Understanding Security Behaviors in Personal Computer Usage: A Threat Avoidance Perspective This study aims to understand the IT threat avoidance behaviors of personal computer users. We tested a research model derived from Technology Threat Avoidance Theory (TTAT) using survey data. We find that users’ IT threat avoidance behavior is predicted by avoidance motivation, which, in turn, is determined by perceived threat, safeguard effectiveness, safeguard cost, and self-efficacy. Users develop a threat perception when they believe that the malicious IT is likely to attack them (perceived susceptibility) and the negative consequences will be severe if they are attacked (perceived severity). When threatened, users are more motivated to avoid the threat if they believe that the safeguarding measure is effective (safeguard effectiveness) and inexpensive (safeguard cost) and they have confidence in using it (self-efficacy). In addition, we find that perceived threat and safeguard effectiveness have a negative interaction on avoidance motivation so that a higher level of perceived threat is associated with a weaker relationship between safeguard effectiveness and avoidance motivation or a higher level of safeguard effectiveness is associated with a weaker relationship between perceived threat and avoidance motivation. These findings provide an enriched understanding about personal computer users’ IT threat avoidance behavior. In this information era, the use of personal computers and the Internet is widely diffused. The US Census Bureau reported that 62 percent of American households owned a computer and 55 percent had Internet access in 2003 (Day et al., 2005) . The latest Pew/Internet survey shows that 74 percent of American adults and 93 percent of teenagers have Internet access at home (Jones and Fox, 2009) . Equipped with the Internet, personal computers are now an important virtual setting for everyday living and work. A wide range of mundane activities such as shopping, chatting, socializing, reading, entertaining, and banking can be done on personal computers. In addition, as organizations have become increasingly virtualized, work is transcending physical restrictions (Handy, 1995) . Employees can choose to work at home or bring unfinished work to their homes because Internet technologies allow them to turn their personal computers into virtual offices. The boundary between work and life has been remarkably blurred. Despite the importance of personal computer use, individual users are particularly vulnerable to IT threats. Unlike employees in organizations and companies, these users do not have to comply with strict IT security policies and it is unlikely that they have strong IT security infrastructure to protect them from malware and hackers. For example, most home users are not computer professionals and lack the expertise to set up a safe home computing system. In addition, unsafe computing behaviors of naïve home users -such as browsing unsafe websites, downloading suspicious software, sharing passwords with family members, and not protecting home wireless networks -expose home computers to many lurking dangers.. Given the pervasiveness of personal computer and Internet use and the blurred line between work and home, IT security breaches on personal computers can cause damages not only to individuals, but also to organizations. On the one hand, users may become victim to identity theft if their personal information is stolen. On the other hand, users' unsafe computing behavior in non-work settings may open a "back door" for hackers to break into their companies' systems. For example, when a user logs into his or her company's intranet from home, hackers can use Trojan to steal the password and use it to illegally access the company's confidential data. Cyber criminals can also turn numerous weakly secured home computers into Zombie computers and use them to create botnets to attack other personal computers and corporate applications. The diffusion of the Internet has made it easy for malicious IT to exploit system vulnerabilities and amplify the negative impact. Many forms of malicious IT such as viruses, worms, spyware, Trojan horses, and botnets have caused enormous financial losses (Bagchi and Udo, 2003) . As the CSI survey shows, in 2009, 64.3 percent of the responding organizations were attacked by malware, and security problems resulted in an average loss of over $234,244 per organization (CSI, 2009) . Consumer Reports' "State of the Net" survey indicates that in a two-year period from 2007 to 2009, the financial losses of U.S. consumers due to viruses and spyware were $5.8 billion and $1.7 billion, respectively (Consumer Reports, 2009 ). Given its huge economic impact, IT security has drawn great attention from information systems (IS) researchers and practitioners (Baskerville, 1993; Dhillon and Backhouse, 2000; Loch et al., 1992) . However, most prior research on IT security has been conducted in organizational settings (D'Arcy et al., 2009; Straub and Welke, 1998) , and little is known about user security behavior in the context of personal computer use.. Researchers have recently noticed that technology alone is insufficient to ensure security and have started to pay attention to the human aspect of security (Anderson and Agarwal, 2006; Aytes and Terry, 2004; Ng et al., 2009; Woon et al., 2005; Workman et al., 2008 ). Yet, knowledge about user security behaviors is far from complete. The purpose of this study is to investigate how personal computer users cope with IT threats. We derive a research model from Technology Threat Avoidance Theory (TTAT; Liang and Xue, 2009 ) to explain how individuals develop threat perceptions, evaluate safeguard measures, and engage in avoidance behavior. Our empirical results provide strong support for our research model. This paper makes several contributions to the IS literature. First, as one of the first attempts to empirically validate TTAT, it investigates an important phenomenon -the security behavior of personal computer users -that is vaguely understood. Our study shows that users are motivated to perform security behaviors if they perceive the threat to be present and avoidable. Second, it shows that both perceived susceptibility and severity of the negative consequences caused by malicious IT motivate users to avoid the IT threat, and their effects are fully mediated by the threat perception. This finding helps to clarify the threat assessment, about which prior studies on IT security have generated inconsistent findings (Ng et al., 2009; Woon et al., 2005; Workman et al., 2008) . Finally, our study reveals a counterintuitive finding -perceived threat and safeguard effectiveness have a negative interaction when they influence avoidance motivation, which has not been reported in empirical studies.. The paper proceeds as follows. We first discuss the theoretical foundation and then describe our research model and hypotheses. Then, we present our methodology, followed by data analysis results. In the discussion section, we highlight major findings and provide implications for research and practice. After discussing limitations and future research directions, we end the paper with a brief conclusion. This study investigates personal computer users' IT threat avoidance behaviors. We derive a research model TTAT and test it using survey data collected from 152 personal computer users. Data analyses reveal several major findings. First, perceived threat, safeguard effectiveness, safeguard cost, and self-efficacy influence avoidance motivation, which determines avoidance behavior. Second, perceived threat is determined by perceived susceptibility and severity and fully mediates their effects. Finally, perceived threat negatively moderates the relationship between safeguard effectiveness and avoidance motivation. These findings provide an enriched understanding of users' IT threat avoidance behavior in the personal computer usage context where security behavior is voluntary. More research is called for to test TTAT more comprehensively and in other settings.. Liang&Xue/Understanding Security Behavior … I had seen someone else doing it before trying it myself … I could call someone for help if I got stuck … someone else helped me get started … I had a lot of time to complete the job … I had just the built-in help facility for assistance … someone showed me how to do it first … I had used similar packages like this one before to do the job Avoidance Motivation (1 = strong disagree, 7 = strongly disagree). I intend to use anti-spyware software to avoid spyware I predict I would use anti-spyware software to avoid spyware I plan to use anti-spyware software to avoid spyware Avoidance Behavior (1 = strong disagree, 7 = strongly disagree). I run anti-spyware software regularly to remove spyware from my computer. I update my anti-spyware software regularly. Notes: SUS = susceptibility; SEV = severity; THR = threat; EFF = safeguard effectiveness; COST = safeguard cost; SE = self-efficacy; AM = avoidance motivation; AB = avoidance behavior. This study empirically investigated why personal computer users we anti-spyware to avoid the threat of spyware. We paid particular attention to perceived threat because it plays a pivotal role in influencing users' avoidance behavior. We validate a research model derived from TTAT (Liang and Xue, 2009 ) using survey data. Data analysis results reveal that the model is able to explain a large amount of variance in users' motivation to avoid IT threats (56 percent) and actual avoidance behavior (21 percent). This paper conveys a simple, yet powerful message -to motivate computer users to avoid IT threats, they need to be convinced that the threats exist and are avoidable. If users fail to see a threat, they will not act to avoid it. If they see the threat but believe it is unavoidable, they will not act to avoid it, either. Thus, both the threat appraisal and the coping appraisal are necessary to motivate security behaviors. Furthermore, we demonstrate that to develop a threat perception, users need to be aware of the likelihood and severity of being attacked by the IT threat. Users evaluate a safeguarding measure from three aspects, taking into account safeguard effectiveness, cost, and their confidence in using the safeguard. We also find that perceived threat and safeguard effectiveness have a negative interaction, suggesting that when the level of either construct is high, the other construct's relationship with avoidance motivation will be weakened. This interesting finding could generate new insights in IT users' threat avoidance behavior.. The only hypothesis not supported is H1c, which proposes the interaction between perceived severity and susceptibility. Based on Aguinis (1995) , we contend that a lack of power might have impeded detection of the interaction. There are three plausible explanations. First, the variance in perceived susceptibility and severity has a range restriction, because our sample is not a random sample and cannot represent the entire personal computer user population (Aguinis, 1995) . Second, the "scale coarseness" of avoidance motivation might have reduced the statistical power (Bobko and Russell, 1994) . Whereas the interaction variable has a possible range of 7 x 7 = 49 distinct responses, avoidance motivation only has a "coarse" 7-point Likert scale. Therefore, information regarding the relationship between avoidance motivation and the interaction variable is lost, and power is reduced. Finally, a large sample size is needed to detect a small moderation effect (Carte and Russell, 2003) . Using G*Power, we estimated the sample size required to achieve the power of 0.8 for a small effect size (ƒ 2 = 0.02) in multiple regression and found the required sample size is 395. If the effect size of

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Image Transformation Based on Learning Dictionaries across Image Spaces Abstract-In this paper, we propose a framework of transforming images from a source image space to a target image space, based on learning coupled dictionaries from a training set of paired images. The framework can be used for applications such as image superresolution and estimation of image intrinsic components (shading and albedo). It is based on a local parametric regression approach, using sparse feature representations over learned coupled dictionaries across the source and target image spaces. After coupled dictionary learning, sparse coefficient vectors of training image patch pairs are partitioned into easily retrievable local clusters. For any test image patch, we can fast index into its closest local cluster and perform a local parametric regression between the learned sparse feature spaces. The obtained sparse representation (together with the learned target space dictionary) provides multiple constraints for each pixel of the target image to be estimated. The final target image is reconstructed based on these constraints. The contributions of our proposed framework are three-fold. 1) We propose a concept of coupled dictionary learning based on coupled sparse coding which requires the sparse coefficient vectors of a pair of corresponding source and target image patches to have the same support, i.e., the same indices of nonzero elements. 2) We devise a space partitioning scheme to divide the high-dimensional but sparse feature space into local clusters. The partitioning facilitates extremely fast retrieval of closest local clusters for query patches. 3) Benefiting from sparse feature-based image transformation, our method is more robust to corrupted input data, and can be considered as a simultaneous image restoration and transformation process. Experiments on intrinsic image estimation and super-resolution demonstrate the effectiveness and efficiency of our proposed method. M ANY problems in computer vision can be cast as transforming images from one form to another. In this work, we assume that the pairs of images have the same content and are in registration with each other, but are visually different. Typical applications include, but are not limited to, super-resolution (SR), face mapping of different styles, estimation of intrinsic images, such as shading and albedo images, and various nonphotorealistic rendering. Some existing methods generate stylistic images based on a single or a pair of reference images [3] , [1] , [2] . Others learn the mapping relations from large training sets of paired images [4] , [6] , [7] , [8] , [49] , [51] . Our work belongs to the second category. A few representative methods of such kind are summarized as follows.. Chang et al. [7] assumed that image patches in source and target spaces form manifolds with similar local geometries, and a target patch could be generated as a linear combination of its neighbors. This approach was applied to super-resolution. Tappen et al. [11] proposed an ExpertBoost algorithm to learn a nonlinear Mixture of Experts estimator, which was parameterized as a set of prototype patches and their corresponding linear regression coefficients. It was applied to image denoising and estimating intrinsic shading/albedo images. Spiritually similar, Kim and Kwon [12] adopted sparse kernel ridge regression to learn a mapping function from paired training patches and applied it to super-resolution.. In general, transforming images from a source space to a target space is difficult. Natural images are very high dimensional, statistically non-Gaussian, and exhibit abundant varying texture patterns. Directly learning image models and their mapping relations may not be feasible. Even working on local patches, the dimensionality is still too high to learn a good and explicit mapping function. The above reviewed methods either use nonparametric approaches, such as nearest neighbor (NN) searching, and learn the mapping relations using the found neighbor training patch pairs [4] , [7] , or use nonlinear regression based on summarizing the training data using a small number of prototype patches [11] , [12] . When dealing with a huge amount of training patches, searching NNs could be prohibitively slow and also costs large memory. Although there exist acceleration methods such as approximate nearest neighbor (ANN) search [22] , [23] , they cannot generally cope with the memory cost problem. For nonlinear regression [11] , [12] , when the prototype number is getting large, significant computation is required, while fewer prototypes cannot approximate the image space well.. We are motivated to find alternative solutions that can address these major challenges. In particular, we consider the more direct approach of learning parametric models.. Note that for an input patch, nonparametric approaches search its NNs at runtime and those NNs correspond to a neighborhood of the query in the image space. If one can divide the space into neighborhoods that are precise and representative and learn mapping relations within each neighborhood offline, it will be not necessary to store all training samples and online search NNs, without sacrifice of the mapping accuracy. In the literature, vector quantization [45] , such as k-means clustering, is popularly used to partition a given space into representative neighborhoods. However, in high-dimensional spaces it is very difficult to achieve precise quantization, which requires a huge number of neighborhoods. It requires high computational complexity to learn and retrieve those neighborhoods, and also large memory to store the neighborhood centroids. One may consider other simpler methods such as scalar or lattice quantizers [45] ; however, their performance is usually poor since their assumptions of data distribution are rarely satisfied by natural images.. The difficulties of working in the original image space motivate us to consider doing the mappings in some projected feature spaces. Recently, sparse representation over a learned dictionary [26] has achieved state-of-the-art performance for many image restoration applications [25] , [27] as well as various image classification tasks [34] , [31] , [35] . It shows that overcomplete sparse models can be well adapted to natural images. Motivated by such progress, we are tempted to learn two separate dictionaries for both the source and target image spaces, and perform partitioning of the learned sparse feature spaces for local parametric image transformation. Instead of doing independent dictionary learning for source and target image spaces, we introduce the concept of coupled dictionary learning based on coupled sparse coding. The coupling is realized by enforcing sparse coefficient vectors of a pair of corresponding source and target image patches to have the same support, i.e., the same indices of nonzero elements. We use an efficient active set block coordinate gradient descent (BCGD) algorithm for coupled sparse coding, which can also efficiently approximate the solution path [42] , [41] . Coupled dictionary learning bridges the processes of source and target dictionary learning, resulting in dictionary atoms that model and correlate different image spaces well. Indeed, coupled sparse coding of image patch pairs can identify the most significant, reduced, and correlated feature subspaces. We devise an efficient scheme to partition the high-dimensional (in case of overcomplete dictionary) but sparse (for nonzero coefficients) feature spaces of training patch samples into easily retrievable local clusters (cells). Our scheme exploits the information of both support patterns and coefficient values of sparse feature vectors, and facilitates efficient retrieval of the closest cluster for a query patch. Our aim is that by doing image mappings in the learned sparse feature spaces, both mapping accuracy and mapping efficiency can be achieved. The mapping efficiency can be realized by local parametric regression and is based on our proposed space partitioning method that facilitates efficient retrieval of local clusters. The mapping accuracy comes from two facts: 1) Sparse feature vectors of training patch samples are highly close to each other within local clusters; 2) it is much easier to model the mapping relations in reduced, significant, and correlated feature subspaces.. Given a query source patch, we compute its sparse feature vector and fast index into its closest local cluster, and then estimate its sparse vector over the optimally learned target space dictionary by local linear regression. The mapping relations have been offline learned. Fig. 1 gives a graphical illustration. In this way, we only need to store the space indexing structure and regression parameters within each local cluster. Our method can effectively deal with the speed and memory cost challenges mentioned above. Compared with alternative approaches that do not enforce coupled sparsity, the number of parameters to be learned and stored is much reduced, and the local regression process is also more stable (cf. Section 3.1.1).. In this paper, we apply our framework to two applications, namely, estimating the intrinsic components (shading and albedo) of an observed image and image superresolution. For the latter one, we achieve performance comparable with the state of the art. Our results on intrinsic image estimation outperform current single-input-imagebased methods. Our method is potentially more robust to corrupted data. In intrinsic image estimation, if noisy images are used as input, we get increased performance gap compared with other methods. In this sense, our method can be considered as a simultaneous image restoration and transformation process.. It is straightforward to apply our framework to other applications such as image denoising. For example, we can generate a training set of paired images by adding Gaussian noise to clean images. Coupled dictionaries can be optimally learned to both model the characteristics of noisy images in the source space, and be used to reconstruct target images of high quality. Given a patch of a noisy test image, its denoised version can be estimated by local regression in the learned, coupled sparse feature spaces. With trained dictionaries specifically for image denoising, our method has the potential to achieve performance comparable to state-ofthe-art techniques based on learned sparse models [25] . In this paper, we propose a learning-based framework for image transformation. Our framework is based on a local regression approach using sparse feature representations over learned dictionaries across image spaces. To learn the dictionaries, we propose the concept of coupled sparsity, and use an active set BCGD algorithm for coupled sparse coding. To learn mapping relations between image spaces, we perform parametric regression within small subsets of training image patch pairs. To this end, we propose a space partitioning scheme that can divide the high-dimensional but sparse feature spaces into easily retrievable local clusters. For any test image patch, our method can efficiently retrieve its closest local cluster and perform regression within the cluster. We applied our framework to intrinsic image estimation and super-resolution and obtained state-of-theart performance. Our method is more robust to corrupted data and can be considered as a simultaneous image restoration and transformation process. [7] , (d) Yang's method [15] , (e) our results by local linear regression, (f) our final results, (g) ground truth. In this work, coupled dictionary learning is performed in a purely unsupervised, data-driven fashion: Dictionaries are learned for better reconstructing training patch pairs. It is possible to extend the current formulation to a supervised setting similar to [36] , [35] , i.e., dictionaries are learned to be better adapted to a specific image transformation task. We leave this extension in future research. In future, we are also interested in extending our method on other image transformation applications. One of the well-established image transformation approaches is Freeman et al.'s NN example-based learning [4] . Given a test image in the source space, each patch of the test image is compared with the training patches in the source image space, and its several nearest neighbors are selected as candidates. To reconstruct the target image, the candidate patches in the target space are selected and stitched using Markov random fields (MRF). The same approach is also applied to face sketch synthesis [52] . Since this method uses only one of the nearest neighbors for reconstruction, it is susceptible to suffering from overfitting, visually producing noisy and/or jaggy artifacts. As extensions, neighbor embedding [7] made benefits of multiple NNs, and mixture of mapping experts [11] was learned by locally linear regression.. In terms of performing mappings in projected feature spaces, Lin and Tang [10] found the most correlative subspaces of different image styles, and performed regression at the reduced subspace dimensions. For each style, it could find only one common subspace, limiting its applicability to only mapping of face images with regular structure. Li and Adelson [9] computed image wavelet coefficients and considered "nested binning" subband coefficients of local neighborhood centered at each training image pixel. However, nested binning simplified highdimensional space partitioning as independent partitioning along each feature dimension (subband), and thus produced lots of empty hyper-rectangular bins.. Yang et al. [15] used sparse coding for super-resolution, which enforces corresponding low-and high-resolution (HR) image patches to share the same sparse feature values. However, this is hard to achieve, even with adapted dictionaries. In practice, either more nonzero elements in a sparse feature vector or dictionaries of many times overcompleteness are required. In [15] , approximately 10 times more overcomplete dictionaries were actually used, and this increases the computation cost of sparse coding. Using sparse representation with more nonzero elements (i.e., nonsparse representation) tends to generate undesired noisy patches, as verified in our super-resolution experiments in Section 8. It is not clear either how their method works on other types of image transformation. In fact, we have conducted experiments on intrinsic image estimation using the method in [15] . Results reported in Section 7 show that it fails at such a general type of image transformation application. Compared with [15] , our method allows the sparse feature vectors of corresponding patches to choose different values, which also leads to a sparser representation than [15] does. Thus, our method has more power and flexibility to describe different image spaces.. Mairal et al. [36] also considered dictionary learning for image transformation. They proposed a supervised learning method to learn a dictionary in the source image space and a corresponding transformation matrix. The learned global transformation matrix was used to map sparse features of source image patches to intensity values of target patches. Instead, our method is based on a local regression approach that learns transformation parameters for each of a great number of local clusters. Consequently, our method admits a much richer model, resulting in improved mapping power than the global method used in [36] . Although supervised learning may result in a dictionary better adapted to the task at hand, the global transformation model used limits its applicability to only image restoration applications, e.g., a classical inverse halftoning problem, as considered in [36] . It is unclear how their method can perform on more general image transformation applications.. Our method also falls in a broader category of piecewise linear estimation (PLE). A popular choice of PLE is based on Gaussian Mixture Models (GMM) [46] , which describes local image patches with a mixture of Gaussian distributions. A key step in GMM model learning is to divide the data (image patches) into precise and representative local clusters. This might be applicable to image restoration problems where it could be case that only patches of the observed (degraded) image itself are involved in clustering. This is exactly the approach taken in [47] , where the maximum a posteriori expectation-maximization (MAP-EM) algorithm was used for the clustering purpose. However, as we discussed in Section 1, for general image mapping problems using a large training set of paired images, it is very difficult to achieve precise clustering, which requires a huge number of local neighborhoods. Similar to k-means clustering, in such a scenario the MAP-EM algorithm considered in [47] could be prohibitively slow. For these methods, it also requires high computational complexity to retrieve local clusters (Gaussians) in the estimation process. Instead, our method uses coupled sparse coding to learn a sparse feature space, based on which an efficient partition scheme is proposed to divide the space into local clusters, i.e., a union of low-dimensional linear subspaces. It also facilitates efficient retrieval of closest local clusters. Reported experiments in Section 5 show the efficacy and efficiency of our proposed subspace partition scheme.. Sparse coding. Given a signal vector x 2 < N and a dictionary D 2 < NÂK whose columns are dictionary atoms, sparse coding finds a sparse coefficient vector a 2 < K by solving min a kx À Dak 2 2 s:t: kak 0 L, where k Á k 0 is the l 0 norm, counting the nonzero elements of a. D is overcomplete when K > N. Each column of D is constrained to have unit l 2 norm. The l 0 norm above can be replaced with l 1 regularization [42] , [43] , resulting in the convex sparse coding problem min a kx À Dak 2 2 þ kak 1 , where balances the sparsity and reconstruction error. D can be chosen as predefined ones or be adapted to training data by learning. Representative dictionary learning algorithms include K-SVD [32] , Fields of Experts (FoE) [27] , and others [26] , [33] .

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


Feature engineering for MEDLINE citation categorization with MeSH Background: Research in biomedical text categorization has mostly used the bag-of-words representation. Other more sophisticated representations of text based on syntactic, semantic and argumentative properties have been less studied. In this paper, we evaluate the impact of different text representations of biomedical texts as features for reproducing the MeSH annotations of some of the most frequent MeSH headings. In addition to unigrams and bigrams, these features include noun phrases, citation meta-data, citation structure, and semantic annotation of the citations. Results: Traditional features like unigrams and bigrams exhibit strong performance compared to other feature sets. Little or no improvement is obtained when using meta-data or citation structure. Noun phrases are too sparse and thus have lower performance compared to more traditional features. Conceptual annotation of the texts by MetaMap shows similar performance compared to unigrams, but adding concepts from the UMLS taxonomy does not improve the performance of using only mapped concepts. The combination of all the features performs largely better than any individual feature set considered. In addition, this combination improves the performance of a state-of-the-art MeSH indexer. Concerning the machine learning algorithms, we find that those that are more resilient to class imbalance largely obtain better performance. Conclusions: We conclude that even though traditional features such as unigrams and bigrams have strong performance compared to other features, it is possible to combine them to effectively improve the performance of the bag-of-words representation. We have also found that the combination of the learning algorithm and feature sets has an influence in the overall performance of the system. Moreover, using learning algorithms resilient to class imbalance largely improves performance. However, when using a large set of features, consideration needs to be taken with algorithms due to the risk of over-fitting. Specific combinations of learning algorithms and features for individual MeSH headings could further increase the performance of an indexing system. Research in MeSH indexing has mostly used the bag-ofwords representation, leaving other more sophisticated features aside. In this paper, we have studied the feasibility of exploiting the syntactic, semantic and structural properties of texts in order to improve classification of MEDLINE citations with different MeSH headings. Our main conclusion is that, even though traditional features such as unigrams and bigrams have strong performance compared to other features, it is possible to combine them to effectively improve the performance of the bag-of-words representation for MeSH indexing. The combination allows improving the performance over the MTI system, that has been shown to be a hard baseline to improve on [28] . We have also found that the selection of the learning algorithm has an influence in the overall performance of the system. Algorithms that are more resilient to the imbalance of the data set show improved performance.. As future work, we plan to take further features into consideration, including the part-of-speech, using automatic summaries from PMC full text articles instead of the abstracts for extracting the classification features, exploiting more in depth the argumentative structure of the abstract, for instance, by including as features only the tokens from relevant sections, and exploring additional features from MEDLINE or full text articles. We have not investigated the combination of learning algorithms in this work, which has previously shown to improve performance [56] . We did not investigate the selection of MeSH headings according to the best performing combination of features and methods [11] . Recent work using deep learning for MeSH indexing [58] shows promising results and could be considered to obtain a better set of features automatically. All this could be explored as future work. Furthermore, since overfitting is a problem with some learning algorithms and feature selection seems to decrease performance [29] , larger citation sets could be considered in further experiments. The experiments show that unigrams and bigrams are a strong baseline compared to other more sophisticated features, which is in agreement with previous work. A conceptual representation based on MetaMap shows similar performance compared to unigrams. In addition, combining the features improves the classification performance. When combining all feature sets, even better results than the MTI system are achieved.. The machine learning algorithm has an influence as well on the performance. Looking at the overall results, SVM-perf has the best average performance for the different feature sets (even better the AdaBoostM1 with oversampling). AdaBoostM1 with oversampling performs better than AdaBoostM1 using the natural class distribution. This means that learning algorithms prepared to deal with data set imbalance perform better. We find as well that when all the features are used, AdaBoostM1 with oversampling has decreased performance, which might be due to over-fitting of the learned model. Previous work on feature selection for MeSH categorization has shown a decrease in performance when feature selection is used [29] , and it has been seen as well for algorithms like SVM in more general domains [57] . A possible solution might be to increase the size of the training set when a large number of features is being used. Figure 1 shows the F-measure per MeSH heading of the best combination of features (TIAB+bigram+all) and the best performance algorithm (SVM-perf ). Our results corroborate previous work that has shown that the performance decreases for the less frequent MeSH headings compared to more frequent ones [11] . We can see this with the most frequent MeSH headings: Humans, Male and Female. Except for a couple of MeSH headings (Animals and Mice), the performance of the algorithms with respect to these MeSH headings decreases. However, we have observed that using learning algorithms resilient to class imbalance shows improved performance that has a positive impact on less frequent ones. The most frequently used feature model is the so called bag-of-words: each position in the input feature vector corresponds to a given word or phrase and stores either a binary value indicating its presence or a numerical value indicating its frequency or even its TF-IDF [12, 13] . Other authors have used phrases rather than individual words [14, 15] , but the results obtained are not significantly better (or are even worse) than those of the bag-ofwords approach. Representations based on named entities have also been explored. They have proved to be useful when a reduction of the number of features is needed [3] , but do not add any classification power.. There is previous work on feature engineering for text categorization not bound to the biomedical domain [3] that examines a large set of text representations, including lexical (e.g., bag-of-words), syntactic (e.g., noun phrases and key phrases) and semantic features (e.g., synonym and hypernym relations and concepts from WordNet). Syntactic features have also been used in combination with other lexical or semantic ones.. Previous work has attempted to improve classification by using bigrams as features [16] . While using bigrams together with unigrams has proved to be potentially beneficial for text classification, using bigrams alone leads in most cases to a decrease in results comparing with the use of bags-of-words.. Lewis [17] was the first to study the effect of syntactic phrases in text categorization, and found that a Naïve Bayesian classifier with only noun phrases yields significantly lower results than a standard classifier based on bag-of-words.. In [18] , part-of-speech tags are appended to words in the feature vector, and the position of each word in the text (whether it appeared in the first quarter, last quarter, or middle half of the document) is also codified. None of these features, however, improves significantly from using unigrams alone. Furnkranz et al. [19] used automatically extracted linguistic patterns as features to a Naïve Bayesian classifier, and got a consistent improvement in precision. More recent efforts have proposed the use of external resources in order to semantically enrich the feature sets. In [20] , for instance, common-sense and domain-specific knowledge are used to enrich the bag-ofwords, using repositories of knowledge such as the Open Directory Project (ODP), Yahoo! Web Directory, and the Wikipedia encyclopedia. Text categorization is the task of automatically assigning pre-defined labels to text [1] . Even though several methods can be used, machine learning is appealing due to the large data sets that are available as training data that allow for automating the development of categorization models effectively.. In the biomedical domain, research in automatic text classification is usually conducted in the context of indexing MEDLINE ® citations with MeSH ® descriptors. There are over 23 M citations in MEDLINE, with a growth rate of over 800 k new citations every year. This growth rate makes it difficult to keep up-to-date with new discoveries. To help cataloguing and searching biomedical documents, the US National Library of Medicine (NLM) ® has produced the medical subject headings (MeSH) controlled vocabulary. Each MEDLINE citation is manually assigned a number of relevant medical subject headings that classify the document according to its topic. Manual indexing is, however, costly. As stated in [2] , MEDLINE indexing is the responsibility of a relatively small group of highly qualified indexing contractors and staff at the NLM who find it difficult to maintain the quality of this huge resource. In this situation, automatic methods to categorize citations might be relevant.. Current work on automatic biomedical text categorization based on machine learning has been dealing largely with bag-of-words representations. Bag-of-words based on unigrams are easy and fast to build and have shown good performance on many text categorization tasks [3] . However, specialized domains such as the biomedical one suffer from a varied terminology which might be too sparse to efficiently train machine learning models. For instance, when building a categorization rule for prostate cancer documents, there are terms with low frequency that are highly relevant for classification, e.g. prostate adenocarcinoma. Ontologies and terminological resources like the Unified Medical Language System (UMLS ® ) [4] can help in the normalization of terms found in text and could be used to derive additional feature sets (e.g., through synonymy and generalization). This normalization has already been proposed for indexing MEDLINE with MeSH controlled vocabulary in the Restrict-to-MESH algorithm [5] .. There is also information latent in the structure of the text that is not usually considered in the categorization task, i.e., if the terms come from a specific section of a citation. For instance, if prostate cancer is mentioned only in the "Background" section, then it is not as relevant as if it is found in the title of the article. This information could be used by the classifier in order to build a better categorization model, as has already been shown useful for the retrieval [6] and the selection of topic relevant sentences [7] [8] [9] .. In addition, there is meta-data that is usually linked to biomedical citations that is not taken into account and could be used to provide a better categorization performance. For instance, there are specialized journals in which certain types of articles are published, e.g. specialized journals for breast cancer like Breast Cancer Research.. Other issues with the features used in text classification are not considered in this work. For instance, there are learning algorithms that benefit from feature selection or better tuning of weights used to represent the features [10] . In previous work, several machine learning (ML) approaches and their combination [11] have been evaluated within the biomedical domain, trying to reproduce, for instance, MeSH indexing. On the other hand, most previous work relies on comparing or combining several ML algorithms (Bayesian models, neural networks, decision trees, regression, etc.) on bag-of-words representations. Little attention has been given to measuring the impact of feature engineering.. The motivation of this work is to evaluate the performance of machine learning algorithms on several types of features in the context of text categorization (in particular, categorization of MEDLINE citations with different MeSH headings), testing representations based on lexical, syntactic, semantic and argumentative information. In addition to evaluating each individual feature set, we test different combinations of features. We also evaluate several machine learning approaches to overcome the imbalance between the small number of positives versus the large number of negatives.. Overall, we find that the bag-of-words representation is a competitive baseline, slightly improved by the use of higher order n-grams. We also find that the combination of different features outperforms the bag-of-word baseline. In addition, a large improvement is obtained by using techniques that target the imbalance in the training set. Finally, we observe that the performance may vary depending on the evaluated MeSH heading, which might imply that optimal performance would require selecting a specific feature set in some particular cases.

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


Exact solution of the robust knapsack problem We consider an uncertain variant of the knapsack problem in which the weight of the items is not exactly known in advance, but belongs to a given interval, and an upper bound is imposed on the number of items whose weight differs from the expected one. For this problem, we provide a dynamic programming algorithm and present techniques aimed at reducing its space and time complexities. Finally, we computationally compare the performances of the proposed algorithm with those of different exact algorithms presented so far in the literature for robust optimization problems.keywords: knapsack problem, robust optimization, dynamic programming. The classical Knapsack Problem (KP) can be described as follows. We are given a set N = {1, . . . , n} of items, each of them with positive profit p j and positive weight w j , and a knapsack capacity c. The problem asks for a subset of items whose total weight does not exceed the knapsack capacity, and whose profit is a maximum. It can be formulated as the follow Integer Linear Program (ILP):. x j ∈ {0, 1}, j ∈ N.. Each variable x j takes value 1 if and only if item j is inserted in the knapsack. This problem is NP-hard, although in practice fairly large instances can be solved to optimality within reasonable running time. Furthermore, dynamic programming algorithms with pseudopolynomial running time are available. A comprehensive survey on all aspects of (KP) was given by Kellerer, Pferschy and Pisinger [10] .. In this paper we consider the following variant of (KP), aimed at modeling uncertainties in the data, in particular in the weights: for each item j the weight may deviate from its given nominal value w j and attain an arbitrary value in some known interval [w j − w j , w j + w j ]. A feasible solution must obey the capacity constraint (2) no matter what the actual weight of each item turns out to be. However, uncertainty is bounded by an integer parameter Γ indicating that at most Γ items in the solution can change from their nominal value w j to an arbitrary value in the interval. Clearly a diminution of a weight below the nominal value does not affect feasibility and in the worst case all changed weights reach their upper limit. Hence a feasible solution consists of a subset of items J ⊆ N such that. We call this problem the Robust Knapsack Problem (RKP). It was recently considered by Monaci and Pferschy [14] who studied the worst-case ratio between the optimal solution value of (KP) and that of (RKP), as well as the ratio between the associated fractional relaxations. A similar setting with the restriction that w j = δ w j for all j for some given constant δ > 0 was introduced by Bertsimas and Sim [3] . Clearly this is a particular case of the model considered in this paper. In the following we assume, without loss of generality, that all input data are integer and items are sorted according to non-increasing w j values. For notational simplicity we define the increased weights byŵ j = w j + w j for all j. In addition, for any given set S of items, we will denote by p(S) = j∈S p j and w(S) = j∈S w j the total profit and weight, respectively, of the items in S.. In this paper we review exact solution algorithms for (RKP). Although it is an NP-hard problem, exact solutions can be found in reasonable time even for large instances (see in Section 6 the computing times for instances up to 5,000 items). Hence it is adequate to look for exact methods in solving (RKP) and it is interesting to compare the behavior of different algorithms. The algorithms proposed in the literature up-to-date present quite distinct features, although two of them can be shown to be very tightly intertwined.. In Section 2 we present a dynamic programming algorithm. The algorithm mimics the well known algorithm for the standard knapsack problem, but is able to take care of the upper weights once the items are sorted according to nonincreasing w j values. This algorithm, developed by the authors, is investigated in detail. In Section 2 we show its correctness, and, as in the usual knapsack algorithm, we show that a similar version obtained by exchanging the roles of weights and values can be also formulated thus paving the way to approximation schemes.. In Section 3 we address the delicate issue of implementing the algorithm with a reduced amount of memory, since, with a large number of items and large data coefficients, space requirements can constitute a problem.. Other exact approaches are presented in Section 4. In particular we review the integer programming model by Bertsimas and Sim [3] (Section 4.1), the iterative approach by Bertsimas and Sim [2] which requires solving n + 1 knapsack instances (Section 4.2) and the Branch-and-Cut algorithm by Fischetti and Monaci [7] in which the robustness requirements is modeled by cutting inequalities (Section 4.3).. The problem we investigate is a special combinatorial optimization problem that has been motivated by a particular modeling of the problem uncertainties. By and large this is the model which has received most attention in the literature, although a lot of research has been done to face problems with uncertain data (see, e.g., the recent survey by Bertsimas, Brown and Caramanis [1] ). As to uncertainty in knapsack problems, few contributions were proposed. (RKP) was first introduced by Bertsimas and Sim [2] , while Klopfenstein and Nace [11] defined a robust chance-constrained variant of the knapsack problem and studied the relation between feasible solutions of this problems an those of (RKP). A polyhedral study of (RKP) was conducted by the same authors in [12] , where some computational experiments with small instances (up to 20 items) were given. Recently, Büsing, Koster and Kutschka [4, 5] addressed the robust knapsack problem within the so-called recoverable robustness context in which one is required to produce a solution that is not necessarily feasible under uncertainty, but whose feasibility can be recovered by means of some legal moves. In [4, 5] , legal moves correspond to the removal of at most K items from the solution, so as to model a telecommunication network problem. For this problem, the authors gave different ILP formulations, cut separation procedures and computational experiments. In this paper we considered the robust knapsack problem, i.e., the uncertain variant of the well-known knapsack problem in which robustness is enforced according to the Bertsimas-Sim definition (see, [3] ). For this problem we presented a dynamic programming algorithm and studied its time and space complexities, as well as techniques aimed at reducing the space complexity. We then computationally tested this algorithm on a large set of randomly generated instances, and compared the performances of the algorithm with those of other exact approaches proposed in the literature for (RKP). Computational results showed that dynamic programming is a viable way for solving (RKP) even for large-sized instances. Computational results show that our dynamic programming algorithm is very effective in solving (RKP) instances where a small number of coefficients change, as its complexity grows linearly with Γ. For larger values of Γ the iterative solution of (KP) instances, as proposed by Bertsimas and Sim [2] , turns out to be the best algorithmic approach. However, the computing time of BS 2 may vary in a substantial way when solving instances of different type -although of the same size -which is not the case for algorithm DP , whose computing time is more stable. This is particularly evident when instances with c = 5000 are considered.. The two algorithms above outperform the remaining two algorithms, BS M ILP and BC. This is not surprising, as the latter are based on the use of a general purpose ILP solver, which is usually less efficient than ad hoc algorithms for knapsack problems. In addition, cutting planes are not the best suitable way to enforce robustness when problems with integer variables are considered, as noted by Fischetti and Monaci [7] , which leads to a number of unsolved instances in our testbed.

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


CENTORI: a global toroidal electromagnetic two-fluid plasma turbulence code A new global two-fluid electromagnetic turbulence code, CENTORI, has been developed for the purpose of studying magnetically-confined fusion plasmas on energy confinement timescales. This code is used to evolve the combined system of electron and ion fluid equations and Maxwell equations in toroidal configurations with axisymmetric equilibria. Uniquely, the equilibrium is co-evolved with the turbulence, and is thus modified by it. CENTORI is applicable to tokamaks of arbitrary aspect ratio and high plasma beta. A predictor-corrector, semi-implicit finite difference scheme is used to compute the time evolution of fluid quantities and fields. Vector operations and the evaluation of flux surface averages are speeded up by choosing the Jacobian of the transformation from laboratory to plasma coordinates to be a function of the equilibrium poloidal magnetic flux. A subroutine, GRASS, is used to co-evolve the plasma equilibrium by computing the steady-state solutions of a diffusion equation with a pseudo-time derivative. The code is written in Fortran 95 and is efficiently parallelized using Message Passing Interface (MPI). Illustrative examples of output from simulations of a tearing mode in a large aspect ratio tokamak plasma and of turbulence in an elongated conventional aspect ratio tokamak plasma are provided. Plasma confinement in tokamak experiments is determined partly by binary Coulomb collisions between charged particles, but mainly by turbulence and instabilities, which occur on scales ranging from particle Larmor radii to the system size. Understanding the nature of this turbulence is a key goal of thermonuclear fusion research, since the confinement time is one of the parameters that must be optimised in order to create burning plasma conditions. In order to simulate turbulence in tokamak plasmas it is necessary to either average the Vlasov equations of the particle species over gyro-angle (the gyrokinetic approach) or take full velocity-space moments of these equations (the fluid approach). The lower dimensionality of fluid models makes it possible to simulate larger systems over longer timescales, and for this reason fluid codes continue to play an important role in tokamak plasma modelling. Some of these codes are based on electrostatic models [1, 2] or employ flux tube geometry [3] , while others are designed specifically for the purpose of simulating edge plasma phenomena, such as edge localised modes (ELMs) [4, 5] . A global magnetohydrodynamic (MHD) code NIMROD [6] has also been applied to the modelling of ELMs [7] , in addition to a range of other MHD instabilities in several different toroidal configurations [8] . In order to model turbulent transport on confinement and resistive diffusion timescales in an electromagnetic global code, it is necessary to include two-fluid effects, and it is also desirable to co-evolve the equilibrium.. In this paper we describe CENTORI (Culham Emulator of Numerical TORI), a new toroidal two-fluid, electromagnetic turbulence simulation code that meets these requirements. It can be used to describe the co-evolution of turbulence, MHD instabilities and equilibrium in tokamak plasmas with arbitrary aspect ratio and high plasma beta (ratio of plasma pressure to magnetic field energy density). It is designed for the specific purpose of simulating global two-fluid electromagnetic tokamak plasma turbulence on confinement timescales, in realistic geometries and in conditions such as those found in the present-day machines MAST [9] and JET [10] , and in the forthcoming international fusion experiment ITER [11] . Turbulent modes in tokamak plasmas are typically drift waves, which are predominantly electrostatic waves driven by temperature or density gradients. An important example is the ion temperature gradient mode, which has wavelengths perpendicular to the magnetic field of the order of the ion Larmor radius ρ i [12] . Many tokamak turbulence codes, such as the electrostatic fluid codes mentioned above and also gyrokinetic codes such as Kinezero [13] , are designed specifically for the modelling of drift waves in a fixed, prescribed plasma equilibrium. CENTORI, on the other hand, is designed to study the interaction between drift waves and MHD instabilities, which generally occur at longer wavelengths, ranging up to the system size, in a co-evolving equilibrium. However fluid codes such as CENTORI cannot be used to model explicitly instabilities that occur on the smallest tokamakrelevant spatial scales, in particular length scales below the ion Larmor radius. Phenomena on the scale of the electron skin depth δ e are specifically excluded from the model used in CENTORI, since electron inertia is neglected (in any event δ e < ρ i unless the plasma beta is less than the electron to ion mass ratio, which is not normally the case in the core region of tokamak plasmas). The drift waves described by gyro-kinetic theory have frequencies of the order of ρ * Ω where ρ * is particle Larmor radius normalised to the equilibrium gradient scale length and Ω is the corresponding cyclotron frequency [14] . Two-fluid theory, on the other hand, can accommodate MHD modes such as global Alfvén eigenmodes [15] , which, in low beta plasmas, have frequencies higher than those of ion drift waves. CENTORI can be used to study processes occurring on timescales ranging from the reciprocal Alfvén frequency to the energy confinement time.. The physics model implemented in CENTORI is very similar to that used in CUTIE, a global two-fluid electromagnetic turbulence code which was based on periodic cylinder geometry and was restricted to large aspect ratio plasmas with circular poloidal cross-section [16] . Despite these restrictions, CUTIE has been used for a number of successful applications. For example, it was recently shown to reproduce experimentally-observed transitions to a high confinement 2 mode of plasma operation via the control of particle fuelling in the COMPASS-D tokamak [17] . This paper is organised as follows. In Section 2 we describe the relationship between laboratory coordinates and plasma coordinates, in which the fluid and Maxwell equations are evolved in CENTORI. The form in which these equations are solved is discussed in Sections 3-5, while initial and boundary conditions are discussed in Section 6. Sections 7 and 8 are concerned respectively with the distinction made in the code between mean and fluctuating quantities, and global quantities evolved by it, such as plasma beta. In Section 9 we describe GRASS, a subroutine of CENTORI which co-evolves the plasma equilibrium using a novel pseudo-transient method. Operational and technical aspects of the CENTORI package and the code structure are discussed in Section 10, while in Section 11 we present some illustrative examples of output from a simulation of a large aspect ratio tokamak plasma. We have presented a comprehensive description of a novel two-fluid electromagnetic plasma turbulence code, CENTORI, together with sample output from a CENTORI simulation of a large aspect ratio tokamak plasma. The code is used to compute self-consistently the time evolution of plasma fluid quantities and fields in a toroidal configuration of arbitrary aspect ratio and plasma beta. The code is parallelised, and the equations are represented in fully finite difference form, ensuring good scalability. The equations are solved in a plasma coordinate system that is defined such that the Jacobian of the transformation from laboratory coordinates is a function only of the equilibrium poloidal flux, thereby accelerating vector operations and the evaluation of flux surface averages. GRASS, a subroutine of CENTORI, is used to determine the plasma equilibrium (and hence the plasma coordinates in which the fluid and Maxwell equations are solved) by computing the steady-state solutions of a diffusion equation with a pseudo-time derivative. The physics model implemented in CENTORI is based solidly on that used in the highly-successful CUTIE code, and we are confident that it will prove to be a powerful tool for the study of heat, particle and momentum transport in tokamak plasmas. In a forthcoming paper we will report the results of the first global simulations, performed using CENTORI, of electromagnetic, nonlinearlysaturated turbulence and transport in a spherical tokamak plasma (MAST). Figure 9 : Contours of toroidal current density fluctuations in poloidal plane at t = 1 ms. The × symbol in the outer midplane indicates the approximate location corresponding to the results shown in Fig. 8 .

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Content-based histopathology image retrieval using a kernel-based semantic annotation framework Large amounts of histology images are captured and archived in pathology departments due to the ever expanding use of digital microscopy. The ability to manage and access these collections of digital images is regarded as a key component of next generation medical imaging systems. This paper addresses the problem of retrieving histopathology images from a large collection using an example image as query. The proposed approach automatically annotates the images in the collection, as well as the query images, with high-level semantic concepts. This semantic representation delivers an improved retrieval performance providing more meaningful results. We model the problem of automatic image annotation using kernel methods, resulting in a unified framework that includes: (1) multiple features for image representation, (2) a feature integration and selection mechanism (3) and an automatic semantic image annotation strategy. An extensive experimental evaluation demonstrated the effectiveness of the proposed framework to build meaningful image representations for learning and useful semantic annotations for image retrieval. The use of digital imaging in histopathology has been rapidly rising during the last few years [1] . Pathology departments using digital microscopy equipments can share slides without sending the glass, and can include images in electronic reports and publications [2] . Then, large amounts of digital histopathology images are constantly acquired as part of the routine operation in these specialized centers. Image collections are stored using information technologies such as Picture Archiving and Communication Systems (PACS), but they remain archived in the long term, basically because after some time they result useless, since the main actual exploit is the one associated to the specific clinical case. Nevertheless, these large image collections are a potential source of information and knowledge, which may support educational activities, research studies and even the clinical decision making process itself, if the right tools to access these collections are developed [3] .. Accessing a collection of histology images can be done using different query paradigms. For instance, using structured queries in conventional databases, using keywords in a text retrieval engine or using example images in a contentbased image retrieval system [4] . In this paper, we consider the problem of retrieving histopathology images from the collection using example images as queries, that is, the user presents reference images to the system, and the system uses the visual content to match similar images in the collection. Using visual contents to search for images is considered a beneficial technology for next generation medical imaging systems [3] , and is also considered one of the major challenges in image retrieval research. The problem underneath an image retrieval system is the mechanism for identifying relevant images, which is mostly a similarity measure between image contents. Different similarity measures have been proposed and studied for medical image retrieval using low-level features, which focus mainly on characterizing visual properties that can be computed from pixels [5, 6] . However, bridging the semantic gap [7] has become the main focus of image retrieval research, i.e. reducing the discrepancy between the information extracted by low-level features and the high level interpretations of human beings on the same images.. This research has led to semantic representations of histology images to address the problem of identifying semantically related images rather than just visually similar images [8] . These strategies aim to provide better search results which are more likely to match contents in the same way as physicians would do. This paper presents a framework to archive and retrieve histopathology images by content. To overcome the problem of delivering semantically valid images for a medical task, we propose an automatic image annotation framework that recognizes high-level concepts after analyzing image visual contents. The main contribution of this work is a strategy to generate multi-feature image representations for the automatic recognition of histopathology concepts. This strategy has been designed as a unified framework based on kernel methods theory, and includes three main aspects for semantic image content recognition: (1) multiple visual features to represent histology image contents (2) appropriate kernel functions to harness the structure of the input data, and (3) the optimal combination of multiple kernel functions according to the underlying image semantics. Kernel functions are fused using a weighted linear combination, whose weights are found by an optimization process that maximizes the correlation between low-level features, represented by kernel functions, and high-level semantic concepts.. The proposed strategy has been implemented and evaluated using a large database of real histopathology images, extracted from medical records of a pathology lab. An extensive validation was conducted using the ground truth provided by pathologists. The experimental evaluation showed that the semantic image annotation leads to an average improvement in the retrieval response of 57% when it is compared to visual search using only low-level features. Also, the results show that a multi-feature representation for visual contents can be progressively improved by operating kernel functions. We found that modeling feature structure and non-linear patterns with kernel functions is more likely to improve the discriminative power of multi-feature representation spaces. The contents of this paper are organized as follows: Section 2 presents a review of previous works related to histology image retrieval. Section 3 introduces the collection of histopathology images used in this study. Section 4 describes the proposed methods for automatic image annotation, based on kernel methods.. The experimental setup and results are presented in Section 5. Finally, Section 6 presents the concluding remarks and future work. This paper presented a novel strategy for automatic annotation of histopathology images. The proposed framework is entirely based on kernel methods, allowing to deal with multiple visual descriptors to build expressive feature spaces.. The generated annotations are used to search images with similar annotations in an image retrieval system under the query-by-example paradigm. We implemented and evaluated the model following an extensive experimentation on real histopathology images. The proposed strategy to retrieve semantically valid results from a large collection of histopathology images showed an average improvement of 57% when compared to visual search, based on low-level features.. In our future work, we consider the use of more specialized visual features for histology images to improve the final search quality, and the automatic analysis of co-occurrence among annotations to differentiate between normal and abnormal images. Digital microscopy is a very broad field of active research, that ranges from image acquisition and compression [9] to automatic disease detection [10] . Contentbased retrieval of microscopy and histology images is one of these areas of research that is receiving increasing attention from researchers. Image retrieval focuses on methods and tools for managing large collections of digital slides, providing effective access to all the available information, in contrast to other research areas that focus on processing individual images for making automatic decisions, such as automated grading [11, 12] or tissue classification [13, 14] .. Image retrieval on pathology image collections was approached by Zheng et al. [15] using low-level features. Four different visual features were studied to measure the discriminative power of similarity measures to correctly identify relevant images given an example query. They reported a correlation between the computed similarity and pathological significance on the tested collection, without the use of domain knowledge. However, to scale up the system performance, low-level features may be insufficient. Tang et al. [8] investigated the role of semantic information to represent local image content in gastro-intestinal tissue images. Their method aim to assign a semantic annotation to each region on the image using machine learning algorithms. The main disadvantage of this approach is the need for manual annotations made on specific regions for a large enough sample of training images. Naik et al. [16] also approached the problem of histology image retrieval using semantic knowledge. They used multiple texture and architectural features of tissues, and employed a boosting algorithm to identify feature weights that maximizes retrieval and classification performance.. In this paper we address the problem of semantic image retrieval for histopathology images, using an automatic annotation strategy. Our previous work on histopathology image retrieval [17] showed the potential of using semantic features to represent image contents. In this paper we extend that work by generalizing the representation of visual contents in a set of multiple heterogeneous features rather than a unique feature vector. Also, we recast the image annotation problem in terms of kernel methods for image representation, feature selection and concept detection, as is presented in the following Sections.

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