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

XRL-Bench: A Benchmark for Evaluating and Comparing Explainable Reinforcement Learning Techniques

Reinforcement Learning (RL) has demonstrated substantial potential across diverse fields, yet understanding its decision-making process, especially in real-world scenarios where rationality and safety are paramount, is an ongoing challenge. This paper delves in to Explainable RL (XRL), a subfield of Explainable AI (XAI) aimed at unravelling the complexities of RL models. Our focus rests on state-explaining techniques, a crucial subset within XRL methods, as they reveal the underlying factors influencing an agent's actions at any given time. Despite their significant role, the lack of a unified evaluation framework hinders assessment of their accuracy and effectiveness. To address this, we introduce XRL-Bench, a unified standardized benchmark tailored for the evaluation and comparison of XRL methods, encompassing three main modules: standard RL environments, explainers based on state importance, and standard evaluators. XRL-Bench supports both tabular and image data for state explanation. We also propose TabularSHAP, an innovative and competitive XRL method. We demonstrate the practical utility of TabularSHAP in real-world online gaming services and offer an open-source benchmark platform for the straightforward implementation and evaluation of XRL methods. Our contributions facilitate the continued progression of XRL technology.Comment: 10 pages, 5 figure

Identification of CFH and FHL2 as biomarkers for idiopathic pulmonary fibrosis

BackgroundIdiopathic pulmonary fibrosis (IPF) is a fatal disease of unknown etiology with a poor prognosis, characterized by a lack of effective diagnostic and therapeutic interventions. The role of immunity in the pathogenesis of IPF is significant, yet remains inadequately understood. This study aimed to identify potential key genes in IPF and their relationship with immune cells by integrated bioinformatics analysis and verify by in vivo and in vitro experiments.MethodsGene microarray data were obtained from the Gene Expression Omnibus (GEO) for differential expression analysis. The differentially expressed genes (DEGs) were identified and subjected to functional enrichment analysis. By utilizing a combination of three machine learning algorithms, specific genes associated with idiopathic pulmonary fibrosis (IPF) were pinpointed. Then their diagnostic significance and potential co-regulators were elucidated. We further analyzed the correlation between key genes and immune infiltrating cells via single-sample gene set enrichment analysis (ssGSEA). Subsequently, a single-cell RNA sequencing data (scRNA-seq) was used to explore which cell types expressed key genes in IPF samples. Finally, a series of in vivo and in vitro experiments were conducted to validate the expression of candidate genes by western blot (WB), quantitative real-time PCR (qRT-PCR), and immunohistochemistry (IHC) analysis.ResultsA total of 647 DEGs of IPF were identified based on two datasets, including 225 downregulated genes and 422 upregulated genes. They are closely related to biological functions such as cell migration, structural organization, immune cell chemotaxis, and extracellular matrix. CFH and FHL2 were identified as key genes with diagnostic accuracy for IPF by three machine learning algorithms. Analysis using ssGSEA revealed a significant association of both CFH and FHL2 with diverse immune cells, such as B cells and NK cells. Further scRNA-seq analysis indicated CFH and FHL2 were specifically upregulated in human IPF tissues, which was confirmed by in vitro and in vivo experiments.ConclusionIn this study, CFH and FHL2 have been identified as novel potential biomarkers for IPF, with potential diagnostic utility in future clinical applications. Subsequent investigations into the functions of these genes in IPF and their interactions with immune cells may enhance comprehension of the disease’s pathogenesis and facilitate the identification of therapeutic targets

Amorphous-Like Ultralow Thermal Transport in Crystalline Argyrodite Cu7PS6

Due to their amorphous-like ultralow lattice thermal conductivity both below and above the superionic phase transition, crystalline Cu- and Ag-based superionic argyrodites have garnered widespread attention as promising thermoelectric materials. However, despite their intriguing properties, quantifying their lattice thermal conductivities and a comprehensive understanding of the microscopic dynamics that drive these extraordinary properties are still lacking. Here, an integrated experimental and theoretical approach is adopted to reveal the presence of Cu-dominated low-energy optical phonons in the Cu-based argyrodite Cu7PS6. These phonons yield strong acoustic-optical phonon scattering through avoided crossing, enabling ultralow lattice thermal conductivity. The Unified Theory of thermal transport is employed to analyze heat conduction and successfully reproduce the experimental amorphous-like ultralow lattice thermal conductivities, ranging from 0.43 to 0.58 W m−1 K−1, in the temperature range of 100–400 K. The study reveals that the amorphous-like ultralow thermal conductivity of Cu7PS6 stems from a significantly dominant wave-like conduction mechanism. Moreover, the simulations elucidate the wave-like thermal transport mainly results from the contribution of Cu-associated low-energy overlapping optical phonons. This study highlights the crucial role of low-energy and overlapping optical modes in facilitating amorphous-like ultralow thermal transport, providing a thorough understanding of the underlying complex dynamics of argyrodites

Evolution of the strange-metal scattering in momentum space of electron-doped La2−xCexCuO4{\rm La}_{2-x}{\rm Ce}_x{\rm CuO}_4

The linear-in-temperature resistivity is one of the important mysteries in the strange metal state of high-temperature cuprate superconductors. To uncover this anomalous property, the energy-momentum-dependent imaginary part of the self-energy Im ${\rm \Sigma}(k, \omega)$ holds the key information. Here we perform systematic doping, momentum, and temperature-dependent angle-resolved photoemission spectroscopy measurements of electron-doped cuprate ${\rm La}_{2-x}{\rm Ce}_x{\rm CuO}_4$ and extract the evolution of the strange metal scattering in momentum space. At low doping levels and low temperatures, Im ${\rm\Sigma} \propto \omega$ dependence dominates the whole momentum space. For high doping levels and high temperatures, Im ${\rm\Sigma} \propto \omega^2$ shows up, starting from the antinodal region. By comparing with the hole-doped cuprates ${\rm La}_{2-x}{\rm Sr}_x{\rm CuO}_4$ and ${\rm Bi}_2{\rm Sr}_2{\rm CaCu}_2{\rm O}_8$, we find a dichotomy of the scattering rate exists along the nodal and antinodal direction, which is ubiquitous in the cuprate family. Our work provides new insight into the strange metal state in cuprates

Genetically predicted causal effects of gut microbiota on spinal pain: a two-sample Mendelian randomization analysis

BackgroundObservational studies have hinted at a correlation between the gut microbiota and spinal pain (SP). However, the impact of the gut microbiota on SP remains inconclusive.MethodsIn this study, we employed a two-sample Mendelian randomization (MR) analysis to explore the causal relationship between the gut microbiota and SP, encompassing neck pain (NP), thoracic spine pain (TSP), low back pain (LBP), and back pain (BP). The compiled gut microbiota data originated from a genome-wide association study (GWAS) conducted by the MiBioGen consortium (n = 18,340). Summary data for NP were sourced from the UK Biobank, TSP from the FinnGen Biobank, and LBP from both the UK Biobank and FinnGen Biobank. Summary data for BP were obtained from the UK Biobank. The primary analytical approach for assessing causal relationships was the Inverse Variance Weighted (IVW) method, supplemented by various sensitivity analyses to ensure result robustness.ResultsThe IVW analysis unveiled 37 bacterial genera with a potential causal relationship to SP. After Benjamini-Hochberg corrected test, four bacterial genera emerged with a strong causal relationship to SP. Specifically, Oxalobacter (OR: 1.143, 95% CI 1.061–1.232, P = 0.0004) and Tyzzerella 3 (OR: 1.145, 95% CI 1.059–1.238, P = 0.0007) were identified as risk factors for LBP, while Ruminococcaceae UCG011 (OR: 0.859, 95% CI 0.791–0.932, P = 0.0003) was marked as a protective factor for LBP, and Olsenella (OR: 0.893, 95% CI 0.839–0.951, P = 0.0004) was recognized as a protective factor for low back pain or/and sciatica. No significant heterogeneity or horizontal pleiotropy was observed through alternative testing methods.ConclusionThis study establishes a causal relationship between the gut microbiota and SP, shedding light on the “gut-spine” axis. These findings offer novel perspectives for understanding the etiology of SP and provide a theoretical foundation for potential interventions targeting the gut microbiota to prevent and treat SP