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Context. We have studied the dense gas morphology and kinematics towards the infrared dark cloud (IRDC) G034.77-00.55, shock-interacting with the SNR W44, to identify evidence of early-stage star formation induced by the shock. Aims. We used high angular resolution N₂H⁺(1−0) images across G034.77-00.55, obtained with the Atacama Large Millimeter/sub-Millimeter Array. N₂H⁺is a well-known tracer of dense and cold material, optimal for identifying gas that has the highest potential to harbour star formation. Methods. The N₂H⁺emission is distributed in two elongated structures, one towards the dense ridge at the edge of the source and one towards the inner cloud. Both elongations are spatially associated with well-defined mass-surface density features. The velocities of the gas in the two structures (i.e. 38–41 km s⁻¹and 41–43 km s⁻¹) are consistent with the lowest velocities of the J- and C-type parts, respectively, of the SNR-driven shock. A third velocity component is present at 43–45.5 km s^–1. The dense gas shows a fragmented morphology with core-like fragments at scales consistent with the Jeans lengths, masses of ~1–20 M_⊙, densities of (n(H₂)≥10⁵cm^–3) sufficient to host star formation in free-fall timescales (a few 10⁴yr), and with virial parameters that suggest a possible collapse. Results. The W44 driven shock may have swept up the encountered material, which is now seen as a dense ridge, almost detached from the main cloud, and an elongation within the inner cloud, well constrained in both N₂H⁺emission and mass surface density. This shock compressed material may have then fragmented into cores that are either in a starless or pre-stellar stage. Additional observations are needed to confirm this scenario and the nature of the cores. 

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. 

Context.Supernova remnants (SNRs) may regulate star formation in galaxies. For example, SNR-driven shocks may form new molecular gas or compress pre-existing clouds and trigger the formation of new stars. Aims.To test this scenario, we measured the deuteration of N₂H⁺, D_frac^N₂H+– a well-studied tracer of pre-stellar cores – across the infrared-dark cloud (IRDC) G034.77-00.55, which is known to be experiencing a shock interaction with the SNR W44. Methods.We use N₂H⁺and N₂D⁺J= 1−0 single pointing observations obtained with the 30m antenna at the Instituto de Radioas-tronomia Millimetrica to infer D_frac^N₂H+towards five positions across the cloud, namely a massive core, different regions across the shock front, a dense clump, an₊d ambient gas. Results.We find D_frac^N₂H+in the range 0.03−0.1, which is several orders of magnitude larger than the cosmic D/H ratio (~10⁻⁵). The D_frac^N₂H+across the shock front is enhanced by more than a factor of 2 (D_frac^N₂H+~ 0.05 - 0.07) with respect to the ambient gas (≤0.03) and simila₊r to that measured generally in pre-stellar cores. Indeed, in the massive core and dense clump regions of this IRDC we measure D_frac^N₂H+~ 0.01. Conclusions.We find enhanced deuteration of N₂H⁺across the region of the shock, that is, at a level that is enhanced with respect to regions of unperturbed gas. It is possible that this has been induced by shock compression, which would then be indirect evidence that the shock is triggering conditions for future star formation. However, since unperturbed dense regions also show elevated levels of deuteration, further, higher-resolution studies are needed to better understand the structure and kinematics of the deuterated material in the shock region; for example, to decipher whether it is still in a relatively diffuse form or is already organised in a population of low-mass pre-stellar cores. 

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.