Reveal flocking of birds flying in fog by machine learning

We study the first-order flocking transition of birds flying in low-visibility conditions by employing three different representative types of neural network (NN) based machine learning architectures that are trained via either an unsupervised learning approach called "learning by confusion" or a widely used supervised learning approach. We find that after the training via either the unsupervised learning approach or the supervised learning one, all of these three different representative types of NNs, namely, the fully-connected NN, the convolutional NN, and the residual NN, are able to successfully identify the first-order flocking transition point of this nonequilibrium many-body system. This indicates that NN based machine learning can be employed as a promising generic tool to investigate rich physics in scenarios associated to first-order phase transitions and nonequilibrium many-body systems.Comment: 7 pages, 3 figure

Joint Device-Edge Digital Semantic Communication with Adaptive Network Split and Learned Non-Linear Quantization

Semantic communication, an intelligent communication paradigm that aims to transmit useful information in the semantic domain, is facilitated by deep learning techniques. Although robust semantic features can be learned and transmitted in an analog fashion, it poses new challenges to hardware, protocol, and encryption. In this paper, we propose a digital semantic communication system, which consists of an encoding network deployed on a resource-limited device and a decoding network deployed at the edge. To acquire better semantic representation for digital transmission, a novel non-linear quantization module is proposed with the trainable quantization levels that efficiently quantifies semantic features. Additionally, structured pruning by a sparse scaling vector is incorporated to reduce the dimension of the transmitted features. We also introduce a semantic learning loss (SLL) function to reduce semantic error. To adapt to various channel conditions and inputs under constraints of communication and computing resources, a policy network is designed to adaptively choose the split point and the dimension of the transmitted semantic features. Experiments using the CIFAR-10 dataset for image classification are employed to evaluate the proposed digital semantic communication network, and ablation studies are conducted to assess the proposed modules including the quantization module, structured pruning and SLL

Scanning-probe and information-concealing machine learning intermediate hexatic phase and critical scaling of solid-hexatic phase transition in biological tissues

We investigate the two-dimensional melting of biological tissues that are modeled by deformable polymeric particles with multi-body interactions described by the Voronoi model. We identify the existence of the intermediate hexatic phase in this system, and the critical scaling of the associated solid-hexatic phase transition with the critical exponent $\nu\approx0.65$ for the divergence of the correlation length. Moreover, we clarify the discontinuous nature of the hexatic-liquid phase transition in this system. These findings are achieved by directly analyzing system's spatial configurations with two generic machine learning approaches developed in this work, dubbed "scanning-probe" via which the possible existence of intermediate phases can be efficiently detected, and "information-concealing" via which the critical scaling of the correlation length in the vicinity of generic continuous phase transition can be extracted. Our work provides new physical insights into the fundamental nature of the two-dimensional melting of biological tissues, and establishes a new type of generic toolbox to investigate fundamental properties of phase transitions in various complex systems.Comment: 8 pages, 5 figure

Noise in Genotype Selection Model

We study the steady state properties of a genotype selection model in presence of correlated Gaussian white noise. The effect of the noise on the genotype selection model is discussed. It is found that correlated noise can break the balance of gene selection and induce the phase transition which can makes us select one type gene haploid from a gene group.Comment: 8 pages, 4 figure

Is the formal energy of the mid-point rule convergent?

AbstractWe obtain some formulae for calculation of the coefficients of four special types of terms in τ2k, k = 1, 2, … (1−1 corresponding to four type of (2k + 1)-vertex free unlabeled trees, k = 1, 2, …, respectively), for a fixed step size τ, in the tree-expansion of the formal energy of the mid-point rule. And, we give an estimate of the difference between the formal energy H and the standard Hamiltonian H in some domain Ω under the assumptions 1.(i)|H is smooth and bounded in Ω, and2.(ii)|the absolute values of the coefficients of the terms in τ2k are uniformly bounded by ησ2k for some constants η ≥ 1, σ > 0 and for any k ≥ 1

Acute aerobic exercise alters executive control network in preadolescent children

The present study aimed to investigate the effect of acute aerobic exercise on executive function (EF) and executive control network (ECN) in preadolescent children, and further explored the neural basis of acute aerobic exercise on EF in these children. We used a within-subjects design with a counterbalanced order. Nine healthy, right-handed children were scanned with resting-state functional magnetic resonance imaging and performed an EF task both in baseline session and exercise session. The exercise session was consisted of 30 minutes of aerobic exercise on a bicycle ergometer at 60% of their estimated maximum heart rate. Compared with the baseline session, acute aerobic exercise benefitted performance in the EF task, increased the functional connectivity between right dorsolateral prefrontal and left cerebellum, further, the increment of functional connectivity was negatively correlated with the EF' s behavioral performance change. These findings suggest that acute aerobic exercise enhances children's EF, and the neural basis may be related to functional connectivity changes in the ECN elicited by acute aerobic exercise.El objetivo de esta investigación es estudiar la influencia del ejercicio aeróbico agudo en la función ejecutiva (EF) y la red de control ejecutiva (REC) en niños preadolescentes, además explorar la base neutral de estos ejercicios aeróbicos en los niños. Hemos utilizado un diseño de orden equilibrado. Nueve niños diestros saludables fueron escaneados con resonancia magnética funcional y se llevaron a cabo tareas de EF, sesiones de ejercicio y sesiones de medición basal. Comparado con las sesiones de base, la sesión de ejercicio consistió en 30 minutos de ejercicios aeróbicos en bicicleta ergométrica al 60% del ritmo cardiaco máximo estimado. Comparado con la sesión basal, el ejercicio aeróbico agudo benefició el desempeño en la tarea EF, aumentó la conectividad funcional entre el prefrontal dorsolateral derecho y el cerebelo izquierdo, además, el incremento de conectividad funcional se correlacionó negativamente con el cambio en el comportamiento del EF. Los resultados de estos estudios demuestran que el ejercicio aeróbico agudo refuerza, y puede provocar ciertos cambios

Current Reversals in a inhomogeneous system with asymmetric unbiased fluctuations

We present a study of transport of a Brownian particle moving in periodic symmetric potential in the presence of asymmetric unbiased fluctuations. The particle is considered to move in a medium with periodic space dependent friction. By tuning the parameters of the system, the direction of current exhibit reversals, both as a function of temperature as well as the amplitude of rocking force. We found that the mutual interplay between the opposite driving factors is the necessary term for current reversals.Comment: 9 pages, 7 figure

Neural Basis of Working Memory Enhancement after Acute Aerobic Exercise: fMRI Study of Preadolescent Children

Working memory lies at the core of cognitive function and plays a crucial role in children’s learning, reasoning, problem solving, and intellectual activity. Behavioral findings have suggested that acute aerobic exercise improves children’s working memory; however, there is still very little knowledge about whether a single session of aerobic exercise can alter working memory’s brain activation patterns, as assessed by functional magnetic resonance imaging (fMRI). Therefore, we investigated the effect of acute moderate-intensity aerobic exercise on working memory and its brain activation patterns in preadolescent children, and further explored the neural basis of acute aerobic exercise on working memory in these children. We used a within-subjects design with a counterbalanced order. Nine healthy, right-handed children were scanned with a Siemens MAGNETOM Trio 3.0 Tesla magnetic resonance imaging scanner while they performed a working memory task (N-back task), following a baseline session and a 30-min, moderate-intensity exercise session. Compared with the baseline session, acute moderate-intensity aerobic exercise benefitted performance in the N-back task, increasing brain activities of bilateral parietal cortices, left hippocampus, and the bilateral cerebellum. These data extend the current knowledge by indicating that acute aerobic exercise enhances children’s working memory, and the neural basis may be related to changes in the working memory’s brain activation patterns elicited by acute aerobic exercise