378 research outputs found
Cross-Attribute Matrix Factorization Model with Shared User Embedding
Over the past few years, deep learning has firmly established its prowess
across various domains, including computer vision, speech recognition, and
natural language processing. Motivated by its outstanding success, researchers
have been directing their efforts towards applying deep learning techniques to
recommender systems. Neural collaborative filtering (NCF) and Neural Matrix
Factorization (NeuMF) refreshes the traditional inner product in matrix
factorization with a neural architecture capable of learning complex and
data-driven functions. While these models effectively capture user-item
interactions, they overlook the specific attributes of both users and items.
This can lead to robustness issues, especially for items and users that belong
to the "long tail". Such challenges are commonly recognized in recommender
systems as a part of the cold-start problem. A direct and intuitive approach to
address this issue is by leveraging the features and attributes of the items
and users themselves. In this paper, we introduce a refined NeuMF model that
considers not only the interaction between users and items, but also acrossing
associated attributes. Moreover, our proposed architecture features a shared
user embedding, seamlessly integrating with user embeddings to imporve the
robustness and effectively address the cold-start problem. Rigorous experiments
on both the Movielens and Pinterest datasets demonstrate the superiority of our
Cross-Attribute Matrix Factorization model, particularly in scenarios
characterized by higher dataset sparsity
6-Hydroximino-4-Aza-A-Homo-Cholest-3-One and Related Analogue as A Potent Inducer of Apoptosis in Cancer Cells
Here we report that 6-hydroximino-4-aza-A-homo-cholest-3-one and 6-hydroxyl-4-aza-A-homo-cholest-3-one, new steroidal lactams were synthesized recently, displayed antiproliferative activity against some cancer cells through inducing cancer cell apoptosis by activation of the intrinsic pathway. The apoptotic function of the compounds was demonstrated by release of cytochrome C, activation of caspase 3 and annexin V labeling. Furthermore, the compound was able to inhibit tumor growth in an athymic mouse model
ViTASD: Robust Vision Transformer Baselines for Autism Spectrum Disorder Facial Diagnosis
Autism spectrum disorder (ASD) is a lifelong neurodevelopmental disorder with
very high prevalence around the world. Research progress in the field of ASD
facial analysis in pediatric patients has been hindered due to a lack of
well-established baselines. In this paper, we propose the use of the Vision
Transformer (ViT) for the computational analysis of pediatric ASD. The
presented model, known as ViTASD, distills knowledge from large facial
expression datasets and offers model structure transferability. Specifically,
ViTASD employs a vanilla ViT to extract features from patients' face images and
adopts a lightweight decoder with a Gaussian Process layer to enhance the
robustness for ASD analysis. Extensive experiments conducted on standard ASD
facial analysis benchmarks show that our method outperforms all of the
representative approaches in ASD facial analysis, while the ViTASD-L achieves a
new state-of-the-art. Our code and pretrained models are available at
https://github.com/IrohXu/ViTASD.Comment: 5 pages, 3 figures, Accepted by the ICASSP 202
Synthesis and Evaluation of Some 17-Acetamidoandrostane and N,N-Dimethyl-7-deoxycholic Amide Derivatives as Cytotoxic Agents: Structure/Activity Studies
Using pregnenolone and 7-deoxycholic acid as starting materials, some 17-acetamidoandrostane and N,N-dimethyl-7-deoxycholic amide derivatives were synthesized. The cytotoxicity of the synthesized compounds was tested in vitro against two tumor cell lines: SGC 7901 (human gastric carcinoma) and Bel 7404 (human liver carcinoma). The result showed that the blockage of the interaction of the amide group with outside groups might cause a decrease of the cytotoxicity, and an O-benzyloximino group at the 3-position of N,N-dimethyl-7-deoxycholic amide could enhance the cytotoxic activity of the compound. The information obtained from the studies provides the structure-activity relationship for these compounds and may be useful for the design of novel chemotherapeutic drugs
Determining the Biodegradability of Leachate Through XAD-8 Adsorption
AbstractKnowledge of biodegradability of leachate during municipal solid waste disposal is important for this process. The traditional indicator BOD5/COD can characterize the leachate stability, but the experiment of BOD5 lasts long time and success or failure is often affected by various factors, such as the seed, solution times, dilution multiple, and high levels of nitrate and nitrite. The present study was built up an innovative method with short time to determine the biodegradability of leachate by using XAD-8 resin. The leachate was sampled from the aerobic, semi-aerobic, and anaerobic degradations of municipal solid waste. The degradability of leachate could be determined using the formula given by 1-2.084CODXAD/COD. When the CODXAD/COD ratio is greater than 0.432, the leachate is expected to be stable
Synthesis and Evaluation of Some 17-Acetamidoandrostane and N,N-Dimethyl-7-deoxycholic Amide Derivatives as Cytotoxic Agents: Structure/Activity Studies
Using pregnenolone and 7-deoxycholic acid as starting materials, some 17-acetamidoandrostane and N,N-dimethyl-7-deoxycholic amide derivatives were synthesized. The cytotoxicity of the synthesized compounds was tested in vitro against two tumor cell lines: SGC 7901 (human gastric carcinoma) and Bel 7404 (human liver carcinoma). The result showed that the blockage of the interaction of the amide group with outside groups might cause a decrease of the cytotoxicity, and an O-benzyloximino group at the 3-position of N,N-dimethyl-7-deoxycholic amide could enhance the cytotoxic activity of the compound. The information obtained from the studies provides the structure-activity relationship for these compounds and may be useful for the design of novel chemotherapeutic drugs
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