On the Value of Online Learning for Radar Waveform Selection

This paper attempts to characterize the kinds of physical scenarios in which an online learning-based cognitive radar is expected to reliably outperform a fixed rule-based waveform selection strategy, as well as the converse. We seek general insights through an examination of two decision-making scenarios, namely dynamic spectrum access and multiple-target tracking. The radar scene is characterized by inducing a state-space model and examining the structure of its underlying Markov state transition matrix, in terms of entropy rate and diagonality. It is found that entropy rate is a strong predictor of online learning-based waveform selection, while diagonality is a better predictor of fixed rule-based waveform selection. We show that these measures can be used to predict first and second-order stochastic dominance relationships, which can allow system designers to make use of simple decision rules instead of more cumbersome learning approaches under certain conditions. We validate our findings through numerical results for each application and provide guidelines for future implementations.Comment: 15 pages, 15 figures. Final version to appear in IEEE Transaction on Radar Systems. arXiv admin note: substantial text overlap with arXiv:2212.0059

Timely Target Tracking in Cognitive Radar Networks

We consider a scenario where a fusion center must decide which updates to receive during each update period in a communication-limited cognitive radar network. When each radar node in the network only is able to obtain noisy state measurements for a subset of the targets, the fusion center may not receive updates on every target during each update period. The solution for the selection problem at the fusion center is not well suited for sequential learning frameworks. We derive an Age of Information-inspired track sensitive metric to inform node selection in such a network and compare it against less-informed techniques.Comment: 6 pages, 6 figure

Experimental Analysis of Reinforcement Learning Techniques for Spectrum Sharing Radar

In this work, we first describe a framework for the application of Reinforcement Learning (RL) control to a radar system that operates in a congested spectral setting. We then compare the utility of several RL algorithms through a discussion of experiments performed on Commercial off-the-shelf (COTS) hardware. Each RL technique is evaluated in terms of convergence, radar detection performance achieved in a congested spectral environment, and the ability to share 100MHz spectrum with an uncooperative communications system. We examine policy iteration, which solves an environment posed as a Markov Decision Process (MDP) by directly solving for a stochastic mapping between environmental states and radar waveforms, as well as Deep RL techniques, which utilize a form of Q-Learning to approximate a parameterized function that is used by the radar to select optimal actions. We show that RL techniques are beneficial over a Sense-and-Avoid (SAA) scheme and discuss the conditions under which each approach is most effective.Comment: Accepted for publication at IEEE Intl. Radar Conference, Washington DC, Apr. 2020. This is the author's version of the wor

Comparison of pretreatment characteristics and treatment outcomes for alcohol-, cocaine-, and multisubstance-dependent patients.

We investigated whether pretreatment characteristics and measures of outcome differed for alcohol-, cocaine-, and multisubstance-dependent patients receiving outpatient substance abuse treatment. One hundred and forty substance dependent individuals (32 alcohol, 76 cocaine, and 32 multisubstance) enrolled in a 12-week outpatient treatment program were compared across measures of addiction severity, personality, and treatment-readiness at admission. In-treatment, end-of-treatment and 9-month follow-up assessments of treatment outcome were then compared across the three groups. Outcome measures included reduction in problem severity, abstinence, retention, number of sessions attended, dropout, and counselor and patient ratings of treatment benefit. At admission, the multisubstance group had a higher proportion of positive urines, reported more severe drug, alcohol and psychiatric problems, and displayed higher impulsivity and anxiety scores than one or both of the other groups. However, multisubstance patients were more treatment ready in terms of adopting a total abstinence orientation than alcohol or cocaine patients. While a significant reduction in symptoms occurred for the total sample during treatment as well as at follow-up, comparisons of outcomes did not consistently favor any particular group. The three groups had equivalent improvements in eleven of fourteen during-treatment and five of seven follow-up measures. Despite pretreatment differences, in severity and treatment-readiness, outcomes were more similar than different for alcohol-, cocaine-, and multisubstance-dependent patients. Clinicians should be cautious about forecasting treatment-outcomes for addicted patients based on their primary substances of abuse

The Atacama Cosmology Telescope: Two-Season ACTPol Spectra and Parameters

We present the temperature and polarization angular power spectra measured by the Atacama Cosmology Telescope Polarimeter (ACTPol). We analyze night-time data collected during 2013-14 using two detector arrays at 149 GHz, from 548 deg$^2$ of sky on the celestial equator. We use these spectra, and the spectra measured with the MBAC camera on ACT from 2008-10, in combination with Planck and WMAP data to estimate cosmological parameters from the temperature, polarization, and temperature-polarization cross-correlations. We find the new ACTPol data to be consistent with the LCDM model. The ACTPol temperature-polarization cross-spectrum now provides stronger constraints on multiple parameters than the ACTPol temperature spectrum, including the baryon density, the acoustic peak angular scale, and the derived Hubble constant. Adding the new data to planck temperature data tightens the limits on damping tail parameters, for example reducing the joint uncertainty on the number of neutrino species and the primordial helium fraction by 20%.Comment: 23 pages, 25 figure

LSST: from Science Drivers to Reference Design and Anticipated Data Products

(Abridged) We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). A vast array of science will be enabled by a single wide-deep-fast sky survey, and LSST will have unique survey capability in the faint time domain. The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the Solar System, exploring the transient optical sky, and mapping the Milky Way. LSST will be a wide-field ground-based system sited at Cerro Pach\'{o}n in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg$^2$ field of view, and a 3.2 Gigapixel camera. The standard observing sequence will consist of pairs of 15-second exposures in a given field, with two such visits in each pointing in a given night. With these repeats, the LSST system is capable of imaging about 10,000 square degrees of sky in a single filter in three nights. The typical 5$\sigma$ point-source depth in a single visit in $r$ will be $\sim 24.5$ (AB). The project is in the construction phase and will begin regular survey operations by 2022. The survey area will be contained within 30,000 deg$^2$ with $\delta<+34.5^\circ$, and will be imaged multiple times in six bands, $ugrizy$, covering the wavelength range 320--1050 nm. About 90\% of the observing time will be devoted to a deep-wide-fast survey mode which will uniformly observe a 18,000 deg$^2$ region about 800 times (summed over all six bands) during the anticipated 10 years of operations, and yield a coadded map to $r\sim27.5$. The remaining 10\% of the observing time will be allocated to projects such as a Very Deep and Fast time domain survey. The goal is to make LSST data products, including a relational database of about 32 trillion observations of 40 billion objects, available to the public and scientists around the world.Comment: 57 pages, 32 color figures, version with high-resolution figures available from https://www.lsst.org/overvie

Evidence of lensing of the cosmic microwave background by dark matter halos

We present evidence of the gravitational lensing of the cosmic microwave background by 1013 solar mass dark matter halos. Lensing convergence maps from the Atacama Cosmology Telescope Polarimeter (ACTPol) are stacked at the positions of around 12 000 optically selected CMASS galaxies from the SDSS-III/BOSS survey. The mean lensing signal is consistent with simulated dark matter halo profiles and is favored over a null signal at 3.2σ significance. This result demonstrates the potential of microwave background lensing to probe the dark matter distribution in galaxy group and galaxy cluster halos

Yoga jam: remixing Kirtan in the Art of Living

Yoga Jam are a group of musicians in the United Kingdom who are active members of the Art of Living, a transnational Hindu-derived meditation group. Yoga Jam organize events—also referred to as yoga raves and yoga remixes—that combine Hindu devotional songs (bhajans) and chants (mantras) with modern Western popular musical genres, such as soul, rock, and particularly electronic dance music. This hybrid music is often played in a clublike setting, and dancing is interspersed with yoga and meditation. Yoga jams are creative fusions of what at first sight seem to be two incompatible phenomena—modern electronic dance music culture and ancient yogic traditions. However, yoga jams make sense if the Durkheimian distinction between the sacred and the profane is challenged, and if tradition and modernity are not understood as existing in a sort of inverse relationship. This paper argues that yoga raves are authenticated through the somatic experience of the modern popular cultural phenomenon of clubbing combined with therapeutic yoga practices and validated by identifying this experience with a reimagined Vedic tradition