SememeASR: Boosting Performance of End-to-End Speech Recognition against Domain and Long-Tailed Data Shift with Sememe Semantic Knowledge

Recently, excellent progress has been made in speech recognition. However, pure data-driven approaches have struggled to solve the problem in domain-mismatch and long-tailed data. Considering that knowledge-driven approaches can help data-driven approaches alleviate their flaws, we introduce sememe-based semantic knowledge information to speech recognition (SememeASR). Sememe, according to the linguistic definition, is the minimum semantic unit in a language and is able to represent the implicit semantic information behind each word very well. Our experiments show that the introduction of sememe information can improve the effectiveness of speech recognition. In addition, our further experiments show that sememe knowledge can improve the model's recognition of long-tailed data and enhance the model's domain generalization ability.Comment: Accepted by INTERSPEECH 202

Text-Only Domain Adaptation for End-to-End Speech Recognition through Down-Sampling Acoustic Representation

Mapping two modalities, speech and text, into a shared representation space, is a research topic of using text-only data to improve end-to-end automatic speech recognition (ASR) performance in new domains. However, the length of speech representation and text representation is inconsistent. Although the previous method up-samples the text representation to align with acoustic modality, it may not match the expected actual duration. In this paper, we proposed novel representations match strategy through down-sampling acoustic representation to align with text modality. By introducing a continuous integrate-and-fire (CIF) module generating acoustic representations consistent with token length, our ASR model can learn unified representations from both modalities better, allowing for domain adaptation using text-only data of the target domain. Experiment results of new domain data demonstrate the effectiveness of the proposed method.Comment: Accepted by INTERSPEECH 2023. arXiv admin note: text overlap with arXiv:2309.0143

Ultrasonic abrasive polishing of additive manufactured parts: An experimental study on the effects of process parameters on polishing performance

The rough surface of metal parts produced by the powder-based layered Additive Manufacturing (AM) technology such as Selective Laser Melting (SLM) is an important problem that needs to be solved. This study introduces obvious improvements in the surface quality of the AM parts by means of ultrasonic abrasive polishing (UAP), which uses cavitation collapse and micro-cut of abrasive particles for finishing surfaces. Experiments were conducted using the orthogonal experimental design method with an L9(34) orthogonal array to investigate the effects of ultrasonic power, machining time, abrasive particle size, and particle concentration on surface roughness Ra and material removal rate (MRR). The wear of the abrasive particles in the slurry was also studied. IN625 nickel-based alloy specimen manufactured by Selective Laser Melting (SLM) was chosen as the target workpiece. The results show that when the ultrasonic output power was too high, both surface quality and machining efficiency were deteriorated. And the surface roughness Ra was not further improved by just increasing the machining time. Severe cavitation erosion occurred in the polishing process and created leftover pits on the workpiece surface, which has a large influence on Ra. The size and amount of the abrasive particles should be within a certain range, which is helpful for material removal and improving the polishing performance. The work is useful for studying the influential process parameters involved in UAP and finding out the appropriate conditions

An Exact Algorithm for Solving Most Relevant Explanation in Bayesian Networks

Most Relevant Explanation (MRE) is a new inference task in Bayesian networks that finds the most relevant partial instantiation of target variables as an explanation for given evidence by maximizing the Generalized Bayes Factor (GBF). No exact algorithm has been developed for solving MRE previously. This paper fills the void and introduces a breadth-first branch-and-bound MRE algorithm based on a novel upper bound on GBF. The bound is calculated by decomposing the computation of the score to a set of Markov blankets of subsets of evidence variables. Our empirical evaluations show that the proposed algorithm scales up exact MRE inference significantly

Multi-Temporal Arable Land Monitoring in Arid Region of Northwest China Using a New Extraction Index

Development of a high-accuracy method to extract arable land using effective data sources is crucial to detect and monitor arable land dynamics, servicing land protection and sustainable development. In this study, a new arable land extraction index (ALEI) based on spectral analysis was proposed, examined by ground truth data, and then applied to the Hexi Corridor in northwest China. The arable land and its change patterns during 1990–2020 were extracted and identified using 40 Landsat TM/OLI images acquired in 1990, 2000, 2010, and 2020. The results demonstrated that the proposed method can distinguish arable land areas accurately, with the User’s (Producer’s) accuracy and overall accuracy (kappa coefficient) exceeding 0.90 (0.88) and 0.89 (0.87), respectively. The mean relative error calculated using field survey data obtained in 2012 and 2020 was 0.169 and 0.191, respectively, indicating the feasibility of the ALEI method in arable land extracting. The study found that arable land area in the Hexi Corridor was 13217.58 km2 in 2020, significantly increased by 25.33% compared to that in 1990. At 10-year intervals, the arable land experienced different change patterns. The study results indicate that ALEI index is a promising tool used to effectively extract arable land in the arid area

Brain Cortical Functional Gradients Predict Cortical Folding Patterns via Attention Mesh Convolution

Since gyri and sulci, two basic anatomical building blocks of cortical folding patterns, were suggested to bear different functional roles, a precise mapping from brain function to gyro-sulcal patterns can provide profound insights into both biological and artificial neural networks. However, there lacks a generic theory and effective computational model so far, due to the highly nonlinear relation between them, huge inter-individual variabilities and a sophisticated description of brain function regions/networks distribution as mosaics, such that spatial patterning of them has not been considered. we adopted brain functional gradients derived from resting-state fMRI to embed the "gradual" change of functional connectivity patterns, and developed a novel attention mesh convolution model to predict cortical gyro-sulcal segmentation maps on individual brains. The convolution on mesh considers the spatial organization of functional gradients and folding patterns on a cortical sheet and the newly designed channel attention block enhances the interpretability of the contribution of different functional gradients to cortical folding prediction. Experiments show that the prediction performance via our model outperforms other state-of-the-art models. In addition, we found that the dominant functional gradients contribute less to folding prediction. On the activation maps of the last layer, some well-studied cortical landmarks are found on the borders of, rather than within, the highly activated regions. These results and findings suggest that a specifically designed artificial neural network can improve the precision of the mapping between brain functions and cortical folding patterns, and can provide valuable insight of brain anatomy-function relation for neuroscience

Maternal methamphetamine exposure influences behavioral sensitization and nucleus accumbens DNA methylation in subsequent generation

The deleterious effects of methamphetamine (METH) exposure extend beyond abusers, and may potentially impact the vulnerability of their offspring in developing addictive behaviors. Epigenetic signatures have been implicated in addiction, yet the characteristics to identify prenatal METH abuse to offspring addiction risk remains elusive. Here, we used escalating doses of METH-exposed mouse model in F0 female mice before and during pregnancy to simulate the human pattern of drug abuse and generated METH-induced behavioral sensitization to investigate the addictive behavior in offspring mice. We then utilized whole genome-bisulfite sequencing (WGBS) to investigate the methylation signature of nucleus accumbens (NAc) in male METH-sensitized mice. Interestingly, male but not female offspring exhibited an enhanced response to METH-induced behavioral sensitization. Additionally, the METH-exposed group of male mice underwent a more comprehensive wave of epigenome remodeling over all genomic elements compared with unexposed groups due to drug exposure history. 104,219 DMCs (METH-SAL vs. SAL-SAL) induced by prenatal METH-exposure were positively correlated with that of postnatal METH-exposure (38,570, SAL-METH vs. SAL-SAL). Moreover, 4,983 DMCs induced by pre- and postnatal METH exposure (METH-METH vs. SAL-METH) were negatively correlated with that of postnatal METH exposure, and 371 commonly changed DMCs between the two comparison groups also showed a significantly negative correlation and 86 annotated genes functionally enriched in the pathways of neurodevelopment and addiction. Key annotated genes included Kirrel3, Lrpprc, and Peg3, implicated in neurodevelopmental processes, were down-regulated in METH-METH group mice compared with the SAL-METH group. Taken together, we render novel insights into the epigenetic correlation of drug exposure and provide evidence for epigenetic characteristics that link maternal METH exposure to the intensity of the same drug-induced behavioral sensitization in adult offspring.Ministry of Education (MOE)Published versionThis research was supported by grants from the National Natural Science Foundation of China (grant No. 81430048, No. 81772034, and No. 32171233), China Postdoctoral Science Foundation (grant No. 2022M712543), and the Ministry of Education (MOE) Tier 3 grant (grant No. MOE 2017-T3-1-002)