Leading Undergraduate Students to Big Data Generation

People are facing a flood of data today. Data are being collected at unprecedented scale in many areas, such as networking, image processing, virtualization, scientific computation, and algorithms. The huge data nowadays are called Big Data. Big data is an all encompassing term for any collection of data sets so large and complex that it becomes difficult to process them using traditional data processing applications. In this article, the authors present a unique way which uses network simulator and tools of image processing to train students abilities to learn, analyze, manipulate, and apply Big Data. Thus they develop students handson abilities on Big Data and their critical thinking abilities. The authors used novel image based rendering algorithm with user intervention to generate realistic 3D virtual world. The learning outcomes are significant

A resonance interpretation for the nonmonotonic behavior of the phi-photoproduction cross section near threshold

We study whether the nonmonotonic behavior found in the differential cross section of the phi-meson photoproduction near threshold can be described by a resonance. The resonant contribution is evaluated by using an effective Lagrangian approach. We find that, with the assumption of a J^P=3/2^- resonance with mass of 2.10 \pm 0.03 GeV and width of 0.465 \pm 0.141 GeV, LEPS data can indeed be well described. The ratio of the helicity amplitudes A_1/2/A_3/2 calculated from the resulting coupling constants differs in sign from that of the known D13(2080). We further find that the addition of this postulated resonance can substantially improve the agreement between the existing theoretical predictions and the recent omega photoproduction data if a large value of the OZI evading parameter x_OZI = 12 is assumed for the resonance.Comment: Contribution to the Proceedings of BARYONS'10 - International conference on the structure of baryons, Dec. 7-11, 2010, Osaka, Japa

An Immersive Telepresence System using RGB-D Sensors and Head Mounted Display

We present a tele-immersive system that enables people to interact with each other in a virtual world using body gestures in addition to verbal communication. Beyond the obvious applications, including general online conversations and gaming, we hypothesize that our proposed system would be particularly beneficial to education by offering rich visual contents and interactivity. One distinct feature is the integration of egocentric pose recognition that allows participants to use their gestures to demonstrate and manipulate virtual objects simultaneously. This functionality enables the instructor to ef- fectively and efficiently explain and illustrate complex concepts or sophisticated problems in an intuitive manner. The highly interactive and flexible environment can capture and sustain more student attention than the traditional classroom setting and, thus, delivers a compelling experience to the students. Our main focus here is to investigate possible solutions for the system design and implementation and devise strategies for fast, efficient computation suitable for visual data processing and network transmission. We describe the technique and experiments in details and provide quantitative performance results, demonstrating our system can be run comfortably and reliably for different application scenarios. Our preliminary results are promising and demonstrate the potential for more compelling directions in cyberlearning.Comment: IEEE International Symposium on Multimedia 201

Synaptic Elasticity

Synapses play a critical role in neural circuits, and their highly specialized structures and biochemical characteristics have been widely studied in learning and memory. Along with their role in signal transmission, synapses also serve as adhesion structures, yet their mechanical characteristics have not received much attention. Given the important role of mechanics in cell adhesion, mechanical studies of synapses could offer insights into synaptic development, maintenance, and function. Here, I investigated synaptic elasticity in cultured rat hippocampal neurons and suggest that mechanical elasticity may be related to synaptic plasticity. I used torsional harmonic atomic force microscopy (TH-AFM) to measure the nanomechanical properties of functional mature excitatory synapses, whose identity and activity was verified by fluorescence microscopy. I combined TH-AFM with transmission electron microscopy and found that high stiffness of synapses originated from postsynaptic spines, not presynaptic boutons. I observed that spines at functional mature excitatory synapses were on average 10 times stiffer than dendritic shafts and that the distribution of spine stiffness exhibited a lognormal-like pattern. Importantly, I found that spine stiffness was correlated with spine size, and it is well established that spine size is correlated with synaptic strength. Based on the stiffness measurements and theoretical modelling of cell adhesion stability, I suggest that stiffness not only helps maintain spine morphology in the presence of synapse adhesion, but also helps stabilize synaptic adhesion. I propose a mechanical synaptic plasticity model. According to this model, mechanical strength leads to functional strength, which could provide a potential causal link between structural plasticity and functional plasticity of synapses