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Solid-phase photo-processes involving icy dust grains greatly affect the chemical evolution of the interstellar medium by leading to the formation of complex organic molecules and by inducing photodesorption. So far, the focus of laboratory studies has been mainly on the impact of energetic ultraviolet (UV) photons on ices, but direct vibrational excitation by infrared (IR) photons is expected to influence the morphology and content of interstellar ices as well. However, little is still known about the mechanisms through which this excess vibrational energy is dissipated, and its implications on the structure and ice photochemistry. In this work, we present a systematic investigation of the behavior of interstellar relevant CO and CH3OH ice analogues upon resonant excitation of vibrational modes using tunable infrared radiation, leading to both the quantification of infrared-induced photodesorption and insights in the impact of vibrational energy dissipation on ice morphology. We utilize an ultrahigh vacuum setup at cryogenic temperatures to grow pure CO and CH3OH ices, as well as mixtures of the two. We expose the ices to intense, near-monochromatic mid-infrared free-electron-laser radiation to selectively excite the species. The dissipation of vibrational energy is observed to be highly dependent on the excited mode and the chemical environment of the ice. All amorphous ices undergo some degree of restructuring towards a more organized configuration upon on-resonance irradiation. Moreover, IR-induced photodesorption is observed to occur for both pure CO and CH3OH ices, with interstellar photodesorption efficiencies of the order of 10 molecules cm-2 s-1 (i.e., comparable to or higher than UV-induced counterparts). Indirect photodesorption of CO upon vibrational excitation of CH3OH in ice mixtures is also observed to occur, particularly in environments rich in methanol., Comment: Accepted for publication in A&A. 19 pages, 14 figures, 2 tables

The evolution of star-forming regions and their thermal balance are strongly influenced by their chemical composition, that, in turn, is determined by the physico-chemical processes that govern the transition between the gas phase and the solid state, specifically icy dust grains (e.g., particles adsorption and desorption). Gas-grain and grain-gas transitions as well as formation and sublimation of interstellar ices are thus essential elements of understanding astrophysical observations of cold environments (e.g., pre-stellar cores) where unexpected amounts of a large variety of chemical species have been observed in the gas phase. Adsorbed atoms and molecules also undergo chemical reactions which are not efficient in the gas phase. Therefore, the parameterization of the physical properties of atoms and molecules interacting with dust grain particles is clearly a key aspect to interpret astronomical observations and to build realistic and predictive astrochemical models. In this consensus evaluation, we focus on parameters controlling the thermal desorption of ices and how these determine pathways towards molecular complexity and define the location of snowlines, which ultimately influence the planet formation process. We review different crucial aspects of desorption parameters both from a theoretical and experimental point of view. We critically assess the desorption parameters commonly used in the astrochemical community for astrophysical relevant species and provide tables with recommended values. In addition, we show that a non-trivial determination of the pre-exponential factor nu using the Transition State Theory can affect the binding energy value. Finally, we conclude this work by discussing the limitations of theoretical and experimental approaches currently used to determine the desorption properties with suggestions for future improvements., Comment: 115 pages, 17 figures

We present Oncilla robot, a novel mobile, quadruped legged locomotion machine. This large-cat sized, 5.1 robot is one of a kind of a recent, bioinspired legged robot class designed with the capability of model-free locomotion control. Animal legged locomotion in rough terrain is clearly shaped by sensor feedback systems. Results with Oncilla robot show that agile and versatile locomotion is possible without sensory signals to some extend, and tracking becomes robust when feedback control is added (Ajaoolleian 2015). By incorporating mechanical and control blueprints inspired from animals, and by observing the resulting robot locomotion characteristics, we aim to understand the contribution of individual components. Legged robots have a wide mechanical and control design parameter space, and a unique potential as research tools to investigate principles of biomechanics and legged locomotion control. But the hardware and controller design can be a steep initial hurdle for academic research. To facilitate the easy start and development of legged robots, Oncilla-robot's blueprints are available through open-source. [...]

In the quest for alternatives to traditional CMOS, it is being suggested that digital computing efficiency and power can be improved by matching the precision to the application. Many applications do not need the high precision that is being used today. In particular, large gains in area- and power efficiency could be achieved by dedicated analog realizations of approximate computing engines. In this work, we explore the use of memristor networks for analog approximate computation, based on a machine learning framework called reservoir computing. Most experimental investigations on the dynamics of memristors focus on their nonvolatile behavior. Hence, the volatility that is present in the developed technologies is usually unwanted and it is not included in simulation models. In contrast, in reservoir computing, volatility is not only desirable but necessary. Therefore, in this work, we propose two different ways to incorporate it into memristor simulation models. The first is an extension of Strukov's model and the second is an equivalent Wiener model approximation. We analyze and compare the dynamical properties of these models and discuss their implications for the memory and the nonlinear processing capacity of memristor networks. Our results indicate that device variability, increasingly causing problems in traditional computer design, is an asset in the context of reservoir computing. We conclude that, although both models could lead to useful memristor based reservoir computing systems, their computational performance will differ. Therefore, experimental modeling research is required for the development of accurate volatile memristor models., Comment: 4 figures, no tables. Submitted to neural computation

The interest in brain-like computation has led to the design of a plethora of innovative neuromorphic systems. Individually, spiking neural networks (SNNs), event-driven simulation and digital hardware neuromorphic systems get a lot of attention. Despite the popularity of event-driven SNNs in software, very few digital hardware architectures are found. This is because existing hardware solutions for event management scale badly with the number of events. This paper introduces the structured heap queue, a pipelined digital hardware data structure, and demonstrates its suitability for event management. The structured heap queue scales gracefully with the number of events, allowing the efficient implementation of large scale digital hardware event-driven SNNs. The scaling is linear for memory, logarithmic for logic resources and constant for processing time. The use of the structured heap queue is demonstrated on field-programmable gate array (FPGA) with an image segmentation experiment and a SNN of 65~536 neurons and 513~184 synapses. Events can be processed at the rate of 1 every 7 clock cycles and a 406$\times$158 pixel image is segmented in 200 ms.

Reservoir computing is a new, powerful and flexible machine learning technique that is easily implemented in hardware. Recently, by using a time-multiplexed architecture, hardware reservoir computers have reached performance comparable to digital implementations. Operating speeds allowing for real time information operation have been reached using optoelectronic systems. At present the main performance bottleneck is the readout layer which uses slow, digital postprocessing. We have designed an analog readout suitable for time-multiplexed optoelectronic reservoir computers, capable of working in real time. The readout has been built and tested experimentally on a standard benchmark task. Its performance is better than non-reservoir methods, with ample room for further improvement. The present work thereby overcomes one of the major limitations for the future development of hardware reservoir computers., Comment: to appear in NIPS 2012

Reservoir computing is a recently introduced, highly efficient bio-inspired approach for processing time dependent data. The basic scheme of reservoir computing consists of a non linear recurrent dynamical system coupled to a single input layer and a single output layer. Within these constraints many implementations are possible. Here we report an opto-electronic implementation of reservoir computing based on a recently proposed architecture consisting of a single non linear node and a delay line. Our implementation is sufficiently fast for real time information processing. We illustrate its performance on tasks of practical importance such as nonlinear channel equalization and speech recognition, and obtain results comparable to state of the art digital implementations., Comment: Contains main paper and two Supplementary Materials