Bottom-Up and Top-Down Reasoning with Hierarchical Rectified Gaussians

Convolutional neural nets (CNNs) have demonstrated remarkable performance in recent history. Such approaches tend to work in a unidirectional bottom-up feed-forward fashion. However, practical experience and biological evidence tells us that feedback plays a crucial role, particularly for detailed spatial understanding tasks. This work explores bidirectional architectures that also reason with top-down feedback: neural units are influenced by both lower and higher-level units. We do so by treating units as rectified latent variables in a quadratic energy function, which can be seen as a hierarchical Rectified Gaussian model (RGs). We show that RGs can be optimized with a quadratic program (QP), that can in turn be optimized with a recurrent neural network (with rectified linear units). This allows RGs to be trained with GPU-optimized gradient descent. From a theoretical perspective, RGs help establish a connection between CNNs and hierarchical probabilistic models. From a practical perspective, RGs are well suited for detailed spatial tasks that can benefit from top-down reasoning. We illustrate them on the challenging task of keypoint localization under occlusions, where local bottom-up evidence may be misleading. We demonstrate state-of-the-art results on challenging benchmarks.Comment: To appear in CVPR 201

ANALISIS KEBIJAKAN JEPANG DALAM PEMBATASAN EKSPOR BAHAN KIMIA TERHADAP KOREA SELATAN TAHUN 2019

This research investigates Japan's policy of restricting chemical exports to South Korea in 2019. The chemicals restricted by Japan include fluorinated polyamide, photoresist, and hydrogen fluoride. Chemicals originating from Japan are crucial materials for South Korea's technology industry. This research is explanatory, aiming to explain why Japan imposed restrictions on chemical exports to South Korea in 2019. The study utilizes foreign policy theory with an adaptive model according to James Nathan Rosenau. This theory elucidates Japan's national interests when implementing the policy of restricting chemical exports to South Korea in 2019 and how domestic and foreign actors were involved in influencing the policies. The relationship between Japan and South Korea has been ongoing since 1910, where historical issues between Japan and South Korea have persisted for a long time. The research findings indicate that historical issues are one of the factors in the formulation of Japan's policy to limit chemical exports to South Korea in 2019. Additionally, Japan restricted chemical exports to South Korea to maintain their national security

What the Oblique Parameters S, T, and U and Their Extensions Reveal About the 2HDM: A Numerical Analysis

The oblique parameters S, T, and U and their higher-order extensions (V, W, and X) are observables that combine electroweak precision data to quantify deviation from the Standard Model. These parameters were calculated at one loop in the basis-independent CP-violating Two-Higgs Doublet Model (2HDM). The scalar parameter space of the 2HDM was randomly sampled within limits imposed by unitarity and found to produce values of the oblique parameters within experimental bounds, with the exception of T. The experimental limits on T were used to predict information about the mass of the charged Higgs boson and the difference in mass between the charged Higgs boson and the heaviest neutral Higgs boson (m_ch - m_3). In particular, it was found that the 2HDM predicts -600 GeV 250 GeV being preferred. The mass scale of the new physics produced by random sampling was consistently fairly high, with the average of the scalar masses falling between 400 and 800 GeV for Y_2 = m_W^2, although the model can be tuned to produce a light neutral Higgs mass (eg, 120 GeV). Hence, the values produced for V, W, and X fell well within .01 of zero, confirming the robustness of the linear expansion approximation. Taking the CP-conserving limit of the model was found to not significantly affect the values generated for the oblique parameters.Comment: 17 pages, 31 figure