All updateable objects in working memory are updated whenever any of them are modified: Evidence from the memory updating paradigm.

In a series of experiments, participants were required to keep track of 1 or 2 working memory (WM) objects, having to update their values in 80% of the trials. Updating cost, defined as the difference between update and non-update trials, was larger when 2 objects were involved compared with when there was only 1 object was involved. This finding was interpreted as evidence that the updating process encompasses both objects in WM, even though only 1 of them is actually updated. This feature of WM updating is limited to objects defined as "updateable," throughout the trial sequence. The results are explained by the need to reprogram the phonological loop when updating or the need for desynchronization followed by resynchronization of WM contents. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Automatic processing of psychological distance: Evidence from a Stroop task.

A picture-word version of the Stroop task was used to test the automatic activation of psychological distance by words carrying various senses of psychological distance: temporal (tomorrow, in a year), social (friend, enemy), and hypotheticality (sure, maybe). The pictures implied depth, with the words appearing relatively close to or distant from the observer. The participants classified the spatial distance of words faster when the word's implicit psychological distance matched its spatial distance (e.g., a geographically close word was classified faster when it was "friend" than when it was "enemy"). The findings are consistent with the idea that psychological distance is accessed automatically, even when it is not directly related to people's current goals, and suggest that psychological distance is an important dimension of meaning, common to spatial distance, temporal distance, social distance, and hypotheticality. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

If multi-agent learning is the answer, what is the question?

The area of learning in multi-agent systems is today one of the most fertile grounds for interaction between game theory and artificial intelligence. We focus on the foundational questions in this interdisciplinary area, and identify several distinct agendas that ought to, we argue, be separated. The goal of this article is to start a discussion in the research community that will result in firmer foundations for the area.¹     

Covalent surface functionalization of nonwoven fabrics with controlled hydrophobicity,water absorption,and pH regulation properties

A modular method for functionalization of nonwoven fabrics was developed using a two-step process. In the first step, the fabrics were grafted with a linker molecule, 10-undecenoyl chloride, via esterification, followed by attachment of a functional material under UV irradiation. Perfluorodecanethiol and 3-mercaptopropionic acid (MPA) were connected to the linker-modified fabrics using thiol-ene click chemistry. Perfluorodecanethiol modified fabrics exhibited hydrophobicity with water contact angle of about 140° while MPA-modified fabrics were able to lower the pH of a solution by about 1.6. We additionally demonstrated the possibility to connect functional polymers to the linker-modified fabrics by radical graft polymerization of acrylic acid; this produced a thin layer of the polymer on the surface of the fabric. Fabrics modified with poly(acrylic acid) exhibited increased hydrophilicity with water contact angle of 0° for both cotton and viscose-polyester fabrics, while the water absorption capability for polypropylene fabrics increased from about 50 to 1200%.     

Separation of Transparent Layers using Focus

Consider situations where the depth at each point in the scene is multi-valued, due to the presence of a virtual image semi-reflected by a transparent surface. The semi-reflected image is linearly superimposed on the image of an object that is behind the transparent surface. A novel approach is proposed for the separation of the superimposed layers. Focusing on either of the layers yields initial separation, but crosstalk remains. The separation is enhanced by mutual blurring of the perturbing components in the images. However, this blurring requires the estimation of the defocus blur kernels. We thus propose a method for self calibration of the blur kernels, given the raw images. The kernels are sought to minimize the mutual information of the recovered layers. Autofocusing and depth estimation in the presence of semi-reflections are also considered. Experimental results are presented.     

Regularized image recovery in scattering media

When imaging in scattering media, visibility degrades as objects become more distant. Visibility can be significantly restored by computer vision methods that account for physical processes occurring during image formation. Nevertheless, such recovery is prone to noise amplification in pixels corresponding to distant objects, where the medium transmittance is low. We present an adaptive filtering approach that counters the above problems: while significantly improving visibility relative to raw images, it inhibits noise amplification. Essentially, the recovery formulation is regularized, where the regularization adapts to the spatially varying medium transmittance. Thus, this regularization does not blur close objects. We demonstrate the approach in atmospheric and underwater experiments, based on an automatic method for determining the medium transmittance.     

An Adaptive Version of the Boost by Majority Algorithm

We propose a new boosting algorithm. This boosting algorithm is an adaptive version of the boost by majority algorithm and combines bounded goals of the boost by majority algorithm with the adaptivity of AdaBoost.The method used for making boost-by-majority adaptive is to consider the limit in which each of the boosting iterations makes an infinitesimally small contribution to the process as a whole. This limit can be modeled using the differential equations that govern Brownian motion. The new boosting algorithm, named BrownBoost, is based on finding solutions to these differential equations.The paper describes two methods for finding approximate solutions to the differential equations. The first is a method that results in a provably polynomial time algorithm. The second method, based on the Newton-Raphson minimization procedure, is much more efficient in practice but is not known to be polynomial.     

Task Switching After Cerebellar Damage.

The authors of this study investigated task switching following cerebellar damage. The study group consisted of 7 children and adolescents (M age = 13.8 years) who underwent surgical removal of a benign posterior fossa tumor. They were tested at a sufficient interval after surgery (M lag = 6.13 years) for restoration of normal cognitive skills and intelligence. Although all showed normal learning of the task compared with control participants, when rapid behavioral changes were required (short preparation time), they exhibited behavioral rigidity manifested by enhanced switching cost. These results are in line with another study on serial reaction time with the same patients (A. Berger et al., in press). They have important implications for our understanding of the cognitive sequelae of early cerebellar damage as well as the involvement of the cerebellum in task switching. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Labeling Schemes for Dynamic Tree Networks

Distance labeling schemes are composed of a marker algorithm for labeling the vertices of a graph with short labels, coupled with a decoder algorithm allowing one to compute the distance between any two vertices directly from their labels (without using any additional information). As applications for distance labeling schemes concern mainly large and dynamically changing networks, it is of interest to study distributed dynamic labeling schemes. The current paper considers the problem on dynamic trees, and proposes efficient distributed schemes for it. The paper first presents a labeling scheme for distances in the dynamic tree model, with amortized message complexity O(log² n) per operation, where n is the size of the tree at the time the operation takes place. The protocol maintains O(log² n) bit labels. This label size is known to be optimal even in the static scenario. A more general labeling scheme is then introduced for the dynamic tree model, based on extending an existing static tree labeling scheme to the dynamic setting. The approach fits a number of natural tree functions, such as distance, separation level, and flow. The main resulting scheme incurs an overhead of an O(log n) multiplicative factor in both the label size and amortized message complexity in the case of dynamically growing trees (with no vertex deletions). If an upper bound on n is known in advance, this method yields a different tradeoff, with an O(log² n/log log n) multiplicative overhead on the label size but only an O(log n/log log n) overhead on the amortized message complexity. In the fully dynamic model the scheme also incurs an increased additive overhead in amortized communication, of O(log² n) messages per operation.     

Novel,Printable Energetic Polymers

Novel, simple‐to‐make energetic mono‐ and bisvinylimidazolium perchlorate monomers are prepared and characterized. These energetic monomers offer the possibility to 3D print energetic polymers that may allow decreasing the content of the energetic filler in energetic devices without compromising their energetic properties. The new printable materials are suited for photocuring‐based additive manufacturing (AM), techniques, offering not only a large degree of control over the mechanical and energetic properties of the resulting solids, but also the production of complex geometries at high resolution.