124 research outputs found
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
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