Decentralized Stochastic Bilevel Optimization with Improved per-Iteration Complexity

Bilevel optimization recently has received tremendous attention due to its great success in solving important machine learning problems like meta learning, reinforcement learning, and hyperparameter optimization. Extending single-agent training on bilevel problems to the decentralized setting is a natural generalization, and there has been a flurry of work studying decentralized bilevel optimization algorithms. However, it remains unknown how to design the distributed algorithm with sample complexity and convergence rate comparable to SGD for stochastic optimization, and at the same time without directly computing the exact Hessian or Jacobian matrices. In this paper we propose such an algorithm. More specifically, we propose a novel decentralized stochastic bilevel optimization (DSBO) algorithm that only requires first order stochastic oracle, Hessian-vector product and Jacobian-vector product oracle. The sample complexity of our algorithm matches the currently best known results for DSBO, and the advantage of our algorithm is that it does not require estimating the full Hessian and Jacobian matrices, thereby having improved per-iteration complexity.Comment: ICML 202

Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography

Data hiding is the process of embedding information into a noise-tolerant signal such as a piece of audio, video, or image. Digital watermarking is a form of data hiding where identifying data is robustly embedded so that it can resist tampering and be used to identify the original owners of the media. Steganography, another form of data hiding, embeds data for the purpose of secure and secret communication. This survey summarises recent developments in deep learning techniques for data hiding for the purposes of watermarking and steganography, categorising them based on model architectures and noise injection methods. The objective functions, evaluation metrics, and datasets used for training these data hiding models are comprehensively summarised. Finally, we propose and discuss possible future directions for research into deep data hiding techniques

Case report: A novel 10.8-kb deletion identified in the β-globin gene through the long-read sequencing technology in a Chinese family with abnormal hemoglobin testing results

BackgroundThalassemia is a common inherited hemoglobin disorder caused by a deficiency of one or more globin subunits. Substitution variants and deletions in the HBB gene are the major causes of β-thalassemia, of which large fragment deletions are rare and difficult to be detected by conventional polymerase chain reaction (PCR)-based methods.Case reportIn this study, we reported a 26-year-old Han Chinese man, whose routine blood parameters were found to be abnormal. Hemoglobin testing was performed on the proband and his family members, of whom only the proband's mother had normal parameters. The comprehensive analysis of thalassemia alleles (CATSA, a long-read sequencing-based approach) was performed to identify the causative variants. We finally found a novel 10.8-kb deletion including the β-globin (HBB) gene (Chr11:5216601-5227407, GRch38/hg38) of the proband and his father and brother, which were consistent with their hemoglobin testing results. The copy number and exact breakpoints of the deletion were confirmed by multiplex ligation-dependent probe amplification (MLPA) and gap-polymerase chain reaction (Gap-PCR) as well as Sanger sequencing, respectively.ConclusionWith this novel large deletion found in the HBB gene in China, we expand the genotype spectrum of β-thalassemia and show the advantages of long-read sequencing (LRS) for comprehensive and precise detection of thalassemia variants