4,945 research outputs found
The Large Deviation Principle and Steady-state Fluctuation Theorem for the Entropy Production Rate of a Stochastic Process in Magnetic Fields
Fluctuation theorem is one of the major achievements in the field of
nonequilibrium statistical mechanics during the past two decades. Steady-state
fluctuation theorem of sample entropy production rate in terms of large
deviation principle for diffusion processes have not been rigorously proved yet
due to technical difficulties. Here we give a proof for the steady-state
fluctuation theorem of a diffusion process in magnetic fields, with explicit
expressions of the free energy function and rate function. The proof is based
on the Karhunen-Lo\'{e}ve expansion of complex-valued Ornstein-Uhlenbeck
process
ARcode: HPC Application Recognition Through Image-encoded Monitoring Data
Knowing HPC applications of jobs and analyzing their performance behavior
play important roles in system management and optimizations. The existing
approaches detect and identify HPC applications through machine learning
models. However, these approaches rely heavily on the manually extracted
features from resource utilization data to achieve high prediction accuracy. In
this study, we propose an innovative application recognition method, ARcode,
which encodes job monitoring data into images and leverages the automatic
feature learning capability of convolutional neural networks to detect and
identify applications. Our extensive evaluations based on the dataset collected
from a large-scale production HPC system show that ARcode outperforms the
state-of-the-art methodology by up to 18.87% in terms of accuracy at high
confidence thresholds. For some specific applications (BerkeleyGW and e3sm),
ARcode outperforms by over 20% at a confidence threshold of 0.8
Classical Stability Margins by PID Control
Proportional-Integral-Derivative (PID) control has been the workhorse of
control technology for about a century. Yet to this day, designing and tuning
PID controllers relies mostly on either tabulated rules (Ziegler-Nichols) or on
classical graphical techniques (Bode). Our goal in this paper is to take a
fresh look on PID control in the context of optimizing stability margins for
low-order (first- and second-order) linear time-invariant systems.
Specifically, we seek to derive explicit expressions for gain and phase margins
that are achievable using PID control, and thereby gain insights on the role of
unstable poles and nonminimum-phase zeros in attaining robust stability. In
particular, stability margins attained by PID control for minimum-phase systems
match those obtained by more general control, while for nonminimum-phase
systems, PID control achieves margins that are no worse than those of general
control modulo a predetermined factor. Furthermore, integral action does not
contribute to robust stabilization beyond what can be achieved by PD control
alone
Influencia de factores ambientales sobre potas oceánicas (Cephalopoda: Ommastrephidae) explotadas comercialmente: un enfoque para la gestión de stocks
Ommastrephid squids are short-lived ecological opportunists and their recruitment is largely driven by the surrounding environment. While recent studies suggest that recruitment variability in several squid species can be partially explained by environmental variability derived from synoptic oceanographic data, assessment of ommastrephid stocks using environmental variability is rare. In thisstudy, we modified asurplus production model to incorporate environmental variability into the assessment of threeommastrephid squids (Ommastrephes bartramii in the northwest Pacific, Illex argentinus in the southwest Atlantic and Dosidicus gigas in the southwest Pacific). We assumed that the key environmental variables—suitable sea surface temperature on spawning grounds during the spawning seasons and feeding grounds during the feeding seasons—have effects on the carrying capacity and the instantaneous population growth rate, respectively, in the surplus production model. For each squid stock, the assessment model with environmental variability had the highest fitting accuracy and the lowest mean squared error and coefficient of variation, and the management reference points based on the optimal model were more precautionary. This study advances our understanding of the interactions between the environment and ommastrephid squid population dynamics and can therefore improve the management of these commercially valuable stocks with a short life cycle.Los miembros de la familia Ommastrephidae (potas) son cefalópodos de vida breve y oportunistas ecológicos, estando sus reclutamientos profundamente influidos por el ambiente circundante. Aunque algunos estudios recientes sugirieron que la variabilidad del reclutamiento en varias especies de esta familia podría explicarse parcialmente por la variabilidad ambiental derivada de datos oceanográficos sinópticos, la gestión de los stocks de omastréfidos empleando factores medioambientales es muy poco frecuente. En el presente trabajo, se ha modificado un modelo de producción generalizada incorporando en él factores ambientales con objeto de ofrecer una herramienta para la gestión y manejo de tres pesquerías: la de Ommastrephes bartramii en el Pacífico Noroeste, la Illex argentinus en el Atlántico sudoeste y la de Dosidicus gigas en el Pacífico sudoeste. Se asumió que los factores ambientales clave: una apropiada temperatura superficial en las áreas de puesta durante las épocas de freza y en las áreas de alimentación durante las estaciones de nutrición, tenían efectos sobre la capacidad de carga y el crecimiento instantáneo de la tasa de crecimiento de la población, respectivamente, en el modelo de producción generalizada. Para el stock de cada especie, el modelo de gestión con las variables ambientales mostró el mayor y más preciso ajuste y el menor error cuadrático y coeficiente de variación; además, los puntos de referencia de manejo basados en el modelo optimizado fueron los más precautorios. El presente estudio significa un avance en nuestro conocimiento sobre las interacciones entre el ambiente y la dinámica de las poblaciones de especies de esta familia de cefalópodos, lo que puede mejorar la gestión de estos stocks de especies de vida breve, cuya importancia comercial es muy grande
Ultrafast quantum state tomography with feed-forward neural networks
Reconstructing the state of many-body quantum systems is of fundamental
importance in quantum information tasks, but extremely challenging due to the
curse of dimensionality. In this work, we present a quantum tomography approach
based on neural networks to achieve the ultrafast reconstruction of multi-qubit
states. Particularly, we propose a simple 3-layer feed-forward network to
process the experimental data generated from measuring each qubit with a
positive operator-valued measure, which is able to reduce the storage cost and
computational complexity. Moreover, the techniques of state decomposition and
-order absolute projection are jointly introduced to ensure the positivity
of state matrices learned in the maximum likelihood function and to improve the
convergence speed and robustness of the above network. Finally, it is tested on
a large number of states with a wide range of purity to show that we can
faithfully tomography 11-qubit states on a laptop within 2 minutes under noise.
Our numerical results also demonstrate that more state samples are required to
achieve the given tomography fidelity for the low-purity states, and the
increased depolarizing noise induces a linear decrease in the tomography
fidelity
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