A metamorphic inorganic framework that can be switched between eight single-crystalline states

The design of highly flexible framework materials requires organic linkers, whereas inorganic materials are more robust but inflexible. Here, by using linkable inorganic rings made up of tungsten oxide (P8W48O184) building blocks, we synthesized an inorganic single crystal material that can undergo at least eight different crystal-to-crystal transformations, with gigantic crystal volume contraction and expansion changes ranging from −2,170 to +1,720 Å3 with no reduction in crystallinity. Not only does this material undergo the largest single crystal-to-single crystal volume transformation thus far reported (to the best of our knowledge), the system also shows conformational flexibility while maintaining robustness over several cycles in the reversible uptake and release of guest molecules switching the crystal between different metamorphic states. This material combines the robustness of inorganic materials with the flexibility of organic frameworks, thereby challenging the notion that flexible materials with robustness are mutually exclusive

Mixed reality application to support infrastructure maintenance

This paper presents a mixed reality (MR) application developed for the head-mounted display Microsoft HoloLens that supports infrastructure maintenance works in buildings with complex infrastructure. The solution is intended to help maintenance workers when they need to track and fix part of the infrastructure by revealing hidden infrastructure, displaying additional information and guiding workers in complex tasks. The application has the potential to improve maintenance worker's tasks as it can help them perform faster and with more accuracy. The work explores the creation of the application and discusses the methodologies used to build an optimal and user-friendly tool. The methodology is based on design science research: an improvement need, and not necessarily a problem, was identified, and from there a solution was conceived. MR has proven to be a major tool for helping in several areas, and this paper can give insights for many future solutions with mixed reality or HoloLens and help them build new and better applications to improve tasks at a job or at home.info:eu-repo/semantics/acceptedVersio

Uncovering cation disorder in ternary Zn1+xGe1−x(N1−xOx)2 and its effect on the optoelectronic properties

Ternary nitride materials, such as ZnGeN2, have been considered as hopeful optoelectronic materials with an emphasis on sustainability. Their nature as ternary materials has been ground to speculation of cation order/disorder as a mechanism to tune their bandgap. We herein studied the model system Zn1+xGe1−x(N1−xOx)2 including oxygen – which is often a contaminant in nitride materials – using a combination of X-ray and neutron diffraction combined with elemental analyses to provide direct experimental evidence for the existence of cation swapping in this class of materials. In addition, we combine our results with UV-VIS spectroscopy to highlight the influence of disorder on the optical bandgap

Differentiable weightless neural networks

We introduce the Differentiable Weightless Neural Network (DWN), a model based on interconnected lookup tables. Training of DWNs is enabled by a novel Extended Finite Difference technique for approximate differentiation of binary values. We propose Learnable Mapping, Learnable Reduction, and Spectral Regularization to further improve the accuracy and efficiency of these models. We evaluate DWNs in three edge computing contexts: (1) an FPGA-based hardware accelerator, where they demonstrate superior latency, throughput, energy efficiency, and model area compared to state-of-the-art solutions, (2) a low-power microcontroller, where they achieve preferable accuracy to XGBoost while subject to stringent memory constraints, and (3) ultralow-cost chips, where they consistently outperform small models in both accuracy and projected hardware area. DWNs also compare favorably against leading approaches for tabular datasets, with higher average rank. Overall, our work positions DWNs as a pioneering solution for edge-compatible high-throughput neural networks.info:eu-repo/semantics/acceptedVersio

Differentiable weightless neural networks

We introduce the Differentiable Weightless Neural Network (DWN), a model based on interconnected lookup tables. Training of DWNs is enabled by a novel Extended Finite Difference technique for approximate differentiation of binary values. We propose Learnable Mapping, Learnable Reduction, and Spectral Regularization to further improve the accuracy and efficiency of these models. We evaluate DWNs in three edge computing contexts: (1) an FPGA-based hardware accelerator, where they demonstrate superior latency, throughput, energy efficiency, and model area compared to state-of-the-art solutions, (2) a low-power microcontroller, where they achieve preferable accuracy to XGBoost while subject to stringent memory constraints, and (3) ultralow-cost chips, where they consistently outperform small models in both accuracy and projected hardware area. DWNs also compare favorably against leading approaches for tabular datasets, with higher average rank. Overall, our work positions DWNs as a pioneering solution for edge-compatible high-throughput neural networks.info:eu-repo/semantics/acceptedVersio

Efficient knowledge aggregation methods for weightless neural networks

Weightless Neural Networks (WNN) are good candidates for Federated Learning scenarios due to their robustness and computational lightness. In this work, we show that it is possible to aggregate the knowledge of multiple WNNs using more compact data structures, such as Bloom Filters, to reduce the amount of data transferred between devices. Finally, we explore variations of Bloom Filters and found that a particular data-structure, the Count-Min Sketch (CMS), is a good candidate for aggregation. Costing at most 3% of accuracy, CMS can be up to 3x smaller when compared to previous approaches, specially for large datasets.info:eu-repo/semantics/publishedVersio

Weightless neural networks as memory segmented bloom filters

Weightless Neural Networks (WNNs) are Artificial Neural Networks based on RAM memory broadly explored as solution for pattern recognition applications. Memory-oriented solutions for pattern recognition are typically very simple, and can be easily implemented in hardware and software. Nonetheless, the straightforward implementation of a WNN requires a large amount of memory resources making its adoption impracticable on memory constrained systems. In this paper, we establish a foundational relationship between WNN and Bloom filters, presenting a novel unified framework which encompasses the two. In particular, we indicate that a WNN can be framed as a memory segmented Bloom filter. Leveraging such finding, we propose a new model of WNNs which utilizes Bloom filters to implement RAM nodes. Bloom filters reduce memory requirements, and allow false positives when determining if a given pattern was already seen in data. We experimentally found that for pattern recognition purposes such false positives can build robustness into the system. The experimental results show that our model using Bloom filters achieves competitive accuracy, training time and testing time, consuming up to 6 orders of magnitude less memory resources when compared against the standard Weightless Neural Network model.info:eu-repo/semantics/acceptedVersio

Br-Induced Suppression of Low-Temperature Phase Transitions in Mixed-Cation Mixed-Halide Perovskites

Mixed-cation mixed-halide lead perovskites have been shown to be excellent candidates for solar energy conversion. However, understanding the structural phases of these mixed-ion perovskites across a wide range of operating temperatures, including very low temperatures for space applications, is crucial. In this study, we investigated the structure of formamidinium-based CsyFA1–yPb(BrxI1–x)3 using low-temperature in situ synchrotron powder X-ray diffraction. Our findings revealed that substituting the I anion with Br in mixed-cation (Cs,FA) perovskites suppressed the phase transformation from tetragonal to orthorhombic at low temperatures. The addition of Br also prevented the formation of nonperovskite secondary phases. We gained fundamental insights into the structural behavior of these materials by creating a low-temperature phase diagram for the compositional set of mixed-cation mixed-halides. This understanding of the structural properties lays the groundwork for designing more robust and efficient energy materials capable of functioning under extreme temperature conditions, including space-based solar energy conversion

Weightless neural networks for efficient edge inference

Weightless neural networks (WNNs) are a class of machine learning model which use table lookups to perform inference, rather than the multiply-accumulate operations typical of deep neural networks (DNNs). Individual weightless neurons are capable of learning non-linear functions of their inputs, a theoretical advantage over the linear neurons in DNNs, yet state-of-the-art WNN architectures still lag behind DNNs in accuracy on common classification tasks. Additionally, many existing WNN architectures suffer from high memory requirements, hindering implementation. In this paper, we propose a novel WNN architecture, BTHOWeN, with key algorithmic and architectural improvements over prior work, namely counting Bloom filters, hardware-friendly hashing, and Gaussian-based nonlinear thermometer encodings. These enhancements improve model accuracy while reducing size and energy per inference. BTHOWeN targets the large and growing edge computing sector by providing superior latency and energy efficiency to both prior WNNs and comparable quantized DNNs. Compared to state-of-the-art WNNs across nine classification datasets, BTHOWeN on average reduces error by more than 40% and model size by more than 50%. We demonstrate the viability of a hardware implementation of BTHOWeN by presenting an FPGA-based inference accelerator, and compare its latency and resource usage against similarly accurate quantized DNN inference accelerators, including multi-layer perceptron (MLP) and convolutional models. The proposed BTHOWeN models consume almost 80% less energy than the MLP models, with nearly 85% reduction in latency. In our quest for efficient ML on the edge, WNNs are clearly deserving of additional attention.info:eu-repo/semantics/acceptedVersio