Secrecy Energy Efficiency of MIMOME Wiretap Channels with Full-Duplex Jamming

Full-duplex (FD) jamming transceivers are recently shown to enhance the information security of wireless communication systems by simultaneously transmitting artificial noise (AN) while receiving information. In this work, we investigate if FD jamming can also improve the systems secrecy energy efficiency (SEE) in terms of securely communicated bits-per- Joule, when considering the additional power used for jamming and self-interference (SI) cancellation. Moreover, the degrading effect of the residual SI is also taken into account. In this regard, we formulate a set of SEE maximization problems for a FD multiple-input-multiple-output multiple-antenna eavesdropper (MIMOME) wiretap channel, considering both cases where exact or statistical channel state information (CSI) is available. Due to the intractable problem structure, we propose iterative solutions in each case with a proven convergence to a stationary point. Numerical simulations indicate only a marginal SEE gain, through the utilization of FD jamming, for a wide range of system conditions. However, when SI can efficiently be mitigated, the observed gain is considerable for scenarios with a small distance between the FD node and the eavesdropper, a high Signal-to-noise ratio (SNR), or for a bidirectional FD communication setup.Comment: IEEE Transactions on Communication

Multiphysics Assessment of Accident Tolerant Fuel, Cladding, and Core Structural Material Concepts

The severe accident at the Fukushima-Daiichi nuclear power plant in 2011 ignited a global research and development effort to replace traditionally-used materials in Light Water Reactors (LWRs) with Accident Tolerant Fuel (ATF) materials. These materials are intended to extend the coping time of nuclear power plants during severe accident scenarios, but must undergo thorough safety and performance evaluations before being implemented. Four ATF concepts are analyzed in this dissertation using state-of-the-art computer modeling tools: (1) iron-chromium-aluminum (FeCrAl) fuel rod cladding, (2) silicon carbide (SiC) fiber-reinforced, SiC matrix composite (SiC/SiC) boiling water reactor (BWR) channel boxes, (3) mixed thorium mononitride (ThN) and uranium mononitride (UN) fuel, (4) and UO2 [uranium dioxide] with embedded high thermal conductivity Mo inserts. The goals and approaches used for each study differed, and portions of this dissertation focused on verifying the accuracy of advanced modeling tools. Although each ATF evaluation is distinct, the underlying theme is the enhancement of safety, efficiency, and economic competitiveness of nuclear power through the use of advanced modeling techniques applied to material characterization studies. Results from the evaluations show the pros and cons of each ATF concept and highlight areas of needed modeling development. Comparisons of simulated and experimental critical heat flux (CHF) data for FeCrAl cladding and subsequent sensitivity analyses emphasized differences between real-world and simulated post-CHF phenomena. The Virtual Environment for Reactor Applications (VERA) multiphysics modeling suite was verified against other widely-used modeling tools for BWR application, and its advanced features were used to generate boundary conditions in SiC/SiC channel boxes used for deformation analyses. Several ThN-UN mixtures were analyzed using reactor physics and thermal hydraulic techniques and were shown to significantly increase the margin to fuel melt compared with UO2 [uranium dioxide] in LWRs. Mo inserts for UO2 [uranium dioxide] were optimized using sensitivity regression techniques and were also shown to significantly increase the margin to fuel melt compared with traditional UO2 [uranium dioxide]

Characterizing the diffusional behavior and trafficking pathways of Kv2.1 using single particle tracking in live cells

2013 Spring.Includes bibliographical references.Studying the diffusion pattern of membrane components yields valuable information regarding membrane structure, organization, and dynamics. Single particle tracking serves as an excellent tool to probe these events. We are investigating of the dynamics of the voltage gated potassium channel, Kv2.1. Kv2.1 uniquely localizes to stable, micro-domains on the cell surface where it plays a non-conducting role. The work reported here examines the diffusion pattern of Kv2.1 and determines alternate functional roles of surface clusters by investigating recycling pathways using single particle tracking in live cells. The movement of Kv2.1 on the cell surface is found to be best modeled by the combination of a stationary and non-stationary process, namely a continuous time random walk in a fractal geometry. Kv2.1 surface structures are shown to be specialized platforms involved in trafficking of Kv channels to and from the cell surface in hippocampal neurons and transfected HEK cells. Both Kv2.1 and Kv1.4, a non-clustering membrane protein, are inserted and retrieved from the plasma membrane at the perimeter of Kv2.1 clusters. From the distribution of cluster sizes, using a Fokker-Planck formalism, we find there is no evidence of a feedback mechanism controlling Kv2.1 domain size on the cell surface. Interestingly, the sizes of Kv2.1 clusters are rather governed by fluctuations in the endocytic and exocytic machinery. Lastly, we pinpoint the mechanism responsible for inducing Kv2.1 non-ergodic dynamics: the capture of Kv2.1 into growing clathrin-coated pits via transient binding to pit proteins

Probabilistic Commonsense Knowledge

Commonsense knowledge is critical to achieving artificial general intelligence. This shared common background knowledge is implicit in all human communication, facilitating efficient information exchange and understanding. But commonsense research is hampered by its immense quantity of knowledge because an explicit categorization is impossible. Furthermore, a plumber could repair a sink in a kitchen or a bathroom, indicating that common sense reveals a probable assumption rather than a definitive answer. To align with these properties of commonsense fundamentally, we want to not only model but also evaluate such knowledge human-like using abstractions and probabilistic principles. Traditional combinatorial probabilistic models, e.g., probabilistic graphical model approaches, have limitations to modeling large-scale probability distributions containing thousands or even millions of commonsensical events. On the other hand, although embedding-based representation learning has the advantage of generalizing to large combinations of events, they suffer from producing consistent probabilities under different styles of queries. Combining benefits from both sides, we introduce probabilistic box embeddings, which represent joint probability distributions on a learned latent space of geometric embeddings. By using box embeddings, it is now possible to handle queries with intersections, unions, and negations in a way similar to Venn diagram reasoning, which has faced difficulty even when using large language models. Meanwhile, existing evaluations do not reflect the probabilistic nature of commonsense knowledge. The popular multiple-choice evaluation style often misleads us into the paradigm that commonsense solved. To fill in the gap, we propose a method of retrieving commonsense related question answer distributions from human annotators as well as a novel method of generative evaluation. We utilize these approaches in two new commonsense datasets. Finally, we draw a connection between the-state-of-art NLP models --- large language models and their ability to perform commonsense reasoning tasks. According to the previous study, large language models would make inconsistent predictions while given different input texts for plausible commonsense situations. We intend to evaluate their performance using more rigorous probabilistic measurements