MelHuBERT: A simplified HuBERT on Mel spectrograms

Self-supervised models have had great success in learning speech representations that can generalize to various downstream tasks. However, most self-supervised models require a large amount of compute and multiple GPUs to train, significantly hampering the development of self-supervised learning. In an attempt to reduce the computation of training, we revisit the training of HuBERT, a highly successful self-supervised model. We improve and simplify several key components, including the loss function, input representation, and training in multiple stages. Our model, MelHuBERT, is able to achieve favorable performance on phone recognition, speaker identification, and automatic speech recognition against HuBERT, while saving 31.2% of the pre-training time, or equivalently 33.5% MACs per one second speech. The code and pre-trained models are available in https://github.com/nervjack2/MelHuBERT.Comment: ASRU 202

Predicting the epidemic threshold of the susceptible-infected-recovered model

Researchers have developed several theoretical methods for predicting epidemic thresholds, including the mean-field like (MFL) method, the quenched mean-field (QMF) method, and the dynamical message passing (DMP) method. When these methods are applied to predict epidemic threshold they often produce differing results and their relative levels of accuracy are still unknown. We systematically analyze these two issues---relationships among differing results and levels of accuracy---by studying the susceptible-infected-recovered (SIR) model on uncorrelated configuration networks and a group of 56 real-world networks. In uncorrelated configuration networks the MFL and DMP methods yield identical predictions that are larger and more accurate than the prediction generated by the QMF method. When compared to the 56 real-world networks, the epidemic threshold obtained by the DMP method is closer to the actual epidemic threshold because it incorporates full network topology information and some dynamical correlations. We find that in some scenarios---such as networks with positive degree-degree correlations, with an eigenvector localized on the high $k$-core nodes, or with a high level of clustering---the epidemic threshold predicted by the MFL method, which uses the degree distribution as the only input parameter, performs better than the other two methods. We also find that the performances of the three predictions are irregular versus modularity

Application of the indirect fluorescent antibody assay in the study of malaria infection in the Yangtze River Three Gorges Reservoir, China

<p>Abstract</p> <p>Background</p> <p>China Yangtze Three Gorges Project (TGP) is one of the biggest construction projects in the world. The areas around the Three Gorge Dam has a history of tertian malaria and subtertian malaria epidemic, but there are no overall data about malaria epidemics before the completion of the project. The objective of this study was to get a reliable baseline on malaria infection in the Yangtze River Three Gorges reservoir area and to provide reference data for future studies about the impact of the project on malaria epidemics.</p> <p>Methods</p> <p>Two surveys of malaria infection were carried out in area, at six-month intervals in May and October 2008. About 3,600 dual specimens blood film samples for parasite diagnosis and filter paper blood spots for serology (using the immunofluorescence antibody test) were collected from the general population, including school populations, whenever possible.</p> <p>Results</p> <p>The overall percentage of positive response of the same population during post-transmission periods was about twice (1.40/0.72) of that in pre-transmission. Positive individuals under 15 years of age were detected in all the localities.</p> <p>Conclusion</p> <p>A certain extent of malaria infection existed in this area. Additional studies are needed to determine the length of malaria experience, and chemotherapeutic intervention as well as the distribution of main vectors for transmission in this area.</p

INFLUENCE OF TRANSGLUTAMINASE-INDUCED CROSS-LINKING ON IN VITRO DIGESTIBILITY OF SOY PROTEIN ISOLATE

ABSTRAC

ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation

With large language models (LLMs) achieving remarkable breakthroughs in natural language processing (NLP) domains, LLM-enhanced recommender systems have received much attention and have been actively explored currently. In this paper, we focus on adapting and empowering a pure large language model for zero-shot and few-shot recommendation tasks. First and foremost, we identify and formulate the lifelong sequential behavior incomprehension problem for LLMs in recommendation domains, i.e., LLMs fail to extract useful information from a textual context of long user behavior sequence, even if the length of context is far from reaching the context limitation of LLMs. To address such an issue and improve the recommendation performance of LLMs, we propose a novel framework, namely Retrieval-enhanced Large Language models (ReLLa) for recommendation tasks in both zero-shot and few-shot settings. For zero-shot recommendation, we perform semantic user behavior retrieval (SUBR) to improve the data quality of testing samples, which greatly reduces the difficulty for LLMs to extract the essential knowledge from user behavior sequences. As for few-shot recommendation, we further design retrieval-enhanced instruction tuning (ReiT) by adopting SUBR as a data augmentation technique for training samples. Specifically, we develop a mixed training dataset consisting of both the original data samples and their retrieval-enhanced counterparts. We conduct extensive experiments on a real-world public dataset (i.e., MovieLens-1M) to demonstrate the superiority of ReLLa compared with existing baseline models, as well as its capability for lifelong sequential behavior comprehension.Comment: Under Revie

Effects of maternal enflurane exposure on NR2B expression in the hippocampus of their offspring

Este trabalho objetiva o estudo da patogênese de deficiência no aprendizado e memória de prole de ratos resultante da anestesia maternal por enflurano, por meio da expressão da subunidade 2B do receptor do ácidoN-metil-D-aspártico (NR2B) no hipocampo dos filhotes. Dividiram-se, aleatoriamente, 30 fêmeas de ratos Sprague-Dawley em três grupos: controle (grupo C), exposição ao enflurano por 4 h (grupo E1) e por 8 h (grupo E2). De oito a 10 dias após o início da gravidez, os ratos dos grupos E1 e E2 inalaram enflurano 1,7% em 2 L/min de oxigênio, por 4 h e 8 h, respectivamente. Ratos do grupo C inalaram apenas 2 L/min de oxigênio. O labirinto de água de Morris foi empregado para analisar as funções de aprendizado e memória da cria em 20 e 30 dias após o nascimento. Utilizaram-se ensaios de RT-PCR e de imuno-histoquímica para medir os níveis de mRNA e expressão da proteína do NR2B, respectivamente. Em comparação com os ratos controle do grupo C, aqueles dos grupos E1 e E2 exibiram latências de escape mais longas, menor número de travessias na plataforma e menos tempo gasto no quadrante alvo no teste de exploração espacial (P ; 0.05) in terms of mRNA levels and protein expression of NR2B. The cognitive function of the offspring is impaired when maternal rats are exposed to enflurane during early pregnancy. A possible mechanism of this effect is related to the down-regulation of NR2B expression

Protein detection using hydrogel-based molecularly imprinted polymers integrated with dual polarisation interferometry

A polyacrylamide-based molecularly imprinted polymer (MIP) was prepared for bovine haemoglobin (BHb). A 3 mg/ml solution of BHb was injected over a dual polarisation interferometer (DPI) sensor to form a physisorbed layer typically of 3.5 ± 0.5 nm thickness. Onto the pre-adsorbed protein layer, MIP and NIP (non-imprinted polymer) were separately injected to monitor the interaction of BHb MIP or NIP particles under different loading conditions with the pre-adsorbed protein layer. In the case of NIP flowing of the protein layer, there was negligible surface stripping of the pre-adsorbed protein. When a protein-eluted sample of MIP particles was flowed over a pre-adsorbed protein layer on the sensor chip, the sensor detected significant decreases in both layer thickness and mass, suggestive that protein was being selectively bound to MIP after being stripped-off from the sensor surface. We also integrated thin-film MIPS for BHb and BSA onto the DPI sensor surface and were able to show that whereas BHb bound selectively and strongly to the BHb MIP thin film (resulting in a sustained increase in thickness and mass), the BHb protein only demonstrated transient and reversible binding on the BSA MIP. MIPs were also tested after biofouling with plasma or serum at various dilutions. We found that serum at 1/100 dilution allowed the MIP to still function selectively. This is the first demonstration of MIPs being integrated with DPI in the development of synthetic receptor-based optical protein sensors. © 2012 Elsevier B.V. All rights reserved