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  1. We measure the performance of separately characterized machine learning-based EDFA models for predicting the optical power spectrum evolution in a 5-span system with six ROADM nodes deployed in the COSMOS testbed, which achieve a mean absolute error of 0.6–0.7 dB after 10 EDFAs under varying channel loading configurations. 
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    Free, publicly-accessible full text available January 1, 2024

    M 22 (NGC 6656) is a chemically complex globular cluster-like system reported to harbour heavy element abundance variations. However, the extent of these variations and the origin of this cluster is still debated. In this work, we investigate the chemical in-homogeneity of M 22 using differential line-by-line analysis of high-quality (R = 110 000, S/N  = 300 per pixel at 514 nm) VLT/UVES spectra of six carefully chosen red giant branch stars. By achieving abundance uncertainties as low as ∼0.01 dex (∼2 per cent), this high precision data validates the results of previous studies and reveals variations in Fe, Na, Si, Ca, Sc, Ti, Cr, Mn, Co, Ni, Zn, Y, Zr, La, Ce, Nd, Sm, and Eu. Additionally, we can confirm that the cluster hosts two stellar populations with a spread of at least 0.24 dex in [Fe/H] and an average s-process abundance spread of 0.65 dex. In addition to global variations across the cluster, we also find non-negligible variations within each of the two populations, with the more metal-poor population hosting larger spreads in elements heavier than Fe than the metal-rich. We address previous works that do not identify anomalous abundances and relate our findings to our current dynamical understanding of the cluster. Given our results, we suggest that M 22 is either a nuclear star cluster, the product of two merged clusters, or an original building block of the Milky Way.

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  3. While current vision algorithms excel at many challenging tasks, it is unclear how well they understand the physical dynamics of real-world environments. Here we introduce Physion, a dataset and benchmark for rigorously evaluating the ability to predict how physical scenarios will evolve over time. Our dataset features realistic simulations of a wide range of physical phenomena, including rigid and soft-body collisions, stable multi-object configurations, rolling, sliding, and projectile motion, thus providing a more comprehensive challenge than previous benchmarks. We used Physion to benchmark a suite of models varying in their architecture, learning objective, input-output structure, and training data. In parallel, we obtained precise measurements of human prediction behavior on the same set of scenarios, allowing us to directly evaluate how well any model could approximate human behavior. We found that vision algorithms that learn object-centric representations generally outperform those that do not, yet still fall far short of human performance. On the other hand, graph neural networks with direct access to physical state information both perform substantially better and make predictions that are more similar to those made by humans. These results suggest that extracting physical representations of scenes is the main bottleneck to achieving human-level and human-like physical understanding in vision algorithms. We have publicly released all data and code to facilitate the use of Physion to benchmark additional models in a fully reproducible manner, enabling systematic evaluation of progress towards vision algorithms that understand physical environments as robustly as people do. 
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  5. We introduce ThreeDWorld (TDW), a platform for interactive multi-modal physical simulation. TDW enables the simulation of high-fidelity sensory data and physical interactions between mobile agents and objects in rich 3D environments. Unique properties include: real-time near-photo-realistic image rendering; a library of objects and environments, and routines for their customization; generative procedures for efficiently building classes of new environments; high-fidelity audio rendering; realistic physical interactions for a variety of material types, including cloths, liquid, and deformable objects; customizable avatars that embody AI agents; and support for human interactions with VR devices. TDW's API enables multiple agents to interact within a simulation and returns a range of sensor and physics data representing the state of the world. We present initial experiments enabled by TDW in emerging research directions in computer vision, machine learning, and cognitive science, including multi-modal physical scene understanding, physical dynamics predictions, multi-agent interactions, models that 'learn like a child', and attention studies in humans and neural networks. 
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  6. A bstract Charged-lepton-flavor-violation is predicted in several new physics scenarios. We update the analysis of τ lepton decays into a light charged lepton ( ℓ = e ± or μ ± ) and a vector meson ( V 0 = ρ 0 , ϕ , ω , K *0 , or $$ \overline{K} $$ K ¯ *0 ) using 980 fb − 1 of data collected with the Belle detector at the KEKB collider. No significant excess of such signal events is observed, and thus 90% credibility level upper limits are set on the τ → ℓV 0 branching fractions in the range of (1.7–4 . 3) × 10 − 8 . These limits are improved by 30% on average from the previous results. 
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    Free, publicly-accessible full text available June 1, 2024
  7. Free, publicly-accessible full text available June 1, 2024
  8. Free, publicly-accessible full text available June 1, 2024