Sudden Decoherence Transitions for Quantum Discord

We investigate the disappearance of discord in 2- and multi-qubit systems subject to decohering influences. We formulate the computation of quantum discord in terms of the generalized Bloch vector, which gives useful insights on the time evolution of quantum coherence for the open system, particularly the comparison of entanglement and discord. And we show that the analytical calculation of the global geometric discord is NP-hard in the number of qubits. We present an efficient numerical method to calculating the quantum discord for a certain important class of multipartite states. In agreement with previous work for 2-qubit cases, (Mazzola et al., Phys. Rev. Lett. 104, 200401 (2010)), we find situations in which there is a sudden transition from classical to quantum decoherence characterized by the discord remaining relatively robust (classical decoherence) until a certain point from where it begins to decay quickly whereas the classical correlation decays more slowly (quantum decoherence). However, we find that as the number of qubits increases, the chance of this kind of transition occurring becomes small.Comment: 15 pages, 2 figure

Complex Scalar Singlet Dark Matter: Vacuum Stability and Phenomenology

We analyze one-loop vacuum stability, perturbativity, and phenomenological constraints on a complex singlet extension of the Standard Model (SM) scalar sector containing a scalar dark matter candidate. We study vacuum stability considerations using a gauge-invariant approach and compare with the conventional gauge-dependent procedure. We show that, if new physics exists at the TeV scale, the vacuum stability analysis and experimental constraints from the dark matter sector, electroweak precision data, and LEP allow both a Higgs-like scalar in the mass range allowed by the latest results from CMS and ATLAS and a lighter singlet-like scalar with weak couplings to SM particles. If instead no new physics appears until higher energy scales, there may be significant tension between the vacuum stability analysis and phenomenological constraints (in particular electroweak precision data) to the extent that the complex singlet extension with light Higgs and singlet masses would be ruled out. We comment on the possible implications of a scalar with ~125 GeV mass and future ATLAS invisible decay searches.Comment: 24 pages, 12 figures; v2 - fixed minor typos, added reference, changed layou

Spatial Pyramid Encoding with Convex Length Normalization for Text-Independent Speaker Verification

In this paper, we propose a new pooling method called spatial pyramid encoding (SPE) to generate speaker embeddings for text-independent speaker verification. We first partition the output feature maps from a deep residual network (ResNet) into increasingly fine sub-regions and extract speaker embeddings from each sub-region through a learnable dictionary encoding layer. These embeddings are concatenated to obtain the final speaker representation. The SPE layer not only generates a fixed-dimensional speaker embedding for a variable-length speech segment, but also aggregates the information of feature distribution from multi-level temporal bins. Furthermore, we apply deep length normalization by augmenting the loss function with ring loss. By applying ring loss, the network gradually learns to normalize the speaker embeddings using model weights themselves while preserving convexity, leading to more robust speaker embeddings. Experiments on the VoxCeleb1 dataset show that the proposed system using the SPE layer and ring loss-based deep length normalization outperforms both i-vector and d-vector baselines.Comment: 5 pages, 2 figures, Interspeech 201

Additional Shared Decoder on Siamese Multi-view Encoders for Learning Acoustic Word Embeddings

Acoustic word embeddings --- fixed-dimensional vector representations of arbitrary-length words --- have attracted increasing interest in query-by-example spoken term detection. Recently, on the fact that the orthography of text labels partly reflects the phonetic similarity between the words' pronunciation, a multi-view approach has been introduced that jointly learns acoustic and text embeddings. It showed that it is possible to learn discriminative embeddings by designing the objective which takes text labels as well as word segments. In this paper, we propose a network architecture that expands the multi-view approach by combining the Siamese multi-view encoders with a shared decoder network to maximize the effect of the relationship between acoustic and text embeddings in embedding space. Discriminatively trained with multi-view triplet loss and decoding loss, our proposed approach achieves better performance on acoustic word discrimination task with the WSJ dataset, resulting in 11.1% relative improvement in average precision. We also present experimental results on cross-view word discrimination and word level speech recognition tasks.Comment: Accepted at 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2019

First-Principles Study of Iron Oxide Polytypes: Comparison of GGA+U and Hybrid Functional Method

Iron oxides are materials of wide interest that exhibit diverse electric, magnetic, optical, and catalytic properties; therefore, many studies to gain complete understanding of their polytypic phase boundary have been pursued. However, first-principles investigations of iron oxides using conventional density functional theory (DFT) calculations often yield a gross error due to the strong electron correlation that is poorly described within (semi) local approximations. This limitation often can be overcome using either the Hubbard correction (DFT+U) or a hybrid functional DFT method. Here, we investigate the diverse polytypic phases of iron monoxide (FeO) by comparing DFT+U and the hybrid-functional method (particularly B3PW91). We found that both methods show reasonable agreement in predicting the properties of the experimentally observed phases (B1, B8, iB8, and B2). However, the DFT+U method overestimates the equilibrium volume of B1 phase and predicts the experimentally undiscovered B4 phases to be nearly as stable as the naturally abundant B1 phase. In addition, B3PW91 predicts a local Jahn–Teller distortion pattern of the B1 phase that is more similar than that predicted by DFT+U to the result of a reported low-temperature neutron diffraction experiment. Using B3PW91, which is considered more convincing, we further discuss that there is no clear phase boundary between the monoclinic and rhombohedral B1 phases under compression but that the compression gradually reduces the local anisotropy to yield a rhombohedral-like phase, which agrees with previous experimental diffraction results. We expect that our comprehensive study demonstrates the virtue of using hybrid-functional DFT methods, particularly in exploring various known and unknown polytypic phases of transition-metal oxides

Ga-doped Pt-Ni Octahedral Nanoparticles as a Highly Active and Durable Electrocatalyst for Oxygen Reduction Reaction

Bimetallic PtNi nanoparticles have been considered as a promising electrocatalyst for oxygen reduction reaction (ORR) in polymer electrolyte membrane fuel cells (PEMFCs) owing to their high catalytic activity. However, under typical fuel cell operating conditions, Ni atoms easily dissolve into the electrolyte, resulting in degradation of the catalyst and the membrane-electrode assembly (MEA). Here, we report gallium-doped PtNi octahedral nanoparticles on a carbon support (Ga-PtNi/C). The Ga-PtNi/C shows high ORR activity, marking an 11.7-fold improvement in the mass activity (1.24 A mgPt-1) and a 17.3-fold improvement in the specific activity (2.53 mA cm-2) compare to the commercial Pt/C (0.106 A mgPt-1 and 0.146 mA cm-2). Density functional theory calculations demonstrate that addition of Ga to octahedral PtNi can cause an increase in the oxygen intermediate binding energy, leading to the enhanced catalytic activity toward ORR. In a voltage-cycling test, the Ga-PtNi/C exhibits superior stability to PtNi/C and the commercial Pt/C, maintaining the initial Ni concentration and octahedral shape of the nanoparticles. Single cell using the Ga-PtNi/C exhibits higher initial performance and durability than those using the PtNi/C and the commercial Pt/C. The majority of the Ga-PtNi nanoparticles well maintain the octahedral shape without agglomeration after the single cell durability test (30,000 cycles). This work demonstrates that the octahedral Ga-PtNi/C can be utilized as a highly active and durable ORR catalyst in practical fuel cell applications

Aluminum nitride waveguide beam splitters for integrated quantum photonic circuits

We demonstrate integrated photonic circuits for quantum devices using sputtered polycrystalline aluminum nitride (AlN) on insulator. The on-chip AlN waveguide directional couplers, which are one of the most important components in quantum photonics, are fabricated and show the output power splitting ratios from 50:50 to 99:1. The polarization beam splitters with an extinction ratio of more than 10 dB are also realized from the AlN directional couplers. Using the fabricated AlN waveguide beam splitters, we observe the Hong-Ou-Mandel interference with a visibility of 91.7 +(-) 5.66 %.Comment: 9 pages, 4 figure