Can Reflection from Grains Diagnose the Albedo?

By radiation transfer models with a realistic power spectra of the projected density distributions, we show that the optical properties of grains are poorly constrained by observations of reflection nebulae. The ISM is known to be hierarchically clumped from a variety of observations (molecules, H I, far-infrared). Our models assume the albedo and phase parameter of the dust, the radial optical depth of the sphere averaged over all directions, and random distributions of the dust within the sphere. The outputs are the stellar extinction, optical depth, and flux of scattered light as seen from various viewing angles. Observations provide the extinction and scattered flux from a particular direction. Hierarchical geometry has a large effect on the flux of scattered light emerging from a nebula for a particular extinction of the exciting star. There is a very large spread in both scattered fluxes and extinctions for any distribution of dust. Consequently, an observed stellar extinction and scattered flux can be fitted by a wide range of albedos. With hierarchical geometry it is not completely safe to determine even relative optical constants from multiwavelength observations of the same reflection nebula. The geometry effectively changes with wavelength as the opacity of the clumps varies. Limits on the implications of observing the same object in various wavelengths are discussed briefly. Henry (2002) uses a recipe to determine the scattered flux from a star with a given extinction. It is claimed to be independent of the geometry. It provides considerably more scattering than our models, probably leading to an underestimate of the grain albedos from the UV Diffuse Galactic Light.Comment: 27 pages, including 7 figures. Accepted by Ap

Diagnose network failures via data-plane analysis

Diagnosing problems in networks is a time-consuming and error-prone process. Previous tools to assist operators primarily focus on analyzing control plane configuration. Configuration analysis is limited in that it cannot find bugs in router software, and is harder to generalize across protocols since it must model complex configuration languages and dynamic protocol behavior. This paper studies an alternate approach: diagnosing problems through static analysis of the data plane. This approach can catch bugs that are invisible at the level of configuration files, and simplifies unified analysis of a network across many protocols and implementations. We present Anteater, a tool for checking invariants in the data plane. Anteater translates high-level network invariants into boolean satisfiability problems, checks them against network state using a SAT solver, and reports counterexamples if violations have been found. Applied to a large campus network, Anteater revealed 23 bugs, including forwarding loops and stale ACL rules, with only five false positives. Nine of these faults are being fixed by campus network operators

Distributed Model-Based Diagnosis using Object-Relational Constraint Databases

This work presents a proposal to diagnose distributed systems utilizing model-based diagnosis using distributed databases. In order to improve aspects as versatility, persistence, easy composition and efficiency in the diagnosis process we use an Object Relational Constraint Database (ORCDB). Thereby we define a distributed architecture to store the behaviour of components as constraints in a relational database to diagnose a distributed system. This work proposes an algorithm to detect which components fail when their information is distributed in several databases, and all the information is not available in a global way. It is also offered a proposal to define, in execution time, the allocation of the sensors in a distributed system.Ministerio de Ciencia y Tecnología DPI2003-07146-C02-0

PhoneMD: Learning to Diagnose Parkinson's Disease from Smartphone Data

Parkinson's disease is a neurodegenerative disease that can affect a person's movement, speech, dexterity, and cognition. Clinicians primarily diagnose Parkinson's disease by performing a clinical assessment of symptoms. However, misdiagnoses are common. One factor that contributes to misdiagnoses is that the symptoms of Parkinson's disease may not be prominent at the time the clinical assessment is performed. Here, we present a machine-learning approach towards distinguishing between people with and without Parkinson's disease using long-term data from smartphone-based walking, voice, tapping and memory tests. We demonstrate that our attentive deep-learning models achieve significant improvements in predictive performance over strong baselines (area under the receiver operating characteristic curve = 0.85) in data from a cohort of 1853 participants. We also show that our models identify meaningful features in the input data. Our results confirm that smartphone data collected over extended periods of time could in the future potentially be used as a digital biomarker for the diagnosis of Parkinson's disease.Comment: AAAI Conference on Artificial Intelligence 201

Accuracy of five algorithms to diagnose gambiense human African trypanosomiasis.

Algorithms to diagnose gambiense human African trypanosomiasis (HAT, sleeping sickness) are often complex due to the unsatisfactory sensitivity and/or specificity of available tests, and typically include a screening (serological), confirmation (parasitological) and staging component. There is insufficient evidence on the relative accuracy of these algorithms. This paper presents estimates of the accuracy of five algorithms used by past Médecins Sans Frontières programmes in the Republic of Congo, Southern Sudan and Uganda

Testosterone deficiency in the adult males

Testosterone deficiency leads to multiple problems but can be difficult to diagnose. However, replacement therapy can be rewarding and a life changer for the patient.peer-reviewe