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Abstract We infer the growth of large scale structure over the redshift range 0.4 ≲z≲ 1 from the cross-correlation of spectroscopically calibrated Luminous Red Galaxies (LRGs) selected from the Dark Energy Spectroscopic Instrument (DESI) legacy imaging survey with CMB lensing maps reconstructed from the latestPlanckand ACT data.We adopt a hybrid effective field theory (HEFT) model that robustly regulates the cosmological information obtainable from smaller scales, such that our cosmological constraints are reliably derived from the (predominantly) linear regime.We perform an extensive set of bandpower- and parameter-level systematics checks to ensure the robustness of our results and to characterize the uniformity of the LRG sample.We demonstrate that our results are stable to a wide range of modeling assumptions, finding excellent agreement with a linear theory analysis performed on a restricted range of scales.From a tomographic analysis of the four LRG photometric redshift bins we find that the rate of structure growth is consistent with ΛCDM with an overall amplitude that is ≃ 5-7% lower than predicted by primary CMB measurements with modest (∼ 2σ) statistical significance.From the combined analysis of all four bins and their cross-correlations withPlanckwe obtainS₈= 0.765 ± 0.023, which is less discrepant with primary CMB measurements than previous DESI LRG crossPlanckCMB lensing results.From the cross-correlation with ACT we obtainS₈= 0.790^+0.024_-0.027, while when jointly analyzingPlanckand ACT we findS₈= 0.775^+0.019_-0.022from our data alone andσ₈= 0.772^+0.020_-0.023with the addition of BAO data.These constraints are consistent with the latestPlanckprimary CMB analyses at the ≃ 1.6-2.2σlevel, and are in excellent agreement with galaxy lensing surveys. 

Abstract Poststarburst galaxies (PSBs) are young quiescent galaxies that have recently experienced a rapid decrease in star formation, allowing us to probe the fast-quenching period of galaxy evolution. In this work, we obtained Hubble Space Telescope (HST)/WFC3 F110W imaging to measure the sizes of 171 massive ( $\log (M_{*}/M_{\odot })\sim 11)$spectroscopically identified PSBs at 1 <z1.3 selected from the DESI Survey Validation luminous red galaxy sample. This statistical sample constitutes an order of magnitude increase from the ∼20 PSBs with space-based imaging and deep spectroscopy. We perform structural fitting of the target galaxies withpysersicand compare them to quiescent and star-forming galaxies in the 3D-HST survey. We find that these PSBs are more compact than the general population of quiescent galaxies, lying systematically ∼0.1 dex below the established size–mass relation. However, their central surface mass densities are similar to those of their quiescent counterparts ( $\log \left({\mathrm{\Sigma }}_{1\mathrm{kpc}}/\right(M_{\odot }{\mathrm{kpc}}^{-2}\left)\right)\sim 10.1$). These findings are easily reconciled by later ex situ growth via minor mergers or a slight progenitor bias. These PSBs are round in projection (b/a_median∼ 0.8), suggesting that they are primarily spheroids, not disks, in 3D. We find no correlation between the time since quenching and light-weighted PSB sizes or central densities. This disfavors apparent structural growth due to the fading of centralized starbursts in this galaxy population. Instead, we posit that the fast quenching of massive galaxies at this epoch occurs preferentially in galaxies with preexisting compact structures. 

The design of heterogeneous systems that include domain specific accelerators is a challenging and time-consuming process. While taking into account area constraints, designers must decide which parts of an application to accelerate in hardware and which to leave in software. Moreover, applications in domains such as Extended Reality (XR) offer opportunities for various forms of parallel execution, including loop level, task level and pipeline parallelism. To assist the design process and expose every possible level of parallelism, we present Trireme , a fully automated tool-chain that explores multiple levels of parallelism and produces domain specific accelerator designs and configurations that maximize performance, given an area budget. FPGA SoCs were used as target platforms and Catapult HLS [7] was used to synthesize RTL using a commercial 12nm FinFET technology. Experiments on demanding benchmarks from the XR domain revealed a speedup of up to 20 ×, as well as a speedup of up to 37 × for smaller applications, compared to software-only implementations. 

Large language models have substantially advanced nuance and context understanding in natural language processing (NLP), further fueling the growth of intelligent conversational interfaces and virtual assistants. However, their hefty computational and memory demands make them potentially expensive to deploy on cloudless edge platforms with strict latency and energy requirements. For example, an inference pass using the state-of-the-art BERT-base model must serially traverse through 12 computationally intensive transformer layers, each layer containing 12 parallel attention heads whose outputs concatenate to drive a large feed-forward network. To reduce computation latency, several algorithmic optimizations have been proposed, e.g., a recent algorithm dynamically matches linguistic complexity with model sizes via entropy-based early exit. Deploying such transformer models on edge platforms requires careful co-design and optimizations from algorithms to circuits, where energy consumption is a key design consideration.