FreePIH: Training-Free Painterly Image Harmonization with Diffusion Model

This paper provides an efficient training-free painterly image harmonization (PIH) method, dubbed FreePIH, that leverages only a pre-trained diffusion model to achieve state-of-the-art harmonization results. Unlike existing methods that require either training auxiliary networks or fine-tuning a large pre-trained backbone, or both, to harmonize a foreground object with a painterly-style background image, our FreePIH tames the denoising process as a plug-in module for foreground image style transfer. Specifically, we find that the very last few steps of the denoising (i.e., generation) process strongly correspond to the stylistic information of images, and based on this, we propose to augment the latent features of both the foreground and background images with Gaussians for a direct denoising-based harmonization. To guarantee the fidelity of the harmonized image, we make use of multi-scale features to enforce the consistency of the content and stability of the foreground objects in the latent space, and meanwhile, aligning both fore-/back-grounds with the same style. Moreover, to accommodate the generation with more structural and textural details, we further integrate text prompts to attend to the latent features, hence improving the generation quality. Quantitative and qualitative evaluations on COCO and LAION 5B datasets demonstrate that our method can surpass representative baselines by large margins

Hot spots and flow structures around an isolated cuboid building subjected to surface warming: Large eddy simulations and wind tunnel measurements

Urban warming is evident in numerous cities. On especially hot days, building surfaces warm up, leading to buoyancy-driven flows adjacent to these surfaces. The dynamics of the flow structures are largely influenced by the interplay between incoming wind and the buoyancy-driven flows. In this study, we used large eddy simulations and wind tunnel measurements to investigate the flow field around an isolated cubic building when different surfaces of the building are warmed. Under conditions of low wind speeds, ranging from 0.5 to 2 m/s, the surface temperatures of the scaled building were maintained between 20 and 95 {\deg}C. As the Richardson number (Ri) varied from 0 to 4.00, the flow, initially dominated by forced convection, shifted to being primarily steered by mixed convection. At low wind speeds and high Ri values, the thermal effect led to noticeable changes in the reattachment and recirculation region lengths, with reductions of up to 48.3% in some cases. At pedestrian levels, thermally induced airflows often created localized hot spots, particularly around building corners and wall sections. This study offers insights into architectural designs that can potentially enhance wind-thermal comfort and improve pollutant dispersion around buildings

A multiobjective single bus corridor scheduling using machine learning-based predictive models

Many real-life optimisation problems, including those in production and logistics, have uncertainties that pose considerable challenges for practitioners. In spite of considerable efforts, the current methods are still not satisfactory. This is primarily caused by a lack of effective methods to deal with various uncertainties. Existing literature comes from two isolated research communities, namely the operations research community and the machine learning community. In the operations research community, uncertainties are often modelled and solved through techniques like stochastic programming or robust optimisation, which are often criticised for their over conservativeness. In the machine learning community, the problem is formulated as a dynamic control problem and solved through techniques like supervised learning and/or reinforcement learning, which could suffer from being myopic and unstable. In this paper, we aim to fill this research gap and develop a novel framework that takes advantages of both short-term accuracy from mathematical models and high-quality future forecasts from machine learning modules. We demonstrate the practicality and feasibility of our approach for a real-life bus scheduling problem and two controlled bus scheduling instances that are generated artificially. To our knowledge, the proposed framework represents the first multi-objective bus-headway-optimisation method for non-timetabled bus schedule with major practical constraints being considered. The advantages of our proposed methods are also discussed, along with factors that need to be carefully considered for practical applications. © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group

Spatio-Temporal Calibration for Omni-Directional Vehicle-Mounted

We present a solution to the problem of spatio-temporal calibration for event cameras mounted on an onmi-directional vehicle. Different from traditional methods that typically determine the camera's pose with respect to the vehicle's body frame using alignment of trajectories, our approach leverages the kinematic correlation of two sets of linear velocity estimates from event data and wheel odometers, respectively. The overall calibration task consists of estimating the underlying temporal offset between the two heterogeneous sensors, and furthermore, recovering the extrinsic rotation that defines the linear relationship between the two sets of velocity estimates. The first sub-problem is formulated as an optimization one, which looks for the optimal temporal offset that maximizes a correlation measurement invariant to arbitrary linear transformation. Once the temporal offset is compensated, the extrinsic rotation can be worked out with an iterative closed-form solver that incrementally registers associated linear velocity estimates. The proposed algorithm is proved effective on both synthetic data and real data, outperforming traditional methods based on alignment of trajectories

Overexpression of Peptide-Encoding OsCEP6.1 Results in Pleiotropic Effects on Growth in Rice (O. sativa)

Plant peptide hormone plays an important role in regulating plant developmental programs via cell-to-cell communication in a non-cell autonomous manner. To characterize the biological relevance of C-TERMINALLY ENCODED PEPTIDE (CEP) genes in rice, we performed a genome-wide search against public databases using bioinformatics approach and identified six additional CEP members. Expression analysis revealed a spatial-temporal pattern of OsCEP6.1 gene in different tissues and at different developmental stages of panicle. Interestingly, the expression level of the OsCEP6.1 was also significantly up-regulated by exogenous cytokinin. Application of a chemically synthesized 15-amino-acid OsCEP6.1 peptide showed that OsCEP6.1 had a negative role in regulating root and seedling growth, which was further confirmed by transgenic lines. Furthermore, the constitutive expression of OsCEP6.1 was sufficient to lead to panicle architecture and grain size variations. Scanning electron microscopy analysis revealed that the phenotypic variation of OsCEP6.1 overexpression lines resulted from decreased cell size but not reduced cell number. Moreover, starch accumulation was not significantly affected. Taken together, these data collectively suggest that the OsCEP6.1 peptide might be involved in regulating the development of panicles and grains in rice

Dissecting Arbitrary-scale Super-resolution Capability from Pre-trained Diffusion Generative Models

Diffusion-based Generative Models (DGMs) have achieved unparalleled performance in synthesizing high-quality visual content, opening up the opportunity to improve image super-resolution (SR) tasks. Recent solutions for these tasks often train architecture-specific DGMs from scratch, or require iterative fine-tuning and distillation on pre-trained DGMs, both of which take considerable time and hardware investments. More seriously, since the DGMs are established with a discrete pre-defined upsampling scale, they cannot well match the emerging requirements of arbitrary-scale super-resolution (ASSR), where a unified model adapts to arbitrary upsampling scales, instead of preparing a series of distinct models for each case. These limitations beg an intriguing question: can we identify the ASSR capability of existing pre-trained DGMs without the need for distillation or fine-tuning? In this paper, we take a step towards resolving this matter by proposing Diff-SR, a first ASSR attempt based solely on pre-trained DGMs, without additional training efforts. It is motivated by an exciting finding that a simple methodology, which first injects a specific amount of noise into the low-resolution images before invoking a DGM's backward diffusion process, outperforms current leading solutions. The key insight is determining a suitable amount of noise to inject, i.e., small amounts lead to poor low-level fidelity, while over-large amounts degrade the high-level signature. Through a finely-grained theoretical analysis, we propose the Perceptual Recoverable Field (PRF), a metric that achieves the optimal trade-off between these two factors. Extensive experiments verify the effectiveness, flexibility, and adaptability of Diff-SR, demonstrating superior performance to state-of-the-art solutions under diverse ASSR environments

Design, Fabrication, and Characterization of a Bifrequency Colinear Array

Ultrasound imaging with high resolution and large penetration depth has been increasingly adopted in medical diagnosis, surgery guidance, and treatment assessment. Conventional ultrasound works at a particular frequency, with a −6 dB fractional bandwidth of ~70 %, limiting the imaging resolution or depth of field. In this paper, a bi-frequency co-linear array with resonant frequencies of 8 MHz and 20 MHz was investigated to meet the requirements of resolution and penetration depth for a broad range of ultrasound imaging applications. Specifically, a 32-element bi-frequency co-linear array was designed and fabricated, followed by element characterization and real-time sectorial scan (S-scan) phantom imaging using a Verasonics system. The bi-frequency co-linear array was tested in four different modes by switching between low and high frequencies on transmit and receive. The four modes included the following: (1) transmit low, receive low, (2) transmit low, receive high, (3) transmit high, receive low, (4) transmit high, receive high. After testing, the axial and lateral resolutions of all modes were calculated and compared. The results of this study suggest that bi-frequency co-linear arrays are potential aids for wideband fundamental imaging and harmonic/sub-harmonic imaging