UR-350 Quantum Game Theory

Quantum computing is a computing paradigm that utilizes the properties of quantum mechanics such as superposition, interface and entanglement for data processing and other tasks. Quantum computing can be used to work on the same problems existing supercomputers do but in a much more efficient manner. Classical game theory is a process of modeling that is widely used in AI applications. The extension of this theory to the quantum field is known as quantum game theory. It can be a promising tool for overcoming critical problems in quantum communication and the implementation of quantum artificial intelligence. Quantum game theory allows the player(s) to formulate strategies differing from the conventional way of playing a game. We review the performance of classical and quantum strategies on five classical games by analyzing the logic and outcomes of each approach

Technology and Research Requirements for Combating Human Trafficking: Enhancing Communication, Analysis, Reporting, and Information Sharing

DHS’ Science & Technology Directorate directed PNNL to conduct an exploratory study on the domain of human trafficking in the Pacific Northwest in order to examine and identify technology and research requirements for enhancing communication, analysis, reporting, and information sharing – activities that directly support efforts to track, identify, deter, and prosecute human trafficking – including identification of potential national threats from smuggling and trafficking networks. This effort was conducted under the Knowledge Management Technologies Portfolio as part of the Integrated Federal, State, and Local/Regional Information Sharing (RISC) and Collaboration Program

Transposable Element Populations Shed Light on the Evolutionary History of Wheat and the Complex Co-Evolution of Autonomous and Non-Autonomous Retrotransposons

Wheat has one of the largest and most repetitive genomes among major crop plants, containing over 85% transposable elements (TEs). TEs populate genomes much in the way that individuals populate ecosystems, diversifying into different lineages, sub-families and sub-populations. The recent availability of high-quality, chromosome-scale genome sequences from ten wheat lines enables a detailed analysis how TEs evolved in allohexaploid wheat, its diploids progenitors, and in various chromosomal haplotype segments. LTR retrotransposon families evolved into distinct sub-populations and sub-families that were active in waves lasting several hundred thousand years. Furthermore, It is shown that different retrotransposon sub-families were active in the three wheat sub-genomes, making them useful markers to study and date polyploidization events and chromosomal rearrangements. Additionally, haplotype-specific TE sub-families are used to characterize chromosomal introgressions in different wheat lines. Additionally, populations of non-autonomous TEs co-evolved over millions of years with their autonomous partners, leading to complex systems with multiple types of autonomous, semi-autonomous and non-autonomous elements. Phylogenetic and TE population analyses revealed the relationships between non-autonomous elements and their mobilizing autonomous partners. TE population analysis provided insights into genome evolution of allohexaploid wheat and genetic diversity of species, and may have implication for future crop breeding

The First Mid-infrared Detection of HNC in the Interstellar Medium: Probing the Extreme Environment toward the Orion Hot Core

We present the first mid-infrared (MIR) detections of HNC and H13CN in the interstellar medium, and numerous, resolved HCN rovibrational transitions. Our observations span 12.8 to 22.9 micron towards the hot core Orion IRc2, obtained with the Echelon-Cross-Echelle Spectrograph aboard the Stratospheric Observatory for Infrared Astronomy (SOFIA). Exceptional, ~5 km/s, resolution distinguishes individual rovibrational transitions of the HNC and HCN P, Q, and R branches; and the H13CN R branch. This allows direct measurement of the species' excitation temperatures, column densities, and relative abundances. HNC and H13CN exhibit a local standard rest velocity of -7 km/s that may be associated with an outflow from nearby radio source I and an excitation temperature of about 100 K. We resolve two velocity components for HCN, the primary component also being at -7 km/s with temperature 165 K. The hottest component, which had never before been observed, is at 1 km/s with temperature 309 K. This is the closest component to the hot core's centre measured to date. The derived 12C/13C=13 is below expectation for Orion's Galactocentric distance, but the derived HCN/HNC=72 is expected for this extreme environment. Compared to previous sub-mm and mm observations, our SOFIA line survey of this region shows that the resolved MIR molecular transitions are probing a distinct physical component and isolating the chemistry closest to the hot core

Drift dynamics in microbial communities and the effective community size

The structure and diversity of all open microbial communities are shaped by individual births, deaths, speciation and immigration events; the precise timings of these events are unknowable and unpredictable. This randomness is manifest as ecological drift in the population dynamics, the importance of which has been a source of debate for decades. There are theoretical reasons to suppose that drift would be imperceptible in large microbial communities, but this is at odds with circumstantial evidence that effects can be seen even in huge, complex communities. To resolve this dichotomy we need to observe dynamics in simple systems where key parameters, like migration, birth and death rates can be directly measured. We monitored the dynamics in the abundance of two genetically modified strains of Escherichia coli, with tuneable growth characteristics, that were mixed and continually fed into 10 identical chemostats. We demonstrated that the effects of demographic (non‐environmental) stochasticity are very apparent in the dynamics. However, they do not conform to the most parsimonious and commonly applied mathematical models, where each stochastic event is independent. For these simple models to reproduce the observed dynamics we need to invoke an “effective community size”, which is smaller than the census community size

Neuroantigen-specific, tolerogenic vaccines: GM-CSF is a fusion partner that facilitates tolerance rather than immunity to dominant self-epitopes of myelin in murine models of experimental autoimmune encephalomyelitis (EAE)

<p>Abstract</p> <p>Background</p> <p>Vaccination strategies that elicit antigen-specific tolerance are needed as therapies for autoimmune disease. This study focused on whether cytokine-neuroantigen (NAg) fusion proteins could inhibit disease in chronic murine models of experimental autoimmune encephalomyelitis (EAE) and thus serve as potential therapeutic modalities for multiple sclerosis.</p> <p>Results</p> <p>A fusion protein comprised of murine GM-CSF as the N-terminal domain and the encephalitogenic MOG35-55 peptide as the C-terminal domain was tested as a tolerogenic, therapeutic vaccine (TTV) in the C57BL/6 model of EAE. Administration of GMCSF-MOG before active induction of EAE, or alternatively, at the onset of EAE blocked the development and progression of EAE. Covalent linkage of the GM-CSF and MOG35-55 domains was required for tolerogenic activity. Likewise, a TTV comprised of GM-CSF and PLP139-151 was a tolerogen in the SJL model of EAE.</p> <p>Conclusion</p> <p>These data indicated that fusion proteins containing GM-CSF coupled to myelin auto-antigens elicit tolerance rather than immunity.</p

Curriculum Guidelines for Undergraduate Programs in Data Science

The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in Data Science. The group consisted of 25 undergraduate faculty from a variety of institutions in the U.S., primarily from the disciplines of mathematics, statistics and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in Data Science