Solving Diffusion ODEs with Optimal Boundary Conditions for Better Image Super-Resolution

Diffusion models, as a kind of powerful generative model, have given impressive results on image super-resolution (SR) tasks. However, due to the randomness introduced in the reverse process of diffusion models, the performances of diffusion-based SR models are fluctuating at every time of sampling, especially for samplers with few resampled steps. This inherent randomness of diffusion models results in ineffectiveness and instability, making it challenging for users to guarantee the quality of SR results. However, our work takes this randomness as an opportunity: fully analyzing and leveraging it leads to the construction of an effective plug-and-play sampling method that owns the potential to benefit a series of diffusion-based SR methods. More in detail, we propose to steadily sample high-quality SR images from pretrained diffusion-based SR models by solving diffusion ordinary differential equations (diffusion ODEs) with optimal boundary conditions (BCs) and analyze the characteristics between the choices of BCs and their corresponding SR results. Our analysis shows the route to obtain an approximately optimal BC via an efficient exploration in the whole space. The quality of SR results sampled by the proposed method with fewer steps outperforms the quality of results sampled by current methods with randomness from the same pretrained diffusion-based SR model, which means that our sampling method "boosts" current diffusion-based SR models without any additional training

An accurate and efficient quasi-dynamic simulation method of electricity-heat multi-energy systems

Quasi-dynamic energy flow computation (EFC) has become a critical tool to determine and predict the states of the multi-energy system (MES), which helps improve MES’ operation efficiency and issues the security warning. However, methods in literature suffer numerical problems including fake oscillations, divergence, etc., Also, with the increasing of system dimensions, the computation efficiency can be hardly guaranteed due to the cross iterations between different nonlinear equations. This paper proposes an accurate and efficient method for quasi-dynamic energy flow computation. Using a scheme with total variation decreasing property, the numerical instability in solutions of thermal dynamics are effectively reduced. By estimating local truncation errors in a cheap way, the simulation step sizes are controlled adaptively and hence the overall simulation efficiency is greatly increased. Numerical tests were performed in a small system and the famous Barry Island system, which verified the advantages of the proposed method in both efficiency and accuracy

AI Illustrator: Translating Raw Descriptions into Images by Prompt-based Cross-Modal Generation

AI illustrator aims to automatically design visually appealing images for books to provoke rich thoughts and emotions. To achieve this goal, we propose a framework for translating raw descriptions with complex semantics into semantically corresponding images. The main challenge lies in the complexity of the semantics of raw descriptions, which may be hard to be visualized (e.g., "gloomy" or "Asian"). It usually poses challenges for existing methods to handle such descriptions. To address this issue, we propose a Prompt-based Cross-Modal Generation Framework (PCM-Frame) to leverage two powerful pre-trained models, including CLIP and StyleGAN. Our framework consists of two components: a projection module from Text Embeddings to Image Embeddings based on prompts, and an adapted image generation module built on StyleGAN which takes Image Embeddings as inputs and is trained by combined semantic consistency losses. To bridge the gap between realistic images and illustration designs, we further adopt a stylization model as post-processing in our framework for better visual effects. Benefiting from the pre-trained models, our method can handle complex descriptions and does not require external paired data for training. Furthermore, we have built a benchmark that consists of 200 raw descriptions. We conduct a user study to demonstrate our superiority over the competing methods with complicated texts. We release our code at https://github.com/researchmm/AI_Illustrator

A Uniform Strain Transfer Scheme for Accurate Distributed Optical Fiber Strain Measurements in Civil Structures

We report a screw-like package design for an embeddable distributed optical fiber strain sensor for civil engineering applications. The screw-like structure is the exterior support for an optical fiber sensor. The bare optical fiber is embedded and secured in a longitudinal groove of the screw-like package using a rigid adhesive. Our packaging scheme prevents damage to the bare optical fiber and ensures that the packaged sensor is accurately and optimally sensing strain fields in civil structures. Moreover, our screw-like design has an equal area in a cross-section perpendicular to and along the screw axis, so strain field distributions are metered faithfully along the length of the embedded optical fiber. Our novel screw-like package optical fiber sensor, interfaced to a Rayleigh scattering-based optical frequency domain reflectometer system enables undistorted, accurate, robust, and spatially-distributed strain measurements in bridges, tunnels, pipelines, buildings, etc. along structural dimensions extending from centimeters to kilometers. Document type: Articl

An Embeddable Optical Strain Gauge based on a Buckled Beam

We report, for the first time, a low cost, compact, and novel mechanically designed extrinsic Fabry-Perot interferometer (EFPI)-based optical fiber sensor with a strain amplification mechanism for strain measurement. The fundamental design principle includes a buckled beam with a coated gold layer, mounted on two grips. A Fabry-Perot cavity is produced between the buckled beam and the endface of a single mode fiber (SMF). A ceramic ferrule is applied for supporting and orienting the SMF. The principal sensor elements are packaged and protected by two designed metal shells. The midpoint of the buckled beam will experience a deflection vertically when the beam is subjected to a horizontally/axially compressive displacement. It has been found that the vertical deflection of the beam at midpoint can be 6-17 times larger than the horizontal/axial displacement, which forms the basis of a strain amplification mechanism. The user-configurable buckling beam geometry-based strain amplification mechanism enables the strain sensor to achieve a wide range of strain measurement sensitivities. The designed EFPI was used to monitor shrinkage of a square brick of mortar. The strain was measured during the drying/curing stage. We envision that it could be a good strain sensor to be embedded in civil materials/structures under a harsh environment for a prolonged period of time

A Uniform Strain Transfer Scheme for Accurate Distributed Optical Fiber Strain Measurements in Civil Structures

We report a screw-like package design for an embeddable distributed optical fiber strain sensor for civil engineering applications. The screw-like structure is the exterior support for an optical fiber sensor. The bare optical fiber is embedded and secured in a longitudinal groove of the screw-like package using a rigid adhesive. Our packaging scheme prevents damage to the bare optical fiber and ensures that the packaged sensor is accurately and optimally sensing strain fields in civil structures. Moreover, our screw-like design has an equal area in a cross-section perpendicular to and along the screw axis, so strain field distributions are metered faithfully along the length of the embedded optical fiber. Our novel screw-like package optical fiber sensor, interfaced to a Rayleigh scattering-based optical frequency domain reflectometer system enables undistorted, accurate, robust, and spatially-distributed strain measurements in bridges, tunnels, pipelines, buildings, etc. along structural dimensions extending from centimeters to kilometers

Effect of Modulating Activity of DLPFC and Gender on Search Behavior: A tDCS Experiment

Studies of search behavior have shown that individuals stop searching earlier and accept a lower point than predicted by the optimal, risk-neutral stopping rule. This behavior may be related to individual risk preferences. Studies have also found correlativity between risk preferences and the dorsolateral prefrontal cortex (DLPFC). As risk attitude plays a crucial role in search behavior, we studied whether modulating the activity of DLPFC, by using a transcranial direct current stimulation (tDCS) device, can change individual search behavior. We performed a sequential search task in which subjects decided when to accept a point randomly drawn from a uniform distribution. A total of 49 subjects (23 females, mean age = 21.84 ± 2.09 years, all right-handed) were recruited at Zhejiang University from May 2017 to September 2017. They repeated the task in 80 trials and received the stimulation at the end of the 40th trial. The results showed that after receiving right anodal/left cathodal stimulation, subjects increased their searching duration, which led to an increase in their accepted point from 778.17 to 826.12. That is, the subjects may have changed their risk attitude to search for a higher acceptable point and received a higher benefit. In addition, the effect of stimulation on search behavior was mainly driven by the female subjects rather than by the male subjects: the female subjects significantly increased their accepted point from 764.15 to 809.17 after right anodal/left cathodal stimulation, while the male subjects increased their accepted point from 794.18 to 845.49, but the change was not significant

A review of enhancement of biohydrogen productions by chemical addition using a supervised machine learning method

In this work, the impact of chemical additions, especially nano‐particles (NPs), was quan-titatively analyzed using our constructed artificial neural networks (ANNs)‐response surface methodology (RSM) algorithm. Fe‐based and Ni‐based NPs and ions, including Mg2+, Cu2+, Na+, NH4+, and K+, behave differently towards the response of hydrogen yield (HY) and hydrogen evolution rate (HER). Manipulating the size and concentration of NPs was found to be effective in enhancing the HY for Fe‐based NPs and ions, but not for Ni‐based NPs and ions. An optimal range of particle size (86–120 nm) and Ni‐ion/NP concentration (81–120 mg L−1) existed for HER. Meanwhile, the manipulation of the size and concentration of NPs was found to be ineffective for both iron and nickel for the improvement of HER. In fact, the variation in size of NPs for the enhancement of HY and HER demonstrated an appreciable difference. The smaller (less than 42 nm) NPs were found to definitely improve the HY, whereas for the HER, the relatively bigger size of NPs (40–50 nm) seemed to significantly increase the H2 evolution rate. It was also found that the variations in the concentration of the investigated ions only statistically influenced the HER, not the HY. The level of response (the enhanced HER) towards inputs was underpinned and the order of significance towards HER was identified as the following: Na+ \u3e Mg2+ \u3e Cu2+ \u3e NH4+ \u3e K+