On-Line Bayesian Speaker Adaptation By Using Tree-Structured Transformation and Robust Priors

This paper presents new results by using our previously proposed on-line Bayesian learning approach for affine transformation parameter estimation in speaker adaptation. The on-line Bayesian learning technique allows updating parameter estimates after each utterance and it can accommodate flexible forms of transformation functions as well as prior probability density functions. We show through experimental results the robustness of heavy tailed priors to mismatch in prior density estimation. We also show that by properly choosing the transformation matrices and depths of hierarchical trees, recognition performance improved significantly

Loose mechanochemical coupling of molecular motors

In living cells, molecular motors convert chemical energy into mechanical work. Its thermodynamic energy efficiency, i.e. the ratio of output mechanical work to input chemical energy, is usually high. However, using two-state models, we found the motion of molecular motors is loosely coupled to the chemical cycle. Only part of the input energy can be converted into mechanical work. Others is dissipated into environment during substeps without contributions to the macro scale unidirectional movement

Mechanism for neurotransmitter-receptor matching.

Synaptic communication requires the expression of functional postsynaptic receptors that match the presynaptically released neurotransmitter. The ability of neurons to switch the transmitter they release is increasingly well documented, and these switches require changes in the postsynaptic receptor population. Although the activity-dependent molecular mechanism of neurotransmitter switching is increasingly well understood, the basis of specification of postsynaptic neurotransmitter receptors matching the newly expressed transmitter is unknown. Using a functional assay, we show that sustained application of glutamate to embryonic vertebrate skeletal muscle cells cultured before innervation is necessary and sufficient to up-regulate ionotropic glutamate receptors from a pool of different receptors expressed at low levels. Up-regulation of these ionotropic receptors is independent of signaling by metabotropic glutamate receptors. Both imaging of glutamate-induced calcium elevations and Western blots reveal ionotropic glutamate receptor expression prior to immunocytochemical detection. Sustained application of glutamate to skeletal myotomes in vivo is necessary and sufficient for up-regulation of membrane expression of the GluN1 NMDA receptor subunit. Pharmacological antagonists and morpholinos implicate p38 and Jun kinases and MEF2C in the signal cascade leading to ionotropic glutamate receptor expression. The results suggest a mechanism by which neuronal release of transmitter up-regulates postsynaptic expression of appropriate transmitter receptors following neurotransmitter switching and may contribute to the proper expression of receptors at the time of initial innervation

The mean velocity of two-state models of molecular motor

The motion of molecular motor is essential to the biophysical functioning of living cells. In principle, this motion can be regraded as a multiple chemical states process. In which, the molecular motor can jump between different chemical states, and in each chemical state, the motor moves forward or backward in a corresponding potential. So, mathematically, the motion of molecular motor can be described by several coupled one-dimensional hopping models or by several coupled Fokker-Planck equations. To know the basic properties of molecular motor, in this paper, we will give detailed analysis about the simplest cases: in which there are only two chemical states. Actually, many of the existing models, such as the flashing ratchet model, can be regarded as a two-state model. From the explicit expression of the mean velocity, we find that the mean velocity of molecular motor might be nonzero even if the potential in each state is periodic, which means that there is no energy input to the molecular motor in each of the two states. At the same time, the mean velocity might be zero even if there is energy input to the molecular motor. Generally, the velocity of molecular motor depends not only on the potentials (or corresponding forward and backward transition rates) in the two states, but also on the transition rates between the two chemical states

Semantic N-gram Language Modeling with the Latent Maximum Entropy Principle

We describe a unified probabilistic framework for statistical language modeling-the latent maximum entropy principle-which can effectively incorporate various aspects of natural language, such as local word interaction, syntactic structure and semantic document information. Unlike previous work on maximum entropy methods for language modeling, which only allow explicit features to be modeled, our framework also allows relationships over hidden features to be captured, resulting in a more expressive language model. We describe efficient algorithms for marginalization, inference and normalization in our extended models. We then present experimental results for our approach on the Wall Street Journal corpus

Learning Mixture Models with the Latent Maximum Entropy Principle

We present a new approach to estimating mixture models based on a new inference principle we have proposed: the latent maximum entropy principle (LME). LME is different both from Jaynes’ maximum entropy principle and from standard maximum likelihood estimation. We demonstrate the LME principle by deriving new algorithms for mixture model estimation, and show how robust new variants of the EM algorithm can be developed. Our experiments show that estimation based on LME generally yields better results than maximum likelihood estimation, particularly when inferring latent variable models from small amounts of data

Boltzmann Machine Learning with the Latent Maximum Entropy Principle

We present a new statistical learning paradigm for Boltzmann machines based on a new inference principle we have proposed: the latent maximum entropy principle (LME). LME is different both from Jaynes maximum entropy principle and from standard maximum likelihood estimation. We demonstrate the LME principle BY deriving new algorithms for Boltzmann machine parameter estimation, and show how robust and fast new variant of the EM algorithm can be developed. Our experiments show that estimation based on LME generally yields better results than maximum likelihood estimation, particularly when inferring hidden units from small amounts of data

Towards Vision Enhancing LLMs: Empowering Multimodal Knowledge Storage and Sharing in LLMs

Recent advancements in multimodal large language models (MLLMs) have achieved significant multimodal generation capabilities, akin to GPT-4. These models predominantly map visual information into language representation space, leveraging the vast knowledge and powerful text generation abilities of LLMs to produce multimodal instruction-following responses. We could term this method as LLMs for Vision because of its employing LLMs for visual-language understanding, yet observe that these MLLMs neglect the potential of harnessing visual knowledge to enhance overall capabilities of LLMs, which could be regraded as Vision Enhancing LLMs. In this paper, we propose an approach called MKS2, aimed at enhancing LLMs through empowering Multimodal Knowledge Storage and Sharing in LLMs. Specifically, we introduce the Modular Visual Memory, a component integrated into the internal blocks of LLMs, designed to store open-world visual information efficiently. Additionally, we present a soft Mixtures-of-Multimodal Experts architecture in LLMs to invoke multimodal knowledge collaboration during generation. Our comprehensive experiments demonstrate that MKS2 substantially augments the reasoning capabilities of LLMs in contexts necessitating physical or commonsense knowledge. It also delivers competitive results on multimodal benchmarks.Comment: 12 pages, 4 figure