1,668 research outputs found
Preserving Stabilization while Practically Bounding State Space
Stabilization is a key dependability property for dealing with unanticipated
transient faults, as it guarantees that even in the presence of such faults,
the system will recover to states where it satisfies its specification. One of
the desirable attributes of stabilization is the use of bounded space for each
variable. In this paper, we present an algorithm that transforms a stabilizing
program that uses variables with unbounded domain into a stabilizing program
that uses bounded variables and (practically bounded) physical time. While
non-stabilizing programs (that do not handle transient faults) can deal with
unbounded variables by assigning large enough but bounded space, stabilizing
programs that need to deal with arbitrary transient faults cannot do the same
since a transient fault may corrupt the variable to its maximum value. We show
that our transformation algorithm is applicable to several problems including
logical clocks, vector clocks, mutual exclusion, leader election, diffusing
computations, Paxos based consensus, and so on. Moreover, our approach can also
be used to bound counters used in an earlier work by Katz and Perry for adding
stabilization to a non-stabilizing program. By combining our algorithm with
that earlier work by Katz and Perry, it would be possible to provide
stabilization for a rich class of problems, by assigning large enough but
bounded space for variables.Comment: Moved some content from the Appendix to the main paper, added some
details to the transformation algorithm and to its descriptio
Self-stabilization Overhead: an Experimental Case Study on Coded Atomic Storage
Shared memory emulation can be used as a fault-tolerant and highly available
distributed storage solution or as a low-level synchronization primitive.
Attiya, Bar-Noy, and Dolev were the first to propose a single-writer,
multi-reader linearizable register emulation where the register is replicated
to all servers. Recently, Cadambe et al. proposed the Coded Atomic Storage
(CAS) algorithm, which uses erasure coding for achieving data redundancy with
much lower communication cost than previous algorithmic solutions.
Although CAS can tolerate server crashes, it was not designed to recover from
unexpected, transient faults, without the need of external (human)
intervention. In this respect, Dolev, Petig, and Schiller have recently
developed a self-stabilizing version of CAS, which we call CASSS. As one would
expect, self-stabilization does not come as a free lunch; it introduces,
mainly, communication overhead for detecting inconsistencies and stale
information. So, one would wonder whether the overhead introduced by
self-stabilization would nullify the gain of erasure coding.
To answer this question, we have implemented and experimentally evaluated the
CASSS algorithm on PlanetLab; a planetary scale distributed infrastructure. The
evaluation shows that our implementation of CASSS scales very well in terms of
the number of servers, the number of concurrent clients, as well as the size of
the replicated object. More importantly, it shows (a) to have only a constant
overhead compared to the traditional CAS algorithm (which we also implement)
and (b) the recovery period (after the last occurrence of a transient fault) is
as fast as a few client (read/write) operations. Our results suggest that CASSS
does not significantly impact efficiency while dealing with automatic recovery
from transient faults and bounded size of needed resources
Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network
Depth estimation from a single image is a fundamental problem in computer
vision. In this paper, we propose a simple yet effective convolutional spatial
propagation network (CSPN) to learn the affinity matrix for depth prediction.
Specifically, we adopt an efficient linear propagation model, where the
propagation is performed with a manner of recurrent convolutional operation,
and the affinity among neighboring pixels is learned through a deep
convolutional neural network (CNN). We apply the designed CSPN to two depth
estimation tasks given a single image: (1) To refine the depth output from
state-of-the-art (SOTA) existing methods; and (2) to convert sparse depth
samples to a dense depth map by embedding the depth samples within the
propagation procedure. The second task is inspired by the availability of
LIDARs that provides sparse but accurate depth measurements. We experimented
the proposed CSPN over two popular benchmarks for depth estimation, i.e. NYU v2
and KITTI, where we show that our proposed approach improves in not only
quality (e.g., 30% more reduction in depth error), but also speed (e.g., 2 to 5
times faster) than prior SOTA methods.Comment: 14 pages, 8 figures, ECCV 201
Exact stabilization of entangled states in finite time by dissipative quantum circuits
Open quantum systems evolving according to discrete-time dynamics are
capable, unlike continuous-time counterparts, to converge to a stable
equilibrium in finite time with zero error. We consider dissipative quantum
circuits consisting of sequences of quantum channels subject to specified
quasi-locality constraints, and determine conditions under which stabilization
of a pure multipartite entangled state of interest may be exactly achieved in
finite time. Special emphasis is devoted to characterizing scenarios where
finite-time stabilization may be achieved robustly with respect to the order of
the applied quantum maps, as suitable for unsupervised control architectures.
We show that if a decomposition of the physical Hilbert space into virtual
subsystems is found, which is compatible with the locality constraint and
relative to which the target state factorizes, then robust stabilization may be
achieved by independently cooling each component. We further show that if the
same condition holds for a scalable class of pure states, a continuous-time
quasi-local Markov semigroup ensuring rapid mixing can be obtained. Somewhat
surprisingly, we find that the commutativity of the canonical parent
Hamiltonian one may associate to the target state does not directly relate to
its finite-time stabilizability properties, although in all cases where we can
guarantee robust stabilization, a (possibly non-canonical) commuting parent
Hamiltonian may be found. Beside graph states, quantum states amenable to
finite-time robust stabilization include a class of universal resource states
displaying two-dimensional symmetry-protected topological order, along with
tensor network states obtained by generalizing a construction due to Bravyi and
Vyalyi. Extensions to representative classes of mixed graph-product and thermal
states are also discussed.Comment: 20 + 9 pages, 9 figure
Self-supervised Multi-level Face Model Learning for Monocular Reconstruction at over 250 Hz
The reconstruction of dense 3D models of face geometry and appearance from a
single image is highly challenging and ill-posed. To constrain the problem,
many approaches rely on strong priors, such as parametric face models learned
from limited 3D scan data. However, prior models restrict generalization of the
true diversity in facial geometry, skin reflectance and illumination. To
alleviate this problem, we present the first approach that jointly learns 1) a
regressor for face shape, expression, reflectance and illumination on the basis
of 2) a concurrently learned parametric face model. Our multi-level face model
combines the advantage of 3D Morphable Models for regularization with the
out-of-space generalization of a learned corrective space. We train end-to-end
on in-the-wild images without dense annotations by fusing a convolutional
encoder with a differentiable expert-designed renderer and a self-supervised
training loss, both defined at multiple detail levels. Our approach compares
favorably to the state-of-the-art in terms of reconstruction quality, better
generalizes to real world faces, and runs at over 250 Hz.Comment: CVPR 2018 (Oral). Project webpage:
https://gvv.mpi-inf.mpg.de/projects/FML
Output Feedback Linearization of Turbidostats After Time Scaling
"© 2019 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works."[EN] Turbidostats are a class of bioreactors gaining interest due to the recent availability of microscale and small-scale devices for characterization and scalingup of the biotechnological systems relevant in the biotech and pharma industries. The goal is to keep cell density constant in continuous operation. Thus, the control law, i.e., the substrate feeding strategy, must guarantee global or semiglobal convergence to an equilibrium point. However, their control is difficult due to the uncertain, time varying, and nonlinear nature of the processes involved. In this brief, we propose an adaptive control law that globally stabilizes the desired biomass setpoint. Furthermore, in a certain region of the state space, the controller linearizes the dynamic behavior after some time scaling. By this way, the orbits of the closedloop system are imposed by the designer. The intrinsic integral action of the gain adaptation rejects the parameter uncertainties. Moreover, the controller implementation only assumes the biomass concentration to be measured. Both the simulated and experimental results show the performance of the controller.This work was supported in part by the National University of La Plata under Grant 11-I127, in part by ANPCyT under Grant PICT2014 2394, in part by CONICET under Grant PIP112 2015 0837, and in part by MINECO/AEI/FEDER, UE under Grant DPI2014-55276-C5-1-R and Grant DPI2017-82896-C2-1-R. The work of F. N. Santos-Navarro was supported by ai2-UPV.De Battista, H.; Picó-Marco, E.; Santos-Navarro, FN.; Picó, J. (2019). Output Feedback Linearization of Turbidostats After Time Scaling. IEEE Transactions on Control Systems Technology. 27(4):1668-1676. https://doi.org/10.1109/TCST.2018.2834882S1668167627
Self-Stabilizing and Private Distributed Shared Atomic Memory in Seldomly Fair Message Passing Networks
We study the problem of privately emulating shared memory in message-passing networks. The system includes clients that store and retrieve replicated information on N servers, out of which e are data-corrupting malicious. When a client accesses a data-corrupting malicious server, the data field of that server response might be different from the value it originally stored. However, all other control variables in the server reply and protocol actions are according to the server algorithm. For the coded atomic storage algorithms by Cadambe et al., we present an enhancement that ensures no information leakage and data-corrupting malicious fault-tolerance. We also consider recovery after the occurrence of transient faults that violate the assumptions according to which the system was designed to operate. After their last occurrence, transient faults leave the system in an arbitrary state (while the program code stays intact). We present a self-stabilizing algorithm, which recovers after the occurrence of transient faults. This addition to Cadambe et al. considers asynchronous settings as long as no transient faults occur. The recovery from transient faults that bring the system counters (close) to their maximal values may include the use of a global reset procedure, which requires the system run to be controlled by a fair scheduler. After the recovery period, the safety properties are provided for asynchronous system runs that are not necessarily controlled by fair schedulers. Since the recovery period is bounded and the occurrence of transient faults is extremely rare, we call this design criteria self-stabilization in the presence of seldom fairness. Our self-stabilizing algorithm uses a bounded amount of storage during asynchronous executions (that are not necessarily controlled by fair schedulers). To the best of our knowledge, we are the first to address privacy, data-corrupting malicious behavior, and self-stabilization in the context of emulating atomic shared memory in message-passing systems
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