more complex team assembly tasks. Our work provides valuable insights into the role and evolution of LLMs in task-oriented social simulations. 

The growing computational demands of deep learning have driven interest in analog neural networks using resistive memory and silicon photonics. However, these technologies face inherent limitations in computing parallelism when used independently. Photonic phase-change memory (PCM), which integrates photonics with PCM, overcomes these constraints by enabling simultaneous processing of multiple inputs encoded on different wavelengths, significantly enhancing parallel computation for deep neural network (DNN) inference and training. This paper presents MERIT, a sustainable DNN accelerator that capitalizes on the non-volatility of resistive memory and the high operating speed of photonic devices. MERIT enables seamless inference and training by loading weight kernels into photonic PCM arrays and selectively supplying light encoded with input features for the forward pass and loss gradients for the backward pass. We compare MERIT with state-of-the-art digital and analog DNN accelerators including TPU, DEAP, and PTC. Simulation results demonstrate that MERIT reduces execution time by 68% and energy consumption by 64% for inference, and reduces execution time by 79% and energy consumption by 84% for training. 

Hydroxyapatite (HAP) exhibits a highly oriented hierarchical structure in biological hard tissues. The formation and selective crystalline orientation of HAP is a process that involves functional biomineralization proteins abundant in acidic residues. To obtain insights into the process of HAP mineralization and acidic residue binding, synthesized HAP with specific lattice planes including (001), (100), and (011) are structurally characterized following the adsorption of aspartic acid (Asp). The adsorption affinity of Asp on HAP surfaces is evaluated quantitatively and demonstrates a high dependency on the HAP morphological form. Among the synthesized HAP nanoparticles (NPs), Asp exhibits the strongest adsorption affinity to short HAP nanorods, which are composed of (100) and (011) lattice planes, followed by nanosheets with a preferential expression of the (001) facet, to which Asp displays a similar but slightly lower binding affinity. HAP nanowires, with the (100) lattice plane preferentially developed, show significantly lower affinity to Asp and evidence of multilayer adsorption compared to the previous two types of HAP NPs. A combination of solid-state NMR (SSNMR) techniques including 13C and 15N CP-MAS, relaxation measurements and 13C−31P Rotational Echo DOuble Resonance (REDOR) is utilized to characterize the molecular structure and dynamics of Asp-HAP bionano interfaces with 13C- and 15N-enriched Asp. REDOR is used to determine 13C−31P internuclear distances, providing insight into the Asp binding geometry where stronger 13C−31P dipolar couplings correlate with binding affinity determined from Langmuir isotherms. The carboxyl sites are identified as the primary binding groups, facilitated by their interaction with surface calcium sites. The Asp chelation conformations revealed by SSNMR are further refined with molecular dynamics (MD) simulation where specific models strongly agree between the SSNMR and MD models for the various surfaces.