SINGINT: Automatic numerical integration of singular integrands We explore the combination of deterministic and Monte Carlo methods to facilitate efficient automatic numerical computation of multidimensional integrals with singular integrands. Two adaptive algorithms are presented that employ recursion and are runtime and memory optimised, respectively. SINGINT, a C implementation of the algorithms, is introduced and its utilisation in the calculation of particle scattering amplitudes is exemplified. While the practitioner can rely on highly efficient and robust automatic routines for the numerical integration of 1-dimensional integrals [1], for multidimensional integrals the situation is more complicated and no mature universal tool has emerged yet.. To guide our discussion we start by recalling the key features of a superior numerical integration routine: It should be efficient, robust and automatic. Efficient means that results of the desired precision are obtained using as little resources as possible in terms of processor time and memory. Robust means that the routine yields accurate results for a variety of integrands of interest, for example, not only smooth ones, but also discontinuous or singular integrands. Automatic implies that the routine requires minimal information about the integrand, or, more precisely, that a "black box" that returns the value of the integrand for any point in the integration volume is all that is required. To devise one routine that excels in all three categories is extremely challenging, but a number of complementary approaches have been developed to satisfy more limited expectations.. Two basic approaches to numerical integration are deterministic integration with integration rules and Monte Carlo integration [2] . Both methods have strengths and weaknesses. Integration rules, on the one hand, yield precise results with relatively few integrand evaluations, but they are not too robust and work best for very smooth functions. Monte Carlo methods, on the other hand, impose few requirements on the integrand, but are known to converge slowly. To bridge the gap, adaptive procedures have been developed, that select and evaluate suitable subregions separately, thus making deterministic integration more robust and Monte Carlo integration more efficient.. As mentioned above, the use of Monte Carlo techniques is suggestive when multidimensional, square-integrable functions with singularities need to be integrated. But, when fast integration or high precision are mandatory pure Monte Carlo methods are not well suited. To enable rapid convergence, integration rule based approaches for singular integrands have been explored in the literature [3] . As a result, highly efficient integrators exist that exploit extrapolation methods and can be used if the location of all singularities is known in analytic form. However, such procedures are not automatic and not applicable if the location of singularities is unknown or difficult to determine.. In this article we therefore present two automatic procedures for the integration of low-dimensional singular integrands that aim to retain the performance advantage of deterministic integration methods. This is achieved by the combined use of integration rules and Monte Carlo sampling. To the best of our knowledge, this combination has not previously been proposed in the literature.. A second objective of this article is to explore the applicability of recursion to adaptive integration. Numerical algorithms have traditionally been implemented in Fortran 77, which does not allow recursion.. 3 One might therefore conjecture that recursive techniques have not been explored by practitioners to the desirable extent. To avoid being limited by such technicalities, SINGINT is implemented in C, which allows to conveniently program with recursion. We introduced two efficient automatic procedures that facilitate the adaptive numerical integration of low-dimensional singular integrands. We showed how the combined use of integration rules and Monte Carlo sampling and the power of recursion enable new runtime and memory optimisations. An application in theoretical particle physics was discussed that demonstrates the practical value of our approach. To allow others to apply the algorithms, we described a C implementation with test examples and explained its use.. With the described physics application in mind, we formulated and implemented the global algorithm of Section 2 specifically for 2-dimensional integrals. However, its generalisation to more than two dimensions is straightforward. There are in fact applications in perturbative field theory, where higherdimensional integrands similar to those of Section 5.2 need to be calculated numerically. We plan to investigate such cases in the future. The global algorithm we suggest decomposes the singular integrand f (x) into a bounded function b c (x) and a singular rest s c (x) by introducing a cut parameter c > 0:. The bounded component b c (x) can then be integrated relying on automatic deterministic methods for maximum efficiency. To integrate the singular com-ponent s c (x) we revert to Monte Carlo integration, an approach that is well suited for irregular or singular integrands and requires no analytic knowledge about the structure of the integrand, thus being automatic, too. These advantages are, however, offset by significantly slower convergence relative to deterministic methods. With regard to our integrand decomposition, this interplay suggests the existence of an optimal range for the cut parameter c. Below that range, unnecessarily large, non-singular regions are Monte Carlo integrated, causing slow overall convergence. Above that range, the deterministic routine has to integrate exceedingly steep peaks in the singular regions, necessitating a high number of function evaluations, which again leads to slow overall convergence. For cut parameters in the optimal range, however, the two effects balance and efficiency is maximal. After both components have been computed, the final result is obtained by adding up the integrals over b c (x) and s c (x). It is obviously independent of the cut parameter c.

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


A Real-Time Computer Vision System for Vehicle Tracking and Traffic Surveillance Increasing congestion on freeways and problems associated with existing detectors have spawned an interest in new vehicle detection technologies such as video image processing. Existing commercial image processing systems work well in free-flowing traffic, but the systems have difficulties with congestion, shadows and lighting transitions. These problems stem from vehicles partially occluding one another and the fact that vehicles appear differently under various lighting conditions.We are developing a feature-based tracking system for detecting vehicles under these challenging conditions. Instead of tracking entire vehicles, vehicle features are tracked to make the system robust to partial occlusion. The system is fully functional under changing lighting conditions because the most salient features at the given moment are tracked. After the features exit the tracking region, they are grouped into discrete vehicles using a common motion constraint.The groups represent individual vehicle trajectories which can be used to measure traditional traffic parameters as well as new metrics suitable for improved automated surveillance. This paper describes the issues associated with feature based tracking, presents the real-time implementation of a prototype system, and the performance of the system on a large data set. In recent years, traffic congestion has become a significant problem. Early solutions attempted to lay more pavement to avoid congestion, but adding more lanes is becoming less and less feasible.. Contemporary solutions emphasize better information and control to use the existing infrastructure more efficiently.. The quest for better traffic information, and thus, an increasing reliance on traffic surveillance, has resulted in a need for better vehicle detection such as wide-area detectors; while the high costs and safety risks associated with lane closures has directed the search towards noninvasive detectors mounted beyond the edge of pavement. One promising approach is vehicle tracking via video image processing, which can yield traditional traffic parameters such as flow and velocity, as well as new parameters such as lane changes and vehicle trajectories.. Because the vehicle tracks, or trajectories, are measured over a length of roadway, rather than at a single point, it is possible to measure true density instead of simply recording detector occupancy. In fact, by averaging trajectories over space and time, the traditional traffic parameters are more stable than corresponding measurements from point detectors, which can only average over time. The additional information from the vehicle trajectories could lead to improved incident detection, both by detecting stopped vehicles within the camera's field of view and by identifying lane change maneuvers or acceleration/deceleration patterns that are indicative of incidents beyond the camera's field of view. The trajectory data could also be used to automate previously labor intensive traffic studies, such as examining vehicle maneuvers in weaving sections or bottlenecks.. The vehicle tracking system can produce individual vehicle data (e.g., spacing, headway, velocity, acceleration), which could lead to better traffic flow modeling and an improved understanding of driver behavior. Finally, our group has demonstrated that the system can extract vehicle signatures and match observations of the same vehicle at multiple detector stations (Huang and Russell, 1998) . This signature matching can be used to measure true link travel time and thus, quantify conditions between widely spaced detectors rather than assuming that local conditions are representative of the entire link.. To be an effective traffic surveillance tool, whether by mimicking loop detectors or actually tracking vehicles, a video image processing system (VIPS) should meet several stringent requirements:. 1) Automatic segmentation of each vehicle from the background and from other vehicles so that all vehicles are detected.. 2) Correctly detect all types of road vehicles -motorcycles, passenger cars, buses, construction equipment, trucks, etc. Even though a number of commercial VIPS for monitoring traffic have been introduced to the market, many of these criteria still cannot be met. Recent evaluations of commercial VIPS found the existing systems have problems with congestion, occlusion, lighting transitions between night/day and day/night, camera vibration due to wind, and long shadows linking vehicles together. We have presented a vehicle detection and tracking system that is designed to operate under these challenging conditions. Instead of tracking entire vehicles, vehicle features are tracked, which makes the system less sensitive to the problem of partial occlusion. The same algorithm is used for tracking in daylight, twilight and nighttime conditions, it is self-regulating by selecting the most salient features for the given conditions.. Common motion over entire feature tracks is used to group features from individual vehicles and reduce the probability that long shadows will link vehicles together. Finally, camera motion during high wind is accounted for by tracking a small number of fiducial points.. The resulting vehicle trajectories can be used to provide traditional traffic parameters as well. as new metrics such as lane changes. The trajectories can be used as input to more sophisticated, automated surveillance applications, e.g., incident detection based on acceleration/deceleration and lane change maneuvers. The vehicle tracker is well suited both for permanent surveillance installations and for short term traffic studies such as examining vehicle movements in weaving sections. The vehicle tracking system can also extract vehicle signatures to match observations between detector stations and quantify conditions over extended links.. A real-time version of the system has been implemented using a network of DSP chips.. The system has been tested on approximately 44 lane-hours of data and has demonstrated good performance under the challenging conditions that have limited earlier VIPS.

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MobiLenin combining a multi-track music video, personal mobile phones and a public display into multi-user interactive entertainment This paper introduces a novel and creative approach for coupling multimedia art with a non-conventional distributed humancomputer interface for multi-user interactive entertainment. The proposed MobiLenin system allows a group of people to interact simultaneously with a multi-track music video shown on a large public display using their personal mobile phones, effectively empowering the group with the joint authorship of the video. The system is realized with a client-server architecture which includes server-driven real-time control of the client UI to guarantee ease of use and a lottery mechanism as an incentive for interaction. Our analysis of the findings of an empirical user evaluation conducted in a true environment of use shows that the MobiLenin system is successful, addressing many of the challenges identified in the literature. The proposed system offers a new form of interactive entertainment for pubs and other public places, and the underlying architecture provides a framework for realizing similar installations with different types of multimedia content. This work introduces the MobiLenin system which combines a multi-track music video with personal mobile phones and a public display into interactive art for the purpose of enticing social interaction between people.. According to Tosa [22] , interactive art can be thought of as an emotion and sympathy interface, and interactive art is a component that provides sympathy with communications. Adams and Moussouri [1] define the interactive experience as something that can actively involve the visitor physically, intellectually, emotionally, and/or socially. Ryan [20] claims that an interactive medium opens its world after the user has made a significant intellectual and emotional investment. According to MaynesAminzade et al. [12] , the greatest challenge does not lie in developing the technology for audience interaction, but in designing engaging activities.. In previous related works people have been able to control onscreen activity by leaning left and right in their seats to steer a race car or move a paddle in the video game Pong, or by batting a beach ball while its shadow is used as a pointing device, or by pointing laser pointers at the screen [12] . In the Cinematrix Interactive Entertainment System [9] the audience members can participate interactively in activities such as maze navigation and opinion polling by displaying the red or the green side of a paddle. Since then relatively little progress has been made in the study of audience interaction [12] .. Churchill et al. [6] argue that there are significant opportunities around the corner for distribution of interactive multimedia digital content designed for social networking and entertainment. Rogers and Lindley [18] state that there has been little research on how deploying public displays in different places invites certain kinds of social interactions. Instilling a sense of community is one motivation in situating large interactive displays in a variety of work and public places [18] [7] . According to Paek et al. [14] , interactive shared displays are most suited for certain types of applications, including "collaborative tools allowing multiple people to contribute to a single goal", and "arena applications involving competitive interaction". The MobiLenin system incorporates both of them to instill a sense of community and to entice social interaction among the users.. Vogel et al. [23] present the questions what kind of input and interface technologies do we need to develop to allow for effective interaction with large public displays? Many large display systems are currently single-user based and require users to take turns when interacting with them. However, there is a growing body of work investigating the use of multi-user interactive displays [4] . Maynes-Aminzade et al. [12] state that systems enabling large audiences to interact offer numerous possibilities for entertainment, but most research on interaction techniques focuses on single users or on small groups.. Magerkurth et al. [10] propose to augment traditional entertainment technology with social and physical elements to form e.g. a new class of hybrid gaming applications. This leads to a thought that mobile devices could be employed for this purpose. Reid et al. [16] report Schminky, a musical game where a PDA is used to interact with other players over a WLAN (Wireless Local Area Network) in a café.. The MobiLenin system offers a solution for realizing multi-user interaction with a public display using personal mobile phones. Public displays and personal mobile phones make an interesting couple in terms of strengths and weaknesses. While shared displays typically offer greater conceptual power and larger presentation space, they often limit interaction to one user at a time. Personal mobile phones, on the other hand, disperse control and access to participating users, though limited conceptual power and smaller screen sizes often hinder dynamic interaction. Thus, connecting shared displays to personal mobile phones is an obvious way to leverage the best of both worlds [15] .. One of the main challenges associated with interactive public displays is how to entice people to interact with them [3] ? Agamanolis [2] concluded that half the battle in designing an interactive situated or public display is designing how the display will invite that interaction. Churchill et al. [6] found that users needed constant encouragement and demonstration to interact with the interactive public display. The MobiLenin system employs a lottery mechanism as an incentive for interaction.. We see that there is a need for designing engaging activities which make the users feel invited, interested and encouraged to interact. At the same time they have to have a reason to focus on what is shown on the public display. To rise to the challenge, we present the MobiLenin system, which builds on the combination of multimedia art, a public display, personal mobile phones and a client-server architecture. The principal idea of the MobiLenin system is that each user can with his/her personal mobile phone interact with an interactive multi-track music video shown on a public display. Each user can individually vote for one of the tracks by selecting the corresponding choice from the menu in the mobile phone application, and the track receiving most votes is shown. Thus, the MobiLenin system empowers the users with a joint authorship of the interactive art piece. As an incentive for interaction, the system contains a lottery mechanism which under adjustable circumstances chooses a winner among the users having voted in a given voting interval. This paper is organized as follows. Section 2 describes the MobiLenin system. Section 3 presents the experimental user evaluation of the system in a true environment of use. Section 4 provides an in-depth analysis in regard to design issues, social experience and collaborative aspects. Section 5 summarizes our major findings for future work, and explains how the MobiLenin architecture could be used for developing other artistic concepts. The MobiLenin system can be expanded in different ways by adopting different content and modifying the script of the interaction. Other possible concepts include interactive storytelling, trivia and multi-player gaming, for example.. The MobiLenin system is scalable so that even large groups of people can interact with the public display and the content. We could ramp up the activity of the current setup into a much more versatile interaction pattern in a more dynamic setting of a live concert. Starting from simple majority votes for the next song to be performed, the interaction could be extended all the way to complex issues such as allowing the audience to interact with the parameters of individual instruments played by the musicians on stage.. The feedback channel provided by the private GUI of the mobile phone application allows addressing individual users or subgroups. For example, the system could ask only one fourth of the audience to wave their hands in the air, or to hug their neighbors, or to shake their hands, or to shout as loud as possible, for the purpose of triggering events full of surprises, something new that the participants are expecting from the interaction.. Further, the virtual interfaces of the private GUIs could be used for clandestine negotiations, but apart from the communications functionality, the virtual domain would still be unaware of the players' state of the diplomacy [10] . In such a constellation the system would allow different participants or even different groups of people to vote for different things or at different times. For example, users or groups would have to compete against each other or the interactive content would include role play characteristics. The system architecture would allow such scenarios since the server drives the voting mechanism and can handle dynamic menu contents even for different users at the same time.. In this paper we did not address service discovery, i.e. the user becoming aware and obtaining the external service or application (s)he is supposed to use. In our case the provider of the interactive art entertainment could advertise the service in its premises, together with instructions for downloading the application from a local Bluetooth service point or from an Internet page. Another technical issue we have yet to address is the scalability of the current implementation, which becomes relevant when we would like to include larger user groups.. We also did not discuss candidate business models. In our system users would have to pay for GPRS data when joining the voting. However, the amount of transferred control data is very small compared to e.g. browsing internet pages. We do not expect it to be a problem, however, as people are increasingly using data connection e.g. to browse the internet with their phone. Hence, they often pay a monthly fee for data access already. Further, mobile phones equipped with WLAN radios are starting to enter the market hence a pub could offer the proposed system as a free service in its WLAN hotspot. We believe that new and exciting forms of interactive art can emerge by further developing hybrid virtual interfaces such as the MobiLenin system. We combined the private GUI of a personal mobile phone with the public GUI of a public display into a realtime hybrid interface which fosters the social dynamics of colocated groups without distracting the user. The personal mobile phones also disperse control, allowing multi-user interaction with a single public display, thus empowering a group of users with the joint authorship of the multimedia art piece. The system also contains a built-in lottery mechanism as an incentive for interaction.. Experimental evaluation in the true environment of use showed that the MobiLenin system succeeds in enticing social interaction at both personal and group level, addressing several of the challenges identified in the literature. The users embraced the joint authorship, which resulted in high social activity among small groups of people. Similarly, the users enjoyed the lottery mechanism: the announcement of the anonymous winner on the public display stirred plenty of general excitement, which then erupted in someone's personal celebration followed by congratulations from the group.. In addition to dispersing the control and facilitating the multi-user interaction with a single public display, employing personal mobile phone as the private GUI brings also other advantages. They are ubiquitous and as such remove the need for any additional, possibly application or service specific user devices. A personal mobile phone is the trusted device and it allows anonymous participation in a group activity, which both contribute towards people feeling invited and uninhibited to interact. Mobile phones also guarantee each user equal power in the interaction and provide a reliable return channel for delivering confidential user specific information back to the user.. Our study confirms that social involvement is much more important than technological involvement. While people are initially amazed at the technology allowing the interaction to occur, within 30 seconds they lose interest if the activity is not inherently entertaining [12] . Our test users commented that in order to keep their interest up in interacting with the music video for a longer time, the system would need to offer something new they have not seen before. Our conclusion is that the key lies in the production of content, so that the user is allowed with a sufficiently rich set of choices to choose from at different times and can anticipate "something new" at the point of interaction. One important issue in producing non-linear content is that the production might require more resources, since multiple tracks need to be produced of one and the same part.

O O O 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 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 B I 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


Applying Ant Colony Optimization to configuring stacking ensembles for data mining An ensemble is a collective decision-making system which applies a strategy to combine the predictions of learned classifiers to generate its prediction of new instances. Early research has proved that ensemble classifiers in most cases can be more accurate than any single component classifier both empirically and theoretically. Though many ensemble approaches are proposed, it is still not an easy task to find a suitable ensemble configuration for a specific dataset. In some early works, the ensemble is selected manually according to the experience of the specialists. Metaheuristic methods can be alternative solutions to find configurations.Ant Colony Optimization (ACO) is one popular approach among metaheuristics. In this work, we propose a new ensemble construction method which applies ACO to the Stacking ensemble construction process to generate domain-specific configurations. A number of experiments are performed to compare the proposed approach with some well-known ensemble methods on 18 benchmark data mining datasets. The approach is also applied to learning ensembles for a real-world cost-sensitive data mining problem. The experiment results show that the new approach can generate better stacking ensembles. Over years of development, it has become more and more difficult to improve significantly the performance of a single classifier. Recently, there has been growing research interest in the method to combine different classifiers together to achieve better performance. The combining method is referred to as Ensemble. In early research, ensembles were proved empirically and theoretically to perform more accurately than any single component classifier in most cases. If an ensemble is generated by a set of classifiers which are trained from the same learning algorithm, this ensemble is a homogeneous ensemble. If an ensemble is generated by a set of classifiers, which are trained from different learning algorithms, this ensemble is a heterogeneous ensemble [Dietterich (2000) ]. For example, Bagging [Breiman (1996) ] and Boosting [Schapire (1990)] are homogeneous ensembles, while Stacking [Wolpert (1992) ] is a heterogeneous ensemble.. To generate an ensemble to achieve expected results, two important things should be considered carefully. The first is to introduce enough diversity into the components of an ensemble.. The second is to choose a suitable combining method to combine the diverse outputs to a single output [Polikar (2006) ]. The diversity is the foundation of an ensemble. However, as the diversity increases, the marginal effect decreases after a certain threshold. The memories and computing cost increase significantly while the performance does not improve steadily. For early Bagging and Boosting methods, the diversity is achieved by using the re-sample strategy. The classifiers included in Bagging are trained with the data subsets, which are randomly sampled from the original dataset. A majority voting scheme is applied as the combining method to make a collective decision. Boosting uses a weighted re-sample strategy. The weights of all instances are initialized equally. If an instance is misclassified, its weight will be increased. Thus it will be more likely to select the misclassified instances into the next training subset. The diversity generating process stops when the errors are too small. The combining scheme of Boosting is a weighted majority voting. Compared to Bagging and Boosting, Stacking does not manipulate the training dataset directly. Instead, an ensemble of classifiers is generated based on two levels.. In the base level, multiple classifiers are trained with different learning algorithms. The diversity is introduced because different learning algorithms make different errors in the same dataset. A meta-classifier is applied to generate the final prediction. The meta-classifier is trained with a learning algorithm using a meta-dataset which combines the outputs of base-level classifiers and the real class label.. One problem of Stacking is how to obtain an "appropriate" configuration of the base-level classifiers and meta-classifier for each domain-specific dataset. The number of base-level classifiers and the kinds of learning algorithms are closely related to the diversity. The kind of meta-classifier is also important to the fusion of the base-level classifiers. However, such configuration is still "Black Art" [Wolpert (1992) ]. Some researchers have proposed different methods to determine the configuration of stacking. Ting and Witten solved two issues about the type of meta-classifier and the kinds of its input attributes [Ting and Witten (1999) ]. Dzeroski andZenko introduced Multi-Response Model Trees as the meta-classifier [Džeroski andZenko (2002) ]. Zheng and Padmanabhan (2007) and Zhu (2010) proposed their Data Envelopment Analysis (DEA) approaches respectively. Ledezma et al. and Ordóñez et al. proposed ap- proaches which search the ensemble configurations using Genetic Algorithms (GAs) [Ledezma et al. (2002) ; Ordóñez et al. (2008) ].. In this work, we propose an approach using Ant Colony Optimization (ACO) to optimize the stacking configuration. ACO is a metaheuristic algorithm which is inspired by the foraging behaviour in real ant colonies. Some approaches were proposed recently to apply ACO in data mining. Parpinelli et al. proposed Ant Miner to extract classification rules [Parpinelli et al. (2002) ]. Some approaches apply ACO in feature subset selection tasks [Al-Ani (2006) ; Zhang et al. (2010) ].. The rest of this paper is organized as follows. In Section 2, the background of this work, including the related ensemble approaches and the ant colony optimization method, is introduced.. In Section 3, the details of our approach are presented. In Section 4, a number of conducted experiments are described to compare our approach with other ensemble methods. Further, the experiment results are presented and discussed in this section. In Section 5, our approach is applied to solve a real-world data mining problem. In the last section, a conclusion is given. In this work, we limit our ACO-Stacking approach to a single performance evaluation criterion for each application. For example, only accuracy is used in the benchmark datasets and only the cumulative profit lift is used for the direct marketing application. ACO has been proved to be strong in multi-criteria optimization problems. One possible future direction is to extend ACOStacking to find multi-criteria ensembles. Furthermore, only two measures for local information (Precision and correlative differences) are selected and applied in the approach. However, many other criteria can be employed as local information, so the best metric for local information can be further explored.. A relatively short execution time is very essential for an application. One future direction of this work is to modify ACO-Stacking to run in parallel to improve the efficiency. Much research has been done to parallelize the ACO approach on a Graphic Processing Unit thereby to accelerate the execution efficiency without many additional resources required.. Ensembles do not only refer to ensembles of classifiers. Nowadays, ensembles are widely used in clustering and regression tasks [Zhou et al. (2001); Fern and Brodley (2003) ]. In the future, we may try to use our ACO-Stacking approach in clustering and regression tasks.

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


Bounded-degree polyhedronization of point sets In 1994 Grünbaum [2] showed, given a point set S in R 3 , that it is always possible to construct a polyhedron whose vertices are exactly S. Such a polyhedron is called a polyhedronization of S. Agarwal et al. [1] extended this work in 2008 by showing that a polyhedronization always exists that is decomposable into a union of tetrahedra (tetrahedralizable). In the same work they introduced the notion of a serpentine polyhedronization for which the dual of its tetrahedralization is a chain. In this work we present an algorithm for constructing a serpentine polyhedronization that has vertices with bounded degree of 7, answering an open question by Agarwal et al. [1] . It is well-known that any set S of points in the plane (not all of which are collinear) admits a polygonalization, that is, there is a simple polygon whose vertex set is exactly S. Similarly, a point set S ⊂ R 3 admits a polyhedronization if there exists a simple polyhedron that has exactly S as its vertices. In 1994 Grünbaum proved that every point set in R 3 admits a polyhedronization. Unfortunately, the polyhedronizations generated by Grünbaum's method can be impossible to tetrahedralize. This is because they may contain Schönhardt polyhedra, a class of non-tetrahedralizable polyhedra [3] .. In 2008, Agarwal, Hurtado, Toussaint, and Trias described a variety of methods for producing polyhedronizations with various properties [1] . One of these methods, called hinge polyhedronization, produces serpentine polyhedronizations, meaning they are composed of tetrahedra whose dual (a graph where each tetrahedron is a node and each edge connects a pair of nodes whose primal entities are tetrahedra sharing a face) is a chain. Serpentine polyhedronizations produced by the hinge polyhedronization method are guaranteed to have two vertices with edges to every other vertex in the set. As a result, two vertices in these constructions have degree n − 1, where n is the number of points in the set. A natural question, and one posed by Agarwal et al., is whether it is always possible to create serpentine polyhedronizations with bounded degree.. In this work we describe an algorithm for constructing serpentine polyhedronizations that have O(1) degree. The constant bound of the produced polyhedronizations is 7, which we show is nearly optimal for all point sets with greater than 12 vertices. Such bounded-degree serpentine polyhedronizations are useful in applications of modeling and graphics where low local complexity is desirable for engineering and computational efficiency. In this paper we show that any point set in 3-space admits a polyhedronization with vertex degree at most 7, while 6 is a simple lower bound. Future work includes showing that either 6 or 7 is the true bound in the worst case. Furthermore, we believe that our technique can be generalized to higher dimensions. In this section we present a high-level overview of the algorithm. Begin with a point set S ⊂ R 3 . Select a face triplet of CH(S) arbitrarily. Call this face triplet T 0 . Let S 0 = S \ T 0 . Assign the labels u 0 , v 0 , w 0 to the nd Canadian Conference on Computational Geometry, 2010 vertices of T 0 and connect the three vertices to form a triangle.. Next we search for a face triplet T 1 of CH(S 0 ) that we can attach to the triangle T 0 via a polyhedron tunnel (see Figure 1) . The tunnel has the face triplet T 0 at one end, the face triplet T 1 at the other end and is disjoint with the interior of CH(S 0 ). The tunnel needs to be tetrahedralizable and the vertices u 0 , v 0 have degree 5 and 4, and w 0 has degree 3. Moreover, the vertices of the face triplet T 1 that we will call u 1 , v 1 , w 1 should have degree 3, 4 and 5, respectively. Note that the constructed tunnel must meet the degree requirements for the vertices of T 0 while it determines the vertex naming assignments for the vertices of T 1 . Figure 1 : Constructing a tunnel between T i , T i+1 . The vertices u i and v i have degree 5 and 4, while w i has degree 3. The other end of the tunnel, T i+1 , has three vertices that will be labeled u i+1 , v i+1 , w i+1 with degree 3, 4 and 5 (shown in parentheses), respectively.. After finding a face triplet T 1 that meets these requirements, the process is repeated for T 1 and S 1 , T 2 and S 2 where S i = S i−1 \T i , until S i contains fewer than three points. At this point a degenerate tunnel is built out of the remaining points and the algorithm stops. In the next two sections we prove that such a construction is always possible, producing a valid serpentine polyhedronization with bounded vertex-degree 7.

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


Encoding Multiple Orientations in a Recurrent Network Models containing recurrent connections amongst the cells within a population can account for a range of empirical data on orientation selectivity in striate cortex. However, existing recurrent models are unable to veridically encode more than one orientation at a time. Underlying this inability is an inherent limitation in the variety of activity pro"les that can be stably maintained. We propose a new recurrent model that can form a broader range of stable population activity patterns. We demonstrate that these patterns preserve information about multiple orientations present in the population inputs. This preservation has signi"cant computational consequences when information encoded in several populations must be integrated to perform behavioral tasks, such as visual discrimination. An important debate in computational neuroscience centers on the origin of selectivities in populations of cortical cells. A focus of this debate has been an extensive set of empirical data on orientation selectivity in primary visual cortex. A central question concerns how the observed sharp tuning of striate cells arises from the broadly tuned purely excitatory LGN input that grows monotonically with contrast. One class of network models posits that the sharp tuning is primarily due to the e!ects of recurrent connections within the striate population. These recurrent models can account for a wide range of the empirical data (reviewed in [5] ).. Carandini and Ringach [1] studied a recurrent model * a simpli"ed version of an earlier model proposed by Somers et al. [4] * that replicates many observed characteristics of a hypercolumn of striate cells. The core of their model is a single equation implementing a feedback "lter with a center-surround weighting function. Their model successfully and succinctly captures a range of results, including sharp orientation tuning and contrast-invariant tuning width. However, when the input to the cell population contains two orientations, their model exhibits peculiar responses: if the two orientations di!er by less than 453, the model responds as if the input contained a single orientation at the mean of the two values; if the angular di!erence between the two orientations exceeds 453, the model responds as if they were nearly orthogonal. The model also cannot signal the presence of three orientations, and creates spurious orthogonal orientations to noisy single orientation inputs. These authors analyzed their model and showed that these e!ects of attraction and repulsion between orientations are unavoidable in a broad class of recurrent models of orientation selectivity.. This model thus makes strong predictions about the visual response of cortical neurons to stimuli containing multiple orientations. Relevant empirical data are scarce. DeAngelis et al. [2] found that the response of a striate cell to its optimal orientation is reduced when the stimulus contains a second orientation. Cell responses to pairs of non-optimally oriented stimuli are not known. Nonetheless, the predictions of the model appear dubious: it predicts that the neurons will be unable to veridically encode multiple orientations, even when the orientations di!er by nearly 453. The aim of the model presented here is to explore if a broader range of activity patterns could be maintained in the population for multiple-orientation stimuli. We also studied the decoding of these patterns to determine whether information about the orientations is preserved. We have proposed a novel model of orientation selectivity in striate cortex that can support a variety of response pro"les, and veridically encodes multiple orientations. Our model can replicate the cross-orientation e!ects observed in one of the only empirical investigations into primary visual cortex responses to multiple orientations [2] . Fig. 3 . Summary of responses to all two-orientation stimuli for both models. The model striate units are arranged according to their preferred orientation (PO). Each horizontal slice shows the unit activities to a particular two-orientation stimulus. The vertical axis represents the di!erence between the two orientations in the stimulus, growing from 03 to 903 (maximally orthogonal) separation. Note that the C}R model response is discontinuous near 453, while our model contains a smooth transition. Our model makes a number of new predictions concerning responses of these cells to stimuli containing multiple orientations: (1) a range of stable activity patterns will be produced for stimuli containing two orientations with di!ering angular spread; (2) inputs containing three orientations separated by 603 will produce trimodal activity patterns; and (3) noisy stimuli containing single orientations will only rarely give rise to spurious bimodal response pro"les.. Each of these points di!er from predictions derived from the Carandini}Ringach model. They also diverge from other models of orientation selectivity, such as that proposed by Pouget et al. [3] . This model forms stable activity patterns in the population such that simple decoding techniques, such as the population vector method, closely approximate maximum likelihood estimation, which is the optimal decoding technique. This model, however, responds to pure noise stimuli as if a single orientation is present, and cannot encode multiple orientations simultaneously.. Finally, we are currently exploring the consequences of this information preservation on processing downstream from V1. It is known that V2 cells respond to illusory contours and "gure-ground information, and we hypothesize that preserving information about multiple orientations within individual V1 populations plays an important role in V2 responses.. Richard Zemel is currently an Assistant Professor in the Departments of Psychology and Computer Science at the University of Arizona. He did four years of postdoctoral work at the Salk Institute and at Carnegie Mellon University before moving to Arizona. He obtained his undergraduate education at Harvard University, and his Ph.D. in Computer Science from the University of Toronto. His research interests include: machine learning, visual perception, and neural coding.. Jonathan Pillow was born in Tucson, Arizona to William and Deborah Pillow, and grew up in Phoenix, AZ, the elder of two boys. He graduated summa cum laude from the University of Arizona in 1997 with a BA in Mathematics and Philosophy. He subsequently studied north-African Literature in Morocco during a one-year tenure as a US Fulbright Scholar. He is currently a Ph.D. student at New York University in the Center for Neural Science. His research interests include information processing in the visual pathway, modelling of primary visual cortex, illusory contour perception, neural population coding, and analysis of timing and variability in neural spike trains. The response of the model to stimuli containing two orientations, of varying angular di!erence, is shown in Fig. 1 . Our model forms di!erent stable activity patterns to all three pairs, and the decoding method accurately recovers the original orientations present in the stimulus. In contrast, the C}R model is unable to veridically encode any of these stimuli.. Our model is also able to maintain a stable response pro"le for stimuli containing three orientations, as well as a clean unimodal response to noisy single-orientation stimuli (see Fig. 2 ). In both cases, the decoding method reconstructs the orientations in the stimulus.. The primary di!erence between the models is highlighted in Fig. 3 , which represents their responses to all possible two-orientation stimuli. Each horizontal slice represents the response to a particular stimulus, and the angular spread between the two orientations increases from 03 to 903 along the y-axis. The C}R model contains a discontinuity near 453, as the population response jumps from uni-to bimodal, while our model contains a smooth transition from one to two orientations. The optimized recurrent weights in our model (see Fig. 4 ) have a similar shape to the center-surround weights used by most recurrent models. The wiggles in the surround of the weights gives them some power at higher frequencies. We are currently analyzing these di!erences in greater detail. A crucial di!erence not apparent in this "gure is that the relative strength of the recurrent versus feedforward weights is smaller in our model than the C}R model. This di!erence likely underlies our model's reduced ability to remove noise and remain strongly contrast invariant, but enhanced ability to encode multiple orientations. The dynamics in our model are very similar to the Carandini}Ringach (C}R) model, with two modi"cations. Both models contain a group of cortical neurons identical except in preferred orientation (!903, 903) , where the response of neurons with preferred orientation to a stimulus containing orientation depends only on ( !). Their model is governed by the following equation:. where <( , t) is the membrane potential of cells in orientation column at time t, is the membrane time constant, <*%, is the excitatory input from LGN, and <#6! and <',& represent intra-cortical excitation and inhibition. These latter two quantities are obtained by convolving the responses of the cells with a narrowly tuned set of excitatory, and a broader pro"le of inhibitory weights, respectively. The combination of these two sets of weights produces a center-surround pro"le. Finally, each cell's response R is a recti"ed linear function of its potential <.. Our model utilizes. (1) optimized recurrent weights, both excitatory and inhibitory. The optimization procedure uses a set of examples, each consisting of the LGN input appropriate to a stimulus containing 1, 2, or 3 bars (or gratings) at random orientations, and target output values for the model striate cells. The weights are modi"ed such that the targets are the stable activity pro"les of the di!erential state update equation (Eq. (1)). The target outputs are obtained by convolving each cell's tuning function, i.e., its expected response to a single-bar stimulus as a function of its orientation, with the set of orientations present in the stimulus. These targets are based on the assumption that a cell's response to a stimulus containing a pair of oriented bars is a scaled sum of its response to the individual bars; this assumption applies to MT cell responses to multiple-direction stimuli [6, 7] . (2) nonlinear activation function. We used the function proposed by Zhang [9] :. R"a log(1#exp(b (<#c)))B (where a, b, c, and d are constants) because it is easy to analytically invert, which facilitates the weight optimization procedure.. A third important addition to our model involves a di!erent way of interpreting the activities of the striate cells. In order to determine what information about orientation is present in the model cell responses, we apply a statistical decoding method that can naturally handle multiple-valued inputs [8] . This method takes into account the cells' tuning functions in "nding the set of inputs most likely to have generated the observed responses. A key feature of this method is that unimodal response pro"les do not necessarily indicate that only one value is present in the input; instead, a broad unimodal pro"le is consistent with a stimulus containing two nearby orientations.

O O 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 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 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 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 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


Performance scalability of decoupled software pipelining Any successful solution to using multicore processors to scale general-purpose program performance will have to contend with rising intercore communication costs while exposing coarsegrained parallelism. Recently proposed pipelined multithreading (PMT) techniques have been demonstrated to have general-purpose applicability and are also able to effectively tolerate intercore latencies through pipelined interthread communication. These desirable properties make PMT techniques strong candidates for program parallelization on current and future multicore processors and understanding their performance characteristics is critical to their deployment. To that end, this paper evaluates the performance scalability of a general-purpose PMT technique called decoupled software pipelining (DSWP) and presents a thorough analysis of the communication bottlenecks that must be overcome for optimal DSWP scalability. This paper analyzed the performance scalability of automatically generated DSWP codes. It demonstrated that superpartitioning of application loops leads to complex inter-thread communication DAGs and that such DAGs interact pathologically with the underlying communication substrate, adversely affecting performance. Analytical expressions derived in this paper can be incorporated into an autoDSWP compiler's partitioning heuristic to statically determine whether a given DSWP partition would lead to these communication pathologies and repartition the code, if needed. Without any communication bottlenecks, DSWP delivers a geometric mean speedup of 1.25X to 1.43X when going from two to eight threads across a variety of benchmarks.. While the speedup achieved by DSWP in innermost and second innermost loops were presented in this paper, initial results from work in progress indicate that there is good potential for pipelined parallelism at outer loop nest levels. Future work will focus on evaluating DSWP's scalability at coarser granularities and improving our autoDSWP compiler's partitioning heuristic based on results from this paper. In contrast to DSWP, which partitions a given loop body across threads so as to keep all recurrences local to a thread, DOACROSS parallelization [Cytron 1986 ] keeps the original loop body unchanged and communicates recurrences across threads. Figure 2 illustrates the space-time behavior of DSWP and DOACROSS parallelization techniques. The dependence graph of a singlethreaded loop body is shown in Figure 2 (a). This is also the DAG SCC for DSWP. The single-threaded DOACROSS and DSWP schedules of this loop are given in Figures 2(b) , (c), and (d), respectively. The y axis represents time and the x axis represents the threads across which the original loop is parallelized. The dashed dark box in the DOACROSS and DSWP schedules highlights how one loop iteration executes in each case. Notice that even though the loop bodies of the individual DSWP threads are different from the original singlethreaded loop body, both the DSWP and DOACROSS parallelizations provide identical performance improvement (i.e., 4X) over single-threaded execution. Therefore, theoretically speaking, the scalability potential is similar in both DSWP and DOACROSS; their speedup being bound by the performance of the slowest SCC or recurrence, respectively. However, their dynamic communication behavior is vastly different, leading to unique performance scalability issues in these techniques. The difference arises from the nature of interthread dependences in the two techniques. Since recurrences are communicated across DOACROSS threads, they are exposed to communication latencies and, hence, are very sensitive to communication costs. For the same reason, DOACROSS is also not effective in dealing with variable latency stalls, since there is no opportunity to build sufficient decoupling between communicating DOACROSS threads. Communication of recurrences between threads in DOACROSS results in lock-step behavior. Consequently, efficient synchronization is critical to scalable DOACROSS performance. On the other hand, DSWP does not suffer from the lock-step synchronization problem because of acyclic interthread dependences. However, it has to contend with performance bottlenecks arising from inefficient interthread operand queueing, as will be shown in Section 4, in order to achieve optimal scalability.. Unlike DSWP and DOACROSS, the applicability of DOALL parallelization [Lundstorm and Barnes 1980 ] is limited to loops without recurrences. In DOALL parallelized programs, each thread executes one or more loop iterations independent of other threads. No interthread dependences need to be communicated in this style of parallelization. Consequently, its performance potential is limited only by the available processor count and the number of iterations in a given loop. Thus, given a loop that does not have any recurrences, DOALL is the most preferred parallelization strategy. If, however, the loop has recurrences, then depending on performance or flexibility requirements, one may use DSWP or DOACROSS parallelization, respectively.. Decoupled access-execute (DAX) architectures [Smith 1982 ] statically partition the code into a memory-access stream and an execute stream comprising ALU operations. Dynamically, the two streams execute in separate processing cores decoupled by FIFOs. This setup proved useful for tolerating memory latencies in applications with high memory-level parallelism (MLP) (i.e., many memory loads can be initiated before any of their results are used). However, the cyclic dependence of the execute stream on the access stream causes the performance of DAX architectures to degenerate to or become worse than the performance of traditional processing cores when handling codes with poor MLP, such as RDS loops, with rising intercore communication costs. DSWP avoids this problem by enforcing acyclic communication among its threads. Since DAX partitions instructions based on their type, it cannot scale to more than two streams with an optional prefetch stream to improve access stream performance [Ro et al. 2006] . DSWP, on the other hand, exposes inherent pipelined parallelism available in program loops by partitioning based on program recurrences and can scale to as many threads as there are SCCs in a given loop.. Techniques like flea-flicker [Barnes et al. 2003 [Barnes et al. , 2005 and dual-core execution [Zhou 2005 ] tolerate load miss latencies by holding missed loads and their dependent instructions in intra-or intercore queues for deferred processing by a second core. When the load miss is resolved, all instructions are committed in sequential order by the second core. This deferred handling enables the first core to continue execution past a load miss. The similarity of these techniques with DSWP is limited to the use of a decoupling queue between cores. These techniques are microarchitectural optimizations to improve load miss handling in single-threaded programs and can complement DSWP.. In addition to the aforementioned techniques, the literature abounds in several speculative multithreading techniques aimed at improving single-threaded performance. While some of these techniques seek to extract more ILP by increasing the effective logical instruction window size, others improve performance by using helper threads to warm up microarchitectural structures of the main program thread. Thread-level speculation [Hammond et al. 2000; Steffan et al. 2005] , multiscalar processors [Sohi et al. 1995] , superthreading [Tsai et al. 1999] , and trace processors [Vajapeyam and Mitra 1997] are representative techniques of the first kind that speculate input dependences of dynamically far-apart regions and spawn them as speculative threads on available cores. The threads are all committed in program order upon validation of their speculative inputs. Subordinate microthreading [Chappel et al. 1999] spawns microcoded threads at strategic points from the main program thread to warm up caches and branch predictors. Speculative pre-computation [Collins et al. 2001; Roth and Sohi 2001] and helper-thread prefetching [Wang et al. 2002] spawn execution-driven speculative threads to warm up cache structures. Instead of throwing away all the work of speculative threads, some techniques optionally register-integrate the computation results of the speculative threads with the main thread.. A main characteristic of these speculative multithreading techniques is that they were evolved to operate under the constraints of the traditional singlethreaded execution model. Instruction fetch and commit are still serialized. In order to overcome the fundamental restrictions imposed by the execution model, architects have gone to great lengths to build copious amounts of buffering in the processing core to hold unissued or uncommitted instructions or to hold speculative architectural state updates of executed-but-uncommitted instructions. Scalability of these speculative techniques depends not only on the inherent parallelism available, but also on the accuracy of speculation as well as the execution efficiency (performance per watt, performance per transistor, etc.) of these techniques.. In contrast, DSWP avoids heavy hardware usage by attacking the fundamental problem of working within a single-threaded execution model and moving to a concurrent multithreaded execution model. Since DSWP is an entirely nonspeculative technique, each DSWP thread performs useful work toward program completion. DSWP threads are typically long-running and do not require frequent thread spawns. Individual, concurrent threads commit register and memory state independently of other threads. Consequently, the amount of storage required to communicate true interthread dependences is insignificant compared to the speculative storage in techniques like TLS and other singlethreaded multicore techniques. These characteristics argue well for DSWP's execution efficiency and scalability.. Next, in Section 3, we discuss details of our evaluation methodology. We present experimental results in Section 4. DSWP, a nonspeculative PMT transformation, started as a mechanism to effectively tolerate variable latency stalls imposed by delinquent memory loads, without resorting to complex speculative issue processors [Rangan et al. 2004] . DSWP was used to parallelize recursive data structure (RDS) loops to execute as two concurrent nonspeculative threads: a critical path (CP) thread comprising the traversal slice and an off-critical path (off-CP) thread comprising the computation slice. For example, consider the loop shown in Figure 1(a) . The traversal slice consists of the critical path code, ptr=ptr->next, and the computation slice consists of ptr->val=ptr->val+1. A DSWP parallelization of this loop yields a traversal and computation thread as shown in Figures 1(b) and (c), respectively. In the figure, the produce function enqueues the pointer onto a queue and the consume function dequeues the pointer. If the queue is full, the produce function will block waiting for a slot in the queue. The consume function will block waiting for data, if the queue is empty. In this way, the traversal and computation threads behave as a traditional decoupled producer-consumer pair.. The above parallelization effectively decouples the execution of the two code slices and allows useful code to be overlapped with long variable-latency instructions without resorting to speculation or extremely large instruction windows. Unidirectional interthread communication enables the use of a decoupling buffer. The decoupling buffer insulates each thread from stalls in the other thread. Further, the reduced size loop of the individual threads allows the program to take better advantage of traversal cache hits to initiate traversal cache misses early. From an ILP standpoint, this allows for an overlap between traversal and computation instructions from distant iterations. Simulation studies have shown that DSWP-parallelized recursive data structure codes running on a dual-core processor achieve latency tolerance not just through a prefetching effect, but also due to DSWP's ability to expose useful nonspeculative parallelism to take advantage of the extra functional units provided by an additional processing core [Rangan et al. 2004] .. In its general form, DSWP tolerates variable latency resulting from not only memory loads, but also variable trip count inner loops, imbalanced if-then-else hammocks, and floating-point operations. It is also an effective means to parallelize resource-constrained applications loops with recurrences across multiple cores. The next subsection describes an automatic DSWP technique to parallelize generic program loops across two or more threads.

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


Statistical Timing Analysis Considering Spatially and Temporally Correlated Dynamic Power Supply Noise Power supply noise is having increasingly more influence on timing, even though noise-aware timing analysis has not yet been fully established, because of several difficulties such as its dependence on input vectors and dynamic behavior. This paper proposes static timing analysis that takes power supply noise into consideration where the dependence of noise on input vectors and spatial and temporal correlations are handled statistically. We construct a statistical model of power supply voltage that dynamically varies with spatial and temporal correlation, and represent it as a set of uncorrelated variables. We demonstrate that power-voltage variations are highly correlated and adopting principal component analysis as an orthogonalization technique can effectively reduce the number of variables. Experiments confirmed the validity of our model and the accuracy of timing analysis. We also discuss the accuracy and CPU time in association with the reduced number of variables.

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Investigating the relations between motivation, tool use, participation, and performance in an e-learning course using web-videoconferencing Web-videoconference systems offer several tools (like chat, audio, and webcam) that vary in the amount and type of information learners can share with each other and the teacher. It has been proposed that tools fostering more direct social interaction and feedback amongst learners and teachers would foster higher levels of engagement. If so, one would expect that the richer the tools used, the higher the levels of learner engagement. However, the actual use of tools and contributions to interactions in the learning situation may relate to students' motivation. Therefore, we investigated the relationship between available tools used, student motivation, participation, and performance on a final exam in an online course in economics (N = 110). In line with our assumptions, we found some support for the expected association between autonomous motivation and participation in web-videoconferences as well as between autonomous motivation and the grade on the final exam. Students' tool use and participation were significantly correlated with each other and with exam scores, but participation appeared to be a stronger predictor of the final exam score than tool use. This study adds to the knowledge base needed to develop guidelines on how synchronous communication in e-learning can be used. This study explored the relationship between available tools used, student motivation, participation, and performance on a final exam in the context of a facultative summer course in economics. Our first hypotheses concerned the relationship between academic motivation and participation, and stated that higher levels of autonomous motivation are associated with more participation in web-videoconferences (H1) and with the use of richer communication tools when taking part in a web-videoconference (H2).. We found partial support for the first hypothesis: the AMS IMTA subscale was significantly associated with the number of times participated. These results are in line with previous findings of Roca and Gagné (2008) and Sørebø et al. (2009) , but in contrast to those studies, we did not find a significant effect of all three subscales related to autonomous motivation. Potentially, this difference could be related to the facultative nature of the course.. We did not find any support for the second hypothesis; there was no relation between autonomous motivation and tool use. A possible explanation for this finding might be that students who participated with chat could still see and hear the other participants (who did have a webcam) as well as the tutor. This might have weakened a potential link between autonomous motivation and tool use, by giving those participants experiences that could also have some beneficial effects on the sense of competency, relatedness, and autonomy (e.g. despite the fact that others could not see or hear them, participants using only chat could have felt a quite high degree of direct and personal contact because they could see and hear others).. An alternative reason might be that (even highly autonomously motivated) students may not have had access to sufficient quality of broadband and technical facilities (e.g., when participating from a holiday or summer job location), which would also weaken a potential link between autonomous motivation and tool use.. Being able to see and hear other students and the tutor while participating with chat might also be the reason why we did not find full support for our third hypothesis that taking part in more web-videoconferences is associated with the use of richer communication tools (H3). That is, there was a significant positive correlation between participation and average tool use, but the Jonckheere-Terpstra test showed a significant trend at the .1 confidence level only in the expected direction.. Regarding the effects on exam scores, we found a trend in line with our expectation that higher levels of autonomous motivation would be associated with higher scores on the final exam (H4), although again only for the IMTA subscale. In addition, while there was a significant positive correlation between the use of communication tools and scores on the final exam (H5), only the number of times students participated in the web-videoconferences predicted scores on the final exam (H6).. The facultative nature of our setting offered a unique possibility to study the relations between student motivation, tool use, and participation in an e-learning course, because students were not obliged to participate or to use certain tools. This setting has several resemblances with distance education programs, where most participants join voluntarily, though some do so out of external motivation (e.g. job prospects), while others join these programs purely because of a specific interest in the topic (e.g. studying economics), and others join for a combination of motivational factors (Bernard et al., 2004; Marks, Sibley, & Arbaugh, 2005) . Similar to other distance education programs where retention rates range from 20-60% (Park & Choi, 2009; Rovai, 2003) , a limitation of the facultative nature of our course is that attrition was quite high. Another limitation is that we cannot draw any conclusions about causal relations between variables. Finally, it is a potential limitation that attendance was used as a proxy for participation. Even though participants attended a webvideoconference, and all who attended communicated, they could have been distracted at least part of the time, and this might be more so when they were not sharing visual information. It should be noted though, that the PBL process (see Loyens et al., 2011) , which lets students take centre stage, but with a tutor present who continuously involved students in the discussion irrespective of the communication tools they used, is likely to have strengthened actual participation and not just attendance.. Future research could aim to experimentally manipulate or control the variables used in this study to enable conclusions regarding causality. In addition, it would be interesting for future research to try and replicate our findings in other facultative and non-facultative elearning courses using web-videoconferences (see also Hrastinski, et al., 2010) .. Nevertheless, the findings that average tool use correlated significantly with participation, and that more participation was associated with higher test scores, are interesting and might contribute to advise and/or guidelines for tutors and students in (facultative) e-learning courses on how to make the most of the learning process. We will first provide some relevant descriptive data before testing the hypotheses. . From the total of 110 participants, 18 never attended a web-videoconference; 27 attended once; 27 attended twice; 16 attended three times and 22 students attended all four videoconferences. Because there is a limited amount of groups and because each group was relatively small, effects nearing significance (i.e. a significance level of p < .1) will also be mentioned.. The first web-videoconference was attended by 70 % (n = 87) participants. Most participants present used all communication tools; a little over 12 % chose not to share visual information with the rest of the group but did use audio and chat; the same amount of participants chose to use chat only, and a minority of 5 % combined the use of webcam with chat but did not participate by using audio. Over time, participation rates decreased, but interestingly, the percentage of participants using webcam, audio, and chat remained almost the same. An additional look at the continuity of tool use shows that from the students who participated all four times, 30% consistently used webcam, audio, and chat and 60% used it three out of four times. In all web-videoconferences, all participating students communicated; only one student consistently used chat and one used chat only in three web-videoconferences plus chat in combination with webcam once. Table 1 shows the correlations between the variables in our research model (displaying Spearman correlation coefficients because of the use of both continuous and categorical variables). Two subscales of the AMS related to intrinsic motivation (IMTK and IMTA) show significant positive correlations with number of web-videoconferences participated, as well as with the final test score. This suggests that intrinsic motivation was positively related to the choice to participate in a web-videoconference. The average tool use, however, did not correlate significantly with any of the independent motivation subscales. In addition, there was a high significant correlation between the number of web-videoconferences participated in and the final exam score. Also, the average tool use correlated significantly with the final exam score.. Finally, 55 % of the participants (n = 60) did not take the final exam. This group contains both students who did not participate in any of the videoconferences and students who did participate once or more (see Figure 3 ). An independent samples t-test shows a significant difference at the .1 level of significance only on the scores of the AMS intrinsic motivation to accomplish (IMTA) subscale between students who did not take the final exam (M = 4.77, SD = .99) and students who did (M = 5.11, SD = 1.07), t(108) = 1.71, p < .1). Table 2 presents the means and standard deviations of motivation, participation and tool use. To test our first hypothesis that higher levels of autonomous motivation would be associated with higher participation in web-videoconferences, an ANOVA on AMS subscales with number of times participated as the independent variable was conducted. It showed a significant difference on the IMTA subscale, F (4,105) = 2.55, p <.05. Post-hoc tests showed that students participating in four web-videoconferences had significantly higher scores on the IMTA subscale than students who did not participate (p < .05), or who participated once (p < .01) or twice (p < .05). Differences on all other subscales were non-significant.

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The role of "leads" in the dynamic OLS estimation of cointegrating regression models In this paper, we consider the role of "leads" of the first difference of integrated variables in the dynamic OLS estimation of cointegrating regression models. We demonstrate that the role of leads is related to the concept of Granger causality and that in some cases leads are unnecessary in the dynamic OLS estimation of cointegrating regression models. Based on a Monte Carlo simulation, we find that the dynamic OLS estimator without leads substantially outperforms that with leads and lags; we therefore recommend testing for Granger noncausality before estimating models.JEL classification: C13; C22 Since the seminal work of Engle and Granger (1987) , cointegrating regressions have become one of the standard tools in analyzing integrated (I(1)) variables. Although the ordinary least squares (OLS) estimator is consistent in the presence of a serial correlation in the error term and/or a correlation between the regressors and cointegration errors, it is well known that the OLS estimator contains the so-called second-order bias. In the literature, there are three typical estimators that deal with this problem: the fully modified OLS estimator proposed by Phillips and Hansen (1990), Park's (1992) canonical cointegrating regression estimator, and the dynamic OLS (DOLS) estimator of Phillips and Loretan (1991) , Saikkonen (1991) , and Stock and Watson (1993) . These three estimators are known to be asymptotically equivalent and efficient. In this paper, we focus on the DOLS estimator among the three estimators and consider the role of "leads" of the first difference of the integrated variables in DOLS.. We investigate the case where leads are unnecessary for the DOLS method, and by using the Monte Carlo simulation, we demonstrate that in such a case, we can expect the improvement of the DOLS estimator in terms of the mean squared error (MSE) by excluding leads from the regressors. In this paper, we considered the role of leads of the first difference of the I(1) regressors in the dynamic OLS estimation. We demonstrated that leads are not necessary in cointegrating regression models when the cointegrating regression error does not Granger-cause the first difference of the I(1) regressors. Based on a Monte Carlo simulation, we found that the dynamic OLS estimator without leads substantially outperforms that with leads and lags when leads are, in fact, unnecessary. Note: "GS001", "GS005", "AIC", "BIC" denote the dynamic OLS estimator with K chosen by the general to specific approach with 1% and 5% significant levels, AIC, and BIC, respectively. "L&L" denotes the dynamic OLS estimator with leads and lags, and "Lags" denotes the dynamic OLS estimator without leads.

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