Estimations of ABL fluxes and other turbulence parameters from Doppler lidar data

Techniques for extracting boundary layer parameters from measurements of a short pulse CO2 Doppler Lidar are described. The radial velocity measurements have a range resolution of 150 m. With a pulse repetition rate of 20 Hz, it is possible to perform scannings in two perpendicular vertical planes in approx. 72 s. By continuously operating the Lidar for about an hour, one can extract stable statistics of the radial velocities. Assuming that the turbulence is horizontally homogeneous, the mean wind, its standard deviations, and the momentum fluxes were estimated. From the vertically pointing beam, the first, second, and third moments of the vertical velocity were also estimated. Spectral analysis of the radial velocities is also performed from which, by examining the amplitude of the power spectrum at the inertial range, the kinetic energy dissipation was deduced. Finally, using the statistical form of the Navier-Stokes equations, the surface heat flux is derived as the residual balance between the vertical gradient of the third moment of the vertical velocity and the kinetic energy dissipation

RNA aptamers generated against oligomeric Abeta40 recognize common amyloid aptatopes with low specificity but high sensitivity.

Aptamers are useful molecular recognition tools in research, diagnostics, and therapy. Despite promising results in other fields, aptamer use has remained scarce in amyloid research, including Alzheimer's disease (AD). AD is a progressive neurodegenerative disease believed to be caused by neurotoxic amyloid beta-protein (Abeta) oligomers. Abeta oligomers therefore are an attractive target for development of diagnostic and therapeutic reagents. We used covalently-stabilized oligomers of the 40-residue form of Abeta (Abeta40) for aptamer selection. Despite gradually increasing the stringency of selection conditions, the selected aptamers did not recognize Abeta40 oligomers but reacted with fibrils of Abeta40, Abeta42, and several other amyloidogenic proteins. Aptamer reactivity with amyloid fibrils showed some degree of protein-sequence dependency. Significant fibril binding also was found for the naïve library and could not be eliminated by counter-selection using Abeta40 fibrils, suggesting that aptamer binding to amyloid fibrils was RNA-sequence-independent. Aptamer binding depended on fibrillogenesis and showed a lag phase. Interestingly, aptamers detected fibril formation with > or =15-fold higher sensitivity than thioflavin T (ThT), revealing substantial beta-sheet and fibril formation undetected by ThT. The data suggest that under physiologic conditions, aptamers for oligomeric forms of amyloidogenic proteins cannot be selected due to high, non-specific affinity of oligonucleotides for amyloid fibrils. Nevertheless, the high sensitivity, whereby aptamers detect beta-sheet formation, suggests that they can serve as superior amyloid recognition tools

Baroque Optics and the Disappearance of the Observer: From Kepler’s Optics to Descartes’ Doubt

In the seventeenth century the human observer gradually disappeared from optical treatises. It was a paradoxical process: the naturalization of the eye estranged the mind from its objects. Turned into a material optical instrument, the eye no longer furnished the observer with genuine representations of visible objects. It became a mere screen, on which rested a blurry array of light stains, accidental effects of a purely causal process. It thus befell the intellect to decipher one natural object—a flat image of no inherent epistemic value—as the vague, reversed reflection of another, wholly independent object. In reflecting on and trespassing the boundaries between natural and artificial, orderly and disorderly, this optical paradox was a Baroque intellectual phenomenon; and it was the origin of Descartes’ celebrated doubt— whether we know anything at all

Latent Gaussian processes for distribution estimation of multivariate categorical data

Multivariate categorical data occur in many applications of machine learning. One of the main difficulties with these vectors of categorical variables is sparsity. The number of possible observations grows exponentially with vector length, but dataset diversity might be poor in comparison. Recent models have gained significant improvement in supervised tasks with this data. These models embed observations in a continuous space to capture similarities between them. Building on these ideas we propose a Bayesian model for the unsupervised task of distribution estimation of multivariate categorical data. We model vectors of categorical variables as generated from a non-linear transformation of a continuous latent space. Non-linearity captures multi-modality in the distribution. The continuous representation addresses sparsity. Our model ties together many existing models, linking the linear categorical latent Gaussian model, the Gaussian process latent variable model, and Gaussian process classification. We derive inference for our model based on recent developments in sampling based variational inference. We show empirically that the model outperforms its linear and discrete counterparts in imputation tasks of sparse data.YG is supported by the Google European fellowship in Machine Learning.This is the final version of the article. It first appeared from Microtome Publishing via http://jmlr.org/proceedings/papers/v37/gala15.htm

Implementing Reinforcement Learning Datacenter Congestion Control in NVIDIA NICs

As communication protocols evolve, datacenter network utilization increases. As a result, congestion is more frequent, causing higher latency and packet loss. Combined with the increasing complexity of workloads, manual design of congestion control (CC) algorithms becomes extremely difficult. This calls for the development of AI approaches to replace the human effort. Unfortunately, it is currently not possible to deploy AI models on network devices due to their limited computational capabilities. Here, we offer a solution to this problem by building a computationally-light solution based on a recent reinforcement learning CC algorithm [arXiv:2207.02295]. We reduce the inference time of RL-CC by x500 by distilling its complex neural network into decision trees. This transformation enables real-time inference within the $\mu$-sec decision-time requirement, with a negligible effect on quality. We deploy the transformed policy on NVIDIA NICs in a live cluster. Compared to popular CC algorithms used in production, RL-CC is the only method that performs well on all benchmarks tested over a large range of number of flows. It balances multiple metrics simultaneously: bandwidth, latency, and packet drops. These results suggest that data-driven methods for CC are feasible, challenging the prior belief that handcrafted heuristics are necessary to achieve optimal performance

Multi-Dimensional Simulations of Pair-Instability Supernovae

We present preliminary results from multidimensional numerical studies of pair instability supernova (PSN), studying the fluid instabilities that occur in multiple spatial dimensions. We use the new radiation-hydrodynamics code, CASTRO, and introduce a new mapping procedure that defines the initial conditions for the multidimensional runs in such a way that conservation of physical quantities is guaranteed at any level of resolution.Comment: Accepted for publication in Computer Physics Communications. 3 pages. 2 fig

On the accuracy of retrieved wind information from Doppler lidar observations

A single pulsed Doppler lidar was successfully deployed to measure air flow and turbulence over the Malvern hills, Worcester, UK. The DERA Malvern lidar used was a CO2 µm pulsed Doppler lidar. The lidar pulse repetition rate was 120 Hz and had a pulse duration of 0.6 µs The system was set up to have 41 range gates with range resolution of 112 m. This gave a theoretical maximum range of approximately 4.6 km. The lidar site was 2 km east of the Malvern hill ridge which runs in a north-south direction and is approximately 6 km long. The maximum height of the ridge is 430 m. Two elevation scans (Range-Height Indicators) were carried out parallel and perpendicular to the mean surface flow. Since the surface wind was primarily westerly the scans were carried out perpendicular and parallel to the ridge of the Malvern hills. The data were analysed and horizontal winds, vertical winds and turbulent fluxes were calculated for profiles throughout the boundary layer. As an aid to evaluating the errors associated with the derivation of velocity and turbulence profiles, data from a simple idealized profile was also analysed using the same method. The error analysis shows that wind velocity profiles can be derived to an accuracy of 0.24 m s-1 in the horizontal and 0.3 m s-1 in the vertical up to a height of 2500 m. The potential for lidars to make turbulence measurements, over a wide area, through the whole depth of the planetary boundary layer and over durations from seconds to hours is discussed

A data recipient centered de-identification method to retain statistical attributes

AbstractPrivacy has always been a great concern of patients and medical service providers. As a result of the recent advances in information technology and the government’s push for the use of Electronic Health Record (EHR) systems, a large amount of medical data is collected and stored electronically. This data needs to be made available for analysis but at the same time patient privacy has to be protected through de-identification. Although biomedical researchers often describe their research plans when they request anonymized data, most existing anonymization methods do not use this information when de-identifying the data. As a result, the anonymized data may not be useful for the planned research project. This paper proposes a data recipient centered approach to tailor the de-identification method based on input from the recipient of the data. We demonstrate our approach through an anonymization project for biomedical researchers with specific goals to improve the utility of the anonymized data for statistical models used for their research project. The selected algorithm improves a privacy protection method called Condensation by Aggarwal et al. Our methods were tested and validated on real cancer surveillance data provided by the Kentucky Cancer Registry

Late-Time Spectral Observations of the Strongly Interacting Type Ia Supernova PTF11kx

PTF11kx was a Type Ia supernova (SN Ia) that showed time-variable absorption features, including saturated Ca II H&K lines that weakened and eventually went into emission. The strength of the emission component of H{\alpha} increased, implying that the SN was undergoing significant interaction with its circumstellar medium (CSM). These features were blueshifted slightly and showed a P-Cygni profile, likely indicating that the CSM was directly related to, and probably previously ejected by, the progenitor system itself. These and other observations led Dilday et al. (2012) to conclude that PTF11kx came from a symbiotic nova progenitor like RS Oph. In this work we extend the spectral coverage of PTF11kx to 124-680 rest-frame days past maximum brightness. These spectra of PTF11kx are dominated by H{\alpha} emission (with widths of ~2000 km/s), strong Ca II emission features (~10,000 km/s wide), and a blue "quasi-continuum" due to many overlapping narrow lines of Fe II. Emission from oxygen, He I, and Balmer lines higher than H{\alpha} is weak or completely absent at all epochs, leading to large observed H{\alpha}/H{\beta} intensity ratios. The broader (~2000 km/s) H{\alpha} emission appears to increase in strength with time for ~1 yr, but it subsequently decreases significantly along with the Ca II emission. Our latest spectrum also indicates the possibility of newly formed dust in the system as evidenced by a slight decrease in the red wing of H{\alpha}. During the same epochs, multiple narrow emission features from the CSM temporally vary in strength. The weakening of the H{\alpha} and Ca II emission at late times is possible evidence that the SN ejecta have overtaken the majority of the CSM and agrees with models of other strongly interacting SNe Ia. The varying narrow emission features, on the other hand, may indicate that the CSM is clumpy or consists of multiple thin shells.Comment: 12 pages, 7 figures, 1 table, re-submitted to Ap

Longtime behavior of nonlocal Cahn-Hilliard equations

Here we consider the nonlocal Cahn-Hilliard equation with constant mobility in a bounded domain. We prove that the associated dynamical system has an exponential attractor, provided that the potential is regular. In order to do that a crucial step is showing the eventual boundedness of the order parameter uniformly with respect to the initial datum. This is obtained through an Alikakos-Moser type argument. We establish a similar result for the viscous nonlocal Cahn-Hilliard equation with singular (e.g., logarithmic) potential. In this case the validity of the so-called separation property is crucial. We also discuss the convergence of a solution to a single stationary state. The separation property in the nonviscous case is known to hold when the mobility degenerates at the pure phases in a proper way and the potential is of logarithmic type. Thus, the existence of an exponential attractor can be proven in this case as well