Multimedia Correlation Analysis in Unstructured Peer-to-Peer Network

Recent years saw the rapid development of peer-topeer (P2P) networks in a great variety of applications. However, similarity-based k-nearest-neighbor retrieval (k-NN) is still a challenging task in P2P networks due to the multiple constraints such as the dynamic topologies and the unpredictable data updates. Caching is an attractive solution that reduces network traffic and hence could remedy the technological constraints of P2P networks. However, traditional caching techniques have some major shortcomings that make them unsuitable for similarity search, such as the lack of semantic locality representation and the rigidness of exact matching on data objects. To facilitate the efficient similarity search, we propose semantic-aware caching scheme (SAC) in this paper. The proposed scheme is hierarchy-free, fully dynamic, non-flooding, and do not add much system overhead. By exploring the content distribution, SAC drastically reduces the cost of similarity-based k-NN retrieval in P2P networks. The performance of SAC is evaluated through simulation study and compared against several search schemes as advanced in the literature

Exascale Deep Learning to Accelerate Cancer Research

Deep learning, through the use of neural networks, has demonstrated remarkable ability to automate many routine tasks when presented with sufficient data for training. The neural network architecture (e.g. number of layers, types of layers, connections between layers, etc.) plays a critical role in determining what, if anything, the neural network is able to learn from the training data. The trend for neural network architectures, especially those trained on ImageNet, has been to grow ever deeper and more complex. The result has been ever increasing accuracy on benchmark datasets with the cost of increased computational demands. In this paper we demonstrate that neural network architectures can be automatically generated, tailored for a specific application, with dual objectives: accuracy of prediction and speed of prediction. Using MENNDL--an HPC-enabled software stack for neural architecture search--we generate a neural network with comparable accuracy to state-of-the-art networks on a cancer pathology dataset that is also $16\times$ faster at inference. The speedup in inference is necessary because of the volume and velocity of cancer pathology data; specifically, the previous state-of-the-art networks are too slow for individual researchers without access to HPC systems to keep pace with the rate of data generation. Our new model enables researchers with modest computational resources to analyze newly generated data faster than it is collected.Comment: Submitted to IEEE Big Dat

Extreme sensitivity of the spin-splitting and 0.7 anomaly to confining potential in one-dimensional nanoelectronic devices

Quantum point contacts (QPCs) have shown promise as nanoscale spin-selective components for spintronic applications and are of fundamental interest in the study of electron many-body effects such as the 0.7 x 2e^2/h anomaly. We report on the dependence of the 1D Lande g-factor g* and 0.7 anomaly on electron density and confinement in QPCs with two different top-gate architectures. We obtain g* values up to 2.8 for the lowest 1D subband, significantly exceeding previous in-plane g-factor values in AlGaAs/GaAs QPCs, and approaching that in InGaAs/InP QPCs. We show that g* is highly sensitive to confinement potential, particularly for the lowest 1D subband. This suggests careful management of the QPC's confinement potential may enable the high g* desirable for spintronic applications without resorting to narrow-gap materials such as InAs or InSb. The 0.7 anomaly and zero-bias peak are also highly sensitive to confining potential, explaining the conflicting density dependencies of the 0.7 anomaly in the literature.Comment: 23 pages, 7 figure

Plasma Dynamics

Contains research objectives and summary of research on nineteen research projects split into five sections.National Science Foundation (Grant ENG75-06242-A01)U.S. Energy Research and Development Administration (Contract E(11-1)-2766)U.S. Air Force - Office of Scientific Research (Grant AFOSR-77-3143)U.S. Energy Research and Development Administration (Contract EY-76-C2-02-3070.*000

Measurement of Energy Correlators inside Jets and Determination of the Strong Coupling Formula Presented

Energy correlators that describe energy-weighted distances between two or three particles in a hadronic jet are measured using an event sample of $\sqrt{s}$=13 TeV proton-proton collisions collected by the CMS experiment and corresponding to an integrated luminosity of 36.3 fb$^{−1}$. The measured distributions are consistent with the trends in the simulation that reveal two key features of the strong interaction: confinement and asymptotic freedom. By comparing the ratio of the measured three- and two-particle energy correlator distributions with theoretical calculations that resum collinear emissions at approximate next-to-next-to-leading-logarithmic accuracy matched to a next-to-leading-order calculation, the strong coupling is determined at the Z boson mass: α$_S$ (m$_Z$)=0.1229 $\frac{0.0040}{-0.0050}$ , the most precise α$_S$m$_Z$ value obtained using jet substructure observable