We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

/pdf/going-deeper-with-convolutions-1yobw2o2ds.pdf

Going deeper with convolutions

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or $N$-way array. Decompositions of higher-order tensors (i.e., $N$-way arrays with $N \geq 3$) have applications in psycho-metrics, chemometrics, signal processing, numerical linear algebra, computer vision, numerical analysis, data mining, neuroscience, graph analysis, and elsewhere. Two particular tensor decompositions can be considered to be higher-order extensions of the matrix singular value decomposition: CANDECOMP/PARAFAC (CP) decomposes a tensor as a sum of rank-one tensors, and the Tucker decomposition is a higher-order form of principal component analysis. There are many other tensor decompositions, including INDSCAL, PARAFAC2, CANDELINC, DEDICOM, and PARATUCK2 as well as nonnegative variants of all of the above. The N-way Toolbox, Tensor Toolbox, and Multilinear Engine are examples of software packages for working with tensors.

/pdf/tensor-decompositions-and-applications-46vl2ih5gn.pdf

Tensor Decompositions and Applications

The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising. Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the denoising performance. Different from the existing discriminative denoising models which usually train a specific model for additive white Gaussian noise at a certain noise level, our DnCNN model is able to handle Gaussian denoising with unknown noise level (i.e., blind Gaussian denoising). With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers. This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks, such as Gaussian denoising, single image super-resolution, and JPEG image deblocking. Our extensive experiments demonstrate that our DnCNN model can not only exhibit high effectiveness in several general image denoising tasks, but also be efficiently implemented by benefiting from GPU computing.

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

We present an approach to interpret the major surfaces, objects, and support relations of an indoor scene from an RGBD image. Most existing work ignores physical interactions or is applied only to tidy rooms and hallways. Our goal is to parse typical, often messy, indoor scenes into floor, walls, supporting surfaces, and object regions, and to recover support relationships. One of our main interests is to better understand how 3D cues can best inform a structured 3D interpretation. We also contribute a novel integer programming formulation to infer physical support relations. We offer a new dataset of 1449 RGBD images, capturing 464 diverse indoor scenes, with detailed annotations. Our experiments demonstrate our ability to infer support relations in complex scenes and verify that our 3D scene cues and inferred support lead to better object segmentation.

/pdf/indoor-segmentation-and-support-inference-from-rgbd-images-1brf1o9jc3.pdf

Indoor segmentation and support inference from RGBD images

Cyclopropenyllithiums as a new source of 1,1-bismetalated cyclopropyl derivatives.

The development of targeted therapies that affect multiple signaling pathways and stimulate antitumor immunity is greatly needed. About 20% of patients with breast cancer overexpress HER2. Small molecules and antibodies targeting HER2 convey some survival benefits; however, patients with advanced disease succumb to the disease under these treatment regimens, possibly because HER2 is not completely necessary for the survival of the targeted cancer cells. In the present study, we show that a polyinosine/polycytosine (pIC) HER2-homing chemical vector induced the demise of HER2-overexpressing breast cancer cells, including trastuzumab-resistant cells. Targeting pIC to the tumor evoked a number of cell-killing mechanisms, as well as strong bystander effects. These bystander mechanisms included type I IFN induction, immune cell recruitment, and activation. The HER2-targeted pIC strongly inhibited the growth of HER2-overexpressing tumors in immunocompetent mice. The data presented here could open additional avenues in the treatment of HER2-positive breast cancer. Cancer Immunol Res; 4(8); 688-97. ©2016 AACR.

https://cancerimmunolres.aacrjournals.org/content/canimm/4/8/688.full.pdf

HER2-Targeted Polyinosine/Polycytosine Therapy Inhibits Tumor Growth and Modulates the Tumor Immune Microenvironment.

While 3D movies are gaining popularity, viewers in a 3D cinema still need to wear cumbersome glasses in order to enjoy them. Automultiscopic displays provide a better alternative to the display of 3D content, as they present multiple angular images of the same scene without the need for special eyewear. However, automultiscopic displays cannot be directly implemented in a wide cinema setting due to variants of two main problems: (i) The range of angles at which the screen is observed in a large cinema is usually very wide, and there is an unavoidable tradeoff between the range of angular images supported by the display and its spatial or angular resolutions. (ii) Parallax is usually observed only when a viewer is positioned at a limited range of distances from the screen. This work proposes a new display concept, which supports automultiscopic content in a wide cinema setting. It builds on the typical structure of cinemas, such as the fixed seat positions and the fact that different rows are located on a slope at different heights. Rather than attempting to display many angular images spanning the full range of viewing angles in a wide cinema, our design only displays the narrow angular range observed within the limited width of a single seat. The same narrow range content is then replicated to all rows and seats in the cinema. To achieve this, it uses an optical construction based on two sets of parallax barriers, or lenslets, placed in front of a standard screen. This paper derives the geometry of such a display, analyzes its limitations, and demonstrates a proof-of-concept prototype.

Cinema 3D: large scale automultiscopic display

We present a 3D light-sensitive display. The display is capable of presenting simple opaque 3D surfaces without self occlusions, while reproducing both viewpoint-sensitive depth parallax and illumination-sensitive variations such as shadows and highlights. Our display is passive in the sense that it does not rely on illumination sensors and on-the-fly rendering of the image content. Rather, it consists of optical elements that produce light transport paths approximating those present in the real scene. Our display uses two layers of Spatial Light Modulators (SLMs) whose micron-sized elements allow us to digitally simulate thin optical surfaces with flexible shapes. We derive a simple content creation algorithm utilizing geometric optics tools to design optical surfaces that can mimic the ray transfer of target virtual 3D scenes. We demonstrate a possible implementation of a small prototype, and present a number of simple virtual 3D scenes.

/pdf/passive-light-and-viewpoint-sensitive-display-of-3d-content-2fbkfupa75.pdf

Passive light and viewpoint sensitive display of 3D content

/pdf/cinema-3d-large-scale-automultiscopic-display-5cprib5o20.pdf

Anat Levin

Papers

Cyclopropenyllithiums as a new source of 1,1-bismetalated cyclopropyl derivatives.

HER2-Targeted Polyinosine/Polycytosine Therapy Inhibits Tumor Growth and Modulates the Tumor Immune Microenvironment.

Cinema 3D: large scale automultiscopic display

Passive light and viewpoint sensitive display of 3D content

Cinema 3D: large scale automultiscopic display