MP-GELU Bayesian Neural Networks: Moment Propagation by GELU Nonlinearity

Bayesian neural networks (BNNs) have been an important framework in the study of uncertainty quantification. Deterministic variational inference, one of the inference methods, utilizes moment propagation to compute the predictive distributions and objective functions. Unfortunately, deriving the moments requires computationally expensive Taylor expansion in nonlinear functions, such as a rectified linear unit (ReLU) or a sigmoid function. Therefore, a new nonlinear function that realizes faster moment propagation than conventional functions is required. In this paper, we propose a novel nonlinear function named moment propagating-Gaussian error linear unit (MP-GELU) that enables the fast derivation of first and second moments in BNNs. MP-GELU enables the analytical computation of moments by applying nonlinearity to the input statistics, thereby reducing the computationally expensive calculations required for nonlinear functions. In empirical experiments on regression tasks, we observed that the proposed MP-GELU provides higher prediction accuracy and better quality of uncertainty with faster execution than those of ReLU-based BNNs.Comment: 9 pages, 1 figure

Random phase-free kinoform for large objects

We propose a random phase-free kinoform for large objects. When not using the random phase in kinoform calculation, the reconstructed images from the kinoform are heavy degraded, like edge-only preserved images. In addition, the kinoform cannot record an entire object that exceeds the kinoform size because the object light does not widely spread. In order to avoid this degradation and to widely spread the object light, the random phase is applied to the kinoform calculation; however, the reconstructed image is contaminated by speckle noise. In this paper, we overcome this problem by using our random phase-free method and error diffusion method

Experimental Demonstrations of Native Implementation of Boolean Logic Hamiltonian in a Superconducting Quantum Annealer

Experimental demonstrations of quantum annealing with native implementation of Boolean logic Hamiltonians are reported. As a superconducting integrated circuit, a problem Hamiltonian whose set of ground states is consistent with a given truth table is implemented for quantum annealing with no redundant qubits. As examples of the truth table, NAND and NOR are successfully fabricated as an identical circuit. Similarly, a native implementation of a multiplier comprising six superconducting flux qubits is also demonstrated. These native implementations of Hamiltonians consistent with Boolean logic provide an efficient and scalable way of applying annealing computation to so-called circuit satisfiability problems that aim to find a set of inputs consistent with a given output over any Boolean logic functions, especially those like factorization through a multiplier Hamiltonian. A proof-of-concept demonstration of a hybrid computing architecture for domain-specific quantum computing is described.Comment: 12 pages, 11 figure

Specialized Pro-Resolving Mediators Do Not Inhibit the Synthesis of Inflammatory Mediators Induced by Tumor Necrosis Factor-α in Synovial Fibroblasts

Background : Tumor necrosis factor (TNF)-α, a proinflammatory cytokine, is involved in the pathogenesis of rheumatoid arthritis (RA). The omega-3 unsaturated fatty acid-derived metabolites resolvin (Rv) D1, RvE1, and maresin-1 (MaR1) have been reported as anti-inflammatory lipid mediators and are known as specialized pro-resolving mediators (SPMs). In this study, we aimed to investigate the anti-inflammatory effects of SPMs on TNF-α-induced responses in synovial fibroblasts. Methods: We investigated the effects of SPMs on gene expression and/or production of cyclooxygenase-2 (COX-2), microsomal prostaglandin E synthase-1 (mPGES-1), interleukin (IL)-6, and matrix metalloproteinase (MMP)-3, which are involved in TNF-α-induced synovitis in RA or OA synovial fibroblasts, by quantitative real-time PCR. We also investigated the effects of SPMs on the mitogen-activated protein kinase (MAPK) signaling pathway by western blotting. Anti-inflammatory effects of SPMs were evaluated by applying SPMs to cultured synovial fibroblasts, followed by TNF-α stimulation. Results: The induction of COX-2, mPGES-1, IL-6, and MMP-3 by TNF-α in synovial fibroblasts was not suppressed by omega 3-derived SPMs regardless of their origin such as RA or OA. SPMs had no effect on lipid mediator receptor gene expression induce by TNF-α and did not inhibit the TNF-α-activated MAPK signaling pathway. The production of COX-2 and IL-6 protein was significantly decreased by p38 inhibitor. Conclusion: Despite reports on the anti-inflammatory effect of omega 3-derived SPMs, its anti-inflammatory effect on TNF-α-induced responses was not observed in synovial fibroblasts. The reason may be that SPMs have no suppressive effect on p38 activation, which plays an important role in the production of inflammatory cytokines in synovial fibroblasts