The False Promise of Principled Negotiations

For over two decades, the method of principled negotiation has been the dominant formative approach to negotiation. Its flagship book, Getting to Yes (Fisher & Ury, 1981; Fisher, Ury, & Patton, 1991) remains the standard presentation of the method. Getting to Yes promotes the method of principled negotiation as an all-purpose strategy of negotiation. The authors of Getting to Yes developed the method of principled negotiation as an alternative to positional bargaining. In this article, the author contends that the method of principled negotiation is not the all-purpose strategy of negotiation promised in Getting to Yes. Furthermore, the author contends that the method of principled negotiation is not a strategy of negotiation at all. In addition, the author contends that by persuading that principled negotiation is an all-purpose strategy Getting to Yes misleads negotiators, hinders the development of actual negotiation strategies, and leads to suboptimal results in many negotiations. In this paper, the author discusses the main concepts used in building the method of principled negotiation and shows that the method is built on incomplete definitions and erroneous assumptions. The author argues in favor of moving beyond the method of principled negotiation in order to find actual solutions to the challenges posed by different negotiations. Thus, the author proposes using a variety of strategies designed to achieve different goals, instead of trying to use, in every case, the “all-purpose” method/strategy of principled negotiation

Sobre el teorema de Liouville para funciones enteras

La idea del artículo es presentar las pruebas del teorema de Liouville sobre funciones enteras. En este trabajo recalcamos dos importantes aplicaciones, una en la demostración del teorema fundamental del álgebra y otra en el área de las aplicaciones conformes. El presente contiene una breve nota histórica de la vida de Joseph Liouville y su trabajo. También contiene la version del teorema de Liouville para funciones doblemente periódicas, funciones armónicas y aplicaciones cuasiconformes

Preseason Lower Extremity Functional Test Scores Are Not Associated With Lower Quadrant Injury - A Validation Study With Normative Data on 395 Division III Athletes

Background: Preseason performance on the lower extremity functional test (LEFT), a timed series of agility drills, has been previously reported to be associated with future risk of lower quadrant (LQ = low back and lower extremities) injury in Division III (D III) athletes.Validation studies are warranted to confirm or refute initial findings. Hypothesis/Purpose: The primary purpose of this study was to examine the ability of the LEFT to discriminate injury occurrence in D III athletes, in order to validate or refute prior findings. It was hypothesized that female and male D III athletes slower at completion of the LEFT would be at a greater risk for a non-contact time-loss injury during sport. Secondary purposes of this study are to report other potential risk factors based on athlete demographics and to present normative LEFT data based on sport participation. Methods: Two hundred and six (females = 104; males = 102) D III collegiate athletes formed a validation sample. Athletes in the validation sample completed a demographic questionnaire and performed the LEFT at the start of their sports preseason. Athletic trainers tracked non-contact time-loss LQ injuries during the season. A secondary analysis of risk based on preseason LEFT performance was conducted for a sample (n = 395) that consisted of subjects in the validation sample (n = 206) as well as athletes from a prior LEFT related study (n = 189). Study Design: Prospective cohort Results: Male athletes in the validation sample completed the LEFT [98.6 (± 8.1) seconds] significantly faster than female athletes [113.1 (± 10.4) seconds]. Male athletes, by sport, also completed the LEFT significantly faster than their female counterparts who participated in the same sport. There was no association between preseason LEFT performance and subsequent injury, by sex, in either the validation sample or the combined sample. Females who reported starting primary sport participation by age 10 were two times (OR = 2.4, 95% CI: 1.2, 4.9; p = 0.01) more likely to experience a non-contact time-loss LQ injury than female athletes who started their primary sport at age 11 or older. Males who reported greater than three hours per week of plyometric training during the six-week period prior to the start of the preseason were four times more likely (OR = 4.0, 95% CI: 1.1, 14.0; p = 0.03) to experience a foot or ankle injury than male athletes who performed three or less hours per week. Conclusions: The LEFT could not be validated as a preseason performance measure to predict future sports injury risk. The data presented in this study may aid rehabilitation professionals when evaluating an injured athlete’s ability to return to sport by comparing their LEFT score to population norms

The Fifth International Conference on Intelligent Environments (IE 09): a report

The development of intelligent environments is considered an important step towards the realization of the ambient intelligence vision. Intelligent environments are technologically augmented everyday spaces, which intuitively support human activity. The IE conferences traditionally provide a leading edge forum for researchers and engineers to present their latest research and to discuss future directions in the area of intelligent environments. This article briefly presents the content of the Fifth International Conference on Intelligent Environments (IE09), which was held July 20–21 at the Castelldefels campus, of the Technical University of Catalonia, near Barcelona, Spain.Postprint (published version

GrabCut-Based Human Segmentation in Video Sequences

In this paper, we present a fully-automatic Spatio-Temporal GrabCut human segmentation methodology that combines tracking and segmentation. GrabCut initialization is performed by a HOG-based subject detection, face detection, and skin color model. Spatial information is included by Mean Shift clustering whereas temporal coherence is considered by the historical of Gaussian Mixture Models. Moreover, full face and pose recovery is obtained by combining human segmentation with Active Appearance Models and Conditional Random Fields. Results over public datasets and in a new Human Limb dataset show a robust segmentation and recovery of both face and pose using the presented methodology

Robotic Grasping using Demonstration and Deep Learning

Robotic grasping is a challenging task that has been approached in a variety of ways. Historically grasping has been approached as a control problem. If the forces between the robotic gripper and the object can be calculated and controlled accurately then grasps can be easily planned. However, these methods are difficult to extend to unknown objects or a variety of robotic grippers. Using human demonstrated grasps is another way to tackle this problem. Under this approach, a human operator guides the robot in a training phase to perform the grasping task and then the useful information from each demonstration is extracted. Unlike traditional control systems, demonstration based systems do not explicitly state what forces are necessary, and they also allow the system to learn to manipulate the robot directly. However, the major failing of this approach is the sheer amount of data that would be required to present a demonstration for a substantial portion of objects and use cases. Recently, we have seen various deep learning grasping systems that achieve impressive levels of performance. These systems learn to map perceptual features, like color images and depth maps, to gripper poses. These systems can learn complicated relationships, but still require massive amounts of data to train properly. A common way of collecting this data is to run physics based simulations based on the control schemes mentioned above, however human demonstrated grasps are still the gold standard for grasp planning. We therefore propose a data collection system that can be used to collect a large number of human demonstrated grasps. In this system the human demonstrator holds the robotic gripper in one hand and naturally uses the gripper to perform grasps. These grasp poses are tracked fully in six dimensions and RGB-D images are collected for each grasp trial showing the object and any obstacles present during the grasp trial. Implementing this system, we collected 40K annotated grasps demonstrations. This dataset is available online. We test a subset of these grasps for their robustness to perturbations by replicating scenes captured during data collection and using a robotic arm to replicate the grasps we collected. We find that we can replicate the scenes with low variance, which coupled with the robotic arm’s low repeatability error means that we can test a wide variety of perturbations. Our tests show that our grasps can maintain a probability of success over 90% for perturbations of up 2.5cm or 10 degrees. We then train a variety of neural networks to learn to map images of grasping scenes to final grasp poses. We separate the task of pose prediction into two separate networks: a network to predict the position of the gripper, and a network to predict the orientation conditioned on the output of the position network. These networks are trained to classify whether a particular position or orientation is likely to lead to a successful grasp. We also identified a strong prior in our dataset over the distribution of grasp positions and leverage this information by tasking the position network to predict corrections to this prior based on the image being presented to it. Our final network architecture, using layers from a pre-trained state of the art image classification network and residual convolution blocks, did not seem able to learn the grasping task. We observed a strong tendency for the networks to overfit, even when the networks had been heavily regularized and parameters reduced substantially. The best position network we were able to train collapses to only predicting a few possible positions, leading to the orientation network to only predict a few possible orientations as well. Limited testing on a robotic platform confirmed these findings