Construction and uses of a concentric catheter for gas sampling in lung airways

A catheter for intra-airway sampling of gas concentrations was constructed from concentric polyethylene tubes. The internal tube (0.58 mm ID, 0.91 mm OD) was connected to a gas analyzer while the external tube (1.20 mm ID, 1.75 mm OD) was constantly flushed by air or a calibration gas, except during sampling. Injection and sampling dead spaces were 0.35 and 0.28 ml, respectively. Delay at 4-ml/min sampling rate was 4.0 +/- 0.2 s. The 0-90% step response to a sudden change in gas composition was 0.24 s when connected to a mass spectrometer. This catheter was used to assess tracer gas dispersion during oscillatory flow (1-20 Hz) in a straight long tube. Local concentrations measured through the catheter, after a small bolus of tracer gas was injected through the external tube, compared favorably with direct measurements through needles inserted via the tube wall and with theoretical predictions. The catheter was also used to measure intra-airway gas concentrations in dog airways during spontaneous breathing, conventional mechanical ventilation, high-frequency ventilation, high-frequency vibration ventilation, and constant-flow ventilation. It ws placed by a fiber-optic bronchoscope and used to measure local quasi-steady concentrations of CO2 and local dispersion with the bolus method. The occurrence of catheter clogging with secretions was substantially reduced with flow through the external tube. Transmitting a calibration gas through the external tube facilitated in situ recalibration of the gas analyzer without removing the catheter. The use of this catheter improved the efficiency and accuracy of measurements of gas concentrations inside lung airways.     

On the bi-enhancement of chordal-bipartite probe graphs

Given a class C of graphs, a graph G=(V,E) is said to be a C-probe graph if there exists a stable (i.e., independent) set of vertices X⊆V and a set F of pairs of vertices of X such that the graph G^′=(V,E∪F) is in the class C. Recently, there has been increasing interest and research on a variety of C-probe graph classes, such as interval probe graphs, chordal probe graphs and chain probe graphs.In this paper we focus on chordal-bipartite probe graphs. We prove a structural result that if B is a bipartite graph with no chordless cycle of length strictly greater than 6, then B is chordal-bipartite probe if and only if a certain “enhanced” graph B^∗ is a chordal-bipartite graph. This theorem is analogous to a result on interval probe graphs in Zhang (1994) [18] and to one on chordal probe graphs in Golumbic and Lipshteyn (2004) [11].     

Extracting certainty from uncertainty: regret bounded by variation in costs

Prediction from expert advice is a fundamental problem in machine learning. A major pillar of the field is the existence of learning algorithms whose average loss approaches that of the best expert in hindsight (in other words, whose average regret approaches zero). Traditionally the regret of online algorithms was bounded in terms of the number of prediction rounds. Cesa-Bianchi, Mansour and Stoltz (Mach. Learn. 66(2–3):21–352, 2007) posed the question whether it is be possible to bound the regret of an online algorithm by the variation of the observed costs. In this paper we resolve this question, and prove such bounds in the fully adversarial setting, in two important online learning scenarios: prediction from expert advice, and online linear optimization.     

A Market-based Solution to the Multiple Traveling Salesmen Problem

This paper describes a market-based solution to the problem of assigning mobile agents to tasks. The problem is formulated as the multiple depots, multiple traveling salesmen problem (MTSP), where agents and tasks operate in a market to achieve near-optimal solutions. We consider both the classical MTSP, in which the sum of all tour lengths is minimized, and the Min-Max MTSP, in which the longest tour is minimized. We compare the market-based solution with direct enumeration in small scenarios, and show that the results are nearly optimal. For the classical MTSP, we compare our results to linear programming, and show that the results are within 1 % of the best cost found by linear programming in more than 90 % of the runs, with a significant reduction in runtime. For the Min-Max case, we compare our method with Carlsson’s algorithm and show an improvement of 5 % to 40 % in cost, albeit at an increase in runtime. Finally, we demonstrate the ability of the market-based solution to deal with changes in the scenario, e.g., agents leaving and entering the market. We show that the market paradigm is ideal for dealing with these changes during runtime, without the need to restart the algorithm, and that the solution reacts to the new scenarios in a quick and near-optimal way.     

Soil Consumed by Chacma Baboons is Low in Bioavailable Iron and High in Clay

Despite widespread consumption of soil among animals, the role of geophagy in health maintenance remains an enigma. It has been hypothesized that animals consume soil for supplementation of minerals and protection against toxins. Most studies determine only the total elemental composition of soil, which may not reflect the amount of minerals available to the consumer. Our aim was to test these hypotheses by evaluating the bioavailability of iron in soil consumed by chacma baboons, using a technique that simulates digestion and adsorption. Our results indicate that, despite variation in absolute iron concentration of soil samples, actual iron bioavailability was low while clay content was quite high. This suggests that iron supplementation is unlikely to be the primary motivation for geophagy in this population, and that detoxification is a plausible explanation. This study demonstrates that more research on bioavailability and clay composition is needed to determine the role geophagy plays in health maintenance.     

Social bookmark weighting for search and recommendation

Social bookmarking enables knowledge sharing and efficient discovery on the web, where users can collaborate together by tagging documents of interests. A lot of attention was given lately for utilizing social bookmarking data to enhance traditional IR tasks. Yet, much less attention was given to the problem of estimating the effectiveness of an individual bookmark for the specific tasks. In this work, we propose a novel framework for social bookmark weighting which allows us to estimate the effectiveness of each of the bookmarks individually for several IR tasks. We show that by weighting bookmarks according to their estimated quality, we can significantly improve social search effectiveness. We further demonstrate that using the same framework, we can derive solutions to several recommendation tasks such as tag recommendation, user recommendation, and document recommendation. Empirical evaluation on real data gathered from two large bookmarking systems demonstrates the effectiveness of the new social bookmark weighting framework.     

Image decomposition via the combination of sparse representations and a variational approach.

The separation of image content into semantic parts plays a vital role in applications such as compression, enhancement, restoration, and more. In recent years, several pioneering works suggested such a separation be based on variational formulation and others using independent component analysis and sparsity. This paper presents a novel method for separating images into texture and piecewise smooth (cartoon) parts, exploiting both the variational and the sparsity mechanisms. The method combines the basis pursuit denoising (BPDN) algorithm and the total-variation (TV) regularization scheme. The basic idea presented in this paper is the use of two appropriate dictionaries, one for the representation of textures and the other for the natural scene parts assumed to be piecewise smooth. Both dictionaries are chosen such that they lead to sparse representations over one type of image-content (either texture or piecewise smooth). The use of the BPDN with the two amalgamed dictionaries leads to the desired separation, along with noise removal as a by-product. As the need to choose proper dictionaries is generally hard, a TV regularization is employed to better direct the separation process and reduce ringing artifacts. We present a highly efficient numerical scheme to solve the combined optimization problem posed by our model and to show several experimental results that validate the algorithm's performance.     

Restoration of a single superresolution image from several blurred,noisy, and undersampled measured images

The three main tools in the single image restoration theory are the maximum likelihood (ML) estimator, the maximum a posteriori probability (MAP) estimator, and the set theoretic approach using projection onto convex sets (POCS). This paper utilizes the above known tools to propose a unified methodology toward the more complicated problem of superresolution restoration. In the superresolution restoration problem, an improved resolution image is restored from several geometrically warped, blurred, noisy and downsampled measured images. The superresolution restoration problem is modeled and analyzed from the ML, the MAP, and POCS points of view, yielding a generalization of the known superresolution restoration methods. The proposed restoration approach is general but assumes explicit knowledge of the linear space- and time-variant blur, the (additive Gaussian) noise, the different measured resolutions, and the (smooth) motion characteristics. A hybrid method combining the simplicity of the ML and the incorporation of nonellipsoid constraints is presented, giving improved restoration performance, compared with the ML and the POCS approaches. The hybrid method is shown to converge to the unique optimal solution of a new definition of the optimization problem. Superresolution restoration from motionless measurements is also discussed. Simulations demonstrate the power of the proposed methodology.