A problem with using derivatives to explain the matching law

Herrnstein (1979) recently claimed that the matching law could be derived from an ordinary differential equation. He failed, however, to analyze the dynamic properties of his proposed equation. I show that it implies behavior cannot stabilize in accordance with the matching law. Consequently, Herrnstein's equation cannot explain why matching behavior has been widely observed.     

Delayed reverberation through time windows as a key to cerebellar function

We present a functional model of the cerebellum comprising cerebellar cortex, inferior olive, deep cerebellar nuclei, and brain stem nuclei. The discerning feature of the model being time coding, we consistently describe the system in terms of postsynaptic potentials, synchronous action potentials, and propagation delays. We show by means of detailed single-neuron modeling that (i) Golgi cells can fulfill a gating task in that they form short and well-defined time windows within which granule cells can reach firing threshold, thus organizing neuronal activity in discrete `time slices', and that (ii) rebound firing in cerebellar nuclei cells is a robust mechanism leading to a delayed reverberation of Purkinje cell activity through cerebellar-reticular projections back to the cerebellar cortex. Computer simulations of the whole cerebellar network consisting of several thousand neurons reveal that reverberation in conjunction with long-term plasticity at the parallel fiber-Purkinje cell synapses enables the system to learn, store, and recall spatio-temporal patterns of neuronal activity. Climbing fiber spikes act both as a synchronization and as a teacher signal, not as an error signal. They are due to intrinsic oscillatory properties of inferior olivary neurons and to delayed reverberation within the network. In addition to clear experimental predictions the present theory sheds new light on a number of experimental observation such as the synchronicity of climbing fiber spikes and provides a novel explanation of how the cerebellum solves timing tasks on a time scale of several hundreds of milliseconds. Received: 23 July 1999 / Accepted in revised form: 31 August 1999     

Sun angle time windows for absconding by the dwarf honeybee, Apis florea

In Asia, the red dwarf honeybee, Apis florea, is notorious for its absconding habit. Interestingly, such colonies show a bimodal frequency distribution about a noonday lull throughout the year. Because slight errors in reading the relative position of the sun near its zenith results in very large orientation errors in the waggle dances of other honeybees in the tropics, we postulated that the frequency distribution of absconding in the red dwarf honeybee relative to local clock time could be explained in similar fashion. The frequency distribution of absconding by the red dwarf honeybee with respect to time was found to be bimodal with a pronounced lull at noonday, which in turn is related to the altitude angle of the sun. So, these bees largely avoid flying off between 12:00 h and 13:00 h on the one hand and that their preferred departure angle of the sun is between 55° and 65° on the other. Given the difficulties of taking an accurate reading of the sun at angles ±6° of the sun's zenith (resulting in a 1 h loss around noon) and the 2 h required to reach consensus over the final direction to be flown, the bees are simply left with two time windows, morning and afternoon, in which to abscond and, indeed some 90% of the red dwarf honeybee colonies do so. The noonday lull is not associated with high temperatures for any given day. Absconding is not inhibited by high temperatures.     

Large time behavior in a nonlinear age-dependent population dynamics problem with spatial diffusion

In this work we analyze the large time behavior in a nonlinear model of population dynamics with age-dependence and spatial diffusion. We show that when t+ either the solution of our problem goes to 0 or it stabilizes to a nontrivial stationary solution. We give two typical examples where the stationary solutions can be evaluated upon solving very simple partial differential equations. As a by-product of the extinction case we find a necessary condition for a nontrivial periodic solution to exist. Numerical computations not described below show a rapid stabilization.This work was partially supported by the Centre National de la Recherche Scientifique through ATP 95939900     

Electrocardiogram data mining based on frame classification by dynamic time warping matching

This paper presents an electrocardiogram (ECG) data mining scheme based on the ECG frame classification realised by a dynamic time warping (DTW) matching technique, which has been used successfully in speech recognition. We use the DTW to classify ECG frames because ECG and speech signals have similar non-stationary characteristics. The DTW mapping function is obtained by searching the frame from its end to start. A threshold is setup for DTW matching residual either to classify an ECG frame or to add a new class. Classification and establishment of a template set are carried out simultaneously. A frame is classified into a category with a minimal residual and satisfying a threshold requirement. A classification residual of 1.33% is achieved by the DTW for a 10-min ECG recording.     

Hybrid simulation and optimization approach for green intermodal transportation problem with travel time uncertainty

The increasing volumes of road transportation contribute to congestion on road, which leads to delays and other negative impacts on the reliability of transportation. Moreover, transportation is one of the main contributors to the growth of carbon dioxide equivalent emissions, where the impact of road transportation is significant. Therefore, governmental organizations and private commercial companies are looking for greener transportation solutions to eliminate the negative externalities of road transportation. In this paper, we present a novel solution framework to support the operational-level decisions for intermodal transportation networks using a combination of an optimization model and simulation. The simulation model includes stochastic elements in form of uncertain travel times, whereas the optimization model represents a deterministic and linear multi-commodity service network design formulation. The intermodal transportation plan can be optimized according to different objectives, including costs, time and \(CO_2e\) emissions. The proposed approach is successfully implemented to real-life scenarios where differences in transportation plans for alternative objectives are presented. The solutions for transportation networks with up to 250 services and 20 orders show that the approach is capable of delivering reliable solutions and identifying possible disruptions and alternatives for adapting the unreliable transportation plans.     

First passage time problem for a drifted Ornstein-Uhlenbeck process

We consider a continuous stochastic process defined as a drifted Ornstein-Uhlenbeck, for which the first passage time is of interest. The process being non-homogeneous, the first passage time probability density function cannot be found analytically, but numerical methods enable to find its estimate. Estimating the first passage time implies solving an unsteady convection-diffusion equation, with variable coefficients, and we use an implicit Euler scheme to solve it. This work is applied to simulated data, and the continuous process is inspired from recent work on biological marker modelling for HIV-positive patients. The first passage time probability density function can be useful to compare the marker progression in different groups. Numerical results show that the first passage time is highly dependent from the process perturbation, and is then more relevant than methods not considering the stochastic process directly to compare the progression.     

Antenna impedance matching with neural networks

Impedance matching between transmission lines and antennas is an important and fundamental concept in electromagnetic theory. One definition of antenna impedance is the resistance and reactance seen at the antenna terminals or the ratio of electric to magnetic fields at the input. The primary intent of this paper is real-time compensation for changes in the driving point impedance of an antenna due to frequency deviations. In general, the driving point impedance of an antenna or antenna array is computed by numerical methods such as the method of moments or similar techniques. Some configurations do lend themselves to analytical solutions, which will be the primary focus of this work. This paper employs a neural control system to match antenna feed lines to two common antennas during frequency sweeps. In practice, impedance matching is performed off-line with Smith charts or relatively complex formulas but they rarely perform optimally over a large bandwidth. There have been very few attempts to compensate for matching errors while the transmission system is in operation and most techniques have been targeted to a relatively small range of frequencies. The approach proposed here employs three small neural networks to perform real-time impedance matching over a broad range of frequencies during transmitter operation. Double stub tuners are being explored in this paper but the approach can certainly be applied to other methodologies. The ultimate purpose of this work is the development of an inexpensive microcontroller-based system.     

Propensity score matching with time-dependent covariates

In observational studies with a time-dependent treatment and time-dependent covariates, it is desirable to balance the distribution of the covariates at every time point. A time-dependent propensity score based on the Cox proportional hazards model is proposed and used in risk set matching. Matching on this propensity score is shown to achieve a balanced distribution of the covariates in both treated and control groups. Optimal matching with various designs is conducted and compared in a study of a surgical treatment, cystoscopy and hydrodistention, given in response to a chronic bladder disease, interstitial cystitis. Simulation studies also suggest that the statistical analysis after matching outperforms the analysis without matching in terms of both point and interval estimations.     

Large-scale biomedical ontology matching with ServOMap

The proliferation of biomedical applications, which rely on different knowledge organization systems, such as ontologies and thesauri raises the issue of the automated identification of the correspondences between these models, in particular for the data integration need. A significant effort has been conducted for tackling this issue of ontology alignment. However, few systems are able to deal with ontologies containing tens of thousands of entities, as it may be the case in the biomedical domain where resources such as SNOMED-CT, the FMA or the NCI thesaurus are commonly used. We present in this paper ServOMap, an efficient system for large-scale ontology alignment. It relies on an Ontology Server (ServO) and uses Information Retrieval techniques for computing similarity between entities. The system participated with two configurations in the 2012 Ontology Alignment Evaluation Initiative campaign. We report the very promising results obtained by the system for large biomedical ontologies alignment. ServOMap is freely available for download at http://code.google.com/p/servo/.     

The problem with the species problem

When Charles Darwin convinced the scientific community that species evolve, the long-held essentialist view of each species as fixed was rejected and a clear conceptual understanding of the term was lost. For the next century, a real species problem existed that became culturally entrenched within the scientific community. Although largely solved decades ago, the species problem remains entrenched today due to a suite of factors. Most of the factors that help maintain its perceived intractability have been revealed and logically dismissed; yet this is not widely known so those factors continue to be influential. It is time to recognize this false foundation and relegate the species problem to history.     

Fungal biodeterioration of stained-glass windows

Biodeterioration of stained-glass windows by fungi was studied using historically accurate glass production methods. Glass reproductions were made according to the chemical composition determined by micro energy dispersive X-Ray Fluorescence of two historical glass windows belonging to King Ferdinand II's collection dating from the 15th and 17th centuries. Three distinct glasses compositions with different colours were selected and reproduced: i) a mixed-alkali colourless glass: ii) a purple potash-glass with manganese as chromophore, and iii) a brown potash-glass coloured by iron ions. The reproduced glass samples, with two initial surface morphologies (corroded and non-corroded), were inoculated with fungi previously isolated and identified on the original stained-glass windows as species of the genera Penicillium and Cladosporium. Physical and chemical glass surface alterations were analysed by means of optical microscopy, Raman microscopy, micro Infrared spectroscopy, and scanning electron microscopy with energy dispersive spectroscopy analysis. Results showed that fungi produced clear damage on all glass surfaces, present as spots and stains, fingerprints, biopiting, leaching and deposition of elements, and formation of biogenic crystals. Therefore, the inoculated fungi were able to biodeteriorate glasses with distinct compositions. Regarding the biodeterioration degree, there were no differences between the initial non-corroded and corroded glass surfaces.