University of Maryland, Baltimore County
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[EB 2011, Young Investigator Awards Symposium]
No full text[Title supplied by cataloger]Alexis M. Stranahan delivering the Herrick Award Lecture at the Young Investigator Awards Symposium, Experimental Biology 2011
[EB 2011, AAA Presidents]
No full text[Title supplied by cataloger]Outgoing President Kathy Jones (R) passes the gavel to incoming President Jeff Laitman at the AAA Awards Banquet at the 2011 Experimental Biology conference
A Utility-Aware Privacy Preserving Framework For Distributed Data Mining With Worst Case Privacy Guarantee
No full textData Mining is the task of finding meaningful patterns from huge amount of data. With the enormous growth of data and their distributed nature, storage of data and analysis of data are often separated, therefore developing the need for the research area of privacy preserving data mining. In other words, privacy preserving data mining is needed when the data is private in nature and revealing of sensitive information needs to the prevented, while still allowing mining of the data with reasonable accuracy. Various data perturbation techniques exist in literature for this purpose. One significant drawback with the existing methods is that they handle average case privacy scenario. But, while dealing with private and business data it would definitely be beneficial to have a privacy framework that would provide a certain guarantee that the data would not be divulged in the worst case. In a distributed data setting, it is often beneficial for organizations to collaboratively perform data mining tasks without giving up their own data. This necessity has developed the research areas of secure multiparty computation and privacy preserving distributed data mining. There exist several protocols that deal with data mining tasks in a distributed scenario but most of these techniques handle a single data mining method. Therefore, if the participating parties are interested in more than one classification methods they will have to go through a series of distributed protocols every time for a different method thus increasing the overhead substantially. Another critical problem with the existing privacy protection techniques is that they do not take the data mining tasks that will be performed on the perturbed data into consideration thus reducing the utility of the perturbation techniques substantially. In a distributed setting the parties are aware of the data mining tasks they would need to perform collaboratively. For example, the collaborative parties are aware that they are building a classification model or predicting an attribute. Therefore, if the data perturbation methods can be pruned according to the need of the end user the utility of the privacy protection techniques can be increased significantly. Here, in this dissertation multiple privacy preserving data mining algorithms have been proposed for multiple data mining methods that address all these above mentioned issues and provide a utility aware approach to privacy preserving data mining in a centralized as well as distributed scenario with worst case privacy guarantee. These algorithms will also allow the end user to perform exploratory data analysis on the perturbed data. Detailed experimental results will demonstrate the effectiveness of these techniques
The City in a Swing Set: The Desegregation of Public Parks in Baltimore
No full textThis thesis chronicles the process of the desegregation of public recreation spaces in Baltimore from 1935-1956. The struggle against segregated public parks in Baltimore shows how actions by everyday people often set the agenda for the actions of institutional organizations like the NAACP, and how such efforts create social change in spaces outside of mandated areas of societal interactions. Without the general public supporting integration for themselves, places like parks would remain effectively segregated, even after discriminatory policies were removed. While a significant portion of this project relates to legal cases, these cases were brought forth only after protest actions by individuals and community-based organizations. The fight culminated in the official desegregation of public park facilities in 1955. Newspaper reports and the official minutes of the Baltimore Bureau of Recreation and Parks uncover the events of these protests; oral histories to bring to light the individual motivations which propelled such actions. Finally, local newspaper reports, oral histories, and published accounts of the events also create a broader picture of how the various communities in Baltimore viewed the fight to desegregate the public parks
Analysis of Effects of Power Supply Noise on Propagation Delay
No full textToday's very deep sub-micron technologies enable highly complex chip designs that operate at very high frequencies. Transistor dimensions have shrunk to a few tens of nanometers. Therefore, to ensure reliable chip operation and to reduce both dynamic and static power consumption the chip's supply voltage must be scaled down. This scaling of the maximum supply voltage reduces noise margins and makes designs susceptible to power supply noise. Power supply noise is the variation in the chip power supply i.e. IR and drops due to the power grid and package parasitics. As the supply voltage drops it causes an increase in the propagation delay. Transition-fault testing is a widely used test technique in the industry that can detect delay defects. In order to minimize the testing overhead, ATPG tools generate vectors that target multiple faults at the same time. However, these vectors cause excessive switching in the circuit and therefore increase power supply noise. As the propagation delay increases with power supply noise this can lead to a significant overkill when using transition test patterns. In order to accurately measure this noise one would need to run dynamic circuit simulations, which is infeasible even for a small portion of the chip. Previous work has demonstrated a convolution-based technique that can be used to compute transient current and voltage at the power pads and at different locations on the power grid. In this work, we use this computation technique to analyze the effect of power supply noise on path propagation delay. We also compare the delays computed using the convolution based technique to full-chip transient simulations
Translating Neuronal Responses From 2D to 3D Microenvironments to Improve The Design of Biomaterials
No full textThe major goal of this work was to examine the dynamic interactions between neurons and three-dimensional (3D) matrices in order to determine the impact of environment dimensionality in neuronal morphological responses and signaling programs. Current strategies for the replacement of organs and tissues rely on the design of components that replicate 3D structure and physiology. While great strides have been made in the development of new biomaterials and neural stem/progenitor cell (NPC) biology, efforts focused on nerve repair have been limited by a poor understanding of how neurons interact with their 3D microenvironment. Tissue engineering has provided numerous 3D substrates that promote neuronal survival and process outgrowth; however, intrinsic neuronal morphologies and the mechanisms regulating neuronal responses to biomaterials are generally overlooked. Therefore, in order to develop physiologically relevant cell culture environments and to ultimately engineer improved models for nerve repair applications, the overall goal of this work was to develop a deeper knowledge of the dynamic three-dimensional interactions between neurons and their extracellular milieu therefore eliciting the mechanisms that regulate neuronal behavior in 3D microenvironments. In this dissertation, we report that a 3D culture platform can invoke the characteristic transformation to the unipolar axonal arbor suffered by sensory neurons within a time frame similar to in vivo, overcoming the loss of this essential milestone in 2D substrates. Additionally, 3D substrates alone provided an environment that promoted growth cone and axonal branching features that reflect morphological patterns observed in vivo. We also demonstrate that microenvironment dimensionality plays a major role in sensory neuron development and regulation of signaling events controlling branching, polarization and outgrowth. We report &beta1-integrin as a key mediator of morphogenic programs in sensory neurons cultured in 3D and in response to the interaction with a surrounding 3D matrix neurons alter &beta1-integrin signaling transduction to the cytoskeleton. All these differences summed together suggest the need for caution when applying conclusions from planar neuronal cell culture to in vivo 3D matrix signaling functions. Lastly, we present a promising strategy to pre-condition NPCs within a novel, hydrolytically degradable hydrogel scaffold based on cross-linked poly(ethylene glycol). This system offers the possibility of optimizing an array of gel bioactive and physical properties which may prove advantageous for future optimization of these scaffolds for specific clinical neurotransplantation applications. This work is major step forward in the understanding of key parameters that guide neuronal development and nerve regeneration and has the potential to lead in the development of novel and improved strategies to treat nerve injuries and neurodegenerative disorders
Progressive Band Prioritization and Selection for Linear Spectral Mixture Analysis
No full textLinear Spectral Mixture Analysis (LSMA) has been widely used in the remote sensing community. It assumes that a data sample vector is linearly mixed by a set of distinct signatures as a linear mixture from which it can be further unmixed as abundance fractions in terms of these signatures. While LSMA has shown to be a promising spectral unmixing technique in remote sensing image analysis, it also suffers from an issue of nonlinear separability encountered in both multispectral and hyperspectral image processing. To resolve this dilemma, a kernel-based LSMA (KLSMA) is proposed in this dissertation, which projects data samples into a high dimensional feature space to solve linearly non-separable problems. Similar techniques are further used to extend Fisher's LSMA (KFLSMA), Weighted Abundance Constrained LSMA (KWAC-LSMA) to their kernel-based versions, referred to as kernel-based FLSMA (KFLSMA), and kernel-based WAC-LSMA (KWAC-LSMA). Since hyperspectral imagery is generally acquired in hundreds of contiguous spectral channels with very high spectral resolution such high inter-band correlation provides high redundant information and can be removed with no significant loss of information. So, it has been a great interest in hyperspectral image analysis to seek a means of how to effectively reduce dimensionality without significantly compromising performance. Band Selection (BS) is one of commonly used approaches for this purpose. However, there are several crucial issues arising in BS which must be addressed, such as the number of bands required for BS to select, p, and what criterion needed to be used to select bands. In order to solve these issues, Band Prioritization (BP) is introduced in this dissertation from which Progressive Band Dimensionality Process (PBDP) is derived to rank bands according to BP which paves the way for a subsequently developed Progressive Band Selection (PBS). Due to practical issues the number of bands to be selected must adapt to various applications instead of being fixed at a constant as BS does. To further mitigate this problem, a new concept of Band Dimensionality Allocation (BDA) is introduced, which allows users to determine band dimensionality dynamically according to specific applications. When PBDP is implemented, one issue may arise in the fact that two highly prioritized bands may also share significant information in common. As a result, if one band is selected, the other band should be considered as a redundant band and must be removed by BS. To address this issue, the Band De-correlation (BD) is further proposed for this purpose. Finally, by implementing the PBDP in conjunction with the BDA and the BD a Progressive Band Selection (PBS) can be derived as an alternative to the traditional BS. The experiments conducted in this dissertation show that the PBS provides significant performance and advantages which could not be achieved by traditional BS
I am very dark, but comely: Consciousness and Black Women in the Fiction of Ecuador's Luz Argentina Chiriboga
No full textThis dissertation explores the journeys of black women protagonists to self-awareness or consciousness in three novels by Afra-Ecuadorian writer Luz Argentina Chiriboga. This study analyzes the five paradigms that come together to create a level of consciousness that may be described as Chiriboga's poetics. These paradigms of consciousness include historical consciousness, Afra-feminist consciousness, erotic consciousness, identity and empowerment. Chiriboga's goal through her literary works is to awaken in her people, the blacks of Esmeraldas, a significant level of consciousness about their history, their culture, and their own empowerment. In her first novel, Tambores bajo de mi piel, the teenager Rebeca leaves her Pacific coast home for the urban city of Quito during the political unrest of the 1960s to attend high school. She experiences various sensual encounters with different men while pining for her ideal man Julio. Through these adventures, Rebeca begins to understand herself thereby achieving a level of empowerment which enables her to return home and run her family's farm. Chiriboga's second novel Jonat�s y Manuela is a historical novel that takes place during Ecuador's period of slavery prior to South American independence. It follows three generations of a family of women during slavery. The two title characters include Jonat�s, the third generation in the family, and her mistress, Manuela who was Sim�n Bolivar's lover. Both eventually assist Bolivar in the war for independence. The last novel, En la noche del viernes, takes place in contemporary Ecuador and follows the journey of the protagonist Susana as she faces racism, betrayal, and a difficult marriage. Both Susana, as well as her friend Luz, overcome many obstacles to reach a high level of consciousness and eventually gain empowerment.
A BIOGEOGRAPHICAL STUDY OF THE AMERICAN CHESTNUT: AN EVALUATION OF INTENTIONAL INTROGRESSION AND A SPATIAL ANALYSIS OF CHESTNUT HABITAT IN MARYLAND
No full textThe American chestnut (Castanea dentat
Recovering from Soft Node Failures in Wireless Sensor Networks using Neural Networks.
No full textIn the past few years, wireless sensor networks (WSNs) have become important in different applications because of their robustness in hostile environments. WSNs need to perform in a timely manner in the face of interference, attacks, accidents, and failures. Being a battery operated system, there is a trade-off between performance and energy utilization. In this thesis we focus WSNs on efficiency and consider ways to improve the performance of WSNs when sensors become damaged, resulting in poor input signal quality. When all other components of the sensor like the processor, memory, and battery working fine, our proposed solution is to learn to undo the damage in software by training on neighbors sensor values