Realistic Image Generation from Text by Using BERT-Based Embedding

Recently, in the field of artificial intelligence, multimodal learning has received a lot of attention due to expectations for the enhancement of AI performance and potential applications. Text-to-image generation, which is one of the multimodal tasks, is a challenging topic in computer vision and natural language processing. The text-to-image generation model based on generative adversarial network (GAN) utilizes a text encoder pre-trained with image-text pairs. However, text encoders pre-trained with image-text pairs cannot obtain rich information about texts not seen during pre-training, thus it is hard to generate an image that semantically matches a given text description. In this paper, we propose a new text-to-image generation model using pre-trained BERT, which is widely used in the field of natural language processing. The pre-trained BERT is used as a text encoder by performing fine-tuning with a large amount of text, so that rich information about the text is obtained and thus suitable for the image generation task. Through experiments using a multimodal benchmark dataset, we show that the proposed method improves the performance over the baseline model both quantitatively and qualitatively

Realistic Image Generation from Text by Using BERT-Based Embedding

Recently, in the field of artificial intelligence, multimodal learning has received a lot of attention due to expectations for the enhancement of AI performance and potential applications. Text-to-image generation, which is one of the multimodal tasks, is a challenging topic in computer vision and natural language processing. The text-to-image generation model based on generative adversarial network (GAN) utilizes a text encoder pre-trained with image-text pairs. However, text encoders pre-trained with image-text pairs cannot obtain rich information about texts not seen during pre-training, thus it is hard to generate an image that semantically matches a given text description. In this paper, we propose a new text-to-image generation model using pre-trained BERT, which is widely used in the field of natural language processing. The pre-trained BERT is used as a text encoder by performing fine-tuning with a large amount of text, so that rich information about the text is obtained and thus suitable for the image generation task. Through experiments using a multimodal benchmark dataset, we show that the proposed method improves the performance over the baseline model both quantitatively and qualitatively

In-Orbit Results and Attitude Analysis of the SNUGLITE Cube-Satellite

SNUGLITE (Seoul National University Global navigation satellite system Laboratory satellITE) is a two-unit cube satellite (CubeSat) with dimensions 10 × 10 × 23 cm that requires an attitude system for missions and ground station telecommunication. A linear-quadratic-Gaussian-based optimal attitude system for the CubeSat platform has been developed using low-cost sensors, with the in-orbit verification of the attitude system being is one of main study objectives. Since launch, the SNUGLITE CubeSat has continuously broadcast in-orbit status information. In this study, a methodology for the analysis of in-orbit attitude estimation results using received data is presented, and this was achieved by comparing two sun-pointing vectors, i.e., the sun-pointing vector calculated using estimated attitude with the positions of the sun and the satellite and the reference vector generated by the power levels of the solar panels. Because the satellite position was required for the attitude analysis, the verification of the performance of the own-developed on-board Global Positioning System (GPS) receiver is also briefly described. Analyses indicate that the attitude estimation of the SNUGLITE CubeSat has achieved an in-orbit real-time pointing accuracy with a root mean square of 6.1°

Coupling Two-Dimensional MoTe₂ and InGaZnO Thin-Film Materials for Hybrid PN Junction and CMOS Inverters

We report the fabrication of hybrid PN junction diode and complementary (CMOS) inverters, where 2D p-type MoTe₂ and n-type thin film InGaZnO (IGZO) are coupled for each device process. IGZO thin film was initially patterned by conventional photolithography either for n-type material in a PN diode or for n-channel of top-gate field-effect transistors (FET) in CMOS inverter. The hybrid PN junction diode shows a good ideality factor of 1.57 and quite a high ON/OFF rectification ratio of ∼3 × 10⁴. Under photons, our hybrid PN diode appeared somewhat stable only responding to high-energy photons of blue and ultraviolet. Our 2D nanosheet–oxide film hybrid CMOS inverter exhibits voltage gains as high as ∼40 at 5 V, low power consumption less than around a few nW at 1 V, and ∼200 μs switching dynamics

One-Pot Synthesis of Indolizines via Sequential Rhodium-Catalyzed [2 + 1]-Cyclopropanation, Palladium-Catalyzed Ring Expansion, and Oxidation Reactions from Pyridotriazoles and 1,3-Dienes

An efficient, one-pot synthetic method for producing functionalized indolizine derivatives was developed via a Rh-catalyzed [2 + 1]-cyclopropanation, Pd-catalyzed ring expansion, and subsequent oxidation using manganese dioxide from pyridotriazoles and 1,3-dienes

Ferroelastic–Ferroelectric Multiferroicity in van der Waals Rhenium Dichalcogenides

© 2022 Wiley-VCH GmbH.2D multiferroics with combined ferroic orders have gained attention owing to their novel functionality and underlying science. Intrinsic ferroelastic–ferroelectric multiferroicity in single-crystalline van der Waals rhenium dichalcogenides, whose symmetries are broken by the Peierls distortion and layer-stacking order, is demonstrated. Ferroelastic switching of the domain orientation and accompanying anisotropic properties is achieved with 1% uniaxial strain using the polymer encapsulation method. Based on the electron localization function and bond dissociation energy of the Re–Re bonds, the change in bond configuration during the evolution of the domain wall and the preferred switching between the two specific orientation states are explained. Furthermore, the ferroelastic switching of ferroelectric polarization is confirmed using the photovoltaic effect. The study provides insights into the reversible bond-switching process and potential applications based on 2D multiferroicity.11Nsciescopu

Low Power Consumption Complementary Inverters with n‑MoS₂ and p‑WSe₂ Dichalcogenide Nanosheets on Glass for Logic and Light-Emitting Diode Circuits

Two-dimensional (2D) semiconductor materials with discrete bandgap become important because of their interesting physical properties and potentials toward future nanoscale electronics. Many 2D-based field effect transistors (FETs) have thus been reported. Several attempts to fabricate 2D complementary (CMOS) logic inverters have been made too. However, those CMOS devices seldom showed the most important advantage of typical CMOS: low power consumption. Here, we adopted p-WSe₂ and n-MoS₂ nanosheets separately for the channels of bottom-gate-patterned FETs, to fabricate 2D dichalcogenide-based hetero-CMOS inverters on the same glass substrate. Our hetero-CMOS inverters with electrically isolated FETs demonstrate novel and superior device performances of a maximum voltage gain as ∼27, sub-nanowatt power consumption, almost ideal noise margin approaching 0.5<i>V</i>_DD (supply voltage, <i>V</i>_DD = 5 V) with a transition voltage of 2.3 V, and ∼800 μs for switching delay. Moreover, our glass-substrate CMOS device nicely performed digital logic (NOT, OR, and AND) and push–pull circuits for organic light-emitting diode switching, directly displaying the prospective of practical applications