A nearly perfect omnidirectional shear-horizontal (SH) wave transducer based on a thickness poled, thickness-shear (d15) piezoelectric ring

The fundamental shear horizontal (SH0) wave in plates is of great importance in the field of nondestructive testing (NDT) and structural health monitoring (SHM) since it is the unique non-dispersive guided wave mode. For practical applications, a phased array system based on omnidirectional SH0 wave transducers is most useful as it can cover a wide range of a plate. However, so far very few omnidirectional SH wave transducers have been developed. In this work, we proposed an omnidirectional SH piezoelectric transducer (OSH-PT) based on a thickness poled piezoelectric ring. The ring is equally divided into twelve sectors and the electric field is circumferentially applied, resulting in a new thickness-shear (d15) mode. Finite element analysis shows that the proposed OSH-PT can excite single-mode SH0 wave and receive the SH0 wave only. Experiments were then conducted to examine the performance of the proposed OSH-PT. Results indicated that it can generate and receive single-mode SH0 wave in a wide frequency range with nearly uniform sensitivities along all directions. Considering its quite simple configuration, compact size and low cost, the proposed OSH-PT is expected to greatly promote the applications of SH waves in the field of NDT and SHM

Bounding the mass of graviton in a dynamic regime with binary pulsars

In Einstein's general relativity, gravity is mediated by a massless spin-2 metric field, and its extension to include a mass for the graviton has profound implication for gravitation and cosmology. In 2002, Finn and Sutton used the gravitational-wave (GW) back-reaction in binary pulsars, and provided the first bound on the mass of graviton. Here we provide an improved analysis using 9 well-timed binary pulsars with a phenomenological treatment. First, individual mass bounds from each pulsar are obtained in the frequentist approach with the help of an ordering principle. The best upper limit on the graviton mass, $m_{g}<3.5\times10^{-20} \, {\rm eV}/c^{2}$ (90% C.L.), comes from the Hulse-Taylor pulsar PSR B1913+16. Then, we combine individual pulsars using the Bayesian theorem, and get $m_{g}<5.2\times10^{-21} \, {\rm eV}/c^{2}$ (90% C.L.) with a uniform prior for $\ln m_g$. This limit improves the Finn-Sutton limit by a factor of more than 10. Though it is not as tight as those from GWs and the Solar System, it provides an independent and complementary bound from a dynamic regime.Comment: 8 pages, 2 figures; accepted by PR

Distant Supervision for Entity Linking

Entity linking is an indispensable operation of populating knowledge repositories for information extraction. It studies on aligning a textual entity mention to its corresponding disambiguated entry in a knowledge repository. In this paper, we propose a new paradigm named distantly supervised entity linking (DSEL), in the sense that the disambiguated entities that belong to a huge knowledge repository (Freebase) are automatically aligned to the corresponding descriptive webpages (Wiki pages). In this way, a large scale of weakly labeled data can be generated without manual annotation and fed to a classifier for linking more newly discovered entities. Compared with traditional paradigms based on solo knowledge base, DSEL benefits more via jointly leveraging the respective advantages of Freebase and Wikipedia. Specifically, the proposed paradigm facilitates bridging the disambiguated labels (Freebase) of entities and their textual descriptions (Wikipedia) for Web-scale entities. Experiments conducted on a dataset of 140,000 items and 60,000 features achieve a baseline F1-measure of 0.517. Furthermore, we analyze the feature performance and improve the F1-measure to 0.545

Large Margin Nearest Neighbor Embedding for Knowledge Representation

Traditional way of storing facts in triplets ({\it head\_entity, relation, tail\_entity}), abbreviated as ({\it h, r, t}), makes the knowledge intuitively displayed and easily acquired by mankind, but hardly computed or even reasoned by AI machines. Inspired by the success in applying {\it Distributed Representations} to AI-related fields, recent studies expect to represent each entity and relation with a unique low-dimensional embedding, which is different from the symbolic and atomic framework of displaying knowledge in triplets. In this way, the knowledge computing and reasoning can be essentially facilitated by means of a simple {\it vector calculation}, i.e. ${\bf h} + {\bf r} \approx {\bf t}$. We thus contribute an effective model to learn better embeddings satisfying the formula by pulling the positive tail entities ${\bf t^{+}}$ to get together and close to {\bf h} + {\bf r} ({\it Nearest Neighbor}), and simultaneously pushing the negatives ${\bf t^{-}}$ away from the positives ${\bf t^{+}}$ via keeping a {\it Large Margin}. We also design a corresponding learning algorithm to efficiently find the optimal solution based on {\it Stochastic Gradient Descent} in iterative fashion. Quantitative experiments illustrate that our approach can achieve the state-of-the-art performance, compared with several latest methods on some benchmark datasets for two classical applications, i.e. {\it Link prediction} and {\it Triplet classification}. Moreover, we analyze the parameter complexities among all the evaluated models, and analytical results indicate that our model needs fewer computational resources on outperforming the other methods.Comment: arXiv admin note: text overlap with arXiv:1503.0815

Convergence and Quantum Advantage of Trotterized MERA for Strongly-Correlated Systems

Strongly-correlated quantum many-body systems are difficult to study and simulate classically. Our recent work [arXiv:2108.13401] proposed a variational quantum eigensolver (VQE) based on the multiscale entanglement renormalization ansatz (MERA) with tensors constrained to certain Trotter circuits. Here, we extend the theoretical analysis, testing different initialization and convergence schemes, determining the scaling of computation costs for various critical spin models, and establishing a quantum advantage. For the Trotter circuits being composed of single-qubit and two-qubit rotations, it is experimentally advantageous to have small rotation angles. We find that the average angle amplitude can be reduced substantially with negligible effect on the energy accuracy. Benchmark simulations show that choosing TMERA tensors as brick-wall circuits or parallel random-pair circuits yields very similar energy accuracies.Comment: 7 pages, 6 figure

Quantum-classical eigensolver using multiscale entanglement renormalization

We propose a variational quantum eigensolver (VQE) for the simulation of strongly-correlated quantum matter based on a multi-scale entanglement renormalization ansatz (MERA) and gradient-based optimization. This MERA quantum eigensolver can have substantially lower computation costs than corresponding classical algorithms. Due to its narrow causal cone, the algorithm can be implemented on noisy intermediate-scale quantum (NISQ) devices and still describe large systems. It is particularly attractive for ion-trap devices with ion-shuttling capabilities. The number of required qubits is system-size independent, and increases only to a logarithmic scaling when using quantum amplitude estimation to speed up gradient evaluations. Translation invariance can be used to make computation costs square-logarithmic in the system size and describe the thermodynamic limit. We demonstrate the approach numerically for a MERA with Trotterized disentanglers and isometries. With a few Trotter steps, one recovers the accuracy of the full MERA.Comment: 14 pages, 9 figures; additional discussions of the computational complexity, layer-transition maps for homogeneous MERA, mid-circuit qubit resets, and data on the quantum advantage; further minor improvements; published versio