Discrete and Continuous-time Soft-Thresholding with Dynamic Inputs

There exist many well-established techniques to recover sparse signals from compressed measurements with known performance guarantees in the static case. However, only a few methods have been proposed to tackle the recovery of time-varying signals, and even fewer benefit from a theoretical analysis. In this paper, we study the capacity of the Iterative Soft-Thresholding Algorithm (ISTA) and its continuous-time analogue the Locally Competitive Algorithm (LCA) to perform this tracking in real time. ISTA is a well-known digital solver for static sparse recovery, whose iteration is a first-order discretization of the LCA differential equation. Our analysis shows that the outputs of both algorithms can track a time-varying signal while compressed measurements are streaming, even when no convergence criterion is imposed at each time step. The L2-distance between the target signal and the outputs of both discrete- and continuous-time solvers is shown to decay to a bound that is essentially optimal. Our analyses is supported by simulations on both synthetic and real data.Comment: 18 pages, 7 figures, journa

Global and local statistical regularities control visual attention to object sequences

Many previous studies have shown that both infants and adults are skilled statistical learners. Because statistical learning is affected by attention, learners' ability to manage their attention can play a large role in what they learn. However, it is still unclear how learners allocate their attention in order to gain information in a visual environment containing multiple objects, especially how prior visual experience (i.e., familiarly of objects) influences where people look. To answer these questions, we collected eye movement data from adults exploring multiple novel objects while manipulating object familiarity with global (frequencies) and local (repetitions) regularities. We found that participants are sensitive to both global and local statistics embedded in their visual environment and they dynamically shift their attention to prioritize some objects over others as they gain knowledge of the objects and their distributions within the task

AutoMoDe – A Transformation Based Approach for the Model-based Design of Embedded Automotive Software

International audienceThe AutoMoDe approach manages the complexity of embedded automotive systems by employing a stream-based development paradigm which is specifically tailored to embedded automotive real-time systems. In this paper the tailoring process is explained by transforming a traction control system from a stream-based model to an embedded real-time software model and afterwards integrating the software model on an embedded automotive rapid development hardware

Impaired object-location learning and recognition memory but enhanced sustained attention in M2 muscarinic receptor-deficient mice

© 2018, The Author(s). Rationale: Muscarinic acetylcholine receptors are known to play key roles in mediating cognitive processes, and impaired muscarinic cholinergic neurotransmission is associated with normal ageing processes and Alzheimer’s disease. However, the specific contributions of the individual muscarinic receptor subtypes (M1–M5) to cognition are presently not well understood. Objectives: The aim of this study was to investigate the contribution of M2-type muscarinic receptor signalling to sustained attention, executive control and learning and memory. Methods: M2 receptor-deficient (M2−/−) mice were tested on a touchscreen-operated task battery testing visual discrimination, behavioural flexibility, object-location associative learning, attention and response control. Spontaneous recognition memory for real-world objects was also assessed. Results: We found that M2−/− mice showed an enhancement of attentional performance, but significant deficits on some tests of learning and memory. Executive control and visual discrimination were unaffected by M2-depletion. Conclusions: These findings suggest that M2 activation has heterogeneous effects across cognitive domains, and provide insights into how acetylcholine may support multiple specific cognitive processes through functionally distinct cholinergic receptor subtypes. They also suggest that therapeutics involving M2 receptor-active compounds should be assessed across a broad range of cognitive domains, as they may enhance some cognitive functions, but impair others

The token’s secret: the two-faced financial incentive of the token economy

Multi-sided platforms are omnipresent in today’s digital world. However, establishing a platform includes challenges: The platform utility usually increases with the number of participants. At an early stage, potential participants expect the platform utility to be low and lack an incentive to join (i.e., “chicken and egg” problem). Blockchain-enabled utility tokens hold the promise to overcome this problem. They supposedly provide a suitable financial incentive for their owners to join the platform as soon as possible. In the first half of 2018, investors seemed to believe in the presumption and spent more than US$ 17.6 billion in token sales. To date, we know little about this financial incentive in the context of the token economy. For this purpose, we model the token value development and the associated incentives in a multi-sided blockchain-enabled platform. The resulting findings suggest that blockchain-enabled utility tokens can help to overcome the “chicken and egg” problem. However, these tokens lead to contradictory incentives for platform participants, and can even inhibit platform usage. The contribution of our work is twofold: First, we develop one of the first models for token value development. Second, our research contributes to a deeper understanding of the utility token’s financial incentive

Large scale reactive additive manufacturing and what to expect when scaling up

Additive manufacturing as a whole offers tremendous savings in time and cost for rapid prototyping and tooling. At present there is a significant number of thermoplastic printers available from small-scale filament-based extrusion to large scale pellet-based extrusion. Thermosets have seen less growth and have been primarily limited to small scale research setups. Recently, a large-scale thermoset printer, the Reactive Additive Manufacturing (RAM) printer was developed (cf. Figure 1). This printer consists of an overall build volume of 450 ft3 and a gantry speed up to 50 in/s. The RAM system is also equipped with a modular pumping station capable of pumping feedstock material at pressures of 3000 psi in 5 or 55 gallon reservoirs. This work intends to reveal the challenges of working with a large scale Direct Ink Writing (DIW) process and how to overcome them. Two material chemistries have been scaled up for this system and are presented herein: a peroxide cured vinyl ester and latent cured epoxy-anhydrides. Factors such as pumpability, printability, and performance vary significantly between these systems and are discussed using rheological characterization, modeling, printing setup and parameters, and part design. Figure Please click Additional Files below to see the full abstract

Multiscale Discriminant Saliency for Visual Attention

The bottom-up saliency, an early stage of humans' visual attention, can be considered as a binary classification problem between center and surround classes. Discriminant power of features for the classification is measured as mutual information between features and two classes distribution. The estimated discrepancy of two feature classes very much depends on considered scale levels; then, multi-scale structure and discriminant power are integrated by employing discrete wavelet features and Hidden markov tree (HMT). With wavelet coefficients and Hidden Markov Tree parameters, quad-tree like label structures are constructed and utilized in maximum a posterior probability (MAP) of hidden class variables at corresponding dyadic sub-squares. Then, saliency value for each dyadic square at each scale level is computed with discriminant power principle and the MAP. Finally, across multiple scales is integrated the final saliency map by an information maximization rule. Both standard quantitative tools such as NSS, LCC, AUC and qualitative assessments are used for evaluating the proposed multiscale discriminant saliency method (MDIS) against the well-know information-based saliency method AIM on its Bruce Database wity eye-tracking data. Simulation results are presented and analyzed to verify the validity of MDIS as well as point out its disadvantages for further research direction.Comment: 16 pages, ICCSA 2013 - BIOCA sessio

Lovey Dove

There are many love words new; Are there any that will do for you? What expression could I take In my confession that I want to make? CHORUS Lovey Dove, cuddle up, Dovey dove, huddle up near me, Honey Lamb, I’m for you, ‘deed I am, Honeydew Dearie! You’re my precious, precious, you’re just love complete; I adore you, for you’re so sweetie, sweet. Tootsie Dear, rest up near, Wootsie Dear, nest up near, Kiss me! If I go, If I should whisper low, say you would miss me! From my heels to head I’ll tell you, I am dead in love. Ev’ry day, more and more, I adore Lovey Dove! Dove! Loving sweethearts always look For sweet phrases in Dan Cupid’s book. Though these love words are not new, These love words, Dearie, all apply to you