Pre-training Graph Transformer with Multimodal Side Information for Recommendation

Side information of items, e.g., images and text description, has shown to be effective in contributing to accurate recommendations. Inspired by the recent success of pre-training models on natural language and images, we propose a pre-training strategy to learn item representations by considering both item side information and their relationships. We relate items by common user activities, e.g., co-purchase, and construct a homogeneous item graph. This graph provides a unified view of item relations and their associated side information in multimodality. We develop a novel sampling algorithm named MCNSampling to select contextual neighbors for each item. The proposed Pre-trained Multimodal Graph Transformer (PMGT) learns item representations with two objectives: 1) graph structure reconstruction, and 2) masked node feature reconstruction. Experimental results on real datasets demonstrate that the proposed PMGT model effectively exploits the multimodality side information to achieve better accuracies in downstream tasks including item recommendation, item classification, and click-through ratio prediction. We also report a case study of testing the proposed PMGT model in an online setting with 600 thousand users

Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization

Recently, the remarkable advance of the Large Language Model (LLM) has inspired researchers to transfer its extraordinary reasoning capability to both vision and language data. However, the prevailing approaches primarily regard the visual input as a prompt and focus exclusively on optimizing the text generation process conditioned upon vision content by a frozen LLM. Such an inequitable treatment of vision and language heavily constrains the model's potential. In this paper, we break through this limitation by representing both vision and language in a unified form. Specifically, we introduce a well-designed visual tokenizer to translate the non-linguistic image into a sequence of discrete tokens like a foreign language that LLM can read. The resulting visual tokens encompass high-level semantics worthy of a word and also support dynamic sequence length varying from the image. Coped with this tokenizer, the presented foundation model called LaVIT can handle both image and text indiscriminately under the same generative learning paradigm. This unification empowers LaVIT to serve as an impressive generalist interface to understand and generate multi-modal content simultaneously. Extensive experiments further showcase that it outperforms the existing models by a large margin on massive vision-language tasks. Our code and models will be available at https://github.com/jy0205/LaVIT

Histone H1 Depletion Impairs Embryonic Stem Cell Differentiation

Pluripotent embryonic stem cells (ESCs) are known to possess a relatively open chromatin structure; yet, despite efforts to characterize the chromatin signatures of ESCs, the role of chromatin compaction in stem cell fate and function remains elusive. Linker histone H1 is important for higher-order chromatin folding and is essential for mammalian embryogenesis. To investigate the role of H1 and chromatin compaction in stem cell pluripotency and differentiation, we examine the differentiation of embryonic stem cells that are depleted of multiple H1 subtypes. H1c/H1d/H1e triple null ESCs are more resistant to spontaneous differentiation in adherent monolayer culture upon removal of leukemia inhibitory factor. Similarly, the majority of the triple-H1 null embryoid bodies (EBs) lack morphological structures representing the three germ layers and retain gene expression signatures characteristic of undifferentiated ESCs. Furthermore, upon neural differentiation of EBs, triple-H1 null cell cultures are deficient in neurite outgrowth and lack efficient activation of neural markers. Finally, we discover that triple-H1 null embryos and EBs fail to fully repress the expression of the pluripotency genes in comparison with wild-type controls and that H1 depletion impairs DNA methylation and changes of histone marks at promoter regions necessary for efficiently silencing pluripotency gene Oct4 during stem cell differentiation and embryogenesis. In summary, we demonstrate that H1 plays a critical role in pluripotent stem cell differentiation, and our results suggest that H1 and chromatin compaction may mediate pluripotent stem cell differentiation through epigenetic repression of the pluripotency genes

Multi-Question Learning for Visual Question Answering

Visual Question Answering (VQA) raises a great challenge for computer vision and natural language processing communities. Most of the existing approaches consider video-question pairs individually during training. However, we observe that there are usually multiple (either sequentially generated or not) questions for the target video in a VQA task, and the questions themselves have abundant semantic relations. To explore these relations, we propose a new paradigm for VQA termed Multi-Question Learning (MQL). Inspired by the multi-task learning, MQL learns from multiple questions jointly together with their corresponding answers for a target video sequence. The learned representations of video-question pairs are then more general to be transferred for new questions. We further propose an effective VQA framework and design a training procedure for MQL, where the specifically designed attention network models the relation between input video and corresponding questions, enabling multiple video-question pairs to be co-trained. Experimental results on public datasets show the favorable performance of the proposed MQL-VQA framework compared to state-of-the-arts

Entorhinal cortex volume, thickness, surface area and curvature trajectories over the adult lifespan

The entorhinal cortex (ERC) acts as a connection between the hippocampus and temporal cortex and plays a key role in memory retrieval and navigation. The morphology of this brain region changes with age. However, there are few quantitative magnetic resonance imaging studies of ERC morphology across the healthy adult lifespan. In this study, we quantified ERC volume, thickness, surface area, and curvature in a large number of subjects spanning seven decades of life. Using structural MRI data from 563 healthy subjects ranging from 19 to 86 years of age, we explored the adult lifespan trajectory of ERC volume, thickness, surface and curvature. ERC volume, thickness, and surface area initially increased with age, reaching a peak at about 32 years, 40 years, and 50 years of age, respectively, after which they decreased with age. ERC volume and surface area were hemispherically leftward asymmetric, whereas ERC thickness was hemispherically rightward asymmetric, with no gender differences. The direction of asymmetry differed across the measures. This informs previous inconsistencies in reports of ERC asymmetry. ERC aging began in mid-adulthood. At this stage of life, it may be important to adopt some strategies to reduce the effects of aging on cognition

Attention-deficit/hyperactivity disorder is characterized by a delay in subcortical maturation

Although previous studies have found that ADHD is characterized by a delay in cortical maturation, it is not clear whether this phenomenon was secondary to developmental trajectories in subcortical regions (caudate, putamen, pallidum, thalamus, hippocampus and amygdala). Using the ADHD-200 dataset, we estimated subcortical volumes in 339 individuals with ADHD and 568 typically developing controls. We defined the growth trajectory of each subcortical structure, delineating a phase of childhood increase followed by an adolescent decrease in subcortical volumes using a quadratic growth model. From these trajectories, the age of attaining peak subcortical volumes was derived and used as an index of subcortical maturation. We found that subcortical structures (caudate, putamen, pallidum, thalamus, hippocampus and amygdala) followed curvilinear trajectories similar to those reported in previous studies. The volumes of these subcortical structures in ADHD were also delayed in the developmental trajectory, which suggested that ADHD may be characterized by a delay in subcortical maturation. This delay may lead to a shift in which individuals with ADHD go through the process of pruning the nerve connections that is part of the normal maturation process during adolescence. Further, we also found that the asymmetric development of subcortical structures was abnormal in ADHD, which resulted from the imbalance of the maturation delay of bilateral subcortical structures. The subcortical maturation delay may play an important role in the pathophysiology of ADHD. Our findings provide new potential targets to investigate the pathophysiology of ADHD

Attention with Long-Term Interval-Based Deep Sequential Learning for Recommendation

Modeling user behaviors as sequential learning provides key advantages in predicting future user actions, such as predicting the next product to purchase or the next song to listen to, for the purpose of personalized search and recommendation. Traditional methods for modeling sequential user behaviors usually depend on the premise of Markov processes, while recently recurrent neural networks (RNNs) have been adopted to leverage their power in modeling sequences. In this paper, we propose integrating attention mechanism into RNNs for better modeling sequential user behaviors. Specifically, we design a network featuring Attention with Long-term Interval-based Gated Recurrent Units (ALI-GRU) to model temporal sequences of user actions. Compared to previous works, our network can exploit the information of temporal dimension extracted by time interval-based GRU in addition to normal GRU to encoding user actions and has a specially designed matrix-form attention function to characterize both long-term preferences and short-term intents of users, while the attention-weighted features are finally decoded to predict the next user action. We have performed experiments on two well-known public datasets as well as a huge dataset built from real-world data of one of the largest online shopping websites. Experimental results show that the proposed ALI-GRU achieves significant improvement compared to state-of-the-art RNN-based methods. ALI-GRU is also adopted in a real-world application and results of the online A/B test further demonstrate its practical value

Prognostic value of fibrosis-5 index combined with C-reactive protein in patients with acute decompensated heart failure

Abstract Background Fibrosis-5 (FIB-5) index is a marker of liver fibrosis and has been shown to have a good prognostic value for patients with acute heart failure (AHF), and C-reactive protein (CRP) has inflammatory properties and predicts adverse prognosis in patients with HF. However, the long-term prognostic value of FIB-5 index combined with CRP in patients with acute decompensated HF (ADHF) is yet unclear. Methods This retrospective study included 1153 patients with ADHF hospitalized from January 2018 to May 2022.The FIB-5 index was calculated as (albumin [g/L]×0.3 + PLT count [109/L]×0.05)−(ALP [U/L]×0.014 + AST to ALT ratio×6 + 14). Patients were stratified into the following four groups according to the median value of FIB-5 index (=-2.11) and CRP (= 4.5): Group 1 had a high FIB-5 index (FIB-5 index >-2.11) and a low CRP (CRP ≤ 4.5); Group 2 had both low FIB-5 index and low CRP; Group 3 had both high FIB-5 index and high CRP; Group 4 had a low FIB-5 index (FIB-5 index ≤-2.11) and a high CRP (CRP > 4.5). The endpoint was major adverse cardiac and cerebral events (MACCEs). Multivariate Cox analysis was used to evaluate the association of the combination with the development of MACCEs. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) analysis were used to compare the accuracy of the combination with a single prognostic factor for predicting the risk of MACCEs. Results During the mean follow-up period of 584 ± 12 days, 488 (42.3%) patients had MACCEs. Kaplan–Meier analysis revealed that the incidence of MACCEs was different in the four groups (P < 0.001). After adjusting for the confounding factors, the hazard ratio (HR) for MACCEs in Group 4 (low FIB-5 index + high CRP) was the highest (Model 1, HR = 2.04, 95%CI 1.58–2.65, P < 0.001; Model 2, HR = 1.67, 95%CI 1.28–2.18, P < 0.001; Model 3, HR = 1.66, 95%CI: 1.27–2.17, P < 0.001). Additionally, the combination of FIB-5 index and CRP enabled more accurate prediction of MACCEs than FIB-5 index alone (NRI, 0.314,95%CI 0.199–0.429; P < 0.001; IDI, 0.023; 95% CI 0.015–0.032; P < 0.001). Conclusions In patients with ADHF, the combination of the FIB-5 index and CRP may be useful in risk stratification in the future

Research on Prediction Model of Interlayer Leakage in Separate Injection Wells in Offshore Oilfields

Abstract In order to ensure effective layered water injection, it is of great significance to establish the functional relationship among the equivalent diameter of damage, leakage and pressure of sealing cylinder. In this paper, the experimental and numerical simulation experiments were carried out to study the relationship between gap flow rate, pressure drop and equivalent diameter of sealing cylinder under the conditions of uniform corrosion, fracture non-uniformity and random groove non-uniform distribution and scratches in different tubing, equivalent diameters and gap widths. Through a lot of data analysis, the relationship between the damage state and the leakage quantity is established and evaluated by physical simulation test. The research shows that the error between the established model function relation and the test data is no more than 10%. By simulating the damage condition of the shock sealing cylinder and quantitatively detecting the defect of the sealing cylinder, the model function relation can well predict the corresponding damage situation. Therefore, this model function can be used to solve sealing cylinder inspection problem, so as to judge the seal defect and leakage quantity which is independent of the experience of the person in charge of the site and able to use the data as a basis for judgment. This research provides a new technology for the effective prediction of the damage of sealing cylinder, and provides theoretical guidance for selecting matching sealing tools and realizing layered mining

Self-Supervised Interest Transfer Network via Prototypical Contrastive Learning for Recommendation

Cross-domain recommendation has attracted increasing attention from industry and academia recently. However, most existing methods do not exploit the interest invariance between domains, which would yield sub-optimal solutions. In this paper, we propose a cross-domain recommendation method: Self-supervised Interest Transfer Network (SITN), which can effectively transfer invariant knowledge between domains via prototypical contrastive learning. Specifically, we perform two levels of cross-domain contrastive learning: 1) instance-to-instance contrastive learning, 2) instance-to-cluster contrastive learning. Not only that, we also take into account users' multi-granularity and multi-view interests. With this paradigm, SITN can explicitly learn the invariant knowledge of interest clusters between domains and accurately capture users' intents and preferences. We conducted extensive experiments on a public dataset and a large-scale industrial dataset collected from one of the world's leading e-commerce corporations. The experimental results indicate that SITN achieves significant improvements over state-of-the-art recommendation methods. Additionally, SITN has been deployed on a micro-video recommendation platform, and the online A/B testing results further demonstrate its practical value. Supplement is available at: https://github.com/fanqieCoffee/SITN-Supplement