Motor learning of novel dynamics is not represented in a single global coordinate system: evaluation of mixed coordinate representations and local learning.

Successful motor performance requires the ability to adapt motor commands to task dynamics. A central question in movement neuroscience is how these dynamics are represented. Although it is widely assumed that dynamics (e.g., force fields) are represented in intrinsic, joint-based coordinates (Shadmehr R, Mussa-Ivaldi FA. J Neurosci 14: 3208-3224, 1994), recent evidence has questioned this proposal. Here we reexamine the representation of dynamics in two experiments. By testing generalization following changes in shoulder, elbow, or wrist configurations, the first experiment tested for extrinsic, intrinsic, or object-centered representations. No single coordinate frame accounted for the pattern of generalization. Rather, generalization patterns were better accounted for by a mixture of representations or by models that assumed local learning and graded, decaying generalization. A second experiment, in which we replicated the design of an influential study that had suggested encoding in intrinsic coordinates (Shadmehr and Mussa-Ivaldi 1994), yielded similar results. That is, we could not find evidence that dynamics are represented in a single coordinate system. Taken together, our experiments suggest that internal models do not employ a single coordinate system when generalizing and may well be represented as a mixture of coordinate systems, as a single system with local learning, or both.

Discriminative Orthogonal Neighborhood-Preserving Projections for Classification

Orthogonal neighborhood-preserving projection (ONPP) is a recently developed orthogonal linear algorithm for overcoming the out-of-sample problem existing in the well-known manifold learning algorithm, i.e., locally linear embedding. It has been shown that ONPP is a strong analyzer of high-dimensional data. However, when applied to classification problems in a supervised setting, ONPP only focuses on the intraclass geometrical information while ignores the interaction of samples from different classes. To enhance the performance of ONPP in classification, a new algorithm termed discriminative ONPP (DONPP) is proposed in this paper. DONPP 1) takes into account both intraclass and interclass geometries; 2) considers the neighborhood information of interclass relationships; and 3) follows the orthogonality property of ONPP. Furthermore, DONPP is extended to the semisupervised case, i.e., semisupervised DONPP (SDONPP). This uses unlabeled samples to improve the classification accuracy of the original DONPP. Empirical studies demonstrate the effectiveness of both DONPP and SDONPP.

NORMAL COORDINATE TREATMENT OF LEU(5)-ENKEPHALIN IN THE CRYSTAL-STATE

Vibrational studies on the neuropentapeptide Leu5-enkephalin were performed for the crystal state where different specific conformations can arise. In the present case, the peptide adopts a double fused folded conformation (beta-turn), the presence of which in the crystal state is directly related to the nature of the solvent used for its crystallization. This study completes other work relating to similar conformations of isolated molecules. It can be seen that specific interactions in the crystal state perturb to a large extent the vibrational relationships between the amide frequencies and the specific sets of dihedral angles characteristic of the particular type of turn. The tyrosyl moiety and its frequency dependence on its hydrogen bond state was especially investigated both for the Fermi resonance and the hydroxyl bending modes.

SYNTHESIS, PROPERTIES AND MOLECULAR-STRUCTURE OF 5-COORDINATE N,N-DIBENZYLDITHIOCARBAMATE COMPLEXES OF TITANOCENE, ZIRCONOCENE AND HAFNOCENE

The five complexes (RC5H4)2M(S2CNBz2)Cl (R = H, CH3; M = Ti, Zr, Hf; Bz = CH2C6H5) have been prepared by the reaction of (RC5H4)2MCl2 with anhydrous sodium salts of dibenzyldithiocarbamate in refluxing CH2Cl2. These complexes have been characterized by elemental analysis, IR and H-1 NMR. X-ray crystal structure determination of Cp2Zr(S2CNBZ2)Cl shows the molecule has a five-coordinate bent metallocene geometry in which the zirconium atom is attached to two eta-5-C5H5 groups, one bidentate dibenzyldithiocarbamate ligand and one chlorine [Zr-Cl, 2.549(1) angstrom; Zr-S, 2.734(1), 2.667(1); Cl-Zr-S, 137.6(1)-degrees and 73.3(1)-degrees; S-Zr-S, 64.3(1)-degrees]. The catalytic system Cp2Ti (S2CNBZ2)Cl-NaH exhibits high initial catalytic activity of hydrogenation of hexene-1 under mild conditions.

SYNTHESIS AND MOLECULAR-STRUCTURE OF THE 5-COORDINATE ZIRCONIUM COMPLEX (CH3C5H4)2ZRCL(ALPHA-C10H7COO)

Bis(methylcyclopentadienyl)chloro(alpha-naphthoyloxy)zirconium, (CH3C5H4)2Zr Cl(alpha-C10H7COO), has been synthesized by the reaction between (MeCp)2ZrCl2 and equimolar sodium alpha-naphthoate in toluene at room temperature. The molecular structure of the title complex was determined by X-ray diffraction. The molecules have a five-coordinate bent metallocene structure in which the alpha-naphthoxyligand is bidentate [Zr-O, 2.317(2), 2.260(2) angstrom]. The remaining distances are [Zr-Cl, 2.521(1) angstrom; Zr-C, 480-2, 565 angstrom; Zr-cent-CH3C5H4 2.217 and 2.230 angstrom].

On The Uniqueness of Correspondence Under Orthographic and Perspective Projections

The task of shape recovery from a motion sequence requires the establishment of correspondence between image points. The two processes, the matching process and the shape recovery one, are traditionally viewed as independent. Yet, information obtained during the process of shape recovery can be used to guide the matching process. This paper discusses the mutual relationship between the two processes. The paper is divided into two parts. In the first part we review the constraints imposed on the correspondence by rigid transformations and extend them to objects that undergo general affine (non rigid) transformation (including stretch and shear), as well as to rigid objects with smooth surfaces. In all these cases corresponding points lie along epipolar lines, and these lines can be recovered from a small set of corresponding points. In the second part of the paper we discuss the potential use of epipolar lines in the matching process. We present an algorithm that recovers the correspondence from three contour images. The algorithm was implemented and used to construct object models for recognition. In addition we discuss how epipolar lines can be used to solve the aperture problem.

Object Recognition By Alignment Using Invariant Projections of Planar Surfaces

In order to recognize an object in an image, we must determine the best transformation from object model to the image. In this paper, we show that for features from coplanar surfaces which undergo linear transformations in space, there exist projections invariant to the surface motions up to rotations in the image field. To use this property, we propose a new alignment approach to object recognition based on centroid alignment of corresponding feature groups. This method uses only a single pair of 2D model and data. Experimental results show the robustness of the proposed method against perturbations of feature positions.

Coordinate-Independent Computations on Differential Equations

This project investigates the computational representation of differentiable manifolds, with the primary goal of solving partial differential equations using multiple coordinate systems on general n- dimensional spaces. In the process, this abstraction is used to perform accurate integrations of ordinary differential equations using multiple coordinate systems. In the case of linear partial differential equations, however, unexpected difficulties arise even with the simplest equations.

Monitoring Activities from Multiple Video Streams: Establishing a Common Coordinate Frame

Passive monitoring of large sites typically requires coordination between multiple cameras, which in turn requires methods for automatically relating events between distributed cameras. This paper tackles the problem of self-calibration of multiple cameras which are very far apart, using feature correspondences to determine the camera geometry. The key problem is finding such correspondences. Since the camera geometry and photometric characteristics vary considerably between images, one cannot use brightness and/or proximity constraints. Instead we apply planar geometric constraints to moving objects in the scene in order to align the scene"s ground plane across multiple views. We do not assume synchronized cameras, and we show that enforcing geometric constraints enables us to align the tracking data in time. Once we have recovered the homography which aligns the planar structure in the scene, we can compute from the homography matrix the 3D position of the plane and the relative camera positions. This in turn enables us to recover a homography matrix which maps the images to an overhead view. We demonstrate this technique in two settings: a controlled lab setting where we test the effects of errors in internal camera calibration, and an uncontrolled, outdoor setting in which the full procedure is applied to external camera calibration and ground plane recovery. In spite of noise in the internal camera parameters and image data, the system successfully recovers both planar structure and relative camera positions in both settings.

Robust Tracking of Human Motion

This technical report presents a combined solution for two problems, one: tracking objects in 3D space and estimating their trajectories and second: computing the similarity between previously estimated trajectories and clustering them using the similarities that we just computed. For the first part, trajectories are estimated using an EKF formulation that will provide the 3D trajectory up to a constant. To improve accuracy, when occlusions appear, multiple hypotheses are followed. For the second problem we compute the distances between trajectories using a similarity based on LCSS formulation. Similarities are computed between projections of trajectories on coordinate axes. Finally we group trajectories together based on previously computed distances, using a clustering algorithm. To check the validity of our approach, several experiments using real data were performed.

Extraction and Clustering of Motion Trajectories in Video

A system is described that tracks moving objects in a video dataset so as to extract a representation of the objects' 3D trajectories. The system then finds hierarchical clusters of similar trajectories in the video dataset. Objects' motion trajectories are extracted via an EKF formulation that provides each object's 3D trajectory up to a constant factor. To increase accuracy when occlusions occur, multiple tracking hypotheses are followed. For trajectory-based clustering and retrieval, a modified version of edit distance, called longest common subsequence (LCSS) is employed. Similarities are computed between projections of trajectories on coordinate axes. Trajectories are grouped based, using an agglomerative clustering algorithm. To check the validity of the approach, experiments using real data were performed.

Active Hidden Models for Tracking with Kernel Projections

We introduce Active Hidden Models (AHM) that utilize kernel methods traditionally associated with classification. We use AHMs to track deformable objects in video sequences by leveraging kernel projections. We introduce the "subset projection" method which improves the efficiency of our tracking approach by a factor of ten. We successfully tested our method on facial tracking with extreme head movements (including full 180-degree head rotation), facial expressions, and deformable objects. Given a kernel and a set of training observations, we derive unbiased estimates of the accuracy of the AHM tracker. Kernels are generally used in classification methods to make training data linearly separable. We prove that the optimal (minimum variance) tracking kernels are those that make the training observations linearly dependent.

Designing three-dimensional transformation optical media using quasiconformal coordinate transformations.

We introduce an approach to the design of three-dimensional transformation optical (TO) media based on a generalized quasiconformal mapping approach. The generalized quasiconformal TO (QCTO) approach enables the design of media that can, in principle, be broadband and low loss, while controlling the propagation of waves with arbitrary angles of incidence and polarization. We illustrate the method in the design of a three-dimensional carpet ground plane cloak and of a flattened Luneburg lens. Ray-trace studies provide a confirmation of the performance of the QCTO media, while also revealing the limited performance of index-only versions of these devices.