Recognising and localising human actions

Human action recognition in challenging video data is becoming an increasingly important research area. Given the growing number of cameras and robots pointing their lenses at humans, the need for automatic recognition of human actions arises, promising Google-style video search and automatic video summarisation/description. Furthermore, for any autonomous robotic system to interact with humans, it must rst be able to understand and quickly react to human actions. Although the best action classication methods aggregate features from the entire video clip in which the action unfolds, this global representation may include irrelevant scene context and movements which are shared amongst multiple action classes. For example, a waving action may be performed whilst walking, however if the walking movement appears in distinct action classes, then it should not be included in training a waving movement classier. For this reason, we propose an action classication framework in which more discriminative action subvolumes are learned in a weakly supervised setting, owing to the diculty of manually labelling massive video datasets. The learned models are used to simultaneously classify video clips and to localise actions to a given space-time subvolume. Each subvolume is cast as a bag-of-features (BoF) instance in a multiple-instance-learning framework, which in turn is used to learn its class membership. We demonstrate quantitatively that even with single xed-sized subvolumes, the classication performance of our proposed algorithm is superior to our BoF baseline on the majority of performance measures, and shows promise for space-time action localisation on the most challenging video datasets. Exploiting spatio-temporal structure in the video should also improve results, just as deformable part models have proven highly successful in object recognition. However, whereas objects have clear boundaries which means we can easily dene a ground truth for initialisation, 3D space-time actions are inherently ambiguous and expensive to annotate in large datasets. Thus, it is desirable to adapt pictorial star models to action datasets without location annotation, and to features invariant to changes in pose such as bag-of-feature and Fisher vectors, rather than low-level HoG. Thus, we propose local deformable spatial bag-of-features (LDSBoF) in which local discriminative regions are split into axed grid of parts that are allowed to deform in both space and time at test-time. In our experimental evaluation we demonstrate that by using local, deformable space-time action parts, we are able to achieve very competitive classification performance, whilst being able to localise actions even in the most challenging video datasets. A recent trend in action recognition is towards larger and more challenging datasets, an increasing number of action classes and larger visual vocabularies. For the global classication of human action video clips, the bag-of-visual-words pipeline is currently the best performing. However, the strategies chosen to sample features and construct a visual vocabulary are critical to performance, in fact often dominating performance. Thus, we provide a critical evaluation of various approaches to building a vocabulary and show that good practises do have a signicant impact. By subsampling and partitioning features strategically, we are able to achieve state-of-the-art results on 5 major action recognition datasets using relatively small visual vocabularies. Another promising approach to recognise human actions first encodes the action sequence via a generative dynamical model. However, using classical distances for their classication does not necessarily deliver good results. Therefore we propose a general framework for learning distance functions between dynamical models, given a training set of labelled videos. The optimal distance function is selected among a family of `pullback' ones, induced by a parametrised mapping of the space of models. We focus here on hidden Markov models and their model space, and show how pullback distance learning greatly improves action recognition performances with respect to base distances. Finally, the action classication systems that use a single global representation for each video clip are tailored for oine batch classication benchmarks. For human-robot interaction however, current systems fall short, either because they can only detect one human action per video frame, or because they assume the video is available ahead of time. In this work we propose an online human action detection system that can incrementally detect multiple concurrent space-time actions. In this way, it becomes possible to learn new action classes on-the-fly, allowing multiple people to actively teach and interact with a robot

Nanophotonic boost of intermolecular energy transfer

We propose a scheme for efficient long-range energy transfer between two distant light emitters separated by more than one wavelength of light, i.e. much beyond the classical Forster radius. A hybrid nanoantenna-waveguide system mediates the transmission of energy, showing enhancements up to 10^8 as compared to vacuum. Our model shows how energy transfer in nanostructured media can be boosted, beyond the simple donor Purcell enhancement, and in particular for large donor-acceptor separations. The scheme we propose connects realistic emitters and could lead to practical on-chip implementations.Comment: 9 pages, 4 figure

Electrically injected cavity polaritons

We have realised a semiconductor quantum structure that produces electroluminescence while operating in the light-matter strong coupling regime. The mid-infrared light emitting device is composed of a quantum cascade structure embedded in a planar microcavity, based on the GaAs/AlGaAs material system. At zero bias, the structure is characterised using reflectivity measurements which show, up to room temperature, a wide polariton anticrossing between an intersubband transition and the resonant cavity photon mode. Under electrical injection the spectral features of the emitted light change drastically, as electrons are resonantly injected in a reduced part of the polariton branches. Our experiment demonstrates that electrons can be selectively injected into polariton states up to room temperature.Comment: 10 pages, 4 figure

The Role of the Speech-Language Pathologist in the Schools for the Treatment of Voice Disorders: Working within the Framework of the Individuals with Disabilities Education Improvement Act

The role of the speech-language pathologist (SLP) has developed considerably over the past 20 years given the medical and technological advances in life-sustaining procedures. Children born with congenital, surgical, or medically fragile conditions become mainstreamed into regular school-based settings, thus extending the traditional role of the treating SLP and multidisciplinary team. Understanding the impact of associated voice disorders on educational performance requires dissemination of additional and important information, as eligibility decisions for students in school-based settings must be made within the framework of the federal legislation and regulations governing the provision of services for students with disabilities. This article discusses how to identify children with voice disorders under the Individuals with Disabilities Education Improvement Act (IDEA), the role of the SLP in various triaging scenarios, and how models of voice therapy can be integrated in a school-based setting

Optical amplification enhancement in photonic crystals

Improving and controlling the efficiency of a gain medium is one of the most challenging problems of laser research. By measuring the gain length in an opal based photonic crystal doped with laser dye, we demonstrate that optical amplification is more than twenty-fold enhanced along the Gamma-K symmetry directions of the face centered cubic photonic crystal. These results are theoretically explained by directional variations of the density of states, providing a quantitative connection between density of the states and light amplification

KM3NeT:a large underwater neutrino telescope in the Mediterranean Sea

High energy neutrinos produced in astrophysical processes will allow for a new way of studying the universe. In order to detect the expected flux of high energy neutrinos from specific astrophysical sources, neutrino telescopes of a scale of a km^3 of water will be needed. A Northern Hemisphere detector is being proposed to be sited in a deep area of the Mediterranean Sea. This detector will provide complimentary sky coverage to the IceCube detector being built at the South Pole. The three neutrino telescope projects in the Mediterranean (ANTARES, NEMO and NESTOR) are partners in an effort to design, and build such a km^3 size neutrino telescope, the KM3NeT. The EU is funding a 3-year Design Study; the status of the Design Study is presented and some technical issues are discussed.Comment: 4 pages, 3 figures, Prepared for the 10th International Conference on Astroparticle and Underground Physics (TAUP 2007), Sendai, Japan, 11-15 Sep 200

Can we infer social preferences from the lab? Evidence from the trust game

We show that a measure of reciprocity derived from the Berg et al. (1995) trust game in a laboratory setting predicts the reciprocal behavior of the same subjects in a real-world situation. By using the Crowne and Marlowe (1960) social desirability scale, we do not find any evidence that a desire to conform to social norms distorts results in the lab, yet we do find evidence that it affects results in the field.

Sensitivity and spectral control of network lasers

Recently, random lasing in complex networks has shown efficient lasing over more than 50 localised modes, promoted by multiple scattering over the underlying graph. If controlled, these network lasers can lead to fast-switching multifunctional light sources with synthesised spectrum. Here, we observe both in experiment and theory high sensitivity of the network laser spectrum to the spatial shape of the pump profile, with some modes for example increasing in intensity by 280% when switching off 7% of the pump beam. We solve the nonlinear equations within the steady state ab-initio laser theory (SALT) approximation over a graph and we show selective lasing of around 90% of the strongest intensity modes, effectively programming the spectrum of the lasing networks. In our experiments with polymer networks, this high sensitivity enables control of the lasing spectrum through non-uniform pump patterns. We propose the underlying complexity of the network modes as the key element behind efficient spectral control opening the way for the development of optical devices with wide impact for on-chip photonics for communication, sensing, and computation

Deploy Energy-efficient Technologies in the Restoration of a Traditional Building in the Historical Center of Catania (Italy)☆

Abstract The policy about energy efficiency of buildings, including minimum energy requirements and energy performance certificate (EPC), have to be also applied to existing buildings in the case of energy retrofit. In this paper, the possible strategies that can be used to reduce the energy needs of traditional massive buildings, that are widespread in the old town of the Mediterranean cities, have been investigated. To this aim, this study evaluates the energy consumption of a massive building placed in Catania city, called "La Casa del Portuale", which was recently refurbished with the aim to host two local administrative centers. The energy needs of this building was evaluated through computer simulation both in the heating and cooling period, on a yearly basis. The activities research were developed analyzing different refurbishment solutions suitable to improve the thermal performance of most traditional buildings without adversely affecting their fabric and character. Therefore, the feasibility comparison has been performed between the examined refurbishment solutions. The results of the proposed research, considering the diffusion of this typology of buildings, could be assumed as reference to a significant portion of the traditional real estate