CB-Conformer: Contextual biasing Conformer for biased word recognition

Due to the mismatch between the source and target domains, how to better utilize the biased word information to improve the performance of the automatic speech recognition model in the target domain becomes a hot research topic. Previous approaches either decode with a fixed external language model or introduce a sizeable biasing module, which leads to poor adaptability and slow inference. In this work, we propose CB-Conformer to improve biased word recognition by introducing the Contextual Biasing Module and the Self-Adaptive Language Model to vanilla Conformer. The Contextual Biasing Module combines audio fragments and contextual information, with only 0.2% model parameters of the original Conformer. The Self-Adaptive Language Model modifies the internal weights of biased words based on their recall and precision, resulting in a greater focus on biased words and more successful integration with the automatic speech recognition model than the standard fixed language model. In addition, we construct and release an open-source Mandarin biased-word dataset based on WenetSpeech. Experiments indicate that our proposed method brings a 15.34% character error rate reduction, a 14.13% biased word recall increase, and a 6.80% biased word F1-score increase compared with the base Conformer

GAIN: A Gated Adaptive Feature Interaction Network for Click-Through Rate Prediction

CTR (Click-Through Rate) prediction has attracted more and more attention from academia and industry for its significant contribution to revenue. In the last decade, learning feature interactions have become a mainstream research direction, and dozens of feature interaction-based models have been proposed for the CTR prediction task. The most common approach for existing models is to enumerate all possible feature interactions or to learn higher-order feature interactions by designing complex models. However, a simple enumeration will introduce meaningless and harmful interactions, and a complex model structure will bring a higher complexity. In this work, we propose a lightweight, yet effective model called the Gated Adaptive feature Interaction Network (GAIN). We devise a novel cross module to drop meaningless feature interactions and preserve informative ones. Our cross module consists of multiple gated units, each of which can independently learn an arbitrary-order feature interaction. We combine the cross module with a deep module into GAIN and conduct comparative experiments with state-of-the-art models on two public datasets to verify its validity. Our experimental results show that GAIN can achieve a comparable or even better performance compared to its competitors. Furthermore, in order to verify the effectiveness of the feature interactions learned by GAIN, we transfer learned interactions to other models, such as Logistic Regression (LR) and Factorization Machines (FM), and find out that their performance can be significantly improved

Distribution in Rat Blood and Brain of TDMQ20, a Copper Chelator Designed as a Drug-Candidate for Alzheimer’s Disease

International audience(1) Background: TDMQ20 is a specific regulator of copper homeostasis in the brain, able to inhibit cognitive impairment in the early stages of Alzheimer’s disease (AD) in mouse models of AD. To promote the further development of this drug-candidate, preliminary data on the pharmacokinetics of TDMQ20 in a mammal model have been collected. Since TDMQ20 should be administered orally, its absorption by the gastrointestinal tract was evaluated by comparison of blood concentrations after administration by oral and IV routes, and its ability to reach its target (the brain) was confirmed by comparison between blood and brain concentrations after oral administration. (2) Methods: plasmatic and brain concentrations of the drug after oral or intravenous treatment of rats at pharmacologically relevant doses were determined as a function of time. (3) Results: oral absorption of TDMQ20 was rapid and bioavailability was high (66% and 86% for males and females, respectively). The drug accumulated in the brain for several hours (brain–plasma ratio 3 h after oral administration = 2.6), and was then efficiently cleared. (4) Conclusions: these data confirm that TDMQ20 efficiently crosses the brain–blood barrier and is a relevant drug-candidate to treat AD

Distribution in Rat Blood and Brain of TDMQ20, a Copper Chelator Designed as a Drug-Candidate for Alzheimer’s Disease

International audience(1) Background: TDMQ20 is a specific regulator of copper homeostasis in the brain, able to inhibit cognitive impairment in the early stages of Alzheimer’s disease (AD) in mouse models of AD. To promote the further development of this drug-candidate, preliminary data on the pharmacokinetics of TDMQ20 in a mammal model have been collected. Since TDMQ20 should be administered orally, its absorption by the gastrointestinal tract was evaluated by comparison of blood concentrations after administration by oral and IV routes, and its ability to reach its target (the brain) was confirmed by comparison between blood and brain concentrations after oral administration. (2) Methods: plasmatic and brain concentrations of the drug after oral or intravenous treatment of rats at pharmacologically relevant doses were determined as a function of time. (3) Results: oral absorption of TDMQ20 was rapid and bioavailability was high (66% and 86% for males and females, respectively). The drug accumulated in the brain for several hours (brain–plasma ratio 3 h after oral administration = 2.6), and was then efficiently cleared. (4) Conclusions: these data confirm that TDMQ20 efficiently crosses the brain–blood barrier and is a relevant drug-candidate to treat AD

Distribution in Rat Blood and Brain of TDMQ20, a Copper Chelator Designed as a Drug-Candidate for Alzheimer’s Disease

(1) Background: TDMQ20 is a specific regulator of copper homeostasis in the brain, able to inhibit cognitive impairment in the early stages of Alzheimer’s disease (AD) in mouse models of AD. To promote the further development of this drug-candidate, preliminary data on the pharmacokinetics of TDMQ20 in a mammal model have been collected. Since TDMQ20 should be administered orally, its absorption by the gastrointestinal tract was evaluated by comparison of blood concentrations after administration by oral and IV routes, and its ability to reach its target (the brain) was confirmed by comparison between blood and brain concentrations after oral administration. (2) Methods: plasmatic and brain concentrations of the drug after oral or intravenous treatment of rats at pharmacologically relevant doses were determined as a function of time. (3) Results: oral absorption of TDMQ20 was rapid and bioavailability was high (66% and 86% for males and females, respectively). The drug accumulated in the brain for several hours (brain–plasma ratio 3 h after oral administration = 2.6), and was then efficiently cleared. (4) Conclusions: these data confirm that TDMQ20 efficiently crosses the brain–blood barrier and is a relevant drug-candidate to treat AD