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  1. Free, publicly-accessible full text available May 14, 2024
  2. Implicit neural representations (INRs) have recently advanced numerous vision-related areas. INR performance depends strongly on the choice of activation function employed in its MLP network. A wide range of nonlinearities have been explored, but, unfortunately, current INRs designed to have high accuracy also suffer from poor robustness (to signal noise, parameter variation, etc.). Inspired by harmonic analysis, we develop a new, highly accurate and robust INR that does not exhibit this tradeoff. Our Wavelet Implicit neural REpresentation (WIRE) uses as its activation function the complex Gabor wavelet that is well-known to be optimally concentrated in space–frequency and to have excellent biases for representing images. A wide range of experiments (image denoising, image inpainting, super-resolution, computed tomography reconstruction, image overfitting, and novel view synthesis with neural radiance fields) demonstrate that WIRE defines the new state of the art in INR accuracy, training time, and robustness. 
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  3. Implicit neural representations (INRs) have recently advanced numerous vision-related areas. INR performance depends strongly on the choice of activation function employed in its MLP network. A wide range of nonlinearities have been explored, but, unfortunately, current INRs designed to have high accuracy also suffer from poor robustness (to signal noise, parameter variation, etc.). Inspired by harmonic analysis, we develop a new, highly accurate and robust INR that does not exhibit this tradeoff. Our Wavelet Implicit neural REpresentation (WIRE) uses as its activation function the complex Gabor wavelet that is well-known to be optimally concentrated in space–frequency and to have excellent biases for representing images. A wide range of experiments (image denoising, image inpainting, super-resolution, computed tomography reconstruction, image overfitting, and novel view synthesis with neural radiance fields) demonstrate that WIRE defines the new state of the art in INR accuracy, training time, and robustness. 
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  4. Engineering in early education provides the foundation for the future of innovation. Reinforcing learning and engineering habits of mind (HoM) at an early age is crucial for expanding students’ higher order thinking, potential for lifelong learning, and sense of agency in their learning experiences. HoM is defined as a set of learned or internalized dispositions that inform an individual's behaviors when confronted with challenges. This study addressed two research questions: (1) Which HoM were articulated by children as they reflected upon their participation in a home-based engineering program? (2) What patterns of the children’s vocabulary align with the HoM framework? Observational methods were used to examine young children’s reflections upon the process of completing low-stakes engineering projects in their home. The participants were 23 children ranging from kindergarten to eighth grade. After they engaged in the ill-structured engineering tasks with family members at home, children joined an online show-and-tell meeting to show their prototype to others while answering various questions about their processes, frustrations, and successes. Findings revealed “Resourcefulness,” “Adapting/Improving,” and “Systems Thinking” as the most common HoM expressed by children through the show-and-tell meetings. Additional analysis also highlighted how children's articulation of learning and engineering habits of mind were logical (i.e., analytical), confident (i.e., clout), and impersonal. Moreover, children’s words were product oriented, predominantly focusing on the materials and tools utilized to create their prototype. The significance of this study highlights how engaging in hands-on engineering projects in the home has the potential to develop children’s dispositions and ways of thinking common to engineers. 
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  5. null (Ed.)
    Cross-modal retrieval aims to learn discriminative and modal-invariant features for data from different modalities. Unlike the existing methods which usually learn from the features extracted by offline networks, in this paper, we pro- pose an approach to jointly train the components of cross- modal retrieval framework with metadata, and enable the network to find optimal features. The proposed end-to-end framework is updated with three loss functions: 1) a novel cross-modal center loss to eliminate cross-modal discrepancy, 2) cross-entropy loss to maximize inter-class variations, and 3) mean-square-error loss to reduce modality variations. In particular, our proposed cross-modal center loss minimizes the distances of features from objects belonging to the same class across all modalities. Extensive experiments have been conducted on the retrieval tasks across multi-modalities including 2D image, 3D point cloud and mesh data. The proposed framework significantly outperforms the state-of-the-art methods for both cross-modal and in-domain retrieval for 3D objects on the ModelNet10 and ModelNet40 datasets. 
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  6. The double-spin-polarization observable E for γ p → pπ0 has been measured with the CEBAF Large Acceptance Spectrometer (CLAS) at photon beam energies Eγ from 0.367 to 2.173 GeV (corresponding to center-ofmass energies from 1.240 to 2.200 GeV) for pion center-ofmass angles, cos θc.m. π0 , between − 0.86 and 0.82. These new CLAS measurements cover a broader energy range and have smaller uncertainties compared to previous CBELSA data and provide an important independent check on systematics. These measurements are compared to predictions as well as new global fits from The George Washington University, Mainz, and Bonn-Gatchina groups. Their inclusion in multipole analyses will allow us to refine our understanding of the single-pion production contribution to the Gerasimov-Drell- Hearn sum rule and improve the determination of resonance properties, which will be presented in a future publication. 
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    Free, publicly-accessible full text available September 1, 2024
  7. Abstract Accurate extreme ultraviolet spectra of open N -shell neodymium (Nd) ions were recorded at the electron beam ion trap facility of the National Institute of Standards and Technology. The measurements were performed for nominal electron beam energies in the range of 0.90 keV to 2.31 keV. The measured spectra were then compared with the spectra simulated by a collisional-radiative model utilizing atomic data produced with a fully relativistic atomic structure code. Consequently, 59 lines from Br-like to Ni-like Nd ions were unambiguously identified, most of which were newly assigned in this study. The wavelengths of 9 known lines from Ni-, Cu- and Zn-like Nd ions were in excellent agreement with previous measurements. 
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