Memory Augmented Graph Neural Networks for Sequential Recommendation

The chronological order of user-item interactions can reveal time-evolving and sequential user behaviors in many recommender systems. The items that users will interact with may depend on the items accessed in the past. However, the substantial increase of users and items makes sequential recommender systems still face non-trivial challenges: (1) the hardness of modeling the short-term user interests; (2) the difficulty of capturing the long-term user interests; (3) the effective modeling of item co-occurrence patterns. To tackle these challenges, we propose a memory augmented graph neural network (MA-GNN) to capture both the long- and short-term user interests. Specifically, we apply a graph neural network to model the item contextual information within a short-term period and utilize a shared memory network to capture the long-range dependencies between items. In addition to the modeling of user interests, we employ a bilinear function to capture the co-occurrence patterns of related items. We extensively evaluate our model on five real-world datasets, comparing with several state-of-the-art methods and using a variety of performance metrics. The experimental results demonstrate the effectiveness of our model for the task of Top-K sequential recommendation.Comment: Accepted by the 34th AAAI Conference on Artificial Intelligence (AAAI 2020

Knowledge-Enhanced Top-K Recommendation in Poincar\'e Ball

Personalized recommender systems are increasingly important as more content and services become available and users struggle to identify what might interest them. Thanks to the ability for providing rich information, knowledge graphs (KGs) are being incorporated to enhance the recommendation performance and interpretability. To effectively make use of the knowledge graph, we propose a recommendation model in the hyperbolic space, which facilitates the learning of the hierarchical structure of knowledge graphs. Furthermore, a hyperbolic attention network is employed to determine the relative importances of neighboring entities of a certain item. In addition, we propose an adaptive and fine-grained regularization mechanism to adaptively regularize items and their neighboring representations. Via a comparison using three real-world datasets with state-of-the-art methods, we show that the proposed model outperforms the best existing models by 2-16% in terms of NDCG@K on Top-K recommendation.Comment: Accepted by the 35th AAAI Conference on Artificial Intelligence (AAAI 2021

Graph Inductive Biases in Transformers without Message Passing

Transformers for graph data are increasingly widely studied and successful in numerous learning tasks. Graph inductive biases are crucial for Graph Transformers, and previous works incorporate them using message-passing modules and/or positional encodings. However, Graph Transformers that use message-passing inherit known issues of message-passing, and differ significantly from Transformers used in other domains, thus making transfer of research advances more difficult. On the other hand, Graph Transformers without message-passing often perform poorly on smaller datasets, where inductive biases are more crucial. To bridge this gap, we propose the Graph Inductive bias Transformer (GRIT) -- a new Graph Transformer that incorporates graph inductive biases without using message passing. GRIT is based on several architectural changes that are each theoretically and empirically justified, including: learned relative positional encodings initialized with random walk probabilities, a flexible attention mechanism that updates node and node-pair representations, and injection of degree information in each layer. We prove that GRIT is expressive -- it can express shortest path distances and various graph propagation matrices. GRIT achieves state-of-the-art empirical performance across a variety of graph datasets, thus showing the power that Graph Transformers without message-passing can deliver.Comment: Published as a conference paper at ICML 2023; 17 page

Textural effect of Pt catalyst layers with different carbon supports on internal oxygen diffusion during oxygen reduction reaction

One way to address the cost issue of polymer electrolyte membrane fuel cells (PEMFCs) is to reduce the amount of platinum used in the cathode catalyst layers (CLs). The oxygen mass transfer resistance of the cathode CLs is the main bottleneck limiting the polarization performance of low Pt-loading membrane electrodes at high current densities. Pt nanoparticles, ionomers, carbon supports, and water in cathode CLs can all affect their oxygen mass transfer resistance. From the perspective of carbon supports, this paper changed the texture of CLs by adding carbon nanotubes (CNTs) or graphene oxide (GO) into carbon black (XC72) and studied its impact on the oxygen mass transfer resistance. A mathematical model was adopted to correlate total mass transfer resistance and internal diffusion efficiency factor with CL structure parameters in order to determine the dominant textural effect of a CL. The results show that adding 30%CNT or 20GO to carbon black of XC72 improved the electrocatalytic performance and mass transfer capability of the composite carbon-supported Pt catalyst layers during oxygen reduction reaction. The study further reveals that the smaller particle-sized carbon material with tiny Pt nanoparticles deposition can minimize the internal oxygen diffusion resistance. A less dense CL structure can reduce the oxygen transfer resistance through the secondary pores. The conclusion obtained can provide guidance for the rational design of optimal cathode CLs of PEMFCs

Postoperative Fever: The Potential Relationship with Prognosis in Node Negative Breast Cancer Patients

Background: Postoperative fever may serve as an indirect sign to reflect the alterations of the host milieu caused by surgery. It still remains open to investigation whether postoperative fever has a bearing on prognosis in patients with lymph node negative breast cancers. Methods: We performed a retrospective study of 883 female unilateral patients with lymph node negative breast cancer. Fever was defined as an oral temperature $38 in one week postoperation. Survival curves were performed with Kaplan-Meier method, and annual relapse hazard was estimated by hazard function. Findings: The fever patients were older than those without fever (P,0.0001). Hypertensive patients had a propensity for fever after surgery (P = 0.011). A statistically significant difference was yielded in the incidence of fever among HR+/ERBB2-, ERBB2+, HR-/ERBB2- subgroups (P = 0.012). In the univariate survival analysis, we observed postoperative fever patients were more likely to recur than those without fever (P = 0.0027). The Cox proportional hazards regression analysis showed that postoperative fever (P = 0.044, RR = 1.89, 95%CI 1.02–3.52) as well as the HR/ERBB2 subgroups (P = 0.013, HR = 1.60, 95%CI 1.09–2.31) was an independent prognostic factor for relapse-free survival. Conclusion: Postoperative fever may contribute to relapse in node negative breast cancer patients, which suggests that changes in host milieu related to fever might accelerate the growth of micro-metastatic foci. It may be more precise t

One-stop stroke management platform reduces workflow times in patients receiving mechanical thrombectomy

Background and purposeClinical outcome in patients who received thrombectomy treatment is time-dependent. The purpose of this study was to evaluate the efficacy of the one-stop stroke management (OSSM) platform in reducing in-hospital workflow times in patients receiving thrombectomy compared with the traditional model.MethodsThe data of patients who received thrombectomy treatment through the OSSM platform and traditional protocol transshipment pathway were retrospectively analyzed and compared. The treatment-related time interval and the clinical outcome of the two groups were also assessed and compared. The primary efficacy endpoint was the time from door to groin puncture (DPT).ResultsThere were 196 patients in the OSSM group and 210 patients in the control group, in which they were treated by the traditional approach. The mean DPT was significantly shorter in the OSSM group than in the control group (76 vs. 122 min; P < 0.001). The percentages of good clinical outcomes at the 90-day time point of the two groups were comparable (P = 0.110). A total of 121 patients in the OSSM group and 124 patients in the control group arrived at the hospital within 360 min from symptom onset. The mean DPT and time from symptom onset to recanalization (ORT) were significantly shorter in the OSSM group than in the control group. Finally, a higher rate of good functional outcomes was achieved in the OSSM group than in the control group (53.71 vs. 40.32%; P = 0.036).ConclusionCompared to the traditional transfer model, the OSSM transfer model significantly reduced the in-hospital delay in patients with acute stroke receiving thrombectomy treatment. This novel model significantly improved the clinical outcomes of patients presenting within the first 6 h after symptom onset

Optimized method for polarization-based image dehazing

Image dehazing is desired under the foggy, rainy weather, or the underwater condition. Since the polarization-based image dehazing utilizes additional polarization information of light to de-scatter, image detail can be recovered well, but how to segment the polarization information of the background radiance and the object radiance becomes the key problem. For solving this problem, a method which combing polarization and contrast enhancement is demonstrated. This method contains two main steps, (a) by seeking the region of large mean intensity, low contrast and large mean degree of polarization, the no-object region can be found, and (b) through defining a weight function and judging whether the dehazed image can achieve high contrast and low information loss, the degree of polarization for object radiance can be estimated. Based on the estimated parameters, the scatter of light by the mediums can be diminished considerably. The theoretical derivation shows that this method can achieve advantages complementation, such as being able to obtain more details like the polarization-based method and high image contrast like the contrast enhancement based method. Besides, it is physically sound and can achieve good dehazing performance under different conditions, which has been verified by different hazing polarization images