Adversarial network coding studies the transmission of data over networks affected by adversarial noise. In this realm, the noise is modeled by an omniscient adversary who is restricted to corrupting a proper subset of the network edges. In 2018, Ravagnani and Kschischang established a combinatorial framework for adversarial networks. The study was recently furthered by Beemer, Kilic and Ravagnani, with particular focus on the one-shot capacity: a measure of the maximum number of symbols that can be transmitted in a single use of the network without errors. In this thesis, both bounds and capacity-achieving schemes are provided for families of adversarial networks in multiple transmission rounds. We also demonstrate scenarios where we transmit more information using a network multiple times for communication versus using the network once. Some results in this thesis are joint work with Giuseppe Cotardo (Virginia Tech), Gretchen Matthews (Virginia Tech) and Alberto Ravagnani (Eindhoven University of Technology). / Master of Science / We study how to best transfer data across a communication network even if there is adversarial interference using network coding. Network coding is used in video streaming, autonomous vehicles, 5G and NextG communications, satellite networks, and Internet of Things (IoT) devices among other applications. It is the process that encodes data before sending it and decodes it upon receipt. It brings advantages such as increased network efficiency, improved reliability, reduced redundancy, enhanced resilience, and energy savings. We seek to enhance this valuable technique by determining optimal ways in which to utilize network coding schemes. We explore scenarios in which an adversary has partial access to a network. To examine the maximum data that can be communicated over one use of a network, we require the intermediate parts of the network process the information before forwarding it in a process called network decoding. In this thesis, we focus on characterizing when using a network multiple times for communication increases the amount of information that is received regardless of the worst-case adversarial attack, building on prior work that shows how underlying structure influences capacity. We design efficient methods for specific networks, to communicate at capacity.

Combining Outputs from Machine Translation Systems By Fahim A. Salim Supervised by: Ing. Zdenek Zabokrtsky, Ph.D Institute of Formal and Applied Linguistics, Charles University in Prague 2010. Abstract: Due to the massive ongoing research there are many paradigms of Machine Translation systems with diverse characteristics. Even systems designed on the same paradigm might perform differently in different scenarios depending upon their training data used and other design decisions made. All Machine Translation Systems have their strengths and weaknesses and often weakness of one MT system is the strength of the other. No single approach or system seems to always perform best, therefore combining different approaches or systems i.e. creating systems of Hybrid nature, to capitalize on their strengths and minimizing their weaknesses in an ongoing trend in Machine Translation research. But even Systems of Hybrid nature has limitations and they also tend to perform differently in different scenarios. Thanks to the World Wide Web and open source, nowadays one can have access to many different and diverse Machine Translation systems therefore it is practical to have techniques which could combine the translation of different MT systems and produce a translation which is better than any of the individual systems....

Die sequenzielle Aktivität von Ortszellen im Hippocampus entspricht vielfach früheren Erlebnissen, was auf eine Rolle in Gedächtnisprozessen hinweist. Jüngere experimentelle Befunde zeigen allerdings, dass Zielorte in sequenzieller Aktivität überrepräsentiert sind. Dies legt eine Rolle dieser Aktivitätsmuster in der Verhaltensplanung nahe, wobei ein detailliertes Verständnis sowohl des Ursprungs als auch der Funktion von Ortszellsequenzen im Hippocampus bislang fehlt. Insbesondere ist nicht bekannt, welcher Mechanismus solche Sequenzen auf adaptive und konstruktive Weise generiert, wodurch effizientes Planen ermöglicht würde. Um der Beantwortung dieser Fragen näher zu kommen, stelle ich ein neu entwickeltes pulscodiertes Netzwerkmodell vor, in dem räumliches Lernen und die Generierung von Sequenzen untrennbar voneinander abhängig sind. Anhand von Simulationen zeige ich, dass dieses Modell die Erzeugung von noch nicht erlebten Sequenztrajektorien in bekannten Umgebungen erklärt, was deren Nutzen für flexible Pfadplanung hervorhebt. Zusätzlich stelle ich die Ergebnisse eines detaillierten Vergleichs zwischen simulierten neuronalen Pulsfolgen und experimentellen Daten auf der Ebene der Populationsdynamik vor. Diese Resultate zeigen, wie sequenzielle räumliche Repräsentationen durch die Interaktion zwischen lokaler oszillatorischer Dynamik und externen Einflüssen geprägt werden.:1. Introduction 2. Neurobiological and theoretical accounts of hippocampal function 3. A computational model of place-cell sequences for goal-finding 4. A statistical note on step size decoding in place-cell sequences 5. Summary and Discussion Bibliography / Hippocampal place-cell sequences observed during awake immobility often represent previous experience, suggesting a role in memory processes. However, recent reports of goals being overrepresented in sequential activity suggest a role in short-term planning, although a detailed understanding of the origins of hippocampal sequential activity and of its functional role is still lacking. In particular, it is unknown which mechanism could support efficient planning by generating place-cell sequences biased toward known goal locations, in an adaptive and constructive fashion. To address these questions, I propose a spiking network model of spatial learning and sequence generation as interdependent processes. Simulations show that this model explains the generation of never-experienced sequence trajectories in familiar environments and highlights their utility in flexible route planning. In addition, I report the results of a detailed comparison between simulated spike trains and experimental data, at the level of network dynamics. These results demonstrate how sequential spatial representations are shaped by the interaction between local oscillatory dynamics and external inputs.:1. Introduction 2. Neurobiological and theoretical accounts of hippocampal function 3. A computational model of place-cell sequences for goal-finding 4. A statistical note on step size decoding in place-cell sequences 5. Summary and Discussion Bibliography