Large Zero Autocorrelation Zone of Golay Sequences and 4q4^q-QAM Golay Complementary Sequences

Sequences with good correlation properties have been widely adopted in modern communications, radar and sonar applications. In this paper, we present our new findings on some constructions of single $H$-ary Golay sequence and $4^q$-QAM Golay complementary sequence with a large zero autocorrelation zone, where $H\ge 2$ is an arbitrary even integer and $q\ge 2$ is an arbitrary integer. Those new results on Golay sequences and QAM Golay complementary sequences can be explored during synchronization and detection at the receiver end and thus improve the performance of the communication system

Topological-Fermi-Liquid to Quantum-Hall-Liquid Transitions: pp-Band and dd-Band Fermions in a Magnetic Field

We find that in a multi-orbital system with intraorbital and interorbital hopping integrals, the Hall conductance exhibits various topological quantum phase transitions (QPTs) induced by on-site orbital polarization: integer quantum Hall (IQH) plateau transitions, and topological Fermi liquid to IQH transitions. Such topological QPTs are demonstrated in two systems: a $p$-band spinless fermionic system realizable with ultracold atoms in optical lattice, and a $d$-band spinful fermionic system closely related to giant orbital Hall effects in transition metals and their compounds.Comment: 4 pages, 4 figure

An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild

Zero-shot learning (ZSL) methods have been studied in the unrealistic setting where test data are assumed to come from unseen classes only. In this paper, we advocate studying the problem of generalized zero-shot learning (GZSL) where the test data's class memberships are unconstrained. We show empirically that naively using the classifiers constructed by ZSL approaches does not perform well in the generalized setting. Motivated by this, we propose a simple but effective calibration method that can be used to balance two conflicting forces: recognizing data from seen classes versus those from unseen ones. We develop a performance metric to characterize such a trade-off and examine the utility of this metric in evaluating various ZSL approaches. Our analysis further shows that there is a large gap between the performance of existing approaches and an upper bound established via idealized semantic embeddings, suggesting that improving class semantic embeddings is vital to GZSL.Comment: ECCV2016 camera-read

Tuning Kinetic Magnetism of Strongly Correlated Electrons via Staggered Flux

We explore the kinetic magnetism of the infinite-$U$ repulsive Hubbard models at low hole densities on various lattices with nearest-neighbor hopping integrals modulated by a staggered magnetic flux $\pm\phi$. Tuning $\phi$ from 0 to $\pi$ makes the ground state (GS) change from a Nagaoka-type ferromagnetic state to a Haerter-Shastry-type antiferromagnetic state at a critical $\phi_c$, with both states being of kinetic origin. Intra-plaquette spin correlation, as well as the GS energy, signals such a quantum criticality. This tunable kinetic magnetism is generic, and appears in chains, ladders and two-dimensional lattices with squares or triangles as elementary constituents.Comment: 4 pages, 5 figures, 1 tabl

Large-Margin Determinantal Point Processes

Determinantal point processes (DPPs) offer a powerful approach to modeling diversity in many applications where the goal is to select a diverse subset. We study the problem of learning the parameters (the kernel matrix) of a DPP from labeled training data. We make two contributions. First, we show how to reparameterize a DPP's kernel matrix with multiple kernel functions, thus enhancing modeling flexibility. Second, we propose a novel parameter estimation technique based on the principle of large margin separation. In contrast to the state-of-the-art method of maximum likelihood estimation, our large-margin loss function explicitly models errors in selecting the target subsets, and it can be customized to trade off different types of errors (precision vs. recall). Extensive empirical studies validate our contributions, including applications on challenging document and video summarization, where flexibility in modeling the kernel matrix and balancing different errors is indispensable.Comment: 15 page