On the hardness of solving edge matching puzzles as SAT or CSP problems

Edge matching puzzles have been amongst us for a long time now and traditionally they have been considered, both, a children’s game and an interesting mathematical divertimento. Their main characteristics have already been studied, and their worst-case complexity has been properly classified as a NP-complete problem. It is in recent times, specially after being used as the problem behind a money-prized contest, with a prize of 2US$ million for the first solver, that edge matching puzzles have attracted mainstream attention from wider audiences, including, of course, computer science people working on solving hard problems. We consider these competitions as an interesting opportunity to showcase SAT/CSP solving techniques when confronted to a real world problem to a broad audience, a part of the intrinsic, i.e. monetary, interest of such a contest. This article studies the NP-complete problem known as edge matching puzzle using SAT and CSP approaches for solving it. We will focus on providing, first and foremost, a theoretical framework, including a generalized definition of the problem. We will design and show algorithms for easy and fast problem instances generation, generators with easily tunable hardness. Afterwards we will provide with SAT and CSP models for the problems and we will study problem complexity, both typical case and worst-case complexity. We will also provide some specially crafted heuristics that result in a boost in solving time and study which is the effect of such heuristics.     

Stochastic Cellular Automata Solutions to the Density Classification Problem

In the density classification problem, a binary cellular automaton (CA) should decide whether an initial configuration contains more 0s or more 1s. The answer is given when all cells of the CA agree on a given state. This problem is known for having no exact solution in the case of binary deterministic one-dimensional CA. We investigate how randomness in CA may help us solve the problem. We analyse the behaviour of stochastic CA rules that perform the density classification task. We show that describing stochastic rules as a “blend” of deterministic rules allows us to derive quantitative results on the classification time and the classification time of previously studied rules. We introduce a new rule whose effect is to spread defects and to wash them out. This stochastic rule solves the problem with an arbitrary precision, that is, its quality of classification can be made arbitrarily high, though at the price of an increase of the convergence time. We experimentally demonstrate that this rule exhibits good scaling properties and that it attains qualities of classification never reached so far.     

Abdominal sensory equipment involved in external host discrimination in a solitary parasitoid wasp

Already parasitized hosts are often of poorer quality than healthy hosts. It is therefore usually advantageous for parasitoid females to recognize and reject them. Parasitized hosts can be identified on the basis of various physical or chemical marks present on the surface or inside the hosts or their surroundings in the case of concealed host. Here we studied host discrimination behaviors of females of a certain population of Pachycrepoideus vindemmiae, a solitary ectoparasitoid, which are known to reject large-sized parasitized hosts after an abdominal examination of their surface. We first investigated females' recognition behaviors of host parasitism status when confronted to small-sized hosts (Drosophila melanogaster pupae) as host size may influence the use of different cues for host selection. We showed that, in such a situation, females also discriminate parasitized hosts after an external host exploration with the tip of their ovipositor sheath (third valvulae). We then described the sense organs present on the different parts of the ovipositor by means of scanning and transmission electron microscopy analysis. As the extremity of the third valvulae bears only one type of sensilla that appears to be chemoreceptors, we considered these sensilla as highly likely to be involved in host discrimination in P. vindemmiae. To our knowledge, this is the first time that receptors located on the ovipositor sheath are described as implicated in host discrimination in parasitoid wasps. We discuss potential chemical markers that might be detected by these receptors.     

Samplinghelper a web-based tool to assess the reliability of sampling strategies in sewers and receiving waters

Sampling is a key step in the analysis of chemical compounds. It is particularly important in the environmental field, for example for wastewater effluents, wet-weather discharges or streams in which the flows and concentrations vary greatly over time. In contrast to the improvements that have occurred in analytical measurement, developments in the field of sampling are less active. However, sampling errors may exceed by an order of magnitude those related to analytical processes. We proposed an Internet-based application based on a sampling theory to identify and quantify the errors in the process of taking samples. This general theory of sampling, already applied to different areas, helps to answer questions related to the number of samples, their volume, their representativeness, etc. The use of the internet to host this application facilitates use of theoretical tools and raise awareness of the uncertainties related to sampling. An example is presented, which highlights the importance of the sampling step in the quality of analytical results.     

Flow profile measurement in microchannel using the optical feedback interferometry sensing technique

The need to accurately measure flow profiles in microfluidic channels is well recognised. In this work, we present a new optical feedback interferometry (OFI) flow sensor that accurately measures local velocity in fluids and enables reconstruction of a velocity profile inside a microchannel. OFI is a self-aligned interferometric technique that uses the laser as both the transmitter and the receiver thus offering high sensitivity, fast response, and a simple and compact optical design. The system described here is based on a commercial semiconductor laser and has been designed to achieve a micrometer-range spatial resolution. The sensor performance was validated by reconstructing the velocity profile inside a circular cross-section flow-channel with 320  $\upmu $ m internal diameter, with a relative error smaller than 1.8 %. The local flow velocity is directly measured, thus avoiding the need for model based profile calculation and uncertainties inherent to this approach. The system was validated by successfully extracting the flow profiles in both Newtonian and shear-thinning liquids.     

ECM: An evidential version of the fuzzy c-means algorithm

A new clustering method for object data, called ECM (evidential c-means) is introduced, in the theoretical framework of belief functions. It is based on the concept of credal partition, extending those of hard, fuzzy, and possibilistic ones. To derive such a structure, a suitable objective function is minimized using an FCM-like algorithm. A validity index allowing the determination of the proper number of clusters is also proposed. Experiments with synthetic and real data sets show that the proposed algorithm can be considered as a promising tool in the field of exploratory statistics.     

Application of the cross entropy method to the GLVQ algorithm

This paper discusses an alternative approach to parameter optimization of well-known prototype-based learning algorithms (minimizing an objective function via gradient search). The proposed approach considers a stochastic optimization called the cross entropy method (CE method). The CE method is used to tackle efficiently the initialization sensitiveness problem associated with the original generalized learning vector quantization (GLVQ) algorithm and its variants. Results presented in this paper indicate that the CE method can be successfully applied to this kind of problem on real-world data sets. As far as known by the authors, it is the first use of the CE method in prototype-based learning.     

Optical flow and advection on 2-Riemannian manifolds: a common framework

Dynamic pattern analysis and motion extraction can be efficiently addressed using optical flow techniques. This article presents a generalization of these questions to non-flat surfaces, where optical flow is tackled through the problem of evolution processes on non-Euclidian domains. The classical equations of optical flow in the Euclidian case are transposed to the theoretical framework of differential geometry. We adopt this formulation for the regularized optical flow problem, prove its mathematical well-posedness and combine it with the advection equation. The optical flow and advection problems are dual: a motion field may be retrieved from some scalar evolution using optical flow; conversely, a scalar field may be deduced from a velocity field using advection. These principles are illustrated with qualitative and quantitative evaluations from numerical simulations bridging both approaches. The proof-of-concept is further demonstrated with preliminary results from time-resolved functional brain imaging data, where organized propagations of cortical activation patterns are evidenced using our approach.     

A review of smart homes- present state and future challenges

In the era of information technology, the elderly and disabled can be monitored with numerous intelligent devices. Sensors can be implanted into their home for continuous mobility assistance and non-obtrusive disease prevention. Modern sensor-embedded houses, or smart houses, cannot only assist people with reduced physical functions but help resolve the social isolation they face. They are capable of providing assistance without limiting or disturbing the resident's daily routine, giving him or her greater comfort, pleasure, and well-being. This article presents an international selection of leading smart home projects, as well as the associated technologies of wearable/implantable monitoring systems and assistive robotics. The latter are often designed as components of the larger smart home environment. The paper will conclude by discussing future challenges of the domain.     

The segmented presentation of visually structured texts: Effects on text comprehension

The effects of a segmented presentation applied to a visually structured text were examined in the context of the explosion of small-screen devices. Empirical research investigating the influence of text signaling on text processing suggests that the text visual structure may influence comprehension by facilitating the construction of a coherent text representation. Undergraduate students were asked to read a text under different segmented conditions varying on the type of information provided about the text visual structure and on the segmentation unit. When the segmented presentation did not supply any information or when it only offered local information about the text visual structure, text comprehension depended on the segmentation unit. When the segmentation unit did not fit the text visual structure, an erroneous text representation was constructed, whereas the compatible segmentation unit led to a correct text comprehension. When the segmented presentation rendered the global text visual structure, the segmentation unit had no effect on comprehension and more readers constructed a correct and close text representation. Thus, the text visual structure seems to play a role in text comprehension and this role has to be taken into account for text segmented presentation.