Apparatus and process for freeform fabrication of composite reinforcement preforms

A solid freeform fabrication process and apparatus for making a three-dimensional reinforcement shape. The process comprises the steps of (1) operating a multiple-channel material deposition device for dispensing a liquid adhesive composition and selected reinforcement materials at predetermined proportions onto a work surface; (2) during the material deposition process, moving the deposition device and the work surface relative to each other in an X-Y plane defined by first and second directions and in a Z direction orthogonal to the X-Y plane so that the materials are deposited to form a first layer of the shape; (3) repeating these steps to deposit multiple layers for forming a three-dimensional preform shape; and (4) periodically hardening the adhesive to rigidize individual layers of the preform. These steps are preferably executed under the control of a computer system by taking additional steps of (5) creating a geometry of the shape on the computer with the geometry including a plurality of segments defining the preform shape and each segment being preferably coded with a reinforcement composition defining a specific proportion of different reinforcement materials; (6) generating programmed signals corresponding to each of the segments in a predetermined sequence; and (7) moving the deposition device and the work surface relative to each other in response to these programmed signals. Preferably, the system is also operated to generate a support structure for any un-supported feature of the 3-D preform shape

Graph-based Alignment and Uniformity for Recommendation

Collaborative filtering-based recommender systems (RecSys) rely on learning representations for users and items to predict preferences accurately. Representation learning on the hypersphere is a promising approach due to its desirable properties, such as alignment and uniformity. However, the sparsity issue arises when it encounters RecSys. To address this issue, we propose a novel approach, graph-based alignment and uniformity (GraphAU), that explicitly considers high-order connectivities in the user-item bipartite graph. GraphAU aligns the user/item embedding to the dense vector representations of high-order neighbors using a neighborhood aggregator, eliminating the need to compute the burdensome alignment to high-order neighborhoods individually. To address the discrepancy in alignment losses, GraphAU includes a layer-wise alignment pooling module to integrate alignment losses layer-wise. Experiments on four datasets show that GraphAU significantly alleviates the sparsity issue and achieves state-of-the-art performance. We open-source GraphAU at https://github.com/YangLiangwei/GraphAU.Comment: 4 page

Contextual Collaboration: Uniting Collaborative Filtering with Pre-trained Language Models

Traditional recommender systems have predominantly relied on identity representations (IDs) to characterize users and items. In contrast, the emergence of pre-trained language model (PLM) en-coders has significantly enriched the modeling of contextual item descriptions. While PLMs excel in addressing few-shot, zero-shot, and unified modeling scenarios, they often overlook the critical collaborative filtering signal. This omission gives rise to two pivotal challenges: (1) Collaborative Contextualization, aiming for the seamless integration of collaborative signals with contextual representations. (2) The necessity to bridge the representation gap between ID-based and contextual representations while preserving their contextual semantics. In this paper, we introduce CollabContext, a novel model that skillfully merges collaborative filtering signals with contextual representations, aligning these representations within the contextual space while retaining essential contextual semantics. Experimental results across three real-world datasets showcase substantial improvements. Through its capability in collaborative contextualization, CollabContext demonstrates remarkable enhancements in recommendation performance, particularly in cold-start scenarios. The code is available after the conference accepts the paper

Group Identification via Transitional Hypergraph Convolution with Cross-view Self-supervised Learning

With the proliferation of social media, a growing number of users search for and join group activities in their daily life. This develops a need for the study on the group identification (GI) task, i.e., recommending groups to users. The major challenge in this task is how to predict users' preferences for groups based on not only previous group participation of users but also users' interests in items. Although recent developments in Graph Neural Networks (GNNs) accomplish embedding multiple types of objects in graph-based recommender systems, they, however, fail to address this GI problem comprehensively. In this paper, we propose a novel framework named Group Identification via Transitional Hypergraph Convolution with Graph Self-supervised Learning (GTGS). We devise a novel transitional hypergraph convolution layer to leverage users' preferences for items as prior knowledge when seeking their group preferences. To construct comprehensive user/group representations for GI task, we design the cross-view self-supervised learning to encourage the intrinsic consistency between item and group preferences for each user, and the group-based regularization to enhance the distinction among group embeddings. Experimental results on three benchmark datasets verify the superiority of GTGS. Additional detailed investigations are conducted to demonstrate the effectiveness of the proposed framework.Comment: 11 pages. Accepted by CIKM'2

Genetic Engineering of Starch Biosynthesis in Maize Seeds for Efficient Enzymatic Digestion of Starch during Bioethanol Production

Maize accumulates large amounts of starch in seeds which have been used as food for human and animals. Maize starch is an importantly industrial raw material for bioethanol production. One critical step in bioethanol production is degrading starch to oligosaccharides and glucose by alpha-amylase and glucoamylase. This step usually requires high temperature and additional equipment, leading to an increased production cost. Currently, there remains a lack of specially designed maize cultivars with optimized starch (amylose and amylopectin) compositions for bioethanol production. We discussed the features of starch granules suitable for efficient enzymatic digestion. Thus far, great advances have been made in molecular characterization of the key proteins involved in starch metabolism in maize seeds. The review explores how these proteins affect starch metabolism pathway, especially in controlling the composition, size and features of starch. We highlight the roles of key enzymes in controlling amylose/amylopectin ratio and granules architecture. Based on current technological process of bioethanol production using maize starch, we propose that several key enzymes can be modified in abundance or activities via genetic engineering to synthesize easily degraded starch granules in maize seeds. The review provides a clue for developing special maize cultivars as raw material in the bioethanol industry

DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation

Graph Neural Network (GNN) based recommender systems have been attracting more and more attention in recent years due to their excellent performance in accuracy. Representing user-item interactions as a bipartite graph, a GNN model generates user and item representations by aggregating embeddings of their neighbors. However, such an aggregation procedure often accumulates information purely based on the graph structure, overlooking the redundancy of the aggregated neighbors and resulting in poor diversity of the recommended list. In this paper, we propose diversifying GNN-based recommender systems by directly improving the embedding generation procedure. Particularly, we utilize the following three modules: submodular neighbor selection to find a subset of diverse neighbors to aggregate for each GNN node, layer attention to assign attention weights for each layer, and loss reweighting to focus on the learning of items belonging to long-tail categories. Blending the three modules into GNN, we present DGRec(Diversified GNN-based Recommender System) for diversified recommendation. Experiments on real-world datasets demonstrate that the proposed method can achieve the best diversity while keeping the accuracy comparable to state-of-the-art GNN-based recommender systems.Comment: 9 pages, WSDM 202

Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering

Collaborative filtering (CF) is a widely employed technique that predicts user preferences based on past interactions. Negative sampling plays a vital role in training CF-based models with implicit feedback. In this paper, we propose a novel perspective based on the sampling area to revisit existing sampling methods. We point out that current sampling methods mainly focus on Point-wise or Line-wise sampling, lacking flexibility and leaving a significant portion of the hard sampling area un-explored. To address this limitation, we propose Dimension Independent Mixup for Hard Negative Sampling (DINS), which is the first Area-wise sampling method for training CF-based models. DINS comprises three modules: Hard Boundary Definition, Dimension Independent Mixup, and Multi-hop Pooling. Experiments with real-world datasets on both matrix factorization and graph-based models demonstrate that DINS outperforms other negative sampling methods, establishing its effectiveness and superiority. Our work contributes a new perspective, introduces Area-wise sampling, and presents DINS as a novel approach that achieves state-of-the-art performance for negative sampling. Our implementations are available in PyTorch

A dynamic prescriptive maintenance model considering system aging and degradation

This paper develops a dynamic maintenance strategy for a system subject to aging and degradation. The influence of degradation level and aging on system failure rate is modeled in an additive way. Based on the observed degradation level at the inspection, repair or replacement is carried out upon the system. Previous researches assume that repair will always lead to an improvement in the health condition of the system. However, in our study, repair reduces the system age but on the other hand, increases the degradation level. Considering the two-fold influence of maintenance actions, we perform reliability analysis on system reliability as a first step. The evolution of system reliability serves as a foundation for establishing the maintenance model. The optimal maintenance strategy is achieved by minimizing the long-run cost rate in terms of the repair cycle. At each inspection, the parameters of the degradation processes are updated with maximum a posteriori estimation when a new observation arrives. The effectiveness of the proposed model is illustrated through a case study of locomotive wheel-sets. The maintenance model considers the influence of degradation and aging on system failure and dynamically determines the optimal inspection time, which is more flexible than traditional stationary maintenance strategies and can provide better performance in the field