Early Recognition of Human Activities from First-Person Videos Using Onset Representations

In this paper, we propose a methodology for early recognition of human activities from videos taken with a first-person viewpoint. Early recognition, which is also known as activity prediction, is an ability to infer an ongoing activity at its early stage. We present an algorithm to perform recognition of activities targeted at the camera from streaming videos, making the system to predict intended activities of the interacting person and avoid harmful events before they actually happen. We introduce the novel concept of 'onset' that efficiently summarizes pre-activity observations, and design an approach to consider event history in addition to ongoing video observation for early first-person recognition of activities. We propose to represent onset using cascade histograms of time series gradients, and we describe a novel algorithmic setup to take advantage of onset for early recognition of activities. The experimental results clearly illustrate that the proposed concept of onset enables better/earlier recognition of human activities from first-person videos

A note on q-Bernoulli numbers and polynomials

By using p-adic q-integrals, we study the q-Bernoulli numbers and polynomials of higher order.Comment: 8 page

A note on q-Euler numbers and polynomials

The purpose of this paper is to construct q-Euler numbers and polynomials by using p-adic q-integral equations on Zp. Finally, we will give some interesting formulae related to these q-Euler numbers and polynomials.Comment: 6 page

A decentralized spectrum allocation and partitioning scheme for a two-tier macro-femtocell network with downlink beamforming

This article examines spectrum allocation and partitioning schemes to mitigate cross-tier interference under downlink beamforming environments. The enhanced SIR owing to beamforming allows more femtocells to share their spectrum with the macrocell and accordingly improves overall spectrum efficiency. We first design a simplified centralized scheme as the optimum and then propose a practical decentralized algorithm that determines which femtocells to use the full or partitioned spectrum with acceptable control overhead. To exploit limited information of the received signal strength efficiently, we consider two types of probabilistic femtocell base station (HeNB) selection policies. They are equal selection and interference weighted selection policies, and we drive their outage probabilities for a macrocell user. Through performance evaluation, we demonstrate that the outage probability and the cell capacity in our decentralized scheme are significantly better than those in a conventional cochannel deployment scheme. Furthermore, we show that the cell utility in our proposed scheme is close to that in the centralized scheme and better than that in the spectrum partitioning scheme with a fixed ratio.open0

Climate Dynamics: A Network-Based Approach for the Analysis of Global Precipitation

Precipitation is one of the most important meteorological variables for defining the climate dynamics, but the spatial patterns of precipitation have not been fully investigated yet. The complex network theory, which provides a robust tool to investigate the statistical interdependence of many interacting elements, is used here to analyze the spatial dynamics of annual precipitation over seventy years (1941-2010). The precipitation network is built associating a node to a geographical region, which has a temporal distribution of precipitation, and identifying possible links among nodes through the correlation function. The precipitation network reveals significant spatial variability with barely connected regions, as Eastern China and Japan, and highly connected regions, such as the African Sahel, Eastern Australia and, to a lesser extent, Northern Europe. Sahel and Eastern Australia are remarkably dry regions, where low amounts of rainfall are uniformly distributed on continental scales and small-scale extreme events are rare. As a consequence, the precipitation gradient is low, making these regions well connected on a large spatial scale. On the contrary, the Asiatic South-East is often reached by extreme events such as monsoons, tropical cyclones and heat waves, which can all contribute to reduce the correlation to the short-range scale only. Some patterns emerging between mid-latitude and tropical regions suggest a possible impact of the propagation of planetary waves on precipitation at a global scale. Other links can be qualitatively associated to the atmospheric and oceanic circulation. To analyze the sensitivity of the network to the physical closeness of the nodes, short-term connections are broken. The African Sahel, Eastern Australia and Northern Europe regions again appear as the supernodes of the network, confirming furthermore their long-range connection structure. Almost all North-American and Asian nodes vanish, revealing that extreme events can enhance high precipitation gradients, leading to a systematic absence of long-range patterns

Topic modeling applied to business research: A latent dirichlet allocation (LDA)-based classification for organization studies

More than 1.5 million academic documents are published each year, and this trend shows an incremental tendency for the following years. One of the main challenges for the academic community is how to organize this huge volume of documentation to have a sense of the knowledge frontier. In this study we applied Latent Dirichlet Allocation (LDA) techniques to identify primary topics in organization studies, and analyzed the relationships between academic impact and belonging to the topics detected by LDA