Quark to Λ\Lambda-hyperon spin transfers in the current-fragmentation region

We perform a study on the struck quark to the $\Lambda$-hyperon fragmentation processes by taking into account the anti-quark fragmentations and intermediate decays from other hyperons. We concentrate on how the longitudinally polarized quark fragments to the longitudinally polarized $\Lambda$, how unpolarized quark and anti-quark fragment to the unpolarized $\Lambda$, and how quark and anti-quark fragment to the $\Lambda$ through the intermediate decay processes. We calculate the effective fragmentation functions in the light-cone SU(6) quark-spectator-diquark model via the Gribov-Lipatov relation, with the Melosh-Wigner rotation effect also included. The calculated results are in reasonable agreement with the HERMES semi-inclusive $ep$ experimental data and the OPAL and ALEPH $e^{+}e^{-}$ annihilation experimental data.Comment: 14 latex pages, 8 figures. Final version for publication in PL

Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes

Despite the recent successes of vanilla Graph Neural Networks (GNNs) on many tasks, their foundation on pairwise interaction networks inherently limits their capacity to discern latent higher-order interactions in complex systems. To bridge this capability gap, we propose a novel approach exploiting the rich mathematical theory of simplicial complexes (SCs) - a robust tool for modeling higher-order interactions. Current SC-based GNNs are burdened by high complexity and rigidity, and quantifying higher-order interaction strengths remains challenging. Innovatively, we present a higher-order Flower-Petals (FP) model, incorporating FP Laplacians into SCs. Further, we introduce a Higher-order Graph Convolutional Network (HiGCN) grounded in FP Laplacians, capable of discerning intrinsic features across varying topological scales. By employing learnable graph filters, a parameter group within each FP Laplacian domain, we can identify diverse patterns where the filters' weights serve as a quantifiable measure of higher-order interaction strengths. The theoretical underpinnings of HiGCN's advanced expressiveness are rigorously demonstrated. Additionally, our empirical investigations reveal that the proposed model accomplishes state-of-the-art (SOTA) performance on a range of graph tasks and provides a scalable and flexible solution to explore higher-order interactions in graphs

Zero-shot Skeleton-based Action Recognition via Mutual Information Estimation and Maximization

Zero-shot skeleton-based action recognition aims to recognize actions of unseen categories after training on data of seen categories. The key is to build the connection between visual and semantic space from seen to unseen classes. Previous studies have primarily focused on encoding sequences into a singular feature vector, with subsequent mapping the features to an identical anchor point within the embedded space. Their performance is hindered by 1) the ignorance of the global visual/semantic distribution alignment, which results in a limitation to capture the true interdependence between the two spaces. 2) the negligence of temporal information since the frame-wise features with rich action clues are directly pooled into a single feature vector. We propose a new zero-shot skeleton-based action recognition method via mutual information (MI) estimation and maximization. Specifically, 1) we maximize the MI between visual and semantic space for distribution alignment; 2) we leverage the temporal information for estimating the MI by encouraging MI to increase as more frames are observed. Extensive experiments on three large-scale skeleton action datasets confirm the effectiveness of our method. Code: https://github.com/YujieOuO/SMIE.Comment: Accepted by ACM MM 202

Spatio-Temporal Branching for Motion Prediction using Motion Increments

Human motion prediction (HMP) has emerged as a popular research topic due to its diverse applications, but it remains a challenging task due to the stochastic and aperiodic nature of future poses. Traditional methods rely on hand-crafted features and machine learning techniques, which often struggle to model the complex dynamics of human motion. Recent deep learning-based methods have achieved success by learning spatio-temporal representations of motion, but these models often overlook the reliability of motion data. Additionally, the temporal and spatial dependencies of skeleton nodes are distinct. The temporal relationship captures motion information over time, while the spatial relationship describes body structure and the relationships between different nodes. In this paper, we propose a novel spatio-temporal branching network using incremental information for HMP, which decouples the learning of temporal-domain and spatial-domain features, extracts more motion information, and achieves complementary cross-domain knowledge learning through knowledge distillation. Our approach effectively reduces noise interference and provides more expressive information for characterizing motion by separately extracting temporal and spatial features. We evaluate our approach on standard HMP benchmarks and outperform state-of-the-art methods in terms of prediction accuracy

A Self-Correcting Sequential Recommender

Sequential recommendations aim to capture users' preferences from their historical interactions so as to predict the next item that they will interact with. Sequential recommendation methods usually assume that all items in a user's historical interactions reflect her/his preferences and transition patterns between items. However, real-world interaction data is imperfect in that (i) users might erroneously click on items, i.e., so-called misclicks on irrelevant items, and (ii) users might miss items, i.e., unexposed relevant items due to inaccurate recommendations. To tackle the two issues listed above, we propose STEAM, a Self-correcTing sEquentiAl recoMmender. STEAM first corrects an input item sequence by adjusting the misclicked and/or missed items. It then uses the corrected item sequence to train a recommender and make the next item prediction.We design an item-wise corrector that can adaptively select one type of operation for each item in the sequence. The operation types are 'keep', 'delete' and 'insert.' In order to train the item-wise corrector without requiring additional labeling, we design two self-supervised learning mechanisms: (i) deletion correction (i.e., deleting randomly inserted items), and (ii) insertion correction (i.e., predicting randomly deleted items). We integrate the corrector with the recommender by sharing the encoder and by training them jointly. We conduct extensive experiments on three real-world datasets and the experimental results demonstrate that STEAM outperforms state-of-the-art sequential recommendation baselines. Our in-depth analyses confirm that STEAM benefits from learning to correct the raw item sequences

Octet Quark Contents from SU(3) Flavor Symmetry

With the parametrization of parton distribution functions (PDFs) of the proton by Soffer \textit{et al.}, we extend the valence quark contents to other octet baryons by utilizing SU(3) flavor symmetry. We find the method practically useful. Fragmentation functions (FFs) are further obtained through the phenomenological Gribov-Lipatov relation at the $x \to 1$ region. Our results are compared with different models, and these different predictions can be discriminated by upcoming experiments.Comment: 6 pages, 5 figures, final version for journal publicatio

The interactions of single-wall carbon nanohorns with polar epithelium

Single-wall carbon nanohorns (SWCNHs), which have multitudes of horn interstices, an extensive surface area, and a spherical aggregate structure, offer many advantages over other carbon nanomaterials being used as a drug nanovector. The previous studies on the interaction between SWCNHs and cells have mostly emphasized on cellular uptake and intracellular trafficking, but seldom on epithelial cells. Polar epithelium as a typical biological barrier constitutes the prime obstacle for the transport of therapeutic agents to target site. This work tried to explore the permeability of SWCNHs through polar epithelium and their abilities to modulate transcellular transport, and evaluate the potential of SWCNHs in drug delivery. Madin-Darby canine kidney (MDCK) cell monolayer was used as a polar epithelial cell model, and as-grown SWCNHs, together with oxidized and fluorescein isothiocyanate-conjugated bovine serum albumin-labeled forms, were constructed and comprehensively investigated in vitro and in vivo. Various methods such as transmission electron microscopy and confocal imaging were used to visualize their intracellular uptake and localization, as well as to investigate the potential transcytotic process. The related mechanism was explored by specific inhibitors. Additionally, fast multispectral optoacoustic tomography imaging was used for monitoring the distribution and transport process of SWCNHs in vivo after oral administration in nude mice, as an evidence for their interaction with the intestinal epithelium. The results showed that SWCNHs had a strong bioadhesion property, and parts of them could be uptaken and transcytosed across the MDCK monolayer. Multiple mechanisms were involved in the uptake and transcytosis of SWCNHs with varying degrees. After oral administration, oxidized SWCNHs were distributed in the gastrointestinal tract and retained in the intestine for up to 36 h probably due to their surface adhesion and endocytosis into the intestinal epithelium. Overall, this comprehensive investigation demonstrated that SWCNHs can serve as a promising nanovector that can cross the barrier of polar epithelial cells and deliver drugs effectively