Learning Compact Compositional Embeddings via Regularized Pruning for Recommendation

Latent factor models are the dominant backbones of contemporary recommender systems (RSs) given their performance advantages, where a unique vector embedding with a fixed dimensionality (e.g., 128) is required to represent each entity (commonly a user/item). Due to the large number of users and items on e-commerce sites, the embedding table is arguably the least memory-efficient component of RSs. For any lightweight recommender that aims to efficiently scale with the growing size of users/items or to remain applicable in resource-constrained settings, existing solutions either reduce the number of embeddings needed via hashing, or sparsify the full embedding table to switch off selected embedding dimensions. However, as hash collision arises or embeddings become overly sparse, especially when adapting to a tighter memory budget, those lightweight recommenders inevitably have to compromise their accuracy. To this end, we propose a novel compact embedding framework for RSs, namely Compositional Embedding with Regularized Pruning (CERP). Specifically, CERP represents each entity by combining a pair of embeddings from two independent, substantially smaller meta-embedding tables, which are then jointly pruned via a learnable element-wise threshold. In addition, we innovatively design a regularized pruning mechanism in CERP, such that the two sparsified meta-embedding tables are encouraged to encode information that is mutually complementary. Given the compatibility with agnostic latent factor models, we pair CERP with two popular recommendation models for extensive experiments, where results on two real-world datasets under different memory budgets demonstrate its superiority against state-of-the-art baselines. The codebase of CERP is available in https://github.com/xurong-liang/CERP.Comment: Accepted by ICDM'2

Incremental Activation Detection for Real-Time fMRI Series Using Robust Kalman Filter

Real-time functional magnetic resonance imaging (rt-fMRI) is a technique that enables us to observe human brain activations in real time. However, some unexpected noises that emerged in fMRI data collecting, such as acute swallowing, head moving and human manipulations, will cause much confusion and unrobustness for the activation analysis. In this paper, a new activation detection method for rt-fMRI data is proposed based on robust Kalman filter. The idea is to add a variation to the extended kalman filter to handle the additional sparse measurement noise and a sparse noise term to the measurement update step. Hence, the robust Kalman filter is designed to improve the robustness for the outliers and can be computed separately for each voxel. The algorithm can compute activation maps on each scan within a repetition time, which meets the requirement for real-time analysis. Experimental results show that this new algorithm can bring out high performance in robustness and in real-time activation detection

Comparative proteomic analysis reveals alterations in development and photosynthesis-related proteins in diploid and triploid rice

Six pairs of primers were designed for gene-specific transcript amplification. (DOC 25 kb

Culture-level dimensions of social axioms and their correlates across 41 cultures

Leung and colleagues have revealed a five-dimensional structure of social axioms across individuals from five cultural groups. The present research was designed to reveal the culture level factor structure of social axioms and its correlates across 41 nations. An ecological factor analysis on the 60 items of the Social Axioms Survey extracted two factors: Dynamic Externality correlates with value measures tapping collectivism, hierarchy, and conservatism and with national indices indicative of lower social development. Societal Cynicism is less strongly and broadly correlated with previous values measures or other national indices and seems to define a novel cultural syndrome. Its national correlates suggest that it taps the cognitive component of a cultural constellation labeled maleficence, a cultural syndrome associated with a general mistrust of social systems and other people. Discussion focused on the meaning of these national level factors of beliefs and on their relationships with individual level factors of belief derived from the same data set.(undefined

Phenomic analysis of chronic granulomatous disease reveals more severe integumentary infections in X-Linked compared with autosomal recessive chronic granulomatous disease

BACKGROUND : Chronic granulomatous disease (CGD) is an inborn error of immunity (IEI), characterised by recurrent bacterial and fungal infections. It is inherited either in an Xlinked (XL) or autosomal recessive (AR) mode. Phenome refers to the entire set of phenotypes expressed, and its study allows us to generate new knowledge of the disease. The objective of the study is to reveal the phenomic differences between XL and AR-CGD by using Human Phenotype Ontology (HPO) terms. METHODS : We collected data on 117 patients with genetically diagnosed CGD from Asia and Africa referred to the Asian Primary Immunodeficiency Network (APID network). Only 90 patients with sufficient clinical information were included for phenomic analysis. We used HPO terms to describe all phenotypes manifested in the patients. RESULTS : XL-CGD patients had a lower age of onset, referral, clinical diagnosis, and genetic diagnosis compared with AR-CGD patients. The integument and central nervous system were more frequently affected in XL-CGD patients. Regarding HPO terms, perianal abscess, cutaneous abscess, and elevated hepatic transaminase were correlated with XL-CGD. A higher percentage of XL-CGD patients presented with BCGitis/BCGosis as their first manifestation. Among our CGD patients, lung was the most frequently infected organ, with gastrointestinal system and skin ranking second and third, respectively. Aspergillus species, Mycobacterium bovis, and Mycobacteirum tuberculosis were the most frequent pathogens to be found. CONCLUSION : Phenomic analysis confirmed that XL-CGD patients have more recurrent and aggressive infections compared with AR-CGD patients. Various phenotypic differences listed out can be used as clinical handles to distinguish XL or AR-CGD based on clinical features.The Society for Relief of Disabled Children and Jeffrey Modell Foundation.https://www.frontiersin.org/journals/immunologydm2022Paediatrics and Child Healt