Genetic Algorithms with Neural Cost Predictor for Solving Hierarchical Vehicle Routing Problems

When vehicle routing decisions are intertwined with higher-level decisions, the resulting optimization problems pose significant challenges for computation. Examples are the multi-depot vehicle routing problem (MDVRP), where customers are assigned to depots before delivery, and the capacitated location routing problem (CLRP), where the locations of depots should be determined first. A simple and straightforward approach for such hierarchical problems would be to separate the higher-level decisions from the complicated vehicle routing decisions. For each higher-level decision candidate, we may evaluate the underlying vehicle routing problems to assess the candidate. As this approach requires solving vehicle routing problems multiple times, it has been regarded as impractical in most cases. We propose a novel deep-learning-based approach called Genetic Algorithm with Neural Cost Predictor (GANCP) to tackle the challenge and simplify algorithm developments. For each higher-level decision candidate, we predict the objective function values of the underlying vehicle routing problems using a pre-trained graph neural network without actually solving the routing problems. In particular, our proposed neural network learns the objective values of the HGS-CVRP open-source package that solves capacitated vehicle routing problems. Our numerical experiments show that this simplified approach is effective and efficient in generating high-quality solutions for both MDVRP and CLRP and has the potential to expedite algorithm developments for complicated hierarchical problems. We provide computational results evaluated in the standard benchmark instances used in the literature

Layer-wise Auto-Weighting for Non-Stationary Test-Time Adaptation

Given the inevitability of domain shifts during inference in real-world applications, test-time adaptation (TTA) is essential for model adaptation after deployment. However, the real-world scenario of continuously changing target distributions presents challenges including catastrophic forgetting and error accumulation. Existing TTA methods for non-stationary domain shifts, while effective, incur excessive computational load, making them impractical for on-device settings. In this paper, we introduce a layer-wise auto-weighting algorithm for continual and gradual TTA that autonomously identifies layers for preservation or concentrated adaptation. By leveraging the Fisher Information Matrix (FIM), we first design the learning weight to selectively focus on layers associated with log-likelihood changes while preserving unrelated ones. Then, we further propose an exponential min-max scaler to make certain layers nearly frozen while mitigating outliers. This minimizes forgetting and error accumulation, leading to efficient adaptation to non-stationary target distribution. Experiments on CIFAR-10C, CIFAR-100C, and ImageNet-C show our method outperforms conventional continual and gradual TTA approaches while significantly reducing computational load, highlighting the importance of FIM-based learning weight in adapting to continuously or gradually shifting target domains.Comment: WACV 202

Introducing Competition to Boost the Transferability of Targeted Adversarial Examples through Clean Feature Mixup

Deep neural networks are widely known to be susceptible to adversarial examples, which can cause incorrect predictions through subtle input modifications. These adversarial examples tend to be transferable between models, but targeted attacks still have lower attack success rates due to significant variations in decision boundaries. To enhance the transferability of targeted adversarial examples, we propose introducing competition into the optimization process. Our idea is to craft adversarial perturbations in the presence of two new types of competitor noises: adversarial perturbations towards different target classes and friendly perturbations towards the correct class. With these competitors, even if an adversarial example deceives a network to extract specific features leading to the target class, this disturbance can be suppressed by other competitors. Therefore, within this competition, adversarial examples should take different attack strategies by leveraging more diverse features to overwhelm their interference, leading to improving their transferability to different models. Considering the computational complexity, we efficiently simulate various interference from these two types of competitors in feature space by randomly mixing up stored clean features in the model inference and named this method Clean Feature Mixup (CFM). Our extensive experimental results on the ImageNet-Compatible and CIFAR-10 datasets show that the proposed method outperforms the existing baselines with a clear margin. Our code is available at https://github.com/dreamflake/CFM.Comment: CVPR 2023 camera-read

Scandium Doping Effect on a Layered Perovskite Cathode for Low-Temperature Solid Oxide Fuel Cells (LT-SOFCs)

Layered perovskite oxides are considered as promising cathode materials for the solid oxide fuel cell (SOFC) due to their high electronic/ionic conductivity and fast oxygen kinetics at low temperature. Many researchers have focused on further improving the electrochemical performance of the layered perovskite material by doping various metal ions into the B-site. Herein, we report that Sc3+ doping into the layered perovskite material, PrBaCo2O5+ (PBCO), shows a positive effect of increasing electrochemical performances. We confirmed that Sc3+ doping could provide a favorable crystalline structure of layered perovskite for oxygen ion transfer in the lattice with improved Gold-schmidt tolerance factor and specific free volume. Consequently, the Sc3+ doped PBCO exhibits a maximum power density of 0.73 W cm(-2) at 500 degrees C, 1.3 times higher than that of PBCO. These results indicate that Sc3+ doping could effectively improve the electrochemical properties of the layered perovskite material, PBCO

The bent conformation of poly(A)-binding protein induced by RNA-binding is required for its translational activation function

A recent study revealed that poly(A)-binding protein (PABP) bound to poly(A) RNA exhibits a sharply bent configuration at the linker region between RNA-recognition motif 2 (RRM2) and RRM3, whereas free PABP exhibits a highly flexible linear configuration. However, the physiological role of the bent structure of mRNA-bound PABP remains unknown. We investigated a role of the bent structure of PABP by constructing a PABP variant that fails to form the poly(A)-dependent bent structure but maintains its poly (A)-binding activity. We found that the bent structure of PABP/poly(A) complex is required for PABP's efficient interaction with eIF4G and eIF4G/eIF4E complex. Moreover, the mutant PABP had compromised translation activation function and failed to augment the formation of 80S translation initiation complex in an in vitro translation system. These results suggest that the bent conformation of PABP, which is induced by the interaction with 30 poly(A) tail, mediates poly(A)-dependent translation by facilitating the interaction with eIF4G and the eIF4G/eIF4E complex. The preferential binding of the eIF4G/eIF4E complex to the bent PABP/poly(A) complex seems to be a mechanism discriminating the mRNA-bound PABPs participating in translation from the idling mRNA-unbound PABPs.111Ysciescopu

eIF2A, an initiator tRNA carrier refractory to eIF2 kinases, functions synergistically with eIF5B

The initiator tRNA (Met-tRNA(i)(Met)) at the P site of the small ribosomal subunit plays an important role in the recognition of an mRNA start codon. In bacteria, the initiator tRNA carrier, IF2, facilitates the positioning of Met-tRNAiMet on the small ribosomal subunit. Eukarya contain the Met-tRNAiMet carrier, eIF2 (unrelated to IF2), whose carrier activity is inhibited under stress conditions by the phosphorylation of its -subunit by stress-activated eIF2 kinases. The stress-resistant initiator tRNA carrier, eIF2A, was recently uncovered and shown to load Met-tRNAiMet on the 40S ribosomal subunit associated with a stress-resistant mRNA under stress conditions. Here, we report that eIF2A interacts and functionally cooperates with eIF5B (a homolog of IF2), and we describe the functional domains of eIF2A that are required for its binding of Met-tRNAiMet, eIF5B, and a stress-resistant mRNA. The results indicate that the eukaryotic eIF5B-eIF2A complex functionally mimics the bacterial IF2 containing ribosome-, GTP-, and initiator tRNA-binding domains in a single polypeptide.112Ysciescopu

Deep learning-based statistical noise reduction for multidimensional spectral data

In spectroscopic experiments, data acquisition in multi-dimensional phase space may require long acquisition time, owing to the large phase space volume to be covered. In such case, the limited time available for data acquisition can be a serious constraint for experiments in which multidimensional spectral data are acquired. Here, taking angle-resolved photoemission spectroscopy (ARPES) as an example, we demonstrate a denoising method that utilizes deep learning as an intelligent way to overcome the constraint. With readily available ARPES data and random generation of training data set, we successfully trained the denoising neural network without overfitting. The denoising neural network can remove the noise in the data while preserving its intrinsic information. We show that the denoising neural network allows us to perform similar level of second-derivative and line shape analysis on data taken with two orders of magnitude less acquisition time. The importance of our method lies in its applicability to any multidimensional spectral data that are susceptible to statistical noise.Comment: 8 pages, 8 figure