Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition

Recurrent Neural Networks (RNNs) and their variants, such as Long-Short Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, have achieved promising performance in sequential data modeling. The hidden layers in RNNs can be regarded as the memory units, which are helpful in storing information in sequential contexts. However, when dealing with high dimensional input data, such as video and text, the input-to-hidden linear transformation in RNNs brings high memory usage and huge computational cost. This makes the training of RNNs unscalable and difficult. To address this challenge, we propose a novel compact LSTM model, named as TR-LSTM, by utilizing the low-rank tensor ring decomposition (TRD) to reformulate the input-to-hidden transformation. Compared with other tensor decomposition methods, TR-LSTM is more stable. In addition, TR-LSTM can complete an end-to-end training and also provide a fundamental building block for RNNs in handling large input data. Experiments on real-world action recognition datasets have demonstrated the promising performance of the proposed TR-LSTM compared with the tensor train LSTM and other state-of-the-art competitors.Comment: 9 page

Maximal Quantum Fisher Information in a Mach-Zehnder Interferometer without initial parity

Mach-Zehnder interferometer is a common device in quantum phase estimation and the photon losses in it are an important issue for achieving a high phase accuracy. Here we thoroughly discuss the precision limit of the phase in the Mach-Zehnder interferometer with a coherent state and a superposition of coherent states as input states. By providing a general analytical expression of quantum Fisher information, the phase-matching condition and optimal initial parity are given. Especially, in the photon loss scenario, the sensitivity behaviors are analyzed and specific strategies are provided to restore the phase accuracies for symmetric and asymmetric losses.Comment: 10 pages, 3 figure

Tetra­aqua­bis(pyridine-3-sulfonato-κN)nickel(II)

In the mol­ecule of the title compound, [Ni(C5H4NO3S)2(H2O)4], the NiII cation is located on an inversion center and is coordinated by four water mol­ecules and two pyridine-3-sulfonate anions with an NiN2O4 distorted octa­hedral geometry. The face-to-face separation of 3.561 (5) Å between parallel pyridine rings indicates the existence of weak π–π stacking between the pyridine rings. The structure also contains inter­molecular O—H⋯O hydrogen bonding and weak C—H⋯O hydrogen bonding

Triaqua­(3-carb­oxy-5-sulfonatobenzoato-κO 1)(1,10-phenanthroline-κ2 N,N′)cobalt(II) monohydrate

In the title compound, [Co(C8H4O7S)(C12H8N2)(H2O)3]·H2O, the CoII cation is coordinated by one sulfoisophthalate dianion, one bidentate phenathroline (phen) mol­ecule and three water mol­ecules in a distorted cis-CoN2O4 octa­hedral geometry. In the crystal structure, aromatic π–π stacking occurs [centroid–centroid distances 3.7630 (14) and 3.7269 (15) Å], as well as an extensive O—H⋯O and C—H⋯O hydrogen-bonding networ