Neural Spectro-polarimetric Fields

Modeling the spatial radiance distribution of light rays in a scene has been extensively explored for applications, including view synthesis. Spectrum and polarization, the wave properties of light, are often neglected due to their integration into three RGB spectral bands and their non-perceptibility to human vision. Despite this, these properties encompass substantial material and geometric information about a scene. In this work, we propose to model spectro-polarimetric fields, the spatial Stokes-vector distribution of any light ray at an arbitrary wavelength. We present Neural Spectro-polarimetric Fields (NeSpoF), a neural representation that models the physically-valid Stokes vector at given continuous variables of position, direction, and wavelength. NeSpoF manages inherently noisy raw measurements, showcases memory efficiency, and preserves physically vital signals, factors that are crucial for representing the high-dimensional signal of a spectro-polarimetric field. To validate NeSpoF, we introduce the first multi-view hyperspectral-polarimetric image dataset, comprised of both synthetic and real-world scenes. These were captured using our compact hyperspectral-polarimetric imaging system, which has been calibrated for robustness against system imperfections. We demonstrate the capabilities of NeSpoF on diverse scenes

AI-KD: Adversarial learning and Implicit regularization for self-Knowledge Distillation

We present a novel adversarial penalized self-knowledge distillation method, named adversarial learning and implicit regularization for self-knowledge distillation (AI-KD), which regularizes the training procedure by adversarial learning and implicit distillations. Our model not only distills the deterministic and progressive knowledge which are from the pre-trained and previous epoch predictive probabilities but also transfers the knowledge of the deterministic predictive distributions using adversarial learning. The motivation is that the self-knowledge distillation methods regularize the predictive probabilities with soft targets, but the exact distributions may be hard to predict. Our method deploys a discriminator to distinguish the distributions between the pre-trained and student models while the student model is trained to fool the discriminator in the trained procedure. Thus, the student model not only can learn the pre-trained model's predictive probabilities but also align the distributions between the pre-trained and student models. We demonstrate the effectiveness of the proposed method with network architectures on multiple datasets and show the proposed method achieves better performance than state-of-the-art methods.Comment: 12 pages, 7 figure

Linearly Replaceable Filters for Deep Network Channel Pruning

Convolutional neural networks (CNNs) have achieved remarkable results; however, despite the development of deep learning, practical user applications are fairly limited because heavy networks can be used solely with the latest hardware and software supports. Therefore, network pruning is gaining attention for general applications in various fields. This paper proposes a novel channel pruning method, Linearly Replaceable Filter (LRF), which suggests that a filter that can be approximated by the linear combination of other filters is replaceable. Moreover, an additional method called Weights Compensation is proposed to support the LRF method. This is a technique that effectively reduces the output difference caused by removing filters via direct weight modification. Through various experiments, we have confirmed that our method achieves state-of-the-art performance in several benchmarks. In particular, on ImageNet, LRF-60 reduces approximately 56% of FLOPs on ResNet-50 without top-5 accuracy drop. Further, through extensive analyses, we proved the effectiveness of our approaches

Synthesis of the C1–C10 Fragment of Madeirolide A

The synthesis of a fully elaborated C1–C10 fragment of madeirolide A has been achieved via a strategy based on a series of stereospecific processes. The concise synthetic route also features an iridium-catalyzed visible light induced radical cyclization for construction of the THP ring and a palladium-catalyzed glycosylation for formation of the α-cineruloside linkage

DynaGAN: Dynamic Few-shot Adaptation of GANs to Multiple Domains

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Dr.3D: Adapting 3D GANs to Artistic Drawings

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BigColor: Colorization using a Generative Color Prior for Natural Images

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Functional Duality of Chondrocyte Hypertrophy and Biomedical Application Trends in Osteoarthritis

Chondrocyte hypertrophy is one of the key indicators in the progression of osteoarthritis (OA). However, compared with other OA indications, such as cartilage collapse, sclerosis, inflammation, and protease activation, the mechanisms by which chondrocyte hypertrophy contributes to OA remain elusive. As the pathological processes in the OA cartilage microenvironment, such as the alterations in the extracellular matrix, are initiated and dictated by the physiological state of the chondrocytes, in-depth knowledge of chondrocyte hypertrophy is necessary to enhance our understanding of the disease pathology and develop therapeutic agents. Chondrocyte hypertrophy is a factor that induces OA progression; it is also a crucial factor in the endochondral ossification. This review elaborates on this dual functionality of chondrocyte hypertrophy in OA progression and endochondral ossification through a description of the characteristics of various genes and signaling, their mechanism, and their distinguishable physiological effects. Chondrocyte hypertrophy in OA progression leads to a decrease in chondrogenic genes and destruction of cartilage tissue. However, in endochondral ossification, it represents an intermediate stage at the process of differentiation of chondrocytes into osteogenic cells. In addition, this review describes the current therapeutic strategies and their mechanisms, involving genes, proteins, cytokines, small molecules, three-dimensional environments, or exosomes, against the OA induced by chondrocyte hypertrophy. Finally, this review proposes that the contrasting roles of chondrocyte hypertrophy are essential for both OA progression and endochondral ossification, and that this cellular process may be targeted to develop OA therapeutics