Neural Word Segmentation with Rich Pretraining

Neural word segmentation research has benefited from large-scale raw texts by leveraging them for pretraining character and word embeddings. On the other hand, statistical segmentation research has exploited richer sources of external information, such as punctuation, automatic segmentation and POS. We investigate the effectiveness of a range of external training sources for neural word segmentation by building a modular segmentation model, pretraining the most important submodule using rich external sources. Results show that such pretraining significantly improves the model, leading to accuracies competitive to the best methods on six benchmarks.Comment: Accepted by ACL 201

Neural Reranking for Named Entity Recognition

We propose a neural reranking system for named entity recognition (NER). The basic idea is to leverage recurrent neural network models to learn sentence-level patterns that involve named entity mentions. In particular, given an output sentence produced by a baseline NER model, we replace all entity mentions, such as \textit{Barack Obama}, into their entity types, such as \textit{PER}. The resulting sentence patterns contain direct output information, yet is less sparse without specific named entities. For example, "PER was born in LOC" can be such a pattern. LSTM and CNN structures are utilised for learning deep representations of such sentences for reranking. Results show that our system can significantly improve the NER accuracies over two different baselines, giving the best reported results on a standard benchmark.Comment: Accepted as regular paper by RANLP 201

Spin relaxation in inhomogeneous magnetic fields with depolarizing boundaries

Field-inhomogeneity-induced relaxation of atomic spins confined in vapor cells with depolarizing walls is studied. In contrast to nuclear spins, such as noble-gas spins, which experience minimal polarization loss at cell walls, atomic spins in uncoated cells undergo randomization at the boundaries. This distinct boundary condition results in a varied dependence of the relaxation rate on the field gradient. By solving the Bloch-Torrey equation under fully depolarizing boundary conditions, we illustrate that the relaxation rate induced by field inhomogeneity is more pronounced for spins with a smaller original relaxation rate (in the absence of the inhomogeneous field). We establish an upper limit for the relaxation rate through calculations in the perturbation regime. Moreover, we connect it to the spin-exchange-relaxation-free magnetometers, demonstrating that its linewidth is most sensitive to inhomogeneous fields along the magnetometer's sensitive axis. Our theoretical result agrees with the experimental data for cells subjected to small pump power. However, deviations in larger input-power scenarios underscore the importance of considering pump field attenuation, which leads to uniformly distributed light shift that behaves as an inhomogeneous magnetic field

Quantum Cloning Machines and the Applications

No-cloning theorem is fundamental for quantum mechanics and for quantum information science that states an unknown quantum state cannot be cloned perfectly. However, we can try to clone a quantum state approximately with the optimal fidelity, or instead, we can try to clone it perfectly with the largest probability. Thus various quantum cloning machines have been designed for different quantum information protocols. Specifically, quantum cloning machines can be designed to analyze the security of quantum key distribution protocols such as BB84 protocol, six-state protocol, B92 protocol and their generalizations. Some well-known quantum cloning machines include universal quantum cloning machine, phase-covariant cloning machine, the asymmetric quantum cloning machine and the probabilistic quantum cloning machine etc. In the past years, much progress has been made in studying quantum cloning machines and their applications and implementations, both theoretically and experimentally. In this review, we will give a complete description of those important developments about quantum cloning and some related topics. On the other hand, this review is self-consistent, and in particular, we try to present some detailed formulations so that further study can be taken based on those results.Comment: 98 pages, 12 figures, 400+ references. Physics Reports (published online

Individuals’ preference on reading pathways influences the involvement of neural pathways in phonological learning

IntroductionExisting behavioral and neuroimaging studies revealed inter-individual variability in the selection of the two phonological routes in word reading. However, it is not clear how individuals’ preferred reading pathways/strategies modulate the involvement of a certain brain region for phonological learning in a new language, and consequently affect their behavioral performance on phonological access.MethodsTo address this question, the present study recruited a group of native Chinese speakers to learn two sets of artificial language characters, respectively, in addressed-phonology training (i.e., whole-word mapping) and assembled-phonology training conditions (i.e., grapheme-to-phoneme mapping).ResultsBehavioral results showed that the more lexical pathways participants preferred, the better they performed on newly-acquired addressed characters relative to assembled characters. More importantly, neuroimaging results showed that participants who preferred lexical pathway in phonological access show less involvement of brain regions for addressed phonology (e.g., the bilateral orbitofrontal cortex and right pars triangularis) in the processing of newly-acquired addressed characters.ConclusionThese results indicated that phonological access via the preferred pathway required less neural resources to achieve better behavioral performance. These above results provide direct neuroimaging evidence for the influence of reading pathway preference on phonological learning