32,372 research outputs found
Generalized (m,k)-Zipf law for fractional Brownian motion-like time series with or without effect of an additional linear trend
We have translated fractional Brownian motion (FBM) signals into a text based
on two ''letters'', as if the signal fluctuations correspond to a constant
stepsize random walk. We have applied the Zipf method to extract the
exponent relating the word frequency and its rank on a log-log plot. We have
studied the variation of the Zipf exponent(s) giving the relationship between
the frequency of occurrence of words of length made of such two letters:
is varying as a power law in terms of . We have also searched how
the exponent of the Zipf law is influenced by a linear trend and the
resulting effect of its slope. We can distinguish finite size effects, and
results depending whether the starting FBM is persistent or not, i.e. depending
on the FBM Hurst exponent . It seems then numerically proven that the Zipf
exponent of a persistent signal is more influenced by the trend than that of an
antipersistent signal. It appears that the conjectured law
only holds near . We have also introduced considerations based on the
notion of a {\it time dependent Zipf law} along the signal.Comment: 24 pages, 12 figures; to appear in Int. J. Modern Phys
Model-free Probabilistic Movement Primitives for physical interaction
Physical interaction in robotics is a complex problem
that requires not only accurate reproduction of the kinematic
trajectories but also of the forces and torques exhibited
during the movement. We base our approach on Movement
Primitives (MP), as MPs provide a framework for modelling
complex movements and introduce useful operations on the
movements, such as generalization to novel situations, time
scaling, and others. Usually, MPs are trained with imitation
learning, where an expert demonstrates the trajectories. However,
MPs used in physical interaction either require additional
learning approaches, e.g., reinforcement learning, or are based
on handcrafted solutions. Our goal is to learn and generate
movements for physical interaction that are learned with imitation
learning, from a small set of demonstrated trajectories.
The Probabilistic Movement Primitives (ProMPs) framework
is a recent MP approach that introduces beneficial properties,
such as combination and blending of MPs, and represents the
correlations present in the movement. The ProMPs provides
a variable stiffness controller that reproduces the movement
but it requires a dynamics model of the system. Learning such
a model is not a trivial task, and, therefore, we introduce the
model-free ProMPs, that are learning jointly the movement and
the necessary actions from a few demonstrations. We derive
a variable stiffness controller analytically. We further extent
the ProMPs to include force and torque signals, necessary for
physical interaction. We evaluate our approach in simulated
and real robot tasks
Robust policy updates for stochastic optimal control
For controlling high-dimensional robots, most stochastic optimal control algorithms use approximations of the system dynamics and of the cost function (e.g., using linearizations and Taylor expansions). These approximations are typically only locally correct, which might cause instabilities in the greedy policy updates, lead to oscillations or the algorithms diverge. To overcome these drawbacks, we add a regularization term to the cost function that punishes large policy update steps in the trajectory optimization procedure. We applied this concept to the Approximate Inference Control method (AICO), where the resulting algorithm guarantees convergence for uninformative initial solutions without complex hand-tuning of learning rates. We evaluated our new algorithm on two simulated robotic platforms. A robot arm with five joints was used for reaching multiple targets while keeping the roll angle constant. On the humanoid robot Nao, we show how complex skills like reaching and balancing can be inferred from desired center of gravity or end effector coordinates
On the entropy production of time series with unidirectional linearity
There are non-Gaussian time series that admit a causal linear autoregressive
moving average (ARMA) model when regressing the future on the past, but not
when regressing the past on the future. The reason is that, in the latter case,
the regression residuals are only uncorrelated but not statistically
independent of the future. In previous work, we have experimentally verified
that many empirical time series indeed show such a time inversion asymmetry.
For various physical systems, it is known that time-inversion asymmetries are
linked to the thermodynamic entropy production in non-equilibrium states. Here
we show that such a link also exists for the above unidirectional linearity.
We study the dynamical evolution of a physical toy system with linear
coupling to an infinite environment and show that the linearity of the dynamics
is inherited to the forward-time conditional probabilities, but not to the
backward-time conditionals. The reason for this asymmetry between past and
future is that the environment permanently provides particles that are in a
product state before they interact with the system, but show statistical
dependencies afterwards. From a coarse-grained perspective, the interaction
thus generates entropy. We quantitatively relate the strength of the
non-linearity of the backward conditionals to the minimal amount of entropy
generation.Comment: 16 page
Some comments on -annihilation branching ratios into -, - and -channels
We give some remarks on the -partial branching ratios in flight at
low momenta of antineutron, measured by OBELIX collaboration. The comparison is
made to the known branching ratios from the -atomic states. The
branching ratio for the reaction is found to be
suppressed in comparison to what follows from the -data. It is also
shown, that there is no so called dynamic I=0-amplitude suppression for the
process .Comment: 8 pages, LaTeX, no figure
Particle-Based Mesoscale Hydrodynamic Techniques
Dissipative particle dynamics (DPD) and multi-particle collision (MPC)
dynamics are powerful tools to study mesoscale hydrodynamic phenomena
accompanied by thermal fluctuations. To understand the advantages of these
types of mesoscale simulation techniques in more detail, we propose new two
methods, which are intermediate between DPD and MPC -- DPD with a multibody
thermostat (DPD-MT), and MPC-Langevin dynamics (MPC-LD). The key features are
applying a Langevin thermostat to the relative velocities of pairs of particles
or multi-particle collisions, and whether or not to employ collision cells. The
viscosity of MPC-LD is derived analytically, in very good agreement with the
results of numerical simulations.Comment: 7 pages, 2 figures, 1 tabl
Quartic double solids with ordinary singularities
We study the mixed Hodge structure on the third homology group of a threefold
which is the double cover of projective three-space ramified over a quartic
surface with a double conic. We deal with the Torelli problem for such
threefolds.Comment: 14 pages, presented at the Conference Arnol'd 7
Halothane hepatitis with renal failure treated with hemodialysis and exchange transfusion
A 38-year-old white female, hepatitis B antigen negative, developed fluminating hepatic failure associated with oliguria and severe azotemia after two halothane anesthesia and without exposure to other hepatotoxic drugs or blood transfusions. She was treated with multiple hemodialysis and exchange blood transfusion. The combined treatment corrected the uremic abnormalities and improved her level of consciousness. The liver and kidney function gradually improved, and she made a complete recovery, the first recorded with hepatic and renal failure under these post-anesthetic conditions. Further evaluation of this combined treatment used for this patient is warranted. © 1974 The Japan Surgical Society
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