Agent-Based System Design for B2B Electronic Commerce ABSTRACT: Agent-based systems are increasingly used to support business-to-business (B2B) electronic commerce and other Internet-based transactions. The design complexit y resulting from the multiple interconnected systems in these domains has to be managed in order to reduce costs and time to market. This paper introduces the Role-Algebraic Multi-Agent System Design (RAMASD) approach. RAMASD utilizes role models as reusable system-building blocks and a role algebra to capture the basic relations of roles. A t wo-sorted algebra is used to define the role algebra's semantics. RAMASD reduces the complexity of designing agent-based B2B e-commerce systems by enabling designers to work at a high level of abstraction and by automatically allocating roles to agents according to applicable role models and design constraints.A case study concerning a B2B electronic market for the automotive industry demonstrates the applicability of RAMASD. The advantages and disadvantages of the proposed approach are discussed, and comparisons with relevant work are made.KEY WORDS AND PHRASES: Agent-oriented software engineering, agent organizations, agent-based e-business.Agent technology has come to the forefront in the software industry because of the advantages that agents have in complex, distributed environments. It is increasingly used in Internet-based transactions, including electronic commerce and cross-organizational workflow management [7, 24] .Designing an agent-based B2B electronic commerce system is a complex process in which it is necessary to define both the structural relationships between agents and individual agent behavior. Many authors view agent-based systems as organized societies of individual computational entities and thus see designing an agent-based system as designing an agent organization (e.g., [14, 38, 43] ).1 Since the criteria affecting agent organization design decisions are numerous and highly dependent on factors that may change dynamically, there is no standard best organization for all situations [34, 38] . Furthermore, to reduce cost and time to market, agent system design decisions should be reusable while both functional and nonfunctional aspects are considered [6] .Existing approaches to agent-based system design fail to address design complexity in that they require the designer to make most of the decisions without the assistance of a software tool [29, 39, 43] . This can be a serious drawback when designing large and complex agent-based B2B e-commerce systems for the real world. Therefore, many authors argue that collective behavior as well as social and organizational abstractions should be considered as first-class design constructs, enabling the agent system designer to reason at a high level of abstraction (e.g., [22, 28] ).This paper proposes an approach that provides semiautomatic support for the high-level design of agent-based B2B electronic commerce systems. It uses role models as basic building blocks, and formalizes the rules and constraints Early research prototypes of agent-based systems were built in an ad hoc manner. The need to engineer agent systems solving real-world problems, however, has given rise to a number of systematic methodologies for agentoriented analysis and design, such as MESSAGE, Gaia, and SODA [9, 28, 43] . All of these methodologies involve analytic and design submodels that emphasize particular aspects of analysis and design. Organizational settings are either specified explicitly in an organizational model (e.g., [9] ) or are defined implicitly from the functionality that agents are assigned (e.g., [22] ). Nonfunctional requirements are not explicitly considered in existing agent-based system engineering methodologies, the only exception being TROPOS, where specific steps in the agent system engineering methodology take nonfunctional requirements into account [8] .The existing approaches to agent-based system design could be further improved by:Introducing a practical way to construct agent system design models from analysis models.The existing approaches to agent-based system engineering typically involve a considerable number of analysis and design models. There are five analysis models and three design models in MESSAGE [9] , three analysis models and four design models in SODA [34] , and two analysis models and three design models in Gaia [46] . The main drawback of these approaches is that after a certain point the design decisions are left solely to the creativity and intuition of the designer. The steps involved in transforming analysis models to design models are not specified in a way that would enable an adequate degree of automation by a software tool.Explicitly considering nonfunctional requirements on design time.The existing agent-based system engineering approaches do not explicitly model nonfunctional aspects on design time, and when they consider them, they do so by adjusting the agent behavior on runtime. As a result, nonfunctional design decisions cannot be reused and runtime reorganization may result in significant resource consumption and system instability. The aim should be to achieve the best possible agent organization at design time. To achieve this, it is necessary to explicitly model and consider nonfunctional aspects before actually deploying a multi-agent system. This idea follows the spirit of INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE 61 similar works that model and study the behavior of a multi-agent system before actual system deployment [30, 34] .Reusing functional, nonfunctional, and organizational settings.The reuse of functional knowledge has long been an issue in software engineering, and it is mandatory in order to reduce cost and time to market of Internet-based B2B e-commerce systems [6] . The view regarding reuse of organizational settings was inspired by the concepts introduced by Zambonelli, Jennings, and Wooldridge [46]. Their work can be extended by classifying known organizational patterns and providing rigorous means for selecting them in a particular design context. If organizational patterns are to be of practical use in implementing largescale, real-world agent applications, a way of easily integrating organizational with functional and nonfunctional design decisions is needed.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Generation of synthetic benchmark electrical load profiles using publicly available load and weather data Electrical load profiles of a particular region are usually required in order to study the performance of renewable energy technologies and the impact of different operational strategies on the power grid. Load profiles are generally constructed based on measurements and load research surveys which are capital and labour-intensive.In the absence of true load profiles, synthetically generated load profiles can be a viable alternative to be used as benchmarks for research or renewable energy investment planning. In this paper, the feasibility of using publicly available load and weather data to generate synthetic load profiles is investigated. An Artificial Neural Network (ANN) based method is proposed to synthesize load profiles for a target region using its Typical Meteorological Year 2 (TMY2) weather data as the input. To achieve this, the proposed ANN models are first trained using TMY2 weather data and load profile data of neighbouring regions as the input and targeted output. The limited number of data points in the load profile dataset and the consequent averaging of TMY2 weather data to match its period resulted in limited data availability for training. This challenge was tackled by incorporating generalization using Bayesian regularization into training. The other major challenge was facilitating ANN extrapolation and this was accomplished by the incorporation of domain knowledge into the input weather data for training. The performance of the proposed technique has been evaluated by simulation studies and tested on three real datasets. Results indicate that the generated synthetic load profiles closely resemble the real ones and therefore can be used as benchmarks. Power utilities across the world are proceeding with deregulation in order to facilitate system expansion by attracting investment from the private sector. As a consequence, in several countries (e.g. UK, India) the electricity sector has been unbundled into generation, transmission, distribution and supply companies. An electric load profile gives the dynamic variability of the energy demand with respect to time for the consumer category considered. The use of load profiles simplifies the arrangement between distribution companies and electricity suppliers, as the total energy record available in conventional energy meters can be distributed through different interval periods [1] . Having load profile information will lower the uncertainty in network management decision making for distribution companies [2] . It will also aid them in pricing competitive electricity options and devising risk hedging strategies for electricity derivatives.. There has been an increase in the deployment of renewable energy systems in recent years for many reasons such as the national and the global carbon emissions targets for combating climate change, growing electricity demand, the need for national energy security and providing energy access to populations without electricity. It would be prudent for the decision makers of developing nations to devise efficient future-minded policies and carefully plan their investment in renewables within their financial constraints. Information passed on to decision makers without the proper inclusion of hourly load dynamics can lead to cost-ineffective suboptimal energy systems which might also prevent countries reaching their set target of renewable energy use [3] . Load profiles are also essential for system operators to plan conventional (dispatchable) generation, to account for the energy import from renewable energy systems and for paying customers.. The standard method of constructing an hourly load profile is by recording the energy consumption, at feeder or substation level, at regular intervals (usually one hour) and dividing this by the number of customers on that feeder to produce an average demand (After Diversity Demand). Hourly load profile measurements at state-wise (sub regional) customer category levels are undertaken in developed nations, but are highly uncommon among developing nations like India owing to the capital requirement. Therefore, load profiling is accomplished by means of load research surveys whenever they are required. Surveying is a labour intensive process and it can take a long time to complete surveying for all states of a country. In many cases the results of these kinds of measurements or surveys when undertaken are archived in internal reports that are not easily accessible [4] .. In the absence of true load profiles based on measurement or load research surveys, synthetically generated load profiles, which can be used as benchmarks, can be a viable alternative for research applications or renewable energy investment planning. In the past, predictive modelling of energy demand has been applied in a number of instances. Several studies have used ANN models for predicting energy consumption of individual and complex buildings [5, 6] . Aydinalp et al describe the modelling of the energy consumption in the Canadian residential sector using ANN models with weather and socio economic factors as inputs [5] . Magnano and Boland describe the synthetic generation of sequences of electricity demand for application in South Australia [7] . However, both these works have based their approaches on large datasets. The large data requirement makes them less easy to implement when the availability of data is limited, which is a typical characteristic of developing nations.. The load demand of a country or region depends on two major factors, the complexity of the economy and the weather of the area [8] . Studies have revealed that a large proportion of the variability in electricity demand is dependent on weather variables such as air temperature, humidity, wind speed, cloud cover and luminosity [9, 10] . The sensitivity of electricity demand in the commercial and residential sectors to meteorological variables is higher than in the industrial sector [11] . Both the weather and illumination components are very dependent on the hour of the day so they will have an impact on the daily load profile.. In the past, several artificial intelligence methods like ANNs, genetic algorithms, fuzzy logic, fuzzy expert systems, self-organising maps, wavelet transform, principal component analysis, grey system theory and support vector regression have been developed for forecasting electricity demand [8, 9, [12] [13] [14] Most of these methods are based on large datasets of historical time series of load data. In some cases, in addition to historical load data, both weather variables (temperature, relative humidity, wind velocity and cloudiness) and socio-economic factors have been used as inputs to the forecasting model [11] . Only recently, the synthetic generation of energy production or consumption using public data is starting to be recognized as a viable alternative to measurement and recording. Vladislavleva et al used genetic programming to demonstrate the feasibility of wind energy prediction by using publicly available weather and energy data for a wind farm in Australia [15] .. In this paper, the feasibility of using publicly available datasets of load profile and weather data to synthetically generate load profiles using ANNs is investigated. A method is proposed to synthetically generate load profiles for the states of a developing nation like India, where measured or surveyed load profiles are not available, using only publicly available weather data of that state and the surveyed or measured load profiles of other neighbouring states. This is based on the assumption that the socio-economic tendencies of all states in that nation are similar. Bilgili et al employed a similar method for predicting a target station's wind speed using reference stations' data [16] .. A general sketch of the proposed model is given in Figure 1 . The developed ANN model is able to synthesize load profiles for a region using only TMY2 weather data. For achieving this, the model is first trained using load profile data of a region neighbouring the target region, with the targeted output and TMY2 weather data of this region as the input. The limited number of data points in the load profile dataset and the consequent averaging of TMY2 weather data to match its period resulted in limited data availability for training. This challenge was tackled by incorporating generalization using Bayesian regularization into training. The other major challenge was facilitating ANN extrapolation and was accomplished by the incorporation of domain knowledge into input weather data for training. It is to be noted that the validity of the benchmark load profiles generated synthetically are conditional and they are meant only to be used as a substitute for load profiling when the necessary load profile data is not available. This synthetic load profile generation also shares a common base with load forecasting in terms of the use of weather and economic factors; however, it does not make use of any mathematical load models or historical time series of load or weather datasets. There are three differences between the proposed method and forecasting: (1) typical meteorological year weather data is used as the input, (2) the load profile generated is for a region different from the training region (where measured data are available), (3) the load profile generated is in the same time frame as the training load profile rather than for the future.. The accuracy of the proposed technique has been evaluated by simulation case studies carried out on three real datasets. The first two case studies provide analysis and validation. In the third case study a modified version of the method is developed for application to a dataset based on research surveys. Results indicate that the synthetic load profiles generated closely resemble real ones and therefore can be used as benchmarks. A comparison of load profiles synthesised by ANN and regression demonstrate the superiority of ANN over regression for synthetic generation of load profiles. The remainder of the paper is organized as follows: the datasets used in this study are described in section 2. In section 3, the formulation of the proposed synthetic load profile generation model is described. The feasibility of the method is investigated and validated by case studies in section 4. Finally, conclusions from this work are presented in section 5. A simple ANN based technique for generating synthetic load profiles using limited publicly available load profiles and weather data has been developed. The synthetically generated load profiles have the same diurnal characteristics as actual load profiles and also show the same correlation to weather and other parameters that affect energy consumption. Both a direct validation through comparison of synthetically generated load profiles to actual load profiles, as well as an indirect validation through synthetic load predictor trainability, show that the proposed method of synthetic generation is simple, reproducible and robust in learning from limited real data. The limitations of practically available load profile data and how the proposed method can be adapted to address these limitations were identified in case study 3. The most important feature of the proposed method is functionality, as it allows realistic benchmark load profiles to be generated for locations which would otherwise rely on other inaccurate methods, such as a constant load assumption. The only limitation is the availability of weather and load data for certain locations, which is required for model training.. This research work is continuing to investigate the potential of extending this method to create daily regional synthetic load profiles based on daily regional weather data. The incorporation of socio-economic factors to the synthetic load profile generation technique is also an area that merit further investigation. Probability distributions of socio-economic factors which affect the load, such as the regional GDP figures or schedule of working hours, could be used to scale the load model.

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


Identification of Imaging Biomarkers in Schizophrenia: A Coefficient-constrained Independent Component Analysis of the Mind Multi-site Schizophrenia Study Abstract A number of recent studies have combined multiple experimental paradigms and modalities to find relevant biological markers for schizophrenia. In this study, we extracted fMRI features maps from the analysis of three experimental paradigms (auditory oddball, Sternberg item recognition, sensorimotor) for a large number (n=154) of patients with schizophrenia and matched healthy controls. We used the general linear model (GLM) and independent component analysis (ICA) to extract feature maps (i.e. ICA component maps and GLM contrast maps), which were then subjected to a coefficient-constrained independent component analysis (CCICA) to identify potential neurobiological markers. A total of 29 different feature maps were extracted for each subject. Our results show a number of optimal feature combinations that reflect a set of brain regions that significantly discriminate between patients and controls in the spatial heterogeneity and amplitude of their feature signals. Spatial heterogeneity was seen in regions such as the superior/middle temporal and frontal gyri, bilateral parietal lobules, and regions of the thalamus. Most strikingly, an ICA feature representing a bilateral frontal pole network was consistently seen in the ten highest feature results when ranked on differences found in the amplitude of their feature signals. The implication of this frontal pole network and the spatial variability which spans regions comprising of bilateral frontal/temporal lobes and parietal lobules suggests that they might play a significant role in the pathophysiology of schizophrenia. One of the major goals in schizophrenia research involves finding a meaningful biological marker that can aid in its diagnosis and further clarify its pathophysiology. We define a biological marker as a consistent quantitative neuroimaging marker that can accurately characterize schizophrenia. In this regard, functional magnetic resonance imaging (fMRI) has been widely used to probe the underlying brain activity that might distinguish schizophrenia patients from a matched control group. Various paradigms have been implemented to specifically test particular sensory and cognitive processes in the brain. These include, but are not limited to the processing of infrequent auditory stimuli (Kiehl et al. 2005b) , working memory (Goldman-Rakic 1994) , sensorimotor procceses (Schroder et al. 1999) , and resting state paradigms (Broyd et al. 2008 ). These studies have produced a rich collection of potential biomarkers, but few studies have delved into the potential "cross-information" between them. In other words, commonalities which exist across these biological markers might provide a more stable cognitive marker for schizophrenia that might not be easily realized by analyzing results from a single task. Our study attempts the exploitation of this joint-information by utilizing a recently developed method known as coefficient-constrained independent component analysis (CCICA) (Sui et al. 2009a ) on a large number of features generated from two popular analysis approaches: the general linear model (GLM) (Friston 1994) and independent component analysis (ICA) (Calhoun et al. 2001) The GLM relies on the convolution of a canonical hemodynamic response with the experimental paradigm to generate a predicted brain response. This predicted response is then regressed against the preprocessed fMRI dataset at every voxel within the brain, resulting in a statistical map that depicts a set of brain regions which are highly correlated with the experimental task. These statistical maps are often referred to as contrast images and the comparison of these maps in schizophrenia patients and matched controls have been the basis for a large number of fMRI studies in schizophrenia (Glahn et al. 2005; Hill et al. 2004; Kindermann et al. 1997 ). ICA has a significantly different approach and instead makes the assumption that the raw fMRI timecourse is a linear mixture of spatially independent components. The spatial component maps that are created are often interpreted as functional connectivity maps and have also been labeled in this context as temporally coherent brain networks (Calhoun 2007a, b) . These networks have been shown to be affected in schizophrenia (Garrity et al. 2007; Kim et al. 2009a) , suggesting that functional connectivity between brain regions can represent another characteristic source for its pathology. Our analysis takes both the contrast maps from SPM and the component maps from ICA as a set of potential neuroimaging biomarkers. Our goal with CCICA then is to find and exploit the joint information in these features that best separates schizophrenia patients from our matched healthy controls.. Many neuroimaging studies have used a common set of tasks to probe potential disease-related biomarkers in patients with schizophrenia and our analysis took advantage of this consistency. In light of this trend, the National Institute of Mental Health's strategic plan for psychopathology has promoted the use of basic tasks in their attempt to diagnose mental illness (http://www.nimh.nih.gov/re search-funding/ rdoc.shtml). One commonly used task has been the auditory oddball discrimination (AOD) task. For schizophrenia patients, a noticeable and consistent reduction in the P300 event-related brain potential relative to matched controls during the detection of a distinct auditory stimuli have suggested the importance of this task in potentially elucidating a meaningful biomarker (Ford 1999 ). Furthermore, this task is often used as a robust measure of brain activity (Kiehl et al. 2005a ) and significant differences in schizophrenia have been found using both a GLM and ICA approach (Kiehl et al. 2005b; Kim et al. 2009b) . Another common task found in studies related to schizophrenia is the maintenance and manipulation of working memory (WM) processes. The consistency of WM deficits appearing in patients with schizophrenia (Lee and Park 2005) including their firstdegree relatives (Meda et al. 2008) , has led to the use of the Sternberg item recognition paradigm (SIRP) (Sternberg 1966) to probe these cognitive deficits (Manoach 2003) . A large multi-site analysis of this task for patients with schizophrenia has shown deficits in multiple networks that included regions of the default-mode network along with areas associated with WM such as the dorsolateral prefrontal cortex (DLFPC) (Kim et al. 2009a ). Finally, many previous studies have focused on sensorimotor gating in schizophrenia to study disease-related cognitive deficits during the execution of simple hand movements along with an auditory stimulus. The sensorimotor task designed for this purpose is a simple block design task has been known to robustly engage the motor and auditory regions of the brain (Machado et al. 2007 ). This task was also successful in finding relevant biomarkers that aided in the classification of patients with schizophrenia (Sui et al. 2009a ). Our study extracted features from all three tasks using ICA and GLM from a large number of patients with schizophrenia and matched controls (n=154) as part of the Mind clinical imaging consortium (MCIC).. The goal of this study was to implement and extend a recently developed algorithm (CCICA) by applying it to a larger and feature rich MCIC fMRI dataset to determine group-discriminative brain regions associated with schizophrenia. As mentioned briefly before, what makes CCICA uniquely suited to finding potential biomarkers is due to its incorporation of group information as a prior, making it sensitive to potential group differences that might exist across these features. Thus, the normal ICA estimation process of maximizing independence also attempts to maximize a group difference criterion, which in our case is a modified t-statistic. More generally, CCICA is attempting to find areas in the brain that tend to covary uniquely, but discriminately between patients and controls. The discriminative patterns that it finds are referred to as independent components and depict a set of brain regions that reflect a group-discriminative biological marker. Based off of previous fMRI studies that utilized CCICA (Sui et al. 2009a) as well as other studies that have attempted to classify schizophrenia patients from controls (Calhoun et al. 2007a, b) , our hypothesis was that CCICA will locate and isolate regions within the prefrontal cortex, thalamus, and bilateral temporal lobes as major group-discriminative regions. Furthermore, we believed that there would be a strong consistency in the type of features that are highly ranked in their group-discriminative scores for both Jdivergence and p-value metrics. Using a recently developed novel algorithm for data fusion, we were able to conglomerate a large number of fMRI features to determine potential biological markers in schizophrenia. However, one of the benefits of CCICA is its ability to use other types of features (such as fractional anisotropy maps or grey/white matter maps) which would allow for multimodal studies of schizophrenia among other disorders. Furthermore, CCICA was able to take advantage of the clinical diagnosis of each participant and use that to guide the extraction of independent components to maximize between-group differences in its resulting components. The p-value metric we used allowed us to assess the degree to which the feature amplitude signals differed between patients and controls while the J-divergence metric allowed us to determine differences in the spatial distribution of that same signal. We found a striking consistency in our top p-value components that stemmed from the ICA SM frontal pole feature set. These components implicated the same bilateral frontal pole regions found from the original feature map and points to these regions as significant markers for pathology. The J-divergence results were more heterogeneous, but marked a set of regions that have been well implicated in previous fMRI studies of schizophrenia. Finally, the results are also consistent with a large number of structural MRI studies that show grey matter volume differences in these regions for schizophrenia patients. The analysis suggests that a significant biological marker for schizophrenia might be related to the functional modulation of a bilateral frontal pole network along with regions in the bilateral temporal lobes and parietal lobules. In order to elucidate potential biomarkers for schizophrenia, our study analyzed a large number of subjects and extracted a total of 29 fMRI features for each subject, which were further analyzed using CCICA. The resulting CCICA components were filtered using an automated artifact removal tool and then ranked based off of their p-values and j-divergences. By taking a data-fusion approach, we hoped to exploit the potential "cross" information that existed between various tasks to determine which biological markers would best discriminate between schizophrenia patients and controls. From the results of our analysis, our most striking finding was seen in the component features associated with the most significant p-values. After filtering, the top ten CCICA components all contained a frontal pole ICA component network from the SM task. The Jdivergence results reflected a significant, but different aspect of potential biomarkers in schizophrenia. The top 2 components in this regard implicated a varied, but well defined set of brain regions that have often been linked to schizophrenia. This included areas such as the superior/ middle temporal gyrus, superior/inferior parietal lobules, and superior/middle frontal gyrus. We found that our results were consistent with our hypothesis of prefrontal dysfunction, but also noticed a significant heterogeneity in the spatial variability of patients with schizophrenia as assessed using our J-divergence scores.. One of the major strengths of this analysis was the incorporation of a various number of functional feature maps and their joint combinations to determine group-discriminative biological markers. Thus, from a total of 29 possible features, it was encouraging to see a single feature type dominate our p-value rankings, specifically the SM frontal pole ICA network. The consistency of this frontal pole network suggests a number of implications for schizophrenia. First, prefrontal dysfunction in schizophrenia has been welldelineated as a consistent area of pathology for a number of years, supported by many fMRI and structural MRI studies using a variety of approaches (Goldman-Rakic and Selemon 1997; Manoach 2003; Meisenzahl et al. 2008) . Secondly, the p-value metric determines the extent to which the amplitude of a feature component signal differs between patients and controls. This contrasts with the j-divergence metric which assesses the degree to which the spatial distribution of the feature component differs between the two groups. From this perspective, our CCICA analysis suggests that prefrontal dysfunction in schizophrenia might represent a strong potential biological marker which might be related more to the modulation of its activity rather than any significant differences in its spatial specificity. The t-statistics associated with these components were negative, suggesting greater activation in schizophrenia patients than healthy controls. Evidence in fMRI data for hyper/hypo frontality in schizophrenia has been mixed and the issue is currently unresolved (Minzenberg et al. 2009 ). However multiple studies have shown a hyperfrontal response in schizophrenia, especially during working memory tasks. Experimental paradigms that test WM processes inside the fMRI scanner have shown hyperfrontality areas such as the posterior parietal regions (Quintana et al. 2003) , ventrolateral prefrontal cortex in first episode patients (Schneider et al. 2007 ) and dorsal lateral prefrontal cortex (Manoach 2003) , Finally, an ICA component nearly identical to the frontal pole component featured in our analysis was significantly different in schizophrenia patients during an auditory oddball task (Kim et al. 2009b) . In that study, a large-scale ICA analysis was performed and controls showed a greater positive modulation of the component's ICA timecourse relative to patients. This particular component was considered to be the most statistically significant of the eight components that passed the study's p-value threshold.. The CCICA components that reflected the greatest Jdivergence scores depict a set of well known regions that 4, 6, 43, 3, 44, 13, 9 25.6/22.2 11.2(−39,−23,62 have been previously implicated in schizophrenia. The ICA DMN2 component from the sensorimotor task and the AOD frontal pole feature component engaged significant areas of the superior and middle temporal gyrus. These regions have been closely linked to the successful processing of auditory stimuli and their dysfunction has been related to the auditory hallucinations that often plague patients with schizophrenia (Barta et al. 1990; Pearlson 1997) . Our results are also consistent with a previous CCICA study that used an identical analysis approach and found the highest ranked J-divergence component to prominently contain bilateral temporal lobes (Sui et al. 2009a) . Furthemore, regions of the medial and superior frontal gyrus were shown to be significantly affected in the top 2 components associated with our high J-divergence measures. The medial and superior frontal gyrus play a prominent role in the characterization of this disorder; its impairment often connected to a dysfunction in executive processes (Davidson and Heinrichs 2003; Minzenberg et al. 2009 ). Looking more closely at the group specific spatial maps for the top J-divergence component (Fig. 5) , it can be seen that patients and controls recruit a significantly different set of brain regions associated with their respective feature maps, suggesting that spatial heterogeneity might be a significant aspect of schizophrenia. The J-divergence results suggest a correlation with the reported grey matter volumetric differences found in structural MRI studies of schizophrenia. Affected regions such as the superior/middle temporal gyrus, the superior/ inferior parietal lobule, and the frontal gyrus from our top 2 components represent a widespread, but consistent set of regions which have shown grey matter volume abnormalities in schizophrenia. Considering that the J-divergence score is associated with the spatial distribution of these feature maps, there might be considerable overlap between these regions and morphometric brain volume studies in schizophrenia. Specifically, the superior and middle temporal gyri along with the thalamus have been one of the most common regions for grey matter differences in metaanalyses of structural MRI studies for schizophrenia. Structurally based studies that have used alternative methods of analysis such as ICA have also found Fig. 5 A closer look at the J-divergence metric for the highest scoring CCICA component. The regions reflect z-scores greater than 2.5 for patients, controls, and both. The region in green shows where both participant groups had z-scores greater than 2.5 and the degree of spatial variability can be seen in the number of regions that are not shared between both groups significant reductions in the thalamus and inferior parietal lobes for patients with schizophrenia (Xu et al. 2009 ). This finding of a more variable localization of correlated activation within the patient group could reflect greater variability in the gross morphology (Park et al. 2004 ) and/or functional organization of the involved regions on the basis of neurodevelopmental abnormalities. It might also reflect the use of more variable strategies to accomplish the tasks. This finding is consistent with prior work that has shown greater heterogeneity in the location of peak fMRI activation (Manoach et al. 2000) and more variable patterns of functional connectivity during working memory tasks in schizophrenia patients compared to controls (Meyer-Lindenberg et al. 2001) . Though it is possible that grey matter volume reductions might not necessarily affect the functional brain activity associated with those regions, a number of studies (Antonova et al. 2005; Calhoun et al. 2006 ) have reported that a significant link might exist and further studies that fuse MRI and fMRI data together might help better elucidate this relationship.. Our results also seem to not only favor specific brain regions, but also a particular experimental paradigm. The SM task was represented in all of our top ten feature combinations when ranked by p-values and fifty percent when ranked on J-divergence scores. The reasons for the SM task to be the most effective paradigm for extracting these group-discriminative components is not entirely clear, but might have something to do with the robust power of this experimental design. Being a simple block-design task, the contrast maps generated in SPM often produce significant effect sizes and this increased power might help better discriminate between schizophrenia patients and controls. Also, the predominance of ICA features in our results show the benefit of including ICA component maps in data-fusion studies. Though there is some bias in the proportion of SPM features versus ICA features, functional connectivity maps generated by ICA represent an intrinsically different category of biological markers. More specifically, the mathematical constraints of the GLM model prevent its feature maps to make any inferences regarding functional connectivity. Furthermore, a number of fMRI studies seem to suggest that schizophrenia might be better characterized by the aberrant connectivity of its brain regions rather than any localized deficit in one area of the brain (Friston 1999 ). This is not to say that the GLM is ineffective in elucidating the pathophysiology of schizophrenia, but that a complimentary approach using ICA might aid in the search for a meaningful biological marker.. There are some limitations to our study that we would like to address. Our first limitation concerns task performance differences, especially in the SIRP task where patients statistically performed worse than controls. Even though this task was relatively simple in comparison to other N-back WM tasks, the well known difficulty in finding schizophrenia patients to perform this task at the same level as controls required us to make some sacrifices in order to increase our sample size. However, it is important to note that the mean average accuracy for both groups was greater than 95% and though there were statistical differences, both groups performed the task, on average, successfully. IQ was also not matched between groups, though patients with schizophrenia show significant differences in IQ versus matched controls. Some studies have attempted to account for this difference in IQ by using it as a covariate in their analyses, but reduced IQ has been known to represent an early manifestation of schizophrenia (Groom et al. 2008) and including this as a covariate might remove some of the interesting variability associated with the disease (Schwarz 1971) . Furthermore, site differences were not taken into account when applying our particular analyses and scanner variability has been an issue concerning multi-site studies (Friedman and Glover 2006; Friedman et al. 2007) , though these studies were performed with often small sample sizes (N=5). We hoped that the inclusion of multiple runs per experimental paradigm and our large sample size would mediate some of the variability issues seen in those previous multi-site studies. Finally, the medication history of patients with schizophrenia was not fully accounted for during this study. Patients were evaluated with schizophrenia and stabilized with medication by a licensed physician, but a full detailed history would allow us to account for some possible confounds associated with these medications.

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Adaptive thresholds for neural networks with synaptic noise The inclusion of a macroscopic adaptive threshold is studied for the retrieval dynamics of both layered feedforward and fully connected neural network models with synaptic noise. These two types of architectures require a different method to be solved numerically. In both cases it is shown that, if the threshold is chosen appropriately as a function of the cross-talk noise and of the activity of the stored patterns, adapting itself automatically in the course of the recall process, an autonomous functioning of the network is guaranteed. This self-control mechanism considerably improves the quality of retrieval, in particular the storage capacity, the basins of attraction and the mutual information content. In general pattern recognition problems, information is mostly encoded by a small fraction of bits and also in neurophysiological studies the activity level of real neurons is found to be low, such that any reasonable network model has to allow variable activity of the neurons. The limit of low activity, i.e., sparse coding is then especially interesting. Indeed, sparsely coded models have a very large storage capacity behaving as 1/(a ln a) for small a, where a is the activity (see, e.g., [1, 2, 3, 4] and references therein). However, for low activity the basins of attraction might become very small and the information content in a single pattern is reduced [4] . Therefore, the necessity for a control of the activity of the neurons has been emphasized such that the latter stays the same as the activity of the stored patterns during the recall process. This has led to several discussions imposing external constraints on the dynamics of the network. However, the enforcement of such a constraint at every time step destroys part of the autonomous functioning of the network, i.e., a functioning that has to be independent precisely from such external constraints or control mechanisms. To solve this problem, quite recently a self-control mechanism has been introduced in the dynamics of networks for so-called diluted architectures [5] . This self-control mechanism introduces a time-dependent threshold in the transfer function [5, 6] . It is determined as a function of both the cross-talk noise and the activity of the stored patterns in the network, and adapts itself in the course of the recall process. It furthermore allows to reach optimal retrieval performance both in the absence and in the presence of synaptic noise [5, 6, 7, 8] . These diluted architectures contain no common ancestors nodes, in contrast with feedforward architectures. It has then been shown that a similar mechanism can be introduced succesfully for layered feedforward architectures but, without synaptic noise [9] . Also for fully connected neural networks, the idea of self-control has been partially exploited for three-state neurons [10] . However, due to the feedback correlations present in such an architecture, the dynamics had to be solved approximately and again, without synaptic noise.. The purpose of the present work is twofold: to generalise this self-control mechanism for layered architectures when synaptic noise is allowed, and to extend the idea of self-control in fully connected networks with exact dynamics and synaptic noise. In both cases it can be shown that it leads to a substantial improvement of the quality of retrieval, in particular the storage capacity, the basins of attraction and the mutual information content.. The rest of the paper is organized as follows. In Sections 2 and 3 the layered network is treated. The precise formulation of the layered model is given in Section 2 and the adaptive threshold dynamics is studied in Section 3. In Sections 4 and 5 the fully connected network is studied. The model setup and its exact threshold dynamics is described in Section 4, the numerical treatment and results are presented in Section 5. Finally, Section 6 contains the conclusions. In this work we have studied the inclusion of an adaptive threshold in sparsely coded layered and fully connected neural networks with synaptic noise. We have presented an analytic form for a self-control threshold, allowing an autonomous functioning of these networks, and compared it, for the layered architecture, with an optimal threshold obtained by maximizing the mutual information which has to be calculated externally each time one of the network parameters (activity, loading, temperature) is changed. The consequences of this selfcontrol mechanism on the quality of the recall process have been studied.. We find that the basins of attraction of the retrieval solutions as well as the storage capacity are enlarged. For some activities the self-control threshold even sets the border between retrieval and non-retrieval. This confirms the considerable improvement of the quality of recall by self-control, also for layered and fully connected network models with synaptic noise.. This allows us to conjecture that self-control might be relevant even for dynamical systems in general, when trying to improve, e.g., basins of attraction.

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AppTCP: The design and evaluation of application-based TCP for e-VLBI in fast long distance networks Electric Very Long Baseline Interferometry (e-VLBI) is a typical astronomical interferometry used in radio astronomy. It allows observations of an object that are made simultaneously by many radio telescopes to be combined, emulating a telescope with the size equal to the maximum separation between the telescopes. The main requirements of transporting e-VLBI data are the high and constant transmission rate. However, the traditional TCP and its variants cannot meet these requirements. In an effort to solve the problem of transporting e-VLBI data in fast long distance networks, we propose an application-based TCP (AppTCP) congestion control algorithm, using Closed-Loop Control theory to keep the stable and constant transmission rate. AppTCP can swiftly reach the required transmission rate by increasing the congestion control window, and keep the transmission rate and allows the other TCP flows to share the remaining bandwidth. We further conduct extensive experiments in both fast long distance network test-bed and actual national networks (i.e., from Beijing to Shanghai in China) and international networks (i.e., from Hongkong in China to Chicago in USA) to evaluate and verify the performance and effectiveness of AppTCP. The results show that the AppTCP can effectively utilize the link capacity and maintain the constant rate during the data transmission, and its performance significantly outperforms that of the existing TCP variants. The efficiency of bulk data transmission over fast long distance networks (FLDnets) is critically required for many scientific applications, e.g., e-VLBI transmissions [1] . In order to support the e-VLBI experiments conducted by global radio telescopes, high volume e-VLBI data need to be transported globally. Although we can provide a link with a high bandwidth for e-VLBI data transmission, we still encounter the short-term congestion resulted from the burst flow in the IP link which greatly degrades the performance of transporting the e-VLBI data. That is because the current version of TCP cannot meet the requirements of e-VLBI data transmission, including reaching a high transmission rate swiftly by aggressively grabbing the available bandwidth from the link, maintaining the transmission rate as long as possible, and sharing the bandwidth friendly with other flows once the required transmission rate is obtained.. A number of improved TCP variants have been proposed to increase the efficiency of TCP in FLDnets, e.g., HSTCP [2] , STCP [3] , HTCP [4] ,Westwood [5] , BIC [6] , CUBIC [7] , Yeah [8] , ACP [9] , etc. Although these TCP variants have achieved success in certain issues, there are still many challenges that cause researchers widespread concerns [10] , such as the efficiency, stability and fairness of TCP for bulk data transmission in FLDnets. To the best of the authors' knowledge, there is no effective approach proposed in the current literature to meet the data transmission requirements of e-VLBI. Although Circuit TCP [11] made a step in this field, however, it completely removes the congestion control mechanism, which makes it only suitable for dedicated optical networks.. In an effort to tackle the problems of e-VLBI data transmission in fast long distance networks, this paper presents a new application-based TCP (AppTCP) based on the Closed-Loop Control theory [12] . With the proposed protocol, the transmission can swiftly reach the required rate by increasing the congestion control window (cwnd) aggressively; in addition, it keeps the transmission rate and allows the other TCP flows to share the remaining bandwidth. We further implement AppTCP on Linux platform, and conduct extensive experiments in the both FLDnet test-bed and the actual national and international networks to verify the efficiency of the proposed AppTCP. The experimental results show that the performance of AppTCP in bulk data transmission in FLDnets significantly outperforms that of the existing TCP variants.. The remainder of the paper is organized as the follows. Section 2 describes the related work. Section 3 presents the design of AppTCP algorithm. Extensive experiments of AppTCP in the FLDnet test-bed and in actual networks are conducted in Section 4 and Section 5, respectively. Finally, Section 6 concludes this study. In order to reduce the jitter of TCP transmission rate and meet the demands of transporting e-VLBI data, this paper has proposed the AppTCP for FLDnets. The Closed-Loop Control theory is adopted in AppTCP to keep the stable transmission rate. To evaluate the performance of AppTCP, extensive experiments have been conducted in the both FLDnet test-bed and in the actual networks, including an international network from HongKong in China to Chicago in USA and a national network from Beijing to Shanghai in China; the performance of the AppTCP has been compared with that of many existing TCP variants. Experimental results have verified that AppTCP can keep a high and constant transmission rate based on the requirements of applications, and can effectively utilize the link capacity and obtain the desirable transmission stability. In addition, AppTCP can also achieve a high degree of friendliness when the required transmission rate is satisfied. This work has its significance for reference of investigating the performance of the TCP owing to its comprehensive comparison with many existing TCP variants in both test-bed and actual networks. To increase the transmission efficiency of the traditional TCP in FLDnets, a number of TCP variants have been proposed. The proposed protocols can be classified into the following categories according to the congestion control strategies: Lossbased Congestion Algorithm (LCA), Delay-based Congestion Algorithm (DCA) and Compound Congestion Algorithm (C-CA) which combines the LCA and DCA. LCA adopts the packet loss as the only indicator of congestion, while DCA uses the variation of Round Trip Time (RTT) to reflect the network's condition. Table 1 shows the classified description of the popular TCP variants. STCP [3] altered standard TCP's AIMD (Additive Increase Multiplicative Decrease) congestion avoidance scheme to MIMD (Multiplicative Increase Multiplicative Decrease). Specifically, STCP increases its cwnd by 0.01 times of the current size on each received ACK (acknowledgement) and decreases it to its 0.875 times upon a packet loss. In contrast, HSTCP [2] still adopts the AIMD scheme, but it polishes the increase and decrease parameters.. BIC [6] adopted a binary search growth and linear growth to adjust its cwnd. Binary search growth is similar to the classical binary search algorithm. If the distance between the current cwnd and the desired cwnd is too large, BIC adopts the linear growth strategy instead of the binary search growth. However, BIC's cwnd updating rate appears too aggressive, especially under short RTT or in low speed networks. CUBIC [7] is the revised version of BIC. It simplified the cwnd updating algorithm of BIC to increase the TCP friendliness.. Vegas [13] was a typical DCA TCP variant and its cwnd is adjusted by the RTT variation of the link. If the RTT is gradually increasing, the link is considered to be congested and the cwnd will be decreased; otherwise, the congestion is alleviated and the cwnd will be increased.. ACP [9] used the estimation of the bottleneck queue size and fairness ration to achieve high utilization and friendly share network resources.. CTCP [14] combined the loss based CAA (Congestion Avoidance Algorithm) and delay based CAA to achieve high bandwidth utilization and TCP friendliness. Delay based CAA is achieved by introducing dwnd (delay window), and then the sending window is controlled by both cwnd and dwnd. The delay-based component has a scalable window increasing rule that not only probes the link capacity, but also reacts early to avoid congestion by sensing the changes of RTT.. In [8] , the network has two status: Fast and Slow. In Fast state, the increase of cwnd is expeditious just like STCP, while in Slow state, cwnd increases moderately as Reno dose to avoid network congestion.. Although these TCP variants have achieved certain success in their respective target areas [15, 16, 17] , there is no effective approach which is proposed to meet the requirements for applications in e-VLBI environments. Therefore, in this paper we target this problem to propose an AppTCP to facilitate the data transmission for e-VLBI.

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Towards an efficient and robust foot classification from pedobarographic images This paper presents a new computational framework for automatic foot classification from digital plantar pressure images. It classifies the foot as left or right and simultaneously calculates two well-known footprint indices: the Cavanagh's arch index and the modified arch index. The accuracy of the framework was evaluated using a set of plantar pressure images from two common pedobarographic devices. The results were outstanding, since all feet under analysis were correctly classified as left or right and no significant differences were observed between the footprint indices calculated using the computational solution and the traditional manual method. The robustness of the proposed framework to arbitrary foot orientations and to the acquisition device was also tested and confirmed. The functional mechanics of the human foot are greatly influenced by the structure of the foot, in particular, by the medial longitudinal arch height (McCrory et al., 1997) .. X-rays and ultrasonic devices can easily carry out this measurement; however, they are relatively expensive. Additionally, X-rays imply a potential health risk due to radiation exposure to subjects undergoing scientific or clinical studies.. Footprint parameters have been widely used as a predictor of arch height and foot classification: low arched, normal, and high arched. The study of footprints has numerous applications such as the characterization of populations, the prevention of injuries or the designing of footwear (Cavanagh and Rodgers, 1987; Dowling and Steele, 2001; Forriol and Pascual, 1990; Hernandez et al., 2007; Jung et al., 2001; Staheli et al., 1987) . Some of the most widely adopted footprint-based measurements include the arch index (AI) (Cavanagh and Rodgers, 1987) , the modified arch index (MAI) (Chu et al., 1995 ), Staheli's index (Staheli et al., 1987) and Chippaux-Smirak's index (Forriol and Pascual, 1990 ).. In the literature, studies reporting significant correlations between the AI or MAI and the plantar arch height can be found. For instance, considering digital plantar pressure images, Chu et al. (1995) found a significant correlation coefficient ( r ) equal to 0.70 − for the AI and the arch height, and of 0.71 − for the MAI and the arch height.. Also using digital plantar images, other authors (Shiang et al., 1998) Most of the previous studies concerning the evaluation of footprint indices were based on conventional ink footprints or on a variation. However, the digital plantar pressure images used by Chu et al. (1995) were manually pre-processed, which is prune to errors and of low reproducibility. On the other hand, Shiang et al. (1998) (Bastos and Tavares, 2004; Tavares et al., 2000; Tavares and Bastos, 2010) , image simulation (Pinho and Tavares, 2004) , image registration, i.e. image alignment, (Harrison and Hillard, 2000; Oliveira et al., 2010; Oliveira and Tavares, 2011; Oliveira et al., 2009; Pataky et al., 2008b) , and statistical analysis (Pataky et al., 2008a; Pataky and Goulermas, 2008) .. The main goal of the present work is to take advantage of those recent techniques of image processing and analysis to build a fully automated computational framework for foot classification and footprint index calculations. As such, the framework should be robust to arbitrary foot orientation, foot type and dimension, and completely independent of the plantar data acquisition device. In this way, the limitations of the accompanying software of the common pedobarographic devices could be overcome.. Hence, it should be noted that the goal of this paper is not a discussion on footprint indices neither a comparison among them. Nevertheless, for further discussion on this topic see Chu et al. (1995) or Razeghi and Batt (2002) . Particularly, an extended review on foot type classification is presented in the latter work; mainly, methods that use visual non-quantitative inspection, anthropometric values, footprint parameters and radiographic evaluation. Concerning the left/right foot image classification, the results show that the computational framework is efficient, accurate and robust to arbitrary foot shapes and orientations. Additionally, the framework calculated the AI from the digital foot images very efficiently and the small differences compared with the manual evaluations (Tables 1 and 2) were not statistically significant. The differences between the AI values of the original and rotated images obtained by the proposed computational framework were also insignificant (Tables 1 and 2) , which means that the framework is very robust to arbitrary foot orientations.. A comparison between the MAI values manually calculated versus the ones computed by the computational framework was not carried out. However, since this index is computed from the same regions used for AI and the values of the pixels used are maintained by the image transformations applied, the conclusions for AI are also valid for MAI.. At a first glance, based on the AI values in Table 1 it appears that the population in this study has predominantly low arch feet. However, the main reason for so small AI values is that these values were obtained from plantar pressure images of subjects in a static position and, consequently, the middle foot / plantar pressure device contact is weaker than when the subjects are walking over it.. Alignment quality is important for the accuracy of footprint indices. Therefore, at the initial development stage, different alignment strategies were tried out. The input foot image was aligned with one image template for each foot type: low arched, normal and high arched. Then, the alignment that led to the lowest SAD value was chosen.. However, the experimental tests showed that similar results could be achieved using just the template image of the normal foot. Thus, since the developed framework should be as fast as possible, the simpler approach was adopted. Also in order to increase the framework accuracy, we tried out a more accurate alignment algorithm (Oliveira and Tavares, 2011) than the cross-correlation based algorithm used in the tests here. Although the alignment quality was slightly improved, the AI values remained almost unaffected. Hence, also based on the requisite for high computational speed, we chose the cross-correlation based alignment algorithm (Oliveira et al., 2010) .. We chose the AI and MAI footprint indices because they are frequently adopted in foot classification. However, other footprint/pressure indices or statistical measurements can easily be assessed in an automated way from the aligned and segmented feet images.. Finally, it should be pointed out that the robustness of the proposed computational framework to arbitrary foot orientations, shapes and dimensions, and its independence to the plantar pressure acquisition device carry significant advantages over the traditional methods and solutions. Hence, with the proposed computational framework, the study/characterization of the plantar pressure distribution of large populations can be easily, efficiently and robustly achieved, since no particular requirements are imposed in terms of foot orientation or characteristics of the devices. The plantar pressure images of the first row were acquired using the Footscan system, and the images of the other rows were acquired using the EMED system. Figure 5 The proposed computational framework was fully implemented in C ++ , using. Microsoft Visual Studio 8, and tested on a notebook PC with an AMD Turion 64 2.0. GHz microprocessor, 1.0 GB of RAM and running Microsoft Windows XP.

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 B I 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 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 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 B 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 O O O O O O O O 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 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 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 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 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 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


Subspace-based support vector machines for pattern classification In this paper, we discuss subspace based support vector machines (SS-SVMs), in which an input vector is classified into the class with the maximum similarity. Namely, for each class we define the weighted similarity measure using the vectors called dictionaries that represent the class and optimize the weights so that the margin between classes is maximized. Because the similarity measure is defined for each class, for a data sample the similarity measure to which the data sample belongs needs to be the largest among all the similarity measures. Introducing slack variables we define these constraints either by equality constraints or inequality constraints. As a result we obtain subspace based least squares SVMs (SSLS-SVMs) and subspace based linear programming SVMs (SSLP-SVMs). To speedup training of SSLS-SVMs, which are similar to LS-SVMs by all-at-once formulation, we also propose SSLS-SVMs by one-against-all formulation, which optimize each similarity measure separately. Using two-class problems, we clarify the difference of In subspace methods (Watanabe & Pakvasa, 1973; Oja, 1983 ) each class region is defined by a set of basis vectors and the similarity of an input vector to a class is measured by the length of projection of the input onto the associated subspace. Similarities of the input vectors serve as discriminant functions.. Various subspace methods, such as class feature compression (CLAFIC) (Watanabe & Pakvasa, 1973) and learning subspace methods (Oja, 1983) , have been proposed. In most cases, principal component analysis (PCA) is used to compute basis vectors of subspaces. The basic idea of PCA is to rotate the coordinates so that data samples are non-correlated and delete the axes that do not contribute in representing the data distribution. To extend PCA for nonlinear problems, kernel PCA (KPCA) has been proposed (Schölkopf et al., 1998 (Schölkopf et al., , 1999 . Recently, using KPCA variants of subspace methods are extended to kernel-based subspace methods, such as kernel mutual subspace methods (KMSMs) (Sakano et al., 2005; Fukui & Yamaguchi, 2007) , kernel constrained mutual subspace methods (KCMSMs) (Fukui et al., 2006) , and kernel orthogonal mutual subspace methods (KOSMSs) (Fukui & Yamaguchi, 2007) .. In subspace methods using KPCA, we set the eigenvalues or 1 to the weights in the similarity measure of each class. However, because each subspace is defined separately and an overlap of subspaces or the margin between classes is not controlled after the definition of the subspaces, these weights may not be optimal from the standpoint of class separability. In our previous work Takeuchi et al., 2009) , we have developed least squares and linear programming support vector machines based on subspace methods.. In this paper, based on Kitamura et al. (2009) ; Takeuchi et al. (2009), we propose subspace based support vector machines (SS-SVMs), which optimize the weights in the similarity measure so that the margin between classes is maximized while minimizing the classification error for the training data. This is the same idea as that of support vector machines (SVMs) (Abe, 2005) . We consider the similarity measure as the separating hyperplane that separates the associated class from the remaining classes and formulate the optimization problem under the linear constraints that the similarity measure associated with a data sample has the highest similarity among all the similarity measures. This formulation is the same as all-at-once SVMs, which are considered to be inefficient. However, because kernel evaluations are done when similarity measures are calculated, kernel evaluations are not necessary during optimization. We define two-types of SS-SVMs: subspace based least squares SVMs (SSLS-SVMs) and subspace based linear programming SVMs (SSLP-SVMs). For SSLS-SVMs with the quadratic objective function and the equality constraints, we derive a set of linear simultaneous equations.. And we formulate SSLP-SVMs by the linear objective function and the in-equality constraints so that they can be solved by linear programming.. To speed up training SSLS-SVMs for large data sets, we propose formulating SSLS-SVMs by one-against-all formulation. By this formulation we can optimize the weights of each class, separately. This paper is organized as follows. In Section 2, we describe kernel-based subspace methods (KSMs) and how to calculate the similarity measures.. In Section 3, we propose SSLS-SVMs and SSLP-SVMs that optimize the weights in similarity measures. In Section 4, we demonstrate the effectiveness of SSLS-SVMs and SSLP-SVMs through computer experiments. And we conclude our work in Section 5. In this paper, we proposed two types of subspace based SVMs (SS-SVMs):. subspace based least squares SVMs (SSLS-SVMs) and subspace based linear programming SVMs (SSLP-SVMs). In SS-SVMs, the similarity measure for each class is assumed as the separating hyperplane that separates the associated class with the remaining classes. Then the margin between classes is maximized under the constraints that the similarity measure associated with the class to which a data sample belongs is the largest among all the similarity measures. This leads to a linear all-at-once SVM. Because all-at-once formulation is inefficient, we also formulated SSLS-SVMs by one-against-all formulation.. According to the computer experiments for two-class problems, both SSLS-SVMs and SSLP-SVMs showed better recognition rates of the test data than the conventional kernel subspace method with equal weights or weights

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


Mod/Resc Parsimony Inference: Theory and application We address in this paper a new computational biology problem that aims at understanding a mechanism that could potentially be used to genetically manipulate natural insect populations infected by inherited, intra-cellular parasitic bacteria. In this problem, that we denote by Mod/Resc Parsimony Inference, we are given a boolean matrix and the goal is to find two other boolean matrices with a minimum number of columns such that an appropriately defined operation on these matrices gives back the input. We show that this is formally equivalent to the Biclique Edge Cover for Bipartite Graphs problem and derive some complexity results for our problem using this equivalence. We provide a new, fixedparameter tractability approach for solving both problems that slightly improves upon a previously published algorithm for the Biclique Edge Cover for Bipartite Graphs. Finally, we present experimental results applying some of our techniques to a real-life dataset. Wolbachia is a genus of inherited, intra-cellular bacteria that infect many arthropod species, including a significant proportion of insects. The bacterium was first identified in 1924 by M. Hertig and S.B. Wolbach in Culex pipiens, a species of mosquito. Wolbachia spreads by altering the reproductive capabilities of its hosts [6] . One of these alterations consists in inducing so-called cytoplasmic incompatibility [7] . This phenomenon, in its simplest expression, results in the death of embryos produced in crosses between males carrying the infection and uninfected females. A more complex pattern is the death of embryos seen in crosses between males and females carrying different Wolbachia strains. The study of Wolbachia and cytoplasmic incompatibility is of interest due to the high incidence of such infections, amongst others in human disease vectors such as mosquitoes, where cytoplasmic incompatibility could potentially be used as a driver mechanism for the genetic manipulation of natural populations.. The molecular mechanisms underlying cytoplasmic incompatibility are currently unknown, but the observations are consistent with a "toxin/antitoxin" model [18] . According to this model, the bacteria present in males modify the sperm (the so-called modification, or mod factor) by depositing a "toxin" during its maturation. Bacteria present in females, on the other hand, deposit an antitoxin (rescue, or resc factor) in the eggs, so that offsprings of infected females can develop normally. The simple compatibility patterns seen in several insect hosts species [1] [2] [3] have led to the general view that cytoplasmic incompatibility relies on a single pair of mod/resc genes. However, more complex patterns, such as those seen in Fig. 1 of the mosquito Culex pipiens [5] , suggest that this conclusion cannot be generalised. Indeed, we show (see Section 5) that eight mod/resc gene pairs are necessary to explain this data.. The aim of this paper is to provide a first model and algorithm to determine the minimum number of mod and resc genes required to explain a compatibility dataset for a given insect host. Such an algorithm will have an important impact on the understanding of the genetic architecture of cytoplasmic incompatibility. Beyond Wolbachia, the method proposed here can be applied to any parasitic bacterium inducing cytoplasmic incompatibility.. Let us now propose a formal description of this problem. Let the compatibility matrix C be an n-by-n matrix describing the observed cytoplasmic compatibility relationships among n strains, with males in rows and females in columns. For the Culex pipiens dataset, the content of the C matrix is directly given by Fig. 1 . For each entry C i, j of this matrix, a value of 1 indicates that the cross between the i'th male and j'th female is incompatible, while a value of 0 indicates that it is compatible. No intermediate levels of incompatibility are observed in Culex pipiens, so that such a discrete code (0 or 1) is sufficient to describe the data. Let the mod matrix M be an n-by-k matrix, with n strains and k mod genes. For each M i, j entry, a 0 indicates that strain i does not carry gene j, and a 1 indicates that it does carry this gene. Similarly, the rescue matrix R is an n-by-k matrix, with n strains and k resc genes, where each R i, j entry indicates whether strain i carries gene j. A cross between male i and female j is compatible only if strain j carries at least all the rescue genes matching the mod genes present in strain i. Using this rule, one can assess whether an (M, R) pair is a solution to the C matrix, that is, to the observed data.. We can easily find non-parsimonious solutions to this problem, that is, large M and R matrices that are solutions to C , as will be proven in the next section. However, solutions may also exist with fewer mod and resc genes. The problem can be summarised as follows: let C (compatibility) be a boolean n-by-n matrix. A pair of n-by-k boolean matrices M (mod) and R (resc) is called a solution to C if, for any row j in R and row i in M, C i, j = 0 if and only if R j, M i, holds for all , 1 k. This appropriately models the fact stated above that, for any cross to be compatible, the female must carry at least all the rescue genes matching the mod genes present in the male. For a given matrix C , we are interested in the minimum value of k for which solutions to C exist, as well as in the set of all solutions for this minimum k. We refer to this problem as the Mod/Resc Parsimony Inference problem (see also Section 2) . Since in some cases, data (on females or males) may be missing, the compatibility matrix C has dimension n-by-m for n not necessarily equal to m. We will consider this more general situation in what follows.. In this paper, we present the Mod/Resc Parsimony Inference problem and prove that it is equivalent to a well-studied graph-theoretical problem known in the literature by the name of Biclique Edge Cover for Bipartite Graphs problem, henceforth called the Biclique Edge Cover problem for simplicity. In this problem, we are given a bipartite graph, and we want to cover its edges with a minimum number of complete bipartite subgraphs (bicliques). This problem is known to be NP-complete, and thus Mod/Resc Parsimony Inference turns out to be NP-complete as well. In Section 4, we investigate a previous fixed-parameter tractability approach [8] for solving the Biclique Edge Cover problem and improve its algorithm. In addition, we show a reduction between this problem and the Clique Edge Cover problem. Finally, in Section 5, we present experimental results where we applied some of these techniques to the Culex pipiens data set presented in Fig. 1 . This provided a finding that is surprising from a biological point of view.

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