No Colloquium: Labor Day, University Holiday
Advanced accelerator research is crucial to the future of high-energy particle physics. Fortunately there have been a plethora of ideas and a great deal of progress on making accelerators that are more compact and economical than the mega-circular colliders and multi-kilometer linacs at the frontier of high energy physics today. This talk will review the status of advanced accelerator research around the world including ideas using laser-drivers and plasma wakefields and the prospect for "high-energy physics on a tabletop." A collaborative USC/UCLA/SLAC experiment on the Stanford Linear Accelerator Center linac will be featured. In addition to setting a record for energy gain in a plasma wakefield device, this experiment has explored a number of rich new beam physics phenomena. One such phenomenon is the apparent "refraction" and total internal reflection of particles at a boundary between gas and plasma. The results have implications for carrying electricity in "vapor wires" that are the analog of fiber optics for light.
In this seminar, I'll present the concept of Dynamic Synapse and review some of the applications using neural networks that incorporate dynamic synapses. In a conventional neural network, a synapse is represented as a number, called synaptic weight, which specifies the strength of the connection between neurons. A neural network can be trained to perform a desired task by changing the synaptic weights according to some learning rules. By representing the synapse as a single number, the conventional neural network is faced with two fundamental limitations: First, although a neuron can be connected to a large number (thousands) of other neurons, it can send only one signal to all these neurons. Second, only the synaptic weight can be tuned during learning which, amounts to merely changing the magnitude of the output signal of a neuron.
Neurons in the brain interact with each other by transmitting sequences of electrical impulses via synapses. Although a number of dynamic processes have been known to exist in the synapse, their role in neural information processing had been unclear. The concept of dynamic synapse was developed as an attempt to explain the function of these dynamic processes: With dynamic processes, a synapse is capable of transforming a sequence of electrical impulses into another sequence of impulses. That is, a synapse can perform pattern transformation function. Furthermore, variations across the many synapses of a single neuron lead to different transformation of the impulse sequence. As a result, dynamic synapses allow a neuron to transmit multiple output signals. We have developed a learning algorithm, which trains each dynamic synapse to perform a proper transformation function such that the neural network can achieve the desired task. To exemplify these concepts, I will review some applications, including word recognition, speaker identification, robot sensor fusion, and biosonar recognition.
October 23 Special Location: SAL 101
Is our Milky Way galaxy teeming with technological creatures? We describe how plausible advances in space astronomy during the present century point us clearly to the answer to this question, whether or not such advances actually occur!
Fundamental photochemical mechanisms in solution can best be interrogated via small molecule systems where clear comparisons with theory are possible. However, small molecules usually require excitation below 300 nm and their reactions, along with the coupled solvents motions, are faster than 100 femtoseconds. This talk will describe the recent application of a sub-50 fs tunable UV source to address such a fundamental photochemical reaction - electron ejection. The detailed dynamics of threshold photodetachment of aqueous anions are mapped by UV-pump/broadband-probe transient absorption spectroscopy and compared with the two-photon ionization of pure solvent.