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  1. Free, publicly-accessible full text available July 1, 2023
  2. We demonstrate the underlying mechanism for one version of quantum-enhanced telescopy, using multiple interconnected Hong-Ou-Mandel interferometers to re-cover the visibility amplitude of the source of light in the presence of arbitrary turbulence.
    Free, publicly-accessible full text available June 1, 2023
  3. Mobile Augmented Reality (AR) demands realistic rendering of virtual content that seamlessly blends into the physical environment. For this reason, AR headsets and recent smartphones are increasingly equipped with Time-of-Flight (ToF) cameras to acquire depth maps of a scene in real-time. ToF cameras are cheap and fast, however, they suffer from several issues that affect the quality of depth data, ultimately hampering their use for mobile AR. Among them, scale errors of virtual objects - appearing much bigger or smaller than what they should be - are particularly noticeable and unpleasant. This article specifically addresses these challenges by proposing InDepth, a real-time depth inpainting system based on edge computing. InDepth employs a novel deep neural network (DNN) architecture to improve the accuracy of depth maps obtained from ToF cameras. The DNN fills holes and corrects artifacts in the depth maps with high accuracy and eight times lower inference time than the state of the art. An extensive performance evaluation in real settings shows that InDepth reduces the mean absolute error by a factor of four with respect to ARCore DepthLab. Finally, a user study reveals that InDepth is effective in rendering correctly-scaled virtual objects, outperforming DepthLab.
    Free, publicly-accessible full text available March 1, 2023
  4. Humans are incredibly good at transferring knowledge from one domain to another, enabling rapid learning of new tasks. Likewise, transfer learning has enabled enormous success in many computer vision problems using pretraining. However, the benefits of transfer in multi-domain learning, where a network learns multiple tasks defined by different datasets, has not been adequately studied. Learning multiple domains could be beneficial, or these domains could interfere with each other given limited network capacity. Understanding how deep neural networks of varied capacity facilitate transfer across inputs from different distributions is a critical step towards open world learning. In this work, we decipher the conditions where interference and knowledge transfer occur in multi-domain learning. We propose new metrics disentangling interference and transfer, set up experimental protocols, and examine the roles of network capacity, task grouping, and dynamic loss weighting in reducing interference and facilitating transfer.
    Free, publicly-accessible full text available January 1, 2023
  5. Free, publicly-accessible full text available January 1, 2023
  6. Mobile Augmented Reality (AR), which overlays digital content on the real-world scenes surrounding a user, is bringing immersive interactive experiences where the real and virtual worlds are tightly coupled. To enable seamless and precise AR experiences, an image recognition system that can accurately recognize the object in the camera view with low system latency is required. However, due to the pervasiveness and severity of image distortions, an effective and robust image recognition solution for “in the wild” mobile AR is still elusive. In this article, we present CollabAR, an edge-assisted system that provides distortion-tolerant image recognition for mobile AR with imperceptible system latency. CollabAR incorporates both distortion-tolerant and collaborative image recognition modules in its design. The former enables distortion-adaptive image recognition to improve the robustness against image distortions, while the latter exploits the spatial-temporal correlation among mobile AR users to improve recognition accuracy. Moreover, as it is difficult to collect a large-scale image distortion dataset, we propose a Cycle-Consistent Generative Adversarial Network-based data augmentation method to synthesize realistic image distortion. Our evaluation demonstrates that CollabAR achieves over 85% recognition accuracy for “in the wild” images with severe distortions, while reducing the end-to-end system latency to as low as 18.2 ms.
    Free, publicly-accessible full text available February 1, 2023
  7. Abstract 316L stainless steel (316L SS) is a flagship material for structural applications in corrosive environments, having been extensively studied for decades for its favorable balance between mechanical and corrosion properties. More recently, 316L SS has also proven to have excellent printability when parts are produced with additive manufacturing techniques, notably laser powder bed fusion (LPBF). Because of the harsh thermo-mechanical cycles experienced during rapid solidification and cooling, LPBF processing tends to generate unique microstructures. Strong heterogeneities can be found inside grains, including trapped elements, nano-inclusions, and a high density of dislocations that form the so-called cellular structure. Interestingly, LPBF 316L SS not only exhibits better mechanical properties than its conventionally processed counterpart, but it also usually offers much higher resistance to pitting in chloride solutions. Unfortunately, the complexity of the LPBF microstructures, in addition to process-induced defects, such as porosity and surface roughness, have slowed progress toward linking specific microstructural features to corrosion susceptibility and complicated the development of calibrated simulations of pitting phenomena. The first part of this article is dedicated to an in-depth review of the microstructures found in LPBF 316L SS and their potential effects on the corrosion properties, with an emphasis on pitting resistance. Themore »second part offers a perspective of some relevant modeling techniques available to simulate the corrosion of LPBF 316L SS, including current challenges that should be overcome.« less
    Free, publicly-accessible full text available April 1, 2023
  8. Abstract Background Genes that belong to the same network are frequently co-expressed, but collectively, how the coordination of the whole transcriptome is perturbed during aging remains unclear. To explore this, we calculated the correlation of each gene in the transcriptome with every other, in the brain of young and older outbred deer mice (P. leucopus and P. maniculatus). Results In about 25 % of the genes, coordination was inversed during aging. Gene Ontology analysis in both species, for the genes that exhibited inverse transcriptomic coordination during aging pointed to alterations in the perception of smell, a known impairment occurring during aging. In P. leucopus, alterations in genes related to cholesterol metabolism were also identified. Among the genes that exhibited the most pronounced inversion in their coordination profiles during aging was THBS4, that encodes for thrombospondin-4, a protein that was recently identified as rejuvenation factor in mice. Relatively to its breadth, abolishment of coordination was more prominent in the long-living P. leucopus than in P. maniculatus but in the latter, the intensity of de-coordination was higher. Conclusions There sults suggest that aging is associated with more stringent retention of expression profiles for some genes and more abrupt changes in others, while more subtle but widespread changes in gene expression appear protective. Ourmore »findings shed light in the mode of the transcriptional changes occurring in the brain during aging and suggest that strategies aiming to broader but more modest changes in gene expression may be preferrable to correct aging-associated deregulation in gene expression.« less
    Free, publicly-accessible full text available December 1, 2022
  9. We explore how different elements of student persistence on computer programming problems may be related to learning outcomes and inform us about which elements may distinguish between productive and unproductive persistence. We collected data from an introductory computer science course at a large midwestern university in the U.S. hosted on an open-source, problem-driven learning system. We defined a set of features quantifying various aspect of persistence during problem solving and used a predictive modeling approach to predict student scores on subsequent and related quiz questions. We focused on careful feature engineering and model interpretation to shed light on the intricacies of both productive and unproductive persistence. Feature importance was analyzed using SHapley Additive exPlanations (SHAP) values. We found that the most impactful features were persisting until solving the problem, rapid guessing, and taking a break, while those with the strongest correlation between their values and their impact on prediction were the number of submissions, total time, and (again) taking a break. This suggests that the former are important features for accurate prediction, while the latter are indicative of the differences between productive persistence and wheel spinning in a computer science context.
    Free, publicly-accessible full text available November 19, 2022
  10. Camacho, Gabriela P (Ed.)
    Abstract The ant genus Nylanderia Emery has a cosmopolitan distribution and includes 150 extant described species and subspecies, with potentially hundreds more undescribed. Global taxonomic revision has long been stalled by strong intra- and interspecific morphological variation, limited numbers of diagnostic characters, and dependence on infrequently collected male specimens for species description and identification. Taxonomy is further complicated by Nylanderia being one of the most frequently intercepted ant genera at ports of entry worldwide, and at least 15 globetrotting species have widespread and expanding ranges, making species-level diagnoses difficult. Three species complexes (‘bourbonica complex’, ‘fulva complex’, and ‘guatemalensis complex’) include globetrotting species. To elucidate the phylogenetic positions of these three complexes and delimit species boundaries within each, we used target enrichment of ultraconserved elements (UCEs) from 165 specimens representing 98 Nylanderia morphospecies worldwide. We also phased the UCEs, effectively doubling sample size and increasing population-level sampling. After recovering strong support for the monophyly of each complex, we extracted COI barcodes and SNPs from the UCE data and tested within-complex morphospecies hypotheses using three molecular delimitation methods (SODA, bPTP, and STACEY). This comparison revealed that most methods tended to over-split taxa, but results from STACEY were most consistent with our morphospeciesmore »hypotheses. Using these results, we recommend species boundaries that are conservative and most congruent across all methods. This work emphasizes the importance of integrative taxonomy for invasive species management, as globetrotting occurs independently across at least nine different lineages across Nylanderia.« less
    Free, publicly-accessible full text available January 1, 2023