Quantifying and Transferring Contextual Information in Object Detection

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Piecewise Euclidean structures and Eberlein's Rigidity Theorem in the singular case

In this article, we generalize Eberlein's Rigidity Theorem to the singular case, namely, one of the spaces is only assumed to be a CAT(0) topological manifold. As a corollary, we get that any compact irreducible but locally reducible locally symmetric space of noncompact type does not admit a nonpositively curved (in the Aleksandrov sense) piecewise Euclidean structure. Any hyperbolic manifold, on the other hand, does admit such a structure.Comment: 28 pages. Published copy, also available at http://www.maths.warwick.ac.uk/gt/GTVol3/paper13.abs.htm

Fermi-liquid ground state in n-type copper-oxide superconductor Pr0.91Ce0.09LaCuO4-y

We report nuclear magnetic resonance studies on the low-doped n-type copper-oxide Pr_{0.91}LaCe_{0.09}CuO_{4-y} (T_c=24 K) in the superconducting state and in the normal state uncovered by the application of a strong magnetic field. We find that when the superconductivity is removed, the underlying ground state is the Fermi liquid state. This result is at variance with that inferred from previous thermal conductivity measurement and contrast with that in p-type copper-oxides with a similar doping level where high-T_c superconductivity sets in within the pseudogap phase. The data in the superconducting state are consistent with the line-nodes gap model.Comment: version to appear in Phys. Rev. Let

Interaction between graphene and SiO2 surface

With first-principles DFT calculations, the interaction between graphene and SiO2 surface has been analyzed by constructing the different configurations based on {\alpha}-quartz and cristobalite structures. The single layer graphene can stay stably on SiO2 surface is explained based on the general consideration of configuration structures of SiO2 surface. It is also found that the oxygen defect in SiO2 surface can shift the Fermi level of graphene down which opens out the mechanism of hole-doping effect of graphene absorbed on SiO2 surface observed in experiments.Comment: 17 pages, 7 figure

Permutable entire functions satisfying algebraic differential equations

It is shown that if two transcendental entire functions permute, and if one of them satisfies an algebraic differential equation, then so does the other one.Comment: 5 page

Raman spectroscopic determination of the length, strength, compressibility, Debye temperature, elasticity, and force constant of the C-C bond in graphene

From the perspective of bond relaxation and vibration, we have reconciled the Raman shifts of graphene under the stimuli of the number-of-layer, uni-axial-strain, pressure, and temperature in terms of the response of the length and strength of the representative bond of the entire specimen to the applied stimuli. Theoretical unification of the measurements clarifies that: (i) the opposite trends of Raman shifts due to number-of-layer reduction indicate that the G-peak shift is dominated by the vibration of a pair of atoms while the D- and the 2D-peak shifts involves z-neighbor of a specific atom; (ii) the tensile strain-induced phonon softening and phonon-band splitting arise from the asymmetric response of the C3v bond geometry to the C2v uni-axial bond elongation; (iii) the thermal-softening of the phonons originates from bond expansion and weakening; and (iv) the pressure- stiffening of the phonons results from bond compression and work hardening. Reproduction of the measurements has led to quantitative information about the referential frequencies from which the Raman frequencies shift, the length, energy, force constant, Debye temperature, compressibility, elastic modulus of the C-C bond in graphene, which is of instrumental importance to the understanding of the unusual behavior of graphene

Ground state and finite temperature signatures of quantum phase transitions in the half-filled Hubbard model on a honeycomb lattice

We investigate ground state and finite temperature properties of the half-filled Hubbard model on a honeycomb lattice using quantum monte carlo and series expansion techniques. Unlike the square lattice, for which magnetic order exists at T=0 for any non-zero $U$, the honeycomb lattice is known to have a semi-metal phase at small $U$ and an antiferromagnetic one at large $U$. We investigate the phase transition at T=0 by studying the magnetic structure$and compressibility using quantum monte carlo simulations and by calculating the sublattice magnetization, uniform susceptibility, spin-wave and single hole ordered phase. Our results are consistent with a single continuous transition at$U_c/t$in the range 4-5. Finite temperature signatures of this phase transition are seen in the behavior of the specific heat,$C(T)$, which changes from a two-peaked structure for$U>U_c$to a one-peaked structure for$U < U_c$. Furthermore, the$U$dependence of the low temperature coefficient of$C(T)$exhibits an anomaly at$U \approx U_c$.Comment: 11 pages, 19 figure

Re-identification by Relative Distance Comparison

Abstract—Matching people across nonoverlapping camera views at different locations and different times, known as person reidentification, is both a hard and important problem for associating behavior of people observed in a large distributed space over a prolonged period of time. Person reidentification is fundamentally challenging because of the large visual appearance changes caused by variations in view angle, lighting, background clutter, and occlusion. To address these challenges, most previous approaches aim to model and extract distinctive and reliable visual features. However, seeking an optimal and robust similarity measure that quantifies a wide range of features against realistic viewing conditions from a distance is still an open and unsolved problem for person reidentification. In this paper, we formulate person reidentification as a relative distance comparison (RDC) learning problem in order to learn the optimal similarity measure between a pair of person images. This approach avoids treating all features indiscriminately and does not assume the existence of some universally distinctive and reliable features. To that end, a novel relative distance comparison model is introduced. The model is formulated to maximize the likelihood of a pair of true matches having a relatively smaller distance than that of a wrong match pair in a soft discriminant manner. Moreover, in order to maintain the tractability of the model in large scale learning, we further develop an ensemble RDC model. Extensive experiments on three publicly available benchmarking datasets are carried out to demonstrate the clear superiority of the proposed RDC models over related popular person reidentification techniques. The results also show that the new RDC models are more robust against visual appearance changes and less susceptible to model overfitting compared to other related existing models. Index Terms—Person reidentification, feature quantification, feature selection, relative distance comparison Ç