Using Pinch Gloves(TM) for both Natural and Abstract Interaction Techniques in Virtual Environments

Usable three-dimensional (3D) interaction techniques are difficult to design, implement, and evaluate. One reason for this is a poor understanding of the advantages and disadvantages of the wide range of 3D input devices, and of the mapping between input devices and interaction techniques. We present an analysis of Pinch Gloves™ and their use as input devices for virtual environments (VEs). We have developed a number of novel and usable interaction techniques for VEs using the gloves, including a menu system, a technique for text input, and a two-handed navigation technique. User studies have indicated the usability and utility of these techniques

TARGET: Rapid Capture of Process Knowledge

TARGET (Task Analysis/Rule Generation Tool) represents a new breed of tool that blends graphical process flow modeling capabilities with the function of a top-down reporting facility. Since NASA personnel frequently perform tasks that are primarily procedural in nature, TARGET models mission or task procedures and generates hierarchical reports as part of the process capture and analysis effort. Historically, capturing knowledge has proven to be one of the greatest barriers to the development of intelligent systems. Current practice generally requires lengthy interactions between the expert whose knowledge is to be captured and the knowledge engineer whose responsibility is to acquire and represent the expert's knowledge in a useful form. Although much research has been devoted to the development of methodologies and computer software to aid in the capture and representation of some types of knowledge, procedural knowledge has received relatively little attention. In essence, TARGET is one of the first tools of its kind, commercial or institutional, that is designed to support this type of knowledge capture undertaking. This paper will describe the design and development of TARGET for the acquisition and representation of procedural knowledge. The strategies employed by TARGET to support use by knowledge engineers, subject matter experts, programmers and managers will be discussed. This discussion includes the method by which the tool employs its graphical user interface to generate a task hierarchy report. Next, the approach to generate production rules for incorporation in and development of a CLIPS based expert system will be elaborated. TARGET also permits experts to visually describe procedural tasks as a common medium for knowledge refinement by the expert community and knowledge engineer making knowledge consensus possible. The paper briefly touches on the verification and validation issues facing the CLIPS rule generation aspects of TARGET. A description of efforts to support TARGET's interoperability issues on PCs, Macintoshes and UNIX workstations concludes the paper

Decision-Directed Hybrid RIS Channel Estimation with Minimal Pilot Overhead

To reap the benefits of reconfigurable intelligent surfaces (RIS), channel state information (CSI) is generally required. However, CSI acquisition in RIS systems is challenging and often results in very large pilot overhead, especially in unstructured channel environments. Consequently, the RIS channel estimation problem has attracted a lot of interest and also been a subject of intense study in recent years. In this paper, we propose a decision-directed RIS channel estimation framework for general unstructured channel models. The employed RIS contains some hybrid elements that can simultaneously reflect and sense the incoming signal. We show that with the help of the hybrid RIS elements, it is possible to accurately recover the CSI with a pilot overhead proportional to the number of users. Therefore, the proposed framework substantially improves the system spectral efficiency compared to systems with passive RIS arrays since the pilot overhead in passive RIS systems is proportional to the number of RIS elements times the number of users. We also perform a detailed spectral efficiency analysis for both the pilot-directed and decision-directed frameworks. Our analysis takes into account both the channel estimation and data detection errors at both the RIS and the BS. Finally, we present numerous simulation results to verify the accuracy of the analysis as well as to show the benefits of the proposed decision-directed framework.Comment: submitted for journal publication, 13 pages, 7 figure

CENP-A Is Dispensable for Mitotic Centromere Function after Initial Centromere/Kinetochore Assembly

Human centromeres are defined by chromatin containing the histone H3 variant CENP-A assembled onto repetitive alphoid DNA sequences. By inducing rapid, complete degradation of endogenous CENP-A, we now demonstrate that once the first steps of centromere assembly have been completed in G1/S, continued CENP-A binding is not required for maintaining kinetochore attachment to centromeres or for centromere function in the next mitosis. Degradation of CENP-A prior to kinetochore assembly is found to block deposition of CENP-C and CENP-N, but not CENP-T, thereby producing defective kinetochores and failure of chromosome segregation. Without the continuing presence of CENP-A, CENP-B binding to alphoid DNA sequences becomes essential to preserve anchoring of CENP-C and the kinetochore to each centromere. Thus, there is a reciprocal interdependency of CENP-A chromatin and the underlying repetitive centromere DNA sequences bound by CENP-B in the maintenance of human chromosome segregation

Design of 2D Time-Varying Vector Fields

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Norme: a chamber opera

This dissertation is an opera for three singers and eleven instrumentalists, divided into six groups: (1) piano; (2) soprano, flute, clarinet, soprano saxophone; (3) harp; (4) soprano, percussion (one player); (5) string quartet; (6) soprano, percussion (one player). The document includes a program note, performance notes, libretto (authored by the composer), notation guide, and score (in C)

Paramètres de reproduction des vaches Kouri au Lac Tchad

Objectif : évaluer les performances de reproduction des vaches de la race Kouri au Tchad et proposer les actions d’amélioration.Méthodologie et résultats : une enquête longitudinale a été réalisée sur 101 vaches durant 10 ans, allant de novembre 2003 à avril 2013 au Centre de Sauvegarde de cette race taurine en pleine zone sahélienne du Tchad. Les performances de reproduction de la vache Kouri ont été: l’âge au premier vêlage de 41,43 ± 0,66 mois (n=70), une durée de gestation moyenne de 298,74 ± 5,11 jours avec un poids moyen des veaux à la naissance de 22,87 3,53 kg, l’intervalle entre vêlages moyen de 477,23 ±118,58 jours (n=126) et a varié en fonction de rang de vêlage, un taux de fécondité moyen de 76,48%, la fertilité en première saillie de 80,77%, un indice coïtal moyen de 1,53 ± 0,14. Les vêlages ont eu lieu toute l’année mais les périodes de forte concentration se situent entre les mois de février et avril.Conclusion et perspectives : Les performances de reproduction de la vache Kouri ont été faibles et ne permettent pas d’atteindre l’objectif d’un intervalle entre vêlages classique de 365 jours. L’intervalle entre vêlages a été plus long que l’intervalle standard d’un an. Ces performances ne peuvent être améliorées que par la mise en place d’une meilleure conduite des pratiques d’élevage et d’un programme de suivi de la reproduction. Ces actions permettront la remise en reproduction des vaches dans les 3 mois après vêlageMots clés : Vache Kouri, Reproduction, Lac-Tchad

Linear and Deep Neural Network-based Receivers for Massive MIMO Systems with One-Bit ADCs

The use of one-bit analog-to-digital converters (ADCs) is a practical solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. However, the distortion caused by one-bit ADCs makes the data detection task much more challenging. In this paper, we propose a two-stage detection method for massive MIMO systems with one-bit ADCs. In the first stage, we propose several linear receivers based on the Bussgang decomposition, that show significant performance gain over existing linear receivers. Next, we reformulate the maximum-likelihood (ML) detection problem to address its non-robustness. Based on the reformulated ML detection problem, we propose a model-driven deep neural network-based (DNN-based) receiver, whose performance is comparable with an existing support vector machine-based receiver, albeit with a much lower computational complexity. A nearest-neighbor search method is then proposed for the second stage to refine the first stage solution. Unlike existing search methods that typically perform the search over a large candidate set, the proposed search method generates a limited number of most likely candidates and thus limits the search complexity. Numerical results confirm the low complexity, efficiency, and robustness of the proposed two-stage detection method.Comment: 12 pages, 10 figure

DNN-based Detectors for Massive MIMO Systems with Low-Resolution ADCs

Low-resolution analog-to-digital converters (ADCs) have been considered as a practical and promising solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. Unfortunately, low-resolution ADCs significantly distort the received signals, and thus make data detection much more challenging. In this paper, we develop a new deep neural network (DNN) framework for efficient and low-complexity data detection in low-resolution massive MIMO systems. Based on reformulated maximum likelihood detection problems, we propose two model-driven DNN-based detectors, namely OBMNet and FBMNet, for one-bit and few-bit massive MIMO systems, respectively. The proposed OBMNet and FBMNet detectors have unique and simple structures designed for low-resolution MIMO receivers and thus can be efficiently trained and implemented. Numerical results also show that OBMNet and FBMNet significantly outperform existing detection methods.Comment: 6 pages, 8 figures, submitted for publication. arXiv admin note: text overlap with arXiv:2008.0375