Adaptive multiscale detection of filamentary structures in a background of uniform random points

We are given a set of $n$ points that might be uniformly distributed in the unit square $[0,1]^2$. We wish to test whether the set, although mostly consisting of uniformly scattered points, also contains a small fraction of points sampled from some (a priori unknown) curve with $C^{\alpha}$-norm bounded by $\beta$. An asymptotic detection threshold exists in this problem; for a constant $T_-(\alpha,\beta)>0$, if the number of points sampled from the curve is smaller than $T_-(\alpha,\beta)n^{1/(1+\alpha)}$, reliable detection is not possible for large $n$. We describe a multiscale significant-runs algorithm that can reliably detect concentration of data near a smooth curve, without knowing the smoothness information $\alpha$ or $\beta$ in advance, provided that the number of points on the curve exceeds $T_*(\alpha,\beta)n^{1/(1+\alpha)}$. This algorithm therefore has an optimal detection threshold, up to a factor $T_*/T_-$. At the heart of our approach is an analysis of the data by counting membership in multiscale multianisotropic strips. The strips will have area $2/n$ and exhibit a variety of lengths, orientations and anisotropies. The strips are partitioned into anisotropy classes; each class is organized as a directed graph whose vertices all are strips of the same anisotropy and whose edges link such strips to their ``good continuations.'' The point-cloud data are reduced to counts that measure membership in strips. Each anisotropy graph is reduced to a subgraph that consist of strips with significant counts. The algorithm rejects $\mathbf{H}_0$ whenever some such subgraph contains a path that connects many consecutive significant counts.Comment: Published at http://dx.doi.org/10.1214/009053605000000787 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Large block inpainting by color continuation analysis

[[abstract]]Automatic inpainting is a mechanism which repairs damaged pictures using an approximation mechanism. The most difficult problem is to inpaint a large damaged area, without knowing its content. One possible solution is to use color interpolation or extrapolation on surrounding pixels. However, spatial characteristics such as edges and pixel continuations are hard to be restored. In this research, we propose a series of automatic algorithms, which is based on an analysis of color continuations. Large damaged blocks are repaired, before the rest smaller potions are repaired by a multiresolution inpainting algorithm. The mechanism is tested on more than 2000 images, including cartoon drawing, photos, Chinese painting, and western painting. Our results prove that, the proposed automatic mechanism fixes damaged image up to a certain degree of satisfaction from the users. The demonstration of our work is available at: http://www.mine.tku.edu.tw/demos/inpaint.[[notice]]補正完畢[[conferencetype]]國際[[conferencedate]]20040105~20040107[[iscallforpapers]]Y[[conferencelocation]]Brisban, Australi

Flexible Sensor Network Reprogramming for Logistics

Besides the currently realized applications, Wireless Sensor Networks can be put to use in logistics processes. However, doing so requires a level of flexibility and safety not provided by the current WSN software platforms. This paper discusses a logistics scenario, and presents SensorScheme, a runtime environment used to realize this scenario, based on semantics of the Scheme programming language. SensorScheme is a general purpose WSN platform, providing dynamic reprogramming, memory safety (sandboxing), blocking I/O, marshalled communication, compact code transport. It improves on the state of the art by making better use of the little available memory, thereby providing greater capability in terms of program size and complexity. We illustrate the use of our platform with some application examples, and provide experimental results to show its compactness, speed of operation and energy efficiency

Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks

Recommendations can greatly benefit from good representations of the user state at recommendation time. Recent approaches that leverage Recurrent Neural Networks (RNNs) for session-based recommendations have shown that Deep Learning models can provide useful user representations for recommendation. However, current RNN modeling approaches summarize the user state by only taking into account the sequence of items that the user has interacted with in the past, without taking into account other essential types of context information such as the associated types of user-item interactions, the time gaps between events and the time of day for each interaction. To address this, we propose a new class of Contextual Recurrent Neural Networks for Recommendation (CRNNs) that can take into account the contextual information both in the input and output layers and modifying the behavior of the RNN by combining the context embedding with the item embedding and more explicitly, in the model dynamics, by parametrizing the hidden unit transitions as a function of context information. We compare our CRNNs approach with RNNs and non-sequential baselines and show good improvements on the next event prediction task

Biases in Interpretation and Memory in Generalized Social Phobia

Two experiments examined the link between interpretation and memory in individuals diagnosed with Generalized Social Phobia (GSP). In Experiment 1, GSP and control participants generated continuations for nonsocial and ambiguous social scenarios. GSP participants produced more socially anxious and negative continuations for the social scenarios than did the controls. On the subsequent test of recalling the social scenarios, intrusion errors that shared meaning with the original continuations were made more frequently by the GSP group, producing false recall with emotionally negative features. To examine whether nonanxious individuals would also produce such errors if given emotional interpretations, in Experiment 2 the authors asked university students to read the scenarios plus endings produced by GSP participants in Experiment 1. The students either constructed vivid mental images of themselves as the main characters or thought about whether the endings provided closure. Low-anxious students in the closure condition produced fewer ending-based intrusions in recalling the social scenarios than did students in the other 3 conditions. Results illustrate the importance of examining the nature of source-monitoring errors in investigations of memory biases in social anxiety

Building Web Based Programming Environments for Functional Programming

Functional programming offers an accessible and powerful algebraic model for computing. JavaScript is the language of the ubiquitous Web, but it does not support functional programs well due to its single-threaded, asynchronous nature and lack of rich control flow operators. The purpose of this work is to extend JavaScript to a language environment that satisfies the needs of functional programs on the Web. This extended language environment uses sophisticated control operators to provide an event-driven functional programming model that cooperates with the browser\u27s DOM, along with synchronous access to JavaScript\u27s asynchronous APIs. The results of this work are used toward two projects: (1) a programming environment called WeScheme that runs in the web browser and supports a functional programming curriculum, and (2) a tool-chain called Moby that compiles event-driven functional programs to smartphones, with access to phone-specific features

Wiener-Hopf solution for impenetrable wedges at skew incidence

A new Wiener-Hopf approach for the solution of impenetrable wedges at skew incidence is presented. Mathematical aspects are described in a unified and consistent theory for angular region problems. Solutions are obtained using analytical and numerical-analytical approaches. Several numerical tests from the scientific literature validate the new technique, and new solutions for anisotropic surface impedance wedges are solved at skew incidence. The solutions are presented considering the geometrical and uniform theory of diffraction coefficients, total fields, and possible surface wave contribution

Meaningful Thickness Detection on Polygonal Curve

International audienceThe notion of meaningful scale was recently introduced to detect the amount of noise present along a digital contour. It relies on the asymptotic properties of the maximal digital straight segment primitive. Even though very useful, the method is restricted to digital contour data and is not able to process other types of geometric data like disconnected set of points. In this work, we propose a solution to overcome this limitation. It exploits another primitive called the Blurred Segment which controls the straight segment recognition precision of disconnected sets of points. The resulting noise detection provides precise results and is also simpler to implement. A first application of contour smoothing demonstrates the efficiency of the proposed method. The algorithms can also be tested online