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Among the unique features of humans, one of the most salient is the ability to effectively cool the body during extreme prolonged activity through the evapotranspiration of water on the skin's surface. The evolution of this novel physiological ability required a dramatic increase in the density and distribution of eccrine sweat glands relative to other mammals and a concomitant reduction of body hair cover. Elucidation of the genetic underpinnings for these adaptive changes is confounded by a lack of knowledge about how eccrine gland fate and density are specified during development. Moreover, although reciprocal changes in hair cover and eccrine gland density are required for efficient thermoregulation, it is unclear if these changes are linked by a common genetic regulation. To identify pathways controlling the relative patterning of eccrine glands and hair follicles, we exploited natural variation in the density of these organs between different strains of mice. Quantitative trait locus mapping identified a large region on mouse Chromosome 1 that controls both hair and eccrine gland densities. Differential and allelic expression analysis of the genes within this interval coupled with subsequent functional studies demonstrated that the level of En1 activity directs the relative numbers of eccrine glands and hair follicles. These findings implicate En1 as a newly identified and reciprocal determinant of hair follicle and eccrine gland density and identify a pathway that could have contributed to the evolution of the unique features of human skin.

In this paper, we obtain minimax and near-minimax nonrandomized decision rules under zero–one loss for a restricted location parameter of an absolutely continuous distribution. Two types of rules are addressed: monotone and nonmonotone. A complete-class theorem is proved for the monotone case. This theorem extends the previous work of Zeytinoglu and Mintz (1984) to the case of 2e-MLR sampling distributions. A class of continuous monotone nondecreasing rules is defined. This class contains the monotone minimax rules developed in this paper. It is shown that each rule in this class is Bayes with respect to nondenumerably many priors. A procedure for generating these priors is presented. Nonmonotone near-minimax almost-equalizer rules are derived for problems characterized by non-2e-MLR distributions. The derivation is based on the evaluation of a distribution-dependent function Qc. The methodological importance of this function is that it is used to unify the discrete- and continuous-parameter problems, and to obtain a lower bound on the minimax risk for the non-2e-MLR case.

The purpose of this paper is to introduce the reader to a novel approach to data fusion. We focus on the latest results which have immediate practical implications. Many tasks in active perception require the ability to combine information from a variety of sensors. Prior to combination, the data must be tested for consistency. Both of these tasks can be viewed as data fusion problems. We examine such problems for location data models. Our approach is based on statistical decision theory. We present the application of the theory to mobile robot localization.

It is clear that much of the world's technological innovation originates from the domain of the startup, an arena in which the United States has historically played a major role. However, while business and other schools have traditionally offered courses in entrepreneurship, only about one third of all new businesses ever reach profitability and less than half of all new startups survive more than five years. Much of the attrition of small businesses is a result of poor preparation of the founders and the failure of academia to treat entrepreneurship as something more than a purely academic subject. A new program at Hofstra University focuses on a fresh approach to preparing Computer Science and Engineering students in entrepreneurship by providing a curriculum specifically designed to meet the myriad challenges encountered by entrepreneurs in the “real” world.

Tracking output is a very attractive source of labeled data sets that, in turn, could be used to train other systems for tracking, detection, recognition and categorization. In this context, long tracking sequences are of particular importance because they provide richer information, multiple views, wider range of appearances. This paper addresses two obstacles to the use of tracking data for training: noise in the tracking data and the unreliability and slow pace of hand labeling. The paper introduces a criterion for detecting inconsistencies (noise) in large data collections and a method for selecting typical representatives of consistent collections. Those are used to build a pipeline which cleanses the tracking data and employs instantaneous (shotgun) labeling of vast numbers of images. The shotgun labeled data is shown to compare favorably with hand labeled data when used in classification tasks. The framework is collaborative - it involves a human-in-the loop but it is designed to minimize the burden on the human.

Creativity and innovativeness are among the most essential attributes of engineering graduates and also of successful entrepreneurs. Entrepreneurship, or the process of starting a new venture, is one of the main roads to new technological innovations. This paper presents two novel models of entrepreneurship education integrated in computer science and computer engineering curricula and geared towards computing students with entrepreneurial intentions. To expose all computing students to entrepreneurial ideas and to spark their entrepreneurial spirit, we also developed several entrepreneurship add-on modules for existing CS and CE disciplines. All these programs have been developed and implemented at Hofstra University, with modules implemented also at Qatar University. Preliminary evaluation results are presented and discussed.

Numerous applications processing 3D point data will gain from the ability to estimate reliably normals and differential geometric properties. Normal estimates are notoriously noisy, the errors propagate and may lead to flawed, inaccurate, and inconsistent curvature estimates. Frankot-Chellappa introduced the use of integrability constraints in normal estimation. Their approach deals with graphs z = f(x,y)- We present a newly discovered general orientability constraint (GOC) for 3D point clouds sampled from general surfaces, not just graphs. It provides a tool to quantify the confidence in the estimation of normals, topology, and geometry from a point cloud. Furthermore, similarly to the Frankot-Chellappa constraint, the GOC can be used directly to extract the topology and the geometry of the manifolds underlying 3D point clouds. As an illustration we describe an automatic Cloud-to-Geometry pipeline which exploits the GOC.

We present an automatic pipeline for recovering the geometry of a 3D scene from a set of unordered, uncalibrated images. The contributions in the paper are the presentation of the system as a whole, from images to geometry, the estimation of the local scale for various scene components in the orientation-topology module, the procedure for orienting the cloud components, and the method for dealing with points of contact. The methods are aimed to process complex scenes and non-uniformly sampled, noisy data sets.

We present a framework to automatically infer topology and geometry from an unorganized 3D point cloud obtained from a 3D scene. If the cloud is not oriented, we use existing methods to orient it prior to recovering the topology. We develop a quality measure for scoring a chosen topology/orientation. The topology is used to segment the cloud into manifold components and later in the computation of shape descriptors.

In this paper we pioneer a method which, given an input of mobile robot pose measurements by a sensor-based localization algorithm, produces a minimax risk fixed-size confidence set estimate for the pose of the agent. This work constitutes the first application to the mobile robotics domain of optimal fixed-size confidence-interval decision theory. The approach is evaluated in terms of theoretical capture probability and empirical capture frequency during actual experiments with the mobile agent. The method is compared to several other procedures including the Kalman filter (minimum mean squared error estimate) and the maximum likelihood estimator (MLE). The minimax approach is found to dominate all the other methods in performance. In particular, for the minimax approach, a very close agreement is achieved between theoretical capture probability and empirical capture frequency. This allows performance to be accurately predicted, greatly facilitating the design of mobile robotic systems, and delineating the tasks that may be performed with a given system.

In this paper we address an application of computer vision which can in the future change completely our way of communicating over the network. We present our version of a testbed for telecollaboration. It is based on a highly accurate and precise stereo algorithm. The results demonstrate the live (on-line) recovery of 3D models of a dynamically changing environment and the simultaneous display and manipulation of the models.

We present our long-term effort focused on building a 3-D model acquisition system for teleimmersion applications. In our project we stress fast processing and high fidelity of the result: it is not just geometric accuracy of the recovered geometric model but also radiometric correctness of the recovered surface texture. We suggest that single-type geometric model is not sufficient for modeling large and complex scenes. In this paper, a hierarchy of representations suitable for 3-D structure models recoverable from visual data is presented and the related problems of data acquisition and fusion, outlier identification, and models reconstruction are discussed.

In the advent of the ubiquity of distributed computing with multimedia capabilities, it is very natural to contemplate collaborations that were not previously possible. In the past scientists, doctors, educators, engineers always had need to share, exchange, debate their work, designs, methodology, knowledge and experience. This was commonly done by attending meetings, publishing in common journals, sometimes books, and visiting each other's laboratories, research institutions, or attending lectures at universities. Recently, advances in computer vision, computer graphics and networking, make possible a new way of communication by immersing participants at remote sites into a common 3D virtual world. The 3D virtual world is high quality, realistic, dynamically updated representation which joins the models of the real worlds at each of the remote sites. This virtual world provides means for telecollaboration, it makes the real-time 3D interaction of people at remote locations possible. We describe the new "teleimmersion" technology. We concentrate on the current state of our research related to the problem of accurate, precise, real-time recovery of the individual remote 3D models, their merging into a common virtual world, and the real-time rendering of this world.

We describe an estimation technique which, given a measurement of the depth of a target from a wide-field-of-view (WFOV) stereo camera pair, produces a minimax risk fixed-size confidence interval estimate for the target depth. This work constitutes the first application to the computer vision domain of optimal fixed-size confidence-interval decision theory. The approach is evaluated in terms of theoretical capture probability and empirical capture frequency during actual experiments with a target on an optical bench. The method is compared to several other procedures including the Kalman Filter. The minimax approach is found to dominate all the other methods in performance. In particular for the minimax approach, a very close agreement is achieved between theoretical capture probability and empirical capture frequency. This allows performance to be accurately predicted, greatly facilitating the system design, and delineating the tasks that may be performed with a given system.

We present new theoretical results which have implications in answering one of the fundamental questions in computer vision: recognition of surfaces and surface shapes. We state the conditions under which: (i) a surface can be recovered, uniquely, from the tangent plane map, in particular from the Gauss map; (ii) a surface shape can be recovered from the metric and the deforming forces. In case where such conditions are not satisfied we classify all exceptions, i.e. the surfaces and surface shapes for which the recovery and registration problems are ill-posed.

We present a new theoretical method and experimental results for direct recovery of the curvatures, the principal curvature directions, and the surface itself by exolicit integration of the Gauss map. The method does not rely on polygonal approximations, smoothing of the data, or model fitting. It is based on the observation that one can recover the surface restoring force from the Gauss map, and (i) applies to orientable surfaces of arbitrary topology (not necessarily closed); (ii) uses only first order linear differential equations; (iii) avoids the use of unstable computations; (iv) provides tools for filtering noise from the sampled data. The method can be used for stable extraction of surfaces and surface shape invariants, in particular, in applications requiring accurate quantitative measurements.

In this paper we summarize the results we have obtained in building a testbed for tele-collaboration. It is based on a highly accurate and precise stereo algorithm. The results demonstrate the live (on-line) recovery of 3-D models of a dynamically changing environment and the simultaneous display and manipulation of the models. Virtual tele-collaboration may soon change completely our way of communicating over the network.

The successful design and operation of autonomous or partially autonomous vehicles which are capable of traversing uncertain terrains requires the application of multiple sensors for tasks such as: local navigation, terrain evaluation, and feature recognition. In applications which include a teleoperation mode, there remains a serious need for local data reduction and decision-making to avoid the costly or impractical transmission of vast quantities of sensory data to a remote operator. There are several reasons to include multi-sensor fusion in a system design: (i) it allows the designer to combine intrinsically dissimilar data from several sensors to infer some property or properties of the environment, which no single sensor could otherwise obtain; and (ii) it allows the system designer to build a robust system by using partially redundant sources of noisy or otherwise uncertain information. At present, the epistemology of multi-sensor fusion is incomplete. Basic research topics include the following taskrelated issues: (i) the value of a sensor suite; (ii) the layout, positioning, and control of sensors (as agents); (iii) the marginal value of sensor information; the value of sensing-time versus some measure of error reduction, e.g., statistical efficiency; (iv) the role of sensor models, as well as a priori models of the environment; and (v) the calculus or calculi by which consistent sensor data are determined and combined. In our research on multi-sensor fusion, we have focused our attention on several of these issues. Specifically, we have studied the theory and application of robust fixed-size confidence intervals as a methodology for robust multi-sensor fusion. This work has been delineated and summarized in Kamberova and Mintz (1990) and McKendall and Mintz (1990a, 1990b). As we noted, this previous research focused on confidence intervals as opposed to the more general paradigm of confidence sets. The basic distinction here is between fusing data characterized by an uncertain scalar parameter versus fusing data characterized by an uncertain vector parameter, of known dimension. While the confidence set paradigm is more widely applicable, we initially chose to address the confidence interval paradigm, since we were simultaneously interested in addressing the issues of: (i) robustness to nonparametric uncertainty in the sampling distribution; and (ii) decision procedures for small sample sizes. Recently, we have begun to investigate the multivariate (confidence set) paradigm. The delineation of optimal confidence sets with fixed geometry is a very challenging problem when: (i) the a priori knowledge of the uncertain parameter vector is not modeled by a Cartesian product of intervals (a hyper-rectangle); and/or (ii) the noise components in the multivariate observations are not statistically independent. Although it may be difficult to obtain optimal fixed-geometry confidence sets, we have obtained some very promising approximation techniques. These approximation techniques provide: (i) statistically efficient fixed-size hyper-rectangular confidence sets for decision models with hyper-ellipsoidal parameter sets; and (ii) tight upper and lower bounds to the optimal confidence coefficients in the presence of both Gaussian and non-Gaussian sampling distributions. In both the univariate and multivariate paradigms, it is assumed that the a priori uncertainty in the parameter value can be delineated by a fixed set in an n-dimensional Euclidean space. It is further assumed, that while the sampling distribution is uncertain, the uncertainty class description for this distribution can be delineated by a given class of neighborhoods in the space of all n-dimensional probability distributions. The following sections of this paper: (i) present a paradigm for multi-sensor fusion based on position data; (ii) introduce statistical and set-valued models for sensor errors and a priori environmental uncertainty; (iii) explain the role of confidence sets in statistical decision theory and sensor fusion; (iv) relate fixed-size confidence intervals to fixedgeometry confidence sets; and (v) examine the performance of fixed-size hyper-cubic confidence sets for decision models with spherical parameter sets in the presence of both Gaussian and non-Gaussian sampling distributions

Many tasks in active perception require that we be able to combine different information from a variety of sensors which relate to one or more features of the environment. Prior to combining these data, we must test our observations for consistency. The purpose of this paper is to examine sensor fusion problems for linear location data models using statistical decision theory (SDT). The contribution of this paper is the application of SDT to obtain: (i) a robust test of the hypothesis that data from different sensors are consistent; and (ii) a robust procedure for combining the data which pass this preliminary consistency test. Here, robustness refers to the statistical effectiveness of the decision rules when the probability distributions of the observation noise and the a priori position information associated with the individual sensors are uncertain. The standard linear location data model refers to observations of the form: Z = θ + V, where V represents additive sensor noise and 0 denotes the "sensed" parameter of interest to the observer. While the theory addressed in this paper applies to many uncertainty classes, the primary focus of this paper is on asymmetric and/or multimodal model, which allow one to account for very general deviations from nominal sampling distributions. This paper extends earlier results in SDT and multi-sensor fusion obtained by Zeytinoglu and Mintz (1984, 1988), and McKendall and Mintz (1988).