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Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM

We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known. To solve this semi-blind deconvolution problem, prior distributions are specified for the PSF and the 3D image. Joint image reconstruction and PSF estimation is then performed within a Bayesian framework, using a variational algorithm to estimate the posterior distribution. The image prior distribution imposes an explicit atomic measure that corresponds to image sparsity. Importantly, the proposed Bayesian deconvolution algorithm does not require hand tuning. Simulation results clearly demonstrate that the semi-blind deconvolution algorithm compares favorably with previous Markov chain Monte Carlo (MCMC) version of myopic sparse reconstruction. It significantly outperforms mismatched non-blind algorithms that rely on the assumption of the perfect knowledge of the PSF. The algorithm is illustrated on real data from magnetic resonance force microscopy (MRFM)

Diffusion-based Image Translation using Disentangled Style and Content Representation

Diffusion-based image translation guided by semantic texts or a single target image has enabled flexible style transfer which is not limited to the specific domains. Unfortunately, due to the stochastic nature of diffusion models, it is often difficult to maintain the original content of the image during the reverse diffusion. To address this, here we present a novel diffusion-based unsupervised image translation method using disentangled style and content representation. Specifically, inspired by the splicing Vision Transformer, we extract intermediate keys of multihead self attention layer from ViT model and used them as the content preservation loss. Then, an image guided style transfer is performed by matching the [CLS] classification token from the denoised samples and target image, whereas additional CLIP loss is used for the text-driven style transfer. To further accelerate the semantic change during the reverse diffusion, we also propose a novel semantic divergence loss and resampling strategy. Our experimental results show that the proposed method outperforms state-of-the-art baseline models in both text-guided and image-guided translation tasks

Patch-wise Graph Contrastive Learning for Image Translation

Recently, patch-wise contrastive learning is drawing attention for the image translation by exploring the semantic correspondence between the input and output images. To further explore the patch-wise topology for high-level semantic understanding, here we exploit the graph neural network to capture the topology-aware features. Specifically, we construct the graph based on the patch-wise similarity from a pretrained encoder, whose adjacency matrix is shared to enhance the consistency of patch-wise relation between the input and the output. Then, we obtain the node feature from the graph neural network, and enhance the correspondence between the nodes by increasing mutual information using the contrastive loss. In order to capture the hierarchical semantic structure, we further propose the graph pooling. Experimental results demonstrate the state-of-art results for the image translation thanks to the semantic encoding by the constructed graphs.Comment: AAAI 202

Stable nanoemulsions for poorly soluble curcumin: From production to digestion response in vitro

Curcumin, a polyphenol, can induce anticancer activity depending on dose. However, oral curcumin administration is limited by its low bioavailability due to aqueous insolubility and instability against physiological conditions. This study aims at formulating nanoemulsions by phase inversion temperature to enhance curcumin loading, stability, antioxidant performance, bioaccessibility, and in vitro absorption. The selection mechanisms for oil phase (coconut oil), surfactant (polyoxyl 40 hydrogenated castor oil), co-surfactant (soy phospholipid), and aqueous phase (2 % wt citrate buffer at pH 4.5) are established. The nanoemulsions show tunable mean droplet size (26–129 nm), high curcumin loading (9.53 ± 0.49 mg/mL), polydispersity 0.05). The curcumin nanoemulsions show ∼ 11 %, 24 %, and 57 % higher retention and ∼ 10 %, 12 %, and 17 % higher antioxidant activity than raw curcumin after 3-hour simulated gastric, intestinal, and physiological incubations, respectively. During in vitro digestion and absorption, the encapsulated curcumin shows higher bioaccessibility and absorption than free curcumin (P < 0.05). The samples are stable during 4-week storage at 4˚C and room temperature without preservatives. These findings suggest the potential to develop a nanoencapsulation strategy, particularly for an oral delivery system of oil-soluble drugs

Unpaired Image-to-Image Translation via Neural Schr\"odinger Bridge

Diffusion models are a powerful class of generative models which simulate stochastic differential equations (SDEs) to generate data from noise. Although diffusion models have achieved remarkable progress in recent years, they have limitations in the unpaired image-to-image translation tasks due to the Gaussian prior assumption. Schr\"odinger Bridge (SB), which learns an SDE to translate between two arbitrary distributions, have risen as an attractive solution to this problem. However, none of SB models so far have been successful at unpaired translation between high-resolution images. In this work, we propose the Unpaired Neural Schr\"odinger Bridge (UNSB), which combines SB with adversarial training and regularization to learn a SB between unpaired data. We demonstrate that UNSB is scalable, and that it successfully solves various unpaired image-to-image translation tasks. Code: \url{https://github.com/cyclomon/UNSB

Embracing Death and the Afterlife: Sculptures of Enma and His Entourage at Rokuharamitsuji

This dissertation investigates a sculptural group of Enma and his entourage that was once enshrined in an Enma hall located within the Kyoto temple Rokuharamitsuji precinct, and hopes to highlight the role that significant yet understudied sculptures played in the development of the cult of Enma and the Ten Kings in premodern Japan. Rokuharamitsuji is of great importance to study the cult of Enma and the Ten Kings not only for its rare early sculptures of Enma and his two assistants created in the thirteenth century when the cult began to flourish in Japan, but also for the later addition of a seventeenth-century Datsueba sculpture, which reveals the evolution of the cult through its incorporation of Japanese popular belief. This study examines how the Rokuharamitsuji sculptural group presented images of hell within a designated space and conveyed messages of salvation to their beholders, responding to the environs of the salvation-oriented temple. It demonstrates that historical, geographical, and cultural attributes of the temple’s surrounding area, namely Rokuhara (a field of skulls), strengthened the belief in Enma and the Ten Kings and contextualized the cult in combination with another belief in Datsueba