Detachable Object Detection: Segmentation and Depth Ordering from Short-Baseline Video We describe an approach for segmenting a moving image into regions that correspond to surfaces in the scene that are partially surrounded by the medium. It integrates both appearance and motion statistics into a cost functional that is seeded with occluded regions and minimized efficiently by solving a linear programming problem. Where a short observation time is insufficient to determine whether the object is detachable, the results of the minimization can be used to seed a more costly optimization based on a longer sequence of video data. The result is an entirely unsupervised scheme to detect and segment an arbitrary and unknown number of objects. We test our scheme to highlight the potential, as well as limitations, of our approach. A "detached object" was defined by Gibson [1] as "a layout of surfaces completely surrounded by the medium." He argued that a "topologically closed surface can be moved without breaking its surface" and that this gives objects "typical affordances like graspability." Unfortunately, unless they are floating in midair, most objects are attached to something. Absent the ability to actively intervene by attempting to grasp an object, the most we can determine from passive imaging data is whether it is partially surrounded by the medium. Therefore, we call a "detachable object" a (compact and simply connected) subset of the domain of an image that back-projects onto a layout of surfaces that is partially surrounded by the medium. These include protruding objects resting on the ground plane or hanging ( Fig. 1 ), but not flat pictures (Fig. 1-bottom) . Detachable objects are defined in the image, rather than the scene, since we want to detect them without having to explicitly reconstruct a spatial layout of surfaces. However, they correspond to regions of space that may be detached, given sufficient force ( Fig. 1-top) .. A direct consequence of the definition is that detachable objects yield occlusion phenomena as a result of either object or viewer motion. A single image is not sufficient to determine whether an object is detachable. At least two are necessary (three, in our approach), and more are beneficial.. In this paper, therefore, we detect detachable objects in two stages: First, we detect occlusion regions. They provide local (and sparse) evidence of detachable objects, as well as local depth ordering constraints. In a second stage, these constraints are integrated into a partition of the entire image into different depth layers (Section 2).. The first stage requires motion. While in some cases two adjacent video frames may be sufficient to generate occlusion regions at the interface with the medium, there is typically no motion discontinuity at the support region where the object is attached (e.g., the foot in Fig. 2-left ). Photometric statistics (color, texture, intensity) may provide evidence of the boundary of the object, but, in general, extended observation is needed to determine whether the contact region changes and therefore the object is detachable. For instance, the right foot in Fig. 2 -right is eventually lifted, the bench in Fig. 6 (top row) is seen against a varying background during the short sequence, and a moving car changes its point of contact with the ground, so they are all detachable, even though at no point are they actually detached.. The partition of the overall detection task into two stages sacrifices end-to-end optimality. However, it comes with a considerable benefit: The first stage admits a convex relaxation [2] ; we show that the second reduces to a linear programming (LP) problem. The key idea of our approach is to use occlusion regions as "seeds" in a supervised segmentation scheme, so the overall system is entirely unsupervised and does not require any manual labeling, scribbling, or annotation. We also perform model selection, that is, the determination of the number of depth layers, all by solving a linear program. If optimality and long-term temporal integration is a concern, we can always use our results to seed a global variational optimization scheme [3] . We have presented a method for detecting "objects" in a scene. While functionally important properties such as graspability cannot be ascertained from passive imaging data, we have defined properties that a moving image of an object must have in order to correlate to topological properties of the scene, such as being partially surrounded by the medium. We have defined these objects as detachable, conscious that this may be a misnomer in some cases, for instance, houses and trees, despite Fig. 1 .. Occlusions play a key role in the detection of detachable objects. Leveraging prior work, we show that once (binary) occlusion regions are available, integrating local ordering information into a coherent depth ordering map can be achieved by simple linear programming. The key to our approach is to convert a supervised segmentation problem into an unsupervised one using occlusions as the supervision mechanism. As a result, we have a fully unsupervised method for detecting and segmenting an unknown number of objects and estimating their number in the meantime, all by solving a linear program.. Our method is not a panacea. Despite our efforts to manage errors in the occlusion detection stage, we still suffer in the case of complete failure of the occlusion detection mechanism. In many cases, this is due to TABLE 1 Performance of Our Approach on the CMU Data Set Computed Based on the Covering Score (12) and Compared to [41] Fig. 7 . Top: Effects of increasing layer cost on model selection. From left to right, the number of regions is estimated as 4 (background, handarm, body and pivot leg, swinging leg), 3 (background, body, and swinging arm), and 2 (whole body). Note that the pivot foot is attached to the ground and is therefore classified as such. Middle: Effects of model selection on representative samples from the CMU data set. Ground truth is on the left, segmentation with manual setting of L ¼ 3 is in the middle, and segmentation with automatic model selection is on the right. Allowing automatic model selection enables the detection of the horse handles as separate detachable objects. On the bottom row, the algorithm fails to detect the closer tissue box as a detachable object because of insufficient parallax, so the box is either lumped with the horizontal cushion or with the entire couch. insufficient motion in the scene, and the results improve under extended temporal observation.. Nevertheless, our results can still be useful as initialization of a more involved optimization over an extended temporal observation, which we and others have already developed.. Our approach has a few tuning parameters, but fewer than most competing schemes since we perform model selection. Of course, even model selection requires tuning the tradeoff between complexity and fidelity, and there is no "right" choice of parameters.. Our approach also shares the limitation of all schemes that break down the original problem (detached object detection, in our case) into a number of sequential steps, whereby failure of the early stages of processing cause failure of the entire pipeline. This predicament comes with the benefit of solving an otherwise very complex computational problem using efficient numerical schemes. This paper relates to a vast literature on video-based segmentation, for instance [4] , [3] , [5] , [6] , [7] . Such approaches work well with few independently moving objects/layers and require a reasonable initialization that is increasingly difficult as the scene becomes more cluttered. Our method does not require knowledge of the number of objects [8] nor any user input, initial bounding boxes [43] , or scribbles [9] , [6] . Instead, occluded regions, detected with [2] , provide the "supervision mechanism" to seed to our method. For this reason, we refer to "short-baseline video" in our title, although the method is agnostic to whether one has short or wide-baseline motion, video or unordered snapshots, ego-motion or object motion, so long as they yield occlusion evidence. In principle, we could forgo a two-step approach and directly segment a video by processing it in a batch as in [10] , [11] ; however, we find that the algorithmic benefits outweigh the loss of end-toend optimality [12] .. Occlusions have, of course, been used before as a cue for layering: Brostow and Essa [13] , however, assume a static camera; Ogale et al. [14] classify three kinds of occlusions to prime motion segmentation; Feldman and Weinshall [15] perform local analysis using spatiotemporal filters to estimate depth ordering. Other methods for layered motion segmentation [16] , [17] , [18] , [19] also take occlusions into account, but use more restrictive parametric motion models, and do not scale well as the number of object increases beyond few. Similarly, [20] , [21] use occlusion boundaries inferred using appearance, motion, and depth cues [22] , [23] , [24] or T-junctions [25] to segment image sequences; however, they require the number of segments to be known a priori. Our work is different in spirit from those attempting to obtain a depth map from a single image [26] , [27] .. We capitalize on efficient supervised segmentation algorithms that, starting from labeled seeds ("scribbles" for background/foreground [28] , [29] ), produce a segmentation by solving a linear program. However, we use the occluded regions as "local scribbles" in a supervised segmentation scheme, Fig. 2 . This evidence is globally integrated into a graph partitioning algorithm that can be solved by linear programming. It assigns each pixel to a depth ordering label that provides a putative segmentation of detachable objects, even in the presence of multiple objects that otherwise cause standard segmentation schemes to lose convexity [30] . This idea is formalized in the next section where we introduce our optimization scheme.. In our implementation, for computational convenience we solve our linear program on a superpixel graph rather than the image lattice. However, this step is not conceptually necessary and can be foregone. Rather than solving (9) on the pixel grid, we precompute a partition of the domain into N nonoverlapping superpixels fs i g N i¼1 such that S N i¼1 s i ¼ D; s i \ s j ¼ ;, 8i 6 ¼ j, as done by Stein and Hebert [22] , using a watershed-based approach driven by a statistical multicue edge detector [36] . However, since superpixels may cross occlusion boundaries, we subdivide them to ensure that each superpixel is a subset of one of the three regions: , c , and Dnð [ c Þ. This superpixelization is not necessary, but it reduces the size of the linear program while enabling integration of simple low-level cues. Since the minimization (9) is already written for a general graph, it does not matter whether it is performed on the pixel grid or the superpixel graph. The only change is the weight matrix w ij , that in the case of superpixels is given by. Iðs i ÞÀ Iðs j ÞÞ 2 þ e Àk vðs i ÞÀ vðs j Þk 2 2 þ þ ð1 À P bðs i ; s j ÞÞ À 0:5 Á ;. ð10Þ. where. where P b : D ! ½0; 1 is the probability of a location being a (material, occlusion, or illumination) boundary, and ðxÞ ¼ 1=ð1 þ e Àx Þ is the sigmoid function. Note that the edge features are incorporated into the computation of the weights since the domain D is partitioned based on P b. In our experiments, we have assigned the weights , , to 0.25, 0.5, and 0.25, respectively, and the slope to 10.. We have used the CMU Occlusion/Object Boundary Dataset 1 [22] to evaluate our approach. It includes 16 test sequences with a variety of indoor and outdoor scenes, mostly seen under small camera motion. It provides ground-truth segmentation for a single reference frame in each sequence. We also report our results on the Soccer sequence [31] to illustrate the typical effect of extended temporal observations and on a sequence portraying a drawing of a girl playing ball near an intersection in West Vancouver (Fig. 4 ).

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


On the Probability of Generating a Lattice We study the problem of determining the probability that m vectors selected uniformly at random from the intersection of the full-rank lattice Λ in R n and the window [0, B) n generate Λ when B is chosen to be appropriately large. This problem plays an important role in the analysis of the success probability of quantum algorithms for solving the Discrete Logarithm Problem in infrastructures obtained from number fields and also for computing fundamental units of number fields.We provide the first complete and rigorous proof that 2n + 1 vectors suffice to generate Λ with constant probability (provided that B is chosen to be sufficiently large in terms of n and the covering radius of Λ and the last n + 1 vectors are sampled from a slightly larger window). Based on extensive computer simulations, we conjecture that only n+1 vectors sampled from one window suffice to generate Λ with constant success probability. If this conjecture is true, then a significantly better success probability of the above quantum algorithms can be guaranteed. Let G be a finite group. Denote by p m (G) the probability that m elements drawn uniformly at random from G with replacement generate G. The problem of determining or bounding this probability is of fundamental interest in group theory and has been extensively studied for various families of groups [Acc96, Pom01] .. The purpose of this paper is to study a very natural generalization of this problem from finite abelian groups to finitely generated abelian torsion-free groups. More precisely, we consider the case of lattices, i.e., discrete subgroups of R n . The problem is now to determine the probability that m vectors selected uniformly at random with replacement from the intersection of the full-rank lattice Λ in R n and a window X ⊂ R n generate Λ. We denote this probability by p m (Λ, X).. Our study this problem of was initially motivated by its relevance to quantum algorithms and quantum cryptanalysis, which we explain in more detail at the end of the paper. But we also believe that this problem is interesting on its own due to its appeal as a very natural and fundamental problem in lattice theory. In fact, it can be viewed as a generalization of the following elementary problem in number theory. For Λ = Z and X = [1, B], the probability p m (Λ, X) corresponds to the probability that m integers chosen uniformly at random from the set {1, . . . , B} with replacement are coprime. It is known that lim B→∞ p m (Z, B) = 1/ζ(m) where ζ denotes the Riemann zeta function. For Λ = Z n and X, the probability p m (Λ, X) is equal to the probability that the m × n matrix whose column vectors are selected uniformly at random from Λ ∩ X is unimodular. This problem was studied for special forms of X asymptotically. For X = [−B, B]. n , B → ∞, it was studied by G. Maze, J. Rosenthal and U. Wagner in [MRW11] , and for X = v + [−B, B]. n , B → ∞, where the entries of the vector v are bounded polynomially in terms of B, by S. Elizalde and K. Woods in [EW07] . In both cases, it was shown that the limit of the probability for B → ∞ is m j=m−n+1 ζ(j) −1 . Both works did not study the problem of bounding the probability in the non-asymptotic case, i.e., in the case where B is fixed.. In this paper, we consider the case where Λ is an arbitrary full-rank lattice and X = [0, B) n for a sufficiently large but fixed B. Ideally, we want to minimize m, while at the same time ensure that the probability p m (Λ, [0, B)) n is bounded from below by a nonzero constant. We use ν(Λ) to denote the covering radius of Λ, λ 1 (Λ) the length of a shortest (nonzero) vector of Λ, and det(Λ) the determinant of Λ.. Our two major contributions to the study of this problem are:. Theorem 1.1. Let Λ be a lattice of full rank in R n , and assume that B ≥ 8n n/2 · ν(Λ) and B 1 ≥ 8n 2 (n + 1)B. Assume that n vectors are selected uniformly at random from Λ ∩ [0, B) n and n + 1 vectors uniformly at random from Λ ∩ [0, B 1 ) n . If the vectors are sampled independently, then the probability that all these vectors generate Λ is at least. Unfortunately, our current approach requires m = 2n + 1 samples and two windows of different sizes to be able to prove that the probability of generating the lattice Λ is bounded from below by a non-zero constant. However, based on extensive numerical evidence, we formulate the following conjecture, which states that only m = n + 1 samples and only one window size suffice to attain a constant probability of generating the lattice. Conjecture 1.2. For every n ∈ N, there exists a constant 0 < c n < 1 and a rational function f n ∈ R(x, y) satisfying. such that the following holds: Let Λ be a lattice in R n and let B > f n (det Λ, λ 1 (Λ)). Then the probability that n+ 1 vectors chosen uniformly at random from Λ ∩ [0, B) n generate the lattice Λ is at least c n . Moreover, the constant c n can be chosen close to n+1 k=2 ζ(k) −1 .

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


Wet paper codes and the dual distance in steganography Abstract. In 1998 Crandall introduced a method based on coding theory to secretly embed a message in a digital support such as an image. Later Fridrich et al. improved this method to minimize the distortion introduced by the embedding; a process called wet paper. However, as previously emphasized in the literature, this method can fail during the embedding step. Here we find sufficient and necessary conditions to guarantee a successful embedding by studying the dual distance of a linear code. Since these results are essentially of combinatorial nature, they can be generalized to systematic codes, a large family containing all linear codes. We also compute the exact number of solutions and point out the relationship between wet paper codes and orthogonal arrays. Steganography is the science of transmitting messages in secret, so that no one other than the sender and receiver may detect the existence of hidden data. It is realized by embedding the information into innocuous cover objects, as digital images. To carry out this process, the sender first extracts a sequence c 1 , . . . , c n , of n bits from the image, e.g. the least significant bits of n pixels gray values. The cover vector c = (c 1 , . . . , c n ) is modified according to a certain algorithm for storing a secret message m 1 , . . . , m r . Then c 1 , . . . , c n are replaced by modified x 1 , . . . , x n in the cover image which is sent through the channel. By using the modified vector x the receiver is able to recover the hidden information. The embedding and recovering algorithms form the steganographic scheme of this system. Formally, a steganographic scheme (or stegoscheme) S of type [n, r] over the binary alphabet F 2 is a pair of functions (emb, rec). By using the embedding function emb : F Since all vectors c and m are in principle equiprobable, it is desirable that all codes C m have the same cardinality. The above equivalence has been extensively exploited to make stegoschemes that minimize the embedding distortion caused in the cover. Crandall [5] proposed the use of linear codes C and the partition of F n 2 into cosets {x + C : x ∈ F n 2 }. The obtained method is currently known as matrix encoding. If H is a parity check matrix for C and dec is syndrome decoding (see [24] as a general reference for all facts concerning error correcting codes), the obtained embedding and recovering maps are emb(c, m) = c − cl(cH T − m) and rec(x) = xH T , where cl(z) denotes the leader of the coset whose syndrome is z, that is the element of smallest Hamming weight whose syndrome is z . Matrix encoding has proved to be very efficient to minimize the embedding distortion in the cover, see [3, 6, 18, 19, 26] .. To reduce the chance of being detected by third parties, the changeable pixels in the cover image should be selected according to the characteristics of the image and the message to hide. In this case the recovering of the hidden data is more difficult, since the receiver does not know what pixels store information. Wet paper codes are designed to lock some components of the cover vector, preventing its modification in the embedding process. Mathematically wet paper codes can be explained as follows: imagine we want to embed a message m = (m 1 , . . . , m r ) ∈ F r 2 into a cover vector c = (c 1 , . . . , c n ) ∈ F n 2 . However, not all of coordinates of c can be used for hiding information: there is a set D ⊆ {1, . . . , n} of δ ≥ r dry coordinates that may be freely modified by the sender, while the other ℓ = n − δ coordinates are wet (or locked) and can not be altered during the embedding process. Let W = {1, . . . , n} \ D. The sets D and W are known to the sender but not to the receiver. Using the matrix encoding method we set emb(c, m) = x ∈ F n 2 with. [S] :. where H is a r × n matrix of full rank r. Locking positions minimize the possibility of detection during transmission but also generates a technical problem, since it is not guaranteed the existence of solutions for [S] . A natural question is to ask for the minimum number of dry coordinates (or equivalently, the maximum number of locked coordinates) necessary (respectively allowed) to make possible this process. We define the wet threshold of H as the minimum number τ of dry coordinates such that the system [S] has a solution for all c ∈ F n 2 , m ∈ F r 2 and W ⊆ {1, . . . , n} with #W ≤ n − τ . The number of extra dry symbols beyond r, τ − r is the strict overhead of the system. It is also of interest to compute the average overhead δ − r, where δ is the average minimum number of dry coordinates such that [S] has a solution over all possible choices of H, c, m and W. In detail, we want to determine (1) necessary and sufficient conditions to ensure that the system [S] has a solution; (2) the probability that [S] has a solution for given n, r and δ; (3) the average overhead δ − r to have a solution.. These problems have already be treated by several authors. Fridrich, Goljan and Soukal [7, 9, 8] studied (2) and showed that we can take r = δ + O(2 −δ/4 ) as δ → ∞, which gives a first answer to (3). Schönfeld and Winkler [18] treated the particular case of BCH codes, giving detailed computer results. Barbier, Augot and Fontaine [1] gave sufficient conditions for the existence of solutions by slightly modifying the problem [S] for linear codes. The case of Reed-Solomon codes has been treated by Fontaine and Galand in [6] . In some of these works the reader may find a study of the embedding efficiency as well as some implementation issues.. The aim of this article is to take another step in this study. We give exact answers to the questions (1) in section 2 and (3) in section 3 above, relating the wet threshold and overhead to well known parameters of the code having H as parity check matrix, and highlighting the role played by the dual distance. The relation with the weight hierarchy of codes is studied in section 4. Finally in section 5 we extend the matrix encoding method to the broad family of systematic codes, showing the relationship between stegoschemes, resilient functions and orthogonal arrays. We show that wet paper codes arising from systematic nonlinear codes may behave better than the ones coming from linear codes, in the sense that they may require less free positions to ensure the existence of solution. Proof. Since G is a parity check matrix of C ⊥ , a nonzero word in C ⊥ with support contained in W imposes a linear condition on the columns of G W and conversely.. More generally, if there exist w independent words of C ⊥ with support in W then we have rank(G W ) = ℓ − w. This suggests that the weight hierarchy of C also plays a role in the study of the solvability of [S] . This study will be conducted later in section 4. Proof. If δ ≥ n − d ⊥ + 1 then #W < d ⊥ and no nonzero codeword of C ⊥ has support contained in W. Conversely, take a codeword of weight d ⊥ and a set W of cardinality n−δ containing its support. Then rank(G W ) < n − δ and the homogeneous system Hx t = 0 has no solution for c such that π W (c) is not in the subspace π W (C) spanned by the rows of. Then when using a parity check matrix of a [n, n−r] code C, at most n−d ⊥ +1 dry symbols are needed to embed r information symbols. The wet threshold of C is τ = n − d ⊥ + 1 and its strict overhead is n − d ⊥ + 1 − r. Remark that according to the Singleton bound applied to C ⊥ we have. Thus, when using a parity check matrix of C to embed information via wet paper codes, the strict overhead is just the Singleton defect of the dual code C ⊥ . columns of H we obtain a matrix whose last row is 0. Note also that the method proposed in [1] allows to embed one information bit for n/2 dry positions modifying one bit of the cover vector. (2) In general it is not simple to construct codes with bounded Singleton defect. An exception are algebraic geometry codes, built from an algebraic curve and two rational divisors, see [16] . It is known that the Singleton defect of a code comming from a curve X is bounded by the genus of X . Therefore it is possible to construct wet paper codes with strict overhead as small as desired. We have obtained necessary and sufficient conditions to make sure the embedding process in the wet paper context. These conditions depend on the dual distance of the involved code. We also gave a sufficient condition in the general case of systematic codes and provided the exact number of solutions. Finally, we showed that systematic codes can be good candidates in the design of wet paper stegoschemes.

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


Performance Based Clustering for Benchmarking of Container Ports: An Application of Dea and Cluster Analysis Technique The operational performance of container ports has received more and more attentions in both academic and practitioner circles, the performance evaluation and process improvement of container ports have also been the focus of several studies. In this paper, Data Envelopment Analysis (DEA), an effective tool for relative efficiency assessment, is utilized for measuring the performances and benchmarking of the 77 world container ports in 2007.The used approaches in the current study consider four inputs (Capacity of Cargo Handling Machines, Number of Berths, Terminal Area and Storage Capacity) and a single output (Container Throughput). The results for the efficiency scores are analyzed, and a unique ordering of the ports based on average cross efficiency is provided, also cluster analysis technique is used to select the more appropriate targets for poorly performing ports to use as benchmarks. Due to the advanced technology and economy of scale in the container transportation operations, the operations efficiency of a container port becomes critically important impacting the overall efficiency of the global logistics network. On the other hand, along with the fast development in the port industry and more efficiently integrated transportation network, the numbers of container ports in those hot economy regions are clearly increased in the recent years, and these ports all face an ever competitive market. Therefore, the proper and precise evaluation of container port performance not only helps a port understand and improve its marketing and competitive position, but also provide a clear and solid base for the local government policy maker for the long term development.. DEA (Data Envelopment Analysis) is a fractional programming model that evaluates the relative efficiency of homogeneous units in the presence of multiple inputs and outputs. The efficiency is defined as the ratio of the weighted sum of outputs to a weighted sum of inputs. DEA has been primarily used to compare the efficiencies of a homogeneous set of decision making units (DMUs) such as schools, hospitals, airlines, manufacturing companies, shops, bank branches, project selection and other environments (Pahlavani, It has been found in the literature that DEA method (Charnes, Cooper and Rhodes, 1978) forms a main stream in the container port performance evaluation. Different version of DEA models are applied for various container port evaluations. Roll and Hayuth (1993) use a CCR 1 DEA model to theoretically rate the efficiency of ports, and 20 container ports with 3 inputs and 4 outputs are evaluated. Martinez-Budria et al (1999) develop a BCC 2 model to examine the relative efficiency of 26 Spanish ports using input and output data during year 1993 to 1997. Tongzon (2001) applies an additive CCR DEA model to empirically test the various factors influencing the efficiency of a 1 The model proposed in Charnes, Cooper and Rhodes (1978) , and named by the first words of these three scholars. 2 The model proposed in Banker, Charnes and Cooper (1984) , and named by the first words of these three scholars.. port. Valentine and Gray (2001) (2004) . There are also DEA based researches concerning the port performance of specified countries, including that Barros (2003a Barros ( , 2003b analyzes the incentive regulation, seaport authorities in Portuguese seaports. Barros and Athanassiou (2004) compare the efficiency from the seaports from Greece and Portugal. Bonilla et al (2004) analyze the efficiency of Spanish port system. Itoh (2002) uses the window DEA model to view the efficiency changes at major Japanese container ports. Martinez Budria et al (1999) study the DEA efficiency of port authorities in Spain.. From the review of above literatures, we can find the operational performances of container ports using DEA have received significant attentions in the literature. However, relatively few academic studies have investigated the complete ranking of container ports and the benchmarking analysis of container ports, which have important implications for the managers of container ports and others.. In this paper, we implement the cross efficiency evaluation method to assess the performance efficiency of 77 world container ports in different countries. Four popular performance measures for the evaluation of container ports, Capacity of Cargo Handling Machines, Number of Berths, Terminal Area and Storage Capacity, are used as the input indexes, one single performance measure, Container Throughput, is considered as the output index. By implementing this DEA analysis, we can calculate the cross-efficiency to provide a unique ordering of the container ports. Using the cluster analysis, we select the more appropriate targets for poorly performing ports to use as benchmarks. An advantage of cross-efficiency over simple efficiency is that the former can be used to rank all DMUs and Doyle and Green (1994) argued that cross efficiency evaluation method is more general, and more powerful than traditional ranking method based on DEA, such as the reference-set count. Cluster analysis is used for identifying benchmarks for poorly performing container ports. Container ports which are inherently similar with respect to their inputs and outputs are classified into the same cluster. The port with the highest score in a given cluster can be used as the primary benchmark for improvement by other ports in that cluster. Besides, some insightful management information on container ports are also revealed.. The rest of this paper is organized as follows. Section 2 gives a brief review of the relevant DEA models. Data descriptions are made in Section 3. Section 4 conducts the numerical testing and analysis. Section 5 draws conclusions and final remarks. In this paper, we have demonstrated a methodology for performance evaluation and process improvement of 77 world container ports. We utilized a combination of multi-factor efficiency models, aggressive cross-efficiency DEA, and clustering methods for evaluating container port performance and in identifying appropriate benchmarks for improving poorly performing ports. Several important and interesting managerial insights were derived from this study. Managers and policy makers for improving the operational efficiencies of ports through benchmarking can utilize the methods in this paper.. As with any technique there are certain limitations to our models. One of the limitations of DEA is its sensitivity to data and parameter selection. If we had selected other parameters (inputs and outputs) or fewer parameters for evaluation, we may have obtained different outcomes. Even though this study provides a random sample of the full population, some extensions to overall industry competitiveness and efficiency may need to be evaluated further. Besides, investigating and evaluating various clustering approaches, factor analysis, and multi-dimensional scaling can all be avenues for future research. In this section, we will provide a brief review of the basic DEA model, cross efficiency DEA model and the benchmarking method based on cluster analysis technique and DEA.

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


Security models for delegated keyword searching within encrypted contents Abstract Within modern internet infrastructure including networks that are ubiquitous, there is often a need for delegatable communication between nodes without compromising the confidentiality of information. In practice, this should be enforced while allowing some basic functionality for intermediate delegated nodes such as searching through encrypted content. This can be achieved using a Public key encryption with keyword search (PEKS) scheme, first proposed by Boneh et al., which enables to search publicly encrypted messages for keywords without revealing any information about the message. The issue of PEKS schemes being vulnerable to keyword guessing attacks (KGAs) was first shown by Byun et al., and two of the most recent PEKS schemes, i.e., due to Within the distributed and ubiquitous nature of modern internet, where delegation of tasks often occurs among nodes, it is crucial that this delegation is performed while retaining some form of security. One issue requiring attention is the need to ensure confidentiality of content as it traverses across nodes within such networks, while still allowing due to efficiency reasons for intermediary nodes to be delegated with the task to search for certain keywords.. Public key encryption with keyword search (PEKS) schemes, first proposed in [4] , can be useful in this respect. Such a scheme allows a user to delegate searching capabilities on publicly encrypted data to a third party (e.g., an email storage provider) without revealing information in the data.. Consider a distributed e-mail system (or a distributed storage system) that consists of three entities, namely an originating sender, a receiver (R), and an email server or storage provider (S). The sender encrypts an email message with a conventional public key encryption scheme (PKE) [9] . S/He also encrypts a keyword (or a list of keywords) associated with the mail content with a PEKS scheme. S/He then appends the PEKS ciphertext to the PKE ciphertext and sends both to the email storage provider. R sends a trapdoor associated with a searching keyword w to the email storage provider S for retrieving all the emails containing the keyword. The PEKS scheme enables S to test whether w is a keyword associated with the email but learns nothing else about the email.. Boneh et al.'s PEKS scheme assumes a secure channel for sending a trapdoor, as observed by Baek et al. [3] . Otherwise, an eavesdropper who captures a trapdoor can run the test function (which is public) to determine the relation between a PEKS ciphertext and the trapdoor. Baek et al. [3] thus proposed a secure channel free PEKs (SCF-PEKS) scheme to remove the requirement of such a secure channel. This scheme is also known as a PEKS scheme with a designated server (dPEKS). A dPEKS scheme ensures that no one except the designated server is able to run the test function (dTest) since dTest is by design a function of the designated server's private key. Recently, some further dPEKS schemes have been proposed i.e., [6, [10] [11] [12] that are either improving the security or efficiency of the Baek et al. [3] scheme.. Byun et al. [5] first addressed the vulnerability of keyword guessing attacks (KGA) on PEKS schemes. It is known that the keyword space of a keyword used by any PEKS scheme is usually small [4] in view of the highly redundant nature of our human language used to represent keywords, e.g., English word. In addition, users tend to query for a small set of keywords, for example, a user may search for some commonly used keywords such as "Urgent" in the "Subject" field of an email. In this case, adversaries are able to exploit this to exhaustively guess some candidate keywords and verify the correctness of their guesses. This results in the leak of information pertaining to encrypted emails. Byun et al. showed that the PEKS schemes of [4] and [8] are susceptible to the attacks. Yau et al. [14] later showed some similar attacks on dPEKS and PEKS schemes of [2, 3] . In a different vein, Jeong et al. [7] showed that consistency implies insecurity to keyword guessing attacks in PEKS, where consistency means that it is not possible to find two keywords such that the Test function states that the PEKS ciphertext of one keyword and the trapdoor for the other keyword contain the same keyword.. To the best of our knowledge, the only security model defined in literature for PEKS against KGA is by Rhee et al. [11] . The other PEKS model is with respect to the indistinguishability goal under chosen keyword attacks (IND-CKA) (e.g., see [4] ). This latter model by definition was not designed to capture KGA and primarily differs in the adversarial goal. Whereas, KGA models have the adversary aim to mount keyword guessing (KG) as a goal, IND has the adversary aiming to break an indistinguishability goal, i.e., being able to distinguish which one of two keywords was used as input to generate a PEKS ciphertext.. In this paper, we define models for KGA for the different settings of PEKS and dPEKS; these models achieve stronger KG notions than the KGA model of [11] , and involve adversarial capability that is more analogous to the adversarial capability of the original IND-CKA model defined by Boneh et al. In contrast, the adversarial capability considered in the Rhee et al. model is much weaker than that of the adversary in the Boneh et al. IND-CKA model. Thus, a security proof within the Rhee et al. KGA model may not be able to fully capture security against KGA in view of its restrictive adversary definition. We then show that our models can capture attacks that exist on three recent dPEKS schemes of [10] [11] [12] that would otherwise not have been captured by the weaker model of Rhee et al. We have proposed security models for both PEKS and dPEKS schemes in the sense of keyword guessing under chosen keyword attacks (KG-CKA). These models are more security sufficient than the KG-CKA model of [11] which is restrictive in the sense the adversary is not allowed any oracle query; so the latter adversarial capability is much weaker than the original adversarial capability considered by the IND-CKA model of Boneh et al. For the dPEKS setting, we focussed on the accessibility of the dTest function as an oracle, and based on this characterization then motivated the difference in the achieved security notions. This consideration corresponds well to the adversarial ability in the IND-CKA dPEKS notions of [12] , and intuitively preserves the adversarial ability in moving from PEKS to dPEKS. We showed the applicability of these models in capturing KGA cryptanalysis of three recent dPEKS schemes of [10] , [12] and [11] ; such results cannot be captured by the weaker KG-CKA model of [11] . Since security models can be used in proving the security of a construction besides being used for cryptanalysis, a strong security model is desirable so that the corresponding proof guarantees security even against powerful adversaries; otherwise security collapses when an adversary exists in practice that has capabilities higher than those considered in the restricted (weaker) security model.

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


A Systematic Two-Layer Approach to Develop Web-based Experimentation Environments for Control Engineering Education LabVIEW and data acquisition boards from National Instruments are used to create the server-side applications, and Easy Java Simulations for the client-side interfaces. For the e-learning layer, the eMersion environment is used to support the required flexible educational scheme. This paper describes the programming techniques implemented and the design considerations that justify these particular choices. As an illustrative casestudy, an example of development of a web-based application is discussed, in which an electrical drive servo-motor is introduced as a convenient setup to practice with motion control applications. Other significant examples of remote experiments developed by the authors are also reported. New technological advances in communication networks are steadily being introduced to improve both the quality of service and the available bandwidth of Internet to accommodate the increasing world-wide demand of resources. The availability and capabilities of these new communication facilities, combined with the fast development of Web technologies and the generalisation of off-the-self components for data acquisition and control of industrial processes, now enable students to avoid attendance to traditional laboratories. Students are encouraged to use tele-presence systems, transforming the remote experimentation from a subject of research into a reality accessible to everybody [1] . Control Engineering is one of the technical areas in which the impact of new technologies to develop novel approaches to distance learning is most noticeable [2] . A great number of research groups, universities, and academic institutions all over the world are currently implementing virtual and remote control laboratories with one objective in common: to enhance traditional teaching processes by taking advantage of modern technologies [3] .. Unfortunately, these developments are often isolated efforts of independent groups that use different programming techniques, different software tools, and different hardware facilities. Based on these premises, it would be very convenient for the control education community to establish a common methodology, procedure, or approach to describe, implement, and interconnect each of the parts that compose a Web-based laboratory. This approach should include a Web-based experimentation platform that supports both the student-teacher interaction and the collaborative processes among students in a way similar to how it takes place in the traditional classroom. This paper gives some recommendations and insights about how to create such virtual and remote control laboratory applications, and how to make them more accessible to students.. The paper is organized as follows. Section 2 describes the experimentation layer, which includes the fundamental aspects required to create virtual and remote control laboratories using LabVIEW and Easy Java Simulations. Section 3 presents the eMersion-based e-learning layer, which implements the complementary Web-resources that users need to make a correct use of the experimental application. Section 4 lists all the steps needed to develop a virtual and remote control laboratory completely integrated in eMersion by using a servo-motor as didactical setup. Section 5 provides two additional examples of remote experiments developed by the authors. Finally, some conclusions and considerations about further work are given. Virtual and remote experimentation for engineering education can be considered as a mature technology. However, the process of transforming a classical control experiment located in a conventional laboratory room into an interactive Web-based laboratory is not an easy task. This paper provides a systematic approach for the fast development of remote laboratories using three tools: Easy Java Simulations, LabVIEW and eMersion.. The proposed component approach eases the development of online experimentation environments. It also provides an effective scheme to switch between simulation and actual operation of real systems, which is a key feature for hands-on learning activities in engineering education.. The proposed approach has been implemented at the UNED for deploying virtual and remote laboratory experiments that are throughout described in this paper.

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


A Bayesian variable selection procedure to rank overlapping gene sets Background: Genome-wide expression profiling using microarrays or sequence-based technologies allows us to identify genes and genetic pathways whose expression patterns influence complex traits. Different methods to prioritize gene sets, such as the genes in a given molecular pathway, have been described. In many cases, these methods test one gene set at a time, and therefore do not consider overlaps among the pathways. Here, we present a Bayesian variable selection method to prioritize gene sets that overcomes this limitation by considering all gene sets simultaneously. We applied Bayesian variable selection to differential expression to prioritize the molecular and genetic pathways involved in the responses to Escherichia coli infection in Danish Holstein cows. Results: We used a Bayesian variable selection method to prioritize Kyoto Encyclopedia of Genes and Genomes pathways. We used our data to study how the variable selection method was affected by overlaps among the pathways. In addition, we compared our approach to another that ignores the overlaps, and studied the differences in the prioritization. The variable selection method was robust to a change in prior probability and stable given a limited number of observations. Bayesian variable selection can prioritize gene sets while also considering the overlaps among them. This can be performed for a large number of genes without overwhelmingly demanding computation. The selection method tends to select one or a few pathways among the highly overlapping pathways. It also makes it possible to determine which pairs of overlapping pathways are harder to prioritize. This can be achieved by studying the latent variables computed in the variable selection.. Our results show that the ANOVA approach can give misleading results by not considering the pathway overlaps. The Bayesian variable selection method gave similar results to the ANOVA method, but it was able to highlight one or a few among the highly overlapping genes.. Cases that would prove difficult for the Bayesian variable selection method include when there are very highly overlapping genes and the overlapping genes are differentially expressed. In such cases, we suggest that the posterior correlations among highly overlapping pathways should be examined to determine whether they are negative and have a high absolute value. Genome-wide expression profiling using microarrays or sequence-based technologies allows us to identify genes and genetic pathways whose expression patterns influence complex traits. It is likely that many phenotypic differences are manifested by small but consistent expression changes in a set of genes (e.g. biological pathways, complexes or modules). Therefore statistical methods have been developed to capture changes in the expression of pre-defined sets of genes. These gene set approaches are complementary to analyses at the singlegene level and represent powerful tools to dissect the complex changes in gene expression that underlie phenotypic traits [1] .. Gene set approaches are based on sets of genes typically defined based on prior biological knowledge, such as genes belonging to the same molecular pathway (Kyoto Encyclopedia of Genes and Genomes (KEGG)) [2] or genes encoding proteins with similar functions (e.g., Gene Ontology (GO)) [3] . One potential problem is that genes can exist in many gene sets and the level of overlap can be substantial. This is ignored in statistical analyses where the gene sets are analyzed individually, which can cause difficulty in interpreting the results, because one cannot determine from the available data which one of the gene sets is more responsible for the effect. In the most extreme case, there could be one or more that are identical. In these cases it is impossible to determine which is responsible for the effect [4] .. One powerful gene set approach is Gene Set Enrichment Analysis (GSEA) [1, 5] . GSEA aggregates the pergene statistics within a gene set, thus making it possible to detect situations where all the genes in a predefined set change in a small but coordinated way. GSEA can be implemented in a manner similar to a linear regression modeling approach that consists of three components: the incidence matrix linking genes to the gene set; the per-gene statistic vector, e.g., the t-statistic, and a per-set summing function. In this way, a large number of gene sets and overlapping gene sets can be viewed as a linear regression with a large number of highly collinear regression variables. This is a typical combinatorial and model selection problem. One way of handling this problem has been to use linear modeling to select a model [4] . This could, for example, be achieved using Akaike's Information Criterion (AIC) [6] . However, the optimizations according to this criterion should in principle compare all 2 p possible models (where P is the number of gene sets). This becomes computationally demanding as the number of gene sets increases.. Another way to consider the overlaps is the TopGO approach [7] . This takes into account the hierarchical structure of the gene sets among GO terms. However, this method is limited to overlaps that appear because of this hierarchical structure. The DAVID gene classification tool is another method that takes account of overlaps [8, 9] . In this tool, the more highly overlapping gene sets are organized as groups [10] . This method analyzes these groups of gene sets instead of the individual gene sets, and makes it possible to score the groups according to the scores of the member gene sets.. In this study we present a gene set approach based on the Bayesian variable selection method, known as Stochastic Search Variable Selection (SSVS) [11] . The Markov chains underlying such Bayesian methods are efficient at handling combinatorial problems such as model selection. This approach can deal with a large number of gene sets and considers the overlaps among the gene sets. Instead of first finding the gene sets with significant effects and thereafter assessing their overlaps and correcting for the correlations among them, this approach should accomplish this process in just one step. The focus now is to investigate how the variable selection procedure analyzes the overlaps among gene sets and how this affects the prioritization of gene sets. Here, we demonstrate the use of this novel gene set approach in a genome-wide expression study of Escherichia coliinduced mastitis (udder infection) in dairy cattle. The gene set approach presented in this study is based on a linear model to identify and prioritize KEGG pathways whose expression levels are associated with bovine mastitis.. Gene sets defined by KEGG pathways KEGG pathways were used as gene sets. These pathways are a collection of high-quality molecular interaction and reaction networks representing the current knowledge of many important biological processes. The use of KEGG pathways as gene sets illustrates situations where the number of gene sets is relatively large and where the level of overlap among the gene sets is substantial. We used 196 KEGG pathways and 3130 bovine Entrez gene identifiers. Only KEGG pathways containing more than four genes were included. The number of genes in a set ranged from five to 793 ('Metabolic Pathways'). The number of occurrences of the same gene across pathways ranged from one to 41. The KEGG pathways were taken from the KEGG database using the genome-wide annotations of bovine Entrez gene identifiers.. Scaling up the analysis to higher dimensions and to cases with increased overlap, as when GO is used to define gene sets, was not expected to be problematic. The current analysis uses little memory and computing time, and both will scale-up linearly in these Bayesian implementations. With higher overlaps among gene sets, worse mixing for gene effects among sets can be expected, probably requiring some increase in the Markov chain Monte Carlo chain length to obtain accurate estimates.

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


Multi-Atlas Segmentation of Biomedical Images: A Survey Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, Brandt, Menzel and Maurer Jr (2004) , Klein, Mensh, Ghosh, Tourville and Hirsch (2005) , and Heckemann, Hajnal, Aljabar, Rueckert and Hammers (2006) , is becoming one of the most widelyused and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of "atlases" (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which roughly spans a decade (2003 -June 2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation.

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


A Graphical Approach to Radio Frequency Quadrupole Design The design of a radio frequency quadrupole, an important section of all ion accelerators, and the calculation of its beam dynamics properties can be achieved using the existing computational tools. These programs, originally designed in 1980s, show effects of aging in their user interfaces and in their output. The authors believe there is room for improvement in both design techniques using a graphical approach and in the amount of analytical calculations before going into CPU burning finite element analysis techniques. Additionally an emphasis on the graphical method of controlling the evolution of the relevant parameters using the drag-to-change paradigm is bound to be beneficial to the designer. A computer code, named DEMIRCI, has been written in C ++ to demonstrate these ideas. This tool has been used in the design of Turkish Atomic Energy Authority (TAEK)'s 1.5 MeV proton beamline at Saraykoy Nuclear Research and Training Center (SANAEM). DEMIRCI starts with a simple analytical model, calculates the RFQ behavior and produces 3D design files that can be fed to a milling machine. The paper discusses the experience gained during design process of SANAEM Project Prometheus (SPP) RFQ and underlines some of DEMIRCI's capabilities. The Radio Frequency Quadrupoles (RFQ) are in use at the low beta sections of all modern ion accelerators since their invention by Kapchinsky and Teplyakov in late 1970s [1] . For light ions such as H + and H − operating in 300-400 MHz range, the RFQ type of choice is the, so called, "4-vane" RFQ [2] . The design of a 4-vane RFQ, which is the focus of this paper, and its manufacture require precise calculation of the relevant parameters, a good understanding of the materials and high precision machining [3] . In fact, this accelerating structure is nothing but a body (cylindrical, square or octagonal vessel) containing four carefully crafted modulated vanes, two vertical and two horizontal symmetrically distributed along the beam axis. The high precision modulation requirement on the vanes can be met by the computerized milling tools, i.e. CNC machines. However, the art of designing an efficient RFQ and the study of its beam dynamics properties necessitate repetitive lengthy calculations: an ideal task for computers. Although the two of commercially available programs [4] [5] , profit from years of experience in accelerator building, the main design ideas and especially the user interaction components can benefit from modern tools and concepts. Additionally, the commonly used Unix-like environment provided by Linux and OSX workstations does not have access directly to these two Microsoft Windows specific software packages.. A new project in the form of a computer code, written in C ++ , called DEMIRCI 1 [6] is started to explore the potential of the modern concepts such as object oriented programming and ROOT environment [8] . This tool helps the designer to create an RFQ model which would achieve certain goals such as a final target energy or a fixed total accelerator length in a fully graphical environment. It calculates a large number of design and beam dynamics parameters such as energy at the end of the cavity, power dissipation and cavity quality factor for each cell. It also allows the designer to visualize a large set of parameters change along the RFQ. Another property of this tool is the interoperability with similar software in the field, either directly using the user interface or by simple exchange of plain text files. This paper focuses on the algorithmic side of the project omitting all the installation details and instructions on how to use the actual code. These details are described elsewhere [6] . However it might be interesting for the reader to know that the project has been used successfully in multiple Unix-like environments such as OSX, Scientific Linux and Ubuntu Linux. At the writing of this note the project source code is under further development and is not open to general public, however a binary at the current state for most common platforms can be obtained by contacting the authors [7] .

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


A surprising Radon transform result and its application to motion detection Abstract-An elliptical region of the plane supports a positivevalued function whose Radon transform depends only on the slope of the integrating line. Any two parallel lines that intersect the ellipse generate equal line integrals of the function. We prove that this peculiar property is unique to the ellipse; no other convex, compact region of the plane supports a nonzero-valued function whose Radon transform depends only on slope.We motivate this problem by considering the detection of a constant-velocity moving object in a sequence of images, in the presence of additive, white, Gaussian noise. The intensity distribution of the object is known, but the velocity is only assumed to lie in some known set, for example, an ellipse or a rectangle. The object is to find a space-time linear filter, operating on the image sequence, whose minimum output signalto-noise ratio (SNR) for any velocity in the set is maximized. For an ellipse (and its special cases, the disk and the linesegment) the special Radon transform property of the ellipse enables us to obtain a closed-form, analytical solution for the minimax filter, which significantly outperforms the conventional three-dimensional (3-D) matched filter. This analytical solution also suggests a constrained minimax filter for other velocity sets, obtainable in closed form, whose SNR can be very close to the minimax SNR. A CHALLENGING problem in image sequence analysis is to detect a moving object in the presence of additive noise [1] , [3] , [4] , [14] - [18] . It is convenient and often reasonable to make several assumptions: that the object is moving with constant velocity, that its image intensity distribution does not change with time, and that the noise is zero mean Gaussian that is uncorrelated over both space and time. We denote the noisy image sequence by where represents time and is Cartesian position within the image frame (1) where is the intensity distribution of the object, is the velocity of the object expressed in some consistent set of units, for example meters/s or pixels/frame, is the position of the object at reference time , and is additive white noise. By assumption, the noise variance is so large that the object cannot be detected Manuscript received June 24, 1997; revised April 3, 1998 . The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Steven D. Blostein.. T. L. Marzetta is with Bell Laboratories, Lucent Technologies, Murray Hill, NJ 07974-0636 USA (e-mail: tlm@research.bell-labs.com).. L. A. Shepp is with the Department of Statistics, Rutgers University, New Brunswick, NJ 08903 USA (e-mail: shepp@stat.rutgers.edu).. Publisher Item Identifier S 1057-7149(99) 06116-3. reliably merely by thresholding the image sequence, or by applying a two-dimensional (2-D) (spatial) matched filter to each image frame. In general we need a more sophisticated three-dimensional (3-D) detection scheme that operates over multiple pixels and multiple frames of the image sequence. Detecting a moving object in a noisy image sequence requires a 3-D filtering operation to improve the SNR. If the object is not resolved by the sensor, then the effective object intensity distribution is the known point-spread function. However, in most applications the velocity of the object is unknown, and one needs a filter that works well over a set of velocities rather than for just one velocity. Under these conditions we have shown that an optimized filter, whose minimum SNR for a specified set of velocities is maximized, can be significantly more efficient than a 3-D matched filter: a SNR improvement of 3.5 dB for a circular disk, and for an elliptical region, an unbounded improvement as the eccentricity approaches one. For an elliptical velocity set (and its special cases, the disk and the line segment) we have a closed form analytical solution for the minimax filter. For other velocity sets we have obtained a useful constrained minimax filter, in closed form, whose SNR can be within 0.4 dB of the minimax SNR. Using constrained minimax filters rather than 3-D matched filters can reduce the size of a filterbank by a factor of five.. For the elliptical velocity set we exploited a special property of the ellipse to obtain an analytical solution for the minimax filter: a convex elliptical region of the plane supports a positive-valued function such that any parallel lines that intersect the ellipse generate equal line integrals. While attempting to extend this solution technique to nonelliptical sets, we obtained the remarkable result that this property of the elliptical set is unique. No other planar convex, compact set supports a nonzero-valued function whose Radon transform depends only on the slope of the integrating line.

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


Estrogen Action in Neuroprotection and Brain Inflammation The fertile period of women's life compared to menopause is associated with a lower incidence of degenerative inflammatory diseases. In brain, estrogens ameliorate brain performance and have positive effects on selected neural pathologies characterized by a strong inflammatory component. We thus hypothesized that the inflammatory response is a target of estrogen action; several studies including ours provided strong evidence to support this prediction. Microglia, the brain's inflammatory cells, and circulating monocytes express the estrogen receptors ER-and ER-and their responsiveness in vivo and in vitro to pro-inflammatory agents, such as lipopolysaccharide (LPS), is controlled by 17 -estradiol (E 2 ). Susceptibility of central nervous system (CNS) macrophage cells to E 2 is also preserved in animal models of neuroinflammatory diseases, in which ER-seems to be specifically involved. At the molecular level, induction of inflammatory gene expression is blocked by E 2 . We recently observed that, differently from conventional antiinflammatory drugs, E 2 stimulates a nongenomic event that interferes with the LPS signal transduction from the plasma membrane to cytoskeleton and intracellular effectors, which results in the inhibition

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Usage and Perceived Impact of Data Warehouses: a Study in Korean Financial Companies As data warehousing has evolved to become the technology of choice for developing data infrastructure in virtually every large company and many other medium-and small-sized firms, data warehousing has also received extensive research attentions from both academics and practitioners. Although several previous studies have investigated success factors and implementation of data warehousing, only few of them have explored end users' perceptions of this system. Moreover, none of these previous studies was done on the company outside North America, especially in any Asian country. Thus, this study was conducted to identify factors affecting end users' usage and perceived impact of data warehouses in Korean financial companies. Results suggest that Data Quality and End User Support and Training are significant factors affecting end users' usage and perceived impact of data warehouses.RESEARCH MODELThe proposed research model includes four independent constructs: Data Quality, Accessibility, Response Time, and End User Support and Training. As specified in the IS Success Model, information quality is one of the dimensions of IS success (DeLone and McLean 1992) . Additionally, data accuracy, format, and preciseness were found as the factors affecting user satisfaction with data warehouses (Chen et al. 2000) . Thus, similar to several previous studies on the effects of information quality upon IS success (e.g., Wixom and Watson 2001), in this study, we measure the independent construct: Data Quality, by assessing the accuracy, timeliness, completeness, relevance, and understandability of data. Another dimension of IS success is system quality (DeLone and McLean 1992) . System quality consists of a variety of measures including ease-of-use, functionality, reliability, flexibility, portability, integration, and importance. One of the reasons for this variety of measures for system quality is that IS success was investigated in different contexts. That is, in The main focus of this study is to empirically investigate the factors that affect System Usage and Perceived Individual Impact of data warehousing users in four Korean financial companies. Results of this study suggest that Data Quality and End User Support and Training are significant factors affecting System Usage and Perceived Individual Impact. Additionally, this study provides consistent results to those results found in several previous studies, regarding the relationships among Perceived Ease of Use, Perceived Usefulness, System Usage, and Perceived Individual Impact. The results also reveal some concerns related to irrelevant user training and slow responses from supporting staff. These concerns may not be easily solved regarding the fact that data warehousing is relatively new for these four Korean companies.. We believe that further investigation of factors affecting System Usage and Perceived Individual Impact of data warehousing is warranted. Similar studies conducted on companies in different countries would provide more details for the generalization of study results. Additional studies to investigate the effects of users' limited technical and/or system knowledge on their perception of system errors would provide additional explanation for the insignificant results found in this study. Furthermore, since this study is cross-sectional, the findings represent a "snap-shot" in time. As users' perceptions may change after they have used data warehouses longer and are more familiar with the systems, a longitudinal study to monitor these changes and their effects would be of important value.. Similar to other empirical studies, this study has certain limitations. Limited sample size (115 respondents) might have affected the integrity of the statistical inference. Finally, certain system or non-system issues discussed in this study might be phenomena local to the studied industrial sector or country. In this sense, generalization of findings may be lacking and researchers should exercise their judgment in extrapolating results of this study from the responding sample to the broader population. The analysis based on survey data reveals factors that lead the data warehousing toward becoming a more effective environment. In particular, this study uncovers areas that warrant more attention if data warehousing is to affect end users' usage and perceived impact. Results of this study indicate that Data Quality significantly affect users' Perceived Ease of Use, Perceived Usefulness, and Perceived Individual Impact. Among various dimensions of Data Quality considered in this study, data relevancy received the highest rating (see Appendix A). Thus, currently the data warehousing systems seem to provide data relevant to users' needs. On the other hand, understandability (i.e., definition of data field, view, and table) appears to be particularly troublesome to users. Although systems provide a short description of each data field, view, and table, this short description is not sufficient for users to understand the data semantics. Accordingly, attention should be placed on providing clear and sufficient definition of data in the warehouses.. Similarly, End User Support and Training also significantly affect Perceived Ease of Use, Perceived Usefulness, and Perceived Individual Impact. For the four Korean financial companies participating in this study, managers have provided their employees sufficient encouragement, help, and support to use data warehouses to fulfill their tasks. Additionally, supporting staffs appear to have sufficient knowledge and experience that enables them to provide the effective solutions to fix technical problems for users. However, training is the issue that needs improvement. Users indicated that they did not receive relevant training that would help them effectively employ data warehouses for their tasks. Another issue where users perceived to be ineffective is the prompt responses from supporting staffs. Users need not only effective solutions to fix their technical problems, but also quick responses from supporting staffs. Thus, companies may consider setting up a group of designated supporting staffs to provide users the initial responses to their problems.. This study renders some interesting results regarding Response Time and Accessibility. However, care should be given in interpreting these results. The results show that Response Time and Accessibility are not significantly associated with Perceived Ease of Use, Perceived Usefulness, and Perceived Individual Impact. It does not mean that these two constructs are not important for the implementation of data warehousing systems in these four Korean financial companies. A possible explanation for these insignificant results is from users' perception on management issues of data warehousing systems. That is, users perceive that management issues such as the lack of relevant training and subsequent system misuse significantly contribute to computing errors and slow system responses (Shin 2003) . Because of the lack of relevant training, most users do not have sufficient knowledge to identify whether computing errors and slow system responses are from ineffective systems design and poor data access tools, especially when the system is relatively new (the four Korean companies in this study have been using their data warehouses for only approximately two years). Thus, users may weight more importance or their dissatisfaction on management issues, rather than technical aspects of the systems. Nevertheless, a firm explanation can only be supported with additional investigations.. Results of this study also support all hypotheses about the associations among Perceived Ease of Use, Perceived Usefulness, System Usage, and Perceived Individual Impact (i.e., H13, H14, and H15). These results are consistent with those findings in several previous studies (e.g., Davis et al. 1989; ; that is, Perceived Ease of Use is a dominant factor in explaining Perceived Usefulness; Perceived Usefulness is then an important factor in determining System Usage; and finally, System Usage significantly leads to Perceived Individual Impact.

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