Iodine Vacancy Redistribution in Organic–Inorganic Halide Perovskite Films and Resistive Switching Effects

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138254/1/adma201700527_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138254/2/adma201700527-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138254/3/adma201700527.pd

Nanoionic Resistive‐Switching Devices

Advances in the understanding of nanoscale ionic processes in solid‐state thin films have led to the rapid development of devices based on coupled ionic–electronic effects. For example, ion‐driven resistive‐switching (RS) devices have been extensively studied for future memory applications due to their excellent performance in terms of switching speed, endurance, retention, and scalability. Recent studies further suggest that RS devices are more than just resistors with tunable resistance; instead, they exhibit rich and complex internal ionic dynamics that equip them with native information‐processing capabilities, particularly in the temporal domain. RS effects induced by the migration of different types of ions, often driven by an electric field, are discussed. It is shown that, by taking advantage of the different state variables controlled by the ionic processes, important synaptic functions can be faithfully implemented in solid‐state devices and networks. Recent efforts on improving the controllability of ionic processes to optimize device performance are also discussed, along with new opportunities for material design and engineering enabled by the ability to control ionic processes at the atomic scale.Solid‐state resistive‐switching devices driven by nanoscale ionic processes are reviewed, with the focus on the rich ionic dynamics that enable natural implementation of a range of biological synaptic and neuron functions. Efforts to control ion redistribution at the atomic scale have led to improved device performance, and enabled applications based on reconfigurable nanostructures and materials through controlled ionic processes in solid‐state devices.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151267/1/aelm201900184_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151267/2/aelm201900184.pd

Photonic Memristor for Future Computing: A Perspective

Photonic computing and neuromorphic computing could address the inherent limitations of traditional von Neumann architecture and gradually invalidate Moore’s law. As photonics applications are capable of storing and processing data in an optical manner with unprecedented bandwidth and high speed, twoâ terminal photonic memristors with a remote optical control of resistive switching behaviors at defined wavelengths ensure the benefit of onâ chip integration, low power consumption, multilevel data storage, and a large variation margin, suggesting promising advantages for both photonic and neuromorphic computing. Herein, the development of photonic memristors is reviewed, as well as their application in photonic computing and emulation on optogeneticsâ modulated artificial synapses. Different photoactive materials acting as both photosensing and storage media are discussed in terms of their opticalâ tunable memory behaviors and underlying resistive switching mechanism with consideration of photogating and photovoltaic effects. Moreover, lightâ involved logic operations, systemâ level integration, and lightâ controlled artificial synaptic memristors along with improved learning tasks performance are presented. Furthermore, the challenges in the field are discussed, such as the lack of a comprehensive understanding of microscopic mechanisms under light illumination and a general constraint of inferior nearâ infrared (NIR) sensitivity.The development of photonic memristors and their application in photonic computing and emulation on optogeneticsâ modulated artificial synapses are reviewed. Photoactive materials as photosensing and storage media are discussed, considering their opticalâ tunable memory behavior and resistive switching mechanism including photogating and photovoltaic effect. Lightâ involved logic operations, system level integration, and artificial synaptic memristors along with improved learning tasks performance are presented.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153103/1/adom201900766.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153103/2/adom201900766_am.pd

3D-VisTA: Pre-trained Transformer for 3D Vision and Text Alignment

3D vision-language grounding (3D-VL) is an emerging field that aims to connect the 3D physical world with natural language, which is crucial for achieving embodied intelligence. Current 3D-VL models rely heavily on sophisticated modules, auxiliary losses, and optimization tricks, which calls for a simple and unified model. In this paper, we propose 3D-VisTA, a pre-trained Transformer for 3D Vision and Text Alignment that can be easily adapted to various downstream tasks. 3D-VisTA simply utilizes self-attention layers for both single-modal modeling and multi-modal fusion without any sophisticated task-specific design. To further enhance its performance on 3D-VL tasks, we construct ScanScribe, the first large-scale 3D scene-text pairs dataset for 3D-VL pre-training. ScanScribe contains 2,995 RGB-D scans for 1,185 unique indoor scenes originating from ScanNet and 3R-Scan datasets, along with paired 278K scene descriptions generated from existing 3D-VL tasks, templates, and GPT-3. 3D-VisTA is pre-trained on ScanScribe via masked language/object modeling and scene-text matching. It achieves state-of-the-art results on various 3D-VL tasks, ranging from visual grounding and dense captioning to question answering and situated reasoning. Moreover, 3D-VisTA demonstrates superior data efficiency, obtaining strong performance even with limited annotations during downstream task fine-tuning

Multi-Fields Modulation of Physical Properties of Oxide Thin Films

Oxide thin films exhibit versatile physical properties such as magnetism, ferroelectricity, piezoelectricity, metal-insulator transition (MIT), multiferroicity, colossal magnetoresistivity, switchable resistivity, etc. More importantly, the exhibited multifunctionality could be tuned by various external fields, which has enabled demonstration of novel electronic devices. In this article, recent studies of the multi-fields modulation of physical properties in oxide thin films have been reviewed. Some of the key issues and prospects about this field are also addressed.Comment: review article, 56 pages, 18 figure

The Rice Pentatricopeptide Repeat Protein PPR756 Is Involved in Pollen Development by Affecting Multiple RNA Editing in Mitochondria.

In land plants, the pentatricopeptide repeat (PPR) proteins form a large family involved in post-transcriptional processing of RNA in mitochondria and chloroplasts, which is critical for plant development and evolutionary adaption. Although studies showed a number of PPR proteins generally influence the editing of organellar genes, few of them were characterized in detail in rice. Here, we report a PLS-E subclass PPR protein in rice, PPR756, loss of function of which led to the abolishment of RNA editing events among three mitochondrial genes includin

Perceive, Ground, Reason, and Act: A Benchmark for General-purpose Visual Representation

Current computer vision models, unlike the human visual system, cannot yet achieve general-purpose visual understanding. Existing efforts to create a general vision model are limited in the scope of assessed tasks and offer no overarching framework to perform them holistically. We present a new comprehensive benchmark, General-purpose Visual Understanding Evaluation (G-VUE), covering the full spectrum of visual cognitive abilities with four functional domains \unicode{x2014} Perceive, Ground, Reason, and Act. The four domains are embodied in 11 carefully curated tasks, from 3D reconstruction to visual reasoning and manipulation. Along with the benchmark, we provide a general encoder-decoder framework to allow for the evaluation of arbitrary visual representation on all 11 tasks. We evaluate various pre-trained visual representations with our framework and observe that (1) Transformer-based visual backbone generally outperforms CNN-based backbone on G-VUE, (2) visual representations from vision-language pre-training are superior to those with vision-only pre-training across visual tasks. With G-VUE, we provide a holistic evaluation standard to motivate research toward building general-purpose visual systems via obtaining more general-purpose visual representations