Super-resolution reconstruction of image sequences

In an earlier work (1999), we introduced the problem of reconstructing a super-resolution image sequence from a given low resolution sequence. We proposed two iterative algorithms, the R-SD and the R-LMS, to generate the desired image sequence. These algorithms assume the knowledge of the blur, the down-sampling, the sequences motion, and the measurements noise characteristics, and apply a sequential reconstruction process. It has been shown that the computational complexity of these two algorithms makes both of them practically applicable. In this paper, we rederive these algorithms as approximations of the Kalman filter and then carry out a thorough analysis of their performance. For each algorithm, we calculate a bound on its deviation from the Kalman filter performance. We also show that the propagated information matrix within the R-SD algorithm remains sparse in time, thus ensuring the applicability of this algorithm. To support these analytical results we present some computer simulations on synthetic sequences, which also show the computational feasibility of these algorithms     

Superresolution restoration of an image sequence: adaptivefiltering approach

This paper presents a new method based on adaptive filtering theory for superresolution restoration of continuous image sequences. The proposed methodology suggests least squares (LS) estimators which adapt in time, based on adaptive filters, least mean squares (LMS) or recursive least squares (RLS). The adaptation enables the treatment of linear space and time-variant blurring and arbitrary motion, both of them assumed known. The proposed new approach is shown to be of relatively low computational requirements. Simulations demonstrating the superresolution restoration algorithms are presented.     

The Metabolic Efficacy of a Cannabidiolic Acid (CBDA) Derivative in Treating Diet- and Genetic-Induced Obesity

Obesity is a global medical problem; its common form is known as diet-induced obesity (DIO); however, there are several rare genetic disorders, such as Prader–Willi syndrome (PWS), that are also associated with obesity (genetic-induced obesity, GIO). The currently available therapeutics for treating DIO and GIO are very limited, and they result in only a partial improvement. Cannabidiolic acid (CBDA), a constituent of Cannabis sativa, gradually decarboxylates to cannabidiol (CBD). Whereas the anti-obesity properties of CBD have been reasonably identified, our knowledge of the pharmacology of CBDA is more limited due to its instability. To stabilize CBDA, a new derivative, CBDA-O-methyl ester (HU-580, EPM301), was synthesized. The therapeutic potential of EPM301 in appetite reduction, weight loss, and metabolic improvements in DIO and GIO was tested in vivo. EPM301 (40 mg/kg/d, i.p.) successfully resulted in weight loss, increased ambulation, as well as improved glycemic and lipid profiles in DIO mice. Additionally, EPM301 ameliorated DIO-induced hepatic dysfunction and steatosis. Importantly, EPM301 (20 and 40 mg/kg/d, i.p.) effectively reduced body weight and hyperphagia in a high-fat diet-fed Magel2^null mouse model for PWS. In addition, when given to standard-diet-fed Magel2^null mice as a preventive treatment, EPM301 completely inhibited weight gain and adiposity. Lastly, EPM301 increased the oxidation of different nutrients in each strain. All together, EPM301 ameliorated obesity and its metabolic abnormalities in both DIO and GIO. These results support the idea to further promote this synthetic CBDA derivative toward clinical evaluation in humans.     

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.