SOSR: Source-Free Image Super-Resolution with Wavelet Augmentation Transformer

Real-world images taken by different cameras with different degradation kernels often result in a cross-device domain gap in image super-resolution. A prevalent attempt to this issue is unsupervised domain adaptation (UDA) that needs to access source data. Considering privacy policies or transmission restrictions of data in many practical applications, we propose a SOurce-free image Super-Resolution framework (SOSR) to address this issue, i.e., adapt a model pre-trained on labeled source data to a target domain with only unlabeled target data. SOSR leverages the source model to generate refined pseudo-labels for teacher-student learning. To better utilize the pseudo-labels, this paper proposes a novel wavelet-based augmentation method, named Wavelet Augmentation Transformer (WAT), which can be flexibly incorporated with existing networks, to implicitly produce useful augmented data. WAT learns low-frequency information of varying levels across diverse samples, which is aggregated efficiently via deformable attention. Furthermore, an uncertainty-aware self-training mechanism is proposed to improve the accuracy of pseudo-labels, with inaccurate predictions being rectified by uncertainty estimation. To acquire better SR results and avoid overfitting pseudo-labels, several regularization losses are proposed to constrain the frequency information between target LR and SR images. Experiments show that without accessing source data, SOSR achieves superior results to the state-of-the-art UDA methods.Comment: 15 pages, 9 figures, 10 table

Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction

User response prediction, which models the user preference w.r.t. the presented items, plays a key role in online services. With two-decade rapid development, nowadays the cumulated user behavior sequences on mature Internet service platforms have become extremely long since the user's first registration. Each user not only has intrinsic tastes, but also keeps changing her personal interests during lifetime. Hence, it is challenging to handle such lifelong sequential modeling for each individual user. Existing methodologies for sequential modeling are only capable of dealing with relatively recent user behaviors, which leaves huge space for modeling long-term especially lifelong sequential patterns to facilitate user modeling. Moreover, one user's behavior may be accounted for various previous behaviors within her whole online activity history, i.e., long-term dependency with multi-scale sequential patterns. In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user. The model also adopts a hierarchical and periodical updating mechanism to capture multi-scale sequential patterns of user interests while supporting the evolving user behavior logs. The experimental results over three large-scale real-world datasets have demonstrated the advantages of our proposed model with significant improvement in user response prediction performance against the state-of-the-arts.Comment: SIGIR 2019. Reproducible codes and datasets: https://github.com/alimamarankgroup/HPM

Effect of Hydrogen Peroxide and Superoxide Anions on Cytosolic Ca2+: Comparison of Endothelial Cells from Large-Sized and Small-Sized Arteries

We compared the Ca2+ responses to reactive oxygen species (ROS) between mouse endothelial cells derived from large-sized arteries, aortas (aortic ECs), and small-sized arteries, mesenteric arteries (MAECs). Application of hydrogen peroxide (H2O2) caused an increase in cytosolic Ca2+ levels ([Ca2+]i) in both cell types. The [Ca2+]i rises diminished in the presence of U73122, a phospholipase C inhibitor, or Xestospongin C (XeC), an inhibitor for inositol-1,4,5-trisphosphate (IP3) receptors. Removal of Ca2+ from the bath also decreased the [Ca2+]i rises in response to H2O2. In addition, treatment of endothelial cells with H2O2 reduced the [Ca2+]i responses to subsequent challenge of ATP. The decreased [Ca2+]i responses to ATP were resulted from a pre-depletion of intracellular Ca2+ stores by H2O2. Interestingly, we also found that Ca2+ store depletion was more sensitive to H2O2 treatment in endothelial cells of mesenteric arteries than those of aortas. Hypoxanthine-xanthine oxidase (HX-XO) was also found to induce [Ca2+]i rises in both types of endothelial cells, the effect of which was mediated by superoxide anions and H2O2 but not by hydroxyl radical. H2O2 contribution in HX-XO-induced [Ca2+]i rises were more significant in endothelial cells from mesenteric arteries than those from aortas. In summary, H2O2 could induce store Ca2+ release via phospholipase C-IP3 pathway in endothelial cells. Resultant emptying of intracellular Ca2+ stores contributed to the reduced [Ca2+]i responses to subsequent ATP challenge. The [Ca2+]i responses were more sensitive to H2O2 in endothelial cells of small-sized arteries than those of large-sized arteries

Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization

Recently, the remarkable advance of the Large Language Model (LLM) has inspired researchers to transfer its extraordinary reasoning capability to both vision and language data. However, the prevailing approaches primarily regard the visual input as a prompt and focus exclusively on optimizing the text generation process conditioned upon vision content by a frozen LLM. Such an inequitable treatment of vision and language heavily constrains the model's potential. In this paper, we break through this limitation by representing both vision and language in a unified form. Specifically, we introduce a well-designed visual tokenizer to translate the non-linguistic image into a sequence of discrete tokens like a foreign language that LLM can read. The resulting visual tokens encompass high-level semantics worthy of a word and also support dynamic sequence length varying from the image. Coped with this tokenizer, the presented foundation model called LaVIT can handle both image and text indiscriminately under the same generative learning paradigm. This unification empowers LaVIT to serve as an impressive generalist interface to understand and generate multi-modal content simultaneously. Extensive experiments further showcase that it outperforms the existing models by a large margin on massive vision-language tasks. Our code and models will be available at https://github.com/jy0205/LaVIT