Shaping metal-organic framework materials with a honeycomb internal structure

© 2018 The Royal Society of Chemistry. A self-assembly technology allows metal-organic framework materials to constitute a honeycomb internal structure while being shaped into millimeter-scale spheres. The ZIF-8 load is up to 83 wt% through solidification of chitosan (CS). This approach can be expanded to other morphologies (fibers) or crystals and is transformative for industrial manufacturing of nanomaterials

Effects of laser fluence on silicon modification by four-beam laser interference

This paper discusses the effects of laser fluence on silicon modification by four-beam laser interference. In this work, four-beam laser interference was used to pattern single crystal silicon wafers for the fabrication of surface structures, and the number of laser pulses was applied to the process in air. By controlling the parameters of laser irradiation, different shapes of silicon structures were fabricated. The results were obtained with the single laser fluence of 354 mJ/cm, 495 mJ/cm, and 637 mJ/cm, the pulse repetition rate of 10 Hz, the laser exposure pulses of 30, 100, and 300, the laser wavelength of 1064 nm, and the pulse duration of 7-9 ns. The effects of the heat transfer and the radiation of laser interference plasma on silicon wafer surfaces were investigated. The equations of heat flow and radiation effects of laser plasma of interfering patterns in a four-beam laser interference distribution were proposed to describe their impacts on silicon wafer surfaces. The experimental results have shown that the laser fluence has to be properly selected for the fabrication of well-defined surface structures in a four-beam laser interference process. Laser interference patterns can directly fabricate different shape structures for their corresponding applications

ILCAS: Imitation Learning-Based Configuration-Adaptive Streaming for Live Video Analytics with Cross-Camera Collaboration

The high-accuracy and resource-intensive deep neural networks (DNNs) have been widely adopted by live video analytics (VA), where camera videos are streamed over the network to resource-rich edge/cloud servers for DNN inference. Common video encoding configurations (e.g., resolution and frame rate) have been identified with significant impacts on striking the balance between bandwidth consumption and inference accuracy and therefore their adaption scheme has been a focus of optimization. However, previous profiling-based solutions suffer from high profiling cost, while existing deep reinforcement learning (DRL) based solutions may achieve poor performance due to the usage of fixed reward function for training the agent, which fails to craft the application goals in various scenarios. In this paper, we propose ILCAS, the first imitation learning (IL) based configuration-adaptive VA streaming system. Unlike DRL-based solutions, ILCAS trains the agent with demonstrations collected from the expert which is designed as an offline optimal policy that solves the configuration adaption problem through dynamic programming. To tackle the challenge of video content dynamics, ILCAS derives motion feature maps based on motion vectors which allow ILCAS to visually ``perceive'' video content changes. Moreover, ILCAS incorporates a cross-camera collaboration scheme to exploit the spatio-temporal correlations of cameras for more proper configuration selection. Extensive experiments confirm the superiority of ILCAS compared with state-of-the-art solutions, with 2-20.9% improvement of mean accuracy and 19.9-85.3% reduction of chunk upload lag.Comment: This work has been submitted to the IEEE Transactions on Mobile Computing for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

ModelScope Text-to-Video Technical Report

This paper introduces ModelScopeT2V, a text-to-video synthesis model that evolves from a text-to-image synthesis model (i.e., Stable Diffusion). ModelScopeT2V incorporates spatio-temporal blocks to ensure consistent frame generation and smooth movement transitions. The model could adapt to varying frame numbers during training and inference, rendering it suitable for both image-text and video-text datasets. ModelScopeT2V brings together three components (i.e., VQGAN, a text encoder, and a denoising UNet), totally comprising 1.7 billion parameters, in which 0.5 billion parameters are dedicated to temporal capabilities. The model demonstrates superior performance over state-of-the-art methods across three evaluation metrics. The code and an online demo are available at \url{https://modelscope.cn/models/damo/text-to-video-synthesis/summary}.Comment: Technical report. Project page: \url{https://modelscope.cn/models/damo/text-to-video-synthesis/summary

Low latency parallel turbo decoding implementation for future terrestrial broadcasting systems

As a class of high-performance forward error correction codes, turbo codes, which can approach the channel capacity, could become a candidate of the coding methods in future terrestrial broadcasting (TB) systems. Among all the demands of future TB system, high throughput and low latency are two basic requirements that need to be met. Parallel turbo decoding is a very effective method to reduce the latency and improve the throughput in the decoding stage. In this paper, a parallel turbo decoder is designed and implemented in field-programmable gate array (FPGA). A reverse address generator is proposed to reduce the complexity of interleaver and also the iteration time. A practical method of modulo operation is realized in FPGA which can save computing resources compared with using division operation. The latency of parallel turbo decoder after implementation can be as low as 23.2 us at a clock rate of 250 MHz and the throughput can reach up to 6.92 Gbps

VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation

A diffusion probabilistic model (DPM), which constructs a forward diffusion process by gradually adding noise to data points and learns the reverse denoising process to generate new samples, has been shown to handle complex data distribution. Despite its recent success in image synthesis, applying DPMs to video generation is still challenging due to high-dimensional data spaces. Previous methods usually adopt a standard diffusion process, where frames in the same video clip are destroyed with independent noises, ignoring the content redundancy and temporal correlation. This work presents a decomposed diffusion process via resolving the per-frame noise into a base noise that is shared among all frames and a residual noise that varies along the time axis. The denoising pipeline employs two jointly-learned networks to match the noise decomposition accordingly. Experiments on various datasets confirm that our approach, termed as VideoFusion, surpasses both GAN-based and diffusion-based alternatives in high-quality video generation. We further show that our decomposed formulation can benefit from pre-trained image diffusion models and well-support text-conditioned video creation.Comment: Accepted to CVPR202

Structural Modulation of Gut Microbiota during Alleviation of Suckling Piglets Diarrhoea with Herbal Formula

To determine whether the traditional Chinese herbal formula of Shen Ling Baizhu (SLB) could modulate the composition of the gut microbiota and alleviate diarrhoea in suckling piglets, twenty-four newly born piglets (Large White × Landrace × Duroc) were selected and allocated to 4 groups (control group and experimental groups I, II, and III) randomly. Faecal microbiome composition was assessed by 16S rRNA gene 454-pyrosequencing. The result indicated that experimental groups I and II exhibited significantly different gut microbiota from the control group. Most notably, the genera Lactobacillus and Bifidobacterium were significantly elevated in experimental group II compared with the control group (P<0.05). Collinsella and Faecalibacterium were also enhanced in experimental group II compared with the control group (P<0.05). The results showed that SLB treatment could modulate the gut microbiota composition of suckling piglets, enriching the amount of beneficial bacteria in particular. The observed changes in the gut microbiota could provide the basis for further research on the pharmacological mechanism of the tested Chinese herbal formula

Influence Pathway Discovery on Social Media

This paper addresses influence pathway discovery, a key emerging problem in today's online media. We propose a discovery algorithm that leverages recently published work on unsupervised interpretable ideological embedding, a mapping of ideological beliefs (done in a self-supervised fashion) into interpretable low-dimensional spaces. Computing the ideological embedding at scale allows one to analyze correlations between the ideological positions of leaders, influencers, news portals, or population segments, deriving potential influence pathways. The work is motivated by the importance of social media as the preeminent means for global interactions and collaborations on today's Internet, as well as their frequent (mis-)use to wield influence that targets social beliefs and attitudes of selected populations. Tools that enable the understanding and mapping of influence propagation through population segments on social media are therefore increasingly important. In this paper, influence is measured by the perceived ideological shift over time that is correlated with influencers' activity. Correlated shifts in ideological embeddings indicate changes, such as swings/switching (among competing ideologies), polarization (depletion of neutral ideological positions), escalation/radicalization (shifts to more extreme versions of the ideology), or unification/cooldown (shifts towards more neutral stances). Case-studies are presented to explore selected influence pathways (i) in a recent French election, (ii) during political discussions in the Philippines, and (iii) for some Russian messaging during the Russia/Ukraine conflict.Comment: This paper is accepted by IEEE CIC as an invited vision pape

SiO line emission from C-type shock waves : interstellar jets and outflows

We study the production of SiO in the gas phase of molecular outflows, through the sputtering of Si--bearing material in refractory grain cores, which are taken to be olivine; we calculate also the rotational line spectrum of the SiO. The sputtering is driven by neutral particle impact on charged grains, in steady--state C-type shock waves, at the speed of ambipolar diffusion. The emission of the SiO molecule is calculated by means of an LVG code. A grid of models has been generated. We compare our results with those of an earlier study (Schilke et al. 1997). Improvements in the treatment of the coupling between the charged grains and the neutral fluid lead to narrower shock waves and lower fractions of Si being released into the gas phase. More realistic assumptions concerning the initial fractional abundance of O2 lead to SiO formation being delayed, so that it occurs in the cool, dense postshock flow. Good agreement is obtained with recent observations of SiO line intensities in the L1157 and L1448 molecular outflows. The inferred temperature, opacity, and SiO column density in the emission region differ significantly from those estimated by means of LVG `slab' models. The fractional abundance of SiO is deduced. Observed line profiles are wider than predicted and imply multiple, unresolved shock regions within the beam.Comment: 1 tex doc, 19 figure

Exites in Cambrian arthropods and homology of arthropod limb branches

Abstract: The last common ancestor of all living arthropods had biramous postantennal appendages, with an endopodite and exopodite branching off the limb base. Morphological evidence for homology of these rami between crustaceans and chelicerates has, however, been challenged by data from clonal composition and from knockout of leg patterning genes. Cambrian arthropod fossils have been cited as providing support for competing hypotheses about biramy but have shed little light on additional lateral outgrowths, known as exites. Here we draw on microtomographic imaging of the Cambrian great-appendage arthropod Leanchoilia to reveal a previously undetected exite at the base of most appendages, composed of overlapping lamellae. A morphologically similar, and we infer homologous, exite is documented in the same position in members of the trilobite-allied Artiopoda. This early Cambrian exite morphology supplements an emerging picture from gene expression that exites may have a deeper origin in arthropod phylogeny than has been appreciated.Copyright © The Authors, 2021. This is an open access article, available to all readers online, published under a creative commons licensing (https://creativecommons.org/licenses/by/4.0/). The attached file is the published version of the article