A Novel Large Moment Antiferromagnetic Order in K0.8Fe1.6Se2 Superconductor

The discovery of cuprate high Tc superconductors has inspired searching for unconventional su- perconductors in magnetic materials. A successful recipe has been to suppress long-range order in a magnetic parent compound by doping or high pressure to drive the material towards a quantum critical point, which is replicated in recent discovery of iron-based high TC superconductors. The long-range magnetic order coexisting with superconductivity has either a small magnetic moment or low ordering temperature in all previously established examples. Here we report an exception to this rule in the recently discovered potassium iron selenide. The superconducting composition is identified as the iron vacancy ordered K0.8Fe1.6Se2 with Tc above 30 K. A novel large moment 3.31 {\mu}B/Fe antiferromagnetic order which conforms to the tetragonal crystal symmetry has the unprecedentedly high an ordering temperature TN = 559 K for a bulk superconductor. Staggeredly polarized electronic density of states thus is suspected, which would stimulate further investigation into superconductivity in a strong spin-exchange field under new circumstance.Comment: 5 figures, 5 pages, and 2 tables in pdf which arXiv.com cannot tak

Error microphone location study for an eight-channel ANC system in free space

© 25th International Congress on Sound and Vibration 2018, ICSV 2018: Hiroshima Calling. All rights reserved. The location of error microphones is one key factor that determines the performance of a multichannel active noise control (ANC) system in terms of global sound power reduction when the number and the location of secondary sources are fixed. In a single channel ANC system, the optimal error microphone location is on a line that is nearly perpendicular to the secondary and primary source axis and closer to the secondary source. This paper investigates the optimal location of the error microphones in an 8-channel ANC system in free space. It is demonstrated that good noise reduction performance can be achieved by placing the error microphones between the primary source and secondary sources and closer to the secondary sources in the low frequency range. Experiments conducted on a gearbox for low frequency noise control show that the averaged sound level reduction at the observation locations 2 meters away is 5.2 dB when the error microphones are placed at 0.2 m inside the secondary source surface

A Cosmological Model with Dark Spinor Source

In this paper, we discuss the system of Friedman-Robertson-Walker metric coupling with massive nonlinear dark spinors in detail, where the thermodynamic movement of spinors is also taken into account. The results show that, the nonlinear potential of the spinor field can provide a tiny negative pressure, which resists the Universe to become singular. The solution is oscillating in time and closed in space, which approximately takes the following form g_{\mu\nu}=\bar R^2(1-\delta\cos t)^2\diag(1,-1,-\sin^2r ,-\sin^2r \sin^2\theta), with $\bar R= (1\sim 2)\times 10^{12}$ light year, and $\delta=0.96\sim 0.99$. The present time is about $t\sim 18^\circ$.Comment: 13 pages, no figure, to appear in IJMP

Crashworthiness design of a steel–aluminum hybrid rail using multi-response objective-oriented sequential optimization

© 2017 Elsevier Ltd Hybrid structures with different materials have aroused increasing interest for their lightweight potential and excellent performances. This study explored the optimization design of steel–aluminum hybrid structures for the highly nonlinear impact scenario. A metamodel based multi-response objective-oriented sequential optimization was adopted, where Kriging models were updated with sequential training points. It was indicated that the sequential sampling strategy was able to obtain a much higher local accuracy in the neighborhood of the optimum and thus to yield a better optimum, although it did lead to a worse global accuracy over the entire design space. Furthermore, it was observed that the steel–aluminum hybrid structure was capable of decreasing the peak force and simultaneously enhancing the energy absorption, compared to the conventional mono-material structure

Modular Equations and Distortion Functions

Modular equations occur in number theory, but it is less known that such equations also occur in the study of deformation properties of quasiconformal mappings. The authors study two important plane quasiconformal distortion functions, obtaining monotonicity and convexity properties, and finding sharp bounds for them. Applications are provided that relate to the quasiconformal Schwarz Lemma and to Schottky's Theorem. These results also yield new bounds for singular values of complete elliptic integrals.Comment: 23 page

ForestHash: Semantic Hashing With Shallow Random Forests and Tiny Convolutional Networks

Hash codes are efficient data representations for coping with the ever growing amounts of data. In this paper, we introduce a random forest semantic hashing scheme that embeds tiny convolutional neural networks (CNN) into shallow random forests, with near-optimal information-theoretic code aggregation among trees. We start with a simple hashing scheme, where random trees in a forest act as hashing functions by setting `1' for the visited tree leaf, and `0' for the rest. We show that traditional random forests fail to generate hashes that preserve the underlying similarity between the trees, rendering the random forests approach to hashing challenging. To address this, we propose to first randomly group arriving classes at each tree split node into two groups, obtaining a significantly simplified two-class classification problem, which can be handled using a light-weight CNN weak learner. Such random class grouping scheme enables code uniqueness by enforcing each class to share its code with different classes in different trees. A non-conventional low-rank loss is further adopted for the CNN weak learners to encourage code consistency by minimizing intra-class variations and maximizing inter-class distance for the two random class groups. Finally, we introduce an information-theoretic approach for aggregating codes of individual trees into a single hash code, producing a near-optimal unique hash for each class. The proposed approach significantly outperforms state-of-the-art hashing methods for image retrieval tasks on large-scale public datasets, while performing at the level of other state-of-the-art image classification techniques while utilizing a more compact and efficient scalable representation. This work proposes a principled and robust procedure to train and deploy in parallel an ensemble of light-weight CNNs, instead of simply going deeper.Comment: Accepted to ECCV 201