Data Correction for Gantry-tilted Local CT Abstract-Gantry-tilted helical multi-slice Computed Tomography (CT) refers to the helical scanning CT system equipped with multi-row detector operating at some gantry tilting angle. Its purpose is to avoid the area which is vulnerable to the X-ray radiation. The local tomography is to reduce the total radiation dose by only scanning the region of interest for image reconstruction. In this paper we consider the scanning scheme, and incorporate the local tomography technique with the gantry-tilted helical multi-slice CT. The image degradation problem caused by gantry tilting is studied, and a new error correction method is proposed to deal with this problem in the local CT. Computer simulation shows that the proposed method can enhance the local imaging performance in terms of image sharpness and artifacts reduction. In the past decade, there are two major technical achievements on Computed Tomography (CT): the helical scanning CT (HCT) and multi-slice CT (MCT) [1] - [4] . The HCT enables a continuous and fast scanning than the step-andshoot operation, while the MCT offers a big volume coverage leading to enhancement of the scanning speed and image resolution in the patient feeding direction.. In medical diagnosis, it sometimes needs to tilt the CT gantry with respect to the patient and couch for some special scanning. After the gantry tilting, however, the geometry of helical MCT is made more complex, and the image quality can be unfortunately degraded. This introduced errors in the projection data set and this problem has been studied and some methods have been proposed for the error correction to improve the processing and image quality of the gantrytilted CT [5] [6] .. The gantry-tilted CT scanning is applied to the situation where higher scanning safety is needed and scanning of the vulnerable to radiation area is to be avoided. In this sense, it is quite meaningful to apply the local tomography technique in the gantry-tilted scanning. The local tomography refers to reconstructing the image of local region of interest (ROI) using only local projection data and aims at reducing the X-ray exposure to the patient.. In this paper we incorporate the wavelet based local tomography ( [7] - [10] ) into the gantry-tilted MCT reconstruction, and propose a data-correction method for reducing the image degradation caused by the gantry tilting. We will show that imaging performance can be enhanced by the proposed method. The outline of this paper is as follows. In Section II the geometry of gantry-tilted helical multi-slice CT scanning is described and the image degradation due to the isocenter derivation in the gantry tilted scanning is analyzed. The proposed error correction method for dealing with the image degradation is presented in Section III, followed by the local reconstruction procedure in Section IV. Computered simulation results of the proposed and existing methods' comparison are given in Section V. The gantry-tilted CT scanning allows scanning the ROI while avoiding the vulnerable-to-radiation area. It is quite meaningful and useful to employ the local tomography technique in the gantry-tilted CT for safer scanning. To solve the image degradation in the gantry-tilted CT, in this paper we analyze the data error caused by the isocenter deviation, and propose a linear interpolation method based on new data correction scheme to form a completed data set for local reconstruction. Comparing with the existing methods, the new proposed approach can provide a better reconstruction performance, as shown in the computer simulation results. The advantages of proposed method are that the data compensation can vary with the ROI's position and shape, and its computational load is considerably light since the compensation matrix can be pre-determined.

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Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs Abstract-The detection of microaneurysms in digital color fundus photographs is a critical first step in automated screening for diabetic retinopathy (DR), a common complication of diabetes. To accomplish this detection numerous methods have been published in the past but none of these was compared with each other on the same data. In this work we present the results of the first international microaneurysm detection competition, organized in The overall results show that microaneurysm detection is a challenging task for both the automatic methods as well as the human expert. There is room for improvement as the best performing system does not reach the performance of the human expert. The data associated with the ROC microaneurysm detection competition will remain publicly available and the website will continue accepting submissions.

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CLUMPY: a code for gamma-ray signals from dark matter structures We present the first public code for semi-analytical calculation of the γ-ray flux astrophysical J-factor from dark matter annihilation/decay in the Galaxy, including dark matter substructures. The core of the code is the calculation of the line of sight integral of the dark matter density squared (for annihilations) or density (for decaying dark matter). The code can be used in three modes: i) to draw skymaps from the Galactic smooth component and/or the substructure contributions, ii) to calculate the flux from a specific halo (that is not the Galactic halo, e.g. dwarf spheroidal galaxies) or iii) to perform simple statistical operations from a list of allowed DM profiles for a given object. Extragalactic contributions and other tracers of DM annihilation (e.g. positrons, antiprotons) will be included in a second release. Despite the several astrophysical evidences pointing at the existence of dark matter (flat rotation curves of galaxies, gravitational lensing, "bullet cluster", etc.), its nature still evades us. This search has become one of the major topic in both particle physics and astrophysics and is tackled using either direct or indirect detection methods. For the former, the hope is to directly witness the interaction of a dark matter particle with a detector. The indirect approaches aim at measuring the end products of dark matter annihilation/decay (e.g., γ-rays, positrons, antiprotons). The detection of γ-rays was soon recognised to be a promising channel [1, 2] . If such a signal is yet to be measured by the existing γ-rays observatories (FERMI, HESS, MAGIC, VERITAS), the prospect may significantly improve with the forthcoming next-generation instruments, such as CTA [3] .. Having a modelling tool that can compute the expected γ-ray flux from dark matter annihilation/decay in a wide range of astrophysical configurations and making it available to the community should prove very useful. This is the motivation for developing the code presented in this paper. This paper highlights clumpy's main features but a more thorough description can be found in the documentation coming with the source code. The paper is organised as follows. Section 2 presents the formulation of the generic γ-ray signal calculation that is performed by clumpy, with special emphasis on the contribution from DM clumps. The code is described in sect. 3, which presents the structure, parameters and functions that are at the core of clumpy. Section 4 deals with two examples of the science that can be performed with clumpy. We conclude and discuss the future developments of the code in sect. 5. The clumpy code is optimised to calculate the line-of-sight integration of the density and/or the density square of the DM distributions (smooth and clump distributions). It can be used by (i) experimentalists looking for realistic γ-ray skymaps to calculate their new instrument sensitivity, or simply to use them in model/template analyses for the DM diffuse emission near the Galactic centre (or for dSphs), (ii) for astrophysicists working on the reconstruction of the DM content of dSphs who wish to calculate the J factor as a by-product of their study, (iii) theoreticians who want to plug their preferred particle physics model and see what is the corresponding γ-ray flux in the Galaxy/dSph. The code is fully documented and provides many functions that makes it versatile and flexible.. Many improvements are possible for a future release. The simplest and more straightforward ones to consider are:. • Extra-galactic signal from the local structures (e.g. local clusters, Nezri et al. in prep.), but also from the highredshift isotropic contribution.. • The possibility to have the so-called Sommerfeld-like enhancements. This requires to couple the calculation of the spatial density of DM to the position-dependant velocity of the DM particles. ./bin/clumpy -g7. Clumpy will display the results automatically but these images have been produced with Gnuplot for aesthetic reasons. Note that ROOT will automatically display the image, but these ones have been plotted using the output files with Gnuplot. These maps are obtained with the following command option: ./bin/clumpy -h5. • Neutrino annihilation spectra and fluxes. Some other improvements are intrinsically more difficult to add, as they depend on astrophysical inputs that are not well determined. Indeed, in the absence of a smoking gun signature for indirect detection (e.g. a line detection), multi-wavelength and multi-messenger analyses seem to be a compulsory approach. To do so, one needs to take into account. • the anti-proton and anti-deuteron signal;. • electron and positron production;. • secondary multi-wavelength emissions from leptons.. Finally, the clumpy code could be coupled to some particle physics code to get more specific annihilation spectra (e.g., darkSUSY, Gondolo et al. [27] ; MicrOMEGAs, Belanger et al. [28] ), or to some cosmic-ray propagation code (e.g. GAL-PROP 9 , DRAGON 10 or USINE, Maurin et al., in preparation). to compare with the Galactic astrophysical backgrounds. This will be considered for a second release of the code. [GW]. [SGP]. [GE] Figure 6 : The geometry as defined in the clumpy simulation. Two frames are used: the Galactic Centre frame and the Galactic coordinates. For a given direction of observation (ψ, θ) (l.o.s. for line of sight), we will run over all the α and β directions within the solid angle ∆Ω. The Galactic Centre frame GC, x, y, z is a cartesian frame, where the (GC, x) and GC, y axes are in the Galactic plane, and the (GC, z) axis is pointing to the North Galactic pole. In this frame, the position of the Earth is (−R ⊙ , 0, 0), where R ⊙ is the distance between the Earth and the Galactic Centre.. The change of coordinates formulae are given in the documentation (geometry.h).

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Position-Invariant Robust Features for Long-Term Recognition of Dynamic Outdoor Scenes A novel Position-Invariant Robust Feature, designated as PIRF, is presented to address the problem of highly dynamic scene recognition. The PIRF is obtained by identifying existing local features (i.e. SIFT) that have a wide baseline visibility within a place (one place contains more than one sequential images). These wide-baseline visible features are then represented as a single PIRF, which is computed as an average of all descriptors associated with the PIRF. Particularly, PIRFs are robust against highly dynamical changes in scene: a single PIRF can be matched correctly against many features from many dynamical images. This paper also describes an approach to using these features for scene recognition. Recognition proceeds by matching an individual PIRF to a set of features from test images, with subsequent majority voting to identify a place with the highest matched PIRF. The PIRF system is trained and tested on 2000+ outdoor omnidirectional images and on COLD datasets. Despite its simplicity, PIRF offers a markedly better rate of recognition for dynamic outdoor scenes (ca. 90%) than the use of other features. Additionally, a robot navigation system based on PIRF (PIRF-Nav) can outperform other incremental topological mapping methods in terms of time (70% less) and memory. The number of PIRFs can be reduced further to reduce the time while retaining high accuracy, which makes it suitable for long-term recognition and localization. Localization is an indispensable capability for both humans and machines. Knowing "Where we are" has always been an important topic in robotics and computer vision communities. Especially for mobile robots, knowing its position is a fundamental requirement for navigation systems. The topic has been studied for more than two decades using several methods: metrical, topological, and hybrid (see [1] , [2] for reviews). Although sonar and laser scanners have traditionally been the sensory modalities of choice [3] , current advances in visual tools have made visual approaches more attractive, providing richer information at a lower price.. From the perspective of computer vision, an efficient robot vision system might need to overcome three difficulties: dynamical changes, viewpoint changes, and scene categorization. In a highly dynamic environment (i), places might look very different over time because of illumination changes (daytime, nighttime) and because of moved objects: parking lots are empty on holidays. These changes are dynamic because their appearances are stable only for only some period of time. Regarding the second sub-problem (ii), different viewpoints often make a scene look different. This problem also includes changes in weather and lighting conditions. An object's appearance might be very different when observed from different camera positions, even if viewed at exactly same time. Scene categorization (ii) describes how the robot understands the scene so that it can categorize new unseen places along with those it has seen previously. Inspired by biology, this ability further reduces the gap separating robots and humans. Recent scene recognition approaches might be divided into three main types:. Object-Based, Region-Based, and Context-Based.. To date, most approaches to scene recognition have been object-based [4] - [6] . Using such approaches, a scene location is recognized by identifying a set of landmarks known to be included in a scene. These approaches are prone to carrying over and amplifying low-level errors along the stream of processing. For instance, upstream identification of small objects (pixel-wise) is hindered by the downstream noise inherent to camera sensors and by varied lighting conditions. This is problematic in spacious environments where landmarks are more dispersed and more distant from the agent. This approach must be environment-specific to ensure the simplicity of selecting a small set of anchor objects as landmarks in an open problem.. For region-based scene recognition, the segmented image regions and their configurational relations are used to form a signature of a location. The major problem hindering this approach is reliable region-based segmentation, in which individual regions must be characterized robustly and associated. Naïve template matching involving a rigid relation is often insufficiently flexible in the face of undersegmentation or over-segmentation, which is often true with unconstrained environments, such as outdoors. Some techniques such as normalized-cut [17] , [35] are useful to improve segmentation quality. Nevertheless, the computation time for image segmentation might be problematic for real time applications.. Context-based approaches, unlike both previously described approaches, bypass traditional processing steps. Context-based approaches examine the input image as a whole and extract a low-dimensional signature that compactly summarizes the image's statistics and semantics. The challenge of discovering a compact and holistic representaCopyright c 2010 The Institute of Electronics, Information and Communication Engineers tion for unconstrained images has therefore prompted considerable research effort recently. Renniger and Malik [18] use a set of texture descriptors and a histogram to create an overall profile of an image. Ulrich and Nourbakhsh [19] build color histograms and perform matching using a voting classifier. Oliva and Torralba [20] encode some spatial information by performing 2D Fourier Transform analyses in individual image subregions on a regularly spaced grid. The resulting spatially arranged set of signatures, one per grid region, is further reduced using principal component analysis (PCA) to yield a unique low-dimensional image classification. In more recent implementations, Torralba et al. [21] used steerable wavelet pyramids instead of the Fourier Transform to solve the robotic localization.. Among the three approaches described above, PIRF is most related to an object-based approach, in the sense that natural landmarks would be used as a signature of place. Our selection is motivated by the observation that outdoor scenes generally include distant objects such as distant buildings or walls. These distant objects seem to appear constantly in scenes irrespective of the camera position. Even in a highly dynamic scene where major components of scenes are changed, these small distant objects still appear. In this case, global representation of whole scenes might be problematic; such scenes include many unstable nearby objects whose subsequent recognition fails. Therefore, we address this highly dynamic scene recognition problem as:. 1. how to detect objects autonomously which are visible to almost every position in such place; 2. how to detect objects autonomously which are unique to a single place; and 3. how to describe such objects precisely despite their distance.. These distant objects can be a good signature of each place; a group of these objects can be used efficiently to identify places. As described herein, we propose a Position-Invariant Robust Feature (PIRF) as an image local feature that solves these three problems. The PIRF is developed upon existing local descriptors such as Scale-Invariant Feature Transformation (SIFT) [7] and Speeded Up Robust Feature (SURF) [13] . Local features extracted from many individual images are filtered to derive the descriptors which appear repeatedly in almost every scene (taken at the same place). These descriptors are averaged to generate a single representative descriptor called PIRF. Filtering is done autonomously using simple feature matching as in an earlier study [7] . Considering Fig. 1 , given several SIFTs extracted from many interesting points of an image (left), and assuming that some SIFTs appear repeatedly in a few more sequential images, PIRFs are then generated by interpolating those corresponding SIFTs. Figure 1 (right) shows PIRFs extracted from standard SIFTs. In this case, almost all PIRFs are only of distant objects, whose appearances are very stable. This solves the first problem. One place can be represented using several PIRFs because one place might include many distant objects; each requires many PIRFs for representation. For later reference in this paper, we call these representative PIRFs (for one individual place) a PIRF-dictionary. For example, three places require three PIRF-dictionaries for representation. Because of the descriptive power of the existing descriptor, detected objects can be described precisely using a set of PIRFs (representatives of slow-moving SIFTs or SURFs). This solves the third problem described earlier. However, it is also clear that collecting many PIRFs will eventually pose the problem of many duplicated PIRFs, which confuse recognition in the long run. Therefore, we additionally propose a technique for eliminating these redundant PIRFs. The technique is incremental; at any time, it can rapidly search for redundant PIRFs and delete them from memory. This solves the second problem above.. We also describe a simple approach to use PIRFs for scene recognition. The recognition system is portrayed in Fig. 2 . Assume that an environment contains five separate places. Each place has its own PIRF-dictionary D i , i ≤ 5 for representation. First, a set of SIFTs is extracted from a testing image. Feature matching is performed to match each single SIFT to a set of PIRFs in each dictionary. A place, whose dictionary contains the highest number of PIRFs that can be matched to the extracted SIFTs, is justified as the winner. In Fig. 2 , for instance, both the first and the second image belong to place 1 because the number of matches between SIFTs and PIRFs in D 1 is the highest. To demonstrate the advantages of PIRF, we test it on 1000+ outdoor omnidirectional images collected from two campuses. Training and testing data are collected, respectively, on holidays and weekdays to address the difficulties of highly dynamic changes over time. The results show that PIRF obtains a markedly higher rate of recognition than other features.. We also describe a simple robot navigation method that uses PIRF (designated as PIRF-Nav) as a basic feature, to confirm PIRF's importance in relation to robotics. The PIRF-Nav is tested on one campus. The image dataset is identical to that used previously for scene recognition. Results show that PIRF-Nav outperforms Incremental Spectral Clustering [15] , [16] in both time and accuracy. The results obtained by our experiments show that PIRF recognizes large sets of both indoor and outdoor images without the help of supervised learning tools such as SVM or HMM. Precisely capturing the points of interest from distant objects, the number of local descriptors can be markedly reduced while preserving their discriminative power.. Regarding accuracy, PIRF clearly outperforms other features in outdoor scenes; it does well even in an indoor environment where distant objects are not ubiquitous. Instead of natural distant objects, PIRF captures nearby objects with a stable appearance. Figure 10 presents samples of testing images with PIRFs matched to the correct dictionaries for both indoors and outdoors. In Fig. 10 (Bottom) PIRF captures the decorations on the wall instead of distant objects.. One concern related to long-term scene recognition is that the number of samples for each place is unbalanced because some places take less time to walk through, but others can take much more time (i.e., the "corridor" dictionary from Ljubljana contains more than 10,000+ PIRFs, whereas the dictionary of "bathroom" contains only ca. 2000 PIRFs). Regarding this concern, thanks to the discriminative power of SIFT, all 40 combined dictionaries of PIRFs are sufficiently distinctive to support recognition.. Another remarkable advantage of PIRF is the reduction rate of memory. Because PIRFs are sufficient to represent the place, the reference images are no longer needed. Most previous approaches work with a database of refer- Fig. 10 The sample images described by PIRF. The images are taken from A28, A21, and C03 respectively. For outdoor scenes, the PIRF tries to describe the distant object such as building. For indoor, it captures the most stable objects such as walls.. ence images [10] , [19] for which the size depends heavily on the area size. As depicted in Fig. 8 , the memory necessary to store all 3091 images (for combined sites) is ca. 991 MB, whereas the memory required for storing PIRFs is 57.65 MB (71669 PIRFs, no reduction). Therefore, using PIRF reduces the necessary memory size by ca. 94%.. The junction detection module, which is used to partition data into classes instead of clustering, is not the main emphasis of this study. This problem might be regarded as a problem of robot perception. A robot who cannot detect the junction would be like a man who forgets to notice an intersection. Such a junction can be treated simply as a normal straight path. Moreover, without a junction detection system, PIRF can be used simply with preliminary partitioned data like those described in earlier studies [31] , [32] , [36] .. A profound effect of using PIRF is the utilization of stable distant object information. However, PIRF has some limitations and limited future research directions for improvement. One disadvantage of the current PIRF implementation is that (i) it strongly relies on the efficiency of the local descriptors. SIFT is a highly discriminative local descriptor. Therefore, the extracted PIRF can capture distinctive features from objects precisely. On the other hand, this disadvantage makes PIRF flexible for use with other local descriptors, i.e. speeded up robust feature (SURF). Second, (ii) PIRF requires input images as the sequences. Although we have claimed that PIRF can solve the kidnapped robot problem (appearance-based localization), it requires that the length of image sequence be sufficient for PIRF extraction (i.e. three images). In other words, PIRF is currently limited to the recognition problem; its descriptive power is too great to be used in the problem of categorization or understanding. Third, in this paper, we use PIRF in a simple manner to recognize scenes. (iii) Collecting numerous PIRFs from many places might finally produce a problem of duplicated features. In addition to our PIRF reduction, vector quantization might be another good choice. Because PIRF is a distinctive feature in a highly dynamic environment, bag-of-PIRF might be a good solution for highly dynamic environments. Finally, although the time in PIRF extraction is faster than that of either Gist or sPACT, its recognition time is slower (although it is faster than ISC). In this study, one place required about 500-1000 PIRFs for representation. Room exists for improvement here; one might be able to compress these PIRFs further to speed up the recognition time.. The PIRF features, despite their simple implementation, can achieve promising localization performance, especially in terms of computation time. Comparing the current ISC method [16] , PIRF is useful to build the topological map incrementally in considerably less time. Although the current PIRF still requires more than a single image for PIRF extraction, the current result appears promising for future improvement. The localization time (single-image classification) is also shortened considerably because the dictionaries are sufficient for place recognition instead of databases of reference images. Importantly, we do not claim that PIRF-Nav is the most suitable navigation approach for PIRF. We merely describe a simple navigation approach to demonstrate that PIRF is useful for the robotic development community. The standard local descriptors used by the BoW approach [23] , [29] do not perform well in highly dynamic scenes. We believe that one might create more efficient robot navigation by considering PIRF, i.e. BoW created from three feature spaces, one of which is PIRF. Various approaches used in the past have addressed Scene and Place recognition. Many effective features and various modes of use have been proposed. Histograms of image properties, e.g. color [19] or image derivatives have been used widely in place recognition. However, after SIFT [7] was popularized among the vision community, it came to dominate feature choice in place recognition systems [15] , [22] , [23] . The SIFT features are invariant to scale and are robust to rotation changes. The 128 dimensional SIFT descriptors have high discriminative power, but are simultaneously robust to local variations [39] . For place recognition, SIFT outperforms edge points [9] , pixel intensities [38] , and steerable pyramids [37] .. As the most appealing descriptor for practical uses, SIFT has been used widely in appearance-based navigation [10] , [11] because matching digital image contents among different views of scene requires a good distinctive invariant feature. Although SIFT satisfies such a requirement, its computation time and memory cost are high. As a remedy, Ledwich and Williams [12] reduced SIFT features by taking advantage of the structure of the indoor environment where average view-depths of most images are short. This makes vertical planes such as walls dominate an image's composition. The rotational invariance of SIFT has also been removed by assuming that the viewpoint for indoor images will be stable to rotation around the view axis, resulting in a non-rotated orientation of the keypoint descriptors located on vertical surfaces. The method is specifically useful for indoor environments.. Meanwhile, Oliva and Torralba [20] suggested that recognition of scenes can be achieved using "global configurations", without detailed object information. Consequently, statistical analysis of SIFT distribution became popular. Torralba et al. [21] use global image features to generate Gaussian Mixture Models for place recognition, using fixed variance. The method gives limited tolerance for appearance variation and is not invariant to translation or scale changes. Lazebnik et al. [9] use the k-means algorithm to cluster SIFT features, and cluster centers were used as the codebook to solve 15-class scene recognition. Cummins and Newman [29] integrate bag-of-visual-words (BoW) into the recursive probabilistic Bayesian framework and achieve performance beyond the localization. That method can determine that a new image has come from a previously unseen place. Later, Angeli et al. [23] proposed incremental BoW. Starting from an empty dictionary, the system can gradually collect new words while localizing places. Recently, Wu and Rehg [36] proposed the spatial Principle Component Analysis on Census Transform (sPACT) as a feature for scene recognition and categorization. Its performance has proven to be better than that of the BoW method [9] . The authors reported the highest accuracy over the KTH-IDOL dataset [31] .. In robotics, scene recognition and localization are also important topics. Several previously proposed methods should also be acknowledged here. Robot localization can be done based on vision alone [19] , [24] , [25] , [30] , or based on combinations of vision and other sensors (e.g., laserscanner, odometer) [19] , [26] - [29] . The methods can be performed either offline or online for use indoors or outdoors. Clemente et al. [26] generated metric maps based only on a hand-held camera. The key ingredient of the method is the inverse-depth representation [34] , which can estimate the depth of local features even in a single observation. The camera motion is an important concern for representing the main map. Royer et al. [25] reported their success in building the 3D reconstruction of map from sequential scenes by calculating the robot motion. Distinct from [25] and [34] , the method of Luo [32] emphasizes singleimage recognition: neither robot motions nor its position is incorporated in the method. The incremental support vector machine (SVM) is used to train the localization system for indoor use. The authors apply two techniques to extend SVM to an incremental version: Fixed Partition and ErrorDriven. Nonetheless, the incremental step used in their work contains some images partitioned by the user. The method must go offline until the step has been completed. In addition, the adopted error-driven technique requires a human to stand by the robot's side and tell it whether the recognition is corrected or not. Valgren and Lilienthal [15] , [16] use incremental spectral clustering (ISC) to cluster images and thereby create a topological map. Their method was reported as a fully incremental mapping method in highly dynamic outdoor environments (across seasons). The number of nodes in the map (data segmentation) is determined autonomously. The incremental BoW [23] is also a fully incremental mapping method. An empty dictionary can be updated incrementally. This method is considered as stateof-the-art vision-based mapping. However, the method addresses only the loop-closure detection problem; all obtained images up to the current time are included in the process of probabilistic decision-making. They did not report the recognition rate of a single image. In other words, their method is especially applicable for robotics. Furthermore, they did not address the problem of highly dynamic changes in scenes; all images in the study seem to be collected on one day.. Here we confirm again that PIRF is new. From a computer vision perspective, PIRF is more discriminative than global features, but less noisy than using standard local features. Unlike other object-based approaches, PIRF can autonomously detect the landmarks which are robust against highly dynamic changes of scenes. From a robotic perspective, navigation based on PIRF (PIRF-Nav) outperforms ISC in terms of time and accuracy. PIRF-Nav is incremental like [23] ; it can recognize single images and close the loops, while its performance is beyond localization like the method of [29] ; it can determine that a new observation come from a previously unseen places. Its robustness against dynamic change also leaves some room for additional improvement and applications (i.e., using PIRF as the local feature for generating BoW).

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


Information about information: a taxonomy of views "Information" is poorly defined in the Information Systems research literature, and is almost always unspecified, a reflexive, all-purpose but indiscriminant solution to an unbounded variety of problems. We present a taxonomy of four views-token, syntax, representation, and adaptation-to enable scholars and practitioners to specify their concept of information. This taxonomy is normative, but we also provide a background review of the etymology and chronology of information, and we sample uses of the term in current IS research. IS research will improve as the term information, via the taxonomy we contribute, is employed more explicitly and consistently.

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


Gene interaction enrichment and network analysis to identify dysregulated pathways and their interactions in complex diseases Background: The molecular behavior of biological systems can be described in terms of three fundamental components: (i) the physical entities, (ii) the interactions among these entities, and (iii) the dynamics of these entities and interactions. The mechanisms that drive complex disease can be productively viewed in the context of the perturbations of these components. One challenge in this regard is to identify the pathways altered in specific diseases. To address this challenge, Gene Set Enrichment Analysis (GSEA) and others have been developed, which focus on alterations of individual properties of the entities (such as gene expression). However, the dynamics of the interactions with respect to disease have been less well studied (i.e., properties of components ii and iii).Results: Here, we present a novel method called Gene Interaction Enrichment and Network Analysis (GIENA) to identify dysregulated gene interactions, i.e., pairs of genes whose relationships differ between disease and control. Four functions are defined to model the biologically relevant gene interactions of cooperation (sum of mRNA expression), competition (difference between mRNA expression), redundancy (maximum of expression), or dependency (minimum of expression) among the expression levels. The proposed framework identifies dysregulated interactions and pathways enriched in dysregulated interactions; points out interactions that are perturbed across pathways; and moreover, based on the biological annotation of each type of dysregulated interaction gives clues about the regulatory logic governing the systems level perturbation. We demonstrated the potential of GIENA using published datasets related to cancer. Conclusions: We showed that GIENA identifies dysregulated pathways that are missed by traditional enrichment methods based on the individual gene properties and that use of traditional methods combined with GIENA provides coverage of the largest number of relevant pathways. In addition, using the interactions detected by GIENA, specific gene networks both within and across pathways associated with the relevant phenotypes are constructed and analyzed. In summary, GIENA generalizes the gene-based enrichment method to detect pathways that are dysregulated in diseases based on changes in multiple types of interactions. Three datasets are used to demonstrate its potential; the results reveal several well-known and biologically meaningful pathways associated with cancer; and the results are highly reproducible. Comparison with GSA indicates that our method is comprehensive and efficient in terms of extracting weak signals and identifying pathways that are statistically significant but that a combination of GSA with GIENA provides the most comprehensive survey of pathway level dysregulation. Figure 6 Venn diagram of comparison of detected cooperation and redundancy interactions. Pathways detected by both profiles are similar (Table 1) ; the comparison of detected interactions also shows high level of similarity. The listed are number of overlapping pathways detected by two profiles divided by the number of pathways detected by the profile in each row. For other two datasets of breast cancer, the results are similar. Genome-wide mRNA expression data provide a rich resource for studying the molecular mechanisms of complex diseases. Through comparison of mRNA expression data between case and control samples, biomarkers and functional molecules significant for diagnosis, prognosis, and treatment have been identified for many complex diseases, including cancers [1, 2] . Extracting signals while rejecting noise in the data and interpreting the results to elucidate biological mechanisms relevant to disease are however, challenging [3] . Lists of hundreds of mRNAs identified as differentially expressed are interesting but can be difficult to interpret in terms of the complex underlying biological processes. In addition, there are in many cases limited overlap between lists of individually dysregulated genes identified by different laboratories that study the same disease [3, 4] . To overcome these challenges, a number of methods that consider genes not as individual entities but as members of biological relevant groups have been developed. Among such methods, gene set enrichment analysis (GSEA, [4] ) is very powerful and highly popular.. While being quite useful for system-level analyses, GSEA and similar methods, such as gene set analysis (GSA, [5] ) have a limitation: they focus only on the molecules (i.e., genes) that comprise a pathway and may neglect the changing interactions among genes within a pathway. Consequently, only pathways enriched in individual differentially expressed genes are detected with statistical significance. However, gene interactions and the dynamics of these interactions are also essential components of pathways and they underlie the orchestration of biological processes at many levels [6] . Interactions are associated with several dynamic characteristics, such as their direction, strength, permanence or transience, and presence or absence [6] . The biological influence of a pathway can be dramatically changed if the dynamics of the interactions in the pathway are altered. Indeed, several studies have demonstrated that the changes in the dynamics of interaction are associated with cancer and other diseases [7] [8] [9] .. In this vein, Zhang et al. have proposed a method in which the interactions were represented by the covariances or correlations between case and control classes, and showed that this approach provides biologically meaningful results [8] . Eddy et al. developed another method called DIfferential RAnk Conservation (DIRAC), which is based on the relative expression ranks of genes in a pathway [10, 11] . A limitation of this method, however, is that it assesses the change in the relationship between genes qualitatively, and misses cases in which (i) changes in expression are not large enough to change the relative order of genes or (ii) the difference between the expressions levels becomes even larger. Watkinson et al. defined the synergy among pairs of genes in terms of the mutual information between phenotype and the clustering of samples induced by the gene expression levels [12] and extracted disease-specific interactions in cancer. Another class of algorithms for system-level analysis of differential gene expression aims to identify dysregulated subnetworks in disease [2] . Using protein-protein interaction (PPI) networks as a template for assessing functional associations among genes, these methods identify groups of functionally related genes that exhibit collective mRNA-level differential expression with respect to disease based on: mutual information, cover-based algorithms and others [13, 14] . These results strongly suggest that dysregulation of interactions is as important a mechanism of disease as dysregulation of genes.. In order to further explore the dysregulation of gene interactions in disease, we have developed Gene Interaction Enrichment and Network Analysis (GIENA), which implements four mathematically simple, yet powerful interaction profile functions to model gene interactions.. The hypothesis behind the analysis, suggested by the work described above, is that dysregulation of interactions, like the dysregulation of individual genes revealed by GSA, is an important set of variables to analyze to provide a comprehensive understanding of mechanisms of disease. GIENA attempts to provide a set of interaction profiles that are associated with universal biological concepts. We then use the canonical pathway information to drive a specific network analysis to indentify hub genes that may mediate communication across pathways. These profiles and their biological interpretation are as follows: (i) the sum of mRNA expression levels, which models cooperation, (ii) the difference between mRNA expression levels models competition, (iii) the maximum mRNA expression level models redundancy, and (iv) the minimum mRNA expression level models dependency between a pair of genes. This framework provides a basis for interrogating both the dynamics of multiple types of interactions and gives clues to the regulatory logic of the perturbed networks, both within pathways and across pathways, as opposed to simply identifying the dysregulated players.. We evaluated these four interaction profiles using previously published mRNA expression datasets associated with cancer [15] [16] [17] [18] [19] . We detected multiple diseaseassociated gene interactions, which we annotated with their biological significance and compared to known literature findings to validate the results. Also, we used the approach to compare data from different experimental studies to examine the robustness of the method. Then, we constructed gene interaction networks based on these detected interactions and analyzed the results as well, in this case to better understand potential novel connections between pathways and to provide testable hypothesis for future experimental validations. Our results show that GIENA is able to reliably detect both known and novel dysregulated canonical pathways and dysregulated interaction networks related to the disease. In addition, the method gives consistent results across datasets from disparate laboratories. Overall, GIENA is systematic approach for the identification of dysregulated interactions at the pathway level and provides specific guidance for interpretation of disease-specific interactions in complex diseases.

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 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 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 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 O O O O O O O O O O


New resistorless tunable voltage-mode universal filter using single VDIBA Abstract To increase the universality of the recently introduced voltage differencing inverting buffered amplifier (VDIBA), this letter presents a new voltage-mode (VM) multi-input-single-output (MISO) universal filter. The proposed filter contains only single VDIBA, two capacitors, and one nMOS transistor, operated in triode region, and is used for resonance angular frequency tuning. Since in the structure no resistors are needed the filter can be classified as resistorless. The VM MISO filter compared with other active building block-based counterparts is very simple, it contains only few transistors, and has the smallest size area. Moreover, no component matching is required and it shows low sensitivity performance. The theoretical results are verified by SPICE simulations using TSMC 0.18 µm level-7 SCN018 CMOS process parameters with ±0.9 V supply voltages. In addition, the behavior of the proposed VM filter was also experimentally verified using commercially available integrated circuits OPA660 and AD830. Active filters are widely used as basic building blocks in analog signal processing. They are intended to replace the RLC filters that mainly suffer from the lack of integrability. Such active filter circuits are successfully used in video signal processing, communication systems, computer systems, telephone circuitry, broadcasting systems, control and instrumentation systems, etc. [1] . Recently, several active building block (ABB)-based minimal configuration voltage-mode (VM) multiinput single-output (MISO) universal filter structures have been reported in the literature in which for specific connection both capacitors are used as input terminal [2] - [13] . Unfortunately, the internal structures of ABBs in all of above reported filters suffer from the excessive number of transistors and the complexity of the used ABBs, which results in high silicon area in case of onchip fabrication. This can be also seen from Table 1 , which summarizes the advantages and disadvantages of previously reported VM MISO filters in [2] - [13] based on various relevant criteria. Full nomenclature of referred ABBs is given in Appendix. With the aim to minimize the silicon area on the chip, very recently the new versatile ABB so-called voltage differencing inverting buffered amplifier (VDIBA) has been presented [14] . Its application possibilities have been shown on the design of a resistorless and electronically tunable dual-output VM first-order all-pass filter [14] , four-phase quadrature oscillator, second-order all-pass filter [15] , single ended and differential mode triangle/square wave generators [16] .. In this letter we propose a new VM MISO universal filter using the just mentioned VDIBA as active element. The proposed filter structure is an alternative realization to recently published CMOS-RC VM MISO universal filter [17] and compared with other ABB-based counterparts in the literature [2] - [13] and CMOS-RC [17] , it shows the following advantages:. (i) compared to circuits in [2] , [3] , [5] - [8] , and [10] - [12] it realizes all five standard filter functions i.e. lowpass filter (LP), band-pass (BP), high-pass (HP), band-reject (BR), and all-pass (AP) without the need of additional inverting voltage inputs, which lead to higher complexity of these solutions, (ii) proposed filter is resistorless i.e. no external resistor is needed, a useful feature that cannot be found in [2] , [4] , [6] - [11] , [13] , and [17] , (iii) it employs only few transistors in total, hence the total transistor area is much smaller than [2] - [13] and [17] , (iv) no passive element or ABB parameter matching is required in the design, which is not available in [3] , [6] - [9] , and [11] , (v) suitable for low-voltage operation, while circuits [2] - [13] and [17] are supplied with ±1.3 V or higher DC voltages.. SPICE simulation results based on Taiwan Semiconductor Manufacturing Company (TSMC) 0.18 µm level-7 SCN018 CMOS process parameters and experimental measurement results using readily available integrated circuits (ICs) OPA660 and AD830 are included to support the theory. This letter presents a new VM MISO universal filter to increase the universality of the recently introduced new 'voltage differencing' device so-called voltage differencing inverting buffered amplifier. The proposed circuit is very simple and realizes all five standard filter functions, i.e. LP, BP, HP, BR, and AP in the same circuit topology without need of additional inverting voltage inputs. The proposed filter achieves easy tuning of the resonance angular frequency without seriously affecting the quality factor. In brief, the new filter offers the following advantages: (i) resistorless circuit, (ii) simple structure containing only few transistors, hence, the total transistor area is much smaller that available circuits in the literature, (iii) no component matching is required, (iv) it shows low sensitivity performance, and (v) suitable for low-voltage operation. SPICE simulations and experimental measurements using commercially available ICs confirm the feasibility of the proposed circuit.

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


Millennials among the Professional Workforce in Academic Libraries: Their Perspective on Leadership Abstract This study explores possible leadership perceptions of Millennials working in academic libraries, specifically their definition, the attributes they associate with leadership, whether they want to assume formal leadership roles, whether they perceive themselves as leaders, and whether they perceive leadership opportunities within their organizations and LIS professional associations. An online survey was utilized to gather the responses and the study participants comprised of Millennials (born 1982 or after) currently working full-time in libraries that were a member of the Committee on Institutional Cooperation (CIC), a consortium of the Big Ten universities and the University of Chicago in 2011–12. The leadership attributes which Millennials indicated as most important (i.e., group/teamwork, communication, respect, vision, and influence) are aspects of leadership that should be reviewed as current administrators prepare and plan professional development programming for future leadership opportunities that will most likely be filled by Millennials. Traditional leadership programming emphasizing individual skills may be less effective for today's Millennials. Incorporating "leadership development programs focused on next-generation leadership skills: collaboration, cross-cultural dialogue, team leadership and service leadership" should be the basis of creating new training models (Baggott, 2009) . Specifically, development opportunities for Millennials to prepare them for leadership roles should include developing effective teams, advance communications skills, and instilling positive organizational values such as respect for others. This study may provide guidance to hiring supervisors in looking for people with leadership skills to understand that the Millennials' comfort areas are teamwork, respect, and communication. This may mean that supervisors as a result, need to redesign positions to take advantage of the teamwork interests and skills while also providing Millennials with more support on the other leadership attributes that they might not be as skilled in.. Results from research conducted by Mackenzie and Smith (2009) related to management education for library directors suggests that there is no agreement from the library profession on how to prepare future librarians for leadership and managerial roles. Despite this disagreement, current training opportunities do exist through programs such as San Jose State University's Executive MLIS program (Blumenstein, 2005) or Simmons College's Managerial Leadership for the Information Professions (Hernon & Schwartz, 2008) . And finally, differences are identified in Millennials' leadership attributes when compared to those identified by current ARL directors, as pointed out in Young et al. (2006) . For example, Millennials identified building working relationships with others as important while ARL directors, in comparison, identified it as only mildly important (a 3 on a scale of 1 to 10 with 10 being most important. This information lends credence to the importance of considering and incorporating the preferences of the up and coming generations as the next round of leadership. A frequency count of leadership attributes referenced by Millennials when defining leadership in their own words revealed that group/teamwork was the top leadership attribute. This is consistent with research and general literature on Millennials as this group is "team oriented," and reflects their role in academic libraries where more than half work as either library assistants or librarians/faculty as opposed to leadership positions. They worked in groups and teams throughout their lives in both academic and extracurricular activities from wearing school uniforms to playing in team sports (Buchanan, 2010; Downing, 2006; Emeagwali, 2011; Howe and Strauss, 2000; Moore and Wells, 2009; Murray, 2011) . Further evaluation of the definitions also revealed that many individuals utilized terms associated with management in their responses as opposed to leadership terms. Vision and influence were the next attributes identified when Millennials define leadership in their own words. When respondents again elaborated in their own words whether they consider themselves a leader, group/teamwork and Communication emerged as top attributes.. The frequency count when respondents identified attributes associated with leadership from the list provided on the survey indicated that communication, respect, and vision were the top attributes identified. While the order may have changed slightly between the lists in terms of responses, if an attribute had a relatively high count on one question, it also had a relatively high count (Baggott, 2009; Buchanan, 2010) , and this researcher expected to find more leadership attributes associated with compassion or empathy in the frequency counts. However, only two individuals used attributes such as "compassion" or "empathy" in their definitions. This small number supports the findings of Twenge, Freeman, and Campbell (2012) which states that while the Millennials were required to do community service during school, it is not an attribute that is widely adopted by the Millennials outside of school requirements.. For the generation that is considered "hyper connected" or "always on", quality face-to-face time, relationships and personal connections appear to be extremely important to the Millennials (Behrstock-Sherratt and Coggshall, 2010; Coley, 2009; Dulin, 2008) . Immediate feedback is a component of face-to-face interaction, and the Millennials experienced immediate feedback since their preschool days (Howe & Strauss, 2000) . Again, the frequency counts did not reveal that many of the Millennials valued these attributes enough to mention them in their definitions of leadership. However, communication is an important component of the above mentioned attributes, and communication was identified as a top attribute as noted earlier (Dulin, 2008; Young et al., 2006) .. A scan of research on Generation Xers shows some similarities with Millennials in terms of "desire for a nurturing environment, teamwork, mentoring, open and frequent communication" and provide somewhat of a central theme for the two cohorts while recognizing that these two cohorts' attributes identified as most desirable are significantly different from the attributes identified as most desirable by academic library directors (Young et al., 2006) . For instance, only a small percentage (17.5) of the Gen-X attributes were ranked as highly by library directors as they were ranked by Gen-Xers (Young et al., 2006) .. The respondents provided similar answers to the questions about their interest in leadership. Both males and females responded positively to the questions of whether they consider themselves a leader, and if not in a leadership position, would they like to assume such a position in their library. When asked about whether barriers exist in their organization or the professional organizations they belong to-there was a difference in the responses. It appears that more respondents (50%) perceived barriers exist in assuming leadership roles in their organizations compared to only 18% of respondents who perceived that barriers exist in assuming leadership in their professional organizations.. Areas for future research include expanding the study to include all of the academic Association of Research Libraries institutions or to include the next tier of college and university libraries within the United States to verify that the results are consistent across public and private institutions as well as across geographic regions. Another area for research potentially includes examining management courses in library schools to determine if the curriculum addresses the changing view of leadership and the role Millennials will play in organizations. Furthermore, the evaluation of the effectiveness of changes in leadership training for preparing Millennials for leadership positions based on these findings would be useful to human resources managers and trainers.

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


Continuous Representations of Time-Series Gene Expression Data We present algorithms for time-series gene expression analysis that permit the principled estimation of unobserved time-points, clustering, and dataset alignment. Each expression profile is modeled as a cubic spline (piecewise polynomial) that is estimated from the observed data and every time point influences the overall smooth expression curve. We constrain the spline coefficients of genes in the same class to have similar expression patterns, while also allowing for gene specific parameters. We show that unobserved time-points can be reconstructed using our method with 10-15% less error when compared to previous best methods. Our clustering algorithm operates directly on the continuous representations of gene expression profiles, and we demonstrate that this is particularly effective when applied to nonuniformly sampled data. Our continuous alignment algorithm also avoids difficulties encountered by discrete approaches. In particular, our method allows for control of the number of degrees of freedom of the warp through the specification of parameterized functions, which helps to avoid overfitting. We demonstrate that our algorithm produces stable low-error alignments on real expression data and further show a specific application to yeast knockout data that produces biologically meaningful results. Principled methods for estimating unobserved time-points, clustering, and aligning microarray gene expression time-series are needed to make such data useful for detailed analysis. Datasets measuring temporal behavior of thousands of genes offer rich opportunities for computational biologists. For example, Dynamic Bayesian Networks may be used to build models and try to understand how genetic responses unfold. However, such modeling frameworks need a sufficient quantity of data in the appropriate format. Current gene expression time-series data often do not meet these requirements, since they may be missing data points, sampled non-uniformly, and measure biological processes that exhibit temporal variation.. In many applications, researchers may face the problem of reconstructing unobserved gene expression values. Values may not have been observed for two reasons. First, errors may occur in the experimental process that lead to corruption or absence of some expression measurements. Second, we may want to estimate expression values at time points different from those originally sampled. In either case, the nature of microarray data makes straightforward interpolation difficult. Data are often very noisy and there are few replicates. Thus, simple techniques such as interpolation of individual genes can lead to poor estimates. Additionally, in many cases there are a large number of missing time-points in a series for any given gene, making gene specific interpolation infeasible. In the case of clustering, the treatment of time-series can be problematic, as a time-series represents a set of dependent experiments. A particular problem arises when series are not sampled uniformly such as in [16, 4, 7] .. Variability in the timing of biological processes further complicates gene expression time-series analysis. The rate at which similar underlying processes such as the cell-cycle unfold can be expected to differ across organisms, genetic variants, and environmental conditions. For instance, Spellman. [16] analyze timeseries data for the yeast cell-cycle in which different methods were used to synchronize the cells. It is clear that the cycle lengths across the different experiments vary considerably, and that the series begin and end at different phases of the cell-cycle. Thus, one needs a method to align such series to make them comparable.. In this paper we use statistical spline estimation to represent time-series gene expression profiles as continuous curves. Our method takes into account the actual duration each time point represents, unlike most previous approaches that treat expression time-series like static data consisting of vectors of discrete samples [7, 12, 9] . Our algorithm generates a set of continuous curves that can be used directly for estimating unobserved data. However, although our method uses spline curves (piecewise polynomials) to represent gene expression profiles, it is not reasonable to fit each gene with an individual spline due to the issues with microarray datasets discussed above. Instead, we constrain the spline coefficients of genes in the same class to covary similarly, while also allowing for gene specific parameters. A class is a set of genes with similar expression profiles that may be constructed using prior biological knowledge or clustering methods. We present a clustering algorithm that infers classes automatically by operating directly on the continuous representations of expression profiles. This is particularly effective when applied to non-uniformly sampled data. However, note that our method does require data that has been sampled at a sufficiently high rate. We demonstrate in Section 6 that our method performs well on several such datasets, but for other datasets that have been sampled at rates too low to capture changes in the underlying biological processes, our method will not be effective. A future direction would be to use our method to determine the quality of the sampling rate.. Our alignment algorithm uses the same spline representation of expression profiles to continuously timewarp series. First, a parameterized function is chosen that maps the time-scale of one series into another.. Because we use parameterized functions, we are explicitly specifying the number of allowed degrees of freedom, which is helpful in avoiding overfitting. Our algorithm seeks to maximize the similarity between the two sets of expression profiles by adjusting the parameters of the warping function.. The remainder of this paper is organized as follows. In Section 2 we present a brief introduction to splines. In Section 3 we discuss our algorithm for estimating unobserved data and in Section 4 we extend this algorithm to perform clustering. In Section 5 we present our alignment algorithm. Section 6 presents applications of our method to expression data and Section 7 concludes the paper and suggests directions for future work. We presented a unified model and algorithms that use statistical spline estimation to represent gene timeseries expression profiles as continuous curves. Results using our approach on a large yeast cell-cycle data set demonstrate that our framework, when used for estimating unobserved time-points, clustering, and alignment of datasets has substantial advantages over other methods that treat time-series as vectors of points. Overall, we believe that as the analysis of dynamic genetic behavior becomes more sophisticated, principled model-based methods such as ours will become essential for reconstructing and combining data.. There are a number of interesting extensions that could be made to our work. Experimental biologists often determine the sampling rate for a time-series experiment based on knowledge about how quickly gene expression values change. These assessments often make little use of information that may be gleaned from previous expression experiments. Our algorithm could be used to find the "right" sampling rate for timeseries experiments, which could lead to substantial time/cost savings or improvements in biological results. Another way of extending this work is to develop a clustering algorithm that uses our alignment method in order to group genes that show similar kinetic changes between datasets. Another open problem is developing a principled method for determining the significance of the alignment error in order to automatically detect genes whose temporal behavior is altered between experiments. In this section we demonstrate the application of our method to expression time-series datasets, showing results for unobserved data estimation, clustering and alignment. Most of our results make use of the cell-cycle time-series data from Spellman ¢ ¡ £ ¦ ¥ [16] . In that paper, the authors identify 800 genes in Recently, several papers have focused on modeling and analyzing the temporal aspects of gene expression data. In Holter. [10] a time translational matrix is used to model the temporal relationships between different modes of the Singular Value Decomposition (SVD). Unlike our work, this method focuses on the SVD modes and not on specific genes. In addition, only relationships between time points that are sampled at the lowest common frequencies can be studied. Thus, not all available expression data can be used. In Zhao. [20] a statistical model is fit to all genes in order to find those that are cell cycle regulated. This method uses a custom tailored model, relying on the periodicity of the specific dataset analyzed, and is thus less general than our approach.. Several [17] several techniques for missing value estimations were explored. However, none of the suggested techniques take into account the actual times the points correspond to, and thus time series data is treated in the same way as static data. As a consequence, their techniques cannot estimate values for time-points between those measured in the original experiments.. There is a considerable statistical literature that deals with the problem of analyzing non-uniformly sampled data. These models, known as mixed-effect models [3] use spline estimation methods to construct a common class profile for their input data. Recently, James and Hastie [11] presented a reduced rank mixed effects model that was used for classifying medical time-series data. In this paper we extend these methods to gene expression data. Unlike the above papers, we focus on the gene specific aspects rather than the common class profile. In addition, we present a method that is able to deal with cases in which class membership is not given. Another difference between this work and [11] is that we do not use a reduced rank approach, since gene expression datasets contain information about thousands of genes.. Many clustering algorithms have been suggested for gene expression analysis (see [13] ). However, as far as we are aware, all these algorithms treat their input as a vector of data points, and do not take into account the actual times at which these points were sampled. In contrast, our algorithm weights time points differently according to the sampling rate.. Aach. [1] presented a method for aligning gene expression time-series that is based on Dynamic Time Warping, a discrete method that uses dynamic programming and is conceptually similar to sequence alignment algorithms. Unlike with our method, the allowed degrees of freedom of the warp operation in Aach. depends on the number of data points in the time-series. Their algorithm also allow mappings of multiple time-points to a single point, thus stopping time in one of the datasets. In contrast, our algorithm avoids temporal discontinuities by using a continuous warping representation. There is also a substantial body of work in the speech recognition and computer vision community that deals with data alignment. For instance, non-stationary Hidden Markov models with warping parameters have been used for alignment of speech data [5] , and mutual information based methods have been used for registering medical images [18] . However, these methods generally assume high resolution data, which is not the case with available gene expression datasets.

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