643 research outputs found
Differential Activation of ER Stress Signal Pathway s Contributes to Palmitate-Induced Hepatocyte Lipoapoptosis
Saturated free fatty acids-induced hepatocyte lipoapoptosis plays a pivotal role in non-alcoholic steatohepatitis. Theactivation of endoplasmic reticulum (ER) stress isinvolved in hepatocyte lipoapoptosis induced by thesaturated free fatty acidpalmitate (PA). However, the underlying mechanismsof the role of ER stress in hepatocyte lipoapoptosis remain largely unclear.In this study, we showed that PA and tunicamycin (Tun), a classic ER stress inducer, resulted in differential activation of ERstress pathways. Our data revealed that PA inducedchronic and persistent ER stress response, but Tuninduced acute and transientER stress response. Compared with Tun treatment, PAinduced much lower glucose-regulated protein 78 (GRP78), a centralregulator of ER homeostasis, accumulation. It is noteworthy that GRP78 over-expression not only inhibited PA-induced ERstress but also decreased PA-induced apoptosis. Taken together, our data suggest that the differentialactivation of ER stresssignal plays an important role in PA-induced hepatocyte lipoapoptosis. More detailed studies on the mechanisms of PA inrepressing the accumulation of GRP78 will contribute to the understanding of molecular mechanisms of lipoapoptosis
DB-LSH: Locality-Sensitive Hashing with Query-based Dynamic Bucketing
Among many solutions to the high-dimensional approximate nearest neighbor
(ANN) search problem, locality sensitive hashing (LSH) is known for its
sub-linear query time and robust theoretical guarantee on query accuracy.
Traditional LSH methods can generate a small number of candidates quickly from
hash tables but suffer from large index sizes and hash boundary problems.
Recent studies to address these issues often incur extra overhead to identify
eligible candidates or remove false positives, making query time no longer
sub-linear. To address this dilemma, in this paper we propose a novel LSH
scheme called DB-LSH which supports efficient ANN search for large
high-dimensional datasets. It organizes the projected spaces with
multi-dimensional indexes rather than using fixed-width hash buckets. Our
approach can significantly reduce the space cost as by avoiding the need to
maintain many hash tables for different bucket sizes. During the query phase of
DB-LSH, a small number of high-quality candidates can be generated efficiently
by dynamically constructing query-based hypercubic buckets with the required
widths through index-based window queries. For a dataset of -dimensional
points with approximation ratio , our rigorous theoretical analysis shows
that DB-LSH achieves a smaller query cost , where
is bounded by while the bound is in the
existing work. An extensive range of experiments on real-world data
demonstrates the superiority of DB-LSH over state-of-the-art methods on both
efficiency and accuracy.Comment: Accepted by ICDE 202
Exploring Users Motivations to Knowledge Contribution at the Creation Stage of Online Communities
The motivation of online community users’ contribution behavior has captured the attention of many scholars in various disciplines. But little empirical research has studied user behaviors according to the different stages of an online community. Based on Iriberri et al. (2009)’s life cycle model of online community, our study specifically focuses on the users’ contribution behavior at the creation stage of an online community. Some constructs of previous studies like trust and online-identity are not able to explain users’ behavior in our context, because identity and trust relationship are not established until growth and mature stage. Given the uniqueness of early participants and online community lifecycle, our study integrates three theoretical perspectives (need fulfillment theory, task-technology fit model and self-verification theory) to propose a research model to understand the participation motives. Furthermore, we introduced a moderator of group-level uniqueness to the self- verification theory
Consensus-Based Group Task Assignment with Social Impact in Spatial Crowdsourcing
Abstract With the pervasiveness of GPS-enabled smart devices and increased wireless communication technologies, spatial crowdsourcing (SC) has drawn increasing attention in assigning location-sensitive tasks to moving workers. In real-world scenarios, for the complex tasks, SC is more likely to assign each task to more than one worker, called group task assignment (GTA), for the reason that an individual worker cannot complete the task well by herself. It is a challenging issue to assign worker groups the tasks that they are interested in and willing to perform. In this paper, we propose a novel framework for group task assignment based on worker groups’ preferences, which includes two components: social impact-based preference modeling (SIPM) and preference-aware group task assignment (PGTA). SIPM employs a bipartite graph embedding model and the attention mechanism to learn the social impact-based preferences of different worker groups on different task categories. PGTA utilizes an optimal task assignment algorithm based on the tree decomposition technique to maximize the overall task assignments, in which we give higher priorities to the worker groups showing more interests in the tasks. We further optimize the original framework by proposing strategies to improve the effectiveness of group task assignment, wherein a deep learning method and the group consensus are taken into consideration. Extensive empirical studies verify that the proposed techniques and optimization strategies can settle the problem nicely
Sequential Recommendation with Diffusion Models
Generative models, such as Variational Auto-Encoder (VAE) and Generative
Adversarial Network (GAN), have been successfully applied in sequential
recommendation. These methods require sampling from probability distributions
and adopt auxiliary loss functions to optimize the model, which can capture the
uncertainty of user behaviors and alleviate exposure bias. However, existing
generative models still suffer from the posterior collapse problem or the model
collapse problem, thus limiting their applications in sequential
recommendation. To tackle the challenges mentioned above, we leverage a new
paradigm of the generative models, i.e., diffusion models, and present
sequential recommendation with diffusion models (DiffRec), which can avoid the
issues of VAE- and GAN-based models and show better performance. While
diffusion models are originally proposed to process continuous image data, we
design an additional transition in the forward process together with a
transition in the reverse process to enable the processing of the discrete
recommendation data. We also design a different noising strategy that only
noises the target item instead of the whole sequence, which is more suitable
for sequential recommendation. Based on the modified diffusion process, we
derive the objective function of our framework using a simplification technique
and design a denoise sequential recommender to fulfill the objective function.
As the lengthened diffusion steps substantially increase the time complexity,
we propose an efficient training strategy and an efficient inference strategy
to reduce training and inference cost and improve recommendation diversity.
Extensive experiment results on three public benchmark datasets verify the
effectiveness of our approach and show that DiffRec outperforms the
state-of-the-art sequential recommendation models
A Learned Index for Exact Similarity Search in Metric Spaces
Indexing is an effective way to support efficient query processing in large
databases. Recently the concept of learned index has been explored actively to
replace or supplement traditional index structures with machine learning models
to reduce storage and search costs. However, accurate and efficient similarity
query processing in high-dimensional metric spaces remains to be an open
challenge. In this paper, a novel indexing approach called LIMS is proposed to
use data clustering and pivot-based data transformation techniques to build
learned indexes for efficient similarity query processing in metric spaces. The
underlying data is partitioned into clusters such that each cluster follows a
relatively uniform data distribution. Data redistribution is achieved by
utilizing a small number of pivots for each cluster. Similar data are mapped
into compact regions and the mapped values are totally ordinal. Machine
learning models are developed to approximate the position of each data record
on the disk. Efficient algorithms are designed for processing range queries and
nearest neighbor queries based on LIMS, and for index maintenance with dynamic
updates. Extensive experiments on real-world and synthetic datasets demonstrate
the superiority of LIMS compared with traditional indexes and state-of-the-art
learned indexes.Comment: 14 pages, 14 figures, submitted to Transactions on Knowledge and Data
Engineerin
Quaternion-Based Graph Convolution Network for Recommendation
Graph Convolution Network (GCN) has been widely applied in recommender
systems for its representation learning capability on user and item embeddings.
However, GCN is vulnerable to noisy and incomplete graphs, which are common in
real world, due to its recursive message propagation mechanism. In the
literature, some work propose to remove the feature transformation during
message propagation, but making it unable to effectively capture the graph
structural features. Moreover, they model users and items in the Euclidean
space, which has been demonstrated to have high distortion when modeling
complex graphs, further degrading the capability to capture the graph
structural features and leading to sub-optimal performance. To this end, in
this paper, we propose a simple yet effective Quaternion-based Graph
Convolution Network (QGCN) recommendation model. In the proposed model, we
utilize the hyper-complex Quaternion space to learn user and item
representations and feature transformation to improve both performance and
robustness. Specifically, we first embed all users and items into the
Quaternion space. Then, we introduce the quaternion embedding propagation
layers with quaternion feature transformation to perform message propagation.
Finally, we combine the embeddings generated at each layer with the mean
pooling strategy to obtain the final embeddings for recommendation. Extensive
experiments on three public benchmark datasets demonstrate that our proposed
QGCN model outperforms baseline methods by a large margin.Comment: 13 pages, 7 figures, 6 tables. Submitted to ICDE 202
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