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  1. 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,more »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.« less
  2. Free, publicly-accessible full text available May 1, 2023
  3. A bstract We present a search for the charged lepton-flavor-violating decays ϒ(1 S ) → ℓ ± ℓ ′ ∓ and radiative charged lepton-flavour-violating decays ϒ(1 S ) → γ ℓ ± ℓ ′ ∓ [ ℓ , ℓ ′ = e, μ, τ ] using the 158 million ϒ(2 S ) sample collected by the Belle detector at the KEKB collider. This search uses ϒ(1 S ) mesons produced in ϒ(2 S ) → π + π − ϒ(1 S ) transitions. We do not find any significant signal, so we provide upper limits on the branching fractions atmore »the 90% confidence level.« less
    Free, publicly-accessible full text available May 1, 2023
  4. A bstract We present the first measurement of the branching fraction of the singly Cabibbo-suppressed (SCS) decay $$ {\Lambda}_c^{+} $$ Λ c + → pη ′ with η ′ → ηπ + π − , using a data sample corresponding to an integrated luminosity of 981 fb − 1 , collected by the Belle detector at the KEKB e + e − asymmetric-energy collider. A significant $$ {\Lambda}_c^{+} $$ Λ c + → pη ′ signal is observed for the first time with a signal significance of 5.4 σ . The relative branching fraction with respect to the normalization modemore »$$ {\Lambda}_c^{+} $$ Λ c + → pK − π + is measured to be $$ \frac{\mathcal{B}\left({\Lambda}_c^{+}\to p\eta^{\prime}\right)}{\mathcal{B}\left({\Lambda}_c^{+}\to {pK}^{-}{\pi}^{+}\right)}=\left(7.54\pm 1.32\pm 0.73\right)\times {10}^{-3}, $$ B Λ c + → pη ′ B Λ c + → pK − π + = 7.54 ± 1.32 ± 0.73 × 10 − 3 , where the uncertainties are statistical and systematic, respectively. Using the world-average value of $$ \mathcal{B}\left({\Lambda}_c^{+}\to {pK}^{-}{\pi}^{+}\right) $$ B Λ c + → pK − π + = (6 . 28 ± 0 . 32) × 10 − 2 , we obtain $$ \mathcal{B}\left({\Lambda}_c^{+}\to p\eta^{\prime}\right)=\left(4.73\pm 0.82\pm 0.46\pm 0.24\right)\times {10}^{-4}, $$ B Λ c + → pη ′ = 4.73 ± 0.82 ± 0.46 ± 0.24 × 10 − 4 , where the uncertainties are statistical, systematic, and from $$ \mathcal{B}\left({\Lambda}_c^{+}\to {pK}^{-}{\pi}^{+}\right) $$ B Λ c + → pK − π + , respectively.« less
    Free, publicly-accessible full text available March 1, 2023
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