GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation

Streaming session-based recommendation (SSR) is a challenging task that requires the recommender system to do the session-based recommendation (SR) in the streaming scenario. In the real-world applications of e-commerce and social media, a sequence of user-item interactions generated within a certain period are grouped as a session, and these sessions consecutively arrive in the form of streams. Most of the recent SR research has focused on the static setting where the training data is first acquired and then used to train a session-based recommender model. They need several epochs of training over the whole dataset, which is infeasible in the streaming setting. Besides, they can hardly well capture long-term user interests because of the neglect or the simple usage of the user information. Although some streaming recommendation strategies have been proposed recently, they are designed for streams of individual interactions rather than streams of sessions. In this paper, we propose a Global Attributed Graph (GAG) neural network model with a Wasserstein reservoir for the SSR problem. On one hand, when a new session arrives, a session graph with a global attribute is constructed based on the current session and its associate user. Thus, the GAG can take both the global attribute and the current session into consideration to learn more comprehensive representations of the session and the user, yielding a better performance in the recommendation. On the other hand, for the adaptation to the streaming session scenario, a Wasserstein reservoir is proposed to help preserve a representative sketch of the historical data. Extensive experiments on two real-world datasets have been conducted to verify the superiority of the GAG model compared with the state-of-the-art methods

Perceive, Excavate and Purify: A Novel Object Mining Framework for Instance Segmentation

Recently, instance segmentation has made great progress with the rapid development of deep neural networks. However, there still exist two main challenges including discovering indistinguishable objects and modeling the relationship between instances. To deal with these difficulties, we propose a novel object mining framework for instance segmentation. In this framework, we first introduce the semantics perceiving subnetwork to capture pixels that may belong to an obvious instance from the bottom up. Then, we propose an object excavating mechanism to discover indistinguishable objects. In the mechanism, preliminary perceived semantics are regarded as original instances with classifications and locations, and then indistinguishable objects around these original instances are mined, which ensures that hard objects are fully excavated. Next, an instance purifying strategy is put forward to model the relationship between instances, which pulls the similar instances close and pushes away different instances to keep intra-instance similarity and inter-instance discrimination. In this manner, the same objects are combined as the one instance and different objects are distinguished as independent instances. Extensive experiments on the COCO dataset show that the proposed approach outperforms state-of-the-art methods, which validates the effectiveness of the proposed object mining framework.Comment: Accepted by CVPR Workshops 202

Intratreatment Tumor Volume Change During Definitive Chemoradiotherapy is Predictive for Treatment Outcome of Patients with Esophageal Carcinoma

Background: This study aimed to assess the predictive value of tumor volume changes of esophagus evaluated by serial computed tomography (CT) scans before, during, and after radical chemoradiotherapy (CRT) for treatment outcomes in patients with esophageal cancer (EC). Methods: Fifty-three patients with histologically confirmed EC were included for analysis. Gross tumor volume of esophagus (GTVe) was manually contoured on the CT images before treatment, at a twentieth fraction of radiotherapy, at completion of CRT and three months after treatment. GTVe reduction ratio (RR) was calculated to reveal changes of tumor volume by time. The Kaplan–Meier method was used to estimate survival and for univariate analysis. The Cox regression model was performed for multivariate analysis. Results: Predominant reduction of GTVe was observed during the first 20 fractions of radiotherapy. Age, pretreatment GTVe, GTVe three months after treatment and GTVe RR at twentieth fraction of radiotherapy were all significantly associated with overall survival (OS) in a univariate analysis. Gender was correlated with locoregional recurrence-free survival (LRRFS) in univariate analysis. Multivariate analysis showed that GTVe ≤20 cc, GTVe RR at twentieth fraction of radiotherapy ≥35% were positive predictive factors of OS and pretreatment GTVe ≤20 cc was prognostic for a favorable LRRFS. Conclusion: Pretreatment tumor volume and intratreatment volume reduction ratio are reliable prognostic factors for esophageal cancer treated with definitive CRT

HYTREL: Hypergraph-enhanced Tabular Data Representation Learning

Language models pretrained on large collections of tabular data have demonstrated their effectiveness in several downstream tasks. However, many of these models do not take into account the row/column permutation invariances, hierarchical structure, etc. that exist in tabular data. To alleviate these limitations, we propose HYTREL, a tabular language model, that captures the permutation invariances and three more structural properties of tabular data by using hypergraphs - where the table cells make up the nodes and the cells occurring jointly together in each row, column, and the entire table are used to form three different types of hyperedges. We show that HYTREL is maximally invariant under certain conditions for tabular data, i.e., two tables obtain the same representations via HYTREL iff the two tables are identical up to permutations. Our empirical results demonstrate that HYTREL consistently outperforms other competitive baselines on four downstream tasks with minimal pretraining, illustrating the advantages of incorporating the inductive biases associated with tabular data into the representations. Finally, our qualitative analyses showcase that HYTREL can assimilate the table structures to generate robust representations for the cells, rows, columns, and the entire table.Comment: NeurIPS 2023 (spotlight

Doping and temperature dependence of electron spectrum and quasiparticle dispersion in doped bilayer cuprates

Within the t-t'-J model, the electron spectrum and quasiparticle dispersion in doped bilayer cuprates in the normal state are discussed by considering the bilayer interaction. It is shown that the bilayer interaction splits the electron spectrum of doped bilayer cuprates into the bonding and antibonding components around the $(\pi,0)$ point. The differentiation between the bonding and antibonding components is essential, which leads to two main flat bands around the $(\pi,0)$ point below the Fermi energy. In analogy to the doped single layer cuprates, the lowest energy states in doped bilayer cuprates are located at the $(\pi/2,\pi/2)$ point. Our results also show that the striking behavior of the electronic structure in doped bilayer cuprates is intriguingly related to the bilayer interaction together with strong coupling between the electron quasiparticles and collective magnetic excitations.Comment: 9 pages, 4 figures, updated references, added figures and discussions, accepted for publication in Phys. Rev.

Microstructure, texture, and mechanical properties of two-pass extruded Mg-5Li-1Al sheet

404-409To overcome the difficult deformation of common Mg alloys, the Mg-5Li-1Al (wt %) alloy sheet with good strength-ductility balance has been successfully fabricated by two-pass extrusion at 280 °C. The microstructural evolution, texture, mechanical properties and stretch formability of the extruded sheets have been investigated. The results show that a refined microstructure can be obtained by two-pass extrusion due to dynamic recrystallization (DRX). The extruded sheet exhibits excellent formability with elongation to failure (FE) of 34% and Erichsen value of 4.82. The superior mechanical properties have been owing to both ultrafine DRX grains and weaken basal texture resulted from lithium addition

Microstructure, texture, and mechanical properties of two-pass extruded Mg-5Li-1Al sheet

To overcome the difficult deformation of common Mg alloys, the Mg-5Li-1Al (wt %) alloy sheet with good strength-ductility balance has been successfully fabricated by two-pass extrusion at 280 °C. The microstructural evolution, texture, mechanical properties and stretch formability of the extruded sheets have been investigated. The results show that a refined microstructure can be obtained by two-pass extrusion due to dynamic recrystallization (DRX). The extruded sheet exhibits excellent formability with elongation to failure (FE) of 34% and Erichsen value of 4.82. The superior mechanical properties have been owing to both ultrafine DRX grains and weaken basal texture resulted from lithium addition