Sharp One-Parameter Mean Bounds for Yang Mean

We prove that the double inequality Jα(a,b)<U(a,b)<Jβ(a,b) holds for all a,b>0 with a≠b if and only if α≤2/(π-2)=0.8187⋯ and β≥3/2, where U(a,b)=(a-b)/[2arctan⁡((a-b)/2ab)], and Jp(a,b)=p(ap+1-bp+1)/[(p+1)(ap-bp)]  (p≠0,-1), J0(a,b)=(a-b)/(log⁡a-log⁡b), and J-1(a,b)=ab(log⁡a-log⁡b)/(a-b) are the Yang and pth one-parameter means of a and b, respectively

RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification

Graph classification is a crucial task in many real-world multimedia applications, where graphs can represent various multimedia data types such as images, videos, and social networks. Previous efforts have applied graph neural networks (GNNs) in balanced situations where the class distribution is balanced. However, real-world data typically exhibit long-tailed class distributions, resulting in a bias towards the head classes when using GNNs and limited generalization ability over the tail classes. Recent approaches mainly focus on re-balancing different classes during model training, which fails to explicitly introduce new knowledge and sacrifices the performance of the head classes. To address these drawbacks, we propose a novel framework called Retrieval Augmented Hybrid Network (RAHNet) to jointly learn a robust feature extractor and an unbiased classifier in a decoupled manner. In the feature extractor training stage, we develop a graph retrieval module to search for relevant graphs that directly enrich the intra-class diversity for the tail classes. Moreover, we innovatively optimize a category-centered supervised contrastive loss to obtain discriminative representations, which is more suitable for long-tailed scenarios. In the classifier fine-tuning stage, we balance the classifier weights with two weight regularization techniques, i.e., Max-norm and weight decay. Experiments on various popular benchmarks verify the superiority of the proposed method against state-of-the-art approaches.Comment: Accepted by the ACM International Conference on Multimedia (MM) 202

ALEX: Towards Effective Graph Transfer Learning with Noisy Labels

Graph Neural Networks (GNNs) have garnered considerable interest due to their exceptional performance in a wide range of graph machine learning tasks. Nevertheless, the majority of GNN-based approaches have been examined using well-annotated benchmark datasets, leading to suboptimal performance in real-world graph learning scenarios. To bridge this gap, the present paper investigates the problem of graph transfer learning in the presence of label noise, which transfers knowledge from a noisy source graph to an unlabeled target graph. We introduce a novel technique termed Balance Alignment and Information-aware Examination (ALEX) to address this challenge. ALEX first employs singular value decomposition to generate different views with crucial structural semantics, which help provide robust node representations using graph contrastive learning. To mitigate both label shift and domain shift, we estimate a prior distribution to build subgraphs with balanced label distributions. Building on this foundation, an adversarial domain discriminator is incorporated for the implicit domain alignment of complex multi-modal distributions. Furthermore, we project node representations into a different space, optimizing the mutual information between the projected features and labels. Subsequently, the inconsistency of similarity structures is evaluated to identify noisy samples with potential overfitting. Comprehensive experiments on various benchmark datasets substantiate the outstanding superiority of the proposed ALEX in different settings.Comment: Accepted by the ACM International Conference on Multimedia (MM) 202

Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts

Graph classification, aiming at learning the graph-level representations for effective class assignments, has received outstanding achievements, which heavily relies on high-quality datasets that have balanced class distribution. In fact, most real-world graph data naturally presents a long-tailed form, where the head classes occupy much more samples than the tail classes, it thus is essential to study the graph-level classification over long-tailed data while still remaining largely unexplored. However, most existing long-tailed learning methods in visions fail to jointly optimize the representation learning and classifier training, as well as neglect the mining of the hard-to-classify classes. Directly applying existing methods to graphs may lead to sub-optimal performance, since the model trained on graphs would be more sensitive to the long-tailed distribution due to the complex topological characteristics. Hence, in this paper, we propose a novel long-tailed graph-level classification framework via Collaborative Multi-expert Learning (CoMe) to tackle the problem. To equilibrate the contributions of head and tail classes, we first develop balanced contrastive learning from the view of representation learning, and then design an individual-expert classifier training based on hard class mining. In addition, we execute gated fusion and disentangled knowledge distillation among the multiple experts to promote the collaboration in a multi-expert framework. Comprehensive experiments are performed on seven widely-used benchmark datasets to demonstrate the superiority of our method CoMe over state-of-the-art baselines.Comment: Accepted by IEEE Transactions on Big Data (TBD 2024

An analytical method to select spindle speed variation parameters for chatter suppression in NC machining

Regenerative chatter vibrations may result in shortened tool life and poor surface finish, and hence should be avoided in practice. Since the Spindle Speed Variation (SSV) method has been certified as a feasible and effective way, the issue of speed variation parameters selection has yet to be solved. Based on the discussions of energy accumulation process in chatter and in vari-speed cutting, an analytical method was proposed to select spindle speed variation parameters. The variation of the phase delay between the inner and the outer modulations was regarded as an important argument that reflects the energy accumulation process. Two phase delay related coefficients were proposed for analyzing the mean energy accumulation process and regional energy accumulation enhancement. The importance of spindle acceleration for chatter suppression was also explained. The phase delay based analytical method was verified by numerical simulation and turning experiments

4,6-Dimethyl-2-(naphthalen-1-yl)pyrimidine

The asymmetric unit of the title compound, C16H14N2, contains two independent mol­ecules in which the dihedral angles between the pyrimidine and naphthaline rings are 38.20 (5) and 39.35 (5)°. Inter­molecular C—H⋯π contacts and π–π stacking inter­actions [centroid–centroid distances = 3.766 (1) and 3.792 (1) Å] are present in the crystal structure

Varioloid A, a new indolyl-6,10b-dihydro-5aH-[1]benzofuro[2,3-b]indole derivative from the marine alga-derived endophytic fungus Paecilomyces varotii EN-291

A new indolyl-6,10b-dihydro-5aH-[1]benzofuro[2,3-b]indole derivative, varioloid A (1), was isolated from the marine alga-derived endophytic fungus Paecilomyces variotii EN-291. Its structure was elucidated on the basis of extensive analysis of 1D and 2D NMR data and the absolute configuration was determined by time-dependent density functional theory-electronic circular dichroism (TDDFT-ECD) calculations. A similar compound, whose planar structure was previously described but the relative and absolute configurations and 13C NMR data were not reported, was also identified and was tentatively named as varioloid B (2). Both compounds 1 and 2 exhibited cytotoxicity against A549, HCT116, and HepG2 cell lines, with IC50 values ranging from 2.6 to 8.2 µg/mL