Region-Aware Portrait Retouching with Sparse Interactive Guidance

Portrait retouching aims to improve the aesthetic quality of input portrait photos and especially requires human-region priority. \pink{The deep learning-based methods largely elevate the retouching efficiency and provide promising retouched results. However, existing portrait retouching methods focus on automatic retouching, which treats all human-regions equally and ignores users' preferences for specific individuals,} thus suffering from limited flexibility in interactive scenarios. In this work, we emphasize the importance of users' intents and explore the interactive portrait retouching task. Specifically, we propose a region-aware retouching framework with two branches: an automatic branch and an interactive branch. \pink{The automatic branch involves an encoding-decoding process, which searches region candidates and performs automatic region-aware retouching without user guidance. The interactive branch encodes sparse user guidance into a priority condition vector and modulates latent features with a region selection module to further emphasize the user-specified regions. Experimental results show that our interactive branch effectively captures users' intents and generalizes well to unseen scenes with sparse user guidance, while our automatic branch also outperforms the state-of-the-art retouching methods due to improved region-awareness.

Identification of S100A8 as a common diagnostic biomarkers and exploring potential pathogenesis for osteoarthritis and metabolic syndrome

BackgroundOsteoarthritis (OA) is the most frequent musculoskeletal disease and the major contributor to disability worldwide. Metabolic syndrome (MetS) has been recognized as being associated with the pathogenesis of osteoarthritis. However, the exact mechanisms and links between the two are not clear.MethodsWe downloaded clinical information data and gene expression profiles for OA and MetS from the database of Gene Expression Omnibus (GEO), and immune related gene (IRG) from the database of Immunology Database and Analysis Portal (IMMPORT). After screening OA-DEG and MetS-DEG, we identified the common immune hub gene by screening the overlapping genes between OA-DEG, MetS-DEG and IRG. Then we conducted single-gene analysis of S100A8, assessed the correlation of S100A8 with immune cell infiltration, and verified the diagnostic value of S100A8 in OA and MetS database respectively.Results323 OA-DEGs,101 MetS-DEGs and an immune-related hub gene, S100A8, were identified. In single gene analysis of S100A8 in OA samples, GSEA suggested that immune-related biological processes were more significantly enriched. The results of immune cell infiltration analysis showed that the enrichment fraction of M2 macrophages was significantly higher in the high S100A8-expressing group, and the level of S100A8 expression was positively correlated with M2 macrophage infiltration. The results of the dataset validation showed that S100A8 expression levels were significantly upregulated in the OA group and performed well in the diagnosis of OA. In single gene analysis of S100A8 in MetS samples, immune cell infiltration analysis showed that monocyte infiltration was higher in the S100A8 high expression samples and that there was a positive correlation between the two. Dataset validation showed that S100A8 is of high value for the diagnosis of MetS. In the validation of the dataset for the four metabolism-related diseases (obesity, diabetes, hypertension and hyperlipidaemia), S100A8 was expressed at higher levels in the disease group and also had a higher diagnostic value for the four metabolism-related diseases.ConclusionS100A8 is a common hub gene and diagnostic biomarker for OA and MetS, and the immune regulation involved in S100A8 may play a central role in the pathogenesis of OA and MetS

Deep Active Alignment of Knowledge Graph Entities and Schemata

Knowledge graphs (KGs) store rich facts about the real world. In this paper, we study KG alignment, which aims to find alignment between not only entities but also relations and classes in different KGs. Alignment at the entity level can cross-fertilize alignment at the schema level. We propose a new KG alignment approach, called DAAKG, based on deep learning and active learning. With deep learning, it learns the embeddings of entities, relations and classes, and jointly aligns them in a semi-supervised manner. With active learning, it estimates how likely an entity, relation or class pair can be inferred, and selects the best batch for human labeling. We design two approximation algorithms for efficient solution to batch selection. Our experiments on benchmark datasets show the superior accuracy and generalization of DAAKG and validate the effectiveness of all its modules.Comment: Accepted in the ACM SIGMOD/PODS International Conference on Management of Data (SIGMOD 2023

New Interpretations of Normalization Methods in Deep Learning

In recent years, a variety of normalization methods have been proposed to help train neural networks, such as batch normalization (BN), layer normalization (LN), weight normalization (WN), group normalization (GN), etc. However, mathematical tools to analyze all these normalization methods are lacking. In this paper, we first propose a lemma to define some necessary tools. Then, we use these tools to make a deep analysis on popular normalization methods and obtain the following conclusions: 1) Most of the normalization methods can be interpreted in a unified framework, namely normalizing pre-activations or weights onto a sphere; 2) Since most of the existing normalization methods are scaling invariant, we can conduct optimization on a sphere with scaling symmetry removed, which can help stabilize the training of network; 3) We prove that training with these normalization methods can make the norm of weights increase, which could cause adversarial vulnerability as it amplifies the attack. Finally, a series of experiments are conducted to verify these claims.Comment: Accepted by AAAI 202

Accelerated Policy Evaluation: Learning Adversarial Environments with Adaptive Importance Sampling

The evaluation of rare but high-stakes events remains one of the main difficulties in obtaining reliable policies from intelligent agents, especially in large or continuous state/action spaces where limited scalability enforces the use of a prohibitively large number of testing iterations. On the other hand, a biased or inaccurate policy evaluation in a safety-critical system could potentially cause unexpected catastrophic failures during deployment. In this paper, we propose the Accelerated Policy Evaluation (APE) method, which simultaneously uncovers rare events and estimates the rare event probability in Markov decision processes. The APE method treats the environment nature as an adversarial agent and learns towards, through adaptive importance sampling, the zero-variance sampling distribution for the policy evaluation. Moreover, APE is scalable to large discrete or continuous spaces by incorporating function approximators. We investigate the convergence properties of proposed algorithms under suitable regularity conditions. Our empirical studies show that APE estimates rare event probability with a smaller variance while only using orders of magnitude fewer samples compared to baseline methods in both multi-agent and single-agent environments.Comment: 10 pages, 5 figure