CamSwarm: Instantaneous Smartphone Camera Arrays for Collaborative Photography

Camera arrays (CamArrays) are widely used in commercial filming projects for achieving special visual effects such as bullet time effect, but are very expensive to set up. We propose CamSwarm, a low-cost and lightweight alternative to professional CamArrays for consumer applications. It allows the construction of a collaborative photography platform from multiple mobile devices anywhere and anytime, enabling new capturing and editing experiences that a single camera cannot provide. Our system allows easy team formation; uses real-time visualization and feedback to guide camera positioning; provides a mechanism for synchronized capturing; and finally allows the user to efficiently browse and edit the captured imagery. Our user study suggests that CamSwarm is easy to use; the provided real-time guidance is helpful; and the full system achieves high quality results promising for non-professional use. A demo video is provided at https://www.youtube.com/watch?v=LgkHcvcyTTM

PanoSwarm: Collaborative and Synchronized Multi-Device Panoramic Photography

Taking a picture has been traditionally a one-persons task. In this paper we present a novel system that allows multiple mobile devices to work collaboratively in a synchronized fashion to capture a panorama of a highly dynamic scene, creating an entirely new photography experience that encourages social interactions and teamwork. Our system contains two components: a client app that runs on all participating devices, and a server program that monitors and communicates with each device. In a capturing session, the server collects in realtime the viewfinder images of all devices and stitches them on-the-fly to create a panorama preview, which is then streamed to all devices as visual guidance. The system also allows one camera to be the host and to send direct visual instructions to others to guide camera adjustment. When ready, all devices take pictures at the same time for panorama stitching. Our preliminary study suggests that the proposed system can help users capture high quality panoramas with an enjoyable teamwork experience. A demo video of the system in action is provided at http://youtu.be/PwQ6k_ZEQSs

Temporal Action Localization in Untrimmed Videos via Multi-stage CNNs

We address temporal action localization in untrimmed long videos. This is important because videos in real applications are usually unconstrained and contain multiple action instances plus video content of background scenes or other activities. To address this challenging issue, we exploit the effectiveness of deep networks in temporal action localization via three segment-based 3D ConvNets: (1) a proposal network identifies candidate segments in a long video that may contain actions; (2) a classification network learns one-vs-all action classification model to serve as initialization for the localization network; and (3) a localization network fine-tunes on the learned classification network to localize each action instance. We propose a novel loss function for the localization network to explicitly consider temporal overlap and therefore achieve high temporal localization accuracy. Only the proposal network and the localization network are used during prediction. On two large-scale benchmarks, our approach achieves significantly superior performances compared with other state-of-the-art systems: mAP increases from 1.7% to 7.4% on MEXaction2 and increases from 15.0% to 19.0% on THUMOS 2014, when the overlap threshold for evaluation is set to 0.5.Comment: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 201

G2R Bound: A Generalization Bound for Supervised Learning from GAN-Synthetic Data

Performing supervised learning from the data synthesized by using Generative Adversarial Networks (GANs), dubbed GAN-synthetic data, has two important applications. First, GANs may generate more labeled training data, which may help improve classification accuracy. Second, in scenarios where real data cannot be released outside certain premises for privacy and/or security reasons, using GAN- synthetic data to conduct training is a plausible alternative. This paper proposes a generalization bound to guarantee the generalization capability of a classifier learning from GAN-synthetic data. This generalization bound helps developers gauge the generalization gap between learning from synthetic data and testing on real data, and can therefore provide the clues to improve the generalization capability

VcsV_{cs} from Pure Leptonic Decays of DsD_s with Radiative Corrections

The radiative corrections to the pure leptonic decay $D_s{\longrightarrow} {\ell}{{\nu}}_{\ell}$ up-to one-loop order is presented. We find the virtual photon loop corrections to $D_s{\longrightarrow} {\tau}{{\nu}}_{\tau}$ is negative and the corresponding branching ratio is larger than $3.51\times 10^{-3}$. Considering the possible experimental resolutions, our prediction of the radiative decay $D_s{\longrightarrow} {\tau}{{\nu}}_{\tau}\gamma$ is not so large as others, and the best radiative channel to determine the $V_{cs}$ or $f_{D_s}$ is $D_s{\longrightarrow} {\mu}{{\nu}}_{\mu}{\gamma}$.Comment: 7 pages, 1 Latex file, 3 PS figure

Learning to Hash for Indexing Big Data - A Survey

The explosive growth in big data has attracted much attention in designing efficient indexing and search methods recently. In many critical applications such as large-scale search and pattern matching, finding the nearest neighbors to a query is a fundamental research problem. However, the straightforward solution using exhaustive comparison is infeasible due to the prohibitive computational complexity and memory requirement. In response, Approximate Nearest Neighbor (ANN) search based on hashing techniques has become popular due to its promising performance in both efficiency and accuracy. Prior randomized hashing methods, e.g., Locality-Sensitive Hashing (LSH), explore data-independent hash functions with random projections or permutations. Although having elegant theoretic guarantees on the search quality in certain metric spaces, performance of randomized hashing has been shown insufficient in many real-world applications. As a remedy, new approaches incorporating data-driven learning methods in development of advanced hash functions have emerged. Such learning to hash methods exploit information such as data distributions or class labels when optimizing the hash codes or functions. Importantly, the learned hash codes are able to preserve the proximity of neighboring data in the original feature spaces in the hash code spaces. The goal of this paper is to provide readers with systematic understanding of insights, pros and cons of the emerging techniques. We provide a comprehensive survey of the learning to hash framework and representative techniques of various types, including unsupervised, semi-supervised, and supervised. In addition, we also summarize recent hashing approaches utilizing the deep learning models. Finally, we discuss the future direction and trends of research in this area

The Pure Leptonic Decays of DsD_s Meson and Their Radiative Corrections

The radiative corrections to the pure leptonic decay $D_s{\longrightarrow} {\ell}{{\nu}}_{\ell}$ up-to one-loop order is presented. We find the virtual photon loop corrections to $D_s{\longrightarrow} {\tau}{{\nu}}_{\tau}$ is negative and the corresponding branching ratio is larger than $3.51\times 10^{-3}$. Considering the possible experimental resolutions, our prediction of the radiative decay $D_s{\longrightarrow} {\tau}{{\nu}}_{\tau}\gamma$ is not so large as others, and the best channel to determine the $V_{cs}$ or $f_{D_s}$ is $D_s{\longrightarrow} {\mu}{{\nu}}_{\mu}{\gamma}$. How to cancel the infrared divergences appearing in the loop calculations, and the radiative decay $D_s{\longrightarrow} {\ell}{{\nu}}_{\ell}{\gamma}$ is shown precisely. It is emphasized that the radiative decay may be separated properly and may compare with measurements directly as long as the theoretical `softness' of the photon corresponds to the experimental resolutions.Comment: 11 pages, 1 Latex file, 8 ps figure

Some Variants of Kuniyoshi-Gasch\"utz Theorem with Applications to Noether's Problem

Let $G$ be a subgroup of $S_{n}$, the symmetric group of degree $n$. For any field $k$, $G$ acts naturally on the rational function field $k(x_{1},\cdots,x_{n})$ via $k$-automorphisms defined by $\sigma\cdot x_{i}:=x_{\sigma\cdot i}$ for any $\sigma\in G$ and $1\leq i\le n$. In this article, we will show that if $G$ is a solvable transitive subgroup of $S_{14}$ and $\text{char}(k)=7$, then the fixed subfield $k(x_{1},\cdots,x_{14})^{G}$ is rational (i.e., purely transcendental) over $k$. In proving the above theorem, we develop some variants of Kuniyoshi-Gasch\"utz Theorem for Noether's problem

Deep Transfer Network: Unsupervised Domain Adaptation

Domain adaptation aims at training a classifier in one dataset and applying it to a related but not identical dataset. One successfully used framework of domain adaptation is to learn a transformation to match both the distribution of the features (marginal distribution), and the distribution of the labels given features (conditional distribution). In this paper, we propose a new domain adaptation framework named Deep Transfer Network (DTN), where the highly flexible deep neural networks are used to implement such a distribution matching process. This is achieved by two types of layers in DTN: the shared feature extraction layers which learn a shared feature subspace in which the marginal distributions of the source and the target samples are drawn close, and the discrimination layers which match conditional distributions by classifier transduction. We also show that DTN has a computation complexity linear to the number of training samples, making it suitable to large-scale problems. By combining the best paradigms in both worlds (deep neural networks in recognition, and matching marginal and conditional distributions in domain adaptation), we demonstrate by extensive experiments that DTN improves significantly over former methods in both execution time and classification accuracy

A classical postselected weak amplification scheme via thermal light cross-Kerr effect

In common sense, postselected weak amplification must be related to destructive interference effect of the meter system, and a single photon exerts no effect on thermal field via cross-phasemodulation (XPM) interaction. In this Letter we present, for the first time, a thermal light cross-Kerr effect. Through analysis, we reveal two unexpected results: i) postselection and weak amplification can be explained at a classical level without destructive interference, and ii) weak amplification and weak value are not one thing. After postselection a new mixed light can be generated which is nonclassical. This scheme can be realized via electromagnetically-induced transparency.Comment: Comments are welcome. 6 pages, 11 figure