Aggregate Implications of Innovation Policy

We present a tractable model of innovating firms and the aggregate economy that we use to assess the link between the responses of firms to changes in innovation policy and the impact of those policy changes on aggregate output and welfare. We argue that the key theoretical determinant of the relative long-run aggregate impact of alternative policies is their impact on the expected profitability of entering firms. We show that, to a first-order approximation, a wide range of policy changes have a long-run aggregate impact in direct proportion to the fiscal expenditures on those policies, and that to evaluate the aggregate impact of such policy changes, there is no need to calculate changes in firms' decisions in response to these policy changes. We use these results to compare the relative magnitudes of the impact on aggregates in the long run of three innovation policies in the United States: the Research and Experimentation Tax Credit, federal expenditure on R&D, and the corporate profits tax. We argue that the corporate profits tax is a relatively important policy through its negative effects on innovation and physical capital accumulation that may well undo the benefits of federal support for R&D. We also use a calibrated version of our model to examine the absolute magnitude of the impact of these policies on aggregates. We show that, depending on the magnitude of spillovers, it is possible for changes in innovation policies to have a very large impact on aggregates in the long run. However, over a 15-year horizon, the impact of changes in innovation policies on aggregate output is not very sensitive to the magnitude of spillovers. On the basis of these results we conclude that, while it is possible to make comparisons about the relative importance of different policies and sharp predictions about their aggregate impact in the medium term, it is very difficult to shed much light on the implications of innovation policies for long-run aggregate outcomes and welfare without accurate estimates as to the magnitude of innovation spillovers.

Deferring the learning for better generalization in radial basis neural networks

Proceeding of: International Conference Artificial Neural Networks — ICANN 2001. Vienna, Austria, August 21–25, 2001The level of generalization of neural networks is heavily dependent on the quality of the training data. That is, some of the training patterns can be redundant or irrelevant. It has been shown that with careful dynamic selection of training patterns, better generalization performance may be obtained. Nevertheless, generalization is carried out independently of the novel patterns to be approximated. In this paper, we present a learning method that automatically selects the most appropriate training patterns to the new sample to be predicted. The proposed method has been applied to Radial Basis Neural Networks, whose generalization capability is usually very poor. The learning strategy slows down the response of the network in the generalisation phase. However, this does not introduces a significance limitation in the application of the method because of the fast training of Radial Basis Neural Networks

Using humanoid robots to study human behavior

Our understanding of human behavior advances as our humanoid robotics work progresses-and vice versa. This team's work focuses on trajectory formation and planning, learning from demonstration, oculomotor control and interactive behaviors. They are programming robotic behavior based on how we humans “program” behavior in-or train-each other

Efficient Model Learning for Human-Robot Collaborative Tasks

We present a framework for learning human user models from joint-action demonstrations that enables the robot to compute a robust policy for a collaborative task with a human. The learning takes place completely automatically, without any human intervention. First, we describe the clustering of demonstrated action sequences into different human types using an unsupervised learning algorithm. These demonstrated sequences are also used by the robot to learn a reward function that is representative for each type, through the employment of an inverse reinforcement learning algorithm. The learned model is then used as part of a Mixed Observability Markov Decision Process formulation, wherein the human type is a partially observable variable. With this framework, we can infer, either offline or online, the human type of a new user that was not included in the training set, and can compute a policy for the robot that will be aligned to the preference of this new user and will be robust to deviations of the human actions from prior demonstrations. Finally we validate the approach using data collected in human subject experiments, and conduct proof-of-concept demonstrations in which a person performs a collaborative task with a small industrial robot

Linear Bellman combination for control of character animation

Controllers are necessary for physically-based synthesis of character animation. However, creating controllers requires either manual tuning or expensive computer optimization. We introduce linear Bellman combination as a method for reusing existing controllers. Given a set of controllers for related tasks, this combination creates a controller that performs a new task. It naturally weights the contribution of each component controller by its relevance to the current state and goal of the system. We demonstrate that linear Bellman combination outperforms naive combination often succeeding where naive combination fails. Furthermore, this combination is provably optimal for a new task if the component controllers are also optimal for related tasks. We demonstrate the applicability of linear Bellman combination to interactive character control of stepping motions and acrobatic maneuvers.Singapore-MIT GAMBIT Game LabNational Science Foundation (U.S.) (Grant 2007043041)National Science Foundation (U.S.) (Grant CCF-0810888)Adobe SystemsPixar (Firm

Searching High Redshift Large-Scale Structures: Photometry of Four Fields Around Quasar Pairs at z~1

We have studied the photometric properties of four fields around the high-redshift quasar pairs QP1310+0007, QP1355-0032, QP0110-0219, and QP0114-3140 at z ~ 1 with the aim of identifying large-scale structures- galaxy clusters or groups- around them. This sample was observed with GMOS in Gemini North and South telescopes in the g', r', i', and z' bands, and our photometry is complete to a limiting magnitude of i' ~ 24 mag (corresponding to ~ M*_i' + 2 at the redshift of the pairs). Our analysis reveals that QP0110-0219 shows very strong and QP1310+0007 and QP1355-0032 show some evidence for the presence of rich galaxy clusters in direct vicinity of the pairs. On the other hand, QP0114-3140 could be an isolated pair in a poor environment. This work suggest that z ~ 1 quasar pairs are excellent tracers of high density environments and this same technique may be useful to find clusters at higher redshifts.Comment: 29 pages, 7 figures, ApJ accepted. Added one figure and 3 references. Some paragraphs was rewritten in sections 1, 3, 5, and 6, as suggested by refere

The human arm as a redundant manipulator: the control of path and joint angles

Cruse H, Brüwer M. The human arm as a redundant manipulator: the control of path and joint angles. Biological cybernetics. 1987;57(1-2):137-144.The movements studied involved moving the tip of a pointer attached to the hand from a given starting point to a given end point in a horizontal plane. Three joints — the shoulder, elbow and wrist —were free to move. Thus the system represented a redundant manipulator. The coordination of the movements of the three joints was recorded and analyzed. The study concerned how the joints are controlled during a movement. The results are used to evaluate several current hypotheses for motor control. Basically, the incremental changes are calculated so as to move the tip of the manipulator along a straight line in the workspace. The values of the individual joints seem to be determined as follows. Starting from the initial values the incremental changes in the three joint angles represent a compromise between two criteria: 1) the amount of the angular change should be about the same in the three joints, and 2) the angular changes should minimize the total cost of the arm position as determined by cost functions defined for each joint as a function of angle. By itself, this mechanism would produce strongly curved trajectories in joint space which could include additional acceleration and deceleration in a joint. These are reduced by the influence of a third criterion which fits with the mass-spring hypothesis. Thus the path is calculated as a compromise between a straight line in workspace and a straight line in joint space. The latter can produce curved paths in the workspace such as were actually found in the experiments. A model calculation shows that these hypotheses can qualitatively describe the experimental findings

Wireless Power Hotspot that Charges All of Your Devices

Each year, consumers carry an increasing number of gadgets on their person: mobile phones, tablets, smartwatches, etc. As a result, users must remember to recharge each device, every day. Wireless charging promises to free users from this burden, allowing devices to remain permanently unplugged. Today's wireless charging, however, is either limited to a single device, or is highly cumbersome, requiring the user to remove all of her wearable and handheld gadgets and place them on a charging pad. This paper introduces MultiSpot, a new wireless charging technology that can charge multiple devices, even as the user is wearing them or carrying them in her pocket. A MultiSpot charger acts as an access point for wireless power. When a user enters the vicinity of the MultiSpot charger, all of her gadgets start to charge automatically. We have prototyped MultiSpot and evaluated it using off-the-shelf mobile phones, smartwatches, and tablets. Our results show that MultiSpot can charge 6 devices at distances of up to 50cm.National Science Foundation (U.S.