Bayesian Speaker Adaptation Based on a New Hierarchical Probabilistic Model

In this paper, a new hierarchical Bayesian speaker adaptation method called HMAP is proposed that combines the advantages of three conventional algorithms, maximum a posteriori (MAP), maximum-likelihood linear regression (MLLR), and eigenvoice, resulting in excellent performance across a wide range of adaptation conditions. The new method efficiently utilizes intra-speaker and inter-speaker correlation information through modeling phone and speaker subspaces in a consistent hierarchical Bayesian way. The phone variations for a specific speaker are assumed to be located in a low-dimensional subspace. The phone coordinate, which is shared among different speakers, implicitly contains the intra-speaker correlation information. For a specific speaker, the phone variation, represented by speaker-dependent eigenphones, are concatenated into a supervector. The eigenphone supervector space is also a low dimensional speaker subspace, which contains inter-speaker correlation information. Using principal component analysis (PCA), a new hierarchical probabilistic model for the generation of the speech observations is obtained. Speaker adaptation based on the new hierarchical model is derived using the maximum a posteriori criterion in a top-down manner. Both batch adaptation and online adaptation schemes are proposed. With tuned parameters, the new method can handle varying amounts of adaptation data automatically and efficiently. Experimental results on a Mandarin Chinese continuous speech recognition task show good performance under all testing conditions

Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications

Recently, Meta AI Research approaches a general, promptable Segment Anything Model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B). Without a doubt, the emergence of SAM will yield significant benefits for a wide array of practical image segmentation applications. In this study, we conduct a series of intriguing investigations into the performance of SAM across various applications, particularly in the fields of natural images, agriculture, manufacturing, remote sensing, and healthcare. We analyze and discuss the benefits and limitations of SAM and provide an outlook on future development of segmentation tasks. Note that our work does not intend to propose new algorithms or theories, but rather provide a comprehensive view of SAM in practice. This work is expected to provide insights that facilitate future research activities toward generic segmentation.Comment: Tech Repor

Ammonium 1-hydr­oxy-2-naphthoate

The title compound, NH4 +·C11H7O3 −, was obtained by slow evaporation of a 30% ammonia solution of 1-hydr­oxy-2-naphthoic acid. The crystal structure is stabilized by inter­molecular N—H⋯O hydrogen bonds, forming layers parallel to the bc plane

MOS2, a Protein Containing G-Patch and KOW Motifs, Is Essential for Innate Immunity in Arabidopsis thaliana

SummaryInnate immunity is critical for sensing and defending against microbial infections in multicellular organisms. In plants, disease resistance genes (R genes) play central roles in recognizing pathogens and initiating downstream defense cascades [1]. Arabidopsis SNC1 encodes a TIR-NBS-LRR-type R protein with a similar structure to nucleotide binding oligomerization domain (Nod) proteins in animals [2, 3]. A point mutation in the region between the NBS and LRR of SNC1 results in constitutive activation of defense responses in the snc1 mutant. Here, we report the identification and characterization of mos2-1, a mutant suppressing the constitutive defense responses in snc1. Analysis of mos2 single mutants indicated that it is not only required for resistance specified by multiple R genes, but also for basal resistance. Map-based cloning of MOS2 revealed that it encodes a novel nuclear protein that contains one G-patch and two KOW domains and has homologs across the animal kingdom. The presence of both G-patch and KOW domains in the MOS2 protein suggests that it probably functions as an RNA binding protein critical for plant innate immunity [4, 5]. Our discovery on the biological functions of MOS2 will shed light on functions of the MOS2 homologs in animals, where they may also play important roles in innate immunity

Characterization of ovarian clear cell carcinoma using target drug-based molecular biomarkers: implications for personalized cancer therapy

Information of antibodies used in immunohistochemistry. Table S2A. Relationship with clinicopathological factors-HGSC. Table S2B. Relationship with clinicopathological factors-CCC. Table S3 Association molecular biomarkers expression and platinum-based chemotherapeutic response. Table S4. Comparison of molecular biomarkers between recurrent and disease-free patients. (DOCX 42 kb

Predicting the Silent Majority on Graphs: Knowledge Transferable Graph Neural Network

Graphs consisting of vocal nodes ("the vocal minority") and silent nodes ("the silent majority"), namely VS-Graph, are ubiquitous in the real world. The vocal nodes tend to have abundant features and labels. In contrast, silent nodes only have incomplete features and rare labels, e.g., the description and political tendency of politicians (vocal) are abundant while not for ordinary people (silent) on the twitter's social network. Predicting the silent majority remains a crucial yet challenging problem. However, most existing message-passing based GNNs assume that all nodes belong to the same domain, without considering the missing features and distribution-shift between domains, leading to poor ability to deal with VS-Graph. To combat the above challenges, we propose Knowledge Transferable Graph Neural Network (KT-GNN), which models distribution shifts during message passing and representation learning by transferring knowledge from vocal nodes to silent nodes. Specifically, we design the domain-adapted "feature completion and message passing mechanism" for node representation learning while preserving domain difference. And a knowledge transferable classifier based on KL-divergence is followed. Comprehensive experiments on real-world scenarios (i.e., company financial risk assessment and political elections) demonstrate the superior performance of our method. Our source code has been open sourced.Comment: Paper was accepted by WWW202