A flow-pattern map for phase separation using the Navier-Stokes Cahn-Hilliard model

We use the Navier-Stokes-Cahn-Hilliard model equations to simulate phase separation with flow. We study coarsening - the growth of extended domains wherein the binary mixture phase separates into its component parts. The coarsening is characterized by two competing effects: flow, and the Cahn-Hilliard diffusion term, which drives the phase separation. Based on extensive two-dimensional direct numerical simulations, we construct a flow-pattern map outlining the relative strength of these effects in different parts of the parameter space. The map reveals large regions of parameter space where a standard theory applies, and where the domains grow algebraically in time. However, there are significant parts of the parameter space where the standard theory does not apply. In one region, corresponding to low values of viscosity and diffusion, the coarsening is accelerated compared to the standard theory. Previous studies involving Stokes flow report on this phenomenon; we complete the picture by demonstrating that this anomalous regime occurs not only for Stokes flow, but also, for flows dominated by inertia. In a second region, corresponding to arbitrary viscosities and high Cahn-Hilliard diffusion, the diffusion overwhelms the hydrodynamics altogether, and the latter can effectively be ignored, in contrast to the prediction of the standard scaling theory. Based on further high-resolution simulations in three dimensions, we find that broadly speaking, the above description holds there also, although the formation of the anomalous domains in the low-viscosity-low-diffusion part of the parameter space is delayed in three dimensions compared to two.Comment: 17 pages, 13 figure

Extending the VEF traces framework to model data center network workloads

Producción CientíficaData centers are a fundamental infrastructure in the Big-Data era, where applications and services demand a high amount of data and minimum response times. The interconnection network is an essential subsystem in the data center, as it must guarantee high communication bandwidth and low latency to the communication operations of applications, otherwise becoming the system bottleneck. Simulation is widely used to model the network functionality and to evaluate its performance under specific workloads. Apart from the network modeling, it is essential to characterize the end-nodes communication pattern, which will help identify bottlenecks and flaws in the network architecture. In previous works, we proposed the VEF traces framework: a set of tools to capture communication traffic of MPI-based applications and generate traffic traces used to feed network simulator tools. In this paper, we extend the VEF traces framework with new communication workloads such as deep-learning training applications and online data-intensive workloads.Ministerio de Ciencia e Innovación y Agencia Estatal de Investigación (MCIN/AEI/10.13039/501100011033) R &D Project Grant (PID2019-109001RA-I00)Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL

Research on Brain and Mind Inspired Intelligence

To address the problems of scientific theory, common technology and engineering application of multimedia and multimodal information computing, this paper is focused on the theoretical model, algorithm framework, and system architecture of brain and mind inspired intelligence (BMI) based on the structure mechanism simulation of the nervous system, the function architecture emulation of the cognitive system and the complex behavior imitation of the natural system. Based on information theory, system theory, cybernetics and bionics, we define related concept and hypothesis of brain and mind inspired computing (BMC) and design a model and framework for frontier BMI theory. Research shows that BMC can effectively improve the performance of semantic processing of multimedia and cross-modal information, such as target detection, classification and recognition. Based on the brain mechanism and mind architecture, a semantic-oriented multimedia neural, cognitive computing model is designed for multimedia semantic computing. Then a hierarchical cross-modal cognitive neural computing framework is proposed for cross-modal information processing. Furthermore, a cross-modal neural, cognitive computing architecture is presented for remote sensing intelligent information extraction platform and unmanned autonomous system

Generalized coherent and intelligent states for exact solvable quantum systems

The so-called Gazeau-Klauder and Perelomov coherent states are introduced for an arbitrary quantum system. We give also the general framework to construct the generalized intelligent states which minimize the Robertson-Schr\"odinger uncertainty relation. As illustration, the P\"oschl-Teller potentials of trigonometric type will be chosen. We show the advantage of the analytical representations of Gazeau-Klauder and Perelomov coherent states in obtaining the generalized intelligent states in analytical way

Initialization Bias of Fourier Neural Operator: Revisiting the Edge of Chaos

This paper investigates the initialization bias of the Fourier neural operator (FNO). A mean-field theory for FNO is established, analyzing the behavior of the random FNO from an ``edge of chaos'' perspective. We uncover that the forward and backward propagation behaviors exhibit characteristics unique to FNO, induced by mode truncation, while also showcasing similarities to those of densely connected networks. Building upon this observation, we also propose a FNO version of the He initialization scheme to mitigate the negative initialization bias leading to training instability. Experimental results demonstrate the effectiveness of our initialization scheme, enabling stable training of a 32-layer FNO without the need for additional techniques or significant performance degradation

CHARA/MIRC observations of two M supergiants in Perseus OB1: temperature, Bayesian modeling, and compressed sensing imaging

Two red supergiants of the Per OB1 association, RS Per and T Per, have been observed in H band using the MIRC instrument at the CHARA array. The data show clear evidence of departure from circular symmetry. We present here new techniques specially developed to analyze such cases, based on state-of-the-art statistical frameworks. The stellar surfaces are first modeled as limb-darkened discs based on SATLAS models that fit both MIRC interferometric data and publicly available spectrophotometric data. Bayesian model selection is then used to determine the most probable number of spots. The effective surface temperatures are also determined and give further support to the recently derived hotter temperature scales of red su- pergiants. The stellar surfaces are reconstructed by our model-independent imaging code SQUEEZE, making use of its novel regularizer based on Compressed Sensing theory. We find excellent agreement between the model-selection results and the reconstructions. Our results provide evidence for the presence of near-infrared spots representing about 3-5% of the stellar flux

Predictive coding and stochastic resonance as fundamental principles of auditory phantom perception

Mechanistic insight is achieved only when experiments are employed to test formal or computational models. Furthermore, in analogy to lesion studies, phantom perception may serve as a vehicle to understand the fundamental processing principles underlying healthy auditory perception. With a special focus on tinnitus—as the prime example of auditory phantom perception—we review recent work at the intersection of artificial intelligence, psychology and neuroscience. In particular, we discuss why everyone with tinnitus suffers from (at least hidden) hearing loss, but not everyone with hearing loss suffers from tinnitus. We argue that intrinsic neural noise is generated and amplified along the auditory pathway as a compensatory mechanism to restore normal hearing based on adaptive stochastic resonance. The neural noise increase can then be misinterpreted as auditory input and perceived as tinnitus. This mechanism can be formalized in the Bayesian brain framework, where the percept (posterior) assimilates a prior prediction (brain’s expectations) and likelihood (bottom-up neural signal). A higher mean and lower variance (i.e. enhanced precision) of the likelihood shifts the posterior, evincing a misinterpretation of sensory evidence, which may be further confounded by plastic changes in the brain that underwrite prior predictions. Hence, two fundamental processing principles provide the most explanatory power for the emergence of auditory phantom perceptions: predictive coding as a top-down and adaptive stochastic resonance as a complementary bottom-up mechanism. We conclude that both principles also play a crucial role in healthy auditory perception. Finally, in the context of neuroscience-inspired artificial intelligence, both processing principles may serve to improve contemporary machine learning techniques