Results 241 to 250 of about 201,081 (282)

Mechanostimulatory Cues Determine Intestinal Fibroblast Fate and Profibrotic Remodeling in a Physiodynamic Human Gut‐on‐a‐Chip

Advanced Science, EarlyView.
A healthy gut barrier shields underlying fibroblasts from luminal shear forces, illustrating that “good fences make good neighbors.” Barrier damage exposes fibroblasts to shear stress, inducing cell death and the emergence of stress‐adapted, profibrotic fibroblasts. Sustained shear exposure promotes the formation of stiff aggregates of mechanoadapative
Soyoun Min, Nam Than, Yong Cheol Shin, Elif G. Ertugral, Chandrasekhar R. Kothapalli, Olumuyiwa Awoniyi, Hyun Jung Kim   +6 more
wiley   +1 more source

In Situ X‐Ray Tomography and Acoustic Emission Monitoring of Damage Evolution in C/C‐SiC Composites Fabricated by Liquid Silicon Infiltration

Advanced Science, EarlyView.
This study investigates how the internal structure of fiber‐reinforced ceramic composites affects their resistance to damage. By combining 3D X‐ray imaging with acoustic emission monitoring during mechanical testing, it reveals how silicon distribution influences crack formation.
Yang Chen, Renato S. M. Almeida, Stefan Flauder, Guillaume Couégnat, Kamen Tushtev, Jürgen Horvath, Peter Wriggers, Kurosch Rezwan   +7 more
wiley   +1 more source

Targeting Peptostreptococcus anaerobius with an Iron‐Based Nanozyme Reverses Ferroptosis Resistance and Enhances Antitumor Immunity in Colorectal Cancer

Advanced Science, EarlyView.
An iron‐based nanozyme selectively eliminates intratumoral P. anaerobius while catalytically generating ROS to induce ferroptosis, synergistically suppressing colorectal cancer growth and activating anti‐tumor immunity through immunogenic cell death. ABSTRACT The intratumoral microbiota is a critical determinant of therapeutic outcomes in colorectal ...
Yinghao Cao, Jun Wang, Hanwenchen Wang, Xiang Sun, Fang Fang, Shiying Li, Jianping Liu, Changrong Shi, Pengyuan Qi, Jianhua Zou, Xiaoyuan Chen, Kailin Cai   +11 more
wiley   +1 more source

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Listwise Collaborative Filtering

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015
Recently, ranking-oriented collaborative filtering (CF) algorithms have achieved great success in recommender systems. They obtained state-of-the-art performances by estimating a preference ranking of items for each user rather than estimating the absolute ratings on unrated items (as conventional rating-oriented CF algorithms do).
Huang, Shanshan, Wang, Shuaiqiang, Liu, Tie-Yan, Ma, Jun, Chen, Zhumin, Veijalainen, Jari   +6 more
openaire   +2 more sources

Adaptive collaborative filtering

Proceedings of the 2008 ACM conference on Recommender systems, 2008
We present a flexible approach to collaborative filtering which stems from basic research results. The approach is flexible in several dimensions: We introduce an algorithm where the loss can be tailored to a particular recommender problem. This allows us to optimize the prediction quality in a way that matters for the specific recommender system.
Markus Weimer, Alexandros Karatzoglou, Alexander J. Smola   +2 more
openaire   +1 more source

Collaborative Filtering with CCAM

2011 10th International Conference on Machine Learning and Applications and Workshops, 2011
Recommender system has become an important research topic since the high interest of academia and industry. As a branch of recommender systems, collaborative filtering (CF) systems take its roots from sharing opinions with others and have been shown to be very effective for generating high quality recommendations.
Meng-Lun Wu, Chia-Hui Chang, Rui-Zhe Liu
openaire   +1 more source

Shared collaborative filtering

Proceedings of the fifth ACM conference on Recommender systems, 2011
Traditional collaborative filtering (CF) methods suffer from sparse or even cold-start problems, especially for new established recommenders. However, since there are now quite a few recommender systems already existing in good working order, their data should be valuable to the new-start recommenders. This paper proposes shared collaborative filtering
Yu Zhao 0002, Xinping Feng, Jianqiang Li 0002, Bo Liu 0010   +3 more
openaire   +1 more source

Discrete Collaborative Filtering

Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, 2016
We address the efficiency problem of Collaborative Filtering (CF) by hashing users and items as latent vectors in the form of binary codes, so that user-item affinity can be efficiently calculated in a Hamming space. However, existing hashing methods for CF employ binary code learning procedures that most suffer from the challenging discrete ...
Hanwang Zhang, Fumin Shen, Wei Liu 0005, Xiangnan He 0001, Huanbo Luan, Tat-Seng Chua   +5 more
openaire   +1 more source

Improved Collaborative Filtering

2011
We consider the interactive model of collaborative filtering, where each member of a given set of users has a grade for each object in a given set of objects. The users do not know the grades at start, but a user can probe any object, thereby learning her grade for that object directly.
Aviv Nisgav, Boaz Patt-Shamir
openaire   +1 more source

Agents for Collaborative Filtering

2003
This paper describes a new generic agent-based framework for collaborative filtering. Usually, collaborative filtering tools use large collaborative document databases to model users’ preferences. Nevertheless, we believe that collaborative filtering can be accomplished with decentralized systems in which user’s preferences are learned from small ...
Fabrício Enembreck, Jean-Paul A. Barthès   +1 more
openaire   +1 more source