Learning from Data with Heterogeneous Noise using SGD

We consider learning from data of variable quality that may be obtained from different heterogeneous sources. Addressing learning from heterogeneous data in its full generality is a challenging problem. In this paper, we adopt instead a model in which data is observed through heterogeneous noise, where the noise level reflects the quality of the data source. We study how to use stochastic gradient algorithms to learn in this model. Our study is motivated by two concrete examples where this problem arises naturally: learning with local differential privacy based on data from multiple sources with different privacy requirements, and learning from data with labels of variable quality. The main contribution of this paper is to identify how heterogeneous noise impacts performance. We show that given two datasets with heterogeneous noise, the order in which to use them in standard SGD depends on the learning rate. We propose a method for changing the learning rate as a function of the heterogeneity, and prove new regret bounds for our method in two cases of interest. Experiments on real data show that our method performs better than using a single learning rate and using only the less noisy of the two datasets when the noise level is low to moderate

Structural and functional study of SNARE machinery in neurotransmitter release

Synaptic neurotransmitter release is the most critical communication process for the connections between neurons and between neurons and target cells. SNAREs (soluble N-ethylmaleimide sensitive factor attachment protein receptors) are believed to be highly involved in docking and fusion of synaptic vesicles to the pre-synaptic plasma membrane. In vivo, synaptic vesicle exocytosis is a regulated and extremely rapid process. Numerous regulatory proteins are also required to achieve the fast speed and Ca2+ dependency of synaptic neurotransmitter release. Our research mainly focuses on investigating the mechanism and protein structural basis of SNARE mediated membrane fusion. Site-directed spin labeling (SDSL) and electron paramagnetic resonance (EPR) spectroscopy are powerful tools to study the structure and membrane topology of membrane proteins in lipid bilayer. We use these advanced techniques to probe the structure of SNARE proteins and the important regulators in neurotransmitter release. Neurotransmitter release takes place on a much shorter time scale compared to other kind of exocytosis and this fast release is accurately coupled with Ca2+ signaling. In this dissertation, we have revealed some structure details that contribute to understand the underlying mechanism of this fine-tuning process. Firstly, the structural analysis of complexin/SNARE complex discloses a balance between different interaction patterns of complexin and SNARE. The exchange of these interaction patterns might switch the complexin function between stimulation and inhibition during different fusion steps. Then we further investigate the linker region structure of SNARE complex in different zippering stages and obtain detailed information about the conformational changes of this linker region during SNARE zippering process. The results we have got from these studies may shed light on the molecular basis of the efficient and precise control of SNARE machinery on neurontransmitter release

Analysis of WiFi and WiMAX and Wireless Network Coexistence

Wireless networks are very popular nowadays. Wireless Local Area Network (WLAN) that uses the IEEE 802.11 standard and WiMAX (Worldwide Interoperability for Microwave Access) that uses the IEEE 802.16 standard are networks that we want to explore. WiMAX has been developed over 10 years, but it is still unknown to most people. However compared to WLAN, it has many advantages in transmission speed and coverage area. This paper will introduce these two technologies and make comparisons between WiMAX and WiFi. In addition, wireless network coexistence of WLAN and WiMAX will be explored through simulation. Lastly we want to discuss the future of WiMAX in relation to WiFi.Comment: 16 pages. ISSN 0974-932