Depth Assisted Full Resolution Network for Single Image-based View Synthesis

Researches in novel viewpoint synthesis majorly focus on interpolation from multi-view input images. In this paper, we focus on a more challenging and ill-posed problem that is to synthesize novel viewpoints from one single input image. To achieve this goal, we propose a novel deep learning-based technique. We design a full resolution network that extracts local image features with the same resolution of the input, which contributes to derive high resolution and prevent blurry artifacts in the final synthesized images. We also involve a pre-trained depth estimation network into our system, and thus 3D information is able to be utilized to infer the flow field between the input and the target image. Since the depth network is trained by depth order information between arbitrary pairs of points in the scene, global image features are also involved into our system. Finally, a synthesis layer is used to not only warp the observed pixels to the desired positions but also hallucinate the missing pixels with recorded pixels. Experiments show that our technique performs well on images of various scenes, and outperforms the state-of-the-art techniques

Perception Driven Texture Generation

This paper investigates a novel task of generating texture images from perceptual descriptions. Previous work on texture generation focused on either synthesis from examples or generation from procedural models. Generating textures from perceptual attributes have not been well studied yet. Meanwhile, perceptual attributes, such as directionality, regularity and roughness are important factors for human observers to describe a texture. In this paper, we propose a joint deep network model that combines adversarial training and perceptual feature regression for texture generation, while only random noise and user-defined perceptual attributes are required as input. In this model, a preliminary trained convolutional neural network is essentially integrated with the adversarial framework, which can drive the generated textures to possess given perceptual attributes. An important aspect of the proposed model is that, if we change one of the input perceptual features, the corresponding appearance of the generated textures will also be changed. We design several experiments to validate the effectiveness of the proposed method. The results show that the proposed method can produce high quality texture images with desired perceptual properties.Comment: 7 pages, 4 figures, icme201

Invariants and Gorenstein projective modules

Invariants with respect to recollements of the stable category of Gorenstein projective A-modules over an algebra A and stable equivalences are investigated. Specifically, the Gorenstein rigidity dimension is introduced. It is shown that the Gorenstein rigidity dimension is invariant with respect to both Morita equivalences and the stable equivalences of Gorenstein projective modules. As a consequence, the Gorenstein rigidity dimension is shown the invariant of derived equivalences. The Gorenstein rigidity dimension is compared along the recollements of the stable category of Gorenstein projective modules. Moreover, the bounds of Gorenstein rigidity dimension is given for several classes of algebras, respectively.Comment: 8 page

Domain Conditioned Adaptation Network

Tremendous research efforts have been made to thrive deep domain adaptation (DA) by seeking domain-invariant features. Most existing deep DA models only focus on aligning feature representations of task-specific layers across domains while integrating a totally shared convolutional architecture for source and target. However, we argue that such strongly-shared convolutional layers might be harmful for domain-specific feature learning when source and target data distribution differs to a large extent. In this paper, we relax a shared-convnets assumption made by previous DA methods and propose a Domain Conditioned Adaptation Network (DCAN), which aims to excite distinct convolutional channels with a domain conditioned channel attention mechanism. As a result, the critical low-level domain-dependent knowledge could be explored appropriately. As far as we know, this is the first work to explore the domain-wise convolutional channel activation for deep DA networks. Moreover, to effectively align high-level feature distributions across two domains, we further deploy domain conditioned feature correction blocks after task-specific layers, which will explicitly correct the domain discrepancy. Extensive experiments on three cross-domain benchmarks demonstrate the proposed approach outperforms existing methods by a large margin, especially on very tough cross-domain learning tasks.Comment: Accepted by AAAI 202

A 0.1–5.0 GHz flexible SDR receiver with digitally assisted calibration in 65 nm CMOS

© 2017 Elsevier Ltd. All rights reserved.A 0.1–5.0 GHz flexible software-defined radio (SDR) receiver with digitally assisted calibration is presented, employing a zero-IF/low-IF reconfigurable architecture for both wideband and narrowband applications. The receiver composes of a main-path based on a current-mode mixer for low noise, a high linearity sub-path based on a voltage-mode passive mixer for out-of-band rejection, and a harmonic rejection (HR) path with vector gain calibration. A dual feedback LNA with “8” shape nested inductor structure, a cascode inverter-based TCA with miller feedback compensation, and a class-AB full differential Op-Amp with Miller feed-forward compensation and QFG technique are proposed. Digitally assisted calibration methods for HR, IIP2 and image rejection (IR) are presented to maintain high performance over PVT variations. The presented receiver is implemented in 65 nm CMOS with 5.4 mm2 core area, consuming 9.6–47.4 mA current under 1.2 V supply. The receiver main path is measured with +5 dB m/+5dBm IB-IIP3/OB-IIP3 and +61dBm IIP2. The sub-path achieves +10 dB m/+18dBm IB-IIP3/OB-IIP3 and +62dBm IIP2, as well as 10 dB RF filtering rejection at 10 MHz offset. The HR-path reaches +13 dB m/+14dBm IB-IIP3/OB-IIP3 and 62/66 dB 3rd/5th-order harmonic rejection with 30–40 dB improvement by the calibration. The measured sensitivity satisfies the requirements of DVB-H, LTE, 802.11 g, and ZigBee.Peer reviewedFinal Accepted Versio

The Color Octet Effect from e+e−→J/ψ+X+γe^+ e^-\to{J/\psi}+X+\gamma at B Factory

We study the initial state radiation process $e^+ e^-\to{J/\psi}+X+\gamma$ for $J/\psi$ production at B factory, and find the cross section is 61% larger than it's Born one for color octet part and is about half as it's Born one for color singlet part. Furthermore, the color singlet and color octet signal are very clearly separated in it's $E_\gamma$ spectra due to kinematics difference. We suggest to measure this $E_\gamma$ spectra at B factory to determine the color octet effect.Comment: 4 pages, 4 figures and 1 tabl