87,498 research outputs found

    Representation and regularity for the Dirichlet problem for real and complex degenerate Hessian equations

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    We consider the Dirichlet problem for positively homogeneous, degenerate elliptic, concave (or convex) Hessian equations. Under natural and necessary conditions on the geometry of the domain, with the C1,1C^{1,1} boundary data, we establish the interior C1,1C^{1,1}-regularity of the unique (admissible) solution, which is optimal even if the boundary data is smooth. Both real and complex cases are studied by the unified (Bellman equation) approach.Comment: 42 pages. Comments are welcom

    Finite groups with small number of cyclic subgroups

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    In this note, we study the finite groups with the number of cylic subgroups no greater than 6.Comment: 4 page

    Interior regularity of fully nonlinear degenerate elliptic equations, I: Bellman equations with constant coefficients

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    This is the first of a series of papers on the interior regularity of fully nonlinear degenerate elliptic equations. We consider a stochastic optimal control problem in which the diffusion coefficients, drift coefficients and discount factor are independent of the spacial variables. Under suitable assumptions, for k=0,1k=0,1, when the terminal and running payoffs are globally Ck,1C^{k,1}, we obtain the Ck,1C^{k,1}-smoothness of the value function, which yields the existence and uniqueness of the solution to the associated Dirichlet problem for the degenerate Bellman equation.Comment: Assumption 2.2 was corrected and then weakened. The original Assumption 2.2 after correction is now Remark 2.1. Minor revision was made accordingly on pages 28 and 29. A few typos were corrected als

    On representation and regularity of viscosity solutions to degenerate Isaacs equations and certain nonconvex Hessian equations

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    We study the smoothness of the upper and lower value functions of stochastic differential games in the framework of time-homogeneous (possibly degenerate) diffusion processes in a domain, under the assumption that the diffusion, drift and discount coefficients are all independent of the spatial variables. Under suitable conditions (see Assumptions 2.1 and 2.2), we obtain the optimal local Lipschitz continuity of the value functions, provided that the running and terminal payoffs are globally Lipschitz. As applications, we obtain the stochastic representation and optimal interior C0,1C^{0,1}-regularity of the unique viscosity solution to the Dirichlet problem for certain degenerate elliptic, nonconvex Hessian equations in suitable domains, with Lipschitz boundary data.Comment: 30 pages. Comments are welcom

    Universal price impact functions of individual trades in an order-driven market

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    The trade size ω\omega has direct impact on the price formation of the stock traded. Econophysical analyses of transaction data for the US and Australian stock markets have uncovered market-specific scaling laws, where a master curve of price impact can be obtained in each market when stock capitalization CC is included as an argument in the scaling relation. However, the rationale of introducing stock capitalization in the scaling is unclear and the anomalous negative correlation between price change rr and trade size ω\omega for small trades is unexplained. Here we show that these issues can be addressed by taking into account the aggressiveness of orders that result in trades together with a proper normalization technique. Using order book data from the Chinese market, we show that trades from filled and partially filled limit orders have very different price impact. The price impact of trades from partially filled orders is constant when the volume is not too large, while that of filled orders shows power-law behavior r∼ωαr\sim \omega^\alpha with α≈2/3\alpha\approx2/3. When returns and volumes are normalized by stock-dependent averages, capitalization-independent scaling laws emerge for both types of trades. However, no scaling relation in terms of stock capitalization can be constructed. In addition, the relation α=αω/αr\alpha=\alpha_\omega/\alpha_r is verified, where αω\alpha_\omega and αr\alpha_r are the tail exponents of trade sizes and returns. These observations also enable us to explain the anomalous negative correlation between rr and ω\omega for small-size trades. We anticipate that these regularities may hold in other order-driven markets.Comment: 17 pages + supplementary figures. The paper has been significantly expanded and more Supplementary Information is adde

    Quadboost: A Scalable Concurrent Quadtree

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    Building concurrent spatial trees is more complicated than binary search trees since a space hierarchy should be preserved during modifications. We present a non-blocking quadtree-quadboost-that supports concurrent insert, remove, move, and contain operations. To increase its concurrency, we propose a decoupling approach that separates physical adjustment from logical removal within the remove operation. In addition, we design a continuous find mechanism to reduce its search cost. The move operation combines the searches for different keys together and modifies different positions with atomicity. The experimental results show that quadboost scales well on a multi-core system with 32 hardware threads. More than that, it outperforms existing concurrent trees in retrieving two-dimensional keys with up to 109% improvement when the number of threads is large. The move operation proved to perform better than the best-known algorithm, with up to 47%

    Uplink Multicell Processing with Limited Backhaul via Per-Base-Station Successive Interference Cancellation

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    This paper studies an uplink multicell joint processing model in which the base-stations are connected to a centralized processing server via rate-limited digital backhaul links. Unlike previous studies where the centralized processor jointly decodes all the source messages from all base-stations, this paper proposes a suboptimal achievability scheme in which the Wyner-Ziv compress-and-forward relaying technique is employed on a per-base-station basis, but successive interference cancellation (SIC) is used at the central processor to mitigate multicell interference. This results in an achievable rate region that is easily computable, in contrast to the joint processing schemes in which the rate regions can only be characterized by exponential number of rate constraints. Under the per-base-station SIC framework, this paper further studies the impact of the limited-capacity backhaul links on the achievable rates and establishes that in order to achieve to within constant number of bits to the maximal SIC rate with infinite-capacity backhaul, the backhaul capacity must scale logarithmically with the signal-to-interference-and-noise ratio (SINR) at each base-station. Finally, this paper studies the optimal backhaul rate allocation problem for an uplink multicell joint processing model with a total backhaul capacity constraint. The analysis reveals that the optimal strategy that maximizes the overall sum rate should also scale with the log of the SINR at each base-station.Comment: JSAC Oct 2013, special issue on VMIM

    Capacity of the Gaussian Relay Channel with Correlated Noises to Within a Constant Gap

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    This paper studies the relaying strategies and the approximate capacity of the classic three-node Gaussian relay channel, but where the noises at the relay and at the destination are correlated. It is shown that the capacity of such a relay channel can be achieved to within a constant gap of \hf \log_2 3 =0.7925 bits using a modified version of the noisy network coding strategy, where the quantization level at the relay is set in a correlation dependent way. As a corollary, this result establishes that the conventional compress-and-forward scheme also achieves to within a constant gap to the capacity. In contrast, the decode-and-forward and the single-tap amplify-and-forward relaying strategies can have an infinite gap to capacity in the regime where the noises at the relay and at the destination are highly correlated, and the gain of the relay-to-destination link goes to infinity.Comment: accepted to communications letter

    Normal property, Jamenson property, CHIP and linear regularity for an infinite system of convex sets in Banach spaces

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    In this paper, we study different kinds of normal properties for infinite system of arbitrarily many convex sets in a Banach space and provide the dual characterization for the normal property in terms of the extended Jamenson property for arbitrarily many weak*-closed convex cones in the dual space. Then, we use the normal property and the extended Jamenson property to study CHIP, strong CHIP and linear regularity for the infinite case of arbitrarily many convex sets and establish equivalent relationship among these properties. In particular, we extend main results in [3] on normal property, Jamenson property, CHIP and linear regularity for finite system of convex sets in a Hilbert space to the infinite case of arbitrarily many convex sets in Banach space setting

    Object Detection based on LIDAR Temporal Pulses using Spiking Neural Networks

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    Neural networks has been successfully used in the processing of Lidar data, especially in the scenario of autonomous driving. However, existing methods heavily rely on pre-processing of the pulse signals derived from Lidar sensors and therefore result in high computational overhead and considerable latency. In this paper, we proposed an approach utilizing Spiking Neural Network (SNN) to address the object recognition problem directly with raw temporal pulses. To help with the evaluation and benchmarking, a comprehensive temporal pulses data-set was created to simulate Lidar reflection in different road scenarios. Being tested with regard to recognition accuracy and time efficiency under different noise conditions, our proposed method shows remarkable performance with the inference accuracy up to 99.83% (with 10% noise) and the average recognition delay as low as 265 ns. It highlights the potential of SNN in autonomous driving and some related applications. In particular, to our best knowledge, this is the first attempt to use SNN to directly perform object recognition on raw Lidar temporal pulses
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