Wideband DOA Estimation via Sparse Bayesian Learning over a Khatri-Rao Dictionary

This paper deals with the wideband direction-of-arrival (DOA) estimation by exploiting the multiple measurement vectors (MMV) based sparse Bayesian learning (SBL) framework. First, the array covariance matrices at different frequency bins are focused to the reference frequency by the conventional focusing technique and then transformed into the vector form. Then a matrix called the Khatri-Rao dictionary is constructed by using the Khatri-Rao product and the multiple focused array covariance vectors are set as the new observations. DOA estimation is to find the sparsest representations of the new observations over the Khatri-Rao dictionary via SBL. The performance of the proposed method is compared with other well-known focusing based wideband algorithms and the Cramer-Rao lower bound (CRLB). The results show that it achieves higher resolution and accuracy and can reach the CRLB under relative demanding conditions. Moreover, the method imposes no restriction on the pattern of signal power spectral density and due to the increased number of rows of the dictionary, it can resolve more sources than sensors

Structural Changes and Regional Disparity in China's Inflation

The inflation problem in China has attracted a great deal of international attention in recent years. This paper examines the time series properties of China's CPI series. It is found that the overall inflation series and the inflation of food, tobacco, clothes, urban transport and urban housing are not persistent. Structural breaks in inflation are found in 2003 and 2004. The degree of rural-urban inflation disparity in China is also investigated. We find evidence that rural residents experience higher inflation than their urban counterparts.Structural Break, Unit Root, ADF Test, Rural and Urban Inflation.

The stability of the tethered trailer and its control

Tethered trailer vehicle is a nonprofessional tractor that drags an unpowered vehicle with rope. In this paper, a nonlinear dynamic model of the tractor is developed. With the Dugoff’s tire model. A new nonlinear tethered tractor-trailer model is created to simulate critical parameters. A trailer front-wheel steering feedback control strategy is derived in order to improve stability and trajectory tracking feature the comparison of the simulation results for tension of the traction rope, the trajectory following resistance, and the handling stability clearly demonstrates the efficacy of the proposed control strategy

Top quark decays with flavor violation in the B-LSSM

The decays of top quark $t\rightarrow c\gamma,\;t\rightarrow cg,\;t\rightarrow cZ,\;t\rightarrow ch$ are extremely rare processes in the standard model (SM). The predictions on the corresponding branching ratios in the SM are too small to be detected in the future, hence any measurable signal for the processes at the LHC is a smoking gun for new physics. In the extension of minimal supersymmetric standard model with an additional local $U(1)_{B-L}$ gauge symmetry (B-LSSM), new gauge interaction and new flavor changing interaction affect the theoretical evaluations on corresponding branching ratios of those processes. In this work, we analyze those processes in the B-LSSM, under a minimal flavor violating assumption for the soft breaking terms. Considering the constraints from updated experimental data, the numerical results imply $Br(t\rightarrow c\gamma)\sim5\times10^{-7}$, $Br(t\rightarrow cg)\sim2\times10^{-6}$, $Br(t\rightarrow cZ)\sim4\times10^{-7}$ and $Br(t\rightarrow ch)\sim3\times10^{-9}$ in our chosen parameter space. Simultaneously, new gauge coupling constants $g_{_B},\;g_{_{YB}}$ in the B-LSSM can also affect the numerical results of $Br(t\rightarrow c\gamma,\;cg,\;cZ,\;ch)$.Comment: 20 pages, 4 figures, published in EPJC. arXiv admin note: substantial text overlap with arXiv:1803.0990

The impact of resumption of former top executives on stock prices: an event study approach

This study explored the impact of resumption of former top executives on stock prices based on market model for the listed corporations in Taiwan stock market. Top executives nowadays confront great challenges in acquiring new corporate accounts to meet agreed targets and drive rapid, profitable growth. Accordingly, corporations commonly decide to reinstate former top executives since their managerial experience is expected to improve corporate performance. The aim of this study is to provide practical guidelines for companies that are considering such decisions and favorable information that can help investors to adjust their portfolios in response to such potential decisions

Lower Bounds for Function Inversion with Quantum Advice

Function inversion is the problem that given a random function $f: [M] \to [N]$, we want to find pre-image of any image $f^{-1}(y)$ in time $T$. In this work, we revisit this problem under the preprocessing model where we can compute some auxiliary information or advice of size $S$ that only depends on $f$ but not on $y$. It is a well-studied problem in the classical settings, however, it is not clear how quantum algorithms can solve this task any better besides invoking Grover's algorithm, which does not leverage the power of preprocessing. Nayebi et al. proved a lower bound $ST^2 \ge \tilde\Omega(N)$ for quantum algorithms inverting permutations, however, they only consider algorithms with classical advice. Hhan et al. subsequently extended this lower bound to fully quantum algorithms for inverting permutations. In this work, we give the same asymptotic lower bound to fully quantum algorithms for inverting functions for fully quantum algorithms under the regime where $M = O(N)$. In order to prove these bounds, we generalize the notion of quantum random access code, originally introduced by Ambainis et al., to the setting where we are given a list of (not necessarily independent) random variables, and we wish to compress them into a variable-length encoding such that we can retrieve a random element just using the encoding with high probability. As our main technical contribution, we give a nearly tight lower bound (for a wide parameter range) for this generalized notion of quantum random access codes, which may be of independent interest.Comment: ITC full versio

Growth rate of liquidity provider's wealth in G3Ms

Geometric mean market makers (G3Ms), such as Uniswap and Balancer, represent a widely used class of automated market makers (AMMs). These G3Ms are characterized by the following rule: the reserves of the AMM must maintain the same (weighted) geometric mean before and after each trade. This paper investigates the effects of trading fees on liquidity providers' (LP) profitability in a G3M, as well as the adverse selection faced by LPs due to arbitrage activities involving a reference market. Our work expands the model described in previous studies for G3Ms, integrating transaction fees and continuous-time arbitrage into the analysis. Within this context, we analyze G3M dynamics, characterized by stochastic storage processes, and calculate the growth rate of LP wealth. In particular, our results align with and extend the results concerning the constant product market maker, commonly referred to as Uniswap v2.Comment: 27 page