Transient radiation and conduction in a slotted slab and a hollow cylinder

Transient radiation and conduction in slotted slab and hollow cylinde

Thouless-Anderson-Palmer Approach for Lossy Compression

We study an ill-posed linear inverse problem, where a binary sequence will be reproduced using a sparce matrix. According to the previous study, this model can theoretically provide an optimal compression scheme for an arbitrary distortion level, though the encoding procedure remains an NP-complete problem. In this paper, we focus on the consistency condition for a dynamics model of Markov-type to derive an iterative algorithm, following the steps of Thouless-Anderson-Palmer's. Numerical results show that the algorithm can empirically saturate the theoretical limit for the sparse construction of our codes, which also is very close to the rate-distortion function.Comment: 10 pages, 3 figure

High performance architecture design for large scale fibre-optic sensor arrays using distributed EDFAs and hybrid TDM/DWDM

A distributed amplified dense wavelength division multiplexing (DWDM) array architecture is presented for interferometric fibre optic sensor array systems. This architecture employs a distributed erbium doped fibre amplifier (EDFA) scheme to decrease the array insertion loss, and employs time division multiplexing (TDM) at each wavelength to increase the number of sensors that can be supported. The first experimental demonstration of this system is reported including results which show the potential for multiplexing and interrogating up to 4096 sensors using a single telemetry fibre pair with good system performance. The number can be increased to 8192 by using dual pump sources

Parameter estimates for fractional autoregressive spatial processes

A binomial-type operator on a stationary Gaussian process is introduced in order to model long memory in the spatial context. Consistent estimators of model parameters are demonstrated. In particular, it is shown that $\hat{d}_N-d=O_P(\frac{(\operatorname {Log}N)^3}{N})$, where $d=(d_1,d_2)$ denotes the long memory parameter.Comment: Published at http://dx.doi.org/10.1214/009053605000000589 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Simultaneous Inference of User Representations and Trust

Inferring trust relations between social media users is critical for a number of applications wherein users seek credible information. The fact that available trust relations are scarce and skewed makes trust prediction a challenging task. To the best of our knowledge, this is the first work on exploring representation learning for trust prediction. We propose an approach that uses only a small amount of binary user-user trust relations to simultaneously learn user embeddings and a model to predict trust between user pairs. We empirically demonstrate that for trust prediction, our approach outperforms classifier-based approaches which use state-of-the-art representation learning methods like DeepWalk and LINE as features. We also conduct experiments which use embeddings pre-trained with DeepWalk and LINE each as an input to our model, resulting in further performance improvement. Experiments with a dataset of $\sim$356K user pairs show that the proposed method can obtain an high F-score of 92.65%.Comment: To appear in the proceedings of ASONAM'17. Please cite that versio

Statistical uncertainty of eddy flux–based estimates of gross ecosystem carbon exchange at Howland Forest, Maine

We present an uncertainty analysis of gross ecosystem carbon exchange (GEE) estimates derived from 7 years of continuous eddy covariance measurements of forest-atmosphere CO2fluxes at Howland Forest, Maine, USA. These data, which have high temporal resolution, can be used to validate process modeling analyses, remote sensing assessments, and field surveys. However, separation of tower-based net ecosystem exchange (NEE) into its components (respiration losses and photosynthetic uptake) requires at least one application of a model, which is usually a regression model fitted to nighttime data and extrapolated for all daytime intervals. In addition, the existence of a significant amount of missing data in eddy flux time series requires a model for daytime NEE as well. Statistical approaches for analytically specifying prediction intervals associated with a regression require, among other things, constant variance of the data, normally distributed residuals, and linearizable regression models. Because the NEE data do not conform to these criteria, we used a Monte Carlo approach (bootstrapping) to quantify the statistical uncertainty of GEE estimates and present this uncertainty in the form of 90% prediction limits. We explore two examples of regression models for modeling respiration and daytime NEE: (1) a simple, physiologically based model from the literature and (2) a nonlinear regression model based on an artificial neural network. We find that uncertainty at the half-hourly timescale is generally on the order of the observations themselves (i.e., ∼100%) but is much less at annual timescales (∼10%). On the other hand, this small absolute uncertainty is commensurate with the interannual variability in estimated GEE. The largest uncertainty is associated with choice of model type, which raises basic questions about the relative roles of models and data

Critical Noise Levels for LDPC decoding

We determine the critical noise level for decoding low density parity check error correcting codes based on the magnetization enumerator (\cM), rather than on the weight enumerator (\cW) employed in the information theory literature. The interpretation of our method is appealingly simple, and the relation between the different decoding schemes such as typical pairs decoding, MAP, and finite temperature decoding (MPM) becomes clear. In addition, our analysis provides an explanation for the difference in performance between MN and Gallager codes. Our results are more optimistic than those derived via the methods of information theory and are in excellent agreement with recent results from another statistical physics approach.Comment: 9 pages, 5 figure

An all-fibre PM MOPA pumped high-power OPO at 3.82 microns based on large aperture PPMgLN

We report a large aperture PPMgLN based OPO generating 21W of average output power at a slope efficiency of 45%, pumped by the output from a polarization maintaining Ytterbium doped fiber MOPA operating at 1060nm producing 58W of average output power and 20ns pulses at a repetition rate of 100kHz. A maximum of 5.5W of optical power was recorded at the idler wavelength of 3.82µm without thermal roll-off. We have experimentally verified that the pulse rise/fall time plays a significant role in the OPO conversion efficiency and that further enhancement in the OPO conversion efficiency will be possible using sub-nanosecond rise and fall times

Virginia Abolishes Locality Rule in Medical Malpractice

The Virginia General Assembly, in recognition of a modem medical society, has abolished the presumption which favors the application of a same or similar locale standard to determine the requisite care of a health care provider. The same or similar standard has been used consistently since 1918 in malpractice litigation and, more recently, in proceedings before the medical malpractice review panels in Virginia. This standard was first adopted by statute in 1977, and though it appeared to intend a broader standard than the local standard, it was in fact interpreted as a clear statutory adoption of the Virginia case law rule. The new statute, repealing the 1977 statute, adopts the statewide standard of care as the presumption which now can only be rebutted by a preponderance of the evidence that, in consideration of local customs and practice, another standard would be more appropriate