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 $P$-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