Suppression of Classical and Quantum Radiation Pressure Noise via Electro-Optic Feedback

We present theoretical results that demonstrate a new technique to be used to improve the sensitivity of thermal noise measurements: intra-cavity intensity stabilisation. It is demonstrated that electro-optic feedback can be used to reduce intra-cavity intensity fluctuations, and the consequent radiation pressure fluctuations, by a factor of two below the quantum noise limit. We show that this is achievable in the presence of large classical intensity fluctuations on the incident laser beam. The benefits of this scheme are a consequence of the sub-Poissonian intensity statistics of the field inside a feedback loop, and the quantum non-demolition nature of radiation pressure noise as a readout system for the intra-cavity intensity fluctuations.Comment: 4 pages, 1 figur

Weakly supervised deep learning for the detection of domain generation algorithms

Domain generation algorithms (DGAs) have become commonplace in malware that seeks to establish command and control communication between an infected machine and the botmaster. DGAs dynamically and consistently generate large volumes of malicious domain names, only a few of which are registered by the botmaster, within a short time window around their generation time, and subsequently resolved when the malware on the infected machine tries to access them. Deep neural networks that can classify domain names as benign or malicious are of great interest in the real-time defense against DGAs. In contrast with traditional machine learning models, deep networks do not rely on human engineered features. Instead, they can learn features automatically from data, provided that they are supplied with sufficiently large amounts of suitable training data. Obtaining cleanly labeled ground truth data is difficult and time consuming. Heuristically labeled data could potentially provide a source of training data for weakly supervised training of DGA detectors. We propose a set of heuristics for automatically labeling domain names monitored in real traffic, and then train and evaluate classifiers with the proposed heuristically labeled dataset. We show through experiments on a dataset with 50 million domain names that such heuristically labeled data is very useful in practice to improve the predictive accuracy of deep learning-based DGA classifiers, and that these deep neural networks significantly outperform a random forest classifier with human engineered features

Molecular Density Functional Theory of Water describing Hydrophobicity at Short and Long Length Scales

We present an extension of our recently introduced molecular density functional theory of water [G. Jeanmairet et al., J. Phys. Chem. Lett. 4, 619, 2013] to the solvation of hydrophobic solutes of various sizes, going from angstroms to nanometers. The theory is based on the quadratic expansion of the excess free energy in terms of two classical density fields, the particle density and the multipolar polarization density. Its implementation requires as input a molecular model of water and three measurable bulk properties, namely the structure factor and the k-dependent longitudinal and transverse dielectric susceptibilities. The fine three-dimensional water structure around small hydrophobic molecules is found to be well reproduced. In contrast the computed solvation free-energies appear overestimated and do not exhibit the correct qualitative behavior when the hydrophobic solute is grown in size. These shortcomings are corrected, in the spirit of the Lum-Chandler-Weeks theory, by complementing the functional with a truncated hard-sphere functional acting beyond quadratic order in density. It makes the resulting functional compatible with the Van-der-Waals theory of liquid-vapor coexistence at long range. Compared to available molecular simulations, the approach yields reasonable solvation structure and free energy of hard or soft spheres of increasing size, with a correct qualitative transition from a volume-driven to a surface-driven regime at the nanometer scale.Comment: 24 pages, 8 figure

Active thermal isolation for temperature responsive sensors

A temperature responsive sensor is located in the airflow over the specified surface of a body and is maintained at a constant temperature. An active thermal isolator is located between this temperature responsive sensor and the specified surface of the body. The temperature of this isolator is controlled to reduce conductive heat flow from the temperature responsive sensor to the body. This temperature control includes: (1) operating the isolator at the same temperature as the constant temperature of the sensor and (2) establishing a fixed boundary temperature which is either less than or equal to or slightly greater than the sensor constant temperature

Pico-strain multiplexed fiber optic sensor array operating down to infra-sonic frequencies

An integrated sensor system is presented which displays passive long range operation to 100 km at pico-strain (pε) sensitivity to low frequencies (4 Hz) in wavelength division multiplexed operation with negligible cross-talk (better than −75 dB). This has been achieved by prestabilizing and multiplexing all interrogation lasers for the sensor array to a single optical frequency reference. This single frequency reference allows each laser to be locked to an arbitrary wavelength and independently tuned, while maintaining suppression of laser frequency noise. With appropriate packaging, such a multiplexed strain sensing system can form the core of a low frequency accelerometer or hydrophone array

Hierarchical Bin Buffering: Online Local Moments for Dynamic External Memory Arrays

Local moments are used for local regression, to compute statistical measures such as sums, averages, and standard deviations, and to approximate probability distributions. We consider the case where the data source is a very large I/O array of size n and we want to compute the first N local moments, for some constant N. Without precomputation, this requires O(n) time. We develop a sequence of algorithms of increasing sophistication that use precomputation and additional buffer space to speed up queries. The simpler algorithms partition the I/O array into consecutive ranges called bins, and they are applicable not only to local-moment queries, but also to algebraic queries (MAX, AVERAGE, SUM, etc.). With N buffers of size sqrt{n}, time complexity drops to O(sqrt n). A more sophisticated approach uses hierarchical buffering and has a logarithmic time complexity (O(b log_b n)), when using N hierarchical buffers of size n/b. Using Overlapped Bin Buffering, we show that only a single buffer is needed, as with wavelet-based algorithms, but using much less storage. Applications exist in multidimensional and statistical databases over massive data sets, interactive image processing, and visualization

The Fabrication, Testing and Simulation of Germanium Thermophotovoltaic Cells

This is the final report on NRL Contract N00173-79-C-0362. The purpose of this investigation was to fabricate germanium photovoltaic cells and to examine the feasibility of using them in a thermophotovoltaic system for the generation of electrical power in space. The energy source was to be solar. Systems aspects of the collection of solar energy and rejection of waste heat were not a part of this study. The strategy employed in this investigation was the following. 1. Fabricate germanium photodiodes. 2. Carefully characterize these photodiodes. 3. Simulate the performance of these photodiodes using a detailed numerical model of the cell and the illuminating spectra. 4. Use this simulation program to project the potential performance of germanium photodiodes in a thermophotovoltaic system under various assumptions about future improvements in diode performance and under various thermophotovoltaic spectral condition

First Measurement of the Clustering Evolution of Photometrically-Classified Quasars

We present new measurements of the quasar autocorrelation from a sample of \~80,000 photometrically-classified quasars taken from SDSS DR1. We find a best-fit model of $\omega(\theta) = (0.066\pm^{0.026}_{0.024})\theta^{-(0.98\pm0.15)}$ for the angular autocorrelation, consistent with estimates from spectroscopic quasar surveys. We show that only models with little or no evolution in the clustering of quasars in comoving coordinates since z~1.4 can recover a scale-length consistent with local galaxies and Active Galactic Nuclei (AGNs). A model with little evolution of quasar clustering in comoving coordinates is best explained in the current cosmological paradigm by rapid evolution in quasar bias. We show that quasar biasing must have changed from b_Q~3 at a (photometric) redshift of z=2.2 to b_Q~1.2-1.3 by z=0.75. Such a rapid increase with redshift in biasing implies that quasars at z~2 cannot be the progenitors of modern L* objects, rather they must now reside in dense environments, such as clusters. Similarly, the duration of the UVX quasar phase must be short enough to explain why local UVX quasars reside in essentially unbiased structures. Our estimates of b_Q are in good agreement with recent spectroscopic results, which demonstrate the implied evolution in b_Q is consistent with quasars inhabiting halos of similar mass at every redshift. Treating quasar clustering as a function of both redshift and luminosity, we find no evidence for luminosity dependence in quasar clustering, and that redshift evolution thus affects quasar clustering more than changes in quasars' luminosity. We provide a new method for quantifying stellar contamination in photometrically-classified quasar catalogs via the correlation function.Comment: 34 pages, 10 figures, 1 table, Accepted to ApJ after: (i) Minor textual changes; (ii) extra points added to Fig.