Efficient and Robust Detection of Duplicate Videos in a Large Database

一种基于nvme热插拔检测方法NVME (Non-Volatile Memory Express) is a high-performance and scalable storage protocol designed to take advantage of the performance of solid-state drives (SSDs) over the older and slower interfaces. NVME drives are becoming more and more popular due to their high speed and low latency. However, one common issue with NVME drives is the hot-plug detection method.NVME是一种高性能和可扩展的存储协议，旨在充分利用固态硬盘(SSD)的性能，相比之下，旧的和慢速的接口就显得不足够强大。

由于其高速和低延迟，NVME驱动器越来越受欢迎。

然而，NVME驱动器的一个常见问题是热插拔检测方法。

The hot-plug detection method for NVME drives refers to the ability to safely remove and add NVME drives from a system while it's powered on. This is an important feature for servers and data centers, as it allows for quick and easy maintenance and upgrading of storage devices without having to power down the entire system. However, the current hot-plug detection methods for NVME drivescan be unreliable, leading to potential data loss or drive damage if not properly handled.NVME驱动器的热插拔检测方法指的是在系统运行时安全地移除和添加NVME驱动器的能力。

电力故障诊断方法研究的一些参考文献Research on the method of power failure diagnosis has been a crucial area of focus in the field of electrical engineering. One important reference in this area is the paper "Power Proxy: Anomaly Detection in Power Usage Data" by Zhang et al. This paper proposes a novel approach using deep learning techniques to detect anomalies in power usage data, which can aid in the diagnosis of power failures. The authors demonstrate the effectiveness of their method through experiments on real-world power usage datasets.电力故障诊断方法的研究一直是电气工程领域的一个重要研究方向。

张等人的论文《Power Proxy: Anomaly Detection in Power Usage Data》是这方面的一个重要参考文献。

这篇论文提出了一种新颖的方法，利用深度学习技术来检测电力使用数据中的异常，有助于诊断电力故障。

作者通过对真实电力使用数据集的实验验证了他们方法的有效性。

In addition to Zhang et al.'s work, another valuable reference is the paper "A Survey on Fault Diagnosis Techniques Through EE Stream Processing" by Wang et al. This survey paper provides a comprehensive overview of fault diagnosis techniques in the contextof electrical engineering stream processing. The authors discuss various methods such as Bayesian networks, neural networks, and decision trees, highlighting their applications in diagnosing power failures. This paper serves as a useful guide for researchers interested in exploring different fault diagnosis techniques.除了张等人的工作，王等人的论文《A Survey on Fault Diagnosis Techniques Through EE Stream Processing》也是一个很有价值的参考文献。

机器视觉行业的职业发展目标与技能特长In the rapidly advancing field of machine vision, professionals can pursue various career development goals and cultivate specific skill sets to stay competitive and excel in their roles. Here are some key career development objectives and sought-after skills in the machine vision industry:1. Develop expertise in image processing algorithms and computer vision techniques.Image processing involves manipulating digital images to extract useful information or enhance visual quality. Professionals can focus on learning various image filtering and enhancement techniques, segmentation algorithms, object detection, recognition, and tracking methods. These skills are essential for enabling machines to understand and interpret visual data.中文翻译：在快速发展的机器视觉领域，专业人士可以追求各种职业发展目标，并培养特定的技能集，以保持竞争力并在其岗位上取得优异成绩。

互相关zncc原理Cross-correlation (互相关) and zero-normalized cross-correlation (ZNCC) are fundamental principles in the field of signal processing and image analysis. These techniques are widely used in various applications such as pattern recognition, feature matching, motion tracking, and image registration.互相关和零标准化互相关（ZNCC）是信号处理和图像分析领域的基本原理。

这些技术广泛应用于模式识别、特征匹配、运动跟踪和图像配准等各种应用中。

From a mathematical perspective, cross-correlation measures the similarity between two signals or images by sliding one over the other and computing the summation of the product of corresponding elements. It is a fundamental operation in signal processing that allows us to detect patterns and identify similarities between different sets of data. ZNCC, on the other hand, is a normalized version of cross-correlation that takes into account the mean and standard deviation of the signals to provide a more robust measure of similarity.从数学角度来看，互相关通过将一个信号或图像滑过另一个，并计算相应元素的乘积之和来衡量它们之间的相似性。

信道信源编码英文文献English:In the field of communication systems, the concepts of channel coding and source coding are fundamental. Channel coding, also known as error-correcting coding, involves adding redundancy to data before transmission to enable error detection and correction at the receiver. This technique is essential for reliable data transmission over noisy communication channels. Source coding, on the other hand, focuses on compressing data to reduce the number of bits required for representation without significant loss of information. Efficient source coding techniques are crucial for optimizing bandwidth usage and storage in various applications such as image and video compression, speech encoding, and data transmissionover networks. Understanding the principles and techniques of both channel coding and source coding is vital for designing efficient and robust communication systems.中文翻译:在通信系统领域，信道编码和信源编码是基础概念。

C S T e c h n i c a l R e p o r t #395, A p r i l 8, 2003Institute of ScientificComputingVolumetric Collision Detectionfor Derformable ObjectsBruno Heidelberger, Matthias Teschner, Markus GrossComputerScience DepartmentETH Zurich, Switzerland e-mail: {heidelberger, teschner, grossm}@inf.ethz.chhttp://graphics.ethz.chVolumetric Collision Detection for Deformable ObjectsBruno Heidelberger Matthias Teschner Markus GrossComputer Graphics LaboratoryETH ZurichFigure1:This sequence shows two deforming objects with150k and5k faces.An explicit representation of the red intersection volume can be computed at interactive frame rate.AbstractWe present a new algorithm for the efficient detection of colli-sions of geometrically complex objects.Our approach requires nei-ther expensive setup nor sophisticated spatial data structures and is hence specifically suitable for handling deformable objects with ar-bitrarily shaped,closed surfaces.The algorithm is based on a Lay-ered Depth Image(LDI)decomposition of the intersection volume. Currently,we have implemented two types of collision queries.The first one comprises an explicit representation of the intersection vol-ume.The second one computes vertex-in-volume tests.All queries are processed on the LDI–based volume representation.Our algo-rithm is very easy to implement and can be accelerated in graphics hardware by using OpenGL.Keywords:Collision Detection,Computational Geometry,Lay-ered Depth Image,Shape Approximation,Deformable Modeling, Physically–based Modeling,Graphics Hardware1IntroductionThe detection of collisions of geometric objects constitutes a fun-damental problem in computer graphics,computational geometry, modeling,robotics,and many other areas.Most research is devoted to the design of algorithms for the efficient computation of queries, such as intersecting surfaces or penetration depth.For complex ob-jects,such queries are computationally expensive,because they re-quire the testing of many individual graphics primitives.Hence,ef-ficient collision detection algorithms are accelerated by spatial data structures including bounding–box hierarchies,distancefields,or other ways of spatial partitioning.Such object representations are built in a pre–processing stage,and once they are created,perform very well for rigid objects.Modern physics–based simulation,as for instance used in game engines or computational surgery,demand the detection of colli-sions of objects deforming over time.In the case of deformable objects,the aforementioned acceleration structures have to be up-dated to adapt to changes of the geometry.While some of these algorithms have been successfully employed in such cases,the ac-celeration structures constitute an additional overhead in memory consumption and computational efficiency.We propose a simple and efficient algorithm based on Layered Depth Images(LDI)[Shade et al.1998]as the fundamental repre-sentation to accelerate collision detection of rigid and deformable objects.While most existing approaches consider object surfaces, our method is inherently volumetric.Rather than explicitly detect-ing surface intersections,the LDI provides a discrete representation of the intersection volume and allows for volume–based collision queries as shown in Fig.1.The generation of LDIs is extremely efficient and can be accelerated by graphics hardware.Various op-timizations minimize buffer read–backs.Our algorithm proceeds in three stages.First,we calculate the intersection of axis–aligned bounding boxes of pairs of objects.If an intersection is non–empty,a second stage computes an LDI rep-resentation of each object within the bounding–box intersection.Fi-nally,we obtain the overall intersection volume by simple Boolean operations on the LDI representation.The simplicity of the algorithm,the absence of expensive setup and the ease of implementation in OpenGL makes it especially at-tractive for real–time simulation and computer games.1.1ContributionsThe contributions of this work can be summarized as follows:•We present a new algorithm for the computation of volumetric penetrations of geometrically complex,deformable objects in real time.Our method handles arbitrarily shaped,closed sur-faces of manifold geometry.•We currently process two different types of collision queries: Thefirst one includes the explicit computation of the intersec-tion volume of two objects.The second one performs vertex-in-volume tests.Our approach is also robust in cases,where one object lies completely inside another object.•Unlike most existing approaches,our method does not require an involved setup or the implementation of a sophisticatedspatial data structure.It is memory efficient,computation-ally economical and performs on deformable models of up to hundred thousands of primitives.1.2Background and Related WorkDue to the importance of the topic,the interference detection of geometrically complex objects has been investigated extensively in various areas.Collision algorithms.In graphics,efficient collision detection is an essential component in physically–based simulationtion [Baraff 2001],[Debunne et al.2001],including cloth [Bridson et al.2002].Further applications can be found in computer animation,medical simulations,and games.Collision detection algorithms based on hierarchies have proven to be very efficient and many BVs have been investigated.Among the acceleration find spheres [Hubbard 1993],[Quinlan 1994],ing boxes [Hughes et al.1996],[van den Bergen oriented bounding boxes [Gottschalk et al.1996],and oriented polytopes [Klosowski et al.1996].Initially,BV approaches were designed for rigid context,the hierarchy is computed in a pre–processing case of deforming objects,however,this hierarchy must at run time.While effort has been spent to optimize BV for deformable objects [Larsson and Akenine-Moeller still pose a substantial computational burden and storage for complex objects.As an additional limitation for cations,BV approaches typically detect intersecting computation of the intersection volume requires an As an alternative to object partitioning,other ploy discretized distance fields as volumetric object for collision detection.The presented results,however,this data structure is less suitable for real–time metrically complex objects [Hirota et al.2000].Recently,various approaches have been introduced that employ graphics hardware for collision detection.In [Baciu et al.1999],a multi–pass rendering method is proposed for collision detection.However,this algorithm is restricted to convex objects.In [Lom-bardo et al.1999],the interaction of a cylindrical tool with de-formable tissue is accelerated by graphics hardware.The method is restricted to collisions of simplified surgical tools with object surfaces.[Hoff III et al.2001]proposes a multi–pass rendering approach for collision detection of 2–D objects,while [Kim et al.2002]and [Kim et al.2003]perform closest–point queries using bounding–volume hierarchies along with a multipass–rendering ap-proach.The aforementioned approaches decompose the objects into convex polytopes.Layered Depth Images (LDI).LDIs have been introduced as an efficient image–based rendering technique.The LDI data structure essentially stores multiple depth values per pixel.Thus,an LDI can be used to approximate the volume of an object.A method for generating LDIs is presented,for instance,in the depth–peeling approach to order–independent transparency [Everitt 2001].This implementation,however,demands a complex setup including two active depth buffers and texture shaders.In contrast,our approach to LDI generation is much simpler by employing a more efficient rendering setup based on plain OpenGL 1.4.Hardware–based approaches to interactive CSG rendering,e.g.[Goldfeather et al.1986]and [Rappoport and Spitz 1997],could also be employed for LDI generation.However,since such CSG rendering implementations have to handle more complex modeling operations they feature more involved rendering setups.Further-more,these CSG rendering approaches do not explicitly compute the resulting solid.2Method This section presents an overview of our algorithm followed by a detailed description of the three stages.Input.Our approach takes 3–D closed objects of manifold geome-try as an input.Although the approach is not confined to triangular (a)Stage 1.(b)Stage 2.(c)Stage 3a.Figure 2:Algorithm overview in 2–D and 3–D.(a)AABB inter-section.(b)LDI generation within the V oI.(c)Computation of the intersection volume.Algorithm overview.Our approach proceeds in three stages.Stage 1computes the AABB intersection for a pair of objects.If the intersection is empty,the two objects do not overlap.If it is not,stages 2and 3are applied to the AABB intersection volume.We will refer to it as V olume-of-Interest (V oI).Stage 2computes two LDIs,one for each object.LDI gener-ation is restricted to the V oI.The depth values of the LDI can be interpreted as the intersections of parallel rays or 3–D scan lines entering or leaving the object.Thus,an LDI classifies the V oI into inside and outside regions with respect to an object.Likewise,we classify the corresponding intersections into entry points and leav-ing points .The concept is similar in spirit to well–known scan–conversion algorithms to fill concave polygons [Foley et al.1990],where intersections of a scan line with a polygon represent transi-tions between interior and exterior.Stage 3performs the actual collision detection.Currently,we distinguish two different types of queries:a)Both LDIs are com-bined using Boolean intersection.If the intersection of all inside regions is not empty,a collision is detected.This operation also pro-vides an explicit representation of the intersection volume.b)Indi-vidual vertices within the V oI of one object are transformed into the LDI of the second object.If such a vertex lies in an inside region,a collision is detected.This query can also be used to check atomic or point–sampled objects for intersection.Fig.2illustrates the three stages of our algorithm.For a detailed description of all stages,refer to Sections2.1–2.3.2.1AABB IntersectionAABB intersections are computed for pairs of objects.If two AABBs do not overlap,the corresponding objects cannot interfere. Otherwise,the intersection volume provides a V oI,which is con-sidered for further processing.Although AABBs do not provide an optimal,i.e.smallest,bounding volume,they can be computed very efficiently.Furthermore,the intersection of two AABBs is again an AABB which keeps subsequent stages of the algorithm simple.Alternative BVs,such as object–oriented bounding boxes [Gottschalk et al.1996]or discrete–oriented polytopes[Klosowski et al.1996],provide tighterfitting object approximations.How-ever,the computation of these representations is more involved and the resulting intersection volume can have a more complex shape making it much more difficult for further processing.In addition, such structures are impractical for deforming objects,which require a dynamic adaptation of the bounding box.In order to simplify and accelerate further computations,the V oI has to meet the following requirements:Firstly,to guarantee proper boundary conditions,we have to render the LDI for each object from the outside.We select one of the six faces of the V oI,the so–called outside face,as the near rendering plane for each object(see Fig.3).If a sorted LDI is generated with respect to this face,en-try points and leaving points alternate.For closed objects,thefirst layer is assumed to contain entry points,the second layer leaving points and so on.Secondly,outside faces for both objects should correspond to opposite sides of the V oI(see Fig.3(a)).This sim-plifies the combination of both LDIs for the computation of the intersection volume.(a)V oI for A and B.(b)Extended V oI to obtainan outside face for A. Figure3:The intersection of the AABBs of two objects A and B provides the V oI.The intersection volume of two AABBs,i.e.the V oI,is bounded by parts of their faces.Fig.3(a)shows a V oI with corresponding outside faces.In some cases,for instance when one box is en-tirely within another box,appropriate outside faces for both objects cannot be found.This problem is solved by extending the V oI.If the outside face of one object isfixed,the opposite face of the V oI can be scaled to touch the bounding box of the other object(see Fig.3(b)).If several eligible pairs of outside faces exist,we choose the ones with the smallest distance between them.This criterion is a fast heuristic to optimize depth complexity for LDI generation assum-ing that depth complexity scales with the distance between the two outside faces.and their opposite faces defining the far planes.The remaining faces define top,bottom,left,and right of the rendering volume. Orthographic projection is used for rendering.The resolution of the LDI,which defines the accuracy of the object approximation,is specified by the user.Objects are rendered n max times for LDI gen-eration,where n max denotes the depth complexity of the relevant part of the object within the V oI.Thus,the computational com-plexity of the LDI generation is O(n max).For details regarding the OpenGL implementation,refer to Sec.3.Thefirst rendering pass generates a single LDI layer and com-putes n max.Depth testing and face culling are disabled.Only the stencil test is employed to discard fragments.The stencil test con-figuration allows thefirst fragment per pixel to pass,while the cor-responding stencil value is incremented.Subsequent fragments are discarded by the stencil test,but still increment the stencil buffer. Hence,after thefirst rendering pass the depth buffer contains the first object layer per pixel and the stencil buffer contains a map rep-resenting the depth complexity per pixel.Thus,n max is found by searching the maximum stencil value.Note,that the max()com-putation constitutes a very small computational burden,given the typical LDI resolution.If n max>1,additional rendering passes2to n max generate the remaining layers.The rendering setup is similar to thefirst pass. However,during the n-th rendering pass,thefirst n fragments per pixel pass the stencil test and,as a consequence,the resulting depth buffer contains the n-th LDI layer.During these passes,the stencil buffer is not incremented,if the stencil test fails.Since fragments are rendered in arbitrary order,the algorithm generates an unsorted LDI.For further processing,the LDI is sorted per pixel.Only thefirst n p layers per pixel are considered,where n p is the depth complexity of this pixel.n p is taken from the stencil buffer as computed in thefirst rendering pass.If n p is smaller than n max,layers n p+1to n max do not contain valid LDI values for this pixel and are discarded(see Fig.5).Although the order in which the fragments of an object are ren-dered is arbitrary,our algorithm relies on consistency across the individual passes.Our LDIs store normalized values.For advanced collision queries,additional information,such as viewing direction and viewing volume are stored as well.The accuracy of the method can be adjusted.In(x,y)-direction, accuracy corresponds with the user–defined LDI resolution.The accuracy in z-direction is given by the depth–buffer resolution.Figure 5:LDI for two pixels,unsorted and sorted with respect to depth values.Compared to alternative data structures,such as bounding–volume hierarchies or distance fields,the LDI representation is very memory efficient.Both the memory consumption and the perfor-mance of the LDI generation depend on the geometric complexity and the depth complexity of the objects.However,our results pre-sented in Sec.4suggest,that the number of LDI layers does not exceed reasonable values even for complex objects.Further,col-lision queries performed on the generated LDIs are very efficient even in the case of a large number of LDI layers.2.3Collision DetectionThe computed LDIs can be utilized to process a variety of collision queries in an efficient way.Our current implementation comprises two different types of collision queries:Intersection volume.Two LDIs can be combined to compute an intersection volume.The LDIs for two colliding objects are dis-cretized with the same resolution on corresponding sampling grids,but with opposite viewing directions.Hence,pixels in bothLDIs represent corresponding volume spans.Therefore,intersection vol-umes can be computed by a pixelwise intersection of the inside re-gions of both LDIs.Ifthe resulting intersection is non–empty,a collision is detected.The sum of all pixelwise intersection regions constitutes a discrete representation of the intersection volume (see Fig.6(a)).(a)Intersection volume.(b)Vertex-in-volume.Figure 6:Two types of collision queries.Pixelwise intersection and testing of the LDIs.Vertex-in-volume.Individual vertices can also be tested against an LDI.To this end,the vertex is transformed into the local coordinate system of the LDI.If the transformed vertex intersects with an in-side region,a collision is detected (see Fig.6(b)).As demonstrated in Fig.11and Fig.12,this query can be used to compute collisionswith atomic objects,such as particles.3Implementation The implementation of V oI computation (stage 1)and collision queries (stage 3)can be easily accomplished by following the de-scriptions in Sec.2.1and Sec.2.3,respectively.This section de-scribes some details regarding the efficient implementation of the LDI generation (see Sec.2.2)using core OpenGL 1.4functionality.The LDI generation is performed in a multi–pass rendering pro-cess following the rendering setup as described in Sec.2.2.To im-prove performance,the color buffer is disabled.Each LDI layer is generated in one rendering pass.Except for the first pass,which computes the depth complexities per pixel,all passes are identical.Our actual OpenGL implementation is very similar to the following pseudo code.rendering setup;//pass 1computes n max and the first LDI layer glClear(GL DEPTH BUFFER BIT |GL STENCIL BUFFER BIT);glStencilFunc(GL GREATER,1,0xff);glStencilOp(GL INCR,GL INCR,GL INCR);render object;depth complexities ←stencil buffer;n max ←max(depth complexities);layer [1]←depth buffer;//passes 2to n max compute the remaining LDI layers n ←2;while n ≤n max begin glClear(GL DEPTH BUFFER BIT |GL STENCIL BUFFER BIT);glStencilFunc(GL GREATER,n,0xff);glStencilOp(GL KEEP,GL INCR,GL INCR);render object;layer [n ]←depth buffer;n ←n +1;end;Each rendering pass actually requires a buffer read–back.Read-ing back buffers from the GPU,however,can be expensive de-pending on the actual architecture.We propose two methods for optimizing the data transfer using OpenGL extensions.Firstly,depth and stencil values of a pixel are usually stored in the same 32–bit word in the framebuffer.This allows to read–back both buffers in a single pass using an OpenGL extension,such as GL NV packed depth stencil.Secondly,all rendering passes can be performed independently from each other except for the first pass.We exploit this fact to render individual layers into differ-ent regions of the framebuffer.Once finished,the whole frame-buffer is read back in a single pass.This optimization reduces the number of read–backs to a maximum of two,assuming that the framebuffer memory is sufficiently large.It reduces stalls in the rendering pipeline and substantially improves the performance of the algorithm.4Results All of the experiments described in this section have been per-formed on a PC with 2.8GHz Intel Pentium IV ,2GB main mem-ory,and NVidia GeForce 4Ti 4600graphics card with 128MB on–board memory.Results for the two types of collision queries are presented using five different test objects (see Fig.7).Intersection volume.In Tab.1,computing times for six cases are given.Since the performance of our algorithm depends on the ge-ometrical complexity of the object and on the depth complexities of both objects within the V oI,we have carried out experiments asshown in Fig.8and Fig.9.In the worst case,when both AABBs coincide,the V oI and the depth complexity are maximal.Hence, both LDIs are computed for the entire object.The performance of these cases can be derived from Tab.2,where timings for the LDI generation for entire objects are given.Objects Faces DepthCom-plexityStage1+2[ms]Stage3a[ms]Fig.Knot/Rabbit12k/50k3/11918 Knot/Rabbit12k/50k4/65619 Rabbit/Reptile50k/110k7/53618 Rabbit/Reptile50k/110k4/24819 Reptile/Santa110k/150k1/32518 Reptile/Santa110k/150k8/611419 Table1:Computation of the intersection volume using various ob-ject pairs as shown in Fig.8and Fig.9.The resolution of the LDI is 128x128.The depth complexities are given for both objects within the V oI.Stage1and2compute the AABBs,the V oI,and both LDIs. Stage3a generates the intersection volume.In the case of rigid ob-jects,stages1and2can be pre–computed.An additional scenario with a very complex intersection volume is illustrated in Fig.10.The Knot and the Dragon have12k faces and520k faces,respectively.Depth complexities within the V oI are 7and8.With an LDI resolution of128x128the intersection volume can be computed in256ms.puting times for the vertex-in-volume test are given in Tab.2.For each object,100,000arbitrarily positioned vertices within the AABB of an object are tested.Fig.11illustrates the experiment for one of the test objects.Object FacesDepthComplexityStage1+2[ms]Stage3b[ms]Knot12k81126Rabbit50k82927Reptile110k128328Santa150k87926Dragon520k1030727 Table2:Vertex-in-volume test for various objects.The V oI com-puted in stage1coincides with the AABB of the object.Stage2 computes the LDI representation for the entire object with a reso-lution of128x128.In stage3,100,000vertices with arbitrary posi-tions within the AABB of the object are tested.In the case of rigid objects,stages1and2can be pre–computed.Fig.11illustrates the test for one object.An application of the vertex-in-volume test is shown in Fig.12. The simulation environment includes20,000particles and the de-forming Santa with10k faces.The object deformation,the particle dynamics,the collision detection and response can be computed and rendered in real time with29fps.5Conclusions5.1DiscussionWe have presented a fast,hardware–accelerated method for volu-metric collision detection.As a central acceleration structure our algorithm computes an LDI that approximates the intersection vol-ume.Unlike many existing collision detection schemes,our method does not require expensive setup or pre–processing and is,hence, specifically suited for deformable objects and dynamic simulation. Currently,we have implemented two different types of collision queries and demonstrated that the method performs at interactive rates up to very complex geometry.As a limitation,our collision detection approach is restricted to objects with closed,watertight surfaces,since it relies on the notion of inside and outside.5.2Future WorkCurrently,collision queries are computed on the CPU.The perfor-mance of the algorithm could be improved by using the GPU.Em-ploying pixel shaders with multiple render targets for LDI genera-tion might support GPU–based queries.While we compute an explicit representation of the intersection volume,additional queries,such as penetration depth or closest point,might be needed for advanced collision response.This is also subject to future research.At this point,self–collisions are not detected.Actually,our al-gorithm could be extended towards self–collisions by treating the front and back faces separately during the rendering process.This would allow us to efficently detect an invalid ordering of front and back faces.6AcknowledgementsThis project is funded by the National Center of Competence in Research“Computational Medicine”(NCCR Co–Me). ReferencesB ACIU,G.,W ONG,W.S.-K.,AND S UN,H.1999.RECODE:an image–based col-lision detection algorithm.The Journal of Visualization and Computer Animation 10,181–192.B ARAFF,D.2001.Collision and contact.SIGGRAPH2001Course Notes.B RIDSON,R.,F EDKIW,R.,AND A NDERSON,J.2002.Robust treatment of colli-sions,contact and friction for cloth animation.In Proc.of the29th annual confer-ence on Computer graphics and interactive techniques,ACM Press,594–603.D EBUNNE,G.,D ESBRUN,M.,C ANI,M.-P.,AND B ARR,A.H.2001.Dynamicreal-time deformations using space&time adaptive sampling.In Proc.of the28th annual conf.on Computer graphics and interactive techniques,ACM Press,31–36.E VERITT, C.2001.Interactive order–independent transparency.Technical report,NVIDIA Corporation.F OLEY,J.D.,VAN D AM,A.,F EINER,S.K.,AND H UGHES,puterGraphics:Principles and Practics.Addison-Wesley Publishing Company.G OLDFEATHER,J.,H ULTQUIST,J.P.M.,AND F UCHS,H.1986.Fast constructive–solid geometry display in the pixel–powers graphics system.In Proc.of the13th annual conference on Computer graphics and interactive techniques,ACM Press, 107–116.G OTTSCHALK,S.,L IN,M.C.,AND M ANOCHA,D.1996.Obbtree:a hierarchicalstructure for rapid interference detection.In Proc.of the23rd annual conference on Computer graphics and interactive techniques,ACM Press,171–180.H IROTA,G.,F ISHER,S.,AND L IN,M.C.2000.Simulation of non–penetratingelastic bodies using distancefields.Technical Report TR00-018,University of North Carolina at Chapel Hill.H OFF III,K.E.,Z AFERAKIS,A.,L IN,M.C.,AND M ANOCHA,D.2001.Fast andsimple2d geometric proximity queries using graphics hardware.In Proc.of2001 Symposium on Interactive3D Graphics,145–148.H UBBARD,P.M.1993.Interactive collision detection.In Proc.IEEE Symposium onResearch Frontiers in Virtual Reality,24–31.H UGHES,M.,D I M ATTIA,C.,L IN,M.C.,AND M ANOCHA,D.1996.Efficient andaccurate interference detection for polynomial deformation and soft object anima-tion.In Proc.of Computer Animation,155–166.K IM ,Y.J.,O TADUY ,M.A.,L IN ,M.C.,AND M ANOCHA ,D.2002.Fast pen-etration depth computation for physically-based animation.In Proc.of the ACM SIGGRAPH symposium on Computer animation ,ACM Press,23–31.K IM ,Y.J.,H OFF III,K.E.,L IN ,M.C.,AND M ANOCHA ,D.2003.Closest point query among the union of convex polytopes using rasterization hardware.Journal of Graphics Tools ,to appear.K LOSOWSKI ,J.T.,H ELD ,M.,M ITCHELL ,J.S.B.,S OWIZRAL ,H.,AND Z IKAN ,K.1996.Efficient collision detection using bounding volume hierarchies of k–DOPs.In Proc.of the 23rd annual conference on Computer graphics and interac-tive techniques ,ACM Press,171–180.L ARSSON ,T.,AND A KENINE -M OELLER ,T.2001.Collision detection for continu-ously deforming bodies.In Proc.of Eurographics 2001,325–333.L OMBARDO ,J.C.,C ANI ,M.-P.,AND N EYRET ,F.1999.Real–time collision detec-tion for virtual surgery.In Proc.of Computer Animation ’99,33–39.Q UINLAN ,S.1994.Efficient distance computation between non–convex objects.In Proc.IEEE Int.Conference on Robotics and Automation ,IEEE,3324–3329.R APPOPORT ,A.,AND S PITZ ,S.1997.Interactive boolean operations for conceptual design of 3–D solids.In Proc.of the 24th annual conference on Computer graphics and interactive techniques ,ACM Press/Addison-Wesley Publishing Co.,269–278.S HADE ,J.,G ORTLER ,S.,WEI H E ,L.,AND S ZELISKI ,yered depth im-ages.In Proc.of the 25th annual conference on Computer graphics and interactive techniques ,ACM Press,231–242.VAN DEN B ERGEN ,G.1997.Efficient collision detection of complex deformable models using aabb trees.Journal of Graphics Tools 2,4,1–13.Figure 7:Test objects:Dragon,Santa,Rabbit,Reptile,Knot.Figure 8:Computation of the intersection volume.AABBs,V oI,and intersection volume are shown for three examples with simpleintersections.Figure 9:Computation of the intersection volume.AABBs,V oI,and intersection volume are shown for three examples with complex intersections.Figure 10:Intersection volume for Knot andDragon.Figure 11:Vertex-in-volume test.Left:The LDI representation is computed for the entire object.V oI and AABB coincide.Right:The vertex-in-volume test is processed for 100,000arbitrarily posi-tionedvertices.Figure 12:Deforming Santa and particles.Red color on the right–hand side indicates actual or recent contact.。

KDD CUP 2009英文关键词：Customer Relationship Management (CRM), marketing databases, French Telecom company Orange, propensity of customers,中文关键词：客户关系管理(CRM)、营销数据库，法国电信公司橙、倾向的客户，数据格式：TEXT数据介绍：Customer Relationship Management (CRM) is a key element of modern marketing strategies. The KDD Cup 2009 offers the opportunity to work on large marketing databases from the French Telecom company Orange to predict the propensity of customers to switch provider (churn), buy new products or services (appetency), or buy upgrades or add-ons proposed to them to make the sale more profitable (up-selling).The most practical way, in a CRM system, to build knowledge on customer is to produce scores. A score (the output of a model) is anevaluation for all instances of a target variable to explain (i.e. churn, appetency or up-selling). Tools which produce scores allow to project, on a given population, quantifiable information. The score is computed using input variables which describe instances. Scores are then used by the information system (IS), for example, to personalize the customer relationship. An industrial customer analysis platform able to build prediction models with a very large number of input variables has been developed by Orange Labs. This platform implements several processing methods for instances and variables selection, prediction and indexation based on an efficient model combined with variable selection regularization and model averaging method. The main characteristic of this platform is its ability to scale on very large datasets with hundreds of thousands of instances and thousands of variables. The rapid and robust detection of the variables that have most contributed to the output prediction can be a key factor in a marketing application.The challenge is to beat the in-house system developed by Orange Labs. It is an opportunity to prove that you can deal with a very large database, including heterogeneous noisy data (numerical and categorical variables), and unbalanced class distributions. Time efficiency is often a crucial point. Therefore part of the competition will be time-constrained to test the ability of the participants to deliver solutions quickly.[以下内容来由机器自动翻译]客户关系管理(CRM) 是现代营销战略的一个关键要素。

hiltiHilti: A Leader in Construction Technology and SolutionsIntroductionIn the realm of construction and engineering, quality tools and equipment are vital for both efficiency and safety. Hilti is a well-known global brand that specializes in providing innovative solutions and technologies for the construction industry. Established in 1941, Hilti has become synonymous with quality, reliability, and excellence. With a strong focus on customer satisfaction and advanced engineering, Hilti continues to be a leader in the construction field.History and BackgroundHilti was founded in the small town of Schaan, Liechtenstein, by brothers Eugen and Martin Hilti. Initially, the company operated as a workshop, producing tools and machines primarily for the local construction industry. However, with a keen eye for innovation, the Hilti brothers quickly realized the potential of their products and expanded their operations.In the 1950s, Hilti introduced the world's first electric hammer drill, revolutionizing the construction industry. This breakthrough technology made drilling faster and more efficient than ever before. As word spread about the superior performance and durability of Hilti tools, the company's reputation grew exponentially.Product Range and SolutionsHilti offers a wide range of products and solutions tailored to the various needs of the construction industry. Their product portfolio includes power tools, anchoring systems, measuring and detection tools, cutting and grinding equipment, and much more.One of Hilti's flagship products is the Hilti TE 1000-AVR Breaker. This powerful and robust tool is highly valued by professionals for its exceptional performance in concrete demolition and renovation tasks. With its vibration reduction system and innovative design, the TE 1000-AVR Breaker ensures both operator comfort and efficiency.Hilti also provides specialized anchoring systems that ensure secure and reliable fastening in concrete, masonry, and steel structures. Their patented Hilti SafeSet™ technology offers aunique combination of speed, precision, and safety in anchor installation. These systems have been used in landmark projects around the world, enabling safe and efficient construction.Another notable product from Hilti is their advanced line of cordless tools, including drills, impact drivers, and saws. These tools combine high performance with long battery life, allowing professionals to work without the limitations of cords and power outlets. With their exceptional durability and ergonomic design, Hilti cordless tools are trusted by construction professionals worldwide.Innovation and ResearchContinuous innovation is at the heart of Hilti's success. The company invests heavily in research and development to provide its customers with cutting-edge solutions. Hilti's engineers and scientists work closely with construction professionals to understand their challenges and develop products that can overcome them.One notable example of Hilti's dedication to innovation is the ON!Track asset management system. This cloud-based software allows construction companies to track and managetheir tools and equipment, leading to improved productivity and cost efficiency. With real-time information on tool locations, maintenance schedules, and usage history, construction managers can optimize their workflows and reduce downtime.Sustainability and Corporate Social ResponsibilityHilti recognizes the importance of sustainability and the need to minimize the environmental impact of construction activities. The company designs its tools and equipment with energy efficiency and long-lasting durability in mind. Hilti also actively promotes recycling initiatives, ensuring that their products can be disposed of responsibly at the end of their lifecycle.In addition, Hilti is committed to supporting the communities in which it operates through various corporate social responsibility initiatives. These include vocational training programs, charitable donations, and volunteering efforts. By empowering individuals and investing in education, Hilti aims to make a positive impact on society.ConclusionHilti's long-standing commitment to innovation, quality, and customer satisfaction has solidified its position as a global leader in the construction industry. With a diverse range of products and solutions, Hilti continues to drive progress and efficiency in construction projects around the world. As the demand for sustainable and innovative technologies increases, Hilti is well-positioned to meet and exceed the expectations of its customers in the years to come.。

Robust Control and Estimation Robust control and estimation are critical aspects of engineering and technology, playing a pivotal role in ensuring the stability and performance of complex systems. These techniques are employed in a wide range of applications, including aerospace, automotive, robotics, and industrial automation. The primary goal of robust control is to design systems that can effectively operate in the presence of uncertainties and variations, while robust estimation focuses on accurately inferring the state of a system despite disturbances and measurement errors. From a technical perspective, robust control involves the design of control systems that can accommodate variations and uncertainties in the system dynamics and external disturbances. This is particularly important in real-world applications where the exact mathematical model of the system may not be known with certainty. Robust control techniques, such as H-infinity control and mu-synthesis, aim to guarantee system stability and performance under a wide range of operating conditions. These methods often involve the use of advanced mathematical tools and optimization algorithms to synthesize controllers that are resilient to uncertainties. In the realm of robust estimation, the focus is on accurately determining the state of a system based on noisy and imperfect measurements. This is crucial for feedback control systems, where the state information is used to compute control actions. Robust estimation techniques, such as Kalman filtering and robust observers, are employed to mitigate the effects of measurement noise and disturbances, providing an accurate estimate of the system state. These methods are essential for applications such as autonomous navigation, sensor fusion, and fault detection, where reliable state estimation is critical for decision-making. Beyond the technical aspects, the significance of robust control and estimation extends to the broader societal and economic impact. In the field of transportation, robust control plays a crucial role in ensuring the safety and efficiency of vehicles, ranging from airplanes to autonomous cars. By enabling systems to operate reliably under varying conditions, robust control contributes to reducing the risk of accidents and improving overall transportation infrastructure. Similarly, in industrial automation, robust estimation techniques enhance the reliability and performance of manufacturing processes, leading tohigher product quality and increased productivity. Moreover, the development of robust control and estimation techniques requires a multidisciplinary approach, encompassing aspects of mathematics, control theory, signal processing, and computer science. This fosters collaboration and knowledge exchange among experts from diverse backgrounds, driving innovation and advancements in the field. Furthermore, the application of these techniques often involves the integration of cutting-edge technologies, such as artificial intelligence, machine learning, and data-driven modeling, leading to the development of more sophisticated and adaptive control systems. From a personal standpoint, the pursuit of robust control and estimation represents a fascinating and intellectually stimulating endeavor. The challenges inherent in dealing with uncertainties and disturbances in real-world systems demand creative and innovative solutions, pushing the boundaries of knowledge and technology. The ability to design control systems that exhibit resilience and adaptability to unforeseen circumstances is not only intellectually rewarding but also holds the potential to make a tangible impact on society by enhancing the safety, reliability, and efficiency of critical systems. In conclusion, robust control and estimation stand as indispensable pillars in the realm of engineering and technology, addressing the complexities and uncertainties inherent in modern systems. From a technical standpoint, these techniques enable the design of control systems that can operate effectively under varying conditions and provide accurate state estimation despite disturbances. Moreover, their impact extends to diverse domains, including transportation, manufacturing, and robotics, where they contribute to safety, reliability, and performance. The multidisciplinary nature of robust control and estimation fosters collaboration and innovation, while the personal and societal implications underscore their significance in shaping the future of technology and engineering.。