Palo Alto Research Center,Xerox (United States)

The Winograd Schema Challenge - a set of twin sentences involving pronoun reference disambiguation that seem to require the use of commonsense knowledge - was proposed by Hector Levesque in 2011. By 2019, a number of AI systems, based on large pre-trained transformer-based language models and fine-tuned on these kinds of problems, achieved better than 90% accuracy. In this paper, we review the history of the Winograd Schema Challenge and discuss the lasting contributions of the flurry of research that has taken place on the WSC in the last decade. We discuss the significance of various datasets developed for WSC, and the research community's deeper understanding of the role of surrogate tasks in assessing the intelligence of an AI system.

In this paper, we propose PATO-a producibility-aware topology optimization (TO) framework to help efficiently explore the design space of components fabricated using metal additive manufacturing (AM), while ensuring manufacturability with respect to cracking. Specifically, parts fabricated through Laser Powder Bed Fusion are prone to defects such as warpage or cracking due to high residual stress values generated from the steep thermal gradients produced during the build process. Maturing the design for such parts and planning their fabrication can span months to years, often involving multiple handoffs between design and manufacturing engineers. PATO is based on the a priori discovery of crack-free designs, so that the optimized part can be built defect-free at the outset. To ensure that the design is crack free during optimization, producibility is explicitly encoded within the standard formulation of TO, using a crack index. Multiple crack indices are explored and using experimental validation, maximum shear strain index (MSSI) is shown to be an accurate crack index. Simulating the build process is a coupled, multi-physics computation and incorporating it in the TO loop can be computationally prohibitive. We leverage the current advances in deep convolutional neural networks and present a high-fidelity surrogate model based on an Attention-based U-Net architecture to predict the MSSI values as a spatially varying field over the part's domain. Further, we employ automatic differentiation to directly compute the gradient of maximum MSSI with respect to the input design variables and augment it with the performance-based sensitivity field to optimize the design while considering the trade-off between weight, manufacturability, and functionality. We demonstrate the effectiveness of the proposed method through benchmark studies in 3D as well as experimental validation.

Wireless Smart Sensor Networks (WSSN) have seen significant advancements in recent years. They act as a core part of structural health monitoring (SHM) systems by facilitating efficient measurement, assessment, and hence maintenance of civil infrastructure. This paper presents the latest technology developments of WSSN in the last ten years, including ones for a single sensor node and those for a network of nodes. Focus is placed on critical aspects of such advancements, including event-triggered sensing, multimeric sensing, edge/cloud computing, time synchronization, real-time data acquisition, decentralized data processing, and long-term reliability. In addition, full-scale applications and demonstrations of WSSN in SHM are also summarized. Finally, the remaining challenges and future research directions of WSSN are discussed to promote the further development and applications.

Additive manufacturing (AM) has significant utility for off-planet fabrication where dedicated infrastructure is severely limited, weight reduction and in situ resource utilization is desirable, and demands for complex systems are high. Direct ink writing (DIW) is a useful AM technique since it enables the deposition of a broad set of materials and the co-printing of multiple materials simultaneously. This allows for the fabrication of complex functional devices and systems in addition to structural objects. To evaluate this technique for space applications, this study characterized DIW in low gravity environments. Parabolic flights were used to simulate Martian, Lunar, and Micro gravity, and the effects that these 3 gravity regimes have on two critical print performance parameters, drooping and slumping, was evaluated using viscous paste inks deposited with an auger-driven extrusion head. In the drooping case, bridging structures were printed across gaps without support material, and the deformation was monitored. In the slumping case, a wall was printed through sequential layer deposition, and the vertical displacement of each layer under reduced gravity was explored. As expected, we found that a reduction in apparent gravity led to a decrease in the droop of a printed line, and as apparent gravitational acceleration is decreased its impact on the magnitude of drooping becomes less significant. For wall structures printed in simulated Lunar or Martian gravity regimes, the total structure height was found to be similar to that of a structure printed under Earth's gravitational conditions. In contrast, for prints performed in microgravity, it was found that slumping was significantly reduced and structure height was larger. These results provide experimental data to enable the design and optimization of appropriate structures and tool paths for printing objects using DIW for off-planet manufacturing.

Generative Artificial Intelligence (AI) models have risen to prominence due to their unparalleled ability to craft and generate digital assets, encompassing text, images, audio, video, and 3D models. Leveraging the capabilities of diffusion models, such as Stable Diffusion and Instruct pix2pix, users can guide AI with specific prompts, streamlining the creative journey for graphic designers. However, the primary application of these models has been to graphic content within desktop interfaces, prompting professionals in interior and architectural design to seek more tailored solutions for their daily operations. To bridge this gap, Augmented Reality (AR) and Mixed Reality (MR) technologies offer a promising solution, transforming traditional 2D artworks into engaging 3D interactive realms. In this paper, we present "Dream Mesh," a MR application MR tool that combines a Speech-to-3D generative workflow besed on DreamFusion model without relying on pre-existing 3D content libraries. This innovative system empowers users to express 3D content needs through natural language input, promising transformative potential in real-time 3D content creation and an enhanced MR user experience.