Artificial sweeteners and perceived obesity and diabetes

Artificial sweeteners have been increasingly incorporated into our diets. Contrary to what is believed to alleviate the obesity and diabetes epidemic seen today, artificial sweeteners have shown to induce the very problem it was meant to repress. Studies found that the consumption of artificial sweeteners ultimately lead to an increased risk of weight gain and diabetes. Exposure has shown to induce problems ranging from dysbiosis, inflammation, overconsumption, metabolic derangements, and much more that highly suggests the counterintuitive effects of artificial sweeteners

Learning From Drift: Federated Learning on Non-IID Data via Drift Regularization

Federated learning algorithms perform reasonably well on independent and identically distributed (IID) data. They, on the other hand, suffer greatly from heterogeneous environments, i.e., Non-IID data. Despite the fact that many research projects have been done to address this issue, recent findings indicate that they are still sub-optimal when compared to training on IID data. In this work, we carefully analyze the existing methods in heterogeneous environments. Interestingly, we find that regularizing the classifier's outputs is quite effective in preventing performance degradation on Non-IID data. Motivated by this, we propose Learning from Drift (LfD), a novel method for effectively training the model in heterogeneous settings. Our scheme encapsulates two key components: drift estimation and drift regularization. Specifically, LfD first estimates how different the local model is from the global model (i.e., drift). The local model is then regularized such that it does not fall in the direction of the estimated drift. In the experiment, we evaluate each method through the lens of the five aspects of federated learning, i.e., Generalization, Heterogeneity, Scalability, Forgetting, and Efficiency. Comprehensive evaluation results clearly support the superiority of LfD in federated learning with Non-IID data

Phase-shifted Adversarial Training

Adversarial training has been considered an imperative component for safely deploying neural network-based applications to the real world. To achieve stronger robustness, existing methods primarily focus on how to generate strong attacks by increasing the number of update steps, regularizing the models with the smoothed loss function, and injecting the randomness into the attack. Instead, we analyze the behavior of adversarial training through the lens of response frequency. We empirically discover that adversarial training causes neural networks to have low convergence to high-frequency information, resulting in highly oscillated predictions near each data. To learn high-frequency contents efficiently and effectively, we first prove that a universal phenomenon of frequency principle, i.e., \textit{lower frequencies are learned first}, still holds in adversarial training. Based on that, we propose phase-shifted adversarial training (PhaseAT) in which the model learns high-frequency components by shifting these frequencies to the low-frequency range where the fast convergence occurs. For evaluations, we conduct the experiments on CIFAR-10 and ImageNet with the adaptive attack carefully designed for reliable evaluation. Comprehensive results show that PhaseAT significantly improves the convergence for high-frequency information. This results in improved adversarial robustness by enabling the model to have smoothed predictions near each data.Comment: Conference on Uncertainty in Artificial Intelligence, 2023 (UAI 2023

Generating Cryptographic S-Boxes Using the Reinforcement Learning

Substitution boxes (S-boxes) are essential components of many cryptographic primitives. The Dijkstra algorithm, SAT solvers, and heuristic methods have been used to find bitsliced implementations of S-boxes. However, it is difficult to apply these methods for 8-bit S-boxes because of their size. Therefore, to implement these S-boxes so that the countermeasure of side-channel attack can be applied efficiently, using structures such as Feistel, Lai-Massey, and MISTY that can be bitsliced implemented with a small number of nonlinear operations has been widely used. Since S-boxes constructed with structures consist of small S-boxes and have specific designs, there are limitations to their cryptographic security and efficiency. In this paper, we propose a new method for generating S-boxes by stacking bitwise operations from the identity function, an approach that is different from existing methods. This method can be expressed in Markov decision process, and reinforcement learning is a suitable solver for Markov decision process. Our goal is to train this method to an agent through reinforcement learning to generate S-boxes to which the masking scheme, which is a countermeasure of side-channel attack, can be efficiently applied. In particular, our method provided various S-boxes superior or comparable to existing S-boxes. We produced 8-bit S-boxes with differential uniformity 16 (resp. 32) and linearity 128 (resp. 128), generated with nine (resp. eight) nonlinear operations, for the first time. To our best knowledge, this is the first study to construct cryptographic S-Box by incorporating reinforcement learning

Dynamic Structure Pruning for Compressing CNNs

Structure pruning is an effective method to compress and accelerate neural networks. While filter and channel pruning are preferable to other structure pruning methods in terms of realistic acceleration and hardware compatibility, pruning methods with a finer granularity, such as intra-channel pruning, are expected to be capable of yielding more compact and computationally efficient networks. Typical intra-channel pruning methods utilize a static and hand-crafted pruning granularity due to a large search space, which leaves room for improvement in their pruning performance. In this work, we introduce a novel structure pruning method, termed as dynamic structure pruning, to identify optimal pruning granularities for intra-channel pruning. In contrast to existing intra-channel pruning methods, the proposed method automatically optimizes dynamic pruning granularities in each layer while training deep neural networks. To achieve this, we propose a differentiable group learning method designed to efficiently learn a pruning granularity based on gradient-based learning of filter groups. The experimental results show that dynamic structure pruning achieves state-of-the-art pruning performance and better realistic acceleration on a GPU compared with channel pruning. In particular, it reduces the FLOPs of ResNet50 by 71.85% without accuracy degradation on the ImageNet dataset. Our code is available at https://github.com/irishev/DSP

Automated Structural Analysis and Quantitative Characterization of Scar Tissue Using Machine Learning

An analysis of scar tissue is necessary to understand the pathological tissue conditions during or after the wound healing process. Hematoxylin and eosin (HE) staining has conventionally been applied to understand the morphology of scar tissue. However, the scar lesions cannot be analyzed from a whole slide image. The current study aimed to develop a method for the rapid and automatic characterization of scar lesions in HE-stained scar tissues using a supervised and unsupervised learning algorithm. The supervised learning used a Mask region-based convolutional neural network (RCNN) to train a pattern from a data representation using MMDetection tools. The K-means algorithm characterized the HE-stained tissue and extracted the main features, such as the collagen density and directional variance of the collagen. The Mask RCNN model effectively predicted scar images using various backbone networks (e.g., ResNet50, ResNet101, ResNeSt50, and ResNeSt101) with high accuracy. The K-means clustering method successfully characterized the HE-stained tissue by separating the main features in terms of the collagen fiber and dermal mature components, namely, the glands, hair follicles, and nuclei. A quantitative analysis of the scar tissue in terms of the collagen density and directional variance of the collagen confirmed 50% differences between the normal and scar tissues. The proposed methods were utilized to characterize the pathological features of scar tissue for an objective histological analysis. The trained model is time-efficient when used for detection in place of a manual analysis. Machine learning-assisted analysis is expected to aid in understanding scar conditions, and to help establish an optimal treatment plan