Basic gestures as spatiotemporal reference frames for repetitive dance/music patterns in samba and charleston

THE GOAL OF THE PRESENT STUDY IS TO GAIN BETTER insight into how dancers establish, through dancing, a spatiotemporal reference frame in synchrony with musical cues. With the aim of achieving this, repetitive dance patterns of samba and Charleston were recorded using a three-dimensional motion capture system. Geometric patterns then were extracted from each joint of the dancer's body. The method uses a body-centered reference frame and decomposes the movement into non-orthogonal periodicities that match periods of the musical meter. Musical cues (such as meter and loudness) as well as action-based cues (such as velocity) can be projected onto the patterns, thus providing spatiotemporal reference frames, or 'basic gestures,' for action-perception couplings. Conceptually speaking, the spatiotemporal reference frames control minimum effort points in action-perception couplings. They reside as memory patterns in the mental and/or motor domains, ready to be dynamically transformed in dance movements. The present study raises a number of hypotheses related to spatial cognition that may serve as guiding principles for future dance/music studies

Plug-in to fear: game biosensors and negative physiological responses to music

The games industry is beginning to embark on an ambitious journey into the world of biometric gaming in search of more exciting and immersive gaming experiences. Whether or not biometric game technologies hold the key to unlock the “ultimate gaming experience” hinges not only on technological advancements alone but also on the game industry’s understanding of physiological responses to stimuli of different kinds, and its ability to interpret physiological data in terms of indicative meaning. With reference to horror genre games and music in particular, this article reviews some of the scientific literature relating to specific physiological responses induced by “fearful” or “unpleasant” musical stimuli, and considers some of the challenges facing the games industry in its quest for the ultimate “plugged-in” experience

Artificial Intelligence and Cognitive Computing

Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that

Alfvén waves underlying ionospheric destabilization: ground-based observations

During geomagnetic storms, terawatts of power in the million mile-per-hour solar wind pierce the Earth’s magnetosphere. Geomagnetic storms and substorms create transverse magnetic waves known as Alfvén waves. In the auroral acceleration region, Alfvén waves accelerate electrons up to one-tenth the speed of light via wave-particle interactions. These inertial Alfvén wave (IAW) accelerated electrons are imbued with sub-100 meter structure perpendicular to geomagnetic field B. The IAW electric field parallel to B accelerates electrons up to about 10 keV along B. The IAW dispersion relation quantifies the precipitating electron striation observed with high-speed cameras as spatiotemporally dynamic fine structured aurora. A network of tightly synchronized tomographic auroral observatories using model based iterative reconstruction (MBIR) techniques were developed in this dissertation. The TRANSCAR electron penetration model creates a basis set of monoenergetic electron beam eigenprofiles of auroral volume emission rate for the given location and ionospheric conditions. Each eigenprofile consists of nearly 200 broadband line spectra modulated by atmospheric attenuation, bandstop filter and imager quantum efficiency. The L-BFGS-B minimization routine combined with sub-pixel registered electron multiplying CCD video stream at order 10 ms cadence yields estimates of electron differential number flux at the top of the ionosphere. Our automatic data curation algorithm reduces one terabyte/camera/day into accurate MBIR-processed estimates of IAW-driven electron precipitation microstructure. This computer vision structured auroral discrimination algorithm was developed using a multiscale dual-camera system observing a 175 km and 14 km swath of sky simultaneously. This collective behavior algorithm exploits the “swarm” behavior of aurora, detectable even as video SNR approaches zero. A modified version of the algorithm is applied to topside ionospheric radar at Mars and broadcast FM passive radar. The fusion of data from coherent radar backscatter and optical data at order 10 ms cadence confirms and further quantifies the relation of strong Langmuir turbulence and streaming plasma upflows in the ionosphere with the finest spatiotemporal auroral dynamics associated with IAW acceleration. The software programs developed in this dissertation solve the century-old problem of automatically discriminating finely structured aurora from other forms and pushes the observational wave-particle science frontiers forward

Learning based biological image analysis

The fate of contemporary scientific research in biology and medicine is bound to the advancements in computational methods. The unprecedented data explosion in microscopy and the crescent interest of life scientists in studying more complex and more subtle interactions stimulate the research for innovative computational solutions on challenging real world applications. Extensions and novel formulations of generic and flexible methods based on learning/inference are necessary to cope with the large variety of the produced data and to avoid continuous reimplementation and heavy parameter tuning. This thesis exploits cutting edge machine learning methods based on structured probabilistic models and weakly supervised learning to provide four novel solutions in the areas of large-scale microscopic imaging and multiple objects tracking. Chapter 2 introduces a weakly supervised learning framework to tackle the problem of detecting defect images while mining massive microscopic imagery databases. This thesis demonstrates accurate prediction with low user annotation effort. Chapter 3 presents a learning approach for counting overlapping objects in images based on local structured predictors. This problem has numerous applications in high throughput microscopy screening such as cells counting for drug toxicity assays. Chapter 4 develops a deterministic graphical model to impose temporal consistency in objects counts when dealing with a video sequence. This Chapter shows that global (temporal and spatial) structural inference consistently improves over local (only spatial) predictions. The method developed in Chapter 4 is used in a novel downstream tracking algorithm which is introduced in Chapter 5. This Chapter tackles, for the first time, the difficult problem of tracking heavily overlapping, translucent and indistinguishable objects. The mutual occlusion event handling of such objects is formulated as a novel structured inference problem based on the minimization of a convex multi-commodity flow energy. The optimal weights of the energy terms are learned with partial user supervision using structured learning with latent variables.To support behavioral biologists, we apply this method to the problem of tracking a community of interacting Drosophila larvae