600 research outputs found
The separate neural control of hand movements and contact forces
To manipulate an object, we must simultaneously control the contact forces exerted on the object and the movements of our hand. Two alternative views for manipulation have been proposed: one in which motions and contact forces are represented and controlled by separate neural processes, and one in which motions and forces are controlled jointly, by a single process. To evaluate these alternatives, we designed three tasks in which subjects maintained a specified contact force while their hand was moved by a robotic manipulandum. The prescribed contact force and hand motions were selected in each task to induce the subject to attain one of three goals: (1) exerting a regulated contact force, (2) tracking the motion of the manipulandum, and (3) attaining both force and motion goals concurrently. By comparing subjects' performances in these three tasks, we found that behavior was captured by the summed actions of two independent control systems: one applying the desired force, and the other guiding the hand along the predicted path of the manipulandum. Furthermore, the application of transcranial magnetic stimulation impulses to the posterior parietal cortex selectively disrupted the control of motion but did not affect the regulation of static contact force. Together, these findings are consistent with the view that manipulation of objects is performed by independent brain control of hand motions and interaction forces
Uniqueness and Generalization in Organizational Psychology: Research as a Relational Practice
The paper addresses the epistemological and theoretical assumptions that underpin the concept of Work and Organizational Psychology as idiographic, situated, and transformative social science. Positioning the connection between uniqueness and generalization inside the debate around organization studies as applied approaches, the contribution highlights the ontological, gnoseological, and methodological implications at stake. The use of practical instead of scientific rationality is explored, through the perspective of a hermeneutic lens, underlining the main features connected to the adoption of an epistemology of practice. Specifically, the contribution depicts the configuration of the applied research as a relational practice, embedded in the unfolding process of generating knowledge dealing with concrete social contexts and particular social objects. The discussion of a case study regarding a field research project allows one to point out challenges and constraints connected to the enactment of the research process as a social accomplishment
The dynamics of motor learning through the formation of internal models
A medical student learning to perform a laparoscopic procedure or a recently paralyzed user of a powered wheelchair must learn to operate machinery via interfaces that translate their actions into commands for an external device. Since the user\u2019s actions are selected from a number of alternatives that would result in the same effect in the control space of the external device, learning to use such interfaces involves dealing with redundancy. Subjects need to learn an externally chosen many-to-one map that transforms their actions into device commands. Mathematically, we describe this type of learning as a deterministic dynamical process, whose state is the evolving forward and inverse internal models of the interface. The forward model predicts the outcomes of actions, while the inverse model generates actions designed to attain desired outcomes. Both the mathematical analysis of the proposed model of learning dynamics and the learning performance observed in a group of subjects demonstrate a first-order exponential convergence of the learning process toward a particular state that depends only on the initial state of the inverse and forward models and on the sequence of targets supplied to the users. Noise is not only present but necessary for the convergence of learning through the minimization of the difference between actual and predicted outcomes
Learning soft task priorities for control of redundant robots
Movement primitives (MPs) provide a powerful
framework for data driven movement generation that has been
successfully applied for learning from demonstrations and robot
reinforcement learning. In robotics we often want to solve a
multitude of different, but related tasks. As the parameters
of the primitives are typically high dimensional, a common
practice for the generalization of movement primitives to new
tasks is to adapt only a small set of control variables, also
called meta parameters, of the primitive. Yet, for most MP
representations, the encoding of these control variables is precoded
in the representation and can not be adapted to the
considered tasks. In this paper, we want to learn the encoding of
task-specific control variables also from data instead of relying
on fixed meta-parameter representations. We use hierarchical
Bayesian models (HBMs) to estimate a low dimensional latent
variable model for probabilistic movement primitives (ProMPs),
which is a recent movement primitive representation. We show
on two real robot datasets that ProMPs based on HBMs
outperform standard ProMPs in terms of generalization and
learning from a small amount of data and also allows for an
intuitive analysis of the movement. We also extend our HBM by
a mixture model, such that we can model different movement
types in the same dataset
Miniaturized and High-Throughput Assays for Analysis of T-Cell Immunity Specific for Opportunistic Pathogens and HIV
Monitoring of antigen-specific T-cell responses is valuable in numerous conditions that include infectious diseases, vaccinations, and opportunistic infections associated with acquired or congenital immune defects. A variety of assays that make use of peripheral lymphocytes to test activation markers, T-cell receptor expression, or functional responses are currently available. The last
group of assays calls for large numbers of functional lymphocytes. The number of cells increases with the number of antigens to be tested. Consequently, cells may be the limiting factor, particularly in lymphopenic subjects and in children, the groups that more often require immune monitoring. We have developed immunochemical assays that measure secreted cytokines in the same wells in which peripheral blood mononuclear cells (PBMC) are cultured. This procedure lent itself to miniaturization and automation. Lymphoproliferation and the enzyme-linked immunosorbent spot (ELISPOT) assay have been adapted to a miniaturized format. Here we provide examples of immune profiles and describe a comparison between miniaturized assays based on cytokine secretion or proliferation. We also demonstrate that these assays are convenient for use in testing antigen specificity in
established T-cell lines, in addition to analysis of PBMC. In summary, the applicabilities of miniaturization to save cells and reagents and of automation to save time and increase accuracy were demonstrated in this study using different methodological approaches valuable in the clinical immunology laboratory
Anemia a cellule falciformi e sindromi correlate: aggiornamenti e prospettive
The presence of hemoglobin S (HbS) in blood is responsible for sickle cell disease when its concentration, for the presence of two copies of HbS gene or one copy of HbS plus another \u3b2-globin variant (such as hemoglobin C or \u3b2-thalassemia), is markedly increased. In this report, we reviewed some recent epidemiological data on the disease prevalence, we discussed pre-analytical as well analytical aspects, relevant to the correct measurement of HbS in blood, and we summarized some important aspects for the management of the sickling crises and for the current and future therapy of this disease
Learning high-level robotic manipulation actions with visual predictive model
Learning visual predictive models has great potential for real-world robot manipulations. Visual predictive models serve as a model of real-world dynamics to comprehend the interactions between the robot and objects. However, prior works in the literature have focused mainly on low-level elementary robot actions, which typically result in lengthy, inefficient, and highly complex robot manipulation. In contrast, humans usually employ top–down thinking of high-level actions rather than bottom–up stacking of low-level ones. To address this limitation, we present a novel formulation for robot manipulation that can be accomplished by pick-and-place, a commonly applied high-level robot action, through grasping. We propose a novel visual predictive model that combines an action decomposer and a video prediction network to learn the intrinsic semantic information of high-level actions. Experiments show that our model can accurately predict the object dynamics (i.e., the object movements under robot manipulation) while trained directly on observations of high-level pick-and-place actions. We also demonstrate that, together with a sampling-based planner, our model achieves a higher success rate using high-level actions on a variety of real robot manipulation tasks
Unilateral versus coordinated effects:comparing the impact on consumer welfare of alternative merger outcomes
The nature of tacitly collusive behaviour often makes coordination unstable, and this may result in periods of breakdown, during which consumers benet from reduced prices. This is allowed for by adding demand uncertainty to the Compte et al. (2002) model of tacit collusion amongst asymmetric rms. Breakdowns occur when a rm cannot exclude the possibility of a deviation by a rival. It is then possible that an outcome with collusive behaviour, subject to long/frequent break downs, can improve consumer welfare compared to an alternative with sustained unilateral conduct. This is illustrated by re-examining the Nestle/Perrier merger analyzed by Compte et al., but now also taking into account the potential for welfare losses arising from unilateral behaviour
Upper Body-Based Power Wheelchair Control Interface for Individuals with Tetraplegia
Many power wheelchair control interfaces are not sufficient for individuals with severely limited upper limb mobility. The majority of controllers that do not rely on coordinated arm and hand movements provide users a limited vocabulary of commands and often do not take advantage of the user's residual motion. We developed a body-machine interface (BMI) that leverages the flexibility and customizability of redundant control by using high dimensional changes in shoulder kinematics to generate proportional control commands for a power wheelchair. In this study, three individuals with cervical spinal cord injuries were able to control a power wheelchair safely and accurately using only small shoulder movements. With the BMI, participants were able to achieve their desired trajectories and, after five sessions driving, were able to achieve smoothness that was similar to the smoothness with their current joystick. All participants were twice as slow using the BMI however improved with practice. Importantly, users were able to generalize training controlling a computer to driving a power wheelchair, and employed similar strategies when controlling both devices. Overall, this work suggests that the BMI can be an effective wheelchair control interface for individuals with high-level spinal cord injuries who have limited arm and hand control
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