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

One video is sufficient? Human activity recognition using active video composition

In this paper, we present a novel human activity recogni-tion approach that only requires a single video example per activity. We introduce the paradigm of active video com-position, which enables one-example recognition of com-plex activities. The idea is to automatically create a large number of semi-artificial training videos called composed videos by manipulating an original human activity video. A methodology to automatically compose activity videos hav-ing different backgrounds, translations, scales, actors, and movement structures is described in this paper. Further-more, an active learning algorithm to model the temporal structure of the human activity has been designed, prevent-ing the generation of composed training videos violating the structural constraints of the activity. The intention is to gen-erate composed videos having correct organizations, and take advantage of them for the training of the recognition system. In contrast to previous passive recognition systems relying only on given training videos, our methodology ac-tively composes necessary training videos that the system is expected to observe in its environment. Experimental re-sults illustrate that a single fully labeled video per activity is sufficient for our methodology to reliably recognize human activities by utilizing composed training videos. 1

First-Person Activity Recognition: What Are They Doing to Me?

This paper discusses the problem of recognizing interaction-level human activities from a first-person view-point. The goal is to enable an observer (e.g., a robot or a wearable camera) to understand ‘what activity others are performing to it ’ from continuous video inputs. These include friendly interactions such as ‘a person hugging the observer ’ as well as hostile interactions like ‘punching the observer ’ or ‘throwing objects to the observer’, whose videos involve a large amount of camera ego-motion caused by physical interactions. The paper investigates multi-channel kernels to integrate global and local motion in-formation, and presents a new activity learning/recognition methodology that explicitly considers temporal structures displayed in first-person activity videos. In our experi-ments, we not only show classification results with seg-mented videos, but also confirm that our new approach is able to detect activities from continuous videos reliably. 1

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

Rates and determinants of antibiotics and probiotics prescription to children in Asia-Pacific countries

Antibiotic therapy may have important side effects. Guidelines recommend the administration of specific probiotics to reduce the risk of antibiotic-associated diarrhoea (AAD). The rates and determinants of antibiotics and co-prescription of probiotics in children remain poorly known in Asia-Pacific countries, which are very heterogenous in terms of economic development, health care organization and health policies. A survey among general practitioners (GPs) and paediatricians was performed in seven countries of the Asia-Pacific area (Australia, Japan, Indonesia, India, China, Singapore, and South Korea). Physicians completed an online questionnaire that explored their current habits and the determinants for prescribing antibiotics and probiotics. For the 731 physicians who completed the questionnaire (390 paediatricians and 341 GPs), 37% of all consultations for a child led to the prescription of antibiotics (ranging from 17% in Australia to 47% in India). A large majority of physicians (84%) agreed that antibiotics disrupted gut microbiota and considered probiotics an effective intervention to prevent AAD (68%). However, only 33% co-prescribed probiotics with antibiotics (ranging from 13% in Japan to 60% in South Korea). The main reasons for prescribing probiotics were previous episodes of AAD (61%), presence of diarrhoea (55%), prolonged antibiotic treatment (54%) or amoxicillin-clavulanic acid therapy (54%). Although current local guidelines recommend the use of selected probiotics in children receiving antibiotics in Asia-Pacific area, the rates of antibiotics and probiotics prescription significantly vary among countries and are deeply affected by country-related cultural and organisational issues

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

CUDA Implementation of a Navier-Stokes Solver on Multi-GPU Desktop Platforms for Incompressible Flows

Graphics processor units (GPU) that are traditionally designed for graphics rendering have emerged as massively-parallel co-processors to the central processing unit (CPU). Small-footprint desktop supercomputers with hundreds of cores that can deliver teraflops peak performance at the price of conventional workstations have been realized. A computational fluid dynamics (CFD) simulation capability with rapid computational turnaround time has the potential to transform engineering analysis and design optimization procedures. We describe the implementation of a Navier-Stokes solver for incompressible fluid flow using desktop platforms equipped with multi-GPUs. Specifically, NVIDIA’s Compute Unified Device Architecture (CUDA) programming model is used to implement the discretized form of the governing equations. The projection algorithm to solve the incompressible fluid flow equations is divided into distinct CUDA kernels, and a unique implementation that exploits the memory hierarchy of the CUDA programming model is suggested. Using a quad-GPU platform, we observe two orders of magnitude speedup relative to a serial CPU implementation. Our results demonstrate that multi-GPU desktops can serve as a cost-effective small-footprint parallel computing platform to accelerate CFD simulations substantially. I. Introductio