Finding the Maximum Eigenvalue of Essentially Nonnegative Symmetric Tensors via Sum of Squares Programming Abstract Finding the maximum eigenvalue of a tensor is an important topic in tensor computation and multilinear algebra. Recently, for a tensor with nonnegative entries (which we refer it as a nonnegative tensor), efficient numerical schemes have been proposed to calculate its maximum eigenvalue based on a Perron-Frobenius-type theorem. In this paper, we consider a new class of tensors called essentially nonnegative tensors, which extends the concept of nonnegative tensors, and examine the maximum eigenvalue of an essentially nonnegative tensor using the polynomial optimization techniques. We first establish that finding the maximum eigenvalue of an essentially nonnegative symmetric tensor is equivalent to solving a sum of squares of polynomials (SOS) optimization problem, which, in its turn, can be equivalently rewritten as a semi-definite programming problem. Then, using this sum of squares programming problem, we also provide upper and lower estimates for the maximum eigenvalue of general symmetric tensors. These upper and lower estimates can be calculated in terms of the entries of the tensor. Numerical examples are also presented to illustrate the significance of the results. The purpose of this article is to study the eigenvalues of a tensor using polynomial optimization techniques. An mth-order n-dimensional tensor A is a multiway array consists of n m entries of real numbers. We say a tensor A is symmetric if and only if the value of its entry is invariant under any permutation of its index. Clearly, when m = 2, a symmetric tensor is nothing but a symmetric matrix. A symmetric tensor uniquely defines an mth-degree homogeneous polynomial function f with real coefficients. Recently, to study the stability of a homogeneous polynomial dynamical system, Qi [1, 2] introduced the definition of eigenvalues of a symmetric tensor and showed that the stability of this dynamical system is tied up with the negativity of the maximum eigenvalue of the corresponding symmetric tensor. Independently, Lim [3] also gave such a definition via a variational approach and established an interesting Perron-Frobenius-type theorem. Recently, the maximum eigenvalue of a symmetric tensor was shown to be ρth-order semismooth with an appropriate estimate on the order ρ in [4] , and numerical study on tensors also has attracted a lot of researchers due to its wide applications in polynomial optimization [2] , hypergraph theory [5, 6] , high-order Markov chains [7] , signal processing [8] , and image science [9] . In particular, various efficiently numerical schemes have been proposed to find the low-rank approximations of a tensor and the eigenvalues/eigenvectors of a tensor (cf. [2, [9] [10] [11] [12] [13] [14] ).. For a nonnegative tensor A, that is, a tensor with all nonnegative entries, various efficient methods for calculating the largest eigenvalue have been proposed recently. In particular, by using the important Perron-Frobenius theorem for nonnegative tensors established in [15] , Ng, Qi, and Zhou [14] proposed an iterative method for finding the maximum eigenvalue of an irreducible nonnegative tensor. The NQZ method in [14] is efficient, but it is not always convergent for irreducible nonnegative tensors. Later on, Chang, Pearson, and Zhang [16] introduced primitive tensors, which are a subclass of irreducible nonnegative tensors, and established the convergence of the NQZ method for primitive tensors. Moreover, Liu, Zhou, and Ibrahim [17] modified the NQZ method so that the modified algorithm is always convergent for finding the largest eigenvalue of an irreducible nonnegative tensor. Recently, Zhang, and Qi [18] established the linear convergence of the NQZ method for essentially positive tensors. Zhang, Qi, and Xu [19] established the linear convergence of the LZI method for weakly positive tensors. Yang and Yang [20, 21] generalized the weak Perron-Frobenius theorem to general nonnegative tensors. Friedland, Gaubert, and Han [22] pointed out that the Perron-Frobenius theorem for nonnegative tensors has a very close link with the Perron-Frobenius theorem for homogeneous monotone maps. They introduced weakly irreducible nonnegative tensors and established the Perron-Frobenius theorem for such tensors. More recently, a numerical method is also presented to calculate the maximum eigenvalue for nonnegative tensors without the irreducible assumption by using a partition technique [23] .. In this paper, we consider a new class of tensors called essentially nonnegative tensors, which extends the nonnegative tensors, and examine the maximum eigenvalue of an essentially nonnegative tensor using the polynomial optimization techniques. We establish that finding the maximum eigenvalue of an essentially nonnegative tensor is equivalent to solving a sum of squares (SOS) polynomial optimization problem, which, in turn, can be equivalently rewritten as a semi-definite programming problem. Using this sum of squares programming problem, we also provide upper and lower estimates of the maximum eigenvalue of general tensors. These upper and lower estimates can be easily calculated in terms of the entries of the tensor. Numerical examples are also provided to illustrate the significance of the upper and lower estimates. Our approach provides the link between the maximum eigenvalue of a symmetric essentially nonnegative tensor and the sum of squares programming problem and leads to easily verifiable upper and lower estimate for the maximum eigenvalue of general tensors.. The organization of this paper is as follows. We first fix the notation and collect some basic definitions in Sect. 2. In Sect. 3, we establish that finding the maximum eigenvalue of an essentially nonnegative tensor is equivalent to solving a sum of squares (SOS) polynomial optimization problem, which, in turn, can be equivalently rewritten as a semi-definite programming problem. In Sect. 4, we provide upper and lower estimates of the maximum eigenvalue of general tensors using the sum of squares (SOS) polynomial optimization techniques. Finally, we conclude our paper and present some future research topics in Sect. 5.

O O B I O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B I I I I O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O


Effects of Deleting Mitochondrial Antioxidant Genes on Life Span ABSTRACT: Reactive oxygen species (ROS) damage biomolecules, accelerate aging, and shorten life span, whereas antioxidant enzymes mitigate these effects. Because mitochondria are a primary site of ROS generation and also a primary target of ROS attack, they have become a major focus area of aging studies. Here, we employed yeast genetics to identify mitochondrial antioxidant genes that are important for replicative life span. In our studies, it was found that among the known mitochondrial antioxidant genes (TTR1, CCD1, SOD1, GLO4, TRR2, TRX3, CCS1, SOD2, GRX5, PRX1), deletion of only three genes, SOD1 (Cu, Zn superoxide dismutase), SOD2 (Manganese-containing superoxide dismutase), and CCS1 (Copper chaperone), shortened the life span enormously. The life span decreased 40% for ∆sod1 mutant, 72% for ∆sod2 mutant, and 50% for ∆ccs1 mutant. Deletion of the other genes had little or no effect on life span. Free radicals generated by aerobic metabolism cause oxidative damage to cell components, such as DNA, proteins, and lipids. Such damage results in altered structure and loss of biological function, which leads to aging and cell death. 1 The mitochondrial respiratory chain on the inner mitochondrial membrane is a major intracellular source of reactive oxygen species (ROS) in eukaryotes. Since ROS are highly reactive and short-lived, mitochondria are continuously exposed to them and accumulate oxidative damage more rapidly than the rest of the cell. 2 Various defense mechanisms operate in mitochondria to minimize the deleterious effects of oxidative stress and maintain the mitochondrial function. Some protective enzymes have direct role in scavenging free radicals, for example, superoxide dismutase is involved in the conversion of superoxide anion to H 2 O 2 , which is further detoxified by catalases or peroxidases. 3 Enzymes that repair the products of oxidatively damaged components form the second line of antioxidant defense. 4 If oxidative stress defense mechanisms are compromised or overwhelmed, disease states may develop. It has been proposed that mitochondrial oxidative damage contributes to neurodegenerative disorders, cancer, and the aging process in humans. 5 Yeast (Saccharomyces cerevisiae) cell possesses a limited life span and is a good model organism to study the role of mitochondrial antioxidant genes in cellular aging. Aging in yeast is determined microscopically by counting the number of daughter cells produced by each cell. In this study, using yeast deletion mutants, the role of mitochondrial antioxidant genes on life span was investigated.

O O O O B I O O O O O O O B O O O B O O O O O O O O O O O B O O O O O B O O O O O O O B O O O O O O O O O B O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O


Atlas.txt: exploring linguistic grounding techniques for communicating spatial information to blind users Abstract This paper describes exploratory research into automatically describing geo-referenced information to blind people. The goal is to produce texts giving an overview of the spatial layout, and a central concern of such texts is that they employ an appropriate linguistic reference frame which enables blind hearers to ground the information. The research presented in this paper was based on two hypotheses: (1) directly perceivable reference frames are easier to ground and (2) spatial descriptions drawn from composite reference frame systems composed of more than one reference frame are easier to ground. An experiment exploring text comprehension on a range of texts employing different reference frame systems is presented. The main results indicate that the second hypothesis is supported. A prototype of a natural language generation system, which generates texts describing geo-referenced information from data, is described. Geo-referenced information like census data is distributed over a geographic area and is often conveyed via shaded thematic choropleth maps, which are inaccessible to blind users, as is shown in Fig. 1 . Although many thematic maps provided by official authorities provide charts along with the maps to make this information accessible, blind users explore charts via a screenreader and spreadsheet software, which restricts them to the mathematical functions on columns and rows like averaging, summation, max and min. This information crucially excludes any sort of overview about global structures or trends in the data which sighted users easily pick up from the corresponding thematic maps. Likewise, while embossed maps can be printed on special paper, haptic exploration does not enable a quick overview of the data [16] since unlike visual perception, which enables Gestalt forms to rapidly emerge from the scene, it proceeds by extensive low-level exploration before any global forms can be detected. Textual information labelling maps, which can be read out via a screenreader, can bridge this gap and provide an overview of how spatial information is laid out, communicating global forms and general trends which might be very hard to otherwise detect. The Atlas.txt project investigates how geo-referenced information like census data is best communicated to blind users, with the goal to proved information which is easy to comprehend and ground, and the user is able to mentally visualise the described distribution of the data. Census data typically involves describing the locations of significant features of a geo-referenced variable or statistic like crime rates in a given area. Typically, the information communicated includes the locations of the maxima and minima of the variable and general trends of the variable across the given region. The goal of texts describing this data is to enable easy mental visualisation of the layout of significant features of the geo-referenced data. The Atlas.txt project involves the development of a prototype data-to-text natural language generation (NLG) system, which aims to process geo-referenced data stored in tables accompanying census maps, and communicate salient information describing the data via easily understood texts. The ideal solution for presenting such data is via multimodal interfaces, so that blind users can hear textual overviews and explore low-level features of the data via exploration in haptic and other modalities. In the context of Atlas.txt, which is an exploratory investigation into automatically communicating geo-referenced information to blind users, the study of the role of non-linguistic modalities has been left for future work.. Given the goal of producing easily understood and grounded texts describing spatial data, the issue of which reference frame to choose becomes central. A linguistic reference frame identifies the location of an object via a spatial coordinate system. These are typically either absolute and involve arbitrary bearings (e.g., North), relative to another object (e.g., ''next to the house''), or intrinsic to an object's perspective (e.g., ''in front of you''), where the object can (for example) be the hearer or speaker. When describing spatial information to blind people, the choice of which reference frame to select becomes central, as visual input to verify the layout of spatial information is unavailable and texts need to be easy to interpret and ground.. One hypothesis argued here is related to the idea that some reference frames might be more familiar to blind hearers and are therefore more readily grounded. For example, describing crime rates as highest ''in Northeastern areas'' might not be as easy to visualise as hearing that crime rates are highest ''at 2-3 o'clock'' for a blind user who has never seen a map but regularly uses a tactile clock. This paper argues that directly perceivable reference frames like clockface or body-centred directions are easier to ground than indirectly perceivable reference frames like cardinal directions.. A second hypothesis hinges on the nature of the survey perspective of space; unlike in a route perspective, (nontactile) maps of large areas are designed with visual perception in mind.. Their advantage over text is that they enable viewers to perceive global forms and patterns easily, and to selectively focus on specific sub-regions. Unlike descriptions in the route perspective, which typically employ egocentric reference frames (i.e., with respect to the person following the route), survey descriptions (which are the focus of this work) tend to avoid egocentric reference frames and often employ relative or absolute reference frames, where objects or regions are either described with respect to other objects or areas in the map, or with respect to an arbitrary frame of reference like the cardinal directions. This leads to a range of possible reference frames which can be used to describe areas or objects on maps in the survey perspective, and the hypothesis put forward here is that employing compound reference frames composed of two (single) reference frames provides additional conceptual perspectives on the described area, which communicates more visualisation clues than are communicated by single reference frames. That is, hearing that, for example, crime rates are highest ''at 2 o'clock, at the top-right of the map'' might be easier to understand than hearing that they are highest ''at 2-3 o'clock''. This paper presents an experiment that investigates comprehension of census texts employing different reference frame strategies in order to determine whether these hypotheses hold. The main findings from the experiment support at least the second hypothesis, as composite descriptions employing cardinal and body-centred, clockface and cardinal, and cardinal and clockface reference frames obtained the highest scores. The first hypothesis seems to be partially supported, as the highest scoring reference frame systems all involved at least one directly perceivable reference frame. The paper starts by describing related work and then presents the experiment and finishes by addressing how the findings from the experiment can be integrated into a natural language generation system producing summaries of spatial data. The results of the experiment include reading times, answering times, comprehension questions, reference frame elicitation, and preference ranking of reference frames. Each of these aspects is discussed in turn below along with some information on the demographics of the participants. The idea of generating textual descriptions of data to make it accessible to the blind is not new; several projects have focused on generating textual descriptions of graphs, [3, 7] .. The iSonic project [17] has focused on communicating census data, the same domain addressed here, but in this project the output modalities were sonification and haptic rather than text. Haptic exploration starts by discovering low-level structures and makes detection of global structures difficult [16] . Sonification also faces similar constraints, as this modality also needs to sweep across the entire data set in order to learn global structures. Aside from the iSonic project, which describes census maps, several projects related to navigational assistance have focused on describing routes to the visually impaired. The work reported in this paper differs from all of these because it aims to (1) effectively describe spatial data via natural language to blind people and (2) focuses on a domain in which spatial descriptions aim to broadly sketch how data is distributed, rather than a route through space.. One question which one might ask in order to automate spatial descriptions of census data for blind people is whether blind people differ from sighted people with respect to spatial learning, and in particular whether they learn best from the same spatial descriptions which are appropriate for sighted people. In terms of spatial learning, Millar [8] argues that efficient and fast coding of spatial relations between objects without considering the position of the body almost certainly needs vision, or at least memories of visual experiences. Millar raises the possibility that vision is only important during a critical period in life, after which the ability for certain spatial processing mechanisms, such as coding within an allocentric reference frame (i.e., one which refers to locations with reference to a given object, e.g., ''in front of the house''), is functional and no longer dependent on vision. Ungar et al. [16] have shown that blind people learning a spatial layout were more successful when they tended to explore relative locations of objects and their locations (1) with respect to the external frame and (2) using two or more exploratory strategies. Several researchers note that certain spatial coding strategies employed by blind people might differ from those employed by sighted people with respect to the preferred reference frame [8, 9] . Millar [8] argues that blind people tend to code spatial information (especially of large spaces) in the form of a local, sequential representation based on routes, whereas sighted people mostly code spatial information in the form of a more global, externally based representation. Noordzij et al. [9] found that this holds for spatial descriptions as well; blind people perform better after listening to route descriptions in contrast with sighted people, however they can build up mental models from both survey and route modes. Turning to learning from spatial descriptions, in [9] it was also showed that early and late-blind people can construct spatial mental models on the basis of verbal descriptions, and it was argued that visual experience does not seem necessary in order to be able to form some spatial representations. Additionally, it was argued that spatial mental model construction may be affected by the way in which navigational skills are learned by blind people, since blind participants performed better in spatial tasks given route descriptions than survey descriptions. They refer to work showing that blind people have different coding strategies [8] or behavioural strategies [14] than sighted people. Brambring [2] elicited spatial descriptions from both blind and sighted participants and found that, while sighted persons gave environmentoriented descriptions, blind participants tended to use descriptions related to their own position. Brambring concluded that sighted persons seem to give global, externally based descriptions and blind people tend to give local, internally based descriptions.. There has also been research on whether the mental models built by sighted people given spatial descriptions indicates that descriptions in both route and survey modes probably produce the same mental models. Taylor and Tversky [12] conducted a study comparing mental models which sighted participants derived from survey vs. route descriptions in which they read texts in either perspective and then answered verbatim or inference questions from both perspectives and drew maps of the environments. In all studies, Taylor and Tversky observed that participants were faster and more accurate answering verbatim rather than to inference questions, suggesting that verbatim questions are verified against a representation of the text of the descriptions. They also noted that participants were as fast and accurate answering inference questions from the read perspective as from the new perspective, suggesting that inference questions are verified against a representation of the situation described by the text. Map drawings were very accurate for both description types. They surmised that readers form the same spatial mental models capturing the spatial relations between landmarks from both survey and route descriptions, and from maps. They refer to work by Thorndyke and Hayes-Roth [15] which argues that route and survey perspectives induce different spatial representations, as they found that subjects who learned an environment by walking through it gave more accurate estimates of local distances, whereas subjects who had studied maps gave more accurate estimates of survey measurements.. However, none of this research evaluates specific linguistic reference frames from absolute, relative and intrinsic modes in terms of how they affect comprehension and grounding of spatial information. In addition, none of these approaches explores whether compound reference frames improve comprehension. The effect of multiple or compound reference frames on spatial description comprehension has not been explored before, but Tenbrink [13] investigates how the principle of redundant verbalisation applies to spatial descriptions in spatially complex tasks involving object identification. Tenbrink found that in situations involving particularly complex scenes, or where different kinds of description strategies were available and equally appropriate, participants tended to use several spatial descriptions where one spatial description would have sufficed to identify the given object. This violation of the Gricean maxim of brevity [4] was preferred in a web study eliciting speaker descriptions. This paper also takes into account additional information manifested as a redundant spatial description, and investigates whether this redundancy aids comprehension of spatial information, where the redundant information in the spatial descriptions considered here makes use of different description strategies, that is, different reference frame choices.

O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Behavioral EMI models of complex digital VLSI circuits Increasing EMI potential of high-performance digital circuits like 32bit microcontrollers demand for switching current models and feasible ways to run netlist-based EMI simulations. A promising modelling approach for digital VLSI circuits is presented and a silicon test vehicle for correlation between models and measurements is described. Electromagnetic interference of complex digital circuits like high-performance 32bit microcontrollers cannot be neglected. Especially the automotive industry demands for EMC-improved systems on silicon. Numerous elements and circuits for noise emission reduction have been invented and implemented in microcontrollers. In most cases, this leads to an EMI reduction, but some questions remain open: -Has the minimum possible EMI been achieved? -Is this the optimum circuit solution e.g. regarding chip size to reduce EMI? Since normally no experiments are allowed on product chips, EMI optimization is often a trial and error procedure causing cost which could have been avoided if EMI models and simulation tools had been available during the chip design phase. This paper describes a modelling approach which is worked on within the European funding project MESDIE in the MEDEA+ frame [10] . It is targeting the generation of simulation models for switching current profiles and realistic power supply system impedance of complex ICs. The ongoing work towards generation of EMI simulation models on chip level consists of 3 parallel approaches [9] : -behavioural models of switching currents caused by synchronously clocked gate arrays in digital modules, based on BSIM transistor models, -backannotation of RLC parasitics of the noise propagation paths, i.e. power supply system, -proof of correlation between behavioural/RLC models and measurements performed on a test chip. Figure 1 shows these 3 parallel approaches with: A = functional modules described by behavioural models of their dynamic current profiles, B = physical structure of power supply system (noise propagation paths) described by RLC parasitics, C = test chip for correlation measurements to improve the quality of models A and B. Whereas current profiles and power supply models are two desired outcomes of the work, the test chip is required for ongoing correlation with and quality improvement of the behavioural and RLC models. The test chip "TASC" offers numerous configurable test structures for the on-silicon measurement of switching currents.. The following chapters describe: 3.1) behavioural modelling approach for current profiles, 3.2) parasitic models of power supply structures, 3.3) TASC test chip. The digital functional modules of complex chips are reduced to simple "replacement gates" which have similar switching current behaviour as the original full module. In that step some assumptions have to be made, e.g. about the activity level. However, since those current models are mainly to be used for comparison of several power routing schemes, absolutely realistic numbers are not required, e.g. worst case or nominal assumptions can be made.. The RLC parameters of the IC's power routing are backannotated from the layout and added to the previously derived behavioural models. This new net list can be simulated e.g. with Spice. It is important to include inductances of supply traces in the simulation net list since their physical influence cannot be neglected.. Both behavioural and RLC models have to be correlated with the physical reality. The test chip "TASC" offers regular modules which can be configured in a wide range. Current and voltage sensors are integrated on silicon. Test modes and measurement sequences are controlled by a comfortable software running on a PC. In addition to onchip measurements in time domain, the conducted and radiated RF noise emission can be measured in frequency domain based on the IEC 61967 standard.. Behavioural models and RLC extractions have already been prepared. The test chip is in fabrication. By end of the year 2002 we will start the test chip analysis and continuously improve the models. This work is supported by the European joint research project MEDEA+ A509 "MESDIE" [10], which deals with analysis and optimization of electromagnetic compatibility aspects on high-speed and high-density silicon and package designs. In that frame one work package aims at the on-chip EMI modelling. One approach was presented in this paper.

O O O O O O O O O O O O O O O O O B O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Innovations in health care services: The CAALYX system Purpose: This paper describes proposed health care services innovations, provided by a system called CAALYX (Complete Ambient Assisted Living eXperiment). CAALYX aimed to provide healthcare innovation by extending the stateof-the-art in tele-healthcare, by focusing on increasing the confidence of elderly people living autonomously, by building on the knowledge base of the most common disorders and respective characteristic vital sign changes for this age group. Methods: A review of the state-of-the-art on health care services was carried out. Then, extensive research was conducted on the particular needs of the elderly in relation to home health services that, if offered to them, could improve their day life by giving them greater confidence and autonomy. To achieve this, we addressed issues associated with the gathering of clinical data and interpretation of these data, as well as possibilities of automatically triggering appropriate clinical measures. Considering this initial work we started the identification of initiatives, ongoing works and technologies that could be used for the development of the system. After that, the implementation of CAALYX was done. Findings: The innovation in CAALYX system considers three main areas of contribution: (i) The Roaming Monitoring System that is used to collect information on the well-being of the elderly users; (ii) The Home Monitoring System that is aimed at helping the elders independently living at home being implemented by a device (a personal computer or a set top box) that supports the connection of sensors and video cameras that may be used for monitoring and for interaction with the elder; (iii) The Central Care Service and Monitoring System that is implemented by a caretaker system where attention and care services are provided to elders, where actors as Caretakers, Doctors and Relatives are logically linked to elders. Innovations in each of these areas are presented here. Conclusions: The ageing European society is placing an added burden on future generations, as the 'elderly-to-workingage-people' ratio is set to steadily increase in the future. Nowadays, quality of life and fitness allows for most older persons to have an active life well into their eighties. Furthermore, many older persons prefer to live in their own house and choose their own lifestyle. The CAALYX system can have a clear impact in increasing older persons' autonomy, by ensuring that they do not need to leave their preferred environment in order to be properly monitored and taken care of. CAALYX (Complete Ambient Assisted Living Experiment) was a two-year project partially funded by the Euro-United Kingdom and University of Limerick, Ireland.. Innovation involves the creation of a product, process, service, market or organizational structure in response to a perceived need. The demographic shift, that envisages an increase in the need to provide effective health care services for older people living in Europe is well known. A total dependency ratio of three inactive persons (elderly dependants of 65 and over, plus young dependants under 15) for every four of working age is predicted by 2050 [1] . In this scenario, the provision of effective health care services is of critical importance to the social and economic welfare of every country.. Increased health care service demands required from an already overworked health care system, has led to urgent need for effective, scalable health-care services. Consequently within EU, significant investment is being provided for research projects that would create effective health-care services. The healthcare innovations provided by CAALYX were in response to this need and specifically had 4 objectives:. 1. To identify which vital signs and observation patterns are most important in determining probable critical states of an elder's health; 2. To develop a geo-located electronic device capable of measuring vital signs and detecting falls of the older person in the domestic environment and outside; 3. To provide secure monitoring of groups of individuals by a caretaker who will decide whether to communicate events identified by the system to the emergency service (112); 4. To implement a social tele-assistance service that can be easily operated by the users.. To achieve these objectives the CAALYX system comprises four subsystems:. 1. Caretaker System: application that is responsible for linking all stakeholders: elders, from their mobile and home system, caretakers, doctors, relatives, the map server and emergency services; 2. Mobile System: responsible for controlling a Body Area Network (BAN) comprised of a set of well-being sensors and for inferring possible concerning states of the elder's health by determining relevant variations in selected physiological parameters; 3. Home System: offers elderly access to their medical data recorded by the BAN and home sensors (as well as their TV) and is the primary means of interaction between the elderly and the CAALYX system; 4. Wearable Light Device (WLD): is a key component of the BAN, merging in one package an ECG instrument, an SpO2 meter, a temperature sensor and a fall and mobility sensor. The WLD has a central role in health monitoring and in the emergency detection functionality of the system. This paper presents the main achievements and innovations from the CAALYX project. The general principle underlying the project was to extend the elder's autonomy, making it easier for the elder to interact with the environment, thus improving his/her daily life. This main objective and the complementary ones were fulfilled, in summary:. • Build and deploy a prototype of the Light Wearable Device with fall and mobility sensor and a set of body vital signs (ECG, heart rate, respiratory rate, temperature and pulse oximetry);. • Build and deploy a prototype of the mobile phone/data logger to manage the elder's Body Area Network (BAN) that manages communications with the caretaker site efficiently;. • Build and deploy a prototype of the caretaker site, including tools for elder management and received data visualization;. • Build and deploy a prototype of the home environment with its architecture, sensing capabilities and elder data display and navigation;. • Test the above system components in a real environment subject to the assessment of actual end-users, thus proving its capability to impact and break the balance of the current welfare system for elderly persons. When considering innovation it must be noted that this activity is not static; new health care services continue to be investigated, developed and implemented. There are always opportunities to innovate within any system environment. The difficult task is identifying innovations which have already occurred, or are in the process of being completed.. By carrying out a comprehensive review of health care services it is possible to identify current state-of-the-art in this domain and where innovation could be directed. Therefore the CAALYX project carried out a detailed review of this domain in order to highlight several interesting opportunities for innovation.. In this section we discuss the analyzed health care services and identify how the CAALYX project could respond in an innovative way to making these services more efficient and effective.

O O O O O O B O O O O O O O O O O O O O O O B O B I I O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O


CrossOverlayDesktop: Dynamic Overlay of Desktop Graphics between Co-located Computers for Multi-User Interaction This paper presents an intuitive interaction technique for data exchange between multiple co-located devices. In the proposed system, CrossOverlayDesktop, desktop graphics of the devices are graphically overlaid with each other (i.e., alphablended). Users can exchange file data by the usual drag-anddrop manipulation through an overlaid area. The overlaid area is determined by the physical six degrees of freedom (6-DOF) correlation of the devices and thus changes according to users' direct movements of the devices. Because familiar operations such as drag-and-drop can be applied to file exchange between multiple devices, seamless, consistent, and thus intuitive multiuser collaboration is realized. Furthermore, dynamic overlay of desktop graphics allows users to intuitively establish communication, identify connected devices, and perform access control. For access control of the data, users can protect their own data by simply dragging them out of the overlaid area, because only the overlaid area becomes a public space. Several proof-of-concept experiments and evaluations were conducted. Results show the effectiveness of the proposed interaction technique. This paper proposed an intuitive interaction technique for information exchange between multiple co-located devices. In the system, desktop graphics of the devices are graphically overlaid with each other according to the 6-DOF correlation of the devices. The proposed file exchange interaction technique allows users to exchange their files through the overlaid area with the familiar drag-and-drop manipulation. A simple average filter is applied to stabilize overlaid desktop graphics against inadvertent motion. Three access control methods were also proposed. In particular, the second method allows users direct and natural manipulations where they control accessibility of their private data just by moving data file icons. The users can also protect their private data by moving their own devices in a manner such that data file icons are outside the public space.. We implemented the proposed methods and confirmed that they worked well in a prototype system. Three pilot studies were also conducted. In the first study, we confirmed that the proposed dynamic overlay of desktop graphics is intuitive even though movement of a device in a 3D physical space results in a 2D movement of the overlaid desktop graphics. In the second study, it was also confirmed that the applied simple stabilization method is effective despite a time delay in the output. In the final study, participants freely tried out all the functions of the system. They could grasp the system concepts quickly. Furthermore, they provided positive responses to all the qualitative measures we examined, such as understandability and difficulty of the proposed interaction technique. These results show that the proposed interaction technique allows seamless, consistent, and thus intuitive multi-user collaboration.. As a future work, we plan to extend the proposed technique to remote user collaboration. Pick-and-Drop (PnD) is one of the most famous data exchange interaction techniques for co-located devices [2] . Here we would like to compare the proposed system (CrossOverlayDesktop: COD) with PnD in terms of their design philosophies. PnD is a very powerful tool for copying of data between computers. It is obvious that users can transfer data from a computer to another with PnD more easily than with COD. However, sharing of data among the users is not explicitly dealt with in PnD. Therefore, PnD is not designed to provide access control of data with the users, which is one of the most important functions for data sharing. On the other hand, data sharing is explicitly considered in COD where the users are allowed to intuitively control access levels of their data. Therefore, in this paper, we showed one possibility to realize intuitive interactions for both copying and sharing of data. As described above, PnD and COD assume completely different interaction scenarios. In the proposed method, an access level for each data file can be assigned from a context menu that appears with a right click on the file icon. However, various possibilities exist for access control implementation. For example, the access level of a data file can be automatically assigned according to the category of coworkers. If a coworker is affiliated with a company, he (or she) may freely access the file (Public). On the other hand, if a coworker belongs to a different organization, the access level may become Semi-Public. Thus, one future work could be to explore other possible access control implementations suitable for the proposed file exchange interaction technique. Intuitive interaction techniques to support direct data exchange between co-located portable devices have been explored in previous studies. Rekimoto proposed an interaction technique called Pick-and-Drop, which allows a user to pick a file on one computer and drop it on another with a touch pen [2] . Although pen ID recognition is required in this technique, Stitching [3] utilizes a pen-based gesture to bind multiple displays. In contrast to these previous studies, the well-known drag-and-drop operation can still be used in file exchange between co-located devices in the interaction technique proposed in this paper. A user can copy a file from another device by simply dragging a file icon through the overlaid desktop area and dropping it on his (or her) own desktop. In this interaction, a touch pen is not always needed; mice or other input interfaces can be used as well.. Several studies have proposed intuitive methods to connect nearby computers. ConnecTables [4] are wheeled tables with mounted LCD displays that can be moved together so that the top edges of the two LCDs meet. The devices then recognize one another through a radio-frequency identification (RFID) tag and a reader mounted on each device. The displays can be joined only at the top edges (i.e., when they face each another). When joined, two ConnecTables create a shared workspace in which users can pass objects back and forth. Rekimoto et al. proposed detecting nearby devices with RFID tags or infrared beaming to establish wireless connections [5] . In these techniques, a user can connect a device by bringing it close to another without having any knowledge about the wireless communication itself. However, as indicated by Hinckley [6] , although wireless networking and location awareness techniques allow devices to communicate and provide some information about the proximity of other devices, a user would have difficulty specifying which devices to connect to when several devices are nearby. This paper explores capabilities of an interaction technique that exploits 6-DOF correlation of co-located devices rather than their proximity. This richer information allows a user not only to establish communication intuitively but also to specify desired devices from multiple nearby devices easily.. Recently, Cao et al. explored co-located multi-user interaction using multiple handheld projectors [7] . Because overlapping of projected working spaces is a natural phenomenon, they realized file exchange through the overlapped area. Although they proposed access control of projected data, all users could see all of the data. They allow users to make their own data invisible to other users; however, the users themselves cannot see this hidden data either. On the other hand, this paper proposes an interaction technique in which users can exchange data only in the overlaid area (i.e., public space). They easily protect private data by simply moving them to the non-overlaid area. In addition, visibility control of file icons and desktop graphics are implemented in the proposed technique.. Several techniques that enable 6-DOF tracking of devices have been developed, such as a vision-based marker tracking method [8] or a magnetic tracking system. However, they require an external apparatus such as a camera and obtrusive visual markers or a transmitter of a magnetic field. On the other hand, many studies have been conducted on ubiquitous high-quality spatial tracking [9] . However, these projects are still in the research phase, and thus are not commercially available. Because we focus on the interaction technique itself, development of 6-DOF tracking technology is outside the focus of this paper. Therefore, an off-the-shelf magnetic tracker is employed in this paper because it offers real-time and precise 6-DOF tracking, and thus allows us to prototype future interaction scenarios. This section describes an implementation of the proposed file exchange interaction technique and shows how it works.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Automatic Detection of White Matter Hyperintensities in Healthy Aging and Pathology Using Magnetic Resonance Imaging: A Review Abstract White matter hyperintensities (WMH) are commonly seen in the brain of healthy elderly subjects and patients with several neurological and vascular disorders. A truly reliable and fully automated method for quantitative assessment of WMH on magnetic resonance imaging (MRI) has not yet been identified. In this paper, we review and compare the large number of automated approaches proposed for segmentation of WMH in the elderly and in patients with vascular risk factors. We conclude that, in order to avoid artifacts and exclude the several sources of bias that may influence the analysis, an optimal method should comprise a careful preprocessing of the images, be based on multimodal, complementary data, take into account spatial information about the lesions and correct for false positives. All these features should not exclude computational leanness and adaptability to available data. According to the STandards for Reporting Vascular changes on nEuroimaging (STRIVE), signal abnormality of variable size in cerebral white matter (WM) that appear hyperintense on T 2 -weighted magnetic resonance imaging (MRI) without cavitation (signal different from cerebrospinal fluid) are defined as white matter hyperintensities (WMH) of presumed vascular origin ). These abnormalities have been commonly found on MRI of clinically healthy elderly people; furthermore, they have been associated with various neurological and geriatric disorders (Kim et al. 2008; Debette and Markus 2010) .. MRI is highly sensitive to the changes affecting cerebral WM. Damaged white matter usually has a prolonged T 2 relaxation time due to increased tissue water content and to degradation of the macromolecular structure of myelin. Therefore, WMH are well depicted with conventional proton density (PD) and T 2 -weighted spin echo or fast spin echo sequences, but are even more conspicuous on fluid-attenuated inversion recovery (FLAIR) images.. The presence, shape and severity of WMH might provide further insight into healthy aging and pathophysiology of various disorders. Unfortunately, since WMH patterns are heterogeneous, ranging from large confluent periventricular WMH to punctate ones in deep WM, their classification is not straightforward. It has been shown that different visual rating scales, such as the Scheltens scale or the Fazekas scale (Scheltens et al. 1998) , are seldom comparable (van Straaten et al. 2006) , are inappropriate when examining longitudinal progression of WMH (Prins et al. 2004) , show poor sensitivity to clinical group differences (Mäntylä et al. 1997) , originate high intra-subject and inter-subject variability (van den Heuvel et al. 2006 ) and significant ceiling/floor effects (van Straaten et al. 2006; Gao et al. 2011) , therefore leading to inconsistencies between WMH studies. As an alternative to a qualitative rating scale, the manual delineation and quantification of WMH areas is a more reliable way to assess WM abnormalities, but the whole process, which is cumbersome and time-consuming for the neuroradiologist, shows high intra-rater and inter-rater variability (Grimaud et al. 1996) .. For all these reasons, given the increased interest in brain research and in the context of clinical studies involving a high number of subjects, an automated approach to detection of WMH is desirable. Although several fully automated methods have been proposed for WMH segmentation, no one clearly outperformed the others. Each of the methods described in the literature has strengths and weaknesses, mainly associated with the imaging modalities used and the abnormalities detected. Several methods have been designed for lesion detection in MS patients (García-Lorenzo et al. 2013; Lladó et al. 2012) , an issue that is similar to that of WMH segmentation. In practice, techniques originally trained on MS patients perform only moderately well when applied to geriatric patients (Admiraal-Behloul et al. 2005 ) and this happens for two main reasons. First, the contrast between gray matter (GM) and WM in MR images decreases with age. Second, the boundaries of MS lesions are sharper than those of WMH.. In this paper we describe the automated methods proposed specifically for WMH segmentation, which were applied to MR images of clinically healthy elderly people and patients with cardiovascular risk factors. Our aim is to compare the advantages and disadvantages of each algorithm, and to discuss the desirable features that should characterize an optimal detection method. In this paper we reviewed the principal approaches to segmentation of WMH from MR images of healthy elderly subjects and patients with vascular pathologies. The need for a fast, accurate and fully automated approach has been underpinned by the efforts that are continuously made to compare and standardize the assessment of WMH load. Currently, qualitative assessment of WMH is achieved through the use of visual rating scales, although Mantyla and colleagues, by comparing 13 different scales, concluded that their heterogeneous properties resulted in inconsistencies in previous studies (Mäntylä et al. 1997) , pointing out that inter-rater variability, along with time consuming procedures, made a true quantitative method superior to any visual rating scale.. Despite the number of proposed methods, an optimal algorithm has not yet been identified. This is probably due to the intrinsic complexity of the problem, as well as to the small samples upon which the majority of methods has been validated, leading to some sort of overfitting. In a recent study (Klöppel et al. 2011) , supervised kNN and SVM performances (Anbeek et al. 2004; Lao et al. 2008 ) outperformed an unsupervised threshold approach, with SVM obtaining the best performance. It is worth pointing out that the unsupervised approach used in this study relied upon FLAIR signal intensity only, without taking into account spatial information or multispectral data. This review has presented several more sophisticated unsupervised methods that have been shown to perform as well as supervised one, without the need for a manually segmented training set.. In order to correctly validate a segmentation method, a ground truth is needed. The majority of studies exploited quantitative measures of WMH load, such as the DSC, while in others (Maillard et al. 2008; Wu et al. 2006; Kruggel et al. 2008; Valdés Hernández et al. 2010; Payne et al. 2002 ) the ground truth was represented by visual rating scores. For completeness, it should be noted that the DSC does not give information about over-or under-segmentation, nor provides any notion about the consistency across disease severity, as the volume correlation does. In addition, WMH in some anatomical regions are more complicated to segment than others, an issue not addressed by global measures like DSC or correlation coefficients. Hence, multiple complementary metrics are needed, and reporting the location of errors would provide a better understanding of the performance of the proposed methods.. The use of multimodal information to perform WMH segmentation has been proved to perform better than the use of a single sequence (Anbeek et al. 2004; Dyrby et al. 2008; Beare et al. 2009; Samaille et al. 2012; Valdés Hernández et al. 2010; Sheline et al. 2008) , since it allows integration of spatial information, extracted from volumetric sequences, thus facilitating elimination of CSF artifacts (Anbeek et al. 2004; Dyrby et al. 2008; Gibson et al. 2010; Samaille et al. 2012; Jeon et al. 2011) . Furthermore, multi-spectral segmentation also allows volume acquisition of lesion subtypes such as lacunar/ subcortical infarcts and Virchow-Robin spaces (for a complete overview of lesion subtypes see Wardlaw et al. 2013) . In particular, Virchow-Robin space segmentations are typically based on T 2 and T 1 CSF intense voxels (for a review see Valdés Hernández et al. 2013) .. Redundant information is provided by PD, T 2 and FLAIR images: in fact, WM abnormalities appear hyperintense in all three sequences. However, the extent of WMH may not look the same on the different sequences (Filippi et al. 1996a, b) , and it was shown that FLAIR imaging present some limitations: it is less sensitive in the posterior fossa (Gawne-Cain et al. 1998 ) and for thalamic lesions (Bastos Leite et al. 2004 ), may overestimate WMH load and has a higher inter-vendor variability (Filippi et al. 1996a (Filippi et al. , b, 1999 Bastianello et al. 1997; Rovaris et al. 1999) . Furthermore, FLAIR may present hyperintense artifacts that can lead to an increase in false positives. This alternating behavior of FLAIR, i.e., losing sensitivity or specificity according to the spatial location of the lesion, might have a biological, albeit still unclear, explanation. A recent study (Haller et al. 2013 ) hypothesized that WMH identified by FLAIR despite the relatively mild demyelination, shown by histopathological comparison, could depend on a relatively high concentration of interstitial water in periventricular/perivascular regions, due, in turn, to agerelated modifications in blood-brain-barrier and plasma properties. On the other hand, the lack of sensitivity in the thalamus and in infratentorial regions could reflect different relaxation characteristics of both normal-appearing and abnormal tissue, accompanied by age-related changes in relaxation times (Bastos Leite et al. 2004) . Finally, during the acquisition of the FLAIR sequence, even a slight spatial inhomogeneity of the inversion pulse profile could influence the sensitivity of FLAIR to WMH in different spatial locations. For all these reasons, the combination of FLAIR with a redundant source (i.e., T 2 ) will increase the certainty of the WMH delineation and reduce false positives.. The accuracy of an automated WMH segmentation procedure is also limited by the scarce knowledge about the spatial distribution of the hyperintensities. Although it is well known that certain locations are more relevant than others (i.e., periventricular watershed), the exact probability of finding a WMH in a specific region is unknown. At the moment, the automatic spatial subdivision of WMH according to their distance from the ventricles has been developed in several methods (Anbeek et al. 2004; Maillard et al. 2008; Brickman et al. 2011; Jeon et al. 2011; Payne et al. 2002; Ramirez et al. 2011) , but further exploration of WMH topographic distribution is required.. In summary, whether supervised or not, from a methodological point of view a good segmentation method should:. -Rely on a good pre-processing stage, in order to be robust to noise; -Be computationally lean; -Use multimodal, complementary data. Anyway, since multimodal images are not always acquired in clinical practice, a good method should be adaptable to the data available in the specific context; -Consider spatial information, both in terms of tissue classes and known topology of WMH; -Be reproducible and applicable to data acquired at different times from different scanners; -Rely on a good, automated method for removing false positives.. In conclusion, advances in new algorithms, as well as new developments in MRI acquisition protocols, should help neuroradiologists to improve the evaluation of WMH, both in clinical studies, investigating their relationship with normal aging and pathology, and in every-day clinical practice.

O O O B I I O O B O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O


Uncovering distinct protein-network topologies in heterogeneous cell populations Background: Cell biology research is fundamentally limited by the number of intracellular components, particularly proteins, that can be co-measured in the same cell. Therefore, cell-to-cell heterogeneity in unmeasured proteins can lead to completely different observed relations between the same measured proteins. Attempts to infer such relations in a heterogeneous cell population can yield uninformative average relations if only one underlying biochemical network is assumed. To address this, we developed a method that recursively couples an iterative unmixing process with a Bayesian analysis of each unmixed subpopulation. Results: Our approach enables to identify the number of distinct cell subpopulations, unmix their corresponding observations and resolve the network structure of each subpopulation. Using simulations of the MAPK pathway upon EGF and NGF stimulations we assess the performance of the method. We demonstrate that the presented method can identify better than clustering approaches the number of subpopulations within a mixture of observations, thus resolving correctly the statistical relations between the proteins. Conclusions: Coupling the unmixing of multiplexed observations with the inference of statistical relations between the measured parameters is essential for the success of both of these processes. Here we present a conceptual and algorithmic solution to achieve such coupling and hence to analyze data obtained from a natural mixture of cell populations. As the technologies and necessity for multiplexed measurements are rising in the systems biology era, this work addresses an important current challenge in the analysis of the derived data. Our results show that the coupling between unmixing of observations and inference of statistical relations is essential and effective. With respect to the unmixing, our method was capable to identify the number of qualitatively distinct subpopulations considerably better than multiparametric proximity based approaches (hierarchical clustering and k-means clustering). Consequently, the statistical relations in each unmixed subpopulation were also correctly recovered, while, without unmixing, uninformative average relations were inferred. As systems biology and personalized medicine are aiming toward reverseengineering and re-engineering signaling networks, they are increasingly challenged by the inter-cellular variability and the large size of the relevant biochemical system. The work presented here offers a conceptual solution as well as an applicable statistical method to address this challenge. In the era of systems biology, single-cell measurement techniques are rapidly expanding with respect to the number of cells that can be analyzed and the number of biochemical species that can be co-measured per cell. The approaches to explore these data have focused so far either on identifying different subpopulations of cells based on multiparametric proximities or on inferring the topology of statistical relations between the parameters for the population as a whole. However, the aim to reach each of these two goals in separate has fundamental problems. In one direction, ignoring the heterogeneity between cell subpopulations will lead to inferring a meaningless average topology of statistical relations of the population as a whole. In the other direction, since statistical relations are inferred from the correlation between the measured parameters, the identification of cell subpopulations based on multiparametric proximities inherently conflicts with the capability to resolve the topology of relations within each subpopulation. Furthermore, protein networks with distinct topologies can be at the same state (i.e., to have high multiparametric proximity) and protein networks with the same topology can be at different states (e.g., at different phases along an oscillatory response). Therefore, attempts to identify cell subpopulations based on multiparametric proximities may actually identify different cellular states but not different types of cells. The method presented here pioneers a comprehensive solution to these fundamental problems by performing the identification of cell subpopulations (i.e. unmixing) and the inference of statistical relations between the measured parameters in one joint analysis. Intentionally, we used snapshot data of a dynamic process (the response of cells to EGF or NGF stimulations) and, respectively, the method we developed does not rely on temporal information nor intends to give a model description of the dynamic itself. Due to that, this method can be applied on the type of single-cell multiparametric measurements currently available such as multicolor flow-cytometry [3, 16] , multiplexed mass cytometry [4, 5] and toponome imaging [6, 7] . The classification of the distinct subpopulations in cell populations sampled at different time points along an experiment can hint toward the dynamic behavior of each subpopulation. However, such traceability of subpopulations along the time points depends on how different is their relative abundance within the whole population and on the sampling rate in comparison to the timescale of the biological process. Advances in multicolor live cell imaging in combination with high-throughput automated microscopy gradually facilitate monitoring increasing numbers of parameters in individual live cells over many cells. The data obtained from such measurements will enable not only tracking the dynamics of the measured parameters in each cell subpopulation but also tracking them in individual cells. This kind of temporal information will help to further improve the identification of the distinct cell subpopulations and the inference of statistical relations between measured parameters in each subpoulation. As indicated by this work, it would be important also for the analysis of such live cell measurement data that unmixing and inference of protein-protein relations will be performed as one process.. The importance to recover single-cell phenotypes out of an uninformative average cell population behavior has been established and exemplified in many systems. Notably, in these examples there was only one measured parameter per cell, often the output of the system, and, therefore, the unmixing was straightforward. However, in order to obtain mechanistic insight into how a biochemical system works it is required to examine the protein network itself, and not only its output. For this, multiple parameters should be co-measured per cell to overcome uncorrelated cell-to-cell variability between these parameters (e.g., due to stochastic noise in expression levels as simulated in this work). We demonstrated here that in such a case unmixing cannot be achieved anymore using the proximity between the values of these parameters, while it can be successfully achieved using the highorder relations between them as captured by UNPBN. Importantly, UNPBN can be straightforwardly extended to incorporate prior knowledge about parts of the network in the individual subpopulations. In order to understand how a protein network gives rise to a cellular function it is essential to quantify the states of the involved proteins and their causal relations. However, it is actually not possible to strictly define out of the proteome the subset of all proteins which are involved in a certain cellular process since these will always have interactions with proteins not included in this subset. In spite of major advances in proteomic [1, 2] and cytometric [3] [4] [5] [6] [7] methods, quantification of the levels and UNPBN addresses the challenge of studying intracellular protein networks caused by unmeasured proteins and inter-cellular heterogeneity. a A biochemical system for which three proteins (x, y, z) are being measured in the same cell while the other proteins are unmeasured. Note that the effects of z on x are mediated by unmeasured proteins (α and β). b Depending on the level and state of these unmeasured proteins, the measured causality between x and z can differ qualitatively between cells. For example, normal and cancer cells have different activity levels of oncogenes and tumor suppressors which here lead to a negative or a positive causal effect of z on x, respectively, thereby to a controlled growth or a constitutive growth. c Left, multiparametric high-throughput single-cell measurements (e.g., flow-cytometry) of a heterogenous sample of cells (e.g., cancer and normal cells). Middle, attempts to statistically infer a single set of relations (here, causal topology) between the measured proteins fail because there are two distinct subpopulations having two distinct sets of relations. At the same time, it is also impossible to identify the two distinct subpopulations as two distinct proximity-based clusters. Right, UNPBN performs unmixing and inference of statistical relations as one process, thus finds the set of sets-of-relations (network topologies) that explains best the observations states. Such qualitative variabilities within the same cell population are generated by epigenetic commitment of cells to different fates (e.g., proliferation versus differentiation) as well as by genetic alterations (Fig. 1b) as in cancer [13, 14] . In many cases the distinct cell subpopulations are spatially intermixed and therefore are harvested together and co-measured within the same sample (e.g., by flow-cytometry). In such cases, causal relations and correlations between measured proteins can be qualitatively different in different cells if they are mediated by nonmeasured proteins which have different states at each subpopulation. Therefore, integration of observations over the cell population toward one model would be invalid and will yield uninformative average relations (Fig. 1c, middle) .. Ultimately, in order to solve this fundamental problem one should identify the number of qualitatively different subpopulations in the data, thus unmix the cells in-silico and resolve separately for each subpopulation the relations between the measured proteins. A recent work suggested to use a mixture model to unravel subpopulations in biochemical systems based on ordinary differential equations and prior knowledge about the number of subpopulations as well as about kinetic constants underlying the differences between them [15] . In this work we developed a Bayesian method for achieving this goal without such prior knowledge. To unmix observations of cells from different subpopulations, we are taking advantage of the high-dimensiona-lity of the observations, as typically obtained from cell-based high-content measurements such as flowcytometry [3, 13, 16, 17] , mass-cytometry [4, 5] and toponome imaging [6, 7] . Within each subpopulation, stochastic cell-to-cell variability in protein expression levels gives rise to high-dimensional probability distributions with the same dimensionality as the number of biochemical species (e.g., proteins) measured in each cell. To this extent, network inference approaches, like Gaussian Bayesian networks (GBN) [18] [19] [20] , to resolve a single statistical model that fits best the data, have been already developed [21] [22] [23] . In this work we use as a basis our previously described nonparametric Bayesian network analysis (NPBN, [21] ) and expand it to allow for different network structures in a mixture of different cell subpopulations (Fig. 1c, right) . In this method, termed hereafter unmixing-via-NPBN (UNPBN), a flexible number of Gaussian Bayesian networks is being fitted to the data and thereby iteratively identifying the number of distinct subpopulations, unmixing the observations and resolving the statistical model for each subpopulation. As a model system to assess and demonstrate our method we simulated the canonical MAPK Raf-Mek-Erk kinases cascade in the context of PC12 cells stimulated by either epidermal growth factor (EGF) or nerve growth factor (NGF) [24] . We show that our method identifies better than common clustering approaches the presence of two subpopulations within a mixture of EGF and NGF stimulated PC12 cells based on the levels of active Raf, Mek and Erk in each cell. This enabled to resolve correctly the statistical relations between Raf, Mek and Erk in each subpopulation.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


USING GENETIC PROGRAMMING TO SELECT THE INFORMATIVE EEG-BASED FEATURES TO DISTINGUISH SCHIZOPHRENIC PATIENTS There is growing interest to analyze electroencephalogram (EEG) signals with the objective of classifying schizophrenic patients from the control subjects. In this study, EEG signals of 15 schizophrenic patients and 19 age-matched control subjects are recorded using twenty surface electrodes. After the preprocessing phase, several features including autoregressive (AR) model coefficients, band power and fractal dimension were extracted from their recorded signals. Three classifiers including Linear Discriminant Analysis (LDA), Multi-LDA (MLDA) and Adaptive Boosting (Adaboost) were implemented to classify the EEG features of schizophrenic and normal subjects. Leave-one (participant)-out cross validation is performed in the training phase and finally in the test phase; the results of applying the LDA, MLDA and Adaboost respectively provided 78%, 81% and 82% classification accuracies between the two groups. For further improvement, Genetic Programming (GP) is employed to select more informative features and remove the redundant ones. After applying GP on the feature vectors, the results are remarkably improved so that the classification rate of the two groups with LDA, MLDA and Adaboost classifiers yielded 82%, 84% and 93% accuracies, respectively. Schizophrenia is a severe and persistent debilitating psychiatric disorder. Diagnosis of schizophrenic patients is mostly performed based on qualitative criteria which are as reliable and accurate as quantitative criteria. According to the diagnostic criteria of the American Psychiatric Association (DSM-IV) [1] , schizophrenic patients show disturbances in thoughts (or cognitions), affects, and perceptions and difficulties in relationships with others. In schizophrenia, a major enduring split exists between affect and thoughts. The hallmark symptoms of schizophrenia are experiences of hallucinations, often of the auditory type, as well as delusions.. In order to have a quantitative criterion to diagnose the schizophrenia, EEG signals can be analyzed with the objective of detecting specific features related to this disease. Numerous studies have been carried out to classify schizophrenic patients from the control subjects according to their EEG signals. It is shown in several researches [2] that EEG abnormalities and paroxysmal dysrhythmias may have a favorable impact on prognosis in schizophrenia. In addition, nonlinear methods have been employed in the analysis of EEG signals in order to classify the two groups [3] [4] [5] . The results revealed differences in underlying characteristics of EEG signal but the classification accuracy of the two groups was not reliable for psychiatrists [3] [4] [5] . Recently, Hornero et al. [6] asked the participants to press the space bar key randomly to generate some time series. This study was aimed at analyzing time series generated by 20 schizophrenic patients and 20 healthy subjects. Three different nonlinear methods, specific for a time series analysis, based on statistical tests namely were used: central tendency from the scatter plots of first differences, approximate entropy, and Lempel-Ziv complexity. The results showed that the time series generated by schizophrenic patients had a lower complexity than that of the control group. In another interesting test for random number generation [9] , participants from both groups were asked to choose a number from one to ten, and this experiment was repeated several times. Their results indicated that schizophrenic patients tended to be more repetitive while normal subjects chose the numbers with more variety.. Sabeti et al. [7] performed comprehensive research on classification of schizophrenic and control participants based on their EEG signals. They used fractal dimension, band power and auto-regressive model coefficients as features from each frame of the signal; then the informative features were selected by L-plus R-Minus method, and finally LDA and Adaboost classifiers were applied to the selected features. In another attempt, Boostani et al. [8] analyzed EEG signals of schizophrenic and normal subjects with the objective of enhancing the classification rate between the two groups. In this way, Boosted version of Direct Linear Discriminant Analysis (BDLDA) as an efficient classifier applied to the EEG features and the achieved results by BDLDA were superior to those determined by LDA and Adaboost classifiers. Moreover, BDLDA results also revealed less standard deviation when compared to others. Robustness of BDLDA results against noise was evaluated under various levels of noise amplitude and the comparison results empirically showed that BDLDA accuracy is less diminished compared to the others when the noise amplitude is increased. In another study, Sabeti et al. [37] performed comprehensive research to see which irregularity-based feature provides more discriminative information between the EEG signals of schizophrenic and normal groups at the resting condition. They came to the conclusion that among the complexity and entropy measures, spectral-entropy provided the best results, but for further improvement, they considered all features together and obtained 90% accuracy between the two groups. From a different point of view, Pressman et al. [10] showed lack of synchronization alternation ability in the schizophrenic patients during a working memory task compared to the controls. This paper is aimed at improving the classification rate of the schizophrenic and control subjects. Our contribution is to enhance the classification rate between the two groups using genetic programming to select informative features where fitness function is evaluated by employing three powerful classifiers including LDA [33] , Multi-LDA (MLDA) [34] and Adaboost [32] . The rest of the paper is structured as follows: In Section 2, a data acquisition process is presented. Feature extraction techniques are illustrated in Section 3. Feature selection methods are presented in Section 4, classifiers are reviewed in Section 5, and a computational procedure is outlined in Section 6. Results and discussions are given in Section 7, and finally the paper is closed with concluding remarks. This study shows that EEG signals can be an efficient tool for distinguishing schizophrenic and control participants. EEG signals are preprocessed, then the descriptive features represent the captured information inside the signals in different domains to highlight their differences. Three well-known classifiers, LDA, MLDA and Adaboost, were trained using leave-one (patient)-out procedure for training in order to remove correlation between the train and test sets. Satisfactory results are fairly obtained by distinguishing the patterns. For further improvement, genetic programming is used for removing redundant and noisy features; the results achieved by Adaboost classifier are considerably enhanced due to Adaboost capability to fit a non-linear boundary among the filtered features while preserving generalization between the train and test results. Experimental results illustrate the effectiveness of the proposed approach that can be a complementary tool to help psychiatrists for more accurate diagnosing schizophrenic patients.

O O O O O O O O O O O B O O O O O O O O B O O O O O O B O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O B O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O B B O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B B O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O B B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O