An Empirical Evaluation of Zero Resource Acoustic Unit Discovery

Acoustic unit discovery (AUD) is a process of automatically identifying a categorical acoustic unit inventory from speech and producing corresponding acoustic unit tokenizations. AUD provides an important avenue for unsupervised acoustic model training in a zero resource setting where expert-provided linguistic knowledge and transcribed speech are unavailable. Therefore, to further facilitate zero-resource AUD process, in this paper, we demonstrate acoustic feature representations can be significantly improved by (i) performing linear discriminant analysis (LDA) in an unsupervised self-trained fashion, and (ii) leveraging resources of other languages through building a multilingual bottleneck (BN) feature extractor to give effective cross-lingual generalization. Moreover, we perform comprehensive evaluations of AUD efficacy on multiple downstream speech applications, and their correlated performance suggests that AUD evaluations are feasible using different alternative language resources when only a subset of these evaluation resources can be available in typical zero resource applications.Comment: 5 pages, 1 figure; Accepted for publication at ICASSP 201

Learning ASR pathways: A sparse multilingual ASR model

Neural network pruning compresses automatic speech recognition (ASR) models effectively. However, in multilingual ASR, language-agnostic pruning may lead to severe performance drops on some languages because language-agnostic pruning masks may not fit all languages and discard important language-specific parameters. In this work, we present ASR pathways, a sparse multilingual ASR model that activates language-specific sub-networks ("pathways"), such that the parameters for each language are learned explicitly. With the overlapping sub-networks, the shared parameters can also enable knowledge transfer for lower-resource languages via joint multilingual training. We propose a novel algorithm to learn ASR pathways, and evaluate the proposed method on 4 languages with a streaming RNN-T model. Our proposed ASR pathways outperform both dense models and a language-agnostically pruned model, and provide better performance on low-resource languages compared to the monolingual sparse models.Comment: Accepted by ICASSP 202

Can command-and-control policy drive low-carbon transition in energy-intensive enterprises? -a study based on evolutionary game theory

There are two views on whether command-and-control policy can promote carbon emission reduction: the “compliance cost” theory and the “innovation compensation” theory. In this paper, we construct an evolutionary game model among energy-intensive enterprises, verification agencies, and local governments from the game theory perspective to explore the impact of command-and-control policy on the low-carbon transition of energy-intensive enterprises. The interaction mechanism of the three actors and the main factors affecting the low-carbon transition of the enterprises are further analyzed with the help of the MATLAB simulation method. The study results show that command-and-control policies can promote the low-carbon transition of enterprises and have a suppressive effect on bribery behavior. In the actual game process, enterprises will compare the cost of low-carbon transition with that of no low-carbon transition. The cost of low-carbon transition is higher when the government’s incentives and penalties are small, so there is a “compliance cost” effect, and the government cannot promote low-carbon transition by increasing the intensity of regulation. On the contrary, when the government’s incentives and penalties are strong enough, enterprises will make a low-carbon transition spontaneously in the face of continuously increasing environmental regulation intensity, which supports the theory of “innovation compensation.” In addition, increasing the profitability of product sales and increasing the cost of bribes are also effective ways to promote low-carbon transition. Finally, relevant policy recommendations were proposed based on the main conclusions. This work opens up a new perspective for environmental regulation theory and provides a theoretical reference and practical basis for developing low-carbon transition

Learning a Dual-Mode Speech Recognition Model via Self-Pruning

There is growing interest in unifying the streaming and full-context automatic speech recognition (ASR) networks into a single end-to-end ASR model to simplify the model training and deployment for both use cases. While in real-world ASR applications, the streaming ASR models typically operate under more storage and computational constraints - e.g., on embedded devices - than any server-side full-context models. Motivated by the recent progress in Omni-sparsity supernet training, where multiple subnetworks are jointly optimized in one single model, this work aims to jointly learn a compact sparse on-device streaming ASR model, and a large dense server non-streaming model, in a single supernet. Next, we present that, performing supernet training on both wav2vec 2.0 self-supervised learning and supervised ASR fine-tuning can not only substantially improve the large non-streaming model as shown in prior works, and also be able to improve the compact sparse streaming model.Comment: 7 pages, 1 figure. Accepted for publication at IEEE Spoken Language Technology Workshop (SLT), 202

Pb2+, Cu2+, Zn2+, Mg2+ and Mn2+ reduce the affinities of flavone, genistein and kaempferol for human serum albumin in vitro

Flavone (Fl), genistein (Gen) and kaempferol (Kol) were studied for their affinities towards human serum albumin (HSA) in the presence and absence of Pb2+,Cu2+,Zn2+,Mg2+ and Mn2+. The fluorescence intensities of HSA decreased with increasing concentration of the three flavonoids. Kaempferol resulted in a blue-shift of the λem of HSA from 336 to 330 nm; flavone showed an obvious red-shift of the λem of HSA from 336 to 342 nm; genistein did not cause an obvious blue-shift or red-shift of the λem of HSA. However, the extents of λem-shifts induced by the flavonoids in the presence of metal ions were much bigger than that in the absence of mental ions. Pb2+,Cu2+,Zn2+,Mg2+ and Mn2+ reduced the quenching constants of the flavonoids for HSA by 14.6% to 60.7% , 28% to 67.9%,3.5% to 59.4%, 23.2% to 63.7% and 14% to 65%, respectively. The affinities of flavone, genistein and kaempferol for HSA decreased about 10.84%, 10.05%and 3.56% in the presence of Pb2+, respectively. Cu2+ decreased the affinities of flavone, genistein and kaempferol for HSA about 14.04%, 5.14%and 8.89%, respectively. Zn2+ decreased the affinities of flavone, genistein and kaempferol for HSA about 3.79%, 0.55% and 3.58%, respectively. Mg2+ decreased the affinities of flavone, genistein and kaempferol for HSA about 16.94%, 2.94%and 7.04%, respectively. Mn2+ decreased the affinities of flavone, genistein and kaempferol for HSA about 14.24%, 3.66% and 4.78%, respectively

A Case-Based Reasoning Framework for Adaptive Prompting in Cross-Domain Text-to-SQL

Recent advancements in Large Language Models (LLMs), such as Codex, ChatGPT and GPT-4 have significantly impacted the AI community, including Text-to-SQL tasks. Some evaluations and analyses on LLMs show their potential to generate SQL queries but they point out poorly designed prompts (e.g. simplistic construction or random sampling) limit LLMs' performance and may cause unnecessary or irrelevant outputs. To address these issues, we propose CBR-ApSQL, a Case-Based Reasoning (CBR)-based framework combined with GPT-3.5 for precise control over case-relevant and case-irrelevant knowledge in Text-to-SQL tasks. We design adaptive prompts for flexibly adjusting inputs for GPT-3.5, which involves (1) adaptively retrieving cases according to the question intention by de-semantizing the input question, and (2) an adaptive fallback mechanism to ensure the informativeness of the prompt, as well as the relevance between cases and the prompt. In the de-semanticization phase, we designed Semantic Domain Relevance Evaluator(SDRE), combined with Poincar\'e detector(mining implicit semantics in hyperbolic space), TextAlign(discovering explicit matches), and Positector (part-of-speech detector). SDRE semantically and syntactically generates in-context exemplar annotations for the new case. On the three cross-domain datasets, our framework outperforms the state-of-the-art(SOTA) model in execution accuracy by 3.7\%, 2.5\%, and 8.2\%, respectively

Interactions between extracellular signal‐regulated kinase 1/2 and P38 Map kinase pathways in the control of RUNX2 phosphorylation and transcriptional activity

RUNX2, a key transcription factor for osteoblast differentiation, is regulated by ERK1/2 and p38 MAP kinase‐mediated phosphorylation. However, the specific contribution of each kinase to RUNX2‐dependent transcription is not known. Here we investigate ERK and p38 regulation of RUNX2 using a unique P‐RUNX2‐specific antibody. Both MAP kinases stimulated RUNX2 Ser319 phosphorylation and transcriptional activity. However, a clear preference for ERK1 versus p38α/β was found when the ability of these MAPKs to phosphorylate and activate RUNX2 was compared. Similarly, ERK1 preferentially bound to a consensus MAPK binding site on RUNX2 that was essential for the activity of either kinase. To assess the relative contribution of ERK1/2 and p38 to osteoblast gene expression, MC3T3‐E1 preosteoblast cells were grown in control or ascorbic acid (AA)‐containing medium ± BMP2/7. AA‐induced gene expression, which requires collagen matrix synthesis, was associated with parallel increases in P‐ERK and RUNX2‐S319‐P in the absence of any changes in P‐p38. This response was blocked by ERK, but not p38, inhibition. Significantly, in the presence of AA, BMP2/7 synergistically stimulated RUNX2 S319 phosphorylation and transcriptional activity without affecting total RUNX2 and this response was totally dependent on ERK/MAPK activity. In contrast, although p38 inhibition partially blocked BMP‐dependent transcription, it did not affect RUNX2 S319 phosphorylation, suggesting the involvement of other phosphorylation sites and/or transcription factors in this response. Based on this work, we conclude that extracellular matrix and BMP regulation of RUNX2 phosphorylation and transcriptional activity in osteoblasts is predominantly mediated by ERK rather than p38 MAPKs. © 2012 American Society for Bone and Mineral Research.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90254/1/561_ftp.pd

Bioactive Lipid Coating of Bone Allografts Direct Engraftment and Fate Determination of Bone Marrow-Derived Cells in Rat GFP Chimeras

Bone grafting procedures are performed to treat wounds incurred during wartime trauma, accidents, and tumor resections. Endogenous mechanisms of repair are often insufficient to ensure integration between host and donor bone and subsequent restoration of function. We investigated the role that bone marrow-derived cells play in bone regeneration and sought to increase their contributions by functionalizing bone allografts with bioactive lipid coatings. Polymer-coated allografts were used to locally deliver the immunomodulatory small molecule FTY720 in tibial defects created in rat bone marrow chimeras containing genetically-labeled bone marrow for monitoring cell origin and fate. Donor bone marrow contributed significantly to both myeloid and osteogenic cells in remodeling tissue surrounding allografts. FTY720 coatings altered the phenotype of immune cells two weeks post-injury, which was associated with increased vascularization and bone formation surrounding allografts. Consequently, degradable polymer coating strategies that deliver small molecule growth factors such as FTY720 represent a novel therapeutic strategy for harnessing endogenous bone marrow-derived progenitors and enhancing healing in load-bearing bone defects