Pengaruh Ukuran Perusahaan Terhadap Kebijakan Dividen Pada Perusahaan Real Estate Dan Property Yang Terdaftar Di Bursa Efek Indonesia Periode 2016 – 2020

Real Estate dan Property merupakan salah satu alternatif investasi yang diminati investor dimana investasi di sektor ini merupakan investasi jangka panjang dan property merupakan aktiva multiguna yang dapat digunakan oleh perusahaan sebagai jaminan, oleh karena itu perusahaan real estate dan property mempunyai struktur modal yang tinggi. Penelitian ini bertujuan untuk mengetahui pengaruh ukuran perusahaan terhadap kebijakan dividen di perusahaan real estate dan property yang terdaftar di Bursa Efek Indonesia Periode 2016 – 2020. Penelitian ini menggunakan metode deskriptif dengan pendekatan kuantitatif. Pengambilan sampel dalam penelitian ini dilakukan dengan menggunakan teknik purposive sampling. Dari analisis dengan menggunakan analisa regresi sederhana diperoleh hasil bahwa ukuran perusahaan berpengaruh positif terhadap kebijakan dividen (DPR) pada perusahaan real estate dan property pada laporan tahunan (annual report) tahun 2016 – 2020

Stimulation of PPC affects the mapping between motion and force signals for stiffness perception but not motion control

How motion and sensory inputs are combined to assess an object’s stiffness is still unknown. Here, we provide evidence for the existence of a stiffness estimator in the human posterior parietal cortex (PPC). We showed previously that delaying force feedback with respect to motion when interacting with an object caused participants to underestimate its stiffness. We found that applying theta-burst transcranial magnetic stimulation (TMS) over the PPC, but not the dorsal premotor cortex, enhances this effect without affecting movement control. We explain this enhancement as an additional lag in force signals. This is the first causal evidence that the PPC is not only involved in motion control, but also has an important role in perception that is disassociated from action. We provide a computational model suggesting that the PPC integrates position and force signals for perception of stiffness and that TMS alters the synchronization between the two signals causing lasting consequences on perceptual behavior

Advances in Human-Robot Handshaking

The use of social, anthropomorphic robots to support humans in various industries has been on the rise. During Human-Robot Interaction (HRI), physically interactive non-verbal behaviour is key for more natural interactions. Handshaking is one such natural interaction used commonly in many social contexts. It is one of the first non-verbal interactions which takes place and should, therefore, be part of the repertoire of a social robot. In this paper, we explore the existing state of Human-Robot Handshaking and discuss possible ways forward for such physically interactive behaviours.Comment: Accepted at The 12th International Conference on Social Robotics (ICSR 2020) 12 Pages, 1 Figur

Adaptation to Delayed Force Perturbations in Reaching Movements

Adaptation to deterministic force perturbations during reaching movements was extensively studied in the last few decades. Here, we use this methodology to explore the ability of the brain to adapt to a delayed velocity-dependent force field. Two groups of subjects preformed a standard reaching experiment under a velocity dependent force field. The force was either immediately proportional to the current velocity (Control) or lagged it by 50 ms (Test). The results demonstrate clear adaptation to the delayed force perturbations. Deviations from a straight line during catch trials were shifted in time compared to post-adaptation to a non-delayed velocity dependent field (Control), indicating expectation to the delayed force field. Adaptation to force fields is considered to be a process in which the motor system predicts the forces to be expected based on the state that a limb will assume in response to motor commands. This study demonstrates for the first time that the temporal window of this prediction needs not to be fixed. This is relevant to the ability of the adaptive mechanisms to compensate for variability in the transmission of information across the sensory-motor system

Force-Field Compensation in a Manual Tracking Task

This study addresses force/movement control in a dynamic “hybrid” task: the master sub-task is continuous manual tracking of a target moving along an eight-shaped Lissajous figure, with the tracking error as the primary performance index; the slave sub-task is compensation of a disturbing curl viscous field, compatibly with the primary performance index. The two sub-tasks are correlated because the lateral force the subject must exert on the eight-shape must be proportional to the longitudinal movement speed in order to perform a good tracking. The results confirm that visuo-manual tracking is characterized by an intermittent control mechanism, in agreement with previous work; the novel finding is that the overall control patterns are not altered by the presence of a large deviating force field, if compared with the undisturbed condition. It is also found that the control of interaction-forces is achieved by a combination of arm stiffness properties and direct force control, as suggested by the systematic lateral deviation of the trajectories from the nominal path and the comparison between perturbed trials and catch trials. The coordination of the two sub-tasks is quickly learnt after the activation of the deviating force field and is achieved by a combination of force and the stiffness components (about 80% vs. 20%), which is a function of the implicit accuracy of the tracking task

Geckos decouple fore- and hind limb kinematics in response to changes in incline

This work is supported by an NSF grant (NSF IOS-1147043) to TE

Proximodistal gradient in the perception of delayed stiffness. In: Virtual Rehabilitation 2009

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Linear and Non-linear Dimensionality-Reduction Techniques on Full Hand Kinematics

The purpose of this study was to find a parsimonious representation of hand kinematics data that could facilitate prosthetic hand control. Principal Component Analysis (PCA) and a non-linear Autoencoder Network (nAEN) were compared in their effectiveness at capturing the essential characteristics of a wide spectrum of hand gestures and actions. Performance of the two methods was compared on (a) the ability to accurately reconstruct hand kinematic data from a latent manifold of reduced dimension, (b) variance distribution across latent dimensions, and (c) the separability of hand movements in compressed and reconstructed representations derived using a linear classifier. The nAEN exhibited higher performance than PCA in its ability to more accurately reconstruct hand kinematic data from a latent manifold of reduced dimension. Whereas, for two dimensions in the latent manifold, PCA was able to account for 78% of input data variance, nAEN accounted for 94%. In addition, the nAEN latent manifold was spanned by coordinates with more uniform share of signal variance compared to PCA. Lastly, the nAEN was able to produce a manifold of more separable movements than PCA, as different tasks, when reconstructed, were more distinguishable by a linear classifier, SoftMax regression. It is concluded that non-linear dimensionality reduction may offer a more effective platform than linear methods to control prosthetic hands