Phase transition in bulk single crystals and thin films of VO2 by nanoscale infrared spectroscopy and imaging

We have systematically studied a variety of vanadium dioxide (VO2) crystalline forms, including bulk single crystals and oriented thin films, using infrared (IR) near-field spectroscopic imaging techniques. By measuring the IR spectroscopic responses of electrons and phonons in VO2 with sub-grain-size spatial resolution (∼20nm), we show that epitaxial strain in VO2 thin films not only triggers spontaneous local phase separations, but also leads to intermediate electronic and lattice states that are intrinsically different from those found in bulk. Generalized rules of strain- and symmetry-dependent mesoscopic phase inhomogeneity are also discussed. These results set the stage for a comprehensive understanding of complex energy landscapes that may not be readily determined by macroscopic approaches

Phonon Polaritons in Monolayers of Hexagonal Boron Nitride.

Phonon polaritons in van der Waals materials reveal significant confinement accompanied with long propagation length: important virtues for tasks pertaining to the control of light and energy flow at the nanoscale. While previous studies of phonon polaritons have relied on relatively thick samples, here reported is the first observation of surface phonon polaritons in single atomic layers and bilayers of hexagonal boron nitride (hBN). Using antenna-based near-field microscopy, propagating surface phonon polaritons in mono- and bilayer hBN microcrystals are imaged. Phonon polaritons in monolayer hBN are confined in a volume about one million times smaller than the free-space photons. Both the polariton dispersion and their wavelength-thickness scaling law are altered compared to those of hBN bulk counterparts. These changes are attributed to phonon hardening in monolayer-thick crystals. The data reported here have bearing on applications of polaritons in metasurfaces and ultrathin optical elements

Synthesis and Biological Study of Adenylyl Cyclase Inhibitors

Adenylyl cyclases (AC) is a critical family of enzymes which modulates the dynamic cellular level of cAMP, cyclic adenosine monophosphate. The study of cAMP showed that it is indispensable for the signal transduction cascades during many physiological processes, such as immune responses and metabolism which highly relate to cancers. Previous studies of AC inhibitors have been limited due to a lack of isoform-selective small molecule modulators. Selectivity of the molecules is imperative to the activation of only the desired AC inhibitor. The design of the described project was to test the structure activity relationship (SAR) by synthesizing a class of AC I inhibitors and then use the results to develop a small molecule with maximum selectivity for therapeutic targeting. Multi-step synthesis featured with epoxide ring-opening reaction followed by the Friedel–Crafts reaction. Compounds were differentiated by changing substituents on the nitrogen atom. The synthetic molecules have been tested via SAR of AC I inhibitor and IC50. Once synthesized, the compounds were tested for their inhibition rate and the results showed that the majority of scaffolds had great SAR rates at 40 µM and two also had impressive rates as low as 4 µM. Further investigation with IC50 studies is on-going. The results suggest that the current synthetic compounds are potentially great AC I inhibitors and further study will continue which will contribute to cancer research

Learning to Solve Tasks with Exploring Prior Behaviour

Postprin

Dual Effects of the US-China Trade War and COVID-19 on United States Imports: Transfer of China's industrial chain?

The trade tension between the U.S. and China since 2018 has caused a steady decoupling of the world's two largest economies. The pandemic outbreak in 2020 complicated this process and had numerous unanticipated repercussions. This paper investigates how U.S. importers reacted to the trade war and worldwide lockdowns due to the COVID-19 pandemic. We examine the effects of the two incidents on U.S. imports separately and collectively, with various economic scopes. Our findings uncover intricate trading dynamics among the U.S., China, and Southeast Asia, through which businesses relocated portions of their global supply chain away from China to avoid high tariffs. Our analysis indicates that increased tariffs cause the U.S. to import less from China. Meanwhile, Southeast Asian exporters have integrated more into value chains centered on Chinese suppliers by participating more in assembling and completing products. However, the worldwide lockdowns over pandemic have reversed this trend as, over this period, the U.S. effectively imported more goods directly from China and indirectly through Southeast Asian exporters that imported from China.Comment: 30 pages, 6 figure

Learning to Solve Tasks with Exploring Prior Behaviours

Demonstrations are widely used in Deep Reinforcement Learning (DRL) for facilitating solving tasks with sparse rewards. However, the tasks in real-world scenarios can often have varied initial conditions from the demonstration, which would require additional prior behaviours. For example, consider we are given the demonstration for the task of \emph{picking up an object from an open drawer}, but the drawer is closed in the training. Without acquiring the prior behaviours of opening the drawer, the robot is unlikely to solve the task. To address this, in this paper we propose an Intrinsic Rewards Driven Example-based Control \textbf{(IRDEC)}. Our method can endow agents with the ability to explore and acquire the required prior behaviours and then connect to the task-specific behaviours in the demonstration to solve sparse-reward tasks without requiring additional demonstration of the prior behaviours. The performance of our method outperforms other baselines on three navigation tasks and one robotic manipulation task with sparse rewards. Codes are available at https://github.com/Ricky-Zhu/IRDEC

Constrained Multiview Representation for Self-supervised Contrastive Learning

Representation learning constitutes a pivotal cornerstone in contemporary deep learning paradigms, offering a conduit to elucidate distinctive features within the latent space and interpret the deep models. Nevertheless, the inherent complexity of anatomical patterns and the random nature of lesion distribution in medical image segmentation pose significant challenges to the disentanglement of representations and the understanding of salient features. Methods guided by the maximization of mutual information, particularly within the framework of contrastive learning, have demonstrated remarkable success and superiority in decoupling densely intertwined representations. However, the effectiveness of contrastive learning highly depends on the quality of the positive and negative sample pairs, i.e. the unselected average mutual information among multi-views would obstruct the learning strategy so the selection of the views is vital. In this work, we introduce a novel approach predicated on representation distance-based mutual information (MI) maximization for measuring the significance of different views, aiming at conducting more efficient contrastive learning and representation disentanglement. Additionally, we introduce an MI re-ranking strategy for representation selection, benefiting both the continuous MI estimating and representation significance distance measuring. Specifically, we harness multi-view representations extracted from the frequency domain, re-evaluating their significance based on mutual information across varying frequencies, thereby facilitating a multifaceted contrastive learning approach to bolster semantic comprehension. The statistical results under the five metrics demonstrate that our proposed framework proficiently constrains the MI maximization-driven representation selection and steers the multi-view contrastive learning process

FastHDRNet: A new efficient method for SDR-to-HDR Translation

Modern displays nowadays possess the capability to render video content with a high dynamic range (HDR) and an extensive color gamut .However, the majority of available resources are still in standard dynamic range (SDR). Therefore, we need to identify an effective methodology for this objective.The existing deep neural networks (DNN) based SDR to HDR conversion methods outperforms conventional methods, but they are either too large to implement or generate some terrible artifacts. We propose a neural network for SDR to HDR conversion, termed "FastHDRNet". This network includes two parts, Adaptive Universal Color Transformation (AUCT) and Local Enhancement (LE). The architecture is designed as a lightweight network that utilizes global statistics and local information with super high efficiency. After the experiment, we find that our proposed method achieves state-of-the-art performance in both quantitative comparisons and visual quality with a lightweight structure and a enhanced infer speed.Comment: 16 pages, 4 figure