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    Radio Frequency Response of the Strongly Interacting Fermi Gases at Finite Temperatures

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    The radio frequency spectrum of the fermions in the unitary limit at finite temperatures is characterized by the sum rule relations. We consider a simple picture where the atoms are removed by radio frequency excitations from the strongly interacting states into a state of negligible interaction. We calculate the moments of the response function in the range of temperature 0.08ϵF<T<0.8ϵF0.08 \epsilon_F < T < 0.8 \epsilon_F using auxiliary field Monte Carlo technique (AFMC) in which continuum auxiliary fields with a density dependent shift are used. We estimate the effects of superfluid pairing from the clock shift. We find a qualitative agreement with the pairing gap - pseudogap transition behavior. We also find within the quasiparticle picture that in order for the gap to come into quantitative agreement with the previously known value at T=0, the effective mass has to be m1.43mm^* \sim 1.43 m. Finally, we discuss implications for the adiabatic sweep of the resonant magnetic field.Comment: 2 figure

    Unsupervised Learning of Complex Articulated Kinematic Structures combining Motion and Skeleton Information

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    In this paper we present a novel framework for unsupervised kinematic structure learning of complex articulated objects from a single-view image sequence. In contrast to prior motion information based methods, which estimate relatively simple articulations, our method can generate arbitrarily complex kinematic structures with skeletal topology by a successive iterative merge process. The iterative merge process is guided by a skeleton distance function which is generated from a novel object boundary generation method from sparse points. Our main contributions can be summarised as follows: (i) Unsupervised complex articulated kinematic structure learning by combining motion and skeleton information. (ii) Iterative fine-to-coarse merging strategy for adaptive motion segmentation and structure smoothing. (iii) Skeleton estimation from sparse feature points. (iv) A new highly articulated object dataset containing multi-stage complexity with ground truth. Our experiments show that the proposed method out-performs state-of-the-art methods both quantitatively and qualitatively
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