THE EFFECT OF TALKER NATIVENESS ON THE PERCEPTION OF VOICING IN SYLLABLE-INITIAL PLOSIVES BY AMERICAN ENGLISH MONOLINGUALS

Ph.D

An active interferometric method for extreme impedance on-wafer device measurements

Nano-scale devices and high-power transistors present extreme impedances, which are far removed from the 50-Ω reference impedance of conventional test equipment, resulting in a reduction in the measurement sensitivity as compared with impedances close to the reference impedance. This letter describes a novel method based on active interferometry to increase the measurement sensitivity of a vector network analyzer for measuring such extreme impedances, using only a single coupler. The theory of the method is explained with supporting simulation. An interferometry-based method is demonstrated for the first time with on-wafer measurements, resulting in an improved measurement sensitivity for extreme impedance device characterization of up to 9%

Context-Aware Generative Adversarial Privacy

Preserving the utility of published datasets while simultaneously providing provable privacy guarantees is a well-known challenge. On the one hand, context-free privacy solutions, such as differential privacy, provide strong privacy guarantees, but often lead to a significant reduction in utility. On the other hand, context-aware privacy solutions, such as information theoretic privacy, achieve an improved privacy-utility tradeoff, but assume that the data holder has access to dataset statistics. We circumvent these limitations by introducing a novel context-aware privacy framework called generative adversarial privacy (GAP). GAP leverages recent advancements in generative adversarial networks (GANs) to allow the data holder to learn privatization schemes from the dataset itself. Under GAP, learning the privacy mechanism is formulated as a constrained minimax game between two players: a privatizer that sanitizes the dataset in a way that limits the risk of inference attacks on the individuals' private variables, and an adversary that tries to infer the private variables from the sanitized dataset. To evaluate GAP's performance, we investigate two simple (yet canonical) statistical dataset models: (a) the binary data model, and (b) the binary Gaussian mixture model. For both models, we derive game-theoretically optimal minimax privacy mechanisms, and show that the privacy mechanisms learned from data (in a generative adversarial fashion) match the theoretically optimal ones. This demonstrates that our framework can be easily applied in practice, even in the absence of dataset statistics.Comment: Improved version of a paper accepted by Entropy Journal, Special Issue on Information Theory in Machine Learning and Data Scienc

FOCUSING THE BALTIC AGRICULTURAL SECTOR TOWARDS THE NEW CONSUMER: ESTONIA'S CASE

This paper analyses some of the current problems emerging in the Baltic economies with special reference to Estonia and the agricultural sector. The prime objectives are to put forward suggestions for improving the agricultural investment, and to improve the marketing towards the modern international consumer. The major points covered are: 1.National changes following the break from USSR in 1991. 2.Re orientation towards the European Union (EU) and international markets. 3.Changes for long run growth in the Estonian agricultural sector.agricultural sector, Estonia

V2X Content Distribution Based on Batched Network Coding with Distributed Scheduling

Content distribution is an application in intelligent transportation system to assist vehicles in acquiring information such as digital maps and entertainment materials. In this paper, we consider content distribution from a single roadside infrastructure unit to a group of vehicles passing by it. To combat the short connection time and the lossy channel quality, the downloaded contents need to be further shared among vehicles after the initial broadcasting phase. To this end, we propose a joint infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communication scheme based on batched sparse (BATS) coding to minimize the traffic overhead and reduce the total transmission delay. In the I2V phase, the roadside unit (RSU) encodes the original large-size file into a number of batches in a rateless manner, each containing a fixed number of coded packets, and sequentially broadcasts them during the I2V connection time. In the V2V phase, vehicles perform the network coded cooperative sharing by re-encoding the received packets. We propose a utility-based distributed algorithm to efficiently schedule the V2V cooperative transmissions, hence reducing the transmission delay. A closed-form expression for the expected rank distribution of the proposed content distribution scheme is derived, which is used to design the optimal BATS code. The performance of the proposed content distribution scheme is evaluated by extensive simulations that consider multi-lane road and realistic vehicular traffic settings, and shown to significantly outperform the existing content distribution protocols.Comment: 12 pages and 9 figure