Imbibition dynamics of nano-particulate ink-jet drops on micro-porous media

Ink-jet printing of nano-metallic colloidal fluids on to porous media such as coated papers has become a viable method to produce conductive tracks for low-cost, disposable printed electronic devices. However, the formation of well-defined and functional tracks on an absorbing surface is controlled by the drop imbibition dynamics in addition to the well-studied post-impact drop spreading behavior. This study represents the first investigation of the real-time imbibition of ink-jet deposited nano-Cu colloid drops on to coated paper substrates. In addition, the same ink was deposited on to a non-porous polymer surface as a control substrate. By using high-speed video imaging to capture the deposition of ink-jet drops, the time-scales of drop spreading and imbibition were quantified and compared with model predictions. The influences of the coating pore size on the bulk absorption rate and nano-Cu particle distribution have also been studied.

Perceptual learning, roving and the unsupervised bias

Perceptual learning improves perception through training. Perceptual learning improves with most stimulus types but fails when . certain stimulus types are mixed during training (roving). This result is surprising because classical supervised and unsupervised neural network models can cope easily with roving conditions. What makes humans so inferior compared to these models? As experimental and conceptual work has shown, human perceptual learning is neither supervised nor unsupervised but reward-based learning. Reward-based learning suffers from the so-called unsupervised bias, i.e., to prevent synaptic " drift" , the . average reward has to be exactly estimated. However, this is impossible when two or more stimulus types with different rewards are presented during training (and the reward is estimated by a running average). For this reason, we propose no learning occurs in roving conditions. However, roving hinders perceptual learning only for combinations of similar stimulus types but not for dissimilar ones. In this latter case, we propose that a critic can estimate the reward for each stimulus type separately. One implication of our analysis is that the critic cannot be located in the visual system. © 2011 Elsevier Ltd.

Optimization of nanotube electrode geometry in a liquid crystal media from wavefront aberrations

This paper presents experimental optimization of number and geometry of nanotube electrodes in a liquid crystal media from wavefront aberrations for realizing nanophotonic devices. The refractive-index gradient profiles from different nanotube geometries-arrays of one, three, four, and five-were studied along with wavefront aberrations using Zernike polynomials. The optimizations help the device to make application in the areas of voltage reconfigurable microlens arrays, high-resolution displays, wavefront sensors, holograms, and phase modulators. © 2012 Optical Society of America.

Bias-free identification of a linear model-predictive steering controller from measured driver steering behavior

Recent developments in modeling driver steering control with preview are reviewed. While some validation with experimental data has been presented, the rigorous application of formal system identification methods has not yet been attempted. This paper describes a steering controller based on linear model-predictive control. An indirect identification method that minimizes steering angle prediction error is developed. Special attention is given to filtering the prediction error so as to avoid identification bias that arises from the closed-loop operation of the driver-vehicle system. The identification procedure is applied to data collected from 14 test drivers performing double lane change maneuvers in an instrumented vehicle. It is found that the identification procedure successfully finds parameter values for the model that give small prediction errors. The procedure is also able to distinguish between the different steering strategies adopted by the test drivers. © 2006 IEEE.

Light-bias induced instability and persistent photoconductivity in In-Zn-O/Ga-In-Zn-O thin film transistors

Stress/recovery measurements demonstrate that even high-performance passivated In-Zn-O/ Ga-In-Zn-O thin film transistors with excellent in-dark stability suffer from light-bias induced threshold voltage shift (ΔV T) and defect density changes. Visible light stress leads to ionisation of oxygen vacancy sites, causing persistent photoconductivity. This makes the material act as though it was n-doped, always causing a negative threshold voltage shift under strong illumination, regardless of the magnitude and polarity of the gate bias.

P-16: Light-bias induced instability and persistent photoconductivity in in-zn-o/ga-in-zn-o thin film transistors

Stress/recovery measurements demonstrate that even highperformance passivated In-Zn-O/ Ga-In-Zn-O thin film transistors with excellent in-dark stability suffer from light-bias induced threshold voltage shift (ΔV T) and defect density changes. Visible light stress leads to ionisation of oxygen vacancy sites, causing persistent photoconductivity. This makes the material act as though it was n-doped, always causing a negative threshold voltage shift under strong illumination, regardless of the magnitude and polarity of the gate bias. © 2011 SID.

Ultrashort pulse generation in diode laser devices

The generation of ultrashort optical pulses by semiconductor lasers has been extensively studied for many years. A number of methods, including gain-/Q-switching and different types of mode locking, have been exploited for the generation of picosecond and sub-picosecond pulses [1]. However, the shortest pulses produced by diode lasers are still much longer and weaker than those that are generated by advanced mode-locked solid-state laser systems [2]. On the other hand, an interesting class of devices based on superradiant emission from multiple contact diode laser structures has also been recently reported [3]. Superradiance (SR) is a transient quantum optics phenomenon based on the cooperative radiative recombination of a large number of oscillators, including atoms, molecules, e-h pairs, etc. SR in semiconductors can be used for the study of fundamental properties of e-h ensembles such as photon-mediated pairing, non-equilibrium e-h condensation, BSC-like coherent states and related phenomena. Due to the intrinsic parameters of semiconductor media, SR emission typically results in the generation of a high-power optical pulse or pulse train, where the pulse duration can be much less than 1 ps, under optimised bias conditions. Advantages of this technique over mode locking in semiconductor laser structures include potentially shorter pulsewidths and much larger peak powers. Moreover, the pulse repetition rate of mode-locked pulses is fixed by the cavity round trip time, whereas the repetition rate of SR pulses is controlled by the current bias and can be varied over a wide range. © 2012 IEEE.

Transcription of multi-genre media archives using out-of-domain data

We describe our work on developing a speech recognition system for multi-genre media archives. The high diversity of the data makes this a challenging recognition task, which may benefit from systems trained on a combination of in-domain and out-of-domain data. Working with tandem HMMs, we present Multi-level Adaptive Networks (MLAN), a novel technique for incorporating information from out-of-domain posterior features using deep neural networks. We show that it provides a substantial reduction in WER over other systems, with relative WER reductions of 15% over a PLP baseline, 9% over in-domain tandem features and 8% over the best out-of-domain tandem features. © 2012 IEEE.

Insula and striatum mediate the default bias.

Humans are creatures of routine and habit. When faced with situations in which a default option is available, people show a consistent tendency to stick with the default. Why this occurs is unclear. To elucidate its neural basis, we used a novel gambling task in conjunction with functional magnetic resonance imaging. Behavioral results revealed that participants were more likely to choose the default card and felt enhanced emotional responses to outcomes after making the decision to switch. We show that increased tendency to switch away from the default during the decision phase was associated with decreased activity in the anterior insula; activation in this same area in reaction to "switching away from the default and losing" was positively related with experienced frustration. In contrast, decisions to choose the default engaged the ventral striatum, the same reward area as seen in winning. Our findings highlight aversive processes in the insula as underlying the default bias and suggest that choosing the default may be rewarding in itself.

Beyond dataset bias: Multi-task unaligned shared knowledge transfer

Many visual datasets are traditionally used to analyze the performance of different learning techniques. The evaluation is usually done within each dataset, therefore it is questionable if such results are a reliable indicator of true generalization ability. We propose here an algorithm to exploit the existing data resources when learning on a new multiclass problem. Our main idea is to identify an image representation that decomposes orthogonally into two subspaces: a part specific to each dataset, and a part generic to, and therefore shared between, all the considered source sets. This allows us to use the generic representation as un-biased reference knowledge for a novel classification task. By casting the method in the multi-view setting, we also make it possible to use different features for different databases. We call the algorithm MUST, Multitask Unaligned Shared knowledge Transfer. Through extensive experiments on five public datasets, we show that MUST consistently improves the cross-datasets generalization performance. © 2013 Springer-Verlag.