Display Calculi for Nominal Tense Logics We define display calculi for nominal tense logics extending the minimal nominal tense logic (MNTL) by addition of primitive axioms. To do so, we use the natural translation of MNTL into the minimal tense logic of inequality (L = ) which is known to be properly displayable by application of Kracht's results. The rules of the display calculus δMNTL for MNTL mimic those of the display calculus δL = for L = . We show that every MNTL-valid formula admits a cut-free derivation in δMNTL. We also show that a restricted display calculus δ − MNTL, is not only complete for MNTL, but it enjoys cut-elimination for arbitrary sequents. Finally, we give a weak Sahlqvist-type theorem for two semantically defined extensions of MNTL. Using Kracht's techniques we obtain sound and complete display calculi for these two extensions based upon δMNTL and δ − MNTL respectively. The display calculi based upon δMNTL enjoy cut-elimination for valid formulae only, but those based upon δ − MNTL enjoy cut-elimination for arbitrary sequents. Background. The addition of names (also called nominals) to modal logics has been investigated recently with different motivations (see e.g. [Or lo84, PT85, Gar86, Bla90] ). A name is usually understood as an atomic proposition that holds true in a unique world of a Kripke model. Usually, the addition of names is intended to increase the expressive power of the initial logics. For instance, although there is no tense formula that characterises the class of irreflexive frames, there is a tense formula with names that characterises this class of frames [Bla93] . Another remarkable breakthrough due to the inclusion of names is the ability to define the intersection operator (see e.g. [PT91] ) although it is known that intersection is not modally definable in the standard modal language [GT75] . Names have also been introduced in information logics [Or lo84, Kon97b, Kon97a] derived from Pawlak's rough set theory [Paw81] where the motivations concern both definability and axiomatisability. Adding the difference operator [ =] , which allows access to worlds different from the current world, is another way to obtain names (see e.g. [Sai88, Koy92, Rij92, Ven93] ). As far as expressive power is concerned, adding [ =] is more powerful than adding only names: in [GG93] , the relationships between names and [ =] are fully established with respect to definability. For instance, all universal first-order conditions on the Kripke reachability relation R (read as a binary predicate symbol) and = (identity) are definable in the bimodal language with the standard necessity operator and the difference operator [Gor90] . In the literature for modal logics with names, much work has been dedicated to the study of their expressive power, decidability, complexity (see e.g. [Bla90, Rij93, GG93, PT91, ABM00]) and to the definition of proof systems [Bla90, PT91, Ven93, Rij93, Sel97, Dem99, Tza99, Are00].. Our contribution. Our main goal is to define cut-free display calculi (see e.g. [Bel82] ) for nominal tense logics and therefore to provide a complementary approach to existing proof systems. In particular, we wish to extend previous results for displaying tense logics (see e.g. [Wan94, Kra96, Wan98] ) to nominals. Display Logic (abbreviated by DL) is a proof-theoretical framework introduced by Belnap [Bel82] that generalises the structural language of Gentzen's sequents by using multiple structural connectives instead of Gentzen's comma. A nice property of DL is its very general cut-elimination theorem [Bel82] . Furthermore, in the rules introducing logical connectives, the principal formula is alone as an antecedent or succedent thereby making the introduction rule a definition of that connective. In that sense, interactions between logical connectives are reduced to the minimum. This can be done since any occurrence of a structure in a sequent can be displayed either as the entire antecedent or as the entire succedent of some sequent "structurally equivalent" to the initial sequent.. So, the first contribution of the paper is the definition of display calculi for certain extensions of the minimal nominal tense logic (MNTL) [Bla90] , by addition of primitive axioms in the sense of [Kra96] . Our first stage consists in defining the display calculus δMNTL by using the natural translation from MNTL into MTL = , the minimal tense logic augmented with the difference operator. Indeed, MTL = is properly displayable 1 in the sense of [Kra96] thanks to the Hilbert-style axiomatisation given in [Rij92, Rij93] (see also [Seg81, Koy92] ). The powerful and sometimes redundant irreflexivity rule (see e.g. [Gab81, Ven93, Bal99] ) is not needed to axiomatise MTL = . The rules for δMNTL mimic those of δMTL = , the display calculus for MTL = . Completeness of δMNTL is first proved by backward translation. The proof also shows that every MNTL-valid formula admits a cut-free derivation in δMNTL. However, to extend δMNTL, the above technique requires Hilbert-style axiomatisations that may contain the irreflexivity rule to appropriately extend MTL = . The corresponding rule in DL lacks various nice properties of the standard display calculi for tense logics. Furthermore, it is not always known when the irreflexivity rule is really needed. We therefore provide a second completeness proof of δMNTL from the Hilbert-style calculus ⊢ MNTL for MNTL given in [Bla90] for which the irreflexivity rule is never needed. The second proof happens to be much more informative since it provides a means to understand the rôle of various structural rules.. Cut-elimination cannot be proved by the technique of the second proof since it relies on the simulation of the modus ponens rule. An interesting, and at first glance very unpleasant, feature of δMNTL is that it does not satisfy the condition (C8) [Bel82] . This condition is central in cut-elimination proofs from [Bel82, Wan98] . Nevertheless, we prove that a slight variant of δMNTL admits cut-elimination for arbitrary sequents by Belnap's conditions while preserving completeness.. Finally, although many extensions of MNTL are not canonical [Bla90] , we show a weak Sahlqvist-style theorem for nominal tense logics. This paves the way to define cut-free display calculi for any extension of ⊢ MNTL by addition of primitive axioms (by using [Kra96] ). Furthermore, we can characterise the semantical extensions of MNTL which correspond to these calculi.. Related work. Most of the proof systems for nominal tense logics are Hilbertstyle ones [Bla90, Bla93, Gor96a] . The situation is similar for modal logics with the difference operator [Seg81, Koy92, Rij92, Rij93, Ven93] . However, prefixed tableaux for several modal logics with the difference operator have been defined in [Dem96, BD97] . Decision procedures have been designed from these calculi [BD97] but a cut rule present in those calculi is not eliminable in many cases for reasons similar to those that apply to calculi from [dM94] . Sequent-style calculi for modal logics with names have been defined in [Kon97a, Kon97b] for the so-called similarity logics based on Pawlak's rough set theory [Paw81] . In [Kon97a, Kon97b] , the nominals play the rôle of prefixes in an elegant manner, although the calculi have no prefixed formulae and only the language of the logic is used. In [Bla00] , sequent calculi for nominal tense logics are given in which the nominals roughly play the role of labels; see also [Sel97, Dem99, Tza99, Are00] .. Our treatment of nominals in our DL calculi is completely different since we instead use the dual nature of a nominal: as atomic proposition and as necessity formula. In that sense, it is similar to the treatment of atomic propositions in display calculi for intuitionistic logic in [Gor95] .. A Sahlqvist theorem for tense logics with the difference operator is given in [Ven93] , but the calculi use the irreflexivity rule; see also [Rij93] .. Other general proof-theoretical frameworks also exist for non-classical logics: Labelled Deductive Systems [Gab96] , Relational Proof Systems [Or lo88, Or lo91, Or lo92] to quote two. But DL has already shown its generality since cut-free display calculi have been defined for substructural logics [Res98, Gor98b, Gor98a] , for modal and polymodal logics [Wan94, Kra96, Wan98] , for intuitionistic logics [Gor95] , for relation algebras [Gor97, DG98] , for logics with relative accessibility relations [DG00a] and for modal provability logics [DG00b, DG01b] .. Plan of the paper. The rest of the paper is structured as follows. In Section 2, we recall the definitions of the logics under study [Bla90, Rij92, Ven93] . In Section 3, we introduce the modal logic of inequality MTL = [Rij92] and formulate its (cut-free) display calculus δMTL = using the methodology of [Kra96] . In Section 4, we define the display calculus δMNTL for MNTL, show its soundness and completeness, and prove it enjoys a weak form of cut-elimination. Section 5 contains a second independent completeness proof that easily extends to extensions of MNTL. In Section 6, we show how cut-elimination can be obtained from Belnap's conditions for a slight variant of δMNTL. Section 7 establishes a weak Sahlqvist-style theorem and by using [Kra96] , defines cut-free display calculi for extensions of MNTL.. This paper is a completed and corrected version of [DG99] . We have defined display calculi for a large class of nominal tense logics. All our calculi enjoy cut-elimination for restricted sequents of the form I ⊢ φ. A simple observation on the use of the rules for introducing nominals shows that a slight variant of all our calculi also enjoy cut-elimination for arbitrary sequents X ⊢ Y.. The following open problems are worth investigating:

O O O B I I 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 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 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 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 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 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 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


Efficient, High-Quality, GPU-Based Visualization of Voxelized Surface Data with Fine and Complicated Structures This paper proposes a GPU-based method that can visualize voxelized surface data with fine and complicated features, has high rendering quality at interactive frame rates, and provides low memory consumption. The surface data is compressed using run-length encoding (RLE) for each level of detail (LOD). Then, the loop for the rendering process is performed on the GPU for the position of the viewpoint at each time instant. The scene is raycasted in planes, where each plane is perpendicular to the horizontal plane in the world coordinate system and passes through the viewpoint. For each plane, one ray is cast to rasterize all RLE elements intersecting this plane, starting from the viewpoint and ranging up to the maximum view distance. This rasterization process projects each RLE element passing the occlusion test onto the screen at a LOD that decreases with the distance of the RLE element from the viewpoint. Finally, the smoothing of voxels in screen space and full screen anti-aliasing is performed. To provide lighting calculations without storing the normal vector inside the RLE data structure, our algorithm recovers the normal vectors from the rendered scene's depth buffer. After the viewpoint changes, the same process is re-executed for the new viewpoint. Experiments using different scenes have shown that the proposed algorithm is faster than the equivalent CPU implementation and other related methods. Our experiments further prove that this method is memory efficient and achieves high quality results. As shown in Fig. 1 , the 3D surface voxel data exists in the x-y-z world coordinate system, where the x-z plane is the horizontal ground plane. As shown in Fig.2 , the algorithm consists of a series of steps, starting with the pre-processing step and ending with rendering the scene and changing the viewpoint. In the pre-processing step, the voxel data is run-lengthencoded for each LOD in the vertical (y) direction. It is important that the encoding direction is vertical, because this leads to a higher average speed of the algorithm for the general case, when the camera looks towards the horizon. The details of this pre-processing step are described in Section 3. As shown in Fig. 2 , after copying the RLE data to the GPU's memory, the loop for visualizing the RLE data from the viewpoint at each time instant starts. As can be seen in Fig. 1 , our proposed method visualizes the scene in planes that are perpendicular to the x-z plane and share the straight line that passes through the viewpoint and is parallel to the y-axis (Down-vector). Raycasting the RLE data in each concentric plane is done step-bystep from near to far along the x-z plane, while the rasterization is done for each step in the vertical direction (parallel to the y axis) from top to bottom. To be more specific, for each step in the x-z-plane, all the RLE elements in the corresponding column are rasterized by projecting them into the screen space. Since the projection of each concentric plane is a line slanted across the screen space, the results of rendering the planes are stored as temporary bitmap for performance reasons. The temporary bitmap is then mapped to the screen using the Pixel Shader. The render loop consists of the following five pipe-lined major steps referenced as 4.1 to 4.6 in Fig. 2 . Note that the corresponding sections and subsections are indicated in the parentheses.. Step 1. Compute the vanishing point of all concentric plane's around the down vector on the CPU. As shown in Fig. 1 , the vanishing point vp is the intersection of the screen plane and Down-vector. The vanishing point needs to be computed first (Section 4.1), before the concentric planes are computed (Section 4.2). Step 2. Compute the concentric plane parameters on the GPU: Each concentric plane's parameters, which are needed for the rendering process, are computed on the GPU (Section 4.3).. Step 3. Render the planes on the GPU: In each concentric plane, a ray is cast in the x-z plane from the viewpoint's x-z-coordinates to the maximal view-distance. For each x-z-position, the corresponding column of all RLE elements is rasterized from top to bottom for the selected LOD (Section 4.4.1) at this distance. For each RLE element, we first perform the projection of the coordinates to the ray-buffer (Section 4.4.2), then, culling is performed (Section 4.4.3). Finally, the element is rasterized as a textured line in the ray-buffer (a temporary bitmap) (Section 4.4.4).. Step 4. Display the temporary bitmap on the screen: The GPU-Pixel Shader is used to rearrange the rows of the temporary texture to a radial pattern of straight lines centered at the vanishing point on the screen (Section 4.5).. Step 5. Improving quality: In the post-processing step, smoothing of voxels is performed to reduce their blocky appearance while antialiasing is included to further improve the rendering quality (Section 4.6).. In order to allow light-calculation without storing normal vectors inside the RLE volume data, we included a special method to recover the normal vectors from the depth buffer (Section 4.6.3). This paper presents an efficient adaptation of the voxel forward projection algorithm to utilize recent graphics hardware. Several improvements to speed up the algorithm, optimize memory consumption, as well as improve the rendering quality were proposed. Initially, the surface data is run-length-encoded to allow fast decoding for rendering on the GPU. According to [8] , RLE is the second fastest algorithm to decode lossless compressed volume data. Experiments using different scenes show the following results. The proposed algorithm is up to seven times as fast as the equivalent CPU implementation, up to three times as fast as basic splatting and even surpasses the performance of the default OpenGL pipeline in terms of primitives per second for some of our test scenes. We were able to further visualize large and highly detailed Table 1 : Performance: We evaluated the performance of our algorithm based on various scenes. Anti-aliasing is set to 2x1. To show the ability to render at high quality, we created a complex test scene with many fine details rendered at 512x384 pixel and 2x2 AA as well as no AA for a comparison. Note that 2x2 AA successfully removes aliasing artifacts for distant pixels.. Fig.14: GPU vs CPU: We compared our GPU implementation running on an NVidia GTX 285 to the CPU (Intel Q6600 4x3Ghz). We split the related methods into three groups: rendering Manuscript received January xx, 20xx. Manuscript revised March xx, 20xx. † The author is with Waseda University, Tokyo, Japan. voxel volume data by using Shear-Warp [3] , raytracing based algorithms and point based rendering. We will briefly overview the most widely used methods in each group and mention their key issues. Shear-Warp [3] renders RLE volume data in a front to back manner to a temporary texture. The temporary texture is then mapped to the screen. It has been proven to be very fast for dense, semi-transparent volume data. However, it requires storing three copies of the volume data in memory, as the data is run-length-encoded for each axis independently: x, y, and z.. Raytracing methods use tree-like structures such as octrees, KD-trees and bounding volume hierarchies (BVHs) to compress the voxel data and accelerate the raytracing process. An octree-based raycaster proposed by A.Knoll at.al. [13] uses a pointer-based octree structure so that large iso-surfaces can be raycast interactively with high quality. The pointer-based octree structure is advantageous in that spatial queries can be made very efficiently. However, it needs to store at least one pointer (usually 4 bytes) for each node, which is more than two times the memory requirement of position data in typical RLE scenes. As a variant of raytracing methods, Gigavoxels [11] uses bricks of volume data in combination with octrees to store voxels for interactive raytracing. The method suits well for raycasting large, semi-transparent volume data. Its features include filtering for high quality, and streaming on demand from the hard-drive to the GPU or CPU for enabling data sets larger than the CPU or GPU memory. However, it is not optimal for visualizing pure opaque surface data, because the used bricks store redundant transparent voxels as well, which increases the memory consumption.. One of the most well-known point based rendering methods, Qsplat [9] , inspired many other researchers to propose similar rendering approaches. As an evolution of Qsplat, FarVoxels [10] improved the basic point-basedrendering by introducing a hybrid method that also utilizes polygonal rendering for geometries close to the viewpoint. However, as these methods employ either point-based rendering or a combination of point-based and polygonal-based rendering, they suffer from the earlier described disadvantages when compared with voxel-based rendering. Traditionally, polygon based rendering has been more efficient than point primitive (splatting) or volume-pixel (voxel) based rendering. However, polygonal models are becoming more and more detailed, leading to dense meshes where each polygon merely covers a few pixels on the screen. Once polygonal meshes become so dense, rendering results by polygons, point primitives or voxels do not show significant differences in quality and rendering speed. This means that voxel and point-based rendering methods gain more importance. This is because as the rasterized size of voxels, splats and polygons become similar, rendering a voxel or splat employs considerably less computation than rendering a polygon. Previously, an advantage of polygon-based rendering was the ability to use repeating textures to save memory. Voxel and splat based rendering inherently use unique texturing, so there is no benefit in memory consumption from using repetitive texturing. However, as there is a recent trend to use unique, non-repeating, textures for each object on the screen (Megatexture technology [15] ), the memory consumption for polygon-based rendering sharply increases. Therefore as this trend continues, the memory consumption of voxel and splat-based rendering becomes comparable with unique textured polygon rendering.. Next, we want to compare the use of level of detail (LOD) between voxels and polygons. LOD is important to accelerate the rendering and decrease the run-time memory consumption. For polygonal objects, LOD is usually implemented as follows. First, a set of polygonal objects with different levels of detail is created by the artist. Then, at run-time, the proper LOD of the object is selected according to the distance from the object to the camera and the object is visualized on the screen. Voxels can handle LOD more efficiently than polygons, because voxel data can be easily down-sampled for representations in lower details. Therefore, it is not necessary for an artist to design separate models of the same object for each level of detail, the different levels of detail can be generated automatically. The final advantage of voxels over polygons we wish to mention is, Boolean operations can be applied much easier to voxels compared to polygons. Despite all these advantages of voxels over polygons, it should be noted that deformations and skeletal animations in real-time still pose a challenge for voxelbased representations. Point and voxel based rendering are very similar. However, the major difference between voxel based rendering and point-based rendering is that voxels occupy a well-defined cubic portion of volume in space, while point-based methods approximate the geometry usually by 2D splats. For reasons related to the fact that splats are 2D approximations of a 3D object, there have to be several exceptions implemented for a point-based algorithm to be robust. Voxel based algorithms are generally more robust without the need for such exceptions, because a voxel covers a well-defined portion of space in 3D.

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


An efficient method of cast shadow removal using multiple features Features of images are often used for cast shadow removal. A technique based on using only a single feature cannot universally distinguish an object pixel from a shadow pixel of a video frame. On the other hand, the use of multiple features increases the computational cost of a shadow removal technique considerably. In this paper, an efficient yet simple method for cast shadow removal from video sequences with static background using multiple features is developed. The basic idea of the proposed technique is that a simultaneous use of a small number of multiple features, if chosen judiciously, can reduce the similarity between object and shadow pixels without an excessive increase in the computational cost. Using the features of gray levels, color composition and gradients of foreground and background pixels, a method is devised to create a complete object mask. First, based on each of the three features, three individual shadow masks are constructed, from which three corresponding object masks are obtained through a simple subtraction operation. The object masks are then merged together to generate a single object mask. Each of the three shadow masks is created so as to cover as many shadow pixels as possible, even if it results in falsely including in them some of the object pixels. As a result, the subsequent object masks may lose some of these pixels. However, the object pixels missed by one of the object masks should be able to be recovered by at least one of the other two, since they are generated based on features complementary to the one used to construct the first one. The final object mask obtained through a logical OR operation of the three individual masks can, therefore, be expected to include most of the object pixels. The proposed method is applied to a number of video sequences. The simulation results demonstrate that the proposed method provides a mechanism for shadow removal that is superior to some of the recently proposed techniques without imparting an excessive computational cost. Segmentation of moving objects from a video sequence is essential in many vision-based applications, such as object recognition, video surveillance and security. For the purpose of segmenting a moving object, the frame of a video sequence containing an object can be regarded to consist of the moving object of interest, its cast shadow and the background. Segmenting a moving object is essentially a construction of an object mask that requires removal not only of the background but of the cast shadow. Segmentation of a moving object from a video sequence is a difficult task in view of the fact that cast shadow moves along with the object even if the background is static.. For a video captured with a fixed camera, Gaussian mixture model (GMM) has been conventionally used to build a background model and then used to generate a static background and a foreground that consists of the moving objects and its cast shadow [1] . In order to remove the cast shadow from the foreground, features such as gray levels, colors and textures have been used in the literature. The method in [2] uses only one feature, namely, the gray level, for the removal of cast shadow. However, if the gray levels of some of the pixels of the object and those of the corresponding 2 background pixels are similar, then, in this method, these object pixels may also be regarded as part of the shadow. The method in [3] has used the fact that the color compositions of the cast shadow pixels are similar to that of the corresponding background pixels. However, some object pixels may be mistaken as shadow pixels when their color composition is similar to that of the background. Thus, it is difficult to distinguish between an object pixel and a shadow pixel by using a single feature when these two pixels are similar with respect to this feature. In order to overcome this problem, multiple features have been used for shadow removal [4] - [7] . In systems using multiple features, multiple modules, one of each feature, are employed. In [4] and [5] , the shadow removal schemes have been developed, in which modules corresponding to the individual features operate in a sequential manner. A drawback of these schemes is that a useful information neglected by a single module could be completely lost or an unnecessary information could propagate through the entire sequential system. In [6] and [7] , architectures for shadow removal have been proposed in which the individual modules operate in parallel. These parallel shadow removal systems provide a superior performance, since each module operating on the same original foreground image can more effectively produce results complementary to one another.. The methods of cast shadow removal using parallel architectures mentioned above are based on choosing a set of complementary features, constructing an object mask corresponding to each of the features chosen, and finally merging them into a single mask. The performance accuracy of these methods depends predominantly on the effectiveness of the techniques in utilizing the complementarity of the features used, and their complexity depends not only on the number of features employed, but also on the simplicity of schemes employed for creating the individual object masks and merging them into a single object mask. In this paper, a simple and efficient method for the removal of cast shadow from a segmented foreground of a frame of video sequences is developed by using the gray level, color composition and gradient features of the image pixels. Similar to other existing methods, the proposed scheme constructs individual masks corresponding to each of the features used. However, unlike existing methods, a greater emphasis is placed in the proposed method in maximizing the detection of shadow and object pixels in creating the respective masks. This is so done by maximizing the likelihood of including each shadow pixel in all of the shadow masks through the use of appropriate threshold parameters even if it does result in falsely including an object pixel in some of shadow masks. At the same time, it is ensured that the likelihood of getting the same object pixel to appear simultaneously in all the shadow masks is minimized by an efficient utilization of the complementarity of the features. The proposed method developed based on this strategy is shown to increase the performance of shadow removal as well as to reduce the complexity. Use of multiple features is essential for removing cast shadows in frames of video sequences, since a single feature cannot effectively distinguish an object pixel from a shadow pixel. However, the complexity of a cast shadow removal technique relies heavily on the number and the nature of the features used. In this paper, a low-complexity scheme for cast shadow removal has been developed by effectively utilizing the complementary nature of gray level, color composition and gradient features of the pixels of a video frame. Each of the features is used to construct a respective shadow mask corresponding to the same video frame. By devising a very simple scheme, the individual shadow masks are used to obtain the corresponding object masks that are then finally merged into a single object mask. The main idea in developing the proposed scheme for providing an accurate object mask has been in minimizing the likelihood of missing a shadow pixel in each of the shadow masks even if it does result in falsely detecting some of the object pixels as shadow pixels but at the same time, ensuring that the same object pixels do not get falsely included in all the shadow masks.. Challenges arising from the formation of cast shadows from the use of multiple illumination sources or from the similarity of the gradient feature of an object pixel and that of a pixel at the edge of the shadow have also been discussed and a simple scheme using morphological operation has been proposed to overcome these problems. The proposed scheme and two other recently reported schemes for cast shadow removal have been applied to the frames of three different video sequences. Subjective and objective results obtained through these experiments have demonstrated the effectiveness and superiority of the proposed scheme at a reasonable computation cost.

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


Experiences of Faculty and Students Using an Audience Response System in the Classroom Learning, as a process, is a dialogue between teacher and learner. 1 During a lecture, as the teacher explains a topic or asks a question, the learner processes the information using current understanding and responds. The teacher compares the response to current theory and if there is a discrepancy provides feedback and/or remediation. Dialogue between individual students and faculty is minimal in the lecture format. As such, the use of audience response systems (ARS) can provide an opportunity for dialogue and improved learning, 2,3 particularly in large classrooms. The audience response system is an innovative technology offering faculty the opportunity to engage students in an otherwise traditional lecture format. The technology also provides a means to evaluate student comprehension, survey student perceptions, record attendance, document program outcomes, implement interactive question/answer games, and review tests. 4-7 Many innovative teaching methods have come and gone over the years, piquing faculty and student interest based on uniqueness or newness. However, each new teaching method requires analysis for its feasibility, cost, and effectiveness. This article presents the experiences of nursing faculty and students with the ARS, perceived advantages and disadvantages, and pedagogical merit.Early scholars used traditional lectures to present information since most documentation was handwritten and not generally available. Most perceived this method as a oneway transmission of information, necessary with large groups of students. The advent of mass-produced books and instant access to Internet information did little to revolutionize the traditional lecture format, leaving it virtually unchanged. Whereas the lecture method limits student to faculty interaction, it remains the easiest to organize,The advent of innovative technologies, such as the audience response system, provides an opportunity to engage students and enhance learning. Based on their experiences, three nursing faculty evaluated the use of an audience response system in four distinct nursing courses through the use of informal survey results. When using the audience response system, the faculty experienced an increased perception of student attentiveness and engagement, high level of class attendance, and enhanced learning. Faculty feelings were mixed concerning the burden in adapting to increased classroom time and increased preparation time. Students' perception of the value of audience response system use was mostly positive, except when responses were included as part of the grade. The majority of the students indicated that use of the audience response system enhanced learning and was a helpful learning method when used with NCLEX-style questions. Overall, faculty believed that the benefits of student engagement and enhanced learning outweighed the burdens of incorporating this new technology in the classroom.

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


Brainstorming:

weighted

voting

prediction

of

inhibitors

for

protein

targets

Abstract

The

"Brainstorming"

approach

presented

in

this

paper

is

a

weighted

voting

method

that

can

improve

the

quality

of

predictions

generated

by

several

machine

learning

(ML)

methods.

First,

an

ensemble

of

heterogeneous

ML

algorithms

is

trained

on

available

experimental

data,

then

all

solutions

are

gathered

and

a

consensus

is

built

between

them.

The

final

prediction

is

performed

using

a

voting

procedure,

whereby

the

vote

of

each

method

is

weighted

according

to

a

quality

coefficient

calculated

using

multivariable

linear

regression

(MLR).

The

MLR

optimization

procedure

is

very

fast,

therefore

no

additional

computational

cost

is

introduced

by

using

this

jury

approach.

Here,

brainstorming

is

applied

to

selecting

actives

from

large

collections

of

compounds

relating

to

five

diverse

biological

targets

of

medicinal

interest,

namely

HIVreverse

transcriptase,

dihydrofolate

reductase,

estrogen

receptor,

and

thrombin.

The

MDL

Drug

Data

Report

(MDDR)

database

was

used

for

selecting

known

inhibitors

for

these

protein

targets,

and

experimental

data

was

then

used

to

train

a

set

of

machine

learning

methods.

The

benchmark

dataset

(available

at

benchmark.tar.gz)

can

be

used

for

further

testing

of

various

clustering

and

machine

learning

methods

when

predicting

the

biological

activity

of

compounds.

Depending

on

the

protein

target,

the

overall

recall

value

is

raised

by

at

least

in

comparison

to

any

single

machine

learning

method

(including

ensemble

methods

like

random

forest)

and

unweighted

simple

majority

voting

procedures.

The

number

of

potential

drug

targets

is

increasing,

mainly

through

genomic

initiatives

high

throughput

experiments

or

microarray

or

cellular

screening

In

the

pharmaceutical

industry

generally,

small

chemical

compound

collections

are

tested

for

single

or

multiple

protein

targets.

Typical

high

throughput

screening

(HTS)

experiments

allow

selection

of

thousands

of

high

activity

leads

against

a

given

protein

target

by

testing

millions

of

compounds.

Unfortunately,

this

approach

can

be

used

only

with

single

proteins,

and

cannot

be

applied

to

larger

number

of

drug

targets.

Using

some

initial

information

about

known

active

molecules

can

reduce

the

scope

of

the

search

for

a

selected

protein

target.

The

knowledge

base

assists

HTS

studies

by

selecting

a

set

of

compounds

for

further

screening

in

virtual

screening

(VS).

VS

methods

typically

use

fingerprint

measures

of

structural

similarity

depend

on

definition

of

distance

in

descriptor

space

Another

approach,

namely

data

fusion,

combines

the

results

of

multiple

similarity

searches

of

chemical

databases

performed

by

different

algorithms,

or

using

different

features

Such

models

are

based

on

frequency

distributions

of

similarity

values

that

are

fused

using

integration

over

regions

defined

by

the

particular

fusion

rule.

Typically,

the

use

of

binary

kernel

discrimination

(BKD)

for

identifying

potential

active

compounds

in

lead-discovery

programs

is

superior

to

methods

based

on

similarity

searching

and

substructural

analysis

but

inferior

to

a

support

vector

machine

New

methods

for

ligand-based

VS

use

data

fusion

and

machine

learning

(ML)

to

enhance

the

effectiveness

of

identification

of

potential

actives

over

typical

similarity

searching

using

a

single

bioactive

reference.

This

basic

search

protocol

can

be

extended

by

the

use

of

group

fusion

to

combine

the

results

of

similarity

searches

when

multiple

reference

structures

are

available.

Similarity

searches

are

typically

based

on

the

assumption

that

the

nearest

neighbors

resulting

from

a

similarity

search

using

a

single

bioactive

reference

structure

are

also

active

Similarly,

various

ML

techniques

or

more

advanced

ensemble

methods

use

certain

chemical

descriptors

in

order

to

represent

a

molecule..

The

most

successful

approaches

for

classification

of

known

drugs

that

exploit

the

chemical

similarity

between

compounds

are

based

on

ML.

The

goal

of

ML

is

to

correctly

classify

the

known

data,

and

use

the

computed

statistical

model

to

describe

the

activity

of

a

new

ligand.

ML

methods

have

progressed

significantly

over

the

past

few

years,

especially

in

the

context

of

predicting

the

characteristics

of

physicochemical

interactions

between

organic

molecules

and

metabolic

enzymes.

Metabolic

stability,

drug

metabolism

and

even

in

vivo

clearance

is

also

addressed

The

modeling

of

relationships

between

the

chemical

structure

of

a

molecule

and

its

metabolic

effect

is

of

great

interest

to

the

pharmaceutical

industry,

especially

given

the

concurrent

expansion

of

experimental

basis.

ML

techniques

have

been

used

with

success

in

massive

screening,

drug

metabolism

prediction,

and

in

classification

or

quantitative

prediction

for

large

and

diverse

compound

sets

Bruce

et

al.

have

carried

out

an

assessment

of

different

ML

techniques

in

the

context

of

cheminformatics,

applying

rigorous

statistical

tests

and

including

several

commonly

used

techniques,

such

as

bootstrap,

bagging,

boosting

and

random

forest

(RF)

Bruce

et

al.

used

eight

data

sets

and

two

different

types

of

descriptors:

descriptors

and

linear

fragment

descriptors.

The

percentage

of

correctly

classified

molecules

was

used

to

validate

the

performance

of

each

method

on

the

basis

of

a

cross

validation.

Using

the

descriptors,

all

methods

correctly

classify

between

and

of

the

molecules

with

a

large

difference

between

individual

data

sets.

Svetnik

et

al.

compared

the

performance

of

non-optimized

standard

implementations

of

RF,

decision

tree

(DT)

and

partial

least

squares

(PLS)

for

six

cheminformatics

data

sets.

In

terms

of

prediction

performance,

RF

ranks

amongst

the

best

algorithms.

DT

performs

uniformly

less

well

than

RF,

and

PLS

comes

close

to

RF

except

for

two

datasets

on

which

it

performs

less

well.

For

some

data

sets

the

authors

compared

their

findings

with

published

results.

For

one

dataset,

RF

was

comparable

to

support

vector

machines

(SVM)

and

artificial

neural

networks

(ANN),

where

the

performance

of

RF

and

SVM

was

similar

and

both

outperformed

ANN.

In

another

article,

Svetnik

et

al.

analyzed

the

performance

of

boosting

in

comparison

to

stochastic

gradient

boosting

with

a

single

DT,

RF,

k-nearest

neighbors

(kNN),

PLS,

naive

Bayesian

(NB)

and

SVM

with

both

linear

and

radial

kernels..

In

our

previous

work,

we

compared

results

for

several

supervised

ML

algorithms,

including

recursive

partitioning

(RP),

SVM,

ANN,

NB

classification,

kNN

with

genetic

algorithm

(GA)-optimized

feature

selection

(kNN/GA),

RF,

DT,

trend

vectors

(TV)

and

ensemble

methods

Here,

I

focus

on

the

core

question,

namely

how

to

efficiently

combine

these

ML

algorithms

into

a

single

meta-predictor

(or

"jury"

system).

In

this

manuscript,

I

present

the

"Brainstorming"

approach,

i.e.,

an

implementation

of

consensus

learning

that

combines

a

variety

of

strong

and

weak

ML

methods

into

a

single

classifier

using

a

weighted

voting

procedure.

This

jury

system

approach

achieves

higher

performance

than

any

single

method

used

in

consensus,

as

confirmed

by

results

coming

from

different

field

of

bioinformatics

Currently,

the

field

of

protein

fold

recognition

is

dominated

by

meta-predictors

such

as

Pcons

Robetta

and

many

others.

Multiple

tests

have

confirmed

that

consensus

methods

are

more

powerful

than

individual

prediction

algorithms

in

terms

of

both

sensitivity

and

specificity,

despite

the

fact

that

some

meta-predictors

use

as

few

as

three

methods

to

build

a

consensus

model.

Here,

different

methods

are

tested

to

estimate

the

quality

of

their

performance

as

individual

predictors,

thereby

avoiding

some

of

the

shortcomings

of

ensemble

methods.

The

methodological

details

are

presented

in

the

Methods

section

below.

The

method

effectively

combines

different

powerful

supervised

ML

methods,

trained

on

an

initial

set

of

active

compounds,

into

a

single

meta-predictor

that

can

be

used

to

search

for

unknown

inhibitors.

Ensemble

methods

that

use

several

different

ML

algorithms

together

with

several

types

of

chemical

descriptors

to

properly

classify

groups

of

compounds

were

proven

here

to

be

the

more

successful

in

terms

of

recall

and

precision

in

comparison

to

single

ML

methods

and

vote

counting.

Consensus

learning

appears

to

enrich

datasets

more

than

any

single

scoring

function.

Multiple

scoring

functions

are

similar

to

repeated

samplings

(the

mean

is

closer

to

the

true

value

than

any

single

value).

Considering

all

compounds

retrieved

by

at

least

one

of

the

seven

methods

for

each

target,

almost

all

actives

were

found

in

the

dataset

of

all

active

compounds.

The

number

of

false

negatives

for

each

target

is

smaller

than

However,

the

sample

size

is

large

since,

for

each

target,

more

than

of

the

compounds

predicted

to

be

active

by

at

least

one

method

are

false

positives.

If

only

compounds

retrieved

by

all

methods

are

considered,

recall

drops

but

is

still

substantial.

At

intermediate

consensus

numbers,

a

large

improvement

in

precision

is

observed

when

going

from

compounds

found

at

least

once

to

those

found

twice

and

three

times.

At

the

same

time,

recall

does

not

decrease

significantly.

Thus,

for

compounds

retrieved

by

at

least

three

methods,

the

average

recall

is

whereas

precision

is

on

average.

Considering

higher

consensus

numbers,

recall

values

are

lower

than

for

SVM

or

RF

applied

alone,

but

a

significant

improvement

in

precision

is

observed,

suggesting

that

a

consensus

approach

is

particularly

effective

at

reducing

the

number

of

false

positives.

Two

possible

applications

of

the

consensus

approach

are

proposed.

In

cases

in

which

only

a

small

number

of

actives

is

known,

selecting

all

actives

identified

by

at

least

one

method

helps

to

reduce

the

number

of

false

negatives.

If

the

objective

is

to

reduce

the

number

of

compounds

to

be

tested,

considering

compounds

that

were

found

by

several

methods

helps

to

reduce

the

number

of

false

positives.

Summarizing,

we

observed

significant

differences

in

the

performance

of

the

methods

used;

however,

the

consensus

learning

that

integrates

these

classification

schemes

is

able

to

boost

precision

and

recall

values

independently

of

the

protein

target,

or

compound

class.

I

conclude

that

brainstorming

is

a

more

efficient

way

to

predict

a

compound's

biological

activity

that

any

single

ML

algorithm..

Some

shortcomings

of

this

method

should

be

stressed.

First,

it

requires

extensive

calculation

of

weights

for

used

ML

methods,

which

is

very

elaborate

to

perform.

Second,

results

depend

on

the

diversity

of

learning

algorithms.

Third,

when

the

number

of

known

actives

is

too

low,

it

is

impossible

to

train

a

set

of

ML

algorithms.

In

such

practical

situations,

I

would

suggest

performing

more

elaborate

QSAR

studies

and

detailed

analysis

of

the

protein

target

active

site,

or

that

docking

software

should

be

used

to

prepare

an

initial

dataset

of

possible

positives

for

further

application

of

ML

algorithms

as

described

in

some

of

our

previous

publications

The

results

of

this

approach

in

the

context

of

activity

prediction

for

small

chemical

molecules

are

presented

in

Table

First,

I

have

prepared

two

training

datasets

for

each

protein

target:

positives

and

negatives.

Then,

I

have

represented

the

training

examples

using

a

predefined

set

of

chemical

descriptors

(see

Materials

and

methods

for

details).

For

each

type

of

descriptor,

a

different

ML

algorithm

was

selected

as

the

best

performing.

In

most

cases,

SVM

and

RF

were

among

the

best.

The

recall

and

precision

values

for

these

best

performing

ML

algorithms

were

then

compared

to

a

simple

voting

procedure.

The

"voting

method

predicts

a

ligand

as

positive

if

at

least

one

ML

algorithm

predicts

it

as

being

active.

In

the

case

of

"voting

n",

at

least

n

ML

algorithms

must

be

in

agreement

in

order

to

predict

a

given

ligand

as

active.

Finally,

the

results

were

compared

to

the

brainstorming

approach,

when

each

method

votes

for

the

final

decision,

with

weight

given

by

the

method's

recall

and

precision

values,

as

computed

for

the

training

dataset.

The

results

for

different

voting

schemes

are

adopted

from

our

previous

studies

Here,

I

compare

those

findings

with

the

results

of

the

novel

brainstorming

procedure

(summarized

in

last

row

of

Table

Almost

all

the

compounds

were

retrieved

by

at

least

one

of

the

seven

ML

methods

for

each

target;

almost

all

were

found

to

be

actives.

However,

because

the

recall

values

obtained

with

the

best

performing

ML

method

alone

are

already

close

to

or

above

any

improvement

compared

to

this

approach

is

small.

In

addition,

a

significant

reduction

in

precision

compared

to

the

best

The

results

of

such

ensemble

prediction

are

then

fused

into

the

single

consensus

prediction

by

a

simple

weighted

voting

procedure.

The

final

output

includes

predicted

class

membership,

a

statistical

model

with

performances

of

each

learning

module,

trained

consensus

and

reliability

scores

for

prediction

(calculated

as

described

in

Methods)

Table

The

recall

(R)

and

precision

(P)

values

for

the

brainstorming

method,

as

compared

with

the

simple

vote

procedure

or

single

machine

learning

(ML)

prediction

The

major

goal

of

this

study

was

to

determine

how

much

one

could

expect

from

the

weighted

voting

technique

in

the

context

of

virtual

HTS.

Above,

I

presented

the

results

of

various

supervised

ML

approaches

that

are

capable

of

learning

and

predicting

the

target-specific

inhibition

likelihood

for

different

chemical

compounds.

The

typical

procedure

begins

initially

with

some

experimental

knowledge

on

inhibitors

for

a

selected

protein

target;

in

addition,

data

from

the

MDDR,

propetiary

compounds

collections,

or

commercial

libraries

can

be

used

here.

Now,

I

would

like

to

compare

those

single

ML

algorithms

with

our

brainstorming

approach,

i.e.,

to

compare

its

performance

within

the

same

or

similar

computational

setup.

Therefore

I

decided

to

use

a

previously

studied

benchmark

dataset

I

previously

collected

results

for

classification

of

different

supervised

ML

methods

for

this

standardized

benchmark

and

compared

their

performance

with

other

studies.

In

addition,

I

provided

the

overall

value

for

classification

error

and

precision/recall

values

for

those

ML

algorithms

Here,

I

show

how

far

one

can

go

with

boosting

the

accuracy

of

multiple

ML

classification

models

by

building

a

consensus

between

them.

The

meta-learning

procedure

is

repeated

separately

for

each

protein

target,

therefore

weights

are

not

universal..

Let

us

assume

that

all

methods

have

equal

recall

and

precision

values,

i.e.,

all

methods

have

identical

quality.

If

the

number

of

methods

predicting

a

given

input

as

a

member

of

the

positive

class

is

equal

to

the

number

of

methods

predicting

it

as

negative

example,

then

the

actual

probability

of

success

will

be

zero.

If

the

negativepredicting

methods

have

weaker

quality

than

the

actual

prediction

that

would

be

given

by

stronger

ML

algorithms,

the

item

will

be

classified

as

active

one.

Even

if

we

have

only

a

single

high

precision

learning

algorithm,

it

will

still

force

the

classification,

as

all

other

methods

are

much

weaker

in

terms

of

their

precision

and

recall

values..

The

model

of

meta-learning

is

based

on

several

assumptions,

as

detailed

in

the

following

sections.

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


Applying ethnography in the analysis and support of expertise in engineering design Whilst many contemporary studies of design have claimed to be using ethnographic methods, the techniques which have been employed often diverge from the characteristics of pure ethnography as used in traditional anthropological and sociological research. We argue that this is entirely appropriate for applied ethnography that is conducted in pursuit of explicit design goals. In this paper, we explore the relationship between pure and applied ethnography, and their use in cognitive and social research. We also discuss how the outcomes of applied ethnography can be applied to i) the design of a computer-based design support tool and ii) the development of controlled experimental studies that retain sufficient ecological validity to capture realistic design expertise. We additionally argue that objectivity in empirical studies of design can be obtained only by triangulating observations across methodologies that embrace both ethnographic and laboratory-based traditions. In this paper we have discussed how the method of ethnography can, and perhaps even should, be adapted for the study of realistic design activities. Applied ethnography differs from a prototypical case of pure ethnography in three principal ways that are associated with its inherent purposiveness: through differences in intensity of observation, in being less independent of prior theory, and in requiring a degree of verifiability or objectivity in interpretation. We have illustrated how using an applied ethnographic technique enabled us to gain important insights into the nature of team design practices that were not possible with more conventional protocol analysis techniques. We were able to sample sufficient data over only a twoweek period to make a major contribution to revising the specification of a design reuse support tool in some important ways. We have also shown how combining the outcomes of applied ethnography with a traditional cognitive method such as the card sort enabled us to verify in a more objective fashion the observations made in previous ethnographic studies.. We argue that it is imperative that the differences between applied community of practitioners).

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


MLPA-conservation mechanism in wireless sensor network environments Transmission using excess power not only shortens the lifetime of sensor nodes, but also introduces interference. Packets should ideally be transmitted with moderate power. This study proposes a multi-level power adjustment (MLPA) mechanism for a wireless sensor network to prolong the lifetime of individual nodes and the overall network. In this study, we constructed an analytical model of the MLPA mechanism with m distinct power levels (m-LPA). For m-LPA, the closed-form expression of the optimal power setting was determined and the mean transmission power was minimized to one-third of the original fixed-transmission power. We found that the average power consumption of our proposed mechanism is 33.93% higher than that of fixed-transmission power. Thus, each node can extend the lifetime by 2.5 times. We have shown the relations between m and density in simulation results. Although the mechanism worked smoothly in this study, the sensors do not need to handle the distance and interference problem. Power consumption has become one of the main topics in research on wireless sensor networks (WSNs). In the past few years, there has been much improvement in the technology and the application of WSNs [1] [2] [3] . Researchers have compiled complete sets of standards for real cases (e.g., IEEE 802.15.4 [4] and Zigbee) spanning a wide range of applications including home security, private sector organizations, health care, and the military. In general, each sensor has limited power and computing resources. Therefore, a major issue is the prolongation of network lifetimes for WSNs.. There are three basic subsystems on a sensor node: a sensing subsystem to collect data from the environment, a processing subsystem to provide computation ability to the sensor node, and a communication subsystem for the transmission of data [5] . These subsystems are all limited by internal sensor-node power. Each sensor node plays its part in performing sensing, computing, or transmitting, always within the context of limited energy. In order to extend the lifetime of WSNs, we focus our attention to transmission problems. Theoretically, transmission power can be separated into n portions. In this study, we define the level is n portions of transmission power. Each sensor must identify the number of poweradjustment level. And each sensor cans effective delivery packets to next node. In addition, each node should save transmission power efficiently. We propose a powercontrol mechanism that achieves efficient sensor-data transmission resting on two special features: (1) an adaptive transmission power rate for packet routing and (2) a prolongation of sensors' lifetime.. The major contributions of this article are as follows.. We provide an multi-level power adjustment (MLPA) mechanism for implementation in a real WSN environment, and we prolong the lifetime of WSNs.. We present a rigorous analysis of an MLPA mechanism and identify near-optimal power configurations for each power level. We present our findings that our proposed mechanism's (2LPA, 200-500 nodes) average power consumption registers a 33.93-55.30% improvement over the original fixed-transmission power method. Thus, the lifetime of each node can extend 2.5 times.. As the simulation results show, we found that when we adjusted the power level to 2 or 3, we could get acceptable performance. If the power level was more than 3, the lifetime performance was better.. The mechanism that we proposed can work smoothly even though to get the distance value between nodes and node is insufficient and even though there is some interference between nodes. In other words, the proposed mechanism can still work even if the between-node distance value is unsatisfactory, the sensor board lacks GPS chips, or the environment exhibits refraction and attenuation. We proposed an MLPA mechanism for implementation in real WSN environments to prolong the lifetime of WSNs. And we present a rigorous analysis of an MLPA mechanism and identify near-optimal power configurations for each power level. On the basis of PAT construction, appropriate transmission power can be adjusted during data transmission. We present our findings that the average power consumption of our proposed mechanism (2LPA, 200-500 nodes) registers a 33.93-55.30% improvement over the original Fixed-TP method. Thus, the lifetime of each node can extend 2.5 times. And we have presented the relations between m and density in simulation results. Setting the appropriate m value will improve lifetime performance.. As the simulation results show, we found that when we adjusted the power level to 2 or 3, we could get acceptable performance. If the power level was more than 3, the lifetime performance was better.. The mechanism that we proposed can work smoothly even though to get the distance value between nodes and node is insufficient and even though there is some interference between nodes. In other words, the proposed mechanism can still work even if the betweennode distance value is unsatisfactory, the sensor board lacks GPS chips, or the environment exhibits refraction and attenuation.. We conclude that the proposed mechanism can work smoothly. If those sensors cannot get the distance between nodes and there is some interference between nodes, our proposed mechanism can work smoothly. Figure 15 Relationship between lifetimes of WSN and density. This section describes the literature's treatment of WSNs and various approaches to determining the transmission ranges for the extension of WSN lifetimes [6] [7] [8] [9] [10] . Conventional WSNs consist of thousands of sensor nodes and sensors that can communicate with each other or with base stations. The coverage area of a WSN depends on the number of sensors deployed [11] [12] [13] . Every sensor node has sensing, processing, transmitting, moving, locating, and electrical power elements; some elements are optional, such as the moving or locating element. Sensor nodes are placed in a particular area and integrated with each other to yield high-quality information about the environment [14] . Every sensor node not only can capture the sensed data and route it to other sensors or base stations, but also can determine methods of routing or scheduling according to the mission, the received data, and the status of the sensor node. A base station is a mobile or static device for connecting a WSN to the Internet, thereby enabling a user to receive sensed data.. In recent years, considerable research has addressed the coordination and management of sensing, as well as sensor-related data collection and processing [1] . Due to the limitations of both electrical power and sensor nodes' communication bandwidth, it is necessary to design a technology that reduces the waste of power and that employs limited bandwidth effectively. The limitations in traditional WSNs create many challenges of design and management such as electric power's relationship to each layer of a network's protocol stack. For example, it is necessary for the network layer to use a power-saving routing method that sends data from sensor nodes to the base station effectively and that increases the network's lifetime satisfactorily.. The routing methods of current WSNs include tabledriven, demand-driven, and hybrid methods. A detailed explanation of these three methods is given below.. (1) Table-driven method: According to the routing table in every sensor node, the sensed data can be transmitted by the default routing path in the routing table such as destination sequenced distance-vector (DSDV) [15] , CGSR [16] , and WRP [17] .. (2) Demand-driven method: This method does not call for a default routing path. Instead, there is an ondemand path, which is found when source nodes must transmit data to a destination node. The routing path is established through flooding that involves node-path information from the source to the destination. AODV [18] and DSR [19] are examples of this kind of routing method. (3) Hybrid method: This method is a combination of the table-driven and demand-driven methods. An example is ZRP [20] .. Whereas at least three methods for solving problems of data transmission and of electrical power are based on routing, some approaches solve such problems by exploiting communication among sensor nodes [6] [7] [8] [9] [10] 21, 22] . These approaches, when successful, adjust communication power according to the between-node distance values [10] , but they must solve problems concerning unknown transmission distance, uncertain range of sensor-node coverage, and nonlinear deployment in real WSNs.. Dhawan et al. [23] proposed a smoothly varying range model to adjust sensing range sensor networks for maximizing the network lifetime. Authors use the GargKönemann algorithm 1 with an approximation ratio to find a monitoring schedule for sensor covers. However, the continuous model needs complicate analysis. Wang and Medidi [24] proposed local sensing radii optimization schemes to minimize the energy consumption and extend the lifetime of networks. Authors assume that the transmission range is at least two times larger than the maximum sensing range, and then focus on optimizing sensing coverage for reliable surveillance and energyefficiency. However, this method requires additional mechanisms and overhead to ensure the connectivity of entire network if obstacles exist between sensors. In [21, 22] , the authors divide the sensing field into a number of smaller areas. Based on those smaller area, sensors could control and minimizing energy consumption.. Because of these uncertainties and other unknowns, real WSNs cannot easily compute the precise communication power, even though some investigations have presented corresponding algorithms. This study proposes a power-adjustable routing policy, in which no sensor node transmits data by static communication power. Rather, each sensor node transmits data using dynamic communication power, determined by the conditions of the immediate environment.

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