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Context.Massive stars have an impact on their surroundings from early in their formation until the end of their lives. However, very little is known about their formation. Episodic accretion may play a crucial role in the process, but only a handful of observations have reported such events occurring in massive protostars.

Aims.We aim to investigate the outburst event from the high-mass star-forming region S255IR where the protostar NIRS3 recently underwent an accretion outburst. We follow the evolution of this source both in photometry and morphology of its surroundings.

Methods.We performed near infrared adaptive optics observations on the S255IR central region using the Large Binocular Telescope in theK_sbroadband as well as the H₂and Brγ narrow-band filters with an angular resolution of ~07″.06, close to the diffraction limit.

Results.We discovered a new near infrared knot north-east of NIRS3 that we interpret as a jet knot that was ejected during the last accretion outburst and observed in the radio regime as part of a follow-up after the outburst. We measured a mean tangential velocity for this knot of 450 ± 50 km s⁻¹. We analysed the continuum-subtracted images from H₂, which traces jet-shocked emission, and Brγ, which traces scattered light from a combination of accretion activity and UV radiation from the central massive protostar. We observed a significant decrease in flux at the location of NIRS3, withK= 13.48 mag being the absolute minimum in the historic series.

Conclusions.Our observations strongly suggest a scenario where the episodic accretion is followed by an episodic ejection response in the near infrared, as was seen in the earlier radio follow-up. The ~2 µm photometry from the past 30 yr suggests that NIRS3 might have undergone another outburst in the late 1980s, making it the first massive protostar with such evidence observed in the near infrared.

 

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. 

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. 

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. 

Measuring deeply virtual Compton scattering (DVCS) on the neutron is one of the necessary steps to understand the structure of the nucleon in terms of generalized parton distributions (GPDs). Neutron targets play a complementary role to transversely polarized proton targets in the determination of the GPD$E$. This poorly known and poorly constrained GPD is essential to obtain the contribution of the quarks’ angular momentum to the spin of the nucleon. DVCS on the neutron was measured for the first time selecting the exclusive final state by detecting the neutron, using the Jefferson Lab longitudinally polarized electron beam, with energies up to 10.6 GeV, and the CLAS12 detector. The extracted beam-spin asymmetries, combined with DVCS observables measured on the proton, allow a clean quark-flavor separation of the imaginary parts of the Compton form factors$\mathcal{H}$and$\mathcal{E}$.

Published by the American Physical Society2024 

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.