Recent progress in random metric theory and its applications to conditional risk measures

The purpose of this paper is to give a selective survey on recent progress in random metric theory and its applications to conditional risk measures. This paper includes eight sections. Section 1 is a longer introduction, which gives a brief introduction to random metric theory, risk measures and conditional risk measures. Section 2 gives the central framework in random metric theory, topological structures, important examples, the notions of a random conjugate space and the Hahn-Banach theorems for random linear functionals. Section 3 gives several important representation theorems for random conjugate spaces. Section 4 gives characterizations for a complete random normed module to be random reflexive. Section 5 gives hyperplane separation theorems currently available in random locally convex modules. Section 6 gives the theory of random duality with respect to the locally $L^{0}-$convex topology and in particular a characterization for a locally $L^{0}-$convex module to be $L^{0}-$pre$-$barreled. Section 7 gives some basic results on $L^{0}-$convex analysis together with some applications to conditional risk measures. Finally, Section 8 is devoted to extensions of conditional convex risk measures, which shows that every representable $L^{\infty}-$type of conditional convex risk measure and every continuous $L^{p}-$type of convex conditional risk measure ($1\leq p<+\infty$) can be extended to an $L^{\infty}_{\cal F}({\cal E})-$type of $\sigma_{\epsilon,\lambda}(L^{\infty}_{\cal F}({\cal E}), L^{1}_{\cal F}({\cal E}))-$lower semicontinuous conditional convex risk measure and an $L^{p}_{\cal F}({\cal E})-$type of ${\cal T}_{\epsilon,\lambda}-$continuous conditional convex risk measure ($1\leq p<+\infty$), respectively.Comment: 37 page

Iterative Detection of Diagonal Block Space Time Trellis Codes, TCM and Reversible Variable Length Codes for Transmission over Rayleigh Fading Channels

Iterative detection of Diagonal Block Space Time Trellis Codes (DBSTTCs), Trellis Coded Modulation (TCM) and Reversible Variable Length Codes (RVLCs) is proposed. With the aid of efficient iterative decoding, the proposed scheme is capable of providing full transmit diversity and a near channel capacity performance. The performance of the proposed scheme was evaluated when communicating over uncorrelated Rayleigh fading channels. Explicitly, significant iteration gains were achieved by the proposed scheme, which was capable of performing within 2~dB from the channel capacity

A Fast DOA Estimation Algorithm Based on Polarization MUSIC

A fast DOA estimation algorithm developed from MUSIC, which also benefits from the processing of the signals' polarization information, is presented. Besides performance enhancement in precision and resolution, the proposed algorithm can be exerted on various forms of polarization sensitive arrays, without specific requirement on the array's pattern. Depending on the continuity property of the space spectrum, a huge amount of computation incurred in the calculation of 4-D space spectrum is averted. Performance and computation complexity analysis of the proposed algorithm is discussed and the simulation results are presented. Compared with conventional MUSIC, it is indicated that the proposed algorithm has considerable advantage in aspects of precision and resolution, with a low computation complexity proportional to a conventional 2-D MUSIC

A Purely Symbol-Based Precoded and LDPC-Coded Iterative-Detection Assisted Sphere-Packing Modulated Space-Time Coding Scheme

In this contribution, we propose a purely symbol-based LDPC-coded scheme based on a Space-Time Block Coding (STBC) signal construction method that combines orthogonal design with sphere packing, referred to here as (STBCSP). We demonstrate that useful performance improvements may be attained when sphere packing aided modulation is concatenated with non-binary LDPC especially, when performing purely symbol-based turbo detection by exchanging extrinsic information between the non-binary LDPC decoder and a rate-1 non-binary inner precoder. We also investigate the convergence behaviour of this symbol-based concatenated scheme with the aid of novel non-binary Extrinsic Information Transfer (EXIT) Charts. The proposed symbol-based turbo-detected STBC-SP scheme exhibits a 'turbo-cliff' at Eb/N0 = 5.0 dB and achieves an Eb/N0 gain of 19.2dB at a BER of 10-5 over Alamouti’s scheme

Spectra of Baryons Containing Two Heavy Quarks in Potential Model

In this work, we employ the effective vertices for interaction between diquarks (scalar or axial-vector) and gluon where the form factors are derived in terms of the B-S equation, to obtain the potential for baryons including a light quark and a heavy diquark. The concerned phenomenological parameters are obtained by fitting data of $B^{(*)}-$mesons instead of the heavy quarkonia. The operator ordering problem in quantum mechanics is discussed. Our numerical results indicate that the mass splitting between $B_{3/2}(V), B_{1/2}(V)$ and $B_{1/2}(S)$ is very small and it is consistent with the heavy quark effective theory (HQET).Comment: 16 page

\Lambda_b \to \Lambda_c P(V) Nonleptonic Weak Decays

The two-body nonleptonic weak decays of \Lambda_b \to \Lambda_c P(V) (P and V represent pseudoscalar and vector mesons respectively) are analyzed in two models, one is the Bethe-Salpeter (B-S) model and the other is the hadronic wave function model. The calculations are carried out in the factorization approach. The obtained results are compared with other model calculations.Comment: 18 pages, Late

Top-N Recommendation on Graphs

Recommender systems play an increasingly important role in online applications to help users find what they need or prefer. Collaborative filtering algorithms that generate predictions by analyzing the user-item rating matrix perform poorly when the matrix is sparse. To alleviate this problem, this paper proposes a simple recommendation algorithm that fully exploits the similarity information among users and items and intrinsic structural information of the user-item matrix. The proposed method constructs a new representation which preserves affinity and structure information in the user-item rating matrix and then performs recommendation task. To capture proximity information about users and items, two graphs are constructed. Manifold learning idea is used to constrain the new representation to be smooth on these graphs, so as to enforce users and item proximities. Our model is formulated as a convex optimization problem, for which we need to solve the well-known Sylvester equation only. We carry out extensive empirical evaluations on six benchmark datasets to show the effectiveness of this approach.Comment: CIKM 201