83,079 research outputs found
How to Deploy a Wire with a Robotic Platform: Learning from Human Visual Demonstrations
In this paper, we address the problem of deploying a wire along a specific path selected by an unskilled user. The robot has to
learn the selected path and pass a wire through the peg table by using the same tool. The main contribution regards the hybrid use
of Cartesian positions provided by a learning procedure and joint positions obtained by inverse kinematics and motion planning.
Some constraints are introduced to deal with non-rigid material without breaks or knots. We took into account a series of metrics
to evaluate the robot learning capabilities, all of them over performed the targets
Flow of non-Newtonian Fluids in Converging-Diverging Rigid Tubes
A residual-based lubrication method is used in this paper to find the flow
rate and pressure field in converging-diverging rigid tubes for the flow of
time-independent category of non-Newtonian fluids. Five converging-diverging
prototype geometries were used in this investigation in conjunction with two
fluid models: Ellis and Herschel-Bulkley. The method was validated by
convergence behavior sensibility tests, convergence to analytical solutions for
the straight tubes as special cases for the converging-diverging tubes,
convergence to analytical solutions found earlier for the flow in
converging-diverging tubes of Newtonian fluids as special cases for
non-Newtonian, and convergence to analytical solutions found earlier for the
flow of power-law fluids in converging-diverging tubes. A brief investigation
was also conducted on a sample of diverging-converging geometries. The method
can in principle be extended to the flow of viscoelastic and
thixotropic/rheopectic fluid categories. The method can also be extended to
geometries varying in size and shape in the flow direction, other than the
perfect cylindrically-symmetric converging-diverging ones, as long as
characteristic flow relations correlating the flow rate to the pressure drop on
the discretized elements of the lubrication approximation can be found. These
relations can be analytical, empirical and even numerical and hence the method
has a wide applicability range.Comment: 36 pages, 14 figures, 5 table
Improving Access to Psychological Therapy: Initial Evaluation of the Two Demonstration Sites
The Government's Improving Access to Psychological Therapy (IAPT) programme aims to implement NICE Guidance for people with depression and anxiety disorders. In the first phase of the programme, two demonstration sites were established in Doncaster and Newham with funding to provide increased availability of cognitive-behaviour therapy-based (CBT) services to those in the community who need them. The services opened in late summer 2006. This paper documents the achievements of the sites up to September 2007 (roughly their first year of operation) and makes recommendations for the future roll out of IAPT services.Cognitive Behavioural Therapy, CBT, Psychological therapy, Evaluation, Cost benefit analysis, IAPT
Robust semicoherent searches for continuous gravitational waves with noise and signal models including hours to days long transients
The vulnerability to single-detector instrumental artifacts in standard
detection methods for long-duration quasimonochromatic gravitational waves from
nonaxisymmetric rotating neutron stars [continuous waves (CWs)] was addressed
in past work [D. Keitel et al., Phys. Rev. D 89, 064023 (2014).] by a Bayesian
approach. An explicit model of persistent single-detector disturbances led to a
generalized detection statistic with improved robustness against such
artifacts. Since many strong outliers in semicoherent searches of LIGO data are
caused by transient disturbances that last only a few hours, we extend the
noise model to cover such limited-duration disturbances, and demonstrate
increased robustness in realistic simulated data. Besides long-duration CWs,
neutron stars could also emit transient signals which, for a limited time, also
follow the CW signal model (tCWs). As a pragmatic alternative to specialized
transient searches, we demonstrate how to make standard semicoherent CW
searches more sensitive to transient signals. Considering tCWs in a single
segment of a semicoherent search, Bayesian model selection yields a new
detection statistic that does not add significant computational cost. On
simulated data, we find that it increases sensitivity towards tCWs, even of
varying durations, while not sacrificing sensitivity to classical CW signals,
and still being robust to transient or persistent single-detector instrumental
artifacts.Comment: 16 pages, 6 figures, REVTeX4.
Integrated mass transportation system study/definition/implementation program definition
Specific actions needed to plan and effect transportation system improvements are identified within the constraints of limited financial, energy and land use resources, and diverse community requirements. A specific program is described which would develop the necessary generalized methodology for devising improved transportation systems and evaluate them against specific criteria for intermodal and intramodal optimization. A consistent, generalized method is provided for study and evaluation of transportation system improvements
PIETOOLS: A Matlab Toolbox for Manipulation and Optimization of Partial Integral Operators
In this paper, we present PIETOOLS, a MATLAB toolbox for the construction and
handling of Partial Integral (PI) operators. The toolbox introduces a new class
of MATLAB object, opvar, for which standard MATLAB matrix operation syntax
(e.g. +, *, ' e tc.) is defined. PI operators are a generalization of bounded
linear operators on infinite-dimensional spaces that form a *-subalgebra with
two binary operations (addition and composition) on the space RxL2. These
operators frequently appear in analysis and control of infinite-dimensional
systems such as Partial Differential equations (PDE) and Time-delay systems
(TDS). Furthermore, PIETOOLS can: declare opvar decision variables, add
operator positivity constraints, declare an objective function, and solve the
resulting optimization problem using a syntax similar to the sdpvar class in
YALMIP. Use of the resulting Linear Operator Inequalities (LOIs) are
demonstrated on several examples, including stability analysis of a PDE,
bounding operator norms, and verifying integral inequalities. The result is
that PIETOOLS, packaged with SOSTOOLS and MULTIPOLY, offers a scalable,
user-friendly and computationally efficient toolbox for parsing, performing
algebraic operations, setting up and solving convex optimization problems on PI
operators
Partial primary reinforcement as a parameter of secondary reinforcement
Thesis (Ph.D.)--Boston UniversityThe problem of this paper is to investigate partial primary reinforcement as a possible parameter of secondary reinforcement. Although partial primary reinforcement is known to be important in many learning situations, there appears to be little systematic knowledge of its relationship to secondary reinforcement.
An experiment was performed in which (1) a neutral stimulus was present on every training trial, (2) a primary reinforcer was present on only some of these trials, (3) after training was completed, a test was made for the secondary reinforcing properties of the neutral stimulus. Six independent groups of albino rats were trained in a simple runway with food as the primary reinforcer and goal box brightness as the neutral stimulus. Each group received a different number of primary reinforcements, namely, 100%, 90%, 80%, 60%, 40%, and 20%, out of one-hundred-twenty training trials. Half of the subjects were trained on a white goal box and half on a black goal box. When training was completed, the alleyway was converted to a T maze with black and white goal boxes. Neither goal box was visible to the subjects until after entrance. The animals were given twenty trials in the T maze, and the number of times they entered each goal box was tabulated.
Analysis of the data revealed that the lower the percentage of reinforcement given during training, the greater were the number of entries into the training box during the test. Some characteristics of the function were: between 100% and 90% the strength of secondary reinforcement did not increase, between 90% and 80% there was a large increase, from 80% to 40% there was a further increase, and from 40% to 20% there was some decrease. It was also revealed that some subjects in the lower percentage of reinforcement groups went either to the training box or to the novel box on every test trial. Other aspects of the data were also analyzed.
From this data a number of conclusions were drawn:
1. Partial primary reinforcement is a parameter of secondary reinforcement. Decrease in partial reinforcement results in an increase in secondary reinforcement various characteristics of this relationship were discussed. It was pointed out that the obtained function might be derived from two separate functions: the relationship of secondary reinforcement to the number of reinforced trials, and the relationship of secondary reinforcement to the number of non-reinforced trials.
2. The fact that some subjects went to the same box on every test trial was explained in terms of the development of strong secondary reinforcement, in the case of subjects who went to the training box, and in terms of the development of strong generalized secondary reinforcement, in the case of subjects who went to the novel box.
3. It has often been reported in the experimental literature that partially reinforced subjects show greater resistance to extinction than continuously reinforced subjects. Our findings can be applied to this phenomenon. Stimuli present during partial reinforcement are apt to acquire greater secondary reinforcing properties than those present during continuous reinforcement, and, hence, the presence of the former during extinction are able to maintain a higher frequency of responding than the presence of the latter. This hypothesis was distinguished from others offered in the literature which purport to explain the greater resistance to extinction in terms of secondary reinforcement.
4. It was pointed out that this experiment revealed a significant variable, secondary reinforcement, which might develop in studies whose training set up resembles ours.
5. Minor findings of the experiment were discussed
Rumba : a Python framework for automating large-scale recursive internet experiments on GENI and FIRE+
It is not easy to design and run Convolutional Neural Networks (CNNs) due to: 1) finding the optimal number of filters (i.e., the width) at each layer is tricky, given an architecture; and 2) the computational intensity of CNNs impedes the deployment on computationally limited devices. Oracle Pruning is designed to remove the unimportant filters from a well-trained CNN, which estimates the filters’ importance by ablating them in turn and evaluating the model, thus delivers high accuracy but suffers from intolerable time complexity, and requires a given resulting width but cannot automatically find it. To address these problems, we propose Approximated Oracle Filter Pruning (AOFP), which keeps searching for the least important filters in a binary search manner, makes pruning attempts by masking out filters randomly, accumulates the resulting errors, and finetunes the model via a multi-path framework. As AOFP enables simultaneous pruning on multiple layers, we can prune an existing very deep CNN with acceptable time cost, negligible accuracy drop, and no heuristic knowledge, or re-design a model which exerts higher accuracy and faster inferenc
Markovian Monte Carlo program EvolFMC v.2 for solving QCD evolution equations
We present the program EvolFMC v.2 that solves the evolution equations in QCD
for the parton momentum distributions by means of the Monte Carlo technique
based on the Markovian process. The program solves the DGLAP-type evolution as
well as modified-DGLAP ones. In both cases the evolution can be performed in
the LO or NLO approximation. The quarks are treated as massless. The overall
technical precision of the code has been established at 0.05% precision level.
This way, for the first time ever, we demonstrate that with the Monte Carlo
method one can solve the evolution equations with precision comparable to the
other numerical methods.Comment: 38 pages, 9 Postscript figure
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