Recent works on learned index open a new direction for the indexing field. The key insight of the learned index is to approximate the mapping between keys and positions with piece-wise linear functions. Such methods require partitioning key space for a better approximation. Although lots of heuristics are proposed to improve the approximation quality, the bottleneck is that the segmentation overheads could hinder the overall performance. This paper tackles the approximation problem by applying a distribution transformation to the keys before constructing the learned index. A two-stage Normalizing-Flow-based Learned index framework (NFL) is proposed, which first transforms the original complex key distribution into a near-uniform distribution, then builds a learned index leveraging the transformed keys. For effective distribution transformation, we propose a Numerical Normalizing Flow (Numerical NF). Based on the characteristics of the transformed keys, we propose a robust After-Flow Learned Index (AFLI). To validate the performance, comprehensive evaluations are conducted on both synthetic and real-world workloads, which shows that the proposed NFL produces the highest throughput and the lowest tail latency compared to the state-of-the-art learned indexes.
We present a new black hole solution in the asymptotic Lifshitz spacetime with a hyperscaling violating factor. A novel computational method is introduced to compute the DC thermoelectric conductivities analytically. We find that both the linear-T and quadratic-T contributions to the resistivity can be realized, indicating that a more detailed comparison with experimental phenomenology can be performed in this scenario.
We reconstruct the information quantities in the holographic relaxed superconductor system and discuss how these quantities behave under non-symmetry configuration. We then combine the effect of the superconductor and the momentum relaxation, and find out that the superconductor effect will change how the momentum relaxation affect these information quantities.
The log-structured merge-tree (LSM-tree)-based key-value store has been widely adopted by many large-scale data storage applications for its excellent write performance. However, such write performance gains mainly come from scarifying read performance due to the leveled and log-structured intrinsic characteristics of the LSM-tree. Therefore, the critical challenge of the existing LSM-tree is how to improve the read efficiency by reducing read amplification. This article for the first time proposes Tidal-tree-Mem, a novel data structure where data flow inside the LSM-tree-like Tidal waves. First, a floating strategy is proposed to allow frequently accessed files at the bottom of the LSM-tree to move to higher positions, reducing read amplification. Second, a stretching strategy is proposed to vary the shape of the LSM-tree to adapt to workloads with different characteristics. To evaluate the performance of Tidal-tree-Mem, we conduct a series of experiments using standard benchmarks from YCSB. The experimental results show that Tidal-tree-Mem can effectively reduce read amplification and the overall latency by over 71.94% and 47.34%, respectively, compared with representative schemes.
Robustness and uncertainty estimation is crucial to the safety of deep neural networks (DNNs) deployed on the edge. The deep ensemble model, composed of a set of individual DNNs (namely members), has strong performance in accuracy, uncertainty estimation, and robustness to out-of-distribution data and adversarial attacks. However, the storage and memory consumption increases linearly with the number of members within an ensemble. Previous works focus on selecting better members, layer-wise low-rank approximation of ensemble parameters, and designing partial ensemble model for reducing the ensemble size, thus lowering storage and memory consumption. In this work, we pay attention to the quantization of the ensemble, which serves as the last mile of network deployment. We propose a differentiable and parallelizable bit sharing scheme that allows the members to share the less significant bits of parameters, without hurting the performance, leaving alone the more significant bits. The intuition is that, numerically, more significant bits (e.g., the bit for the sign) are more useful in distinguishing a member from other members. For real deployment of the bit-sharing scheme, we further propose an efficient encoding-decoding scheme with minimal storage overhead. The experimental results show that, BitsEnsemble reduces the storage size of ensemble for over $22\times $ , with only $0.36\times $ increase in training latency, and no sacrifice of inference latency. The code is available in https://github.com/ralphc1212/bitsensemble .
We derive new black hole solutions in Einstein-Maxwell-axion-dilaton theory with a hyperscaling violation exponent. We then examine the corresponding anomalous transport exhibited by cuprate strange metals in the normal phase of high-temperature superconductors via gauge-gravity duality. Linear-temperature-dependence resistivity and quadratic-temperature-dependence inverse Hall angle can be achieved. In the high-temperature regime, the heat conductivity and Hall Lorenz ratio are proportional to the temperature. The Nernst signal first increases as temperature goes up, but it then decreases with increasing temperature in the high-temperature regime.
We study the transport coefficients, including the conductivities and shear viscosity of the nonrelativistic field theory dual to the Lifshitz black brane with multiple $U(1)$ gauge fields by virtue of the gauge/gravity duality. Focusing on the case of double $U(1)$ gauge fields, we systematically investigate the electric, thermal, and thermoelectric conductivities for the dual nonrelativistic field theory. In the large frequency regime, we find a nontrivial power law behavior in the electric alternating current conductivity when the dynamical critical exponent $z>1$ in ($2+1$)-dimensional field theory. The relations between this novel feature and the ``symmetric hopping model'' in condensed matter physics are discussed. In addition, we also show that the Kovtun-Starinets-Son bound for the shear viscosity to the entropy density is not violated by the additional $U(1)$ gauge fields and dilaton in the Lifshitz black brane.
Xian-Hui Ge, Yu Tian, Shang-Yu Wu, and Shao-Feng Wu 4 Shanghai Key Laboratory of High Temperature Superconductors, Department of Physics, Shanghai University, Shanghai 200444, P.R. China School of Physics, University of Chinese Academy of Sciences, Beijing, 100049, P.R. China Department of Electrophysics, National Chiao Tung University, Hsinchu 300, R. China Shanghai Key Lab for Astrophysics, 100 Guilin Road, 200234 Shanghai, P. R. China (Dated: July 5, 2016)
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