Neighbor Based Enhancement for the Long-Tail Ranking Problem in Video Rank Models

Rank models play a key role in industrial recommender systems, advertising, and search engines. Existing works utilize semantic tags and user-item interaction behaviors, e.g., clicks, views, etc., to predict the user interest and the item hidden representation for estimating the user-item preference score. However, these behavior-tag-based models encounter great challenges and reduced effectiveness when user-item interaction activities are insufficient, which we called "the long-tail ranking problem". Existing rank models ignore this problem, but its common and important because any user or item can be long-tailed once they are not consistently active for a short period. In this paper, we propose a novel neighbor enhancement structure to help train the representation of the target user or item. It takes advantage of similar neighbors (static or dynamic similarity) with multi-level attention operations balancing the weights of different neighbors. Experiments on the well-known public dataset MovieLens 1M demonstrate the efficiency of the method over the baseline behavior-tag-based model with an absolute CTR AUC gain of 0.0259 on the long-tail user dataset.Comment: 5 page

Advances in fundamental and translational breast cancer research in 2022

According to the data released by the International Cancer Research Institute of the World Health Organization (WHO), breast cancer is the malignant tumor with the highest incidence rate in women, and seriously endangers women's health.Many impressive advances have been achieved in fundamental and translational research of breast cancer, which deepened the understanding of the molecular nature of breast cancer and provided new scientific strategies for the precision treatment of breast cancer. This paper summarized the annual advances in fundamental and translational breast cancer research in 2022 in seven sections: tumor metabolism, tumor microenvironment, microbes and tumors, tumor metastasis, tumor drug resistance and drug screening, multi-omics research and artificial intelligence. Furthermore, we proposed an outlook on the future directions of breast cancer research to provide a reference for future studies

HEAD: an FHE-based Privacy-preserving Cloud Computing Protocol with Compact Storage and Efficient Computation

Fully homomorphic encryption (FHE) provides a natural solution for privacy-preserving cloud computing, but a straightforward FHE protocol may suffer from high computational overhead and a large ciphertext expansion rate, especially for computation-intensive tasks over large data, which are the main obstacles toward practical privacy-preserving cloud computing. In this paper, we present HEAD, a generic privacy-preserving cloud computing protocol that can be based on most mainstream (typically a BGV or GSW style scheme) FHE schemes with more compact storage and less computational costs than the straightforward FHE counterpart. In particular, our protocol enjoys a ciphertext/plaintext expansion rate of 1 (i.e., no expansion) in a cloud computing server, instead of a factor of hundreds of thousands. This is achieved by means of ``pseudorandomly masked\u27\u27 ciphertexts, and the efficient transformations of them into FHE ciphertexts to facilitate privacy-preserving cloud computing. Depending on the underlying FHE in use, our HEAD protocol can be instantiated with the three masking techniques, namely modulo-subtraction-masking, modulo-division-masking, and XOR-masking, to support the decimal integer, real, or binary messages. Thanks to these masking techniques, various homomorphic computation tasks are made more efficient and less prone to noise accumulation. Furthermore, our multi-input masking and unmasking operations are more flexible than the FHE SIMD-batching, by supporting an on-demand configuration of FHE during each cloud computing request. We evaluate the performance of HEAD protocols on BFV, BGV, CKKS, and FHEW schemes based on the PALISADE and SEAL libraries, which confirms the theoretical analysis of the storage savings, the reduction in terms of computational complexity and noise accumulation. For example, in the BFV computation optimization, the sum or product of eight ciphertexts overhead is reduced from 336.3 ms to 6.3 ms, or from 1219.4 ms to 9.5 ms, respectively. We also embed HEAD into a mainstream database, PostgreSQL, in a client-server cloud storage and computing style. Compared with a straightforward FHE protocol, our experiments show that HEAD does not incur ciphertext expansion, and exhibits at least an order of magnitude saving in computing time at the server side for various tasks (on a hundred ciphertexts), by paying a reasonable price in client pre-processing time and communication. Our storage advantage not only gets around the database storage limitation but also reduces the I/O overhead

Determining the Balance Between Drug Efficacy and Safety by the Network and Biological System Profile of Its Therapeutic Target

One of the most challenging puzzles in drug discovery is the identification and characterization of candidate drug of well-balanced profile between efficacy and safety. So far, extensive efforts have been made to evaluate this balance by estimating the quantitative structure–therapeutic relationship and exploring target profile of adverse drug reaction. Particularly, the therapeutic index (TI) has emerged as a key indicator illustrating this delicate balance, and a clinically successful agent requires a sufficient TI suitable for it corresponding indication. However, the TI information are largely unknown for most drugs, and the mechanism underlying the drugs with narrow TI (NTI drugs) is still elusive. In this study, the collective effects of human protein–protein interaction (PPI) network and biological system profile on the drugs' efficacy–safety balance were systematically evaluated. First, a comprehensive literature review of the FDA approved drugs confirmed their NTI status. Second, a popular feature selection algorithm based on artificial intelligence (AI) was adopted to identify key factors differencing the target mechanism between NTI and non-NTI drugs. Finally, this work revealed that the targets of NTI drugs were highly centralized and connected in human PPI network, and the number of similarity proteins and affiliated signaling pathways of the corresponding targets was much higher than those of non-NTI drugs. These findings together with the newly discovered features or feature groups clarified the key factors indicating drug's narrow TI, and could thus provide a novel direction for determining the delicate drug efficacy-safety balance

Natural Coevolution of Tumor and Immunoenvironment in Glioblastoma.

Isocitrate dehydrogenase (IDH) wild-type glioblastoma (GBM) has a dismal prognosis. A better understanding of tumor evolution holds the key to developing more effective treatment. Here we study GBM\u27s natural evolutionary trajectory by using rare multifocal samples. We sequenced 61,062 single cells from eight multifocal IDH wild-type primary GBMs and defined a natural evolution signature (NES) of the tumor. We show that the NES significantly associates with the activation of transcription factors that regulate brain development, including MYBL2 and FOSL2. Hypoxia is involved in inducing NES transition potentially via activation of the HIF1A-FOSL2 axis. High-NES tumor cells could recruit and polarize bone marrow-derived macrophages through activation of the FOSL2-ANXA1-FPR1/3 axis. These polarized macrophages can efficiently suppress T-cell activity and accelerate NES transition in tumor cells. Moreover, the polarized macrophages could upregulate CCL2 to induce tumor cell migration. SIGNIFICANCE: GBM progression could be induced by hypoxia via the HIF1A-FOSL2 axis. Tumor-derived ANXA1 is associated with recruitment and polarization of bone marrow-derived macrophages to suppress the immunoenvironment. The polarized macrophages promote tumor cell NES transition and migration. This article is highlighted in the In This Issue feature, p. 2711