Articulated mesh animation from multi-view silhouettes Details in mesh animations are difficult to generate but they have great impact on visual quality. In this work, we demonstrate a practical software system for capturing such details from multi-view video recordings. Given a stream of synchronized video images that record a human performance from multiple viewpoints and an articulated template of the performer, our system captures the motion of both the skeleton and the shape. The output mesh animation is enhanced with the details observed in the image silhouettes. For example, a performance in casual loose-fitting clothes will generate mesh animations with flowing garment motions. We accomplish this with a fast pose tracking method followed by nonrigid deformation of the template to fit the silhouettes. The entire process takes less than sixteen seconds per frame and requires no markers or texture cues. Captured meshes are in full correspondence making them readily usable for editing operations including texturing, deformation transfer, and deformation model learning. Meshes are most often animated by hand using keyframing and procedural deformations. Adding detail with this approach is tedious because it requires setting more than a hundred parameters. As a result, most animated characters appear to wear skin-tight garments that move stiffly without flowing. Procedural approaches that take physics into account can generate these details with more ease and efficiency but they are difficult to control when the goal is to match a particular motion or performance.. In this paper, we pursue a complementary direction to create detailed mesh animations through observation rather than simulation. Motion capture of moving meshes provides an alternative to efficient generation of detailed animations. The potential value of this approach is readily apparent in the success of motion capture for skeletal animation. There, observed motions of the skeleton are edited, transformed, interpolated, and recomposed to produce new high-quality animations. The relative simplicity of that approach has already taken root in the film industry where recorded motions are often used instead of more traditional animation techniques. What current motion capture lacks, however, is the ability to efficiently acquire both the internal skeleton and the external shape. A multi-view studio provides a set of synchronized high-definition silhouette videos by recording a performance from several angles using multiple calibrated cameras. Our software pipeline, shown in Figure 3 , also uses a template mesh rigged with a skeleton that matches the physical dimensions of the performer: the skeleton is positioned within the template mesh and each vertex is assigned a weight that is used to deform the template with linear blend skinning (LBS). Our software outputs a sequence of joint configurations and vertex positions to represent the pose and shape of the performer in every frame of the multi-view sequence.. The software pipeline proceeds in two stages: skeleton tracking and surface refinement. In the first stage (Section 2), the system 97:2 • D. Vlasic et al. Our approach demonstrates that by taking advantage of geometric information in the silhouettes, we can obtain detailed mesh animations with full correspondence and correct topology. The output mesh animations are suitable for editing with methods such as texturing and deformation transfer. The key insight is to use skeletal pose estimation for gross deformation followed by iterative nonrigid shape matching to fit the image data. Moreover, an interactive mechanism for correcting occasional pose estimation mistakes allowed us to obtain high-quality tracking of complex sequences with little effort. Our results show that silhouettes alone can convey rich and complex visual details despite inaccuracies away from the contours.. Because our method only relies on silhouettes, it is completely immune to color noise, lighting, and color calibration problems. However, this reliance leads to two limitations. First, our method cannot reproduce the surface accurately away from the contours: it has to rely on the template to interpolate geometric information. This is especially problematic for unarticulated objects such as long scarves, faces, or flowing hair. Using color information in a manner that does not sacrifice robustness would improve their reconstruction. Second, our method is sensitive to errors in the silhouettes, and will produce incorrect geometry when visual hulls are noisy. However, this is not a serious problem in a studio setting where chroma-keying methods can be used to obtain clean silhouettes.. We found that the quality of the output animation strongly depends on the quality of the template. While a space-carved template can convey the character of the motion convincingly, the final appearance is much better for templates with accurate and highresolution detail, such as those obtained with a laser scanner. Additionally, pose estimation is sensitive to the accuracy of the template skeleton proportions and degrees of freedom. Fine-tuning the skeleton is currently a manual task, but it may be possible to extract an improved skeleton automatically from our output sequences and use it to improve tracking of the same performer.. Another issue we have not completely addressed is texture sliding, which is still occasionally visible despite bilateral filtering. We believe that this problem traces back to temporally inconsistent ray constraints used in shape estimation. A possible remedy is to permit motions of a constrained point within the tangent plane of the silhouette cone. This would allow more natural configurations of the mesh and the resulting optimization procedure would still be efficient to solve.. An interesting application of our results would be to learn a model of shape motion that takes clothing and dynamics into account. Such a model could simplify mesh animation from skeletal motion capture data and retain the details acquired during performance. Another possible future direction would be to provide an interface to edit and clean up the output mesh sequences, similar to our user interface for pose correction. Standard mesh editing techniques are not well suited for this purpose because they do not account for temporal consistency. An integrated mesh-editing framework could take advantage of the video data to assist the user and improve reconstruction.. Because future research can greatly benefit from a large volume of data, we have built a studio that will enable us to capture more performances, including multiple people interacting, animals, and props. We hope that we can support further improvement of mesh capture and processing techniques by acquiring a large data set and sharing it with other researchers. : Frame-to-frame correspondence allows a static texture to be easily applied to the entire motion.. Figure 10: Top row: the template mesh and two frames of animation. Bottom row: the template mesh geometry is edited and the change is propagated to the two frames using deformation transfer. We tested our method on eleven sequences with five subjects captured in two different studios. The accompanying video demonstrates the ability of our method to produce meshes that capture fine motion details for very fast and challenging motions. Figure 8 shows reconstructed shapes from five of these sequences. We evaluate our algorithm according to several criteria: computation efficiency, the amount of user interaction needed, and robustness to parameter changes. We demonstrate the utility of our resulting meshes by applying a static texture and a geometric deformation to some sequences. Traditional motion capture estimates skeleton motion using information from markers or sensors on the body. While these systems are accurate, they still require manual adjustments to clean up recorded data. Furthermore, recording requires skin-tight garments to ensure that markers move rigidly with the corresponding limb. Markerless motion capture addresses these limitations by estimating pose directly from multi-view video [Ménier et al. 2006; Cheung et al. 2005; Theobalt et al. 2002; de Aguiar et al. 2004] . However, these methods are less reliable when limbs are not clearly distinguishable (e.g., when arms are close to the body). Manual clean-up is often necessary, but the above methods do not discuss ways to assist in this process.. Pose estimation does not capture the fine detail on the surface of the shape. Richer templates with more degrees of freedom can capture time-varying geometric detail better. Sand et al. [2003] capture some of those effects with silhouette-bounded needles radiating from each bone, without maintaining frame-to-frame correspondence. Park and Hodgins [2006] improve on these results to record detailed bulging and jiggling using a traditional motioncapture system with many markers. Other researchers have shown how to design patterns that can be printed on clothing to enable highly detailed estimation of garment motion [Scholz et al. 2005; White et al. 2007] . Markers, either attached or printed, make it less convenient to record arbitrary human performances in casual clothing. Purely vision-based techniques rely on texture cues to track motion of a few carefully selected points [de Aguiar et al. 2007b; de Aguiar et al. 2007a] . These approaches are not as reliable on fast motions, especially when there are few textures cues (e.g., pants in Figure 1 ).. The multi-view stereo literature provides a powerful collection of tools for recovering a single static mesh [Seitz et al. 2006] . Some of the best methods produce extremely accurate results but they are computationally expensive, requiring an hour of computation or more for a single frame [Hornung and Kobbelt 2006; Furukawa and Ponce 2006; Esteban and Schmitt 2004] . Faster carving methods sacrifice quality but capture meshes at speeds more practical for processing video streams [Rander et al. 1997; Starck and Hilton 2007; Goldlücke et al. 2007 ]. These methods do not make assumptions about the observed deformations and geometry, allowing them to capture arbitrary deforming surfaces, not just articulated characters. They, however, do not always reconstruct the topology correctly and produce uncorresponded results (Figure 2 ). We have data sets from two different sources. In both cases the setup consists of a ring of eight cameras looking down at the performer. The first data set was provided by Starck and Hilton [2007] . Their video streams are recorded at 25 FPS at 1920 by 1080 pixel resolution. The second source was our studio where we capture video at 1600 by 1200 resolution also at 25 FPS. We calibrated our cameras using an LED and software by Svoboda et al. [2005] . The template mesh may be obtained by various means, such as 3D scanning [Levoy et al. 2000] , static multi-view stereo of a single frame [Seitz et al. 2006] , or manual modeling (as in [Park and Hodgins 2006] ). For the Starck and Hilton data set, we use a good frame obtained by their multi-view stereo method [2007] as the template. For our data sets we use a 3D scan of the performer (see Figure 8 ) obtained with a Cyberware scanner. Each template mesh was decimated to 10,000 vertices.. We manually built 40-DOF skeletons to fit the dimensions of each performer and embedded them within the template meshes (Figure 3, left) . We attached each mesh to its skeleton with linear blend skinning using weights generated with Pinocchio [Baran and Popović 2007] .

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


Exploring the feasibility and accuracy of Latent Semantic Analysis based text mining techniques to detect similarity between patent documents and scientific publications In this study, we examine and validate the use of existing text mining techniques (based on the vector space model and latent semantic indexing) to detect similarities between patent documents and scientific publications. Clearly, experts involved in domain studies would benefit from techniques that allow similarity to be detectedand hence facilitate mapping, categorization and classification efforts. In addition, given current debates on the relevance and appropriateness of academic patenting, the ability to assess content-relatedness between sets of documents -in this case, patents and publications -might become relevant and useful. We list several options available to arrive at content based similarity measures. Different options of a vector space model and latent semantic indexing approach have been selected and applied to the publications and patents of a sample of academic inventors (n=6). We also validated the outcomes by using independently obtained validation scores of human raters. While we conclude that text mining techniques can be valuable for detecting similarities between patents and publications, our findings also indicate that the various options available to arrive at similarity measures vary considerably in terms of accuracy: some generally accepted text mining options, like dimensionality reduction and LSA, do not yield the best results when working with smaller document sets. Implications and directions for further research are discussed. Science and technology policies rely on a diverse set of indicators pertaining to scientific, technological and innovative activities both on a national and regional scale (e.g. NSF, 2006; European Commission, Third European Report on S&T Indicators, 2003) . Such indicators are used to map and compare activities by country or region but also to analyse specific fields and domains. Both bibliometric indicators pertaining to scientific activity (publications) and technometric indicators pertaining to technology (patents) figure prominently in such analysis. At the same time, it can be noted that the majority of studies and analyses undertaken in this area focus on mere counts and/or quantitative attributes of relevant documents (e.g. number of publications, patents, number of references, number of citations); less attention is paid to analysis in which the content of the underlying documents is the focal point of attention.. In this study, we investigate the feasibility and relevancy of content (lexical) analysis implying both patent and publication documents. Text analysis is already being used in efforts to delineate specific domains or subfields. Until now, such demarcation has relied heavily on expert opinions to identify appropriate sets of terms and/or classes in available classification schemes (e.g. Hicks et al., 1986; Hinze, 1996; Rabeharisoa, 1992 , in fuel cells; Noyons et al., 1994, in laser medicine; Schmoch, 2004, in genetics; Glänzel et al., 2004, in biotechnology and Meyer, 2000 , in nanoscience and nanotechnology). Clearly, experts involved in domain studies would benefit from automated results that indicate similarity and hence enable mapping, categorization or classification. But not only domain studies would profit from methodologies that permit the identification of content similarity across different sets of documents. The current debate on the relevance and appropriateness of academic patenting (Van Looy et al., 2004 Calderini et al., 2005; Azoulay et al., 2006; Fabrizio and Diminin, 2005; Murray and Stern, 2005; Meyer, 2006) reveals that, under certain conditions, combining scientific and technological activities yields certain beneficial effects, including scientific productivity. At the same time, it can be noted that the occurrence of such beneficial effects may be partly dependent on the (topic) relatedness of both activities. Further analysis of whether and to what extent knowledge spillover dynamics -between scientific and technological activity realms -are present and result in positive 'reinforcing' rather than 'jeopardizing' dynamics might benefit from the ability to assess content-relatedness between sets of documents -in this case, patents and publications.. While our previous research focused on the relevance of lexical text analysis for the purpose of domain studies by targeting one activity realm, namely patent documents, (Van Dromme, Magerman et al. 2006 ), the present analysis includes both patent and publication documents stemming from one and the same academic researcher. Contrary to domain studies, which often involve thousands of documents, the number of relevant documents under consideration is much smaller in this case. So, a first question that arises is related to whether traditional assumptions applied in large-scale text mining applications are as relevant for small-scale applications, such as the one envisaged here. In addition, combining different types of document -i.e. scientific publications and patent documents -introduces an additional level of complexity, which justifies further analysis to assess the relevance and accuracy of text mining algorithms.. In this study, we aim to assess the accuracy of Latent Semantic Analysis based lexical text analysis techniques to construct distance measures that are well suited to grasping similarities between patent and publication text documents. The paper is structured as follows; in the following section, we first elaborate upon current practices developed in the field of text mining and Latent Semantic Analysis. It will become apparent that different options and methods are available to arrive at similarity measures. This variety of possible approaches is then translated into the research design: for the academic inventors under study (n=6), we will calculate a set of distance measures (n=23) for all scientific papers and patent documents based on the content of the documents (title and abstract). It will become clear that different options and calculation methods indeed yield different outcomes. Hence, in a next step of the analysis, we compare the accuracy of the measures obtained by comparing them with independently obtained assessments of similarity. This will allow us not only to draw conclusions on the feasibility of the overall approach; our findings also suggest tentative propositions on the methods and options that are best suited to small-scale applications, implying documents of a heterogeneous nature.

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Dynamic register file resizing and frequency scaling to improve embedded processor performance and energy-delay efficiency With CMOS scaling leading to ever increasing levels of transistor integration on a chip, designers of high-performance embedded processors have ample area available to increase processor resources in order to improve performance. However, increasing resource sizes can increase power dissipation and also reduce access time, which can limit maximum achievable operating frequency. In this paper, we explore optimizations for the processor register file (RF), to improve performance and reduce the energy-delay product. We show that while increasing the size of the RF can potentially increase the IPC, overall it results in an increase in program execution time. In response we propose L2MRFS -a dynamic register file resizing scheme in tandem with frequency scaling, which exploits L2 cache misses to noticeably improve processor performance (11% on average) and also significantly reduce the energy-delay product (7%). With the advent of the digital convergence era, high performance embedded applications are increasingly using complex out-of-order superscalar embedded processors to meet performance goals. Examples of such embedded processors include the NEC's VR5500 and VR77100 Star Sapphire [6] , and the IBM PowerPC 750FX [7] processors. Technology scaling into the ultra deep submicron (UDSM) region has allowed hundreds of millions of gates to be integrated onto a single chip. Designers thus have ample silicon budget to add more processor resources (e.g., increasing register file size, reorder buffer size, etc) in order to exploit application parallelism and improve performance. However, restrictions with the power budget and practically achievable operating clock frequencies act as limiting factors that prevent unbounded increases in processor resource sizes. Increasing register file (RF) size for instance, increases its access time, which reduces processor frequency, since access times to the multi-ported RF is one of the most critical timing factors that determines the achievable processor operating frequency. In this paper, we present a novel technique for dynamically resizing the RF (L2MRFS) which in tandem with dynamic frequency scaling (DFS) significantly improves the performance and reduces energydelay product for embedded processors. Our approach exploits L2 cache misses, adaptively reducing RF size during the period when there is no pending L2 cache miss, and using a larger RF during the L2 cache miss period. To enable single cycle access during RF resizing, the processor frequency is dynamically scaled. Such a dynamic frequency scaling (DFS) should be done fast, otherwise it can negatively impact performance during dynamic RF resizing. In our studied processor, IBM PowerPC 750FX, DFS is done in effectively zero cycle using a dual PLL architecture [11] . The RF size adaptation is realized using a circuit modification scheme that comes with minimal hardware modification, unlike costly banking or clustering techniques. Experimental results show that L2MRFS noticeably improves performance and also significantly reduces energy-delay product for out-of-order embedded processers. In this paper, we presented a novel technique for dynamically resizing the RF (L2MRFS) which in tandem with dynamic frequency scaling (DFS) significantly improves the performance and reduces energydelay product for embedded processors. Our approach exploits L2 cache misses, adaptively reducing RF size during the period when there is no pending L2 cache miss, and using a larger RF during the L2 cache miss period. The RF size adaptation is realized using a circuit modification scheme that comes with minimal hardware modification, unlike costly banking or clustering techniques. Our extensive experimental results have shown that L2MRFS noticeably improves performance (11% on average) and also significantly reduces energy-delay product (7%) for out-of-order embedded processers. In this section we present experimental results to show how the dynamic RF resizing approach impacts processor performance. We use SPEC2K benchmarks executed with reference data sets, and compiled with the O4 option using the Compaq compiler. The architecture was simulated using an extensively modified version of MASE (SimpleScalar 4.0) [9] . The benchmarks were fast-forwarded for 500 million instructions, then fully simulated for 1 billion instructions. We used a modified version of CACTI4 [8] for estimating access time for the RF. For estimating energy consumption of our adaptive technique we integrate Wattch [10] into our simulator infrastructure. We used process parameters for a 70nm process at 595MHz with 1V supply voltage.. In Figure 7 (a) and (b) we report the result of using the proposed L2MRFS technique. In Figure 7 (a) we report the performance improvement in terms of normalized execution time when we apply L2MRFS to Conf_1 (referred to as DYN_Conf_1) compare with Conf_1 and for DYN_Conf_2 compare to Conf_2. From Figure 7 (a), a performance improvement can be seen across all benchmarks for DYN_Conf_1 when normalized to Conf_1; the performance improvement is almost 6% on average. The performance improvement for DYN_Conf_2 is even more, up to 14% for crafty and 11% on average. In Figure 7 (b) we report the energy-delay products of DYN_Conf_1 and DYN_Conf_2 compared to Conf_1 and Conf_2 respectively. As can be seen for all the benchmarks our dynamic RF resizing technique results in overall reduction in energy-delay product. The average energy-delay is reduced by 3.5% and 7% respectively for DYN_Conf_1 and DYN_Conf_2 compare to Conf_1 and Conf_2. Finally, it should be noted that while DYN_conf_2 delivers lower energy-delay and higher performance in terms of execution time, it requires more silicon budget and dissipates more power compared to DYN_Conf_1 (results are not reported due to space limitation). This in fact is due to having larger resources such IQ, RF and ROB. There has been a lot of research that has proposed altering the structure of the register file (RF) for improving performance. One set of techniques uses localities of communication to split the microarchitecture into distributed clusters, each containing a subset of the RF. [1]- [2] . A critical issue in the design of such systems is the heuristics used to map instructions to clusters. These schemes have the potential to scale to larger issue widths but require complex intercluster control logic to map instructions to clusters and to handle inter-cluster dependencies. Alternatively, other approaches retain a centralized microarchitecture, but partition the processor units such as the RF [3] - [4] to reduce access time and energy dissipation. Partitioning the RF into multiple banks for instance reduces the ports on the partitions, which reduces the pre-charging and sensing times and the related energy dissipation. But these reductions come at the cost of value multiplexing and port conflict problems. The major drawback of all these banking techniques in general is in the complexity that speculation adds and more specifically the complexity they introduce on handling the coherency in register caches and banking conflicts. This added complexity becomes even more critical for embedded processors that work in resource-constrained environments. None of the above schemes exploit the L2 cache misses for dynamic register file resizing. The technique that comes closest to our work is [5] , which performs early register de-allocation based on L2 misses to improve performance. However, such a scheme increases complexity since it requires additional resources such as bit vectors to identify the sources and the destinations of the load-dependent instructions, additional bits in the ROB and possibly a backup register file to store de-allocated values. In contrast, we propose a much simpler architecture and circuit level approach that dynamically adapts the RF size on L2 cache misses to achieve significant performance improvements.

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


Evolving small-board Go players using coevolutionary temporal difference learning with archives We apply Coevolutionary Temporal Difference Learning (CTDL) to learn small-board Go strategies represented as weighted piece counters. CTDL is a randomized learning technique which interweaves two search processes that operate in the intra-game and inter-game mode. Intra-game learning is driven by gradient-descent Temporal Difference Learning (TDL), a reinforcement learning method that updates the board evaluation function according to differences observed between its values for consecutively visited game states. For the inter-game learning component, we provide a coevolutionary algorithm that maintains a sample of strategies and uses the outcomes of games played between them to iteratively modify the probability distribution, according to which new strategies are generated and added to the sample. We analyze CTDL's sensitivity to all important parameters, including the trace decay constant that controls the lookahead horizon of TDL, and the relative intensity of intra-game and inter-game learning. We also investigate how the presence of memory (an archive) affects the search performance, and find out that the archived approach is superior to other techniques considered here and produces strategies that outperform a handcrafted weighted piece counter strategy and simple liberty-based heuristics. This encouraging result can be potentially generalized not only to other strategy representations used for small-board Go, but also to various games and a broader class of problems, because CTDL is generic and does not rely on any problem-specific knowledge. Despite having been a subject of artificial intelligence research for more than 40 years, the game of Go remains a great challenge as the best computer players continue to yield to professionals. This is a result of the huge combinatorial complexity, which is much higher for this game than for other popular two-player deterministic board games-there are about 10 170 board states and the game tree has an average branching factor of around 200. These figures, together with other specific features of Go, make it impossible to directly adopt techniques that proved successful in other board games, like chess or checkers (Mechner, 1998; Johnson, 1997) .. Many of canonical Go-playing programs are precisely tuned expert systems founded on a thorough human analysis of the game. Such programs typically employ a multitude of rules elicited from professional Go players in order to recognize particular board patterns and react to them. However, this knowledge-based approach is constrained by the extent and quality of the available knowledge, and by the designer's ability to articulate it in a playing program. These shortcomings, which manifest themselves when applying the knowledge-based methodology to most games, turn out to be particularly painful for Go.. No wonder that some computer Go researchers abandon this cognitive perspective, which aims at mimicking expert reasoning, in favor of a behavioral perspective that does not care much whether the player's perception of the game state and internal strategy representation exhibit analogies to human players. Approaches that belong to the latter group typically assume that a player has no initial knowledge about game specifics (apart from the game definition) and involve some form of learning to automatically harvest it from the outcomes of games played against opponents. The major differences between the representatives of this trend consist in when (in the course of learning) the outcomes of such interactions are transformed into knowledge and how it is done. Such a learning task can be formalized as maximization of the expected outcome of the game (or the probabil-ity of winning for binary-outcome games), which is some function of strategy parameters. The form of that function is unknown to the learner and, for nontrivial games like Go, very complex, which precludes any attempts at solving this problem analytically. Also, the sheer size of the domain of that function (the search space) is immense even for simple representations of the game strategy. Thus, some form of random sampling of the search space becomes inevitable, which explains the popularity of Monte Carlo (MC) techniques in this context (see, e.g., Müller, 2009) .. The primary contribution of this paper is a method that assumes the aforementioned behavioral perspective and employs a randomized search simultaneously in two different modes: local (intra-strategy) and global (interstrategy). To conduct the search in the former mode, we employ gradient-based temporal difference learning that works with a single strategy at a time and trains it by a randomized self-play. For the inter-strategy mode, our method relies on coevolutionary learning which maintains a population of strategies, makes them play against each other, and uses the outcomes of games to guide the process of random sampling of the search space in subsequent iterations. The proposed approach, termed Coevolutionary Temporal Difference Learning (CTDL), hybridizes thus two radically different techniques that complement each other in terms of exploration and exploitation of the search space.. This paper is organized as follows. In Section 2, we shortly present the game of Go and its customization adopted for this study. In Section 3, we detail the CTDL approach, starting from describing its constituents: temporal difference learning and coevolutionary learning. Section 5 presents the results of an extensive computational experiment and their analysis. In Sections 6 and 7, we discuss the results and conclude this contribution. Where appropriate, we refer to and review the related work; a comprehensive review of all AI methods applied to computer Go can be found in the work of Bouzy and Cazenave (2001) . There are at least two general lessons that can be learned from this study. Firstly, we can conclude that different modes of adopting the Monte Carlo methodology in a learning algorithm may lead to fundamentally different dynamics of the learning process and final outcomes. Secondly, using qualitatively different modes of randomization can be synergetic, leading to substantially better performance when compared with the constituent methods (and randomization modes).. Although our evolved WPC players would most probably yield to other contemporary strategies that use more sophisticated representations, we need to emphasize that our primary objective was to hybridize two al- Fig. 6 . Genotypic changes observed in the best-of-generation individuals of an example CTDL+HoF run sampled every 40000 games (the first generation in the upper-left corner, arranged row-wise). Individuals are illustrated as 5 × 5 Go boards colored accordingly to the corresponding WPC weights (white = −1, black = 1). WPCs were scaled using the maximum absolute weight.. gorithms that learn fully autonomously and study the relative gains that result from their synergy. To quote Arthur Lee Samuel's declaration, The temptation to improve the machine's game by giving it standard openings or other man-generated knowledge of playing techniques has been consistently resisted (Samuel, 1959, p. 215) . This result confirms our former observations (Szubert et al., 2009 ), when we demonstrated that hybridizing coevolution with TD(0) proves beneficial when learning the strategy of the game of Othello. Here, we come to similar conclusions for the game of small-board Go, and additionally note that extending the lookahead horizon by using TD(λ) with λ close to 1 can boost the performance of CTDL even further. Adding a simple coevolutionary archive to this mixture makes it even better. Thus, there is growing evidence to support our claim that hybridizing coevolution with temporal difference learning can be beneficial.. In the context of games, this result is still preliminary due to the small board size, and claiming that CTDL scales well with the board size would be premature. Evolving an effective strategy for larger boards is much more challenging, in particular for the game of Go, where absolute positions are of minor importance compared to the 'topology' of the board state. Approaching such a problem using the WPC for strategy representation would not make sense. Note, however, that CTDL is a generic coevolutionary metaheuristic: none of its constituent methods is aware of strategy encoding, as they simply process vectors of numbers (strategy parameters). Thus, CTDL can be applied to other strategy representations that can be encoded by vectors, including representations that already proved successful on selected games (like n-tuples). We look forward to investigate such scenarios, presuming that for problems and representations on which TDL and CEL alone perform well, CTDL could act in a synergetic way.. Apart from being encouraging from the practical viewpoint, CTDL seems to rise also interesting theoretical issues that deserve further research. One of them pertains to the way CTDL performs the local search. In essence, by relying on a randomly perturbed self-play, TD serves only as a substitute for local search, as it has no access to the objective fitness function. Nevertheless, it positively contributes to our hybrid. Thus, it turns out that we can do a kind of local search without objective information about solution performance. This sounds both puzzling and appealing, as normally an objective quality measure is an indispensable prerequisite for local search. By analogy to the terms memetic algorithms and Lamarckian evolution that are usually used to refer to various hybrids of evolution and local search, which typically alternate genetic search for the population and local search for individual solutions, this paradigm can be termed coevolutionary memetic algorithm or Lamarckian coevolution. We plan to elaborate on this observation in further research and hypothesize that some findings from the memetic algorithms literature are potentially applicable to our approach. An in-depth conceptual analysis of the interplay between intra-game learning (temporal difference) and inter-game learning (coevolution) in CTDL reveals that it is more intricate and sophisticated than it may appear at a first glance. Though TDL and CEL employ the WPC in exactly the same way when playing a game, they attach a fundamentally different interpretation to WPC weights during learning. TDL attempts to modify the weights so as to faithfully model the true value function describing winning chances for each game state. It does so because its training formula (3), when applied to the terminal state, substitutes the actual game outcome (+1, 0, or −1) in place of the learner's estimate P t+1 . CEL, on the contrary, does not refer to any such absolute values, so only the ordering of state values is relevant for it. In effect, TDL is trying to solve a bit different, more constrained (and thus presumably more difficult) problem. Technically, for each local minimum w min of the error function that the gradient-descent TDL aims at (including the global minima), there are infinitely many other WPCs that produce identical behavior of the player against any strategy.. 2 Each of such strategies is equally desirable from the viewpoint of CEL, but many of them would be considered completely worthless by TDL (e.g., because of overestimating the true state values).. With TDL and CEL guiding the search process in different directions, their efforts can happen to cancel each other and render CTDL ineffective. However, the experimental results clearly demonstrate that this is not the case, most probably because, in a highly dimensional search space (25 elements of the WPC), it is very unlikely for TDL and CEL to adopt strictly opposite search directions. TDL, at least on average, benefits from disturbances introduced by CEL, which force it to consider solutions that it would not come upon otherwise, and probably helps it escape local minima. This is parallel to observations in the field of optimization, where the search algorithm can benefit from being allowed to consider infeasible solutions that do not meet some of the assumed constraints, as this opens new 'shortcuts' in the search space (Michalewicz, 1996) .. It might also be the case that the loss of performance resulting from the incompatibility discussed here is compensated by synergy of other features of both methods. For that instance, TDL, as a gradient-based technique, is able to link the outcome P of its actions independently to each parameter of the solution (element of the WPC matrix) and calculate the desired correction vector (cf. Eq. 3). It is also capable to simultaneously update all strategy parameters (weights). With this skill, it complements evolution, which is devoid of such an ability. All players considered in the remaining part of this paper rely on the Weighted Piece Counter (WPC) to represent their strategies. The WPC is a matrix (unrolled here to a vector for brevity) that assigns a weight w i to each board intersection i and uses a scalar product to calculate the utility f of a board state b:. where s is the board size and b i is +1, −1, or 0 if, respectively, intersection i is occupied by the black player or the white player, or remains empty. This means that we employed the simplest form of the direct coding of the board state termed Koten board representation by Mayer (2007) , in which exactly one input for each intersection is provided for the evaluation function f . Although such an input signal does not carry information about the states of neighboring intersections, which seems to be essential in Go, the direct board encoding is frequently used in related studies (Runarsson and Lucas, 2005; Schraudolph et al., 2001) . While the Go board has eight axes of symmetry and the WPC could be simplified to cover just 1/8 of the board, we do not reveal this fact to the learners and let them learn WPC for the entire board, following the behavioral perspective mentioned in Introduction. The players interpret the values of f in a complementary manner: the black player prefers moves leading to states with larger values, while smaller values are favored by the white player. Alternatively, the WPC may be viewed as an artificial neural network comprising a single linear neuron with inputs connected to board intersections. The fact that the WPC weighs the occupancy of each board intersection independently makes it probably the least sophisticated strategy representation for board games. One can also argue whether the WPC is appropriate for the rather weakly-positional game of Go (as compared to, e.g., Othello). Therefore, in objective terms, we do not anticipate the strategies elaborated in the following experiment to beat the top-ranked computer players. However, in the context of this study, this should not be perceived as a hindrance, as our primary goal is to investigate the interplay between coevolutionary learning and TDL in the hybridized approach, and the potential impact it has on the player's performance as measured against the constituent strategies, with a hope that at least some of the conclusions can be generalized to more sophisticated strategy representations.. Still, the WPC's simplicity and its positional character bring substantial advantages, fast board evaluation being the most prominent one. WPC strategies can also be easily interpreted and compared by inspecting the weight values. For instance, Table 1 presents the weight matrix of a sample player for 5 × 5 Go that clearly aims at occupying the center of the board while avoiding the corners. In order to evaluate the idea of hybridizing coevolution with temporal difference learning, several experiments comparing CTDL, CEL, TDL, and their extensions with HoF were conducted. All algorithms were implemented using our coevolutionary algorithms library called cECJ (Szubert, 2010) built upon the Evolutionary Computation in Java (ECJ) framework (Luke, 2010) . It was assumed that the uttermost element influencing the time of training is the time required to play a game, so the time consumed by such operations as selection, mutation, evaluation was neglected. In other words, our unit of computational effort is a single game. To provide fair comparison, all runs were stopped when the number of games played reached 2000000. For statistical confidence, each experiment was repeated 25 times.

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Crossing the hurdle: the determinants of individual scientific performance Abstract An original cross-sectional dataset referring to a medium-sized Italian university is implemented in order to analyze the determinants of scientific research production at individual level. The dataset includes 942 permanent researchers of various scientific sectors for a 3-year time-span (2008)(2009)(2010). Three different indicators-based on the number of publications and/or citations-are considered as response variables. The corresponding distributions are highly skewed and display an excess of zero-valued observations. In this setting, the goodness-of-fit of several Poisson mixture regression models are explored by assuming an extensive set of explanatory variables. As to the personal observable characteristics of the researchers, the results emphasize the age effect and the gender productivity gap-as previously documented by existing studies. Analogously, the analysis confirms that productivity is strongly affected by the publication and citation practices adopted in different scientific disciplines. The empirical evidence on the connection between teaching and research activities suggests that no univocal substitution or complementarity thesis can be claimed: a major teaching load does not affect the odds to be a non-active researcher and does not significantly reduce the number of publications for active researchers. In addition, new evidence emerges on the effect of researchers administrative tasks-which seem to be negatively related with researcher's productivityand on the composition of departments. Researchers' productivity is apparently enhanced by operating in department filled with more administrative and technical staff, and it is not significantly affected by the composition of the department in terms of senior/junior researchers. During the past 30 years, there has been a growing interest in the role of academic research activity and in its contribution to economic growth and social development. One of the least studied and most puzzling feature of this debate is the question of individual scientific productivity. Researcher's activity is basically a multi-output activity, producing outcomes as research, teaching and others products (newspapers articles, medical protocols, etc.) with a relevant impact on society. This idea is so widespread that in the national research assessment exercises-as an example in the British Research Excellence Framework 2014-information is collected on all these different activities in order to evaluate not only the quality of research produced by universities, but even their multifaceted societal impact. At the best of our knowledge, no study addresses the question of the determinants of researchers' overall production, even if many papers solely focus on one dimension of their multi-output activities.. The idea that scientific publications represent the essence of the research activity is widely accepted (Wooton 2013) . In this respect, two different streams of literature have been developed. The first stream is focused on describing the laws underlying the frequency distribution of researcher's publications-following the tradition started by Lotka (1926) . The second stream deals with the determinants of individual productivity. These works aim to define the factors affecting research productivity by using tools as correlation analysis or regression modelling.. A common feature of this literature consists in considering the number of publications of a researcher as a proxy for quantifying her/his productivity. The main drawback of this indicator stems on the fact that each publication counts for one: a short paper addressing a limited issue counts as a seminal paper. So, the holy grail of scientometric research is the construction of indicators addressing-at the same time-the issue of productivity and quality of scientific work also at a researcher level (van Leeuwen et al. 2003) . To this aim, the most-used strategy in empirical research consists in the substitution of the notion of ''research quality'' with the notion of ''scientific impact'' (as defined in Martin and Irvine 1983) which can be more easily handled using citation data. A first possible approach may be based on counting a subset of the publications of a researcher, such as the highly-cited papers or those published in ''top'' journals-so defined in reference to the impact factor or to other similar indicators. A different approach aims to obtain composite indicators of productivity and impact of a researcher considering her/his published articles as well as the citations they received. Among them, the most widespread indicator is the h-index (Hirsch 2005) .. The aim of this article is to contribute to the debate on the determinants of individual scientific production by stressing the attention on two original issues. The first issue deals with the adoption of regression models able to properly handle the specific nature of the data. Three standard different indicators, based on the number of publications and/or their citations, are considered as response variables. They are integer-valued and display highly-skewed distributions which are in addition zero-inflated, i.e. an excess of zeroes is present. Many existing papers dealing with modelling the determinants of scientific production do not specifically account the skewed and zero-inflated nature of the data Matt 2004, 2006; Lissoni et al. 2011; Rivera-Huerta et al. 2011) . In order to address the skew distribution of research output-and eventually acknowledging the presence of zero excess-quantile regression may be adopted (Kelchtermans and Veugelers 2011) . In the present paper, an alternative approach, based on appropriate GLM models expressly conceived for count data with zero excesses, is proposed. Quite surprisingly some of these models, even if wellestablished in other disciplines, have been neglected in the framework of the analysis of production process of academic research. The second issue focuses on the joint use of an extensive set of explanatory variables, which have been considered by adopting separate analyses in the previous literature. Indeed, the proposed models consider as possible determinants of the researcher's individual academic productivity: (i) some personal observable characteristics, such as gender and age; (ii) some individual career features, such as academic position, seniority, typology of labour contract; (iii) the scientific field in which the scholar works; (iv) her/his teaching and administrative tasks; (v) her/his departmental working context. These ideas are carried out on a large original dataset referred to a set of about one thousand scholars belonging to a medium-sized Italian university.. The remainder of the paper is subdivided into four sections. In section ''Literature review'', the main literature addressing the issues of researcher's production is surveyed. In Section ''Materials and methods'', the data and the methodologies adopted are illustrated. In Section ''Results and discussion'', the main results of our empirical analysis are presented and discussed. Finally, ''Conclusions'' section are drawn. This article contributes to the stream of literature investigating the individual determinants of researcher performance. We analyse original data referring to a medium-sized Italian university which employs 942 researchers covering many scientific fields in Life Sciences, Physical and Engineering Sciences and Social Sciences and Humanities. All the researchers have a permanent position in the university and obey the same rules defined at a national level for recruitment, career, didactic charges, administrative duties and wages. Data refers to a 3-year time-span (2008) (2009) (2010) .. With respect to previous literature, we adopt eight regression models to manage skewed count data with an excess of zero-valued observations, which are often the main features of the response variables adopted to quantify research production. Among these models, the Hurdle Negative Binomial exhibits a good fitting and appears to be reasonably coherent with the underlying generating data process. This model can be interpreted as follows: all the researchers are considered as potentially productive; when a researcher passes the hurdle by writing a paper and becoming active in the time period considered, her/his performance is described by the count component of the model. Moreover, the odds to be non-active is modelled by the zero component of the model. In order to highlight the determinants of a researcher production, we introduced an extensive set of explanatory variables, which, to the best of our knowledge, were not jointly explored by previous literature in a unique framework. A first result, widely known in literature, is that the different publication and citation practices adopted in different research fields have a strong impact on productivity of active researcher and on the odds to be non-active. This strong evidence suggests to consider a research priority the investigation of the processes giving rise to these sectorial differences, which are substantially unexplained in our model.. Results regarding personal observable characteristics of researchers confirm the evidences of previous studies. In particular, our data add evidence to the gender productivity puzzle and to the age-effect in publication activity. Academic position positively influences researcher's productivity, while seniority tends to have a negative effect. The evidence of our analysis does not allow to draw a clear-cut conclusion about the relation between teaching and research activities. A major teaching load does not affect the odds to be a non-active researcher and it does not reduce significantly the number publications for active researchers. The number of thesis supervised increases the number of publications in the repository. This evidence suggests that no univocal substitution or complementarity hypothesis can be claimed. Also in this case an in-deep analysis of factual evidence and underlying processes is straightforward to gain a better understanding of the phenomena.. On the contrary, a clear result emerges about administrative tasks. These appear to negatively affect research productivity, especially when research outcomes filtered by a reviewing process are considered. To the best of our knowledge, this is a new piece of evidence that deserves further scrutiny. Our data also allowed to analyse the effect of the departmental working context on productivity. A first clear result is that operating in a department filled with more administrative and technical staff enhances productivity. On the contrary, mixed evidences emerge when the composition of the department in terms of senior/junior researchers is considered.. However, we caution against generalizations of our results since the adopted database include scientists working in a single university and we are aware that more general country-level investigation on the main determinants of scientific production should be undertaken.. We believe that further research is needed in order to create improved measures of the effort devoted to institutional duties and university governance and to better understand if and how these activities could affect scientific production. Similarly, additional proxies for the teaching load have to be exploited. To this purpose, future research could surely benefit from the availability of information concerning researcher's final outputs (publications, patents, products, etc.), but also projects, scientific areas, teaching and administrative and institutional activities. Indeed, the evaluation of the effects of administrative and teaching tasks on scientific output is mandatory to verify if current incentive policies for stimulating research production are effective.

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


On-line emotion recognition in a 3-D activation-valence-time continuum using acoustic and linguistic cues Abstract For many applications of emotion recognition, such as virtual agents, the system must select responses while the user is speaking. This requires reliable on-line recognition of the user's affect. However most emotion recognition systems are based on turnwise processing. We present a novel approach to on-line emotion recognition from speech using Long Short-Term Memory Recurrent Neural Networks. Emotion is recognised frame-wise in a two-dimensional valence-activation continuum. In contrast to current state-of-the-art approaches, recognition is performed on low-level signal frames, similar to those used for speech recognition. No statistical functionals are applied to low-level feature contours. Framing at a higher level is therefore unnecessary and regression outputs can be produced in real-time for every low-level input frame. We also investigate the benefits of including linguistic features on the signal frame level obtained by a keyword spotter. Emotionally sensitive virtual agents need to be able to estimate a user's emotion in real-time. However most Automatic Emotion Recognition (AER) systems are designed for supra-segmental units of speech [50] , such as complete sentences or fragments of sentences [30, 44] . This means that no output is generated by the system until the user pauses for a long time, and that changes in emotion during a segment are ignored. Furthermore the fragments-in most casesare pre-segmented and all results obtained have the precondition of perfect segmentation. However, in a real-world, on-line AER system, the segmentation is not known and methods such as Voice Activity Detection (VAD) and energy thresholding must be implemented. These, however, will not achieve perfect segmentation on, e.g. the sentence level. All those aspects highlight that it is extremely important to close or at least narrow the gap between the human ability to permanently observe, question, update, and refine the estimation of the current affect of a conversational partner, and the simple evaluation of features at the end of a speech turn as it is applied for many virtual agents (e.g. [40] )-even though this does not mean that human perception is continuous at all points.. In this article, we introduce a novel approach to fully continuous emotion recognition in a 3-D valence-activationtime continuum. The novelty of our technique is that it explicitly models time as a third dimension-similar to automatic speech recognition systems.. The motivation for time-continuous emotion recognition is clearly given by applications where emotion must be evaluated incrementally in real-time, e.g. in-car driver monitoring for safety enhancement, or virtual agents as in our case: in the SEMAINE system [27] customised and immediate feedback based on the emotional state of the user has to be produced. Taking into account current affective cues, responses have to be prepared already before the user has finished speaking and hypotheses have to be generated onthe-fly. It might not make sense at first to detect emotion from speech every few milliseconds, since emotion remains bound to syllables or even words [31] . However, if the output is smoothed over short time periods, reliable estimates of quasi-instantaneous emotions can be given, without the need to identify word boundaries, for example. Moreover, if word boundaries are known, e.g. from an Automatic Speech Recognition (ASR) unit, the emotion output can be mapped to the word segments and averaged to transcribe the emotional content of every word instantaneously.. In the context of emotion-related virtual agents Long Short-Term Memory models have been suggested [22] . Stemming from a similar motivation, our on-line speechbased emotion recognition approach is based on Long ShortTerm Memory Recurrent Neural Networks (LSTM-RNN). Those networks directly operate on short time frames of low-level audio descriptors such as Energy, Pitch, and MelFrequency-Cepstral Coefficients (MFCC) and do not require any segmentation besides the low-level framing. Additionally, linguistic features are incorporated via early fusion. Due to the regression output in an emotion space spanned by valence and activation (as in [4, 15, 44] , for example), the approach is no more limited to detection of discrete emotion classes. For every low-level audio input frame an output value ranging from −1 to +1 for each emotional dimension is generated.. This article is structured as follows: the next section addresses the problem of continuous emotion recognition in more detail. In Sect. 3 we briefly outline the SEMAINE architecture, which reveals the structure of a virtual agent system demanding for incremental emotion recognition. Section 4 describes the acoustic and linguistic features which were extracted from the audio signal. Section 5 presents and discusses the propagated LSTM-RNN architecture as well as other Neural Network architectures, to which we compare the performance of the LSTM-RNN. Section 6 introduces the naturalistic emotion database used for experimental evaluations in Sect. 7. Section 8 shows results obtained on this database. We conclude the article in Sect. 9 with an outlook for future trends in fully continuous emotion recognition.

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


Integral equations, large and small forcing functions: Periodicity The defining property of an integral equation with resolvent R(t, s) is the relation between a(t) and t 0 R(t, s)a(s)ds for functions a(t) in a given vector space. We study the behavior of a solution of an integral equationwhen a 1 (t) is periodic, C(t + T, s + T ) = C(t, s), while a 2 (t) is typified by (t + 1) β with 0 < β < 1. There is a resolvent, R(t, s), so thatWe show that the integral t 0 R(t, s)a 2 (s)ds so closely approximates a 2 (t) that the only trace of that large function, a 2 (t), in the solution is an L p -function, p < ∞. In short, that large function a 2 (t) has essentially no long term effect on the solution which turns out to be the sum of a periodic function, a function tending to zero, and an L p -function. The noteworthy property here is that with great precision the integral t 0 R(t, s)a(s)ds can duplicate vector spaces of functions both large and small, both monotone and oscillatory; however, it cannot duplicate a given nontrivial periodic function a(t) other than k 1 + β ds is an L p approximation to sin(t + 1) β for 0 < β < 1, but contraction mappings show us that precisely at β = 1 that approximation fails and sin(t + 1) − t 0 R(t, s) sin(s + 1)ds approaches a nontrivial periodic function. It is a classical, but elementary, exercise to show that if t 0 |C(t, s)|ds ≤ α < 1 then every solution of x(t) = a(t) − t 0 C(t, s)x(s)ds is bounded for every a ∈ BC, the space of bounded continuous functions. Moreover, it is a simple consequence of Perron's theorem that every solution of that equation is bounded for every a ∈ BC if and only if sup t≥0 t 0 |R(t, s)|ds < ∞ where R(t, s) is the resolvent. Such facts promote the conventional idea that when an integral equation has a nice kernel the solution follows the forcing function. To motivate our work, if we encounter x(t) = sin t + (t + 1)

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


A decision-tree model of balance scale development We present an alternative model of human cognitive development on the balance scale task. Study of this task has inspired a wide range of human and computational work. The task requires that children predict the outcome of placing a discrete number of weights at various distances on either side of a fulcrum. Our model+ which features the symbolic learning algorithm C4.5 as a transition mechanism, exhibits regularities found in the human data including orderly stage progression, U-shaped development, and the torque difference effect. Unlike previous successful models of the task, the current model uses a single free parameter, is not restricted in the size of the balance scale that it can accommodate, and does not require the assumption of a highly structured output representation or a training environment biased towards weight or distance information. The model makes a number of predictions differing from those of previous computational efforts. Within the past decade a number of symbolic and connectionist learning methods have been applied to cognitive development's balance scale task. The aim of this body of research has been to investigate the use of machine learning methods as models of developmental transition, to explore the range of assumptions under which psychologically accurate models of the task can be achieved, and, most important, to assemble predictions about the task and the changes that children's thinking undergoes during the course of development.. The balance scale task consists of showing a child a balance scale supported by blocks so that it stays in the balanced position. Next, a discrete number of weights are placed around one of a number of evenly spaced pegs on either side of the fulcrum, and it becomes the child's task to predict which arm will go down, or whether the scale will balance, once the supporting blocks are removed. A five-peg, five-weight balance scale appears in Figure 1 .. The psychological task requires the integration of the dimensions of weight and distance through the course of development. Perfect performance on this task can be achieved by computing torques for both the left and right arms by multiplying weight by distance, and the side with the largest torque goes down. If torques are equal, then the scale will balance. Siegler (1976 Siegler ( , 1981 types is used to gage the level of expertise that the child has acquired and to gain insight into the types of information that the child uses to solve balance scale problems.. The first three types of problems are referred to as simple problems since the dimension of greater magnitude determines the outcome. Balance problems have equal numbers of weights placed at equal distances from the fulcrum resulting in the scale balancing.. Distances on either side of the fulcrum are equal for weight problems, resulting in the dropping of the side with more weight. For distance problems, the arm with weights placed at a greater distance goes down since the two sides have equal weights.. The final three problem types are referred to as conflict problems since the cue of weight conflicts with the cue of distance. These problems have greater weight on one arm and greater distance on the other, with no simple way of determining the outcome. The side with the greater weight or distance drops respectively in conflict-weight and conflict-distance problems, while the scale balances for conflict-balance problems. Siegler (1976 Siegler ( , 1981 has reported that children's performance on the balance scale task progresses through four distinct stages. In stage 1, children use only weight information to determine if the scale will balance. In stage 2, children emphasize weight information but use distance if weights on either side of the fulcrum are equal. In stage 3 both weight and distance information is utilized for simple problems, but children seem to respond indecisively when one arm has greater weight and the other greater distance. By stage 4, there is a correct integration of weight and distance information resulting in the near flawless performance of the task. Each of these rules makes specific predictions about the pattern of errors that can be expected to occur across the different problem types. Figure 1 presents the predicted percentages of correct responses, broken down by problem type, for each of these four stages.. While orderly stage progression constitutes a major regularity of balance scale development, a second regularity can be observed by examining the predicted pattern of errors in Figure 1 for conflict-weight problems. In stages 1 and 2, children answer these problems correctly since they generally rely on the weight dimension to determine their response. In stage 3 however, when weight and distance cues are in conflict, children often perform poorly on the same type of problems that they had previously answered correctly. This situation is rectified by stage 4 however, at which point correct answers re-occur. This trend is referred to in the developmental literature as U-shaped development, reflecting the pattern of the longitudinal plot of performance. The U is created by initially good performance followed by a regression and a later recovery, on conflict-weight problems.. A third major balance scale regularity was reported by Ferretti and Butterfield (1986) . These researchers discovered that the rule classifications of many children systematically varied when assessed with different sets of testing problems drawn from the theoretically equivalent problem types. It was discovered that children's judgements about problems with a greater absolute difference in the amount of torque between the two arms (torque difference) were more often correct than similar types of problems with smaller torque differences. As a result, children assessed with problems using larger torque differences are classified at a higher developmental stage by Siegler's rule assessment procedure than if they were assessed with smaller torque difference problems. This last phenomenon is dubbed the torque difference effect (TDE) . In summary, we have presented a model of human cognitive development on the balance scale task using the symbolic C4.5 learning algorithm to induce decision trees capable of capturing the major trends present in the human developmental data. This model stands in contrast to the previously limited symbolic approaches to the problem, providing a successful alternative set of assumptions and competing predictions, to connectionist accounts of the data. Our demonstration suggests that symbolic learning algorithms warrant investigation as transition mechanisms in cognitive development.. 6. The use of a discrete, symbolic representation yields identical results, however this prevents generalization to continuously represented problems that the model was not trained on.

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Pose estimation for objects with planar surfaces using eigenimage and range data analysis In this paper we present a novel method for estimating the object pose for 3D objects with welldefined planar surfaces. Specifically, we investigate the feasibility of estimating the object pose using an approach that combines the standard eigenspace analysis technique with range data analysis. In this sense, eigenspace analysis was employed to constrain one object rotation and reject surfaces that are not compatible with a model object. The remaining two object rotations are estimated by computing the normal to the surface from the range data. The proposed pose estimation scheme has been successfully applied to scenes defined by polyhedral objects and experimental results are reported. This paper described the development of a two-stage pose estimation algorithm. In the standard form the eigenspace analysis technique has several limitations such as sensitivity to illumination changes, background conditions and partial occlusion. To address these issues we have applied an edge-based segmentation in order to decompose the input image into disjoint regions that describe the scene objects. However, to determine the 6 DOF object pose using the standard eigenspace technique is not a practical approach since a prohibitive number of images are required to sample the object's appearance in all possible orientations. To overcome this issue we employed range data to constrain two object rotations while the estimation of the remaining object rotation is determined by applying eigenimage analysis. It is worth noting that this pose estimation scheme has the advantage that no spatial relationships between adjacent scene surfaces are necessary to determine the pose of the scene object. The experimental results indicate that reasonable accurate pose estimation is obtainable from this approach and we believe that this pose estimation technique is particularly useful when dealing with polyhedral objects or objects with well-defined surfaces. Demo software available on the following web page: http://www.eeng.dcu.ie/∼vsg/code.html.

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Complementary code keying with PIC based microcontrollers for the wireless radio communications The IEEE 802.11b is a Direct Sequence Spread Spectrum (DSSS) system very similar in concept to the CDMA Wireless, using a spread spectrum chip sequence. In the 802.11b the transmission medium is wireless and the operating frequency band is 2.4GHz, 802.11b provides 5.5 and 11 Mbps payload data rates in addition to the 1 and 2 Mbps rates provided by 802.11. To provide the higher rates, 8 chip Complementary Code Keying (CCK) is employed as the modulation scheme.Demodulatin of the CCK modulated signal is done coherently by a RAKE receiver implementation which features a channel matched filter and Fast Walsh Transform block.This paper presents implementation of PIC16F84 as a CCK encoder. All low cost PIC based microcontrollers can be used, too. Here we describe the building blocks design of transmitter such as CCK encoder (digital part) and QPSK modulator (analogue part). CCK is a variation on M -ary Orthogonal Keying modulation, which uses I/Q modulation architecture with complex symbol structures. CCK allows for multi-channel operation using the DSSS channel structure scheme [2] . The spreading employs the same chipping rate and spectrum shape as the Barker's code word [4] .. The CCK uses 6 bits to encode the code send, this increase the speed of the 802.11 by 6. The chipping rate is 11 MHz [1] , which is the same as the DSSS system as described in 802.11 [2] , thus providing the same occupied channel bandwidth.. CCK is an M-ary Orthogonal Keying modulation where one of M unique (nearly orthogonal) signal codeword is chosen for transmission [5] . The spread function for CCK is chosen from a set of M nearly orthogonal vectors by the data word. CCK uses one vector from a set of 64 complex (QPSK) vectors for the symbol and thereby modulates 6 bits (1 of 64) on each 8 chips spreading code symbol ( Figure 1 ) [6] . Two more bits are sent by QPSK modulating the whole code symbol. This results in modulating 8 bits onto each symbol. The formula that defines the CCK codeword has 4 phase terms. One of them modulates all of the chips (ϕ 1 ) and this is used for the QPSK rotation of the whole code vector. The 3 others modulate every odd chip (ϕ 2 ), every odd pair of chips (ϕ 3 ) and every odd quad of chips (ϕ 4 ) respectively. (1) Here, C is the code word with LSB first to MSB last. The data bit stream is partitioned into bytes as (d7, d6, d5…d0), where d0 is the LSB and is first in time. The 8 bits are used to encode the phase parameters ϕ 1 -ϕ 4 according to scheme shown in Table 1 . The encoding is based on differential QPSK modulation as specified in Table 2 . This paper describes a new method to improve the performance of block codes for general multipath environments in radio transmission. We showed that CCK codes have robust performance in a variety of multipath environments.. DSSS with use of Barker's code [4] is more stable according to the long range and distance between transmitter and receiver in comparison with CCK. The multi-path performance of CCK is better than MBOK. The CCK waveform has better E b /N 0 performance than DPSK.. Transmitter's part of the CCK transceiver is a low cost effective, but the receiver's part is more complex.

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