In this work, three lattice-free (LF) discriminative training criteria for purely sequence-trained neural network acoustic models are compared on LVCSR tasks, namely maximum mutual information (MMI), boosted maximum mutual information (bMMI) and state-level minimum Bayes risk (sMBR). We demonstrate that, analogous to LF-MMI, a neural network acoustic model can also be trained from scratch using LF-bMMI or LF-sMBR criteria respectively without the need of cross-entropy pre-training. Furthermore, experimental results on Switchboard-300hrs and Switchboard+Fisher-2100hrs datasets show that models trained with LF-bMMI consistently outperform those trained with plain LF-MMI and achieve a relative word error rate (WER) reduction of 5% over competitive temporal convolution projected LSTM (TDNN-LSTMP) LF-MMI baselines.
This is an implementation of Monotonic Calibrated Interpolated Look-Up Tables in TensorFlow. These are fast-to-evaluate and interpretable lattice models, also known as interpolated look-up tables. This library also provides a rich and intuitive set of regularizations and monotonicity constraints configurable per feature. This tutorial contains more detailed explanation about lattice models and usage in TensorFlow, and check out API docs for python APIs. TensorFlow Lattice is not an official Google product.
People have known for decades that quantum computing will one day become a serious threat to our standard forms of encryption because of their enormous power in computing certain tough problems. Until just a few years ago, quantum computers were a 50-year-old concept first introduced by Richard Feynman in 1959. In 2009, Yale scientists created a two-qubit chip, and IBM now has a 20-qubit system, the IBM Q, that it has made available to outside developers for testing applications. Read also: FBI won't say how many investigations are hindered by encryption This acceleration in quantum computing technologies has brought forward the that day when quantum computers will break our public/private key encryption. Welcome to the future transparency of today.
The same phenomenon has been seen with ultracold atoms in optical lattices, but it is not expected to occur in a uniform system. Meinert et al. observed Bloch oscillations of an impurity atom in one-dimensional tubes of strongly interacting cesium atoms--a system without built-in periodicity. Owing to the strong interactions, the bosonic atoms stayed away from one another, forming an effective lattice. The researchers observed reflections of the impurity atoms of this effective lattice in momentum space, with the lattice constant corresponding to the interatomic distance of the host gas.
This paper is the continuation of our research work about linguistic truth-valued concept lattice. In order to provide a mathematical tool for mining tacit knowledge, we establish a concrete model of 6-ary linguistic truth-valued concept lattice and introduce a mining algorithm through the structure consistency. Specifically, we utilize the attributes to depict knowledge, propose the 6-ary linguistic truth-valued attribute extended context and congener context to characterize tacit knowledge, and research the necessary and sufficient conditions of forming tacit knowledge. We respectively give the algorithms of generating the linguistic truth-valued congener context and constructing the linguistic truth-valued concept lattice.