Inductive queries for a drug designing robot scientist

It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments

Astronomical spectrograph calibration with broad-spectrum frequency combs

Broadband femtosecond-laser frequency combs are filtered to spectrographically resolvable frequency-mode spacing, and the limitations of using cavities for spectral filtering are considered. Data and theory are used to show implications to spectrographic calibration of high-resolution, astronomical spectrometers

Tunneling Ionization Rates from Arbitrary Potential Wells

We present a practical numerical technique for calculating tunneling ionization rates from arbitrary 1-D potential wells in the presence of a linear external potential by determining the widths of the resonances in the spectral density, rho(E), adiabatically connected to the field-free bound states. While this technique applies to more general external potentials, we focus on the ionization of electrons from atoms and molecules by DC electric fields, as this has an important and immediate impact on the understanding of the multiphoton ionization of molecules in strong laser fields.Comment: 13 pages, 7 figures, LaTe

Extreme ultraviolet interferometry measurements with high-order harmonics

We demonstrate that high-order harmonics generated by short, intense laser pulses in gases provide an interesting radiation source for extreme ultraviolet interferometry, since they are tunable, coherent, of short pulse duration, and simple to manipulate. Harmonics from the 9th to the 15th are used to measure the thickness of an aluminum layer. The 11th harmonic is used to determine the spatial distribution of the electron density of a plasma produced by a 300-ps laser. Electronic densities higher than 2-10(20) electrons/cm(3) are measured. (C) 2000 Optical Society of America. OCIS codes: 190.0190, 190.4160, 190.7110, 120.3180, 140.7240, 350.4500

Time-dependent calculation of ionization in Potassium at mid-infrared wavelengths

We study the dynamics of the Potassium atom in the mid-infrared, high intensity, short laser pulse regime. We ascertain numerical convergence by comparing the results obtained by the direct expansion of the time-dependent Schroedinger equation onto B-Splines, to those obtained by the eigenbasis expansion method. We present ionization curves in the 12-, 13-, and 14-photon ionization range for Potassium. The ionization curve of a scaled system, namely Hydrogen starting from the 2s, is compared to the 12-photon results. In the 13-photon regime, a dynamic resonance is found and analyzed in some detail. The results for all wavelengths and intensities, including Hydrogen, display a clear plateau in the peak-heights of the low energy part of the Above Threshold Ionization (ATI) spectrum, which scales with the ponderomotive energy Up, and extends to 2.8 +- 0.5 Up.Comment: 15 two-column pages with 15 figures, 3 tables. Accepted for publication in Phys. Rev A. Improved figures, language and punctuation, and made minor corrections. We also added a comparison to the ADK theor

Controlling photonic structures using optical forces

The downscaling of optical systems to the micro and nano-scale results in very compliant systems with nanogram-scale masses, which renders them susceptible to optical forces. Here we show a specially designed resonant structure for enabling efficient static control of the optical response with relatively weak repulsive and attractive optical forces. Using attractive gradient optical forces we demonstrate a static mechanical deformation of up to 20 nanometers in the resonator structure. This deformation is enough to shift the optical resonances by roughly 80 optical linewidths.Comment: Body: 7 pages, 3 figures; Appendix: 14, 5 figure

The inevitable QSAR renaissance

QSAR approaches, including recent advances in 3D-QSAR, are advantageous during the lead optimization phase of drug discovery and complementary with bioinformatics and growing data accessibility. Hints for future QSAR practitioners are also offered

Price Effects of Sovereign Debt Auctions in the Euro-Zone: The Role of the Crisis

Exploring the period since the inception of the euro, we show that secondary-market yields on Italian public debt increase in anticipation of auctions of new issues and decrease after the auction, while no or a smaller such effect is present for German public debt. However, these yield movements on the Italian debt are largely confined to the period of the crisis since mid-2007. We also find that there is some tendency of the yield movements to be larger when the demand for the new issue is smaller relative to its supply. Our results are consistent with a framework in which a small group of primary dealers require compensation for inventory risk and this compensation needs to be higher when market uncertainty is larger. We also find that the secondary-market behaviour of series with a maturity close to the auctioned series, but for which there is no auction, is very similar to the secondary-market behaviour of the auctioned series. These findings support an explanation of yield movements based on the behaviour of primary dealers with limited risk-bearing capacity

DPRESS: Localizing estimates of predictive uncertainty

<p>Abstract</p> <p>Background</p> <p>The need to have a quantitative estimate of the uncertainty of prediction for QSAR models is steadily increasing, in part because such predictions are being widely distributed as tabulated values disconnected from the models used to generate them. Classical statistical theory assumes that the error in the population being modeled is independent and identically distributed (IID), but this is often not actually the case. Such inhomogeneous error (heteroskedasticity) can be addressed by providing an individualized estimate of predictive uncertainty for each particular new object <it>u</it>: the standard error of prediction <it>s</it>_ucan be estimated as the non-cross-validated error <it>s</it>_t*for the closest object <it>t</it>* in the training set adjusted for its separation <it>d </it>from <it>u </it>in the descriptor space relative to the size of the training set.</p> <p><display-formula><graphic file="1758-2946-1-11-i1.gif"/></display-formula></p> <p>The predictive uncertainty factor <it>γ</it>_t*is obtained by distributing the internal predictive error sum of squares across objects in the training set based on the distances between them, hence the acronym: <it>D</it>istributed <it>PR</it>edictive <it>E</it>rror <it>S</it>um of <it>S</it>quares (DPRESS). Note that <it>s</it>_t*and <it>γ</it>_t*are characteristic of each training set compound contributing to the model of interest.</p> <p>Results</p> <p>The method was applied to partial least-squares models built using 2D (molecular hologram) or 3D (molecular field) descriptors applied to mid-sized training sets (<it>N </it>= 75) drawn from a large (<it>N </it>= 304), well-characterized pool of cyclooxygenase inhibitors. The observed variation in predictive error for the external 229 compound test sets was compared with the uncertainty estimates from DPRESS. Good qualitative and quantitative agreement was seen between the distributions of predictive error observed and those predicted using DPRESS. Inclusion of the distance-dependent term was essential to getting good agreement between the estimated uncertainties and the observed distributions of predictive error. The uncertainty estimates derived by DPRESS were conservative even when the training set was biased, but not excessively so.</p> <p>Conclusion</p> <p>DPRESS is a straightforward and powerful way to reliably estimate individual predictive uncertainties for compounds outside the training set based on their distance to the training set and the internal predictive uncertainty associated with its nearest neighbor in that set. It represents a sample-based, <it>a posteriori </it>approach to defining applicability domains in terms of localized uncertainty.</p