Dynamical tunneling in mushroom billiards

We study the fundamental question of dynamical tunneling in generic two-dimensional Hamiltonian systems by considering regular-to-chaotic tunneling rates. Experimentally, we use microwave spectra to investigate a mushroom billiard with adjustable foot height. Numerically, we obtain tunneling rates from high precision eigenvalues using the improved method of particular solutions. Analytically, a prediction is given by extending an approach using a fictitious integrable system to billiards. In contrast to previous approaches for billiards, we find agreement with experimental and numerical data without any free parameter.Comment: 4 pages, 4 figure

A STOL airworthiness investigation using a simulation of an augmentor wing transport. Volume 2: Simulation data and analysis

A simulator study of STOL airworthiness was conducted using a model of an augmentor wing transport. The approach, flare and landing, go-around, and takeoff phases of flight were investigated. The simulation and the data obtained are described. These data include performance measures, pilot commentary, and pilot ratings. A pilot/vehicle analysis of glide slope tracking and of the flare maneuver is included

Empirical Studies of Evolving Systems

This paper describes the results of the working group investigating the issues of empirical studies for evolving systems. The groups found that there were many issues that were central to successful evolution and this concluded that this is a very important area within software engineering. Finally nine main areas were selected for consideration. For each of these areas the central issues were identified as well as success factors. In some cases success stories were also described and the critical factors accounting for the success analysed. In some cases it was later found that a number of areas were so tightly coupled that it was important to discuss them together

Revealing and Resolving the Restrained Enzymatic Cleavage of DNA Self-Assembled Monolayers on Gold: Electrochemical Quantitation and ESI-MS Confirmation

Herein we report a combined electrochemical and ESI-MS study of the enzymatic hydrolysis efficiency of DNA self-assembled monolayers (SAMs) on gold, platform systems for understanding nucleic acid surface chemistry and for constructing DNA-based biosensors. Our electrochemical approach is based on the comparison of the amounts of surface-tethered DNA nucleotides before and after Exonuclease I (Exo I) incubation using electrostatically bound [Ru(NH3)6]3+ as redox indicators. It is surprising to reveal that the hydrolysis efficiency of ssDNA SAMs does not depend on the packing density and base sequence, and that the cleavage ends with surface-bound shorter strands (9-13 mers). The ex-situ ESI-MS observations confirmed that the hydrolysis products for ssDNA SAMs (from 24 to 56 mers) are dominated with 10-15 mer fragments, in contrast to the complete digestion in solution. Such surface-restrained hydrolysis behavior is due to the steric hindrance of the underneath electrode to the Exo I/DNA binding, which is essential for the occurrence of Exo I-catalyzed processive cleavage. More importantly, we have shown that the hydrolysis efficiency of ssDNA SAMs can be remarkably improved by adopting long alkyl linkers (locating DNA strands further away from the substrates)

Development of improved semi-organic structural adhesives for elevated temperature applications Technical summary report, 1 ~JUL. 1964 - 29 ~FEB. 1968

Titanium chelate polymer adhesive formulation for aluminum joint curing in high temperature application

The long noncoding RNA, treRNA, decreases DNA damage and is associated with poor response to chemotherapy in chronic lymphocytic leukemia.

The study of long noncoding RNAs (lncRNAs) is an emerging area of cancer research, in part due to their ability to serve as disease biomarkers. However, few studies have investigated lncRNAs in chronic lymphocytic leukemia (CLL). We have identified one particular lncRNA, treRNA, which is overexpressed in CLL B-cells. We measured transcript expression in 144 CLL patient samples and separated samples into high or low expression of treRNA relative to the overall median. We found that high expression of treRNA is significantly associated with shorter time to treatment. High treRNA also correlates with poor prognostic indicators such as unmutated IGHV and high ZAP70 protein expression. We validated these initial findings in samples collected in a clinical trial comparing the nucleoside analog fludarabine alone or in combination with the alkylating agent cyclophosphamide in untreated CLL samples collected prior to starting therapy (E2997). High expression of treRNA was independently prognostic for shorter progression free survival in patients receiving fludarabine plus cyclophosphamide. Given these results, in order to study the role of treRNA in DNA damage response we generated a model cell line system where treRNA was over-expressed in the human B-CLL cell line OSU-CLL. Relative to the vector control line, there was less cell death in OSU-CLL over-expressing treRNA after exposure to fludarabine and mafosfamide, due in part to a reduction in DNA damage. Therefore, we suggest that treRNA is a novel biomarker in CLL associated with aggressive disease and poor response to chemotherapy through enhanced protection against cytotoxic mediated DNA damage

The Case for Learned Index Structures

Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not. In this exploratory research paper, we start from this premise and posit that all existing index structures can be replaced with other types of models, including deep-learning models, which we term learned indexes. The key idea is that a model can learn the sort order or structure of lookup keys and use this signal to effectively predict the position or existence of records. We theoretically analyze under which conditions learned indexes outperform traditional index structures and describe the main challenges in designing learned index structures. Our initial results show, that by using neural nets we are able to outperform cache-optimized B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over several real-world data sets. More importantly though, we believe that the idea of replacing core components of a data management system through learned models has far reaching implications for future systems designs and that this work just provides a glimpse of what might be possible

Open-Ended Evolutionary Robotics: an Information Theoretic Approach

This paper is concerned with designing self-driven fitness functions for Embedded Evolutionary Robotics. The proposed approach considers the entropy of the sensori-motor stream generated by the robot controller. This entropy is computed using unsupervised learning; its maximization, achieved by an on-board evolutionary algorithm, implements a "curiosity instinct", favouring controllers visiting many diverse sensori-motor states (sms). Further, the set of sms discovered by an individual can be transmitted to its offspring, making a cultural evolution mode possible. Cumulative entropy (computed from ancestors and current individual visits to the sms) defines another self-driven fitness; its optimization implements a "discovery instinct", as it favours controllers visiting new or rare sensori-motor states. Empirical results on the benchmark problems proposed by Lehman and Stanley (2008) comparatively demonstrate the merits of the approach